From 0ceae846faea7624862a44a1bedc38e21aa3b679 Mon Sep 17 00:00:00 2001 From: Matthias Groncki Date: Sat, 11 Apr 2015 08:39:06 +0200 Subject: [PATCH 01/25] delete old versions and checkpoints --- .gitignore | 1 + ExposureSimulation_AmericanMC_1.ipynb | 4101 ------------------------- Untitled0.py | 14 - YieldCurve_pure_Python.ipynb | 267 -- 4 files changed, 1 insertion(+), 4382 deletions(-) create mode 100644 .gitignore delete mode 100644 ExposureSimulation_AmericanMC_1.ipynb delete mode 100644 Untitled0.py delete mode 100644 YieldCurve_pure_Python.ipynb diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..763513e --- /dev/null +++ b/.gitignore @@ -0,0 +1 @@ +.ipynb_checkpoints diff --git a/ExposureSimulation_AmericanMC_1.ipynb b/ExposureSimulation_AmericanMC_1.ipynb deleted file mode 100644 index 61fe85a..0000000 --- a/ExposureSimulation_AmericanMC_1.ipynb +++ /dev/null @@ -1,4101 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Exposure Simulation with American Monte Carlo I\n", - "==========================\n", - "Copyright (C) Matthias Groncki, 2015\n", - "\n", - "This Ipython Notebook script is free-software/open-source.\n", - "\n", - "You can redistribute it and/or modify it\n", - "under the terms of the QuantLib license. \n", - "\n", - "The license is also available online at\n", - ".\n", - "\n", - "This program is distributed in the hope that it will be useful, but WITHOUT\n", - "ANY WARRANTY; \n", - "\n", - "without even the implied warranty of MERCHANTABILITY or FITNESS\n", - "FOR A PARTICULAR PURPOSE.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import QuantLib as ql" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "today = ql.Date(27, 3, 2015)\n", - "ql.Settings.instance().setEvaluationDate(today)\n", - "cal = ql.TARGET()\n", - "fwd = ql.QuoteHandle(ql.SimpleQuote(0.03))\n", - "yts = ql.RelinkableYieldTermStructureHandle(ql.FlatForward(0,cal, fwd, ql.Actual365Fixed()))\n", - "vts = ql.RelinkableBlackVolTermStructureHandle(ql.BlackVarianceCurve(today, \n", - " [today+ql.Period(\"1y\")], \n", - " [0.1], \n", - " ql.Actual365Fixed()))\n", - "vts.enableExtrapolation()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s0 = ql.QuoteHandle(ql.SimpleQuote(100.0))" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "process = ql.BlackScholesProcess(s0, yts, vts)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "dt = 1./12.\n", - "srdt = np.sqrt(dt)\n", - "tg = np.arange(0,10,dt)" - ] - }, - { - "cell_type": "code", - "execution_count": 276, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "N = 1000\n", - "S = np.zeros((N,len(tg)))\n", - "for n in range(0, N):\n", - " S[n,0] = s0.value()\n", - " for i in range(1, len(tg)):\n", - " S[n,i] = process.evolve(tg[i-1], S[n, i-1], dt, np.random.randn())" - ] - }, - { - "cell_type": "code", - "execution_count": 277, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEACAYAAAC57G0KAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzsnWd4VEUXgN+7m94goYUeCEjvvQkXkaI0AaVKkSqICEgv\n", - "oqiAqIgoTXr9AFGkSGfovSNICyW0UNN7du/3YzZmiQEChBKc93n2YXfu3LtnN8uZc8+cAgqFQqFQ\n", - "KBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgU/2nMwBFgle31KOCqbewI0MBu7hDgHHAaqPv8\n", - "RFQoFArFk9APWAistL3+zDaWnKLAUcAR8APOA6bnIJ9CoVAokpEa5ZsLeAuYAWi2Mc3uuT1NgMVA\n", - 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"# Setup portfolio \n", - "# List of Call-Option (Strike, Maturity)\n", - "portfolio = [(103, 1), (115, 10)]\n", - "V = np.zeros((len(portfolio), len(S), len(tg)))\n", - "for d in range(0, len(portfolio)):\n", - " deal = portfolio[d]\n", - " strike = deal[0]\n", - " maturity = deal[1]\n", - " payoff = ql.PlainVanillaPayoff(ql.Option.Call, deal[0])\n", - " for i in range(0, N):\n", - " for j in range(0, len(tg)):\n", - " t = tg[j]\n", - " if t > deal[1]:\n", - " V[d,i,j] = 0\n", - " else:\n", - " V[d,i,j] = ql.BlackCalculator(payoff, S[i,j] * np.exp(-0.03 * (maturity-t)), np.sqrt(maturity-t) * 0.1).value() " - ] - }, - { - "cell_type": "code", - "execution_count": 282, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEACAYAAAC57G0KAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd0VNXXhp87k56QQgg1QOi9SAcBudIVqQoiVUQRK/Ah\n", - "WCEiNkQU5GdXBFQUVGx04YAgTXqTKqHXEAKkZ+Z+f+yJDDGBJCSE4HnWymJy65kB3ntmn73fDRqN\n", - 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"\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "# Setup portfolio \n", - "# List of Call-Option (Strike, Maturity)\n", - "portfolio = [(103, 1), (115, 10)]\n", - "V = np.zeros((len(portfolio), len(S), len(tg)))\n", - "for d in range(0, len(portfolio)):\n", - " deal = portfolio[d]\n", - " strike = deal[0]\n", - " maturity = deal[1]\n", - " payoff = ql.PlainVanillaPayoff(ql.Option.Call, deal[0])\n", - " for j in range(0, len(tg)-1):\n", - " for i in range(0, len(S)):\n", - " t = tg[119-j]\n", - " if t == maturity:\n", - " #print(d,i,j,t, S[i,j])\n", - " V[d,i,119-j] = max(0, S[i,119-j]-strike)\n", - " elif t > maturity:\n", - " V[d,i,119-j] = 0\n", - " elif t < maturity and j>0:\n", - " x0 = np.ones(N)\n", - " x1 = S[:,120-j]\n", - " x2 = 0.5*(3*x1*2-1)\n", - " x3 = 0.5*(5*x1**3-3*x1)\n", - " b = np.exp(-0.03*dt)*V[d, : ,120-j]\n", - " a = np.array([np.ones(N), S[:,120-j], S[:,120-j]**2, S[:,120-j]**3]).T\n", - " x = np.linalg.lstsq(a,b)\n", - " a*x" - ] - }, - { - "cell_type": "code", - "execution_count": 283, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "b = S[:,12]-103\n", - "b[b<0] = 0\n", - "b=np.exp(-0.03*dt)*b" - ] - }, - { - "cell_type": "code", - "execution_count": 287, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1.05034060e+02, 3.14602179e+02, 2.89672217e+06],\n", - " [ 1.07796749e+02, 3.22890247e+02, 3.13137134e+06],\n", - " [ 1.09135664e+02, 3.26906991e+02, 3.24951251e+06],\n", - " ..., \n", - " [ 1.17044244e+02, 3.50632732e+02, 4.00840109e+06],\n", - " [ 1.13855744e+02, 3.41067231e+02, 3.68964633e+06],\n", - " [ 1.10861593e+02, 3.32084779e+02, 3.40613729e+06]])" - ] - }, - "execution_count": 287, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x0 = np.ones(N)\n", - "x1 = S[:,11]\n", - "x2 = 0.5*(3*x1*2-1)\n", - "x3 = 0.5*(5*x1**3-3*x1)\n", - "x4 = 1./8.*(35*x1**4-20*x1**2+3)\n", - "a = np.array([x1,x2,x3]).T\n", - "a[b>0]" - 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"metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/Untitled0.py b/Untitled0.py deleted file mode 100644 index b90c530..0000000 --- a/Untitled0.py +++ /dev/null @@ -1,14 +0,0 @@ -# -*- coding: utf-8 -*- -# 3.0 - -# - -import numpy as np - -# - -print "Hallo World" - -# - - diff --git a/YieldCurve_pure_Python.ipynb b/YieldCurve_pure_Python.ipynb deleted file mode 100644 index f9bf74b..0000000 --- a/YieldCurve_pure_Python.ipynb +++ /dev/null @@ -1,267 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import scipy.interpolate as intp\n" - ] - }, - { - "cell_type": "code", - "execution_count": 138, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def daysInBetweenAct(d0, d1):\n", - " return (d0-d1) / np.timedelta64(1,'D')\n", - "\n", - "def yearFractionAct360(d0, d1):\n", - " return daysInBetweenAct(d0, d1) / 360.\n", - "\n", - "def yearFractionAct365(d0, d1):\n", - " return daysInBetweenAct(d0, d1) / 365.\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 132, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def discountCurve(times, zeroRates):\n", - " dt = times[1:]-times[:-1]\n", - " dx = zeroRates[1:] - zeroRates[:-1]\n", - " dx_dt = dx / dt\n", - " def discount(t):\n", - " if t <= times[0]:\n", - " r = zeroRates[0]\n", - " elif t >= times[-1]:\n", - " r = zeroRates[-1]\n", - " else:\n", - " idx = np.min(np.argwhere(times>=t)) - 1\n", - " r = zeroRates[idx] + dx_dt[idx] * (times[idx+1] - t)\n", - " return np.exp(-r*t)\n", - " return np.vectorize(discount)\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "def index(curve, tenor):\n", - " def fixing(t):\n", - " return (curve(t)/curve(t+tenor)-1)*tenor\n", - " return np.vectorize(fixing)\n", - "\n", - "def swapCashflows(self, start, maturity, nominal, fixedRate, fixedLegTenor, floatTenor, fwdCurve):\n", - " fix_schedule = np.arrange(start, maturity, fixedLegTenor)\n", - " dt = fix_schedule[1:]-fix_schedule[:-1]\n", - " fix_leg = nominal * dt * fixedRate\n", - " dt = fix_schedule[1:]-fix_schedule[:-1]\n", - " float_leg = nominal * dt * idx(float_schedule[:-1])\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 133, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "-0.49502491687458405" - ] - }, - "execution_count": 133, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tenors = np.array([1, 2 , 3])\n", - "zcs = np.array([0.02, 0.0125, 0.0175])\n", - "-zeroCurve(tenors, zcs)(0.5)/2" - ] - }, - { - "cell_type": "code", - "execution_count": 134, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import pycppad" - ] - }, - { - "cell_type": "code", - "execution_count": 135, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "a_zcs = pycppad.independent(zcs)\n", - "a_y = np.array([zeroCurve(tenors, a_zcs)(0.5)])\n", - "f = pycppad.adfun(a_zcs, a_y)" - ] - }, - { - "cell_type": "code", - "execution_count": 136, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-0.49502492, 0. , 0. ]])" - ] - }, - "execution_count": 136, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "f.jacobian(zcs)" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "dy = zcs[1:]-zcs[:-1]\n", - "dx = tenors[1:]-tenors[:-1]" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0.99875078, 0.99750312])" - ] - }, - "execution_count": 98, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.exp(0.5*-dy/dx)" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "idx = np.min(np.argwhere(tenors>=3)) - 1\n", - "idx" - ] - }, - { - "cell_type": "code", - "execution_count": 125, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pay = np.datetime64(\"2015-07-01\")\n", - "today = np.datetime64(\"2015-04-06\")" - ] - }, - { - "cell_type": "code", - "execution_count": 127, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "86.0" - ] - }, - "execution_count": 127, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(pay-today) / np.timedelta64(1, \"D\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} From 37b4270d1882d3e2eb1f670d1bd8df810adbe03f Mon Sep 17 00:00:00 2001 From: Matthias Groncki Date: Sat, 11 Apr 2015 08:40:51 +0200 Subject: [PATCH 02/25] Clean chekcpoints --- .gitignore | 2 +- ...xpectedExposureSimulation-checkpoint.ipynb | 4540 ----------------- ...reSimulation_AmericanMC_1-checkpoint.ipynb | 3384 ------------ .../First_example-checkpoint.ipynb | 483 -- .../YieldCurve_pure_Python-checkpoint.ipynb | 91 - 5 files changed, 1 insertion(+), 8499 deletions(-) delete mode 100644 .ipynb_checkpoints/ExpectedExposureSimulation-checkpoint.ipynb delete mode 100644 .ipynb_checkpoints/ExposureSimulation_AmericanMC_1-checkpoint.ipynb delete mode 100644 .ipynb_checkpoints/First_example-checkpoint.ipynb delete mode 100644 .ipynb_checkpoints/YieldCurve_pure_Python-checkpoint.ipynb diff --git a/.gitignore b/.gitignore index 763513e..aa6ae88 100644 --- a/.gitignore +++ b/.gitignore @@ -1 +1 @@ -.ipynb_checkpoints +./.ipynb_checkpoints diff --git a/.ipynb_checkpoints/ExpectedExposureSimulation-checkpoint.ipynb b/.ipynb_checkpoints/ExpectedExposureSimulation-checkpoint.ipynb deleted file mode 100644 index aec7103..0000000 --- a/.ipynb_checkpoints/ExpectedExposureSimulation-checkpoint.ipynb +++ /dev/null @@ -1,4540 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "collapsed": false - }, - "source": [ - "# Expected Exposure simulation with QuantLib and Python" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Copyright (c) 2015 Matthias Groncki*\n", - "\n", - "\n", - "Redistribution and use in source and binary forms, with or without\n", - "modification, are permitted provided that the following conditions are met:\n", - "\n", - " - Redistributions of source code must retain the above copyright notice,\n", - " this list of conditions and the following disclaimer.\n", - "\n", - "THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n", - "AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n", - "IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\n", - "DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE LIABLE\n", - "FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\n", - "DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\n", - "SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\n", - "CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\n", - "OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\n", - "OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n", - "\n", - "This disclaimer is taken from the QuantLib license" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# import the used libraries\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import QuantLib as ql\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Setting evaluation date\n", - "today = ql.Date(7,4,2015)\n", - "ql.Settings.instance().setEvaluationDate(today)" - ] - }, - { - "cell_type": "code", - "execution_count": 294, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Setup Marketdata\n", - "rate = ql.SimpleQuote(0.03)\n", - "rate_handle = ql.QuoteHandle(rate)\n", - "dc = ql.Actual365Fixed()\n", - "yts = ql.FlatForward(today, rate_handle, dc)\n", - "yts.enableExtrapolation()\n", - "hyts = ql.RelinkableYieldTermStructureHandle(yts)\n", - "t0_curve = ql.YieldTermStructureHandle(yts)\n", - "euribor6m = ql.Euribor6M(hyts)" - ] - }, - { - "cell_type": "code", - "execution_count": 295, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Setup a dummy portfolio with two Swaps\n", - "def makeSwap(start, maturity, nominal, fixedRate, index, typ=ql.VanillaSwap.Payer):\n", - " \"\"\"\n", - " creates a plain vanilla swap with fixedLegTenor 1Y\n", - " \n", - " parameter:\n", - " \n", - " start (ql.Date) : Start Date\n", - " \n", - " maturity (ql.Period) : SwapTenor\n", - " \n", - " nominal (float) : Nominal\n", - " \n", - " fixedRate (float) : rate paid on fixed leg\n", - " \n", - " index (ql.IborIndex) : Index\n", - " \n", - " return: tuple(ql.Swap, list) Swap and all fixing dates\n", - " \n", - " \n", - " \"\"\"\n", - " end = ql.TARGET().advance(start, maturity)\n", - " fixedLegTenor = ql.Period(\"1y\")\n", - " fixedLegBDC = ql.ModifiedFollowing\n", - " fixedLegDC = ql.Thirty360(ql.Thirty360.BondBasis)\n", - " spread = 0.0\n", - " fixedSchedule = ql.Schedule(start,\n", - " end, \n", - " fixedLegTenor, \n", - " index.fixingCalendar(), \n", - " fixedLegBDC,\n", - " fixedLegBDC, \n", - " ql.DateGeneration.Backward,\n", - " False)\n", - " floatSchedule = ql.Schedule(start,\n", - " end,\n", - " index.tenor(),\n", - " index.fixingCalendar(),\n", - " index.businessDayConvention(),\n", - " index.businessDayConvention(),\n", - " ql.DateGeneration.Backward,\n", - " False)\n", - " swap = ql.VanillaSwap(typ, \n", - " nominal,\n", - " fixedSchedule,\n", - " fixedRate,\n", - " fixedLegDC,\n", - " floatSchedule,\n", - " index,\n", - " spread,\n", - " index.dayCounter())\n", - " return swap, [index.fixingDate(x) for x in floatSchedule][:-1]\n", - "\n", - "portfolio = [makeSwap(today + ql.Period(\"2d\"),\n", - " ql.Period(\"5Y\"),\n", - " 1e6,\n", - " 0.03,\n", - " euribor6m),\n", - " makeSwap(today + ql.Period(\"2d\"),\n", - " ql.Period(\"4Y\"),\n", - " 5e5,\n", - " 0.03,\n", - " euribor6m,\n", - " ql.VanillaSwap.Receiver),\n", - " ]" - ] - }, - { - "cell_type": "code", - "execution_count": 296, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2233.46835369\n", - "-884.749675579\n" - ] - } - ], - "source": [ - "# Setup pricing engine and calculate the npv\n", - "engine = ql.DiscountingSwapEngine(hyts)\n", - "\n", - "for deal, fixingDates in portfolio:\n", - " deal.setPricingEngine(engine)\n", - " print(deal.NPV())" - ] - }, - { - "cell_type": "code", - "execution_count": 297, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Stochastic Process " - ] - }, - { - "cell_type": "code", - "execution_count": 298, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Assume the model is already calibrated either historical or market implied\n", - "volas = [ql.QuoteHandle(ql.SimpleQuote(0.0075)),\n", - " ql.QuoteHandle(ql.SimpleQuote(0.0075))]\n", - "meanRev = [ql.QuoteHandle(ql.SimpleQuote(0.02))]\n", - "model = ql.Gsr(t0_curve, [today+100], volas, meanRev, 16.)" - ] - }, - { - "cell_type": "code", - "execution_count": 299, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "process = model.stateProcess()" - ] - }, - { - "cell_type": "code", - "execution_count": 300, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Define evaluation grid\n", - "date_grid = [today + ql.Period(i,ql.Weeks) for i in range(0,52*6)]\n", - "for deal in portfolio:\n", - " date_grid += deal[1]\n", - "\n", - "date_grid = np.unique(np.sort(date_grid))\n", - "time_grid = np.vectorize(lambda x: ql.ActualActual().yearFraction(today, x))(date_grid)\n", - "dt = time_grid[1:] - time_grid[:-1]" - ] - }, - { - "cell_type": "code", - "execution_count": 301, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Random number generator\n", - "seed = 1\n", - "urng = ql.MersenneTwisterUniformRng(seed)\n", - "usrg = ql.MersenneTwisterUniformRsg(len(time_grid)-1,urng)\n", - "generator = ql.InvCumulativeMersenneTwisterGaussianRsg(usrg)" - ] - }, - { - "cell_type": "code", - "execution_count": 303, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Generate N paths\n", - "N = 1500\n", - "\n", - "x = np.zeros((N, len(time_grid)))\n", - "y = np.zeros((N, len(time_grid)))\n", - "for n in range(0,N):\n", - " dWs = generator.nextSequence().value()\n", - " for i in range(1, len(time_grid)):\n", - " x[n,i] = process.expectation(time_grid[i-1], \n", - " x[n,i-1], \n", - " dt[i-1]) + dWs[i-1] * process.stdDeviation(time_grid[i-1],\n", - " x[n,i-1],\n", - " dt[i-1])\n", - " y[n,i] = (x[n,i] - process.expectation(0,0,time_grid[i])) / process.stdDeviation(0,0,time_grid[i])" - ] - }, - { - "cell_type": "code", - "execution_count": 304, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAYAAAAEACAYAAAC6d6FnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd4FdXWuN85Nb0X0umh994H6SK9dwREUBAQQVDxICBI\n", - "B6VJ7x1BivSh915DL4EkJCE9OckpM78/Jt7LtVz5Pq8/P73zPs95kpnZM3ufk5O19l5r7bVAQ0ND\n", - "Q0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ+O/lGZADHAPGP0rbebmX78KVHzl\n", - "vA+wBbgN3AJq/HHD1NDQ0ND4T6IH7gMFASNwBSj5kzYtgD35v1cHzrxybSXwdv7vBsD7jxqohoaG\n", - "hsZ/lprA3leOP85/vcpCoPMrxzFAMKqwf/iHjk5DQ0ND41fR/c77w4DYV46f5Z/7rTbhQCEgCVgO\n", - "XAIWA26/czwaGhoaGq/J71UAymu2E37hPgNQCZif/zObn68eNDQ0NDT+IAy/8/7nQMQrxxGoM/x/\n", - "1yY8/5yQ3/Z8/vkt/LICuA8U+Z3j1NDQ0Phv4wFQ9I/swJDfSUHAxG87gWvwr07gY0Dx/N8twFe/\n", - 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"RK5CYII=\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# plot the paths\n", - "for i in range(0,N):\n", - " plt.plot(time_grid, x[i,:])" - ] - }, - { - "cell_type": "code", - "execution_count": 305, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#Swap pricing under each scenario\n", - "npv_matrix = np.zeros((N,len(date_grid)))\n", - "for p in range(0,N):\n", - " for t in range(0, len(date_grid)):\n", - " date = date_grid[t]\n", - " ql.Settings.instance().setEvaluationDate(today)\n", - " # update fixing\n", - " x_p_t = y[p,t]\n", - " zeroBonds = [model.zerobond(time_grid[t]+i, \n", - " time_grid[t],\n", - " x_p_t) for i in range(1,10)]\n", - " ql.Settings.instance().setEvaluationDate(date)\n", - " yc = ql.DiscountCurve([date] \n", - " + [date+ql.Period(i,ql.Years)\n", - " for i in range(1,10)], \n", - " [1] + zeroBonds, \n", - " ql.Actual365Fixed())\n", - " yc.enableExtrapolation()\n", - " hyts.linkTo(yc)\n", - " if euribor6m.isValidFixingDate(date):\n", - " fixing = -np.log(model.zerobond(time_grid[t]+0.5, time_grid[t], x_p_t)) / 0.5\n", - " euribor6m.addFixing(date, fixing)\n", - " for deal, fixing in portfolio:\n", - " npv_matrix[p,t] += deal.NPV()\n", - " ql.IndexManager.instance().clearHistories()\n", - "ql.Settings.instance().setEvaluationDate(today)\n", - "hyts.linkTo(yts)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'time_grid' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m300\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtime_grid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnpv_matrix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Time in years\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"NPV\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'time_grid' is not defined" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot the first 30 NPV paths\n", - "plt.figure(figsize=(7,5), dpi=300)\n", - "for i in range(0,30):\n", - " plt.plot(time_grid, npv_matrix[i,:])\n", - "plt.xlabel(\"Time in years\")\n", - "plt.ylabel(\"NPV\")\n", - "plt.title(\"Simulated npv paths\")" - ] - }, - { - "cell_type": "code", - "execution_count": 315, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 315, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAdAAAAFRCAYAAADEsfx9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd8G+X9x99nyyuJM5wdZzg7ZAIJYUOOHWZaymihQEuB\n", - "Nm1p2aPw4yiUFlrKKLtQVllhb8q6sCFAQgIkIXs5ibN3PHW/P77PRSdZkmVZju3k+3699PLpdCc9\n", - "Gr7P850PKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKI3AGcD/Gum5HwFu\n", - 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"TkSuQmCC\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Calculate the exposure\n", - "E = npv_matrix.copy()\n", - "E[E<0]=0\n", - "# Plot the first 30 exposure paths\n", - "plt.figure(figsize=(7,5), dpi=600)\n", - "for i in range(0,30):\n", - " plt.plot(time_grid, E[i,:])\n", - "plt.xlabel(\"Time in years\")\n", - "plt.ylabel(\"Exposure\")\n", - "plt.ylim([-10000,70000])\n", - "plt.title(\"Simulated exposure paths\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 308, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Calculate the expected exposure\n", - "E = npv_matrix.copy()\n", - "E[E<0]=0\n", - "EE = np.sum(E, axis=0)/N" - ] - }, - { - "cell_type": "code", - "execution_count": 321, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAcoAAAFRCAYAAAD0Pe1cAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xm4HFWZx/FvZSEkBAgQhhASEkAChAGRAAEUvMoWEdkE\n", - 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"plt.ylim([-2000,10000])\n", - "plt.title(\"Expected Exposure (netting set)\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 322, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAcgAAAFRCAYAAADwyD1hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3XmYHFW5x/FvhewkJGxCEgIBWR4CRPawG1AgcNm8guB1\n", - "Q1FEFHBDjMolKiqiyHIRXEBZlE3BCMiORBAlkSUQCIEECJAACWvYIcm894/3NF2Z9Mz0THfXqZr6\n", - "fZ6nn6k+3V31TlfPvH2WOgdERERERERERERERERERERERERERERERERERKSGvYG/ZHi8ScBvMzxe\n", - "T3wFOCV2ECIiUts84E3gNeA54PfAyuGxqcBb4bHKbXx4rA14vd1j3+zkOHcD2zcp5jZgg9T9CcDT\n", - "Tdp3q0xgxRgHhLI1M49GSqtP7ABECsSA/YChwNbAtsD3Uo99OTxWuU1LvXZcu8d+3sExtgNWAaY3\n", - "Me6kifuK5R3geuDTsQOR8lCCFOmZZ4AbgM2avN998NpoWhvwReBR4GXg7HaPfw6YBbwUYlo3lN8e\n", - "ft4PvIonl+uAkXgt9lVgBDAZuDg8d0w43qeBJ4Hnge+kjjUIuDAcaxbwLTqvkZ4OLAQWAw9Qfb8G\n", - "4F8SnsRr4+cCA/Ea+fXtYlw7vGYq8F+dHEtERCJ5AvhQ2B4NPAh8P9y/DTiig9e1Ae+v8xhXAN+o\n", - "8fqr8ZrlaGAR3k8JcCAwB9gE/8L7XeDOdq9NN7F+kBUT2kmsmCB/jSexccDbYf/g/YC3AcOAUXjS\n", - 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"metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# calculate the maximum pfe\n", - "MPFE = np.max(PFE_curve)\n", - "MPFE" - ] - }, - { - "cell_type": "code", - "execution_count": 312, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "39643.707316833963" - ] - }, - "execution_count": 312, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# alternative pfe 95% quantile of the maxima of each exposure paths\n", - "PFE = np.sort(np.max(E,axis=1))[0.95*N]\n", - "PFE" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/.ipynb_checkpoints/ExposureSimulation_AmericanMC_1-checkpoint.ipynb b/.ipynb_checkpoints/ExposureSimulation_AmericanMC_1-checkpoint.ipynb deleted file mode 100644 index a0bdc9f..0000000 --- a/.ipynb_checkpoints/ExposureSimulation_AmericanMC_1-checkpoint.ipynb +++ /dev/null @@ -1,3384 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Exposure Simulation with American Monte Carlo I\n", - "==========================\n", - "Copyright (C) Matthias Groncki, 2015\n", - "\n", - "This Ipython Notebook script is free-software/open-source.\n", - "\n", - "You can redistribute it and/or modify it\n", - "under the terms of the QuantLib license. \n", - "\n", - "The license is also available online at\n", - ".\n", - "\n", - "This program is distributed in the hope that it will be useful, but WITHOUT\n", - "ANY WARRANTY; \n", - "\n", - "without even the implied warranty of MERCHANTABILITY or FITNESS\n", - "FOR A PARTICULAR PURPOSE.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import QuantLib as ql" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "today = ql.Date(27, 3, 2015)\n", - "ql.Settings.instance().setEvaluationDate(today)\n", - "cal = ql.TARGET()\n", - "fwd = ql.QuoteHandle(ql.SimpleQuote(0.03))\n", - "yts = ql.RelinkableYieldTermStructureHandle(ql.FlatForward(0,cal, fwd, ql.Actual365Fixed()))\n", - "vts = ql.RelinkableBlackVolTermStructureHandle(ql.BlackVarianceCurve(today, \n", - " [today+ql.Period(\"1y\")], \n", - " [0.1], \n", - " ql.Actual365Fixed()))\n", - "vts.enableExtrapolation()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s0 = ql.QuoteHandle(ql.SimpleQuote(100.0))" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "process = ql.BlackScholesProcess(s0, yts, vts)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "dt = 1./12.\n", - "srdt = np.sqrt(dt)\n", - "tg = np.arange(0,10,dt)" - ] - }, - { - "cell_type": "code", - "execution_count": 276, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "N = 1000\n", - "S = np.zeros((N,len(tg)))\n", - "for n in range(0, N):\n", - " S[n,0] = s0.value()\n", - " for i in range(1, len(tg)):\n", - " S[n,i] = process.evolve(tg[i-1], S[n, i-1], dt, np.random.randn())" - ] - }, - { - "cell_type": "code", - "execution_count": 277, - 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} - ], - "source": [ - "for i in range(0,N):\n", - " plt.plot(tg, S[i,])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "Classical exposure simulation using closed formula for pricing the option on the grid\n", - "--------------------------------------------------------------------------------" - ] - }, - { - "cell_type": "code", - "execution_count": 278, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Setup portfolio \n", - "# List of Call-Option (Strike, Maturity)\n", - "portfolio = [(103, 1), (115, 10)]\n", - "V = np.zeros((len(portfolio), len(S), len(tg)))\n", - "for d in range(0, len(portfolio)):\n", - " deal = portfolio[d]\n", - " strike = deal[0]\n", - " maturity = deal[1]\n", - " payoff = ql.PlainVanillaPayoff(ql.Option.Call, deal[0])\n", - " for i in range(0, N):\n", - " for j in range(0, len(tg)):\n", - " t = tg[j]\n", - " if t > deal[1]:\n", - " V[d,i,j] = 0\n", - " else:\n", - " V[d,i,j] = ql.BlackCalculator(payoff, S[i,j] * np.exp(-0.03 * (maturity-t)), np.sqrt(maturity-t) * 0.1).value() " - ] - }, - { - "cell_type": "code", - "execution_count": 279, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEACAYAAAC57G0KAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd0VNXXhp87k56QQgg1QOi9SAcBudIVqQoiVUQRK/Ah\n", - "WCEiNkQU5GdXBFQUVGx04YAgTXqTKqHXEAKkZ+Z+f+yJDDGBJCSE4HnWymJy65kB3ntmn73fDRqN\n", - "RqPRaDQajUaj0Wg0Go1Go9FoNBqNRqPRaDQajUaj0Wg0/0l8gHXAFmAX8Lpre2FgCbAXWAwEu53z\n", - "HLAP2A20u2Ej1Wg0Gk228XP96QGsBZoDE4BRru2jgTdcr6sjDwNPIALYD9hu1EA1Go1GkzP8gD+B\n", - "GsjsvJhre3HX7yCz99Fu5ywEmtyoAWo0Go3mMlmZXduQWfkpQAE7EXE/5dp/istiXxI46nbuUaBU\n", - "roxUo9FoNNnCIwvHOIG6QBCwCDDT7bdcP5lxtX0ajUajySOyIvBpxALzgPrIrL04cBIoAZx2HXMM\n", - "KO12TrhrW3r2AxWyO1iNRqP5j3MAqJhbFyvC5QwZX+B3oDWyyJoWa3+Wfy+yegHlXIMxMriuntVf\n", - "JjK/B3ATEZnfA7iJiMzvAdxEROb3AG4isqWd15rBlwCmI3F4GzATWApsBmYDDwFRQE/X8btc23cB\n", - "qcBj2R2QRqPRaHKHawn8dqBeBtvPAW0yOec1149Go9Fo8hGdo57/LM/vAdxELM/vAdxELM/vAdxE\n", - "LM/vAeQhuRZPv5nQYRuNRvNfpxaihbWycU6B0M4CMUiNRqPJQyYhtULTs3FOgdDOAjFIjUajySO8\n", - 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" for j in range(0, len(tg)-1):\n", - " for i in range(0, len(S)):\n", - " t = tg[119-j]\n", - " if t == maturity:\n", - " #print(d,i,j,t, S[i,j])\n", - " V[d,i,119-j] = max(0, S[i,119-j]-strike)\n", - " elif t > maturity:\n", - " V[d,i,119-j] = 0\n", - " elif t < maturity and j>0:\n", - " x0 = np.ones(N)\n", - " x1 = S[:,120-j]\n", - " x2 = 0.5*(3*x1*2-1)\n", - " x3 = 0.5*(5*x1**3-3*x1)\n", - " b = np.exp(-0.03*dt)*V[d, : ,120-j]\n", - " a = np.array([np.ones(N), S[:,120-j], S[:,120-j]**2, S[:,120-j]**3]).T\n", - " x = np.linalg.lstsq(a,b)\n", - " a*x" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "b = S[:,12]-103\n", - "b[b<0] = 0\n", - "b=np.exp(-0.03*dt)*b" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "x0 = np.ones(N)\n", - "x1 = S[:,11]\n", - "x2 = 0.5*(3*x1*2-1)\n", - "x3 = 0.5*(5*x1**3-3*x1)\n", - "x4 = 1./8.*(35*x1**4-20*x1**2+3)\n", - "a = np.array([x0,x1,x2,x3]).T\n", - "a[b>0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "np.sum(np.linalg.lstsq(a[b>0],b[b>0])[0]*a, axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "V[0,:,11]" - ] - }, - { - "cell_type": "code", - "execution_count": 242, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 5.71033823, 0. , 0. , 0. ,\n", - " 0. , 0. , 0.51982112, 0. ,\n", - " 6.85074025, 0. , 12.26251575, 0. ,\n", - " 1.65815024, 0. , 0. , 0. ,\n", - " 16.4980129 , 1.86114817, 0. , 0. ,\n", - " 1.28618839, 0. , 9.09221276, 16.63990127,\n", - " 0. , 0. , 7.30353411, 0.42212328,\n", - " 6.40863998, 0. , 0. , 13.40383763,\n", - " 0. , 2.73942937, 2.44788028, 0. ,\n", - " 9.89982434, 0. , 13.24887741, 0. ,\n", - " 4.67784449, 0. , 13.33633536, 2.77396567,\n", - " 0. , 7.81115419, 2.4082896 , 7.971613 ,\n", - " 6.47568208, 0. , 7.33137147, 0. ,\n", - " 0. , 0. , 0. , 4.00539734,\n", - " 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. ,\n", - " 4.21191027, 13.65583205, 0. , 0. ,\n", - " 8.20985923, 0. , 0. , 4.45462913,\n", - " 1.57270765, 0. , 0. , 6.85770758,\n", - " 0.60494483, 0. , 0. , 4.97376353,\n", - " 0. , 0. , 18.16266835, 0. ,\n", - " 0.25711498, 5.44433569, 0.09687961, 0. ,\n", - " 0. , 0. , 0. , 6.04225351,\n", - " 2.90458908, 5.38610568, 15.77616361, 0. ,\n", - " 18.39508234, 3.49858737, 1.30847667, 1.50142978])" - ] - }, - "execution_count": 242, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/.ipynb_checkpoints/First_example-checkpoint.ipynb b/.ipynb_checkpoints/First_example-checkpoint.ipynb deleted file mode 100644 index 1295427..0000000 --- a/.ipynb_checkpoints/First_example-checkpoint.ipynb +++ /dev/null @@ -1,483 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# First Example using QuantLib" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Copyright (c) 2015 Matthias Groncki*\n", - "\n", - "\n", - "Redistribution and use in source and binary forms, with or without\n", - "modification, are permitted provided that the following conditions are met:\n", - "\n", - " - Redistributions of source code must retain the above copyright notice,\n", - " this list of conditions and the following disclaimer.\n", - "\n", - "THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n", - "AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n", - "IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\n", - "DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE LIABLE\n", - "FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\n", - "DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\n", - "SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\n", - "CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\n", - "OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\n", - "OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n", - "\n", - "This disclaimer is taken from the QuantLib license" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import QuantLib as ql\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Set Evaluation Date\n", - "today = ql.Date(31,3,2015)\n", - "ql.Settings.instance().setEvaluationDate(today)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Pricing a plain vanilla interest rate swap" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Flat yield curve\n", - "rate = ql.SimpleQuote(0.03)\n", - "rate_handle = ql.QuoteHandle(rate)\n", - "dc = ql.Actual365Fixed()\n", - "disc_curve = ql.FlatForward(today, rate_handle, dc)\n", - "disc_curve.enableExtrapolation()\n", - "hyts = ql.YieldTermStructureHandle(disc_curve)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot the yield curve" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEZCAYAAACJjGL9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3XmcHFW5//FPJ5MECFkIhD0wiIqIsoiEHUZQDFyEC6gg\n", - "ILIIUfGCAhqCeglwr2xXcEN+imjgAgFFUOBqZNEgIItIEsKSmIQESEAIURFREOT5/fGcYWom3T3d\n", - "M9V9qvt8369Xvaaru6rrOYF5nqk6VeeAiIiIiIiIiIiIiIiIiIiIiIiIiIiIiDRQJ/AGMCRyHDFN\n", - "B86JHYQIpP2LKPEtBf4OvBSWvwLr1/kdXcDT/WwzHXg1c5yXgI/UeZxms7BUsgFwOfAM/u/2ODAN\n", - "WKPhkUlyVCgkJgP2B0aFZTTwxwYd5/zMcUYBP67zOzryDiqj0u9hqcL744B7gRHATvi/2weAMcDm\n", - "Azj+0AHsIwlRoZBWcAzwGP6X82LghPD+SOAXwIYM/IzkeGAhsBL4Gf6Xerc3gM8AfwjLNOCb4bNh\n", - "wMvABWF9deAVYGxY/zHwLPAX4E7gnZnvnQ5cCvwc+Bt+VrQd8FBow7XAalViPgV4ETgSeCq8twz4\n", - "PDCP8pfuZgHHhddHA/cAFwEv4Je4/gxsldl+PH62t05Y3x+YE7a7B3h3lfhERHKzBNi7zPud9E50\n", - "+wGbhdd74Al6u7C+J/1fevoh5a/37wWsALYFhuNF4M7M528Av8ST/wjgfcDD4bNdgEXAfZnvmp3Z\n", - 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"plt.plot(tg, discount(tg))\n", - "plt.xlabel(\"time\")\n", - "plt.ylabel(\"discount factor\")\n", - "plt.title(\"Flat Forward Curve\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##Setup the swap" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "start = ql.TARGET().advance(today, ql.Period(\"2D\"))\n", - "\n", - "end = ql.TARGET().advance(start, ql.Period(\"10Y\"))\n", - "\n", - "nominal = 1e7\n", - "\n", - "typ = ql.VanillaSwap.Payer\n", - "\n", - "fixRate = 0.03\n", - "\n", - "fixedLegTenor = ql.Period(\"1y\")\n", - "\n", - "fixedLegBDC = ql.ModifiedFollowing\n", - "\n", - "fixedLegDC = ql.Thirty360(ql.Thirty360.BondBasis)\n", - "\n", - "index = ql.Euribor6M(ql.YieldTermStructureHandle(disc_curve))\n", - "\n", - "spread = 0.0\n", - "\n", - "fixedSchedule = ql.Schedule(start,\n", - " end,\n", - " fixedLegTenor,\n", - " index.fixingCalendar(),\n", - " fixedLegBDC,\n", - " fixedLegBDC,\n", - " ql.DateGeneration.Backward,\n", - " False)\n", - "\n", - "floatSchedule = ql.Schedule(start,\n", - " end,\n", - " index.tenor(),\n", - " index.fixingCalendar(),\n", - " index.businessDayConvention(),\n", - " index.businessDayConvention(),\n", - " ql.DateGeneration.Backward,\n", - " False)\n", - "\n", - "swap = ql.VanillaSwap(typ,\n", - " nominal,\n", - " fixedSchedule,\n", - " fixRate,\n", - " fixedLegDC,\n", - " floatSchedule,\n", - " index,\n", - " spread,\n", - " index.dayCounter())\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##Setup pricing engine" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "engine = ql.DiscountingSwapEngine(ql.YieldTermStructureHandle(disc_curve))" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "swap.setPricingEngine(engine)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Calculate NPV and fair rate" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "40868.85186864901" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "swap.NPV()" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.0304803675048478" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "swap.fairRate()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/.ipynb_checkpoints/YieldCurve_pure_Python-checkpoint.ipynb b/.ipynb_checkpoints/YieldCurve_pure_Python-checkpoint.ipynb deleted file mode 100644 index 3f99f6c..0000000 --- a/.ipynb_checkpoints/YieldCurve_pure_Python-checkpoint.ipynb +++ /dev/null @@ -1,91 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import scipy.interpolate as intp" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def zeroCurve(times, zeroRates):\n", - " interpolator = intp.interp1d(times, zeroRates)\n", - " def discount(t):\n", - " if t < times[0]:\n", - " r = zeroRates[0]\n", - " elif t > times[-1]:\n", - " r = zeroRates[-1]\n", - " else:\n", - " r = interpolator(t)\n", - " return np.exp(-r*t)\n", - " return discount" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.99916701379245831" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tenors = [1, 2 , 3]\n", - "zcs = [0.01, 0.0125, 0.0175]\n", - "zeroCurve(tenors, zcs)(1./12.)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} From 803cc672ecbd833231267bb685ff1c1a55fab841 Mon Sep 17 00:00:00 2001 From: Matthias Groncki Date: Sat, 11 Apr 2015 08:45:39 +0200 Subject: [PATCH 03/25] Added a brief description of this repository --- README.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/README.md b/README.md index 9dbb3ac..056da39 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,6 @@ IPythonScripts ============== + +Here you find my IPython (Jupyter) notebooks from my blog (ipythonquant.wordpress.org). + +Feel free to download the samples and play around with them. From c6b6788267104067d6082184a1929ee35ee543f2 Mon Sep 17 00:00:00 2001 From: Matthias Groncki Date: Sat, 11 Apr 2015 10:14:58 +0200 Subject: [PATCH 04/25] Some small improvements to ExposureSimulation --- ExpectedExposureSimulation.ipynb | 128 +++++++++++++++++++++---------- 1 file changed, 88 insertions(+), 40 deletions(-) diff --git a/ExpectedExposureSimulation.ipynb b/ExpectedExposureSimulation.ipynb index aec7103..93aca8b 100644 --- a/ExpectedExposureSimulation.ipynb +++ b/ExpectedExposureSimulation.ipynb @@ -66,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 294, + "execution_count": 3, "metadata": { "collapsed": false }, @@ -85,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": 295, + "execution_count": 4, "metadata": { "collapsed": false }, @@ -160,7 +160,7 @@ }, { "cell_type": "code", - "execution_count": 296, + "execution_count": 5, "metadata": { "collapsed": false }, @@ -169,23 +169,24 @@ "name": "stdout", "output_type": "stream", "text": [ - "2233.46835369\n", - "-884.749675579\n" + "The slowest run took 4.41 times longer than the fastest. This could mean that an intermediate result is being cached \n", + "10000 loops, best of 3: 34.5 Β΅s per loop\n" ] } ], "source": [ + "%%timeit\n", "# Setup pricing engine and calculate the npv\n", "engine = ql.DiscountingSwapEngine(hyts)\n", - "\n", "for deal, fixingDates in portfolio:\n", " deal.setPricingEngine(engine)\n", - " print(deal.NPV())" + " deal.NPV()\n", + " #print(deal.NPV())" ] }, { "cell_type": "code", - "execution_count": 297, + "execution_count": 6, "metadata": { "collapsed": true }, @@ -196,7 +197,7 @@ }, { "cell_type": "code", - "execution_count": 298, + "execution_count": 7, "metadata": { "collapsed": false }, @@ -211,7 +212,7 @@ }, { "cell_type": "code", - "execution_count": 299, + "execution_count": 8, "metadata": { "collapsed": true }, @@ -222,11 +223,19 @@ }, { "cell_type": "code", - "execution_count": 300, + "execution_count": 9, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "27.927\n" + ] + } + ], "source": [ "# Define evaluation grid\n", "date_grid = [today + ql.Period(i,ql.Weeks) for i in range(0,52*6)]\n", @@ -235,12 +244,14 @@ "\n", "date_grid = np.unique(np.sort(date_grid))\n", "time_grid = np.vectorize(lambda x: ql.ActualActual().yearFraction(today, x))(date_grid)\n", - "dt = time_grid[1:] - time_grid[:-1]" + "dt = time_grid[1:] - time_grid[:-1]\n", + "\n", + "print(len(time_grid)*1500*2*29e-6)" ] }, { "cell_type": "code", - "execution_count": 301, + "execution_count": 10, "metadata": { "collapsed": true }, @@ -255,31 +266,52 @@ }, { "cell_type": "code", - "execution_count": 303, + "execution_count": 13, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 loops, best of 3: 19.7 s per loop\n" + ] + } + ], "source": [ + "#%%timeit\n", "# Generate N paths\n", "N = 1500\n", - "\n", "x = np.zeros((N, len(time_grid)))\n", "y = np.zeros((N, len(time_grid)))\n", + "pillars = np.array([0.0, 0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n", + "zero_bonds = np.zeros((N, len(time_grid), 12))\n", + "for j in range(12):\n", + " zero_bonds[:, 0, j] = model.zerobond(pillars[j],\n", + " 0,\n", + " 0)\n", "for n in range(0,N):\n", " dWs = generator.nextSequence().value()\n", " for i in range(1, len(time_grid)):\n", - " x[n,i] = process.expectation(time_grid[i-1], \n", + " t0 = time_grid[i-1]\n", + " t1 = time_grid[i]\n", + " x[n,i] = process.expectation(t0, \n", " x[n,i-1], \n", - " dt[i-1]) + dWs[i-1] * process.stdDeviation(time_grid[i-1],\n", + " dt[i-1]) + dWs[i-1] * process.stdDeviation(t0,\n", " x[n,i-1],\n", " dt[i-1])\n", - " y[n,i] = (x[n,i] - process.expectation(0,0,time_grid[i])) / process.stdDeviation(0,0,time_grid[i])" + " y[n,i] = (x[n,i] - process.expectation(0,0,t1)) / process.stdDeviation(0,0,t1)\n", + " for j in range(12):\n", + " zero_bonds[n, i, j] = model.zerobond(t1+pillars[j],\n", + " t1,\n", + " y[n, i])\n", + " " ] }, { "cell_type": "code", - "execution_count": 304, + "execution_count": 12, "metadata": { "collapsed": false }, @@ -2758,7 +2790,7 @@ "RK5CYII=\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2773,41 +2805,57 @@ }, { "cell_type": "code", - "execution_count": 305, + "execution_count": 24, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 loops, best of 3: 1min 10s per loop\n" + ] + } + ], "source": [ + "%%timeit\n", "#Swap pricing under each scenario\n", - "npv_matrix = np.zeros((N,len(date_grid)))\n", + "npv_matrix = np.zeros((N,len(date_grid), len(portfolio)))\n", "for p in range(0,N):\n", " for t in range(0, len(date_grid)):\n", " date = date_grid[t]\n", - " ql.Settings.instance().setEvaluationDate(today)\n", - " # update fixing\n", - " x_p_t = y[p,t]\n", - " zeroBonds = [model.zerobond(time_grid[t]+i, \n", - " time_grid[t],\n", - " x_p_t) for i in range(1,10)]\n", " ql.Settings.instance().setEvaluationDate(date)\n", - " yc = ql.DiscountCurve([date] \n", - " + [date+ql.Period(i,ql.Years)\n", - " for i in range(1,10)], \n", - " [1] + zeroBonds, \n", + " ycDates = [date, \n", + " date + ql.Period(6, ql.Months)] \n", + " ycDates += [date + ql.Period(i,ql.Years) for i in range(1,11)]\n", + " yc = ql.DiscountCurve(ycDates, \n", + " zero_bonds[p, t, :], \n", " ql.Actual365Fixed())\n", " yc.enableExtrapolation()\n", " hyts.linkTo(yc)\n", " if euribor6m.isValidFixingDate(date):\n", - " fixing = -np.log(model.zerobond(time_grid[t]+0.5, time_grid[t], x_p_t)) / 0.5\n", + " fixing = euribor6m.fixing(date)\n", " euribor6m.addFixing(date, fixing)\n", - " for deal, fixing in portfolio:\n", - " npv_matrix[p,t] += deal.NPV()\n", + " for i in range(len(portfolio)):\n", + " npv_matrix[p, t, i] = deal.NPV()\n", " ql.IndexManager.instance().clearHistories()\n", "ql.Settings.instance().setEvaluationDate(today)\n", "hyts.linkTo(yts)" ] }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Calculate the portfolio npv by netting all NPV\n", + "portfolio_npv = np.sum(npv_matrix,axis=2)" + ] + }, { "cell_type": "code", "execution_count": 3, @@ -2840,7 +2888,7 @@ "# Plot the first 30 NPV paths\n", "plt.figure(figsize=(7,5), dpi=300)\n", "for i in range(0,30):\n", - " plt.plot(time_grid, npv_matrix[i,:])\n", + " plt.plot(time_grid, portfolio_npv[i,:])\n", "plt.xlabel(\"Time in years\")\n", "plt.ylabel(\"NPV\")\n", "plt.title(\"Simulated npv paths\")" @@ -3914,7 +3962,7 @@ ], "source": [ "# Calculate the exposure\n", - "E = npv_matrix.copy()\n", + "E = portfolio_npv.copy()\n", "E[E<0]=0\n", "# Plot the first 30 exposure paths\n", "plt.figure(figsize=(7,5), dpi=600)\n", From 78dfe9162bc6d4f6fa8f3d210d1dd3fe9b4c9383 Mon Sep 17 00:00:00 2001 From: Matthias Groncki Date: Sat, 11 Apr 2015 10:25:42 +0200 Subject: [PATCH 05/25] Bugfix in Exposure Simulaton --- ExpectedExposureSimulation.ipynb | 4942 ++++++++++++++++++++---------- 1 file changed, 3386 insertions(+), 1556 deletions(-) diff --git a/ExpectedExposureSimulation.ipynb b/ExpectedExposureSimulation.ipynb index 93aca8b..94f2b41 100644 --- a/ExpectedExposureSimulation.ipynb +++ b/ExpectedExposureSimulation.ipynb @@ -164,18 +164,9 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The slowest run took 4.41 times longer than the fastest. This could mean that an intermediate result is being cached \n", - "10000 loops, best of 3: 34.5 Β΅s per loop\n" - ] - } - ], + "outputs": [], "source": [ - "%%timeit\n", + "#%%timeit\n", "# Setup pricing engine and calculate the npv\n", "engine = ql.DiscountingSwapEngine(hyts)\n", "for deal, fixingDates in portfolio:\n", @@ -266,19 +257,11 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1 loops, best of 3: 19.7 s per loop\n" - ] - } - ], + "outputs": [], "source": [ "#%%timeit\n", "# Generate N paths\n", @@ -2790,7 +2773,7 @@ "RK5CYII=\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2805,23 +2788,15 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 13, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1 loops, best of 3: 1min 10s per loop\n" - ] - } - ], + "outputs": [], "source": [ - "%%timeit\n", + "#%%timeit\n", "#Swap pricing under each scenario\n", - "npv_matrix = np.zeros((N,len(date_grid), len(portfolio)))\n", + "npv_cube = np.zeros((N,len(date_grid), len(portfolio)))\n", "for p in range(0,N):\n", " for t in range(0, len(date_grid)):\n", " date = date_grid[t]\n", @@ -2838,7 +2813,7 @@ " fixing = euribor6m.fixing(date)\n", " euribor6m.addFixing(date, fixing)\n", " for i in range(len(portfolio)):\n", - " npv_matrix[p, t, i] = deal.NPV()\n", + " npv_cube[p, t, i] = portfolio[i][0].NPV()\n", " ql.IndexManager.instance().clearHistories()\n", "ql.Settings.instance().setEvaluationDate(today)\n", "hyts.linkTo(yts)" @@ -2846,38 +2821,1873 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Calculate the portfolio npv by netting all NPV\n", - "portfolio_npv = np.sum(npv_matrix,axis=2)" + "portfolio_npv = np.sum(npv_cube,axis=2)" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { - "ename": "NameError", - "evalue": "name 'time_grid' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m300\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m 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"execute_result" } From 0a05f2c1e9a97d1560746aa489d6e7f61b9c7c30 Mon Sep 17 00:00:00 2001 From: Matthias Groncki Date: Sat, 18 Apr 2015 11:30:40 +0200 Subject: [PATCH 06/25] Add new notebook. CVA Calculation example --- CVA_calculation_I.ipynb | 11380 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 11380 insertions(+) create mode 100644 CVA_calculation_I.ipynb diff --git a/CVA_calculation_I.ipynb b/CVA_calculation_I.ipynb new file mode 100644 index 0000000..e0048b3 --- /dev/null +++ b/CVA_calculation_I.ipynb @@ -0,0 +1,11380 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "# CVA calculation with QuantLib and Python" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Copyright (c) 2015 Matthias Groncki*\n", + "\n", + "\n", + "Redistribution and use in source and binary forms, with or without\n", + "modification, are permitted provided that the following conditions are met:\n", + "\n", + " - Redistributions of source code must retain the above copyright notice,\n", + " this list of conditions and the following disclaimer.\n", + "\n", + "THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n", + "AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n", + "IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\n", + "DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE LIABLE\n", + "FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\n", + "DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\n", + "SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\n", + "CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\n", + "OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\n", + "OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n", + "\n", + "This disclaimer is taken from the QuantLib license" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# import the used libraries\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import QuantLib as ql\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Setting evaluation date\n", + "today = ql.Date(7,4,2015)\n", + "ql.Settings.instance().setEvaluationDate(today)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Setup Marketdata\n", + "rate = ql.SimpleQuote(0.03)\n", + "rate_handle = ql.QuoteHandle(rate)\n", + "dc = ql.Actual365Fixed()\n", + "yts = ql.FlatForward(today, rate_handle, dc)\n", + "yts.enableExtrapolation()\n", + "hyts = ql.RelinkableYieldTermStructureHandle(yts)\n", + "t0_curve = ql.YieldTermStructureHandle(yts)\n", + "euribor6m = ql.Euribor6M(hyts)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Setup a dummy portfolio with two Swaps\n", + "def makeSwap(start, maturity, nominal, fixedRate, index, typ=ql.VanillaSwap.Payer):\n", + " \"\"\"\n", + " creates a plain vanilla swap with fixedLegTenor 1Y\n", + " \n", + " parameter:\n", + " \n", + " start (ql.Date) : Start Date\n", + " \n", + " maturity (ql.Period) : SwapTenor\n", + " \n", + " nominal (float) : Nominal\n", + " \n", + " fixedRate (float) : rate paid on fixed leg\n", + " \n", + " index (ql.IborIndex) : Index\n", + " \n", + " return: tuple(ql.Swap, list) Swap and all fixing dates\n", + " \n", + " \n", + " \"\"\"\n", + " end = ql.TARGET().advance(start, maturity)\n", + " fixedLegTenor = ql.Period(\"1y\")\n", + " fixedLegBDC = ql.ModifiedFollowing\n", + " fixedLegDC = ql.Thirty360(ql.Thirty360.BondBasis)\n", + " spread = 0.0\n", + " fixedSchedule = ql.Schedule(start,\n", + " end, \n", + " fixedLegTenor, \n", + " index.fixingCalendar(), \n", + " fixedLegBDC,\n", + " fixedLegBDC, \n", + " ql.DateGeneration.Backward,\n", + " False)\n", + " floatSchedule = ql.Schedule(start,\n", + " end,\n", + " index.tenor(),\n", + " index.fixingCalendar(),\n", + " index.businessDayConvention(),\n", + " index.businessDayConvention(),\n", + " ql.DateGeneration.Backward,\n", + " False)\n", + " swap = ql.VanillaSwap(typ, \n", + " nominal,\n", + " fixedSchedule,\n", + " fixedRate,\n", + " fixedLegDC,\n", + " floatSchedule,\n", + " index,\n", + " spread,\n", + " index.dayCounter())\n", + " return swap, [index.fixingDate(x) for x in floatSchedule][:-1]\n", + "\n", + "portfolio = [makeSwap(today + ql.Period(\"2d\"),\n", + " ql.Period(\"5Y\"),\n", + " 1e6,\n", + " 0.03,\n", + " euribor6m),\n", + " makeSwap(today + ql.Period(\"2d\"),\n", + " ql.Period(\"4Y\"),\n", + " 5e5,\n", + " 0.03,\n", + " euribor6m,\n", + " ql.VanillaSwap.Receiver),\n", + " ]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%%timeit\n", + "# Setup pricing engine and calculate the npv\n", + "engine = ql.DiscountingSwapEngine(hyts)\n", + "for deal, fixingDates in portfolio:\n", + " deal.setPricingEngine(engine)\n", + " deal.NPV()\n", + " #print(deal.NPV())" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Stochastic Process " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Assume the model is already calibrated either historical or market implied\n", + "volas = [ql.QuoteHandle(ql.SimpleQuote(0.0075)),\n", + " ql.QuoteHandle(ql.SimpleQuote(0.0075))]\n", + "meanRev = [ql.QuoteHandle(ql.SimpleQuote(0.02))]\n", + "model = ql.Gsr(t0_curve, [today+100], volas, meanRev, 16.)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "process = model.stateProcess()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6.786\n" + ] + } + ], + "source": [ + "# Define evaluation grid\n", + "date_grid = [today + ql.Period(i,ql.Months) for i in range(0,12*6)]\n", + "for deal in portfolio:\n", + " date_grid += deal[1]\n", + "\n", + "date_grid = np.unique(np.sort(date_grid))\n", + "time_grid = np.vectorize(lambda x: ql.ActualActual().yearFraction(today, x))(date_grid)\n", + "dt = time_grid[1:] - time_grid[:-1]\n", + "\n", + "print(len(time_grid)*1500*2*29e-6)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Random number generator\n", + "seed = 1\n", + "urng = ql.MersenneTwisterUniformRng(seed)\n", + "usrg = ql.MersenneTwisterUniformRsg(len(time_grid)-1,urng)\n", + "generator = ql.InvCumulativeMersenneTwisterGaussianRsg(usrg)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%%timeit\n", + "# Generate N paths\n", + "N = 1500\n", + "x = np.zeros((N, len(time_grid)))\n", + "y = np.zeros((N, len(time_grid)))\n", + "pillars = np.array([0.0, 0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n", + "zero_bonds = np.zeros((N, len(time_grid), 12))\n", + "\n", + "for j in range(12):\n", + " zero_bonds[:, 0, j] = model.zerobond(pillars[j],\n", + " 0,\n", + " 0)\n", + "for n in range(0,N):\n", + " dWs = generator.nextSequence().value()\n", + " for i in range(1, len(time_grid)):\n", + " t0 = time_grid[i-1]\n", + " t1 = time_grid[i]\n", + " x[n,i] = process.expectation(t0, \n", + " x[n,i-1], \n", + " dt[i-1]) + dWs[i-1] * process.stdDeviation(t0,\n", + " x[n,i-1],\n", + " dt[i-1])\n", + " y[n,i] = (x[n,i] - process.expectation(0,0,t1)) / process.stdDeviation(0,0,t1)\n", + " for j in range(12):\n", + " zero_bonds[n, i, j] = model.zerobond(t1+pillars[j],\n", + " t1,\n", + " y[n, i])\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": [ + 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p in range(0,N):\n", + " for t in range(0, len(date_grid)):\n", + " date = date_grid[t]\n", + " ql.Settings.instance().setEvaluationDate(date)\n", + " ycDates = [date, \n", + " date + ql.Period(6, ql.Months)] \n", + " ycDates += [date + ql.Period(i,ql.Years) for i in range(1,11)]\n", + " yc = ql.DiscountCurve(ycDates, \n", + " zero_bonds[p, t, :], \n", + " ql.Actual365Fixed())\n", + " yc.enableExtrapolation()\n", + " hyts.linkTo(yc)\n", + " if euribor6m.isValidFixingDate(date):\n", + " fixing = euribor6m.fixing(date)\n", + " euribor6m.addFixing(date, fixing)\n", + " for i in range(len(portfolio)):\n", + " npv_cube[p, t, i] = portfolio[i][0].NPV()\n", + " ql.IndexManager.instance().clearHistories()\n", + "ql.Settings.instance().setEvaluationDate(today)\n", + "hyts.linkTo(yts)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Calculate the discounted npvs\n", + "discounted_cube = np.zeros(npv_cube.shape)\n", + "for i in range(npv_cube.shape[2]):\n", + " discounted_cube[:,:,i] = npv_cube[:,:,i] * discount_factors" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Calculate the portfolio npv by netting all NPV\n", + "portfolio_npv = np.sum(npv_cube,axis=2)\n", + "discounted_npv = np.sum(discounted_cube, axis=2)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAucAAAJoCAYAAADF4U/FAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXmcj9UXx9/HjBljX7PvS9kTiiR3CC0kUqSNlESlPa22\n", + "NumXFElKJakkJQml5pJKJGkRZSvZIsLYxozz++M+k68x+8Lgvl+v5zXfeb7Pc+99nu927rnnfI6o\n", + 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"ax1.set_title(\"Simulated npv paths\")\n", + "ax2.set_xlabel(\"Time in years\")\n", + "ax2.set_ylabel(\"NPV in time 0 Euros\")\n", + "ax2.set_title(\"Simulated discounted npv paths\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAucAAAJoCAYAAADF4U/FAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3XeYXVX1xvHvGwIBklBC7yCEEgTpkeqJIkWqihRBKRGE\n", + "gKACCqISVEBAQfpPEaRJbwIBJJSNoBTpCNIJQjABA4Res35/7D1wM5lyJzN35kLez/PMM/eeus/U\n", + "dfZZe21FBGZmZmZm1vf69XUDzMzMzMwsc3BuZmZmZtYkHJybmZmZmTUJB+dmZmZmZk3CwbmZmZmZ\n", + "WZNwcG5mZmZm1iQcnJuZdUDSjpL+2qBjnynpl404dhvn2kXSrb1xrhmJpCUlTZHk/6dm1iP8x8TM\n", + "ZniS1pP0D0mvSpok6TZJawBExJ8jYuMGnTrKRz1tTJJGNqgdVidJ4yR9sa/bYWafXv37ugFmZn1J\n", + 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range(0,n):\n", + " ax1.plot(time_grid, E[i,:])\n", + "for i in range(0,n):\n", + " ax2.plot(time_grid, dE[i,:])\n", + "ax1.set_xlabel(\"Time in years\")\n", + "ax1.set_ylabel(\"Exposure\")\n", + "ax1.set_ylim([-10000,70000])\n", + "ax1.set_title(\"Simulated exposure paths\")\n", + "ax2.set_xlabel(\"Time in years\")\n", + "ax2.set_ylabel(\"Discounted Exposure\")\n", + "ax2.set_ylim([-10000,70000])\n", + "ax2.set_title(\"Simulated discounted exposure paths\")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Calculate the expected exposure\n", + "E = portfolio_npv.copy()\n", + "E[E<0]=0\n", + "EE = np.sum(E, axis=0)/N\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Calculate the discounted expected exposure\n", + "dE = discounted_npv.copy()\n", + "dE[dE<0] = 0\n", + "dEE = np.sum(dE, axis=0)/N\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAfoAAAJoCAYAAACOUyOcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3XmcXFWZ//HPlyysCSGAAcI6GJAoiKLEDY3+EFERXMEF\n", + "BjWKDirq6CjoqDgq7gvogA4qBEQUFxhQRAISxWUMIggS2YQACSQghAQIQiDP749zCi5Fd7o66ap7\n", + "6vb3/Xr1q2/d9VQlXc99zjn3HEUEZmZm1kzr1F0AMzMz6x4HejMzswZzoDczM2swB3ozM7MGc6A3\n", + "MzNrMAd6MzOzBnOgN7Nhk/RmSRfXXQ4zG5oDvVkPSFogaYWkeyo/x9VYnrmSZtV1fTPrnbF1F8Bs\n", + "lAhgv4j4Vd0FyTxS1gAkjY2Ih+ouh9lIckZvVjNJJ0j6ceX15yVdkJdnSloo6ShJd0i6UdIbK/uu\n", + "K+lLkm6StDifa73K9gMkXS5pmaTrJb1E0meAvYBvVGsWJD1J0hxJd0q6WtLrKufZVNLZ+Tx/BHYc\n", + "4j09S9LvJS3N139BXv+c/D62zq+fKukuSTvl1wskHSnpqrz+u5LWrZz37ZKuy2X8X0lbVrZ9VdKS\n", + 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"outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAcoAAAFRCAYAAAD0Pe1cAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xm8XHV9//HXO0Ag7HsgAdkMKAoCQaIiNeCGIosrLkXU\n", + "VO2PIqC2SmjV1FYFK6LUYl1YAhUUES1URHCJYq1EdiSiIARIIAECJGEP8Pn98f0OmVzuPXdu7p3z\n", + "PXPv+/l4zOOeOXPmnM9MJvOZ766IwMzMzPo3rnQAZmZmTeZEaWZmVsGJ0szMrIITpZmZWQUnSjMz\n", + "swpOlGZmZhWcKM1GgKT3SbqidBxNIen1kn5Y4/VmSvpWXddbHZKOlnRi6Ths6JworQhJ8yU9Kml5\n", + "2+3UgvHMkTSjS+feXtIzfV7rcklv78b1GuJzwBdG4kT5vdux7f50SXe1HxMRX4iID47E9UZCfzEC\n", + "3wLeI2mLEjHZ6luzdAA2ZgXwpoj4RelAsjpm3tgoIp6p4TojStK4ocQt6aXAhhExdyTDGMFzFRER\n", + "T0j6CfBe4OTS8VjnXKK0xpH0dUkXtN0/SdLP8vZ0SQtyVdt9km6X9O62Y9eW9CVJd0halM+1Ttvj\n", + "h0q6TtJSSbfmKsLPAfsBX2sv2Up6gaTLJS2RdHN7CVDSZpIuyue5EthpNV/reEnXSjo6319D0v9K\n", + 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expected exposure path\n", + "plt.figure(figsize=(7,5), dpi=300)\n", + "plt.plot(time_grid, dEE)\n", + "plt.xlabel(\"Time in years\")\n", + "plt.ylabel(\"Discounting Expected Exposure\")\n", + "plt.ylim([-2000,10000])\n", + "plt.title(\"Expected Exposure (netting set)\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAcgAAAFRCAYAAADwyD1hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xmc3FWV///XG8ISIBABDUkIghAcIiAIEhCRAApRZ1iU\n", + "9TtuI+ISUYdxA0cl6oigAoIKjsgSUKL8cECQRZYxKipEkT1GYCRAAgkQQgiyhpzfH/cWqTTVne50\n", + "Vd1Pdb2fj0c/uup+lnurs5y+597PvYoIzMzMbEWrlW6AmZlZFTlAmpmZNeAAaWZm1oADpJmZWQMO\n", + "kGZmZg04QJqZmTXgAGlWMZL2k3RJG+s7TtJZ7apvVUg6WtKJpdth3cUB0qyfJM2R9LSkJZLmSzpX\n", + 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"output_type": "execute_result" + } + ], + "source": [ + "# calculate the maximum pfe\n", + "MPFE = np.max(PFE_curve)\n", + "MPFE" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "38683.65078838585" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# alternative pfe 95% quantile of the maxima of each exposure paths\n", + "PFE = np.sort(np.max(E,axis=1))[0.95*N]\n", + "PFE" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Setup Default Curve \n", + "pd_dates = [today + ql.Period(i, ql.Years) for i in range(11)]\n", + "hzrates = [0.02 * i for i in range(11)]\n", + "pd_curve = ql.HazardRateCurve(pd_dates,hzrates,ql.Actual365Fixed())\n", + "pd_curve.enableExtrapolation()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAmoAAAJoCAYAAADWGs10AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xm8rWP9//HX25B5Hr84IlPmECHqCCVCqYgUQiKlb5Pq\n", + "W9GvQqUylQwnhEiaaFCkE5mHYz7GHBxjGY+pcD6/P65rs84+++y99tprrete93o/H4/12Huvda/7\n", + "/tyL+3M+67qvQRGBmZmZmVXPbKUDMDMzM7OhuVAzMzMzqygXamZmZmYV5ULNzMzMrKJcqJmZmZlV\n", + "lAs1MzMzs4pyoWYjkrS/pEclPSNpkTHua4qkLdsV2yiPPV3SG1p87yzjlrS5pNsHbfuO/PtXJJ3U\n", + "WsRm1mmSjpf01Tbs51RJ32xHTCMc51BJp7f43j0lXTrM63+U9JGhtpU0TdIKrRzXxsaFWs3louH5\n", + "XGQ9KekySftJUpPvnxP4PrBlRCwYEU+OMaTIj6YSTkP80yQ9IukUSfONMYZWvBr3TC9EXBoRbxy0\n", + "7cBrh0XEvgCSVsjFoq87s2FI2kzS5ZKekvS4pH9IenMnjhUR+0fEt9qxK2aRI3LR80rOY09LmiRp\n", + "uzEcpyMiYtuIGDInR8QCETEFuleUWuJ/MOovgPdExILA8sARwMHAhCbfvzQwNzC5M+GNaCD+BYD1\n", + 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np.vectorize(pd_curve.hazardRate)(times)\n", + "f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(10,10))\n", + "ax1.plot(times, dp)\n", + "ax2.plot(times, sp)\n", + "ax3.plot(times, dd)\n", + "ax4.plot(times, hr)\n", + "ax1.set_xlabel(\"Time in years\")\n", + "ax2.set_xlabel(\"Time in years\")\n", + "ax3.set_xlabel(\"Time in years\")\n", + "ax4.set_xlabel(\"Time in years\")\n", + "ax1.set_ylabel(\"Probability\")\n", + "ax2.set_ylabel(\"Probability\")\n", + "ax3.set_ylabel(\"Density\")\n", + "ax4.set_ylabel(\"HazardRate\")\n", + "ax1.set_title(\"Default Probability\")\n", + "ax2.set_title(\"Survival Probability\")\n", + "ax3.set_title(\"Default density\")\n", + "ax4.set_title(\"Harzard rate\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Calculation of the default probs\n", + "defaultProb_vec = np.vectorize(pd_curve.defaultProbability)\n", + "dPD = defaultProb_vec(time_grid[:-1], time_grid[1:])" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "586.17763177944278" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Calculation of the CVA\n", + "recovery = 0.4\n", + "CVA = (1-recovery) * np.sum(dEE[1:] * dPD)\n", + "CVA" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} From 4a5efbd501d052bff88be4c682e6e8d1a07b6e05 Mon Sep 17 00:00:00 2001 From: Matthias Groncki Date: Sun, 26 Apr 2015 10:37:15 +0200 Subject: [PATCH 07/25] Added Script for swaption pricing --- MonteCarloPricing-Swaption.ipynb | 1962 ++++++++++++++++++++++++++++++ 1 file changed, 1962 insertions(+) create mode 100644 MonteCarloPricing-Swaption.ipynb diff --git a/MonteCarloPricing-Swaption.ipynb b/MonteCarloPricing-Swaption.ipynb new file mode 100644 index 0000000..c592ca1 --- /dev/null +++ b/MonteCarloPricing-Swaption.ipynb @@ -0,0 +1,1962 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "#Swaption Pricing : Monte-Carlo Methods" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Copyright (c) 2015 Matthias Groncki*\n", + "\n", + "\n", + "Redistribution and use in source and binary forms, with or without\n", + "modification, are permitted provided that the following conditions are met:\n", + "\n", + " - Redistributions of source code must retain the above copyright notice,\n", + " this list of conditions and the following disclaimer.\n", + "\n", + "THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n", + "AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n", + "IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\n", + "DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE LIABLE\n", + "FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\n", + "DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\n", + "SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\n", + "CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\n", + "OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\n", + "OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n", + "\n", + "This disclaimer is taken from the QuantLib license" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# import the used libraries\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import QuantLib as ql\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Setting evaluation date\n", + "today = ql.Date(7,4,2015)\n", + "ql.Settings.instance().setEvaluationDate(today)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Setup Marketdata\n", + "rate = ql.SimpleQuote(0.03)\n", + "rate_handle = ql.QuoteHandle(rate)\n", + "dc = ql.Actual365Fixed()\n", + "yts = ql.FlatForward(today, rate_handle, dc)\n", + "yts.enableExtrapolation()\n", + "hyts = ql.RelinkableYieldTermStructureHandle(yts)\n", + "t0_curve = ql.YieldTermStructureHandle(yts)\n", + "euribor6m = ql.Euribor6M(hyts)\n", + "cal = ql.TARGET()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Setup a dummy portfolio with two Swaps\n", + "def makeSwap(start, maturity, nominal, fixedRate, index, typ=ql.VanillaSwap.Payer):\n", + " \"\"\"\n", + " creates a plain vanilla swap with fixedLegTenor 1Y\n", + " \n", + " parameter:\n", + " \n", + " start (ql.Date) : Start Date\n", + " \n", + " maturity (ql.Period) : SwapTenor\n", + " \n", + " nominal (float) : Nominal\n", + " \n", + " fixedRate (float) : rate paid on fixed leg\n", + " \n", + " index (ql.IborIndex) : Index\n", + " \n", + " return: tuple(ql.Swap, list) Swap and all fixing dates\n", + " \n", + " \n", + " \"\"\"\n", + " end = ql.TARGET().advance(start, maturity)\n", + " fixedLegTenor = ql.Period(\"1y\")\n", + " fixedLegBDC = ql.ModifiedFollowing\n", + " fixedLegDC = ql.Thirty360(ql.Thirty360.BondBasis)\n", + " spread = 0.0\n", + " fixedSchedule = ql.Schedule(start,\n", + " end, \n", + " fixedLegTenor, \n", + " index.fixingCalendar(), \n", + " fixedLegBDC,\n", + " fixedLegBDC, \n", + " ql.DateGeneration.Backward,\n", + " False)\n", + " floatSchedule = ql.Schedule(start,\n", + " end,\n", + " index.tenor(),\n", + " index.fixingCalendar(),\n", + " index.businessDayConvention(),\n", + " index.businessDayConvention(),\n", + " ql.DateGeneration.Backward,\n", + " False)\n", + " swap = ql.VanillaSwap(typ, \n", + " nominal,\n", + " fixedSchedule,\n", + " fixedRate,\n", + " fixedLegDC,\n", + " floatSchedule,\n", + " index,\n", + " spread,\n", + " index.dayCounter())\n", + " return swap, [index.fixingDate(x) for x in floatSchedule][:-1]\n", + "\n", + "\n", + "def makeSwaption(swap, callDates, settlement):\n", + " if len(callDates) == 1:\n", + " exercise = ql.EuropeanExercise(callDates[0])\n", + " else:\n", + " exercise = ql.BermudanExercise(callDates)\n", + " return ql.Swaption(swap, exercise, settlement)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lets start with an european swaption" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a forward starting plain vanilla swap using the helper function above and create a european swaption which allows us to to enter the swap in one year from today." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "settlementDate = today + ql.Period(\"1Y\")\n", + "\n", + "swaps = [makeSwap(settlementDate,\n", + " ql.Period(\"5Y\"),\n", + " 1e6,\n", + " 0.03047096,\n", + " euribor6m)\n", + " ]\n", + "\n", + "calldates = [euribor6m.fixingDate(settlementDate)]\n", + " \n", + "swaptions = [makeSwaption(swap, \n", + " calldates, \n", + " ql.Settlement.Physical) \n", + " for swap, fd in swaps]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pricing of the underlying at time 0 using the QuantLib pricing engine" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Swap NPV at time 0: 0.0091\n" + ] + } + ], + "source": [ + "#%%timeit\n", + "# Setup pricing engine and calculate the npv of the underlying swap\n", + "engine = ql.DiscountingSwapEngine(hyts)\n", + "for deal, fixingDates in swaps:\n", + " deal.setPricingEngine(engine)\n", + " print(\"Swap NPV at time 0: %.4f\" % deal.NPV())\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Setup the Gaussian Shortrate model (a.k.a Hull White model)\n", + "\n", + "Don't worry about calibration, assume we know the calbriated model parameter" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Stochastic Process " + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Assume the model is already calibrated either historical or market implied\n", + "volas = [ql.QuoteHandle(ql.SimpleQuote(0.0075)),\n", + " ql.QuoteHandle(ql.SimpleQuote(0.0075))]\n", + "meanRev = [ql.QuoteHandle(ql.SimpleQuote(0.02))]\n", + "model = ql.Gsr(t0_curve, [today+100], volas, meanRev, 16.)\n", + "process = model.stateProcess()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Calculate the swaption price using an integral pricing engine" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "swaptionEngine = ql.Gaussian1dSwaptionEngine(model)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Swaption NPV : 12898.40\n" + ] + } + ], + "source": [ + "for swaption in swaptions:\n", + " swaption.setPricingEngine(swaptionEngine)\n", + " print(\"Swaption NPV : %.2f\" % swaption.NPV())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "###Pricing with an Monte Carlo method" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "####Create a swap path pricer in Python \n", + "\n", + "Convert all Dates into times in years (using the same DayCounter as in the yieldTermStructure and store all fix cashflows in a numpy array." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "mcDC = yts.dayCounter()\n", + "\n", + "def timeFromReferenceFactory(daycounter, ref):\n", + " \"\"\"\n", + " returns a function, that calculate the time in years\n", + " from a the reference date *ref* to date *dat* \n", + " with respect to the given DayCountConvention *daycounter*\n", + " \n", + " Parameter:\n", + " dayCounter (ql.DayCounter)\n", + " ref (ql.Date)\n", + " \n", + " Return:\n", + " \n", + " f(np.array(ql.Date)) -> np.array(float)\n", + " \"\"\"\n", + " def impl(dat):\n", + " return daycounter.yearFraction(ref, dat)\n", + " return np.vectorize(impl)\n", + "\n", + "timeFromReference = timeFromReferenceFactory(mcDC, today)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "fixed_leg = swap.leg(0)\n", + "n = len(fixed_leg)\n", + "fixed_times = np.zeros(n)\n", + "fixed_amounts = np.zeros(n)\n", + "for i in range(n):\n", + " cf = fixed_leg[i]\n", + " fixed_times[i] = timeFromReference(cf.date())\n", + " fixed_amounts[i] = cf.amount()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "float_leg = swap.leg(1)\n", + "n = len(float_leg)\n", + "float_times = np.zeros(n)\n", + "float_dcf = np.zeros(n)\n", + "ibor_dcf = np.zeros(n)\n", + "ibor_start_time = np.zeros(n)\n", + "ibor_end_time = np.zeros(n)\n", + "for i in range(n):\n", + " cf = ql.as_floating_rate_coupon(float_leg[i])\n", + " iborIndex = cf.index()\n", + " value_date = cf.referencePeriodStart()\n", + " index_mat = cf.referencePeriodEnd()\n", + " float_dcf[i] = cf.accrualPeriod()\n", + " ibor_start_time[i] = timeFromReference(value_date)\n", + " ibor_end_time[i] = timeFromReference(index_mat)\n", + " ibor_dcf[i] = iborIndex.dayCounter().yearFraction(value_date,\n", + " index_mat)\n", + " float_times[i] = timeFromReference(cf.date())\n", + "\n", + "df_times = np.concatenate([fixed_times, ibor_start_time, ibor_end_time, float_times])\n", + "df_times = np.unique(df_times)\n", + "\n", + "fix_idx = np.in1d(df_times, fixed_times, True).nonzero()\n", + "float_idx = np.in1d(df_times, float_times, True).nonzero()\n", + "iborstart_idx = np.in1d(df_times, ibor_start_time, True).nonzero()\n", + "iborend_idx = np.in1d(df_times, ibor_end_time, True).nonzero()" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.99726027])" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "callTimes = timeFromReference(calldates)\n", + "callTimes" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "Calculate the index fixings for all fixing times and discount factors for all payment dates" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0090561973629519343" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#%%timeit\n", + "t = 0\n", + "x_t = 0\n", + "discount = np.vectorize(lambda T: model.zerobond(T, t, x_t))\n", + "dfs = discount(df_times)\n", + "np.sum((dfs[iborstart_idx] / dfs[iborend_idx] - 1) / ibor_dcf * float_dcf * dfs[float_idx] * 1e6) - np.sum(fixed_amounts *dfs[fix_idx])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Monte Carlo Simulation\n", + "\n", + "#### Generate time grid and paths" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Define evaluation grid\n", + "date_grid = [today] + calldates\n", + "\n", + "date_grid = np.unique(np.sort(date_grid))\n", + "time_grid = np.vectorize(lambda x: ql.Actual365Fixed().yearFraction(today, x))(date_grid)\n", + "dt = time_grid[1:] - time_grid[:-1]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Random number generator\n", + "seed = 1\n", + "urng = ql.MersenneTwisterUniformRng(seed)\n", + "usrg = ql.MersenneTwisterUniformRsg(len(time_grid)-1,urng)\n", + "generator = ql.InvCumulativeMersenneTwisterGaussianRsg(usrg)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%%timeit\n", + "# Generate N paths\n", + "N = 100000\n", + "m = len(time_grid)\n", + "x = np.zeros((N, m))\n", + "y = np.zeros((N, m))\n", + "numeraires = np.zeros((N,m))\n", + "dfs = np.zeros((N, m, len(df_times)))\n", + "\n", + "for n in range(0,N):\n", + " numeraires[n, 0] = model.numeraire(0, 0)\n", + " \n", + "for n in range(0,N):\n", + " dWs = generator.nextSequence().value()\n", + " for i in range(1, len(time_grid)):\n", + " t0 = time_grid[i-1]\n", + " t1 = time_grid[i]\n", + " x[n,i] = process.expectation(t0, \n", + " x[n,i-1], \n", + " dt[i-1]) + dWs[i-1] * process.stdDeviation(t0,\n", + " x[n,i-1],\n", + " dt[i-1])\n", + " y[n,i] = (x[n,i] - process.expectation(0,0,t1)) / process.stdDeviation(0,0,t1)\n", + " numeraires[n ,i] = model.numeraire(t1, y[n, i])\n", + " dfs[n,i] = np.vectorize(lambda T : model.zerobond(T, t1, y[n,i]))(df_times) " + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEACAYAAABcXmojAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + 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"execution_count": 52, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%%timeit\n", + "floatLeg_npv = np.sum((dfs[:,-1, iborstart_idx][:,0,:] / dfs[:, -1, iborend_idx][:,0,:] - 1) / ibor_dcf * float_dcf * dfs[:,-1, float_idx][:,0,:] * 1e6, \n", + " axis = 1) \n", + "fixedLeg_npv = np.sum(fixed_amounts * dfs[:, -1, fix_idx][:,0,:], axis=1)\n", + "npv = (floatLeg_npv - fixedLeg_npv)\n", + "npv[npv < 0] = 0\n", + "npv = npv / numeraires[:,-1] * numeraires[:,0]" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "12841.486989266908" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(npv)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} From 6485d161835ae02d5e66f786a987587bbcce3cbf Mon Sep 17 00:00:00 2001 From: Matthias Groncki Date: Sat, 2 May 2015 09:24:31 +0200 Subject: [PATCH 08/25] Some small fixes in notation and code clean up --- MonteCarloPricing-Swaption.ipynb | 1593 +++--------------------------- 1 file changed, 149 insertions(+), 1444 deletions(-) diff --git a/MonteCarloPricing-Swaption.ipynb b/MonteCarloPricing-Swaption.ipynb index c592ca1..3e157a3 100644 --- a/MonteCarloPricing-Swaption.ipynb +++ b/MonteCarloPricing-Swaption.ipynb @@ -38,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 103, "metadata": { "collapsed": false }, @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 104, "metadata": { "collapsed": false }, @@ -66,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 105, "metadata": { "collapsed": false }, @@ -86,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 106, "metadata": { "collapsed": false }, @@ -171,7 +171,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 107, "metadata": { "collapsed": false }, @@ -203,7 +203,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 108, "metadata": { "collapsed": false }, @@ -220,9 +220,9 @@ "#%%timeit\n", "# Setup pricing engine and calculate the npv of the underlying swap\n", "engine = ql.DiscountingSwapEngine(hyts)\n", - "for deal, fixingDates in swaps:\n", - " deal.setPricingEngine(engine)\n", - " print(\"Swap NPV at time 0: %.4f\" % deal.NPV())\n" + "for swap, fixingDates in swaps:\n", + " swap.setPricingEngine(engine)\n", + " print(\"Swap NPV at time 0: %.4f\" % swap.NPV())\n" ] }, { @@ -236,7 +236,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 109, "metadata": { "collapsed": true }, @@ -247,7 +247,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 110, "metadata": { "collapsed": false }, @@ -270,7 +270,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 111, "metadata": { "collapsed": false }, @@ -281,7 +281,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 112, "metadata": { "collapsed": false }, @@ -320,7 +320,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 113, "metadata": { "collapsed": false }, @@ -351,9 +351,9 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 114, "metadata": { - "collapsed": true + "collapsed": false }, "outputs": [], "source": [ @@ -369,7 +369,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 115, "metadata": { "collapsed": false }, @@ -379,51 +379,77 @@ "n = len(float_leg)\n", "float_times = np.zeros(n)\n", "float_dcf = np.zeros(n)\n", - "ibor_dcf = np.zeros(n)\n", - "ibor_start_time = np.zeros(n)\n", - "ibor_end_time = np.zeros(n)\n", + "accrual_start_time = np.zeros(n)\n", + "accrual_end_time = np.zeros(n)\n", + "nominals = np.zeros(n)\n", "for i in range(n):\n", + " # convert base classiborstart_idx Cashflow to\n", + " # FloatingRateCoupon\n", " cf = ql.as_floating_rate_coupon(float_leg[i])\n", " iborIndex = cf.index()\n", " value_date = cf.referencePeriodStart()\n", " index_mat = cf.referencePeriodEnd()\n", + " # year fraction\n", " float_dcf[i] = cf.accrualPeriod()\n", - " ibor_start_time[i] = timeFromReference(value_date)\n", - " ibor_end_time[i] = timeFromReference(index_mat)\n", - " ibor_dcf[i] = iborIndex.dayCounter().yearFraction(value_date,\n", - " index_mat)\n", + " # calculate the start and end time\n", + " accrual_start_time[i] = timeFromReference(value_date)\n", + " accrual_end_time[i] = timeFromReference(index_mat)\n", + " # payment time\n", " float_times[i] = timeFromReference(cf.date())\n", - "\n", - "df_times = np.concatenate([fixed_times, ibor_start_time, ibor_end_time, float_times])\n", - "df_times = np.unique(df_times)\n", - "\n", - "fix_idx = np.in1d(df_times, fixed_times, True).nonzero()\n", - "float_idx = np.in1d(df_times, float_times, True).nonzero()\n", - "iborstart_idx = np.in1d(df_times, ibor_start_time, True).nonzero()\n", - "iborend_idx = np.in1d(df_times, ibor_end_time, True).nonzero()" + " # nominals \n", + " nominals[i] = cf.nominal()" ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 116, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0.99726027])" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "callTimes = timeFromReference(calldates)\n", - "callTimes" + "# Store all times for which we need a discount factor in one array\n", + "df_times = np.concatenate([fixed_times, \n", + " accrual_start_time, \n", + " accrual_end_time, \n", + " float_times])\n", + "df_times = np.unique(df_times)" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Store indices of fix leg payment times in \n", + "# the df_times array\n", + "fix_idx = np.in1d(df_times, fixed_times, True)\n", + "fix_idx = fix_idx.nonzero()\n", + "# Indices of the floating leg payment times \n", + "# in the df_times array\n", + "float_idx = np.in1d(df_times, float_times, True)\n", + "float_idx = float_idx.nonzero()\n", + "# Indices of the accrual start and end time\n", + "# in the df_times array\n", + "accrual_start_idx = np.in1d(df_times, accrual_start_time, True)\n", + "accrual_start_idx = accrual_start_idx.nonzero()\n", + "accrual_end_idx = np.in1d(df_times, accrual_end_time, True)\n", + "accrual_end_idx = accrual_end_idx.nonzero()" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Convert call date to time\n", + "callTimes = timeFromReference(calldates)" ] }, { @@ -432,34 +458,52 @@ "collapsed": true }, "source": [ - "Calculate the index fixings for all fixing times and discount factors for all payment dates" + "####Test the t0 Swap Pricing" ] }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 119, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.0090561973629519343" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "#%%timeit\n", "t = 0\n", "x_t = 0\n", + "# Calculate all discount factors\n", "discount = np.vectorize(lambda T: model.zerobond(T, t, x_t))\n", "dfs = discount(df_times)\n", - "np.sum((dfs[iborstart_idx] / dfs[iborend_idx] - 1) / ibor_dcf * float_dcf * dfs[float_idx] * 1e6) - np.sum(fixed_amounts *dfs[fix_idx])" + "# Calculate fixed leg npv\n", + "fix_leg_npv = np.sum(fixed_amounts * dfs[fix_idx])\n", + "# Estimate the index fixings\n", + "index_fixings = (dfs[accrual_start_idx] / dfs[accrual_end_idx] - 1) \n", + "index_fixings /= float_dcf\n", + "# Calculate the floating leg npv\n", + "float_leg_npv = np.sum(nominals * index_fixings * float_dcf * dfs[float_idx])\n", + "npv = float_leg_npv - fix_leg_npv\n" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Swap NPV at time 0: 0.0091\n", + "Error : 0.00000000\n" + ] + } + ], + "source": [ + "npv\n", + "print(\"Swap NPV at time 0: %.4f\" % npv)\n", + "print(\"Error : %.8f\" % (npv - swap.NPV()))" ] }, { @@ -473,7 +517,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 121, "metadata": { "collapsed": false }, @@ -489,7 +533,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 122, "metadata": { "collapsed": false }, @@ -504,7 +548,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": null, "metadata": { "collapsed": false }, @@ -512,1420 +556,81 @@ "source": [ "#%%timeit\n", "# Generate N paths\n", - "N = 100000\n", + "M = 100000\n", "m = len(time_grid)\n", - "x = np.zeros((N, m))\n", - "y = np.zeros((N, m))\n", - "numeraires = np.zeros((N,m))\n", - "dfs = np.zeros((N, m, len(df_times)))\n", + "x = np.zeros((M, m))\n", + "y = np.zeros((M, m))\n", + "numeraires = np.zeros((M,m))\n", + "dfs = np.zeros((M, m, len(df_times)))\n", "\n", - "for n in range(0,N):\n", + "for n in range(0,M):\n", " numeraires[n, 0] = model.numeraire(0, 0)\n", " \n", - "for n in range(0,N):\n", + "for n in range(0,M):\n", " dWs = generator.nextSequence().value()\n", " for i in range(1, len(time_grid)):\n", " t0 = time_grid[i-1]\n", " t1 = time_grid[i]\n", - " x[n,i] = process.expectation(t0, \n", - " x[n,i-1], \n", - " dt[i-1]) + dWs[i-1] * process.stdDeviation(t0,\n", - " x[n,i-1],\n", - " dt[i-1])\n", - " y[n,i] = (x[n,i] - process.expectation(0,0,t1)) / process.stdDeviation(0,0,t1)\n", + " e = process.expectation(t0, \n", + " x[n,i-1], \n", + " dt[i-1])\n", + " std = process.stdDeviation(t0,\n", + " x[n,i-1],\n", + " dt[i-1])\n", + " x[n,i] = e + dWs[i-1] * std \n", + " e_0_0 = process.expectation(0,0,t1)\n", + " std_0_0 = process.stdDeviation(0,0,t1)\n", + " y[n,i] = (x[n,i] - e_0_0) / std_0_0\n", " numeraires[n ,i] = model.numeraire(t1, y[n, i])\n", " dfs[n,i] = np.vectorize(lambda T : model.zerobond(T, t1, y[n,i]))(df_times) " ] }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 136, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEACAYAAABcXmojAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - 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"dfs[:,-1, float_idx].shape" - ] - }, - { - "cell_type": "code", - "execution_count": 52, + "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#%%timeit\n", - "floatLeg_npv = np.sum((dfs[:,-1, iborstart_idx][:,0,:] / dfs[:, -1, iborend_idx][:,0,:] - 1) / ibor_dcf * float_dcf * dfs[:,-1, float_idx][:,0,:] * 1e6, \n", + "# Calculate NPV of the underlying swap at expiry\n", + "index_fixings = dfs[:,-1, accrual_start_idx][:,0,:] / dfs[:, -1, accrual_end_idx][:,0,:] - 1\n", + "index_fixings /= float_dcf\n", + "floatLeg_npv = np.sum(index_fixings * float_dcf * dfs[:,-1, float_idx][:,0,:] * nominals, \n", " axis = 1) \n", "fixedLeg_npv = np.sum(fixed_amounts * dfs[:, -1, fix_idx][:,0,:], axis=1)\n", "npv = (floatLeg_npv - fixedLeg_npv)\n", + "# Apply payoff function \n", "npv[npv < 0] = 0\n", - "npv = npv / numeraires[:,-1] * numeraires[:,0]" + "# Deflate NPV\n", + "npv = npv / numeraires[:,-1] \n", + "npv = np.mean(npv) * numeraires[0,0]" ] }, { "cell_type": "code", - "execution_count": 53, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "12841.486989266908" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "np.mean(npv)" + "print(\"MC NPV: %.4f\" % npv)\n", + "print(\"Error : %.4f \" % (npv-swaption.NPV()))" ] }, { @@ -1940,21 +645,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.4.3" } }, "nbformat": 4, From 29c6bb2924a3b5a31ddef6dc18cb039c09e6bbc3 Mon Sep 17 00:00:00 2001 From: Matthias Groncki Date: Tue, 19 May 2015 17:18:28 +0200 Subject: [PATCH 09/25] test --- StochProcess..Basics.ipynb | 876 +++++++++++++++++++++++++++++++++++++ 1 file changed, 876 insertions(+) create mode 100644 StochProcess..Basics.ipynb diff --git a/StochProcess..Basics.ipynb b/StochProcess..Basics.ipynb new file mode 100644 index 0000000..a8a0eed --- /dev/null +++ b/StochProcess..Basics.ipynb @@ -0,0 +1,876 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt \n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Stochastische Prozesse" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "Was ist ist ein stochastischer Prozess?\n", + "\n", + "Wikipedia beschreibt einen stochastischen Prozess als\n", + "\n", + "die mathematische Beschreibung von zeitlich geordneten, zufΓ€lligen VorgΓ€ngen.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "\n", + "\n", + "**\"Defintion\":**\n", + "\n", + "Sei $(\\Omega, P, F)$ ein Wahrscheinlichkeitsraum, $T$ eine Indexmenge (ΓΌblicherweise T=N oder T=R). \n", + "Eine Folge von von Zufallsvariablen $X_t$ mit $t \\in T$ mit $X_t : \\Omega \\to R$ einen stochastischen Prozess.\n", + "\n", + "\n", + "**Beispiele**\n", + "\n", + "- Aktienkurse, Zinsen, Fx-Kurse \n", + "\n", + "- Warteschlange an der Supermarktkasse\n", + "\n", + "- Arbeitslosenzahlen\n", + "\n", + "- ...\n", + "\n", + "\n", + "Eine Realisierung / Beobachtung eines Stochastischen Prozess nennt man auch **Pfad**.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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FYXsXSKjySxWfaxXgZOAWVeaHfUOBRaqcX0KdLsDLQNsU01TxsXxsbY2vMIWx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\ndGtDx+h0HYKI3ANMGiioPK/WIXQ7JnYz0dPhqor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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dt = 1./100.\n", + "T = np.arange(0, 1, dt)\n", + "for i in range(0,5):\n", + " X = np.cumsum(np.sqrt(dt) * np.random.randn(T.shape[0]))\n", + " plt.plot(T,X)\n", + "plt.xlabel(\"T\")\n", + "plt.ylabel(\"X_t\")\n", + "plt.title(\"Pfade eines stochastischen Prozess\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Wiener Prozess\n", + "\n", + "Eine besondere Klasse ist der Wiener Prozess. Er hat folgende Eigenschaften:\n", + "\n", + "- $W(0) = 0$\n", + "\n", + "- $W(t_2) - W(t_1) \\sim N(0,t_2-t_1)$\n", + "\n", + "- Differenzen sind i.i.d\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Stochastische Differentialgleichungen\n", + "\n", + "In der Bewertung von Finanzinstrumente spielen stochastiche Prozesse eine wichtige Rolle. \n", + "\n", + "###Beispiel Call Option\n", + "\n", + "Wir haben eine Aktie, den Aktienkurs den wir beobachten folgt einem stochastischen Prozess $S_t$. \n", + "\n", + "Eine Call-Option gibt dem Optionshalter das Recht zum Zeitpunkt $T$ (OptionExpiry) die Aktie zu einem festgelegten Kurs $K$ (Strike) zu kaufen.\n", + "\n", + "Zum AusΓΌbungszeitpunkt ist der Wert der Option mit $V(T) = \\max(0.0, S_T-K)$ gegeben.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "K = 130\n", + "S_T = np.linspace(100, 160, 50)\n", + "plt.plot(S_T, np.fmax(S_T-K, 0) )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Aber was ist der Wert zum heutigen Zeitpunkt t=0?\n", + "\n", + "Unter gewissen Vorraussetzungen kann man zeigen, das der heutige Wert den erwarteten abdiskontierten Auszahlung entspricht\n", + "\n", + "$V(0) = E[P(0,T) V(T, S_T) | F_0]$\n", + "\n", + "Wie kΓΆnnen wir den Erwartungswert berechnen? Wir mΓΌssen dazu die Verteilung von V(T, S_T) kennnen. Die Verteilung ist abhΓ€ngig vom Prozess.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Black Scholes SDGL" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "Mit einer stochastischen Differentialgleichung wird die *Dynamik* des Prozesses beschrieben. Üblicherweise hat eine SDGL die Form\n", + "\n", + "$ dS_t = \\mu(t, S_t) dt + \\sigma(t, S_t) dW_t$\n", + "\n", + "mit dem Wiener prozess $W_t$. \n", + "\n", + "Die Γ„nderung des Prozesses von $t$ nach $t+\\delta t$ fΓΌr sehr kleine (unendlich kleine) $\\delta t$ wird mit dieser Formel beschrieben. Es besteht aus einem deterministischen Teil $\\mu(t, S_t)dt$ und einem stochastishen Teil beschrieben $\\sigma(t, S_t) dW$. \n", + "\n", + "Ein sehr bekanntes Modell ist das Black-Scholes Model mit der SDGL:\n", + "\n", + "$ dS_t = r S_t dt + \\sigma S_t dW$\n", + "\n", + "mir dem risikolosen Zins $r$. FΓΌr dieses Modell gibt es eine LΓΆsung fΓΌr die Gleichung\n", + "\n", + "$S(t) = S_0\\exp((r-\\frac{\\sigma^2}{2})t+\\sigma W_t)$\n", + "\n", + "In diesem Model sind die relativen VerΓ€nderung von S_0 nach S_t lognormal verteilt.\n", + "\n", + "$\\log(\\frac{S_t}{S_0}) = (r-\\frac{\\sigma^2}{2})t+\\sigma W_t$\n", + "\n", + "In diesem Modell gibt es sogar geschlossene Formen fΓΌr die Preise von einer Calloption:\n", + "\n", + "$V(0) = S_0\\Phi(d1) - K \\exp(-rT) \\Phi (d2) $\n", + "\n", + "mit $d1=$, $d2=$ und $\\Phi$ die Verteilungsfunktion der Standardnormalverteilung.\n", + "\n", + "\n", + "....\n", + "\n", + "Was kann man machen wenn es keine geschlossene Formel gibt?\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "source": [ + "###Monte Carlo Methoden" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Wie lassen sich Erwartungswerte schΓ€tzen?\n", + "\n", + "**Mittelwert** \n", + "\n", + "Sei $X_i$ unabhΓ€ngig und identisch verteilte Zufallsvariablen mit $E[X_i] = \\mu$. Dann ist \n", + "\n", + "$\\hat{\\mu} = \\frac{1}{n} \\sum_i^n X_i $\n", + "\n", + "ein erwartungstreuer und konsistenter SchΓ€tzer.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "####Beispiel 1:Berechnung von $\\pi$ mit MC\n", + "\n", + "- Dartwurf auf ein Quadrat 2 x 2\n", + "\n", + "- In dem Quadrat ist der Einheits-Kreis gezeichnet\n", + "\n", + "- $X_i = 1$, fall der Dart innerhalb des Kreises landet und sonst Null.\n", + "\n", + "- Wir nehmen dass wir beim werfen nicht zielen und somit jeder Punkte auf der FlΓ€che mit der gleichen Wahrscheinlichkeit getroffen werden. Die WΓΌrfe sind unabhΓ€ngig und die Wahrscheinlichkeit das ein Dart im Kreis landet liegt bei $\\frac{1}{4}\\pi$. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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V9Evb17ci4pxhfH7bHXFZd8hH8w/l6ZK+7+cj67F5WpUFIbxm/nc5Fg7sZUg7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\n8/8BG0UfwDLM+7sAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pi = 3.000000\n" + ] + }, + { + "data": { + "image/png": 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lh1QpdZjXaIgb3D8AbwLeAHwWONMzapbDid/GRGIc8D7geOAO4PMR3FhuVNYr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\nAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pi = 3.300000\n" + ] + }, + { + "data": { + "image/png": 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c7dSEJVARXqse4OrdgTh/dY0WjtM201WY7xuBHg36xaCje3N5UPTDqesjqXsW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\ntK18rFQq5nU5Tq//ktxpjZB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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pi = 3.150000\n" + ] + }, + { + "data": { + "image/png": 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VtJVwUSllxSqaXSWraLVeFMfQOG47Utp0LHCaZfuS4L5pMSDTwNAyJne/wCE7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\nAtcdBS5A68bFsv/L1FdAV9CYh70G3ISWLoewr5B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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pi = 3.147000\n" + ] + } + ], + "source": [ + "def MCSimulation1(N):\n", + " x = np.linspace(-1,1)\n", + " y = np.sqrt(1-x**2)\n", + " plt.plot(x,y, color='b')\n", + " plt.plot(x,-y, color='b')\n", + " dart = np.random.uniform(-1,1, size=(2,N))\n", + " getroffen = np.sum(dart**2, axis=0)<=1\n", + " plt.scatter(dart[0,~getroffen], dart[1,~getroffen], color='r')\n", + " plt.scatter(dart[0,getroffen], dart[1,getroffen], color='b')\n", + " plt.title(\"Dartwurf N=%i\"%N)\n", + " plt.show()\n", + " print(\"Pi = %f\" % (4.*float(np.sum(getroffen))/float(N)) )\n", + "\n", + "np.random.seed(1)\n", + "MCSimulation1(4)\n", + "MCSimulation1(40)\n", + "MCSimulation1(400)\n", + "MCSimulation1(4000)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "###Beispiel 2. Calloption mit Monte Carlo Simulation\n", + "\n", + "- r = 0.03 \n", + "- $\\sigma= 0.2$\n", + "- T = 1\n", + "- K = 103\n", + "- S_0 = 100" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "np.random.seed(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import scipy.stats as ss\n", + "\n", + "#Black and Scholes\n", + "def d1(S0, K, r, sigma, T):\n", + " return (np.log(S0/K) + (r + sigma**2 / 2) * T)/(sigma * np.sqrt(T))\n", + " \n", + "def d2(S0, K, r, sigma, T):\n", + " return (np.log(S0 / K) + (r - sigma**2 / 2) * T) / (sigma * np.sqrt(T))\n", + " \n", + "def BlackScholesCall(S0, K, r, sigma, T):\n", + " return S0 * ss.norm.cdf(d1(S0, K, r, sigma, T)) - K * np.exp(-r * T) * ss.norm.cdf(d2(S0, K, r, sigma, T))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def payOff(S_T, K, T, r):\n", + " return np.fmax(S_T-K, 0.0)*np.exp(-r*T)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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q+w/y1ey1uG6eoPY1FXYo5CaeOOLZEZg0+n/f1q9/dFdMzNigdtGb6PDhQSrs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CIUna3lHuqhLcb14119bxSlwckWsb7S5g8Psm2F1qUgU/v20OeDRMcIhZgdoEZBAS9oyS53VA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PPTrCvnRHz4NXcWd2hLjdHRBFx8GT45Q+TPS/rG8MdwHwr/CkebQL1RYdSYfwJn5yvE2yv1\nXF4t+MSfQ4OPUFSX5u+Wy3Tz3++5L01VMzaeuj+epcnpCMqGx+FW1pvx7b1Zn209X4TWwMeelfKG\noS8oRUegl9Y8rWbNysS8hYvZernBjMweF+YE7M20wa+0Gx+vd3uURv55oInWuof197uIwXoFqKe1\nvqjEL2dqrUsmOfaxGnkAZZo2YmJ6MHbsVE6cOE+bNpX1xIn3LLUT768NEceyZuAfAdWxVgJKqRqI\nHO++DnR4tyc9FyM39XLg0nMLY5vdPLR1KCNGbCIurhligKYD63U6LoCpzNqIIFMTQxvJBvcAmg4z\n3Z5dwStl95LXleu1bUTXiiGrTeO0F2ynEXbPZu4wHBK2iN3lGDBwKuWAFmjdgmtbsnJsZiRR5/3I\nXv+icayj36DPcngtfY5jc7qyKt6JLcB3BARo5AHtAxT1xnv2XOZe8MGnFxKIngzMD8Dw6suRAW05\n005BuaS8+0RjnhlhNpTWhpFibMIaFw+E8jbI0MYyZZq+yIvXBaFiHkltbC3phjFANDBZa65Z/VDM\nmBHAhQvD6datEvnzhyBuqL/uaaNIkcLcvLkCf/+8jBx5hhw5LiOU0B7aMBLE6LxCCe3fiU6DSxPV\n/C95EfyEtoTJTLMIGzf+yujRuVq5uzdbHBo6DAlYdkTrY8o0+yP881qJ8zcSMNUsX20sby2qHLvd\np/+UqXHPrff0yfLSC/bwds1D67Dhk7CgiTv++F3PA04DbbWlqW4NQkXgma053W6ty/TmpNyhJW/O\nn3D1rXVF83/2DL85rQr1vhS1/6Pcw0OqH2v+W68inpxckZ3f2+RmWV7gt3N+fntjnGhU5VXsoa7h\nc4iPGoislqohK6D2wDlgAdFsZhKTyYK5zt3FbW3dti8Ed+vmQeTpMzh7F8Dm4uF3ZuvVHW9Mcduc\n3/WbF1pfH6yDiENWYYvQetxdYy/3y/cqPp7IJk3C3Ox2O9BxYSmaNjrKovqBPWiyqk10gw3hR+vG\nvZTLRux3kc7M9hpOded4Jq2eh7t7LCuuzeHNWM3adZfx+mh+n+OxKz/KR6Uf53PN6E24kzPVVirc\nj8KK17BV/I5A14/ot+k8B3K4X7/kdvPAkSwc3JaHXfmew7VxboaFXCYsUxwu2SLJUiyWa97xuAPe\nKpZrQIXK/fVZi4SxB3idzz73Z+bMGVy+HMEzdVu55p3fu/pZ2sxZRLvCN/jpURr5kgg9rCbiC1uD\nzFg6a62zWPso4GrC34mOfexGPgFq7txcTJy4CQ+P7IwY0REvryWIq6k8orOesBVCAkZbeestf3bv\nLkStWoZes+Z24NEqSxgIvFKNau+PZWwjO/bCdux53XDzjlYxRLjFRflGue9xwWUzsAIwDW2kXNcx\nEUxllkKYCF0MfTdjxxLrKopkrTZCjLgTYly+RanMdjLPDKdg1WP0mn2TMr8aBGy2vldl4CkNlW96\nepbcV7DgvM9athw9d8yYCKvt4kAzQjfl5NiMEkScqOcZ5bn57SVv5at1oNYZG7a+COuhVMIWQ0x5\nG7YCe9l7LT/5O2cl68qk1FRTmcuBEEMbyVL7lGm+CjTRhtEmuf8nMz71ET51GUMbaaPzpROWcmFP\nJLawH3kpmIgbojfQj9jYvbzzTiQHDjSnXr2NbNw4mtBQDbj1pGfVVrTqFUlkpmMcOzJUD62AUjmQ\nld54tJ5hqU+ajZ577vz8kJDS0S4u0/KEhg5F61hlmpWA1UDN+724RMdHr3Yj2rfUmpDc+z7+LHOV\n5xpEjG29+K/4LLrQuXNk9/FhY6ZMfAcsNgLIjdy71WPxWLaH0S210j4l1XD3635R2tXH5dRv+WtG\n7s1feMnUXPO/u+Fm/6nR/ryr3/9u5AuuXNMlmHgrhmy/2RWt+jzro2N8nz3n7lXXr9Fq5hY+zpeG\nNg4AqGDlhBj7EUABruHKF06KMSMo65qZlss+ZnSVE5HAQQ+3ktu2TQt9dmvp8tGvvDU8e5Zbty7H\nK/VHydOnj/w2ePDAHcWK9as1ffp32jC0FWdZDmyNbtDgjGtcXHOgHgk1U5VqfZge87bnbOYxbegZ\nW4V1IydMr3TtR2Cm0hRe+B03mxxhl2cszdE6zjSVT7xmwcUoivT5rpX/jcULfOi/ew37J7uyemI5\nwnL7gt5Lns0/0aZbVEGPq83Kng+vUeWcLfrpo2E38+fH5/xg3FwmE517B2FZI8ly3ovjRa+xAGHt\nbMKKY0n3aAO8zWczFjMqOBhn50XYTnWhG9OAYscm82Gh60xRUPBR++RfQVwO4Yj/KRphhGRJtM9V\nrXXWJMf9a4w8gFLKFT+/3ylRogyjRimcnG4hfuHE2zGgOO+8M5KDB+vz2WcnyJatOGL49yIRfSnY\nMGxYJrZsmYDMIrMBC5xwmtXppRWhZiPXPxv+ykcdv8EJcatU0vDHudxsmtuF06sb3xakckF06V0A\n10LHyDJ5EJ2XNWfdzFfZxR2apoheCZ/XFTEAqxERs4KIi6CcNozLFk+7CzDR6nNF5NptB/4K6to1\n95+lS7cInDvX2+/GDc/3O3c+Mb9BA1PbbNutfbZow4hVwSoz0AlNX99w3xztN7bPVP1w9eOFLhfa\nBRxAjN/+v/n7SF/6fon4rtsmTt4CMJVZHIlXlDP0vTN1ZZrbgHe1YaTsq00CU5mzgWuGFjdGcrAm\nKD2AEfp+hZPvAyvWkiDOFon4mH9G4hZ52bx5PoGBmujoTECoL76HhjK0cClK+X3O55u3sOXIQhY2\ntGEbaKmOFgXWmTD8GQgMhGMjoUjvwYOXzWjZsi3irtmGuAYDtWEsSEMffZAM1QV5Oneuef769VUN\nmjSJXr5oUav1a+M3Ao18/6JH3h9p4LMPfbUqP50uVCUobM4Hn1LkqD9TBvrfjLJ/O3oF1SqeJXLK\nL/zqFsdzQO4rnixv2456W3O6rwkZO71wFHnrunDV7VSWW9rFnnWXX5jfkYPFyXa4GLUa/kq0Wwx7\njuQ4snRwt8HlwzzCngU+Jn/H3ynQdQDLV9ZlxoyIGXZ71R7R0RtO+vDFr4XxbnaYHh6x+CqNy1VP\np7hVT5W/cS17cZfJL7T/ucb+/WW+mDChfM3p08OP5c69H0l6mx77zDOHnLSWVXYiZtoWNeudeNyD\nKtE3ouDX72++4ONag52DIC5c/fgtq1ofoIiCuiQup2gqpxhcxt7U3v2Gbxp5+fB7HfLiEhlLWM7v\niHcZhCREdQKaAOvJHHWegA3Nq5Wa4/12wA/uH2xsdG7PqkuembJHq7DyO+bGuDIoOfqqrCD1Ntpv\nOMGGl5oTHT2dvpeHICv98kATHajDUGqukkn042HXKKU+QGRQByLUrQtKqVxASHLuGiRglwBTa20+\ncCceAEopd5Rahs3mR1zcEmRlsiVJVmk/hDb2tNb6rMWXL4/MXgsj/nnZYmIys3XrcfLl66q7dNl2\nuw3TrIdwqacDJb1vUPapvyhR80/iq23B2TmW+HgbcUouiVaWr9rFjuv6Ovz+3kjWIXSqxElXoUhC\n0P6kDB5lmuOBAtow2if6skWQItE70PqitV8F6zvX0AEBxy75+LTwjI6edN3LSw3q12/vj/XqFUWu\nb6sEyQEVrBQSSOyK3OjhiBvqF8DUgTpcScr/EiQJqmtSxpCpzHFADkMb3ZL0+yngR6BIeqQLTGVm\nQ15gLQxt3KNoqZTqhLja9iP++tZa62TdRWmBRXVsgExyznLixLvMmfM2GzbcwG7vAaxvTevFnej0\ndAQRS/KS92VDG7esvtZBAvUVAwiIKgXjL0KP1yD0Pbmer6H1dWWaDRCZhDPATm0YPTLU13z5Gqnr\n11c0b9o0esnChV/btPYHqse58+HG2ZzcdlZ18Zkc2DRO2VWxL8fOxDnufcPQZ1SwckfuWQ9g8uRf\nsPfdwtMKOpz3Ivc3T/mdy+xZOcePpdFrazUdRZZKp0motgb5naPjhtU2r7s2WRGdudxh//idFdXx\nJa2dzmyrQgUUU7l6dQJt284FTmphgH2LrEIzA/2ueLAh+zCyAnUH/sm4EetwOpqF2tXO8Yzdyal7\nUMPFX5XdmTmgxIV9e4vzYR8PzjZ00lG3XWlHVJ/xF2k4pIDTmHHhvtuqhXpQuO7Y13PEZ31KfTh9\n+ozByze2BWom5r9b19YVWNSO7/x681nRNRsaTR89ZdJhfans/Lv263gyF2Vuvo9/VKXqWVcXG5bp\nA68R4/zYt11rcr0I4Ysht/8xatWsrD/88OY918U5/ll8Ls4jc00Prl2bSeWbgzD4CKjBVEZxlWoA\nWby8/K6FhfV71DN5f631JYtlsQqogVQ7CtVaj1NKvQX4/tsCrylBKeWKUBLrIw9uESRJYy1iUAch\n8YbjKTaS0JZpeiGSCBOQpenniVL0uwElEUNzADigDeOGFaDMZZ0rKWINbVxM93cyTQ+Eo/y2NoxF\nKezjicwQR2vDuFM1XthDnYD34pXaXnnGDNuuokXdEEN/1wzYMvjlEWPfBMkM3gxcIg53TvI0Htwi\nJ4cQvfJQ4Kc2m9qE9F/ZfzPQxtDG5kR9SlPANTmYyuwEvAFUNbRhVedSnogGS13u6K/8iDChOuoU\n9L3TCvXtt26sWrWGRYtq4OQ0i2vXXg8hxBVhGpWfxaw5c5mboCwZlPBiWaFWjL/EpVYv83IW4OeZ\nsLIHkJBZebt90yyI0A3f0oaRLpLAXe3kzduKmzd/eLpdu8OLFiz4qrKLy/pTYWHNiYt7vgMd/FvS\nwv6yrevNyPiYEVrrb28fF6ycEZpubaA4kENpjleKKB9xrNa7FbNeORp3zN/nCMoWB9qGxgbaRnyM\nOzFXcuBbOQwXn/iiR2zfv/IVuSvupHasM79nDuNFQxvxSsrq7QI6a5HiHQvUIEmeiApWWYN/Y1mP\n7VSbWN3n1e5rB7xxjaoFbURP8mXX4FBqhsXh6QJscOXyruxO3ze8ENep2qyGo3bNqr29iIed7fum\nk8+m+XVyx9cOvztv3vgOI0asWV21andtGHeSnCSA+y2SuPVCCAHlkVjYOmQiszaAkAuI62k8+/bt\neWbTaP/+bc9VGj+e9X8WeHEdPXv2ItLpbdqVakLets1RV+w827S2/vDD3bfPM3uLCyPynSO6vhe3\njs8mKuo1ghiNuFzr60BhJlovnF8JCHj6URv5dYiOsh0YrLUOsSiU3yPL1xP8yyiU6YF14yUY/VJA\nr7Rmet5uwzRLIMyBQ0BPbRg3UjnkH4cyzdpIZmk5bRj36Ggr0/wM8NKG0emegwGENjhIw+D+AwYc\nmN66dSTJGPq7DglW3sgs3wewcwMXfmUUmdlAYxHzQoK0ATmv5Tzeaksr7wV1FlS/Pv76pUQB1zIJ\nHPv0wHpZrgJ+NbQxwXLP/KBQez7l049KUMIAqsYSe2sZy56JIupsO9pNtmG7gPDrjyWuQJUalGHU\n4MKFlYSHa2y25vrkyQ0WPXUNQqcbbFFOcyDsm3yI//4ZZ5wHz2KWLZbY6d10txS54v8kVO7cnQkP\n/xIfHxeio8EwdLk8TXXQrAK2vp8oLoTuPc/rr3tit5fRWp9Nto1g5UH5Sb3xKRvEqQU/c3L2RcQO\nxFo/E34PA76jXshVJJHofWBASABLgTXO3NpRh5aLgPJfQtN3od4+CMsKAWi9O7lzAyzNV2d+9rM9\nOhzNduP085eDb3pwzQ/4VAXxQfvNNZ9/aWe2oCIXC5U46Vlcz6qzfOOPNX75BFipA/U15LneDGS5\n4u3dJ/vSpWUQt/MEhBwQi+jdZ0Xu82i4XaqxHVA/DluDS/g77YovH37o2+27rh+5UnXoUFx276bT\nsGF6MYAyzdLAIuIIY1gpN6LfLsKVX9xo27abnjZtnjJND1a7/8aE16vBX7OIjelJEAOQe6OODtS3\nn1Vlmp8CeQgIaOFIhvoXQpmmO+KrbQK8qI173QgP4Zy+yIwjMzJrzoP47xNKE/6iDWOXMs02yM1d\nKYHal0J7Bdf361e01r59MxbVrWvvPHz4qUh395b3M/T3tCFGbj0wRWs9DUAFKy9bvK1Z1SNVp/9V\n+C/PWOfYzeRudYTCvbPr+o1bpaFNhQTG45Ekm+vArRBCCgFb3uCN8R54vPMSL+0oRalCQqBhORLo\nzBxBRL6NbOyRj3w3ilP8skIVRBK/XkotIK6GD6/Db799zN9/V6BevTXcutVMm6bdesl8gRiJtveU\nLBQG2njk+owKIUQjsYmnDW38ndbxfBAopWoh+QlbpD4C24AehjZ+Uab5DJ999gM7d9qZMqWKbtw4\nqRtDIavTHsBz2jB2pPm8plnJ78aNhbPHjo1stOnv7Dv4OFt21h0vzJe/ALuLQOMzEBetdfvkjjeV\n6YJ4C17zcVn8UwHb1A4vt+J6l91s69iG8Fd20iIoBLcjfhx8q0P9H/9o9FY/bM57gTfueu6UKg5U\nIKGcomkWQQx8Ke4Ua2mSdNWkTDMb8L6NuFb1z8z5tvD6eS0qVMC3eHEinZxoZxiiCppofzegKvHU\nZGzJnlxYUITTo2wMHnycatUu0/qDikSFb4MddQniecRO1NGB+mSiNnojOTE1CAi44TDy/2Io02yL\n0OXGAJPvlwH7D5xrLhIQ/BhZbeVC3BXLECXHLkhCz1TkQd2UQlMo06yD8LLjyhw/PnR39+5tjuXK\nVa/NqFF/7ylSpFE6DX1BxAUWqLX+KuFzU5lVo52jl3Yc0HFoaB7/Twk/GkN8zIfADB14r/yEZSwb\nImnn/siM0RfI4oqrR13qRjahiVsZyrjEEbfdC68FCJtp/z0CdEr5IQyZBSGETESosAURv/5dmizK\nNL24cqUzP/zwNr/8koeSJbdTr15fPWHClkTf5XVkfOukleVjKrMnMpusYeh7aZEPC5bRXAusNbRx\nO06mhg71Ys6cQ7z4ojdt2gwHpmvDiLPcf18hrKzW2jDOK6V6W3+/fd8sbblmneKVmrCobt2wt3v2\nDH11as5uVbc6r0LKXn5tudV2AW9orZcm6WsZxGd/BehuaOMsSr0Y5cSnE2pz6LWt5PW0c8ol3tbf\nZe3a+oh7tZPVt0O9RlsAAB76SURBVGDE1fKONox7pcCFQTYECXBHISSGTMik4WqirQrwDZ07/8GZ\nM9OQEoGj0+LuswKs75JjRT+iO2XDyzeGCxXiie3gR1D7msiKv4EOvLOCUab5NLIKr6MN47Cj/N9/\nAMo0CyH8+Qigd1IVzH/oHK2RmWJFbRjhiT4PQJah5RDtj1XA9ym6aeSYZohuSEfEkM5R8fGbzr7w\nwj7PqKigYb16Hfy8ZcsaCYbeikXksdp/HqlsvzDxg2VRBBcjAdL+Cfo3pjK/PJsb507zqcf6Z1sT\nF9kfSWr6AZiiA/W+RFnCo5CZchDwQwghIFWSOmt0Wzv23cc5vjwPeXJ44dUQCEhqsO/6nkIS+AOY\nHELIJ4j+TzOgSUAIFwADu709y5e/yKxZNjJl2kpUVA999WpSFdNmiEumhqGNUymdLyms2f9i4FBC\nEt2jgKnMD5H4UPOkLiqlVClsto3MmHGIIkVsyOx9FHAY6E5AQCwy+30GMYCHEWmHew2euMw+Rfj+\nvVVIyF9IQlmVSa8zsPIOliDU4FVKKQMJNJfRWt8wlZkfoXe2tPrweeIXdV+lxh2E7mOhf+OlS5dd\n9faehbjE2ib42S3q6+sIMWQu8L42jKvKNGsgmjR1EIM9PUFnypKj8EXuM9kuXjzBiy92QupHdNRa\n35MAlRqUYhBu+9+g6hRv3nwhnrCVozk08Q2gvQ7Ut1VrlWkWQGSKb4scOoz8fwRWUKc/suz8CJig\njeQTgjLQtj8yE2qrDWNjMv//FKFlHkM0V/IAQ7RhzE9m304I3bJVwkzfeljGAK2HffPNhEELF37w\nW6VK8RNefPHKzmLFsiMzoFiEBnoOeUhisFxEyGx6HQEBCgnk9cLNbSRLl/4UsMGtxJBJrPizJusa\nrOUFQxs3VLDyR3yUfYjBzg2yEIUTXhzElyNeUV5xpc6WKlTibIlivhG+kVEuUav+zvv3DxtLbtwN\nXAoJColFDPYzQANDGylqFSmlCrngskmjL8Q66ahOrt0LPBfTNPuwkl/FH3M7e5MjR+KIijpETMxg\nre+tRWsqsyxSt/S5pEJwaYGpzOxIkLynoY001d59EJjKbIeMzVMpvQCVUoOAdqxaNRNX12BEq2gM\nAQE+yMwTxE8diyWrjLCo7FYDHsh93gt5QXySIEZmuX3GA42n9uftcnuZBTQztLFVKfV5TnK6L2BB\nKND1fE5+eO0TCl3PQjzibltOQMAJxED3B9bg6lqDefMU/v7rgP7JFZ5RppkDeWE8z9WrJzl7Njfr\n16/k55+PExmZl+yUIhsFiSOSKC4TyQVucZYoziNaSN0RHZ4uWt8pYJ9eKEV34E1mBr3LpY/nU6Dz\n3+Rp1SZhMmQ9Z+uBudowPkp0PRxG/r8E6009HXENvJqcUU5newpxqxzUhvFWCvt4I5l1HohUsjcy\no/9AG8anifYbgDBUmmjjXj+xpbD5VY6rV0N+HDnSu8yJE42O5snjMr9+/UuL69YdfyJXrq+0YUQo\n0+wDjATeRmZXTREBp0NAVo4cycHEiZ64uNh5/fXjJaMKXZw0hMuekRjhhH/1Bm/sOcCBajjTED88\nyM9XqqFaX39//adKnCvR2i3Wrdxx/+PbtxbdevhM1jMKRU64vWUHvupqdu3dzex2O0M5gcKYGL+6\nmIWj3JkRGX2jxt6s5xgz1K5iw6/v7rQ087VOO8rU/lYvmDCb2b+TQoayZaA3AyMMfe8LM60wlfk0\nYjznW21liMefhvOUQtwXje+XOW3Jd6xFJBzGWp8VQdx+axDCRaz1uYeCH6uAswk/eEqiZCPEWL1O\nMoql1j07EujwaW/GljzI6OMcb+ePf9M44oaFOt1YFvxFvhMnC9IBCdqeBZoRE9OU0aPd2b8/mr59\n+1O7tp25c+exaFE4YWHVtdYn7vOdcpLZay4+EXXJG3+JvNjJiRu++KGJII5DaFxR+GHDBycyAxBD\nDBGcIgvjsLFIB+oUY1hpgRqeOStuYetRzl/w9K/OyAtrIpBQWSwS6JbYresw8v9BWDf5C8iydwlC\ndcwQA0eZZhcs6mBy6e6J9qsCeGjD+MP6uzDywM5AZnbBSPp5I20YJ+/Tjg9yQ3ZytdsXzh4zZseL\nISG1lDCSliCaOBtUSEiCAmYfbRgLlWn6ITQ8qeI0aNAtdu3qj8zspwHOBSnY7HmeLxNAgNqW+dC+\nGX1sV67XLnPsh/ZOWz2ieA1Rx/wYmJsg7XxP/4KVl/W9zJCgkLeR5XhZRBIiXJmmf/ZLPNv9SwbV\n/JNyi9pw6/t2LJ3Zk6LZL3PS1U5HQxvayqRdgMxEtyByAJcMLfx9U5lu1nnWGdp45z6XKE2wXhjT\nEbdaV0MbW1I5JD1teyC+/zeBYYY2Zqd2jFKqABKYbYCwpb5HJG8/sXbIjuRK1NFQ+xJk2g5Xn4Zx\nmeA3tN6X6jk+/vh9tm17rdXX4dH94vvk3MrWc18WNv86ErGrKTNnLsLLa1CCtIXFeluEr28cs2Zt\nwNe3IfJC70BAQFVEBbWx1vqu8nuWq68zMJHW7KU8hVGsQ1a+O4FdycV/AFSw8kRiW7UQ12U9JHA+\nH2HtpGslroJVFiR57k8dKKURLVfudCQD/QSidXXXasRh5P/DUKaZBTGwTREVwpBUDkl6fH6E695Q\nG8bODJw/D5IpG44kojRNT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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def pathGeneration(N, S_0, sigma, r, T, dt):\n", + " t = np.arange(0, T+dt, dt)\n", + " dW = np.sqrt(dt) * np.random.randn(N, len(t)-1)\n", + " S = np.zeros((N, len(t)))\n", + " S[:, 0] = S_0\n", + " S[:,1:] = S_0*np.cumprod(np.exp((r-sigma**2/2)*dt+sigma*dW), axis=1)\n", + " return t, S\n", + "\n", + "t, S1 = pathGeneration(100, 100, 0.1, 0.03, 1, 1/52.)\n", + "\n", + "for i in range(0, 100):\n", + " plt.plot(t, S1[i,:])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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Au9Zj3twY1bC0GX/DayS3vY4KXcKXN0h2LnS74FAE16UrVAyc52OBZu6p9JGU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\ns+fwwjaD1IX0+m24+j9C8L9DM07v83FSUL1NmvW/ucbah3+fv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K2qOgHQIA2ZuBe0aqDm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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "N = 10000\n", + "t, S = pathGeneration(N, 100, 0.1, 0.03, 1, 1)\n", + "\n", + "for i in range(0, 100):\n", + " plt.plot(t, S[i,:])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "N = np.linspace(100,2e6, 70)\n", + "V_MC = np.zeros(N.shape)\n", + "V_BS = np.zeros(N.shape)\n", + "V_BS[:] = BlackScholesCall(100, 103, 0.03, 0.1, 1)\n", + "for i in range(N.shape[0]):\n", + " t, S = pathGeneration(N[i], 100, 0.1, 0.03, 1, 1)\n", + " V_MC[i] = np.mean(payOff(S[:,-1], 103, 1, 0.03))\n", + " \n", + "plt.plot(N,V_MC)\n", + "plt.plot(N,V_BS)\n", + "plt.ylim(np.min(V_MC)-0.1, np.max(V_BS)+0.1)\n", + "plt.legend([\"MC\", \"BS\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "N = np.array([10**i for i in range(1,8)])\n", + "V_MC = np.zeros((7,20))\n", + "\n", + "for i in range(N.shape[0]):\n", + " for j in range(0, 20):\n", + " t, S = pathGeneration(N[i], 100, 0.1, 0.03, 1, 1)\n", + " V_MC[i, j] = np.mean(payOff(S[:,-1], 103, 1, 0.03))" + ] + }, + { + "cell_type": "code", + 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4.006704\n", + "13 4.535982 4.206602 4.237326 4.014739 4.005382 3.997234 4.009190\n", + "14 8.195545 3.122013 4.033490 4.001259 4.011972 4.012704 4.006725\n", + "15 4.146682 3.982844 4.011506 3.957476 4.040843 4.000705 4.009913\n", + "16 4.491857 3.696805 3.974811 4.035294 4.030621 4.018620 4.007754\n", + "17 7.630725 3.880077 3.996466 4.064760 3.986851 3.998750 4.006122\n", + "18 2.949070 4.360241 3.992905 3.943630 3.997630 4.011300 4.005711\n", + "19 4.911999 4.364572 4.002349 3.997426 3.994726 4.020332 4.009137" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "pd.DataFrame(V_MC, index=N).T" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "10 3.765250\n", + "100 0.314827\n", + "1000 0.025343\n", + "10000 0.002690\n", + "100000 0.000500\n", + "1000000 0.000041\n", + "10000000 0.000004\n", + "dtype: float64" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.DataFrame(V_MC, index=N).T.var()" + ] + } + ], + "metadata": { + "celltoolbar": "Slideshow", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.4.3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} From 1d3df9ef8e0ffaf35c790200eece79aed69515f1 Mon Sep 17 00:00:00 2001 From: Matthias Groncki Date: Tue, 19 May 2015 18:58:53 +0200 Subject: [PATCH 10/25] Added slides --- StochProcess..Basics.ipynb | 818 ++-- StochProcess..Basics.slides.html | 6557 ++++++++++++++++++++++++++++++ 2 files changed, 7113 insertions(+), 262 deletions(-) create mode 100644 StochProcess..Basics.slides.html diff --git a/StochProcess..Basics.ipynb b/StochProcess..Basics.ipynb index a8a0eed..0416843 100644 --- a/StochProcess..Basics.ipynb +++ b/StochProcess..Basics.ipynb @@ -4,7 +4,10 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": true + "collapsed": true, + "slideshow": { + "slide_type": "skip" + } }, "outputs": [], "source": [ @@ -21,7 +24,7 @@ } }, "source": [ - "## Stochastische Prozesse" + "## Stochastische Prozesse und eine kurze EinfΓΌhrung in Monte-Carlo Simulationen" ] }, { @@ -32,7 +35,7 @@ } }, "source": [ - "Was ist ist ein stochastischer Prozess?\n", + "##Was ist ist ein stochastischer Prozess?\n", "\n", "Wikipedia beschreibt einen stochastischen Prozess als\n", "\n", @@ -44,7 +47,7 @@ "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "subslide" + "slide_type": "slide" } }, "source": [ @@ -56,6 +59,17 @@ "Eine Folge von von Zufallsvariablen $X_t$ mit $t \\in T$ mit $X_t : \\Omega \\to R$ einen stochastischen Prozess.\n", "\n", "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ "**Beispiele**\n", "\n", "- Aktienkurse, Zinsen, Fx-Kurse \n", @@ -65,22 +79,34 @@ "- Arbeitslosenzahlen\n", "\n", "- ...\n", - "\n", - "\n", - "Eine Realisierung / Beobachtung eines Stochastischen Prozess nennt man auch **Pfad**.\n" + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "Eine Realisierung / Beobachtung eines Stochastischen Prozess nennt man auch **Pfad**." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { - "collapsed": false + "collapsed": false, + "slideshow": { + "slide_type": "fragment" + } }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 2, @@ -89,9 +115,9 @@ }, { "data": { - "image/png": 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DjwEXpu4M5pYngKs1oedVRBkAaEK/BB7ETE6lIkk5DMs4ehbwOLB5UEqpnATEmZRBON9i\n4GTgU2BUXVEGFSXXUUY7YRM4PgWKBRmiqi+llPERguPUASQpWwMvAr2wzv8YTejL4didwGrYW3ql\nOp1gBvoS2EsT+nEJZVoCHwH7akLfD/suB1bThJ4aPnfBlq3cThP6bWVkqo34immO4+QESUoTrAM+\nXxP6eHDkPgZsg4VxXgpspQmdW0XnG4jNB+iWScFIUkYBH2lCkyn7WmEvn22AhcD7wPWa0OFVIVNt\no86ZjBzHqfsEv8EwYIwm9HEATejrWErrUcDNwGFVpQwC9wJNsHUH0uXZH9gYm+C2BE3oVCxx3tFY\niowvseUtnYArBMdxKsuh2ASywWn7rwEmYKOGjKadihIc1qcCN4X8QcCS2c83A6doQudnqHoLkMAc\n3ydU1ny1ouEmI8dxKkxILfElMEATNR8OLknpB1wB7KYJnRT8BO00ob1LKC/AM8CNmtCiGhS1xnEf\nguM4VY4kpQA4EZtZfJgmdErKsTOwCV/75VC+AcAlWMTQ/cCmmtDpuZKntuAKwXGcKiO8TR8ADAW+\nBz4DdsNy/PwpSWkOfIU5dr/InaQgSTkOuBs4XRMewg7uVHYcp2o5DTPHDAoTus7GlMLwoCzOxyag\n5VQZAGhC7wG2wnwETgXxEYLjOMsRJpJ9i8X6f5KyvyGWc+wDLCNpZ03ojNxI6ZSGjxAcx6kqjgXe\nT1UGAJrQv4EDw3aLK4MVi1qR3M5xnNpDyPl/DnBIpuOa0OmSlM7AnBoVzKl2XCE4jpNOP2CiJnRc\nSQU0obNqUB6nhnCF4DjOEiQp9YAhQP8ci+LkAPchOI6TSm9gqib0zVwL4tQ8PkJwHKd48tmp2Ojg\n4ByL4+QIVwiOs5IjSdkFWwRmGrCDJvSrHIvk5AhXCI6zEiNJ2QH4D7YIzFOe7G3lxiemOc5KSnAg\nv4+tYlauheud2o9PTHMcpzycCPyGrXHsOD5CcJyVEUnKmsDnQLSirxO8slKRvtN9CI6zAiJJ2RHo\nCVymCV2YochVwAhXBk4qrhAcJ0dIUvYEFmhCx1Rhm1tj6xd3xKKG2kpSBmhCF6eU2R3YI5RxnCW4\nychxckDIGvoN0BDopQkdW4E28oGHgM2A+kADQIDLsTWHC4D/AmM1oWeHlNWnA+cCvatSETm1DzcZ\nOU7d4VjgI+A24ClJym6a0AnlbONEoBWWhG4hsAD4KWUt4QWSlH2BNyUpf2CKozWwrSZ0chVcg7OC\n4SMEx6lhUkYHvTShH0pSegNXAztn21FLUloB40OdiVmUfQN4CVtRLNPi884Khi+h6Th1AEnKv4Ge\nmtD9U/YNwlJOD6aMCWIpC8V/rAktzPKceal+BGfFx+chOE4tJ4wOzgOSqfs1obdgaaeTwCuSlNIc\nvgcCHYArsz2vKwMnG1whOE7NchzwkSb0w/QDmtAiYAvgeeANScpJ6WUkKa2Bm4GBbvpxqho3GTlO\nDSBJWR84HjgBMxctpxDSyrfHFMNLwJma0EWSlMOAW4GrNKHXV7fMTt3GfQiOU41IUu4AXtaEPl1G\nuUZYjH97YANgF2Ab4EHgrrKcwCntNAcexyKIfgF2APpqQt+v8EU4Kw2uEBynkkhSmgDraEInpe3f\nG3gE+AHYPJPTV5KyCXAS0AeYikUSfYtFAz2tCf2rAvIUANdicwrO1oT+Wd42nJUTVwiOUwlSonci\nYCdN6Gdh/yrABCzu/3pgsCb01ZR6DUK9zYG7gbs1oT/WrPSOsyweZeQ4leMkbKLXIOB5Scq6Yf8F\nwDhN6CuYQjgjrd4QzKyznib0YlcGTl3FFYLjAJKUzljIZ29N6AjgDkwpdAUGslQJPAJsEcxDhPDQ\nU4CTS0gi5zh1BlcIzkpPcAKPBM5JWT7yKuBD4C0gqQmdDqAJ/RtbbvIMSUoecBdQqAmdWvOSO07V\n4rmMnJWa4De4EfMR3F+8XxOqkpSTgXHAfWnVhgFfYQ7methownHqPO5UdlZqJClnAUdjTuQ55ag3\nDJtktmWx89lxahN1LtupiNwH7AP8oqpdcimLU3eQpNyFhXPeoAldUIl2DgVOA3YojzIIXAq86srA\nWZHI6QhBRHYG/gBGlKQQfITgpCJJWQ/4GHgXWB9z5sYZytUDhgP3aEJfz3B8RyxUdHdN6PhqFdpx\nckCdCztV1TeB2bmUwalz9AaeBPbFwkFHSFJuCYvFpHINsDVwV5gnsISgVJ4EjnJl4DhL8Sgjp67R\nF3hYE6ohhURnoBPwiCSlPoAkpR+wH5bqYRJwZnHlMPP3UeB6TehLNS2849RmXCE4dQZJShegGfBm\n8T5N6Fxgb2z5yFGSlG7AddjiM7MxH8EZkpS2oUoS+B1LB+E4Tgp1IuxURApTPsaqy9uMnZWCvsCj\n6bn9NaF/S1IOwdJGFAEHa0I/D8e+l6TcANwkSbkNW3NgS18fwFnREJEIS7tS8TZyHXYqIm2B59yp\n7JRGmAQ2GdhXE/ppKWU6px8PPoRPgbWAAzI5oR1nRaPOJbcTkUeBbsDqWHrfi1V1eFoZVwgOkpRd\ngNs0UbHwZEnKtpiyuLdqJXOc2kmdm4egqr1zeX6nbCQpUtr6vjVIX+ChilbWhL4HvFd14jjOioc7\nlZ0SCbH8o8Ns3lzK0Rg4GIsOchynmnCF4JTGUKAxcJ4k5V+5EECSsjYQA49rQqfkQgbHWVlwheBk\nRJLSB+iFxfM/ClyYAxk2Ad4BRmEpph3HqUZyHmVUFu5UrnkkKZsDrwC7aUI/k6SsCUwEttGEflfJ\nti8B6gNDSvNNhNQSTwNnhfUJHMcpB3UudYVT+wgzeZ8EBhUnbtOE/gLcBFxeybYPBo4C9sLSTpRU\nrhPwFNDPlYHj1ByuEJx0DgSmakIfS9t/PdBNkrJ1RRqVpGyIrSNwKLAnMECScmKGcusCLwJnemoJ\nx6lZ6sRMZadGOQW4NX2nJvQPSUoSeFySch9mzvkim5DUsCLZE0BCE/pB2NcTeEOS8g/wjCb0V0lK\nE2A0MEwTWuEQU8dxKob7EJwlhFxBLwFtM60PHFYXi7BRxAHAn8ARmtBP0srtAVyCZbL9CVu4/mfg\nyFQFIknZDFuOchNgMTAPeBb4dy2Z++A4dZY6N1M5G1wh1BySlDuAGZrQZBZlBUtFfT2wtyb0o7B/\nL+AB4CTgL2BtoAm2LsEfpbS1JtAa+FgTuqgKLsdxVmpcITgVRpLSFMsVtIkmdEY56h2IrSm8H9ap\n3wfsrwl9tzrkdBwnO+pc6gqnVnE08HJ5lAGAJvTp4Ad4ATP77KsJHVcdAjqOU734CMEpNtlMBI7X\nhL5ZVvkS2tgJmFtSJlLHcWqWapuHICKnZbPPqbPsAcwH3qpoA5rQt1wZOE7dJtt5CP0z7BtQhXI4\nOSKMDi4FLvfIHsdZuSnVhyAivYE+wPoi8lzKodWA/1WnYE6NcQD2HDyRa0Ecx8ktZTmV3wZmAP/C\n1qAttkfNxVagcuowkpR8bHRwri8p6ThOqQpBVX8AfgC2K62ciLyjqttXpWBOxZCkrA7M0YT+k0Xx\n3sAcLFWE4zgrOVWVy6hhFbXjZIEkRSQp9TPsOwr4Hrg/rC9cWhsFQBK4wH0HjuOAJ7ercwQn8H3A\nLEnKPZKUrpKUZsDDwHlAT6AdZWcmPRH41hecdxynGFcIdY/Tgc2BzYBvsMVrpmF5g7YJM4T3Bw6S\npJycXlmSkh/WJDgXOKPGpHYcp9ZT6sQ0EVlPNfOyhSKys6pNYhKRj1V1i2oR0CemLUGSsicwHNi2\neDnJYBpqqQn9Ma1sO2xewTXYEpSTsOUwH8FeBHprQn+uOemdmkSE+kBHVT7JcKw7UKDKKzUvmVNT\nVHkuIxH5DrgTuFbVEo6JyNpYxFFHVd0q7OuiaoupVDUrukKQpLQHftSEzi+jXAfgTeAgTWhWE8jC\nymfnYdlENwQWATdiaag9gdwKigh7AzcAawBvAP9WZZoIDTBTYm9s1brNVZlWBefrDmytyjWVbcup\nOqpDITQHrgJ2AAYDXTCTxTXA7arVH6pYFxVCCOd8HOt4J5RSbm/gP8DXWGrojGXDegIfADdpQu+q\noEz1gOaa0JkVqe/UfkToAgwFNsB+p68BQ7A1Lq4DDseCDo4HTgW2AHqpoqG+ACcAz2WrKETYClvD\nYh5QqMr9VXhJTiWo8uR2qjobOEFEBgP/BaYD26sua55wluM4zLmrwCGZCkhS9sWcw7sBnYAiScoV\nWKefrmiHAp8Bd1dUoBCG6spgBUCEfwG/qbIwdOI7Yz6hLbGXtQNUWRCKF4rwOHAZtvbEPaqoCJcD\nH2FKYqQIedgyqScA6wCJLOTYEHgOGAh8BbwuwgRVPqjCy3VqkGxHCNsB52Br4fYATlPV12pEwDo2\nQggRP18CB2FrE/csXps4pcx+wL3AfprQ98K+DYARwO9AH03orLB/T+AuYDNN6OwauxCn1iHCxpgp\naCegAfas/IHloboWGKHK3+Voryu2INHmwJWYWfES4GbM/1Bi5yDC2sBYYKgqd4V9BwX5tlHll1Am\nAsaq4i+RNUx1+RCGATeo2kQnEdk87Jusqr0rIW92AtY9hXAt0FQTerwk5Sygqyb0sJTjPTDH7nJp\nooNZZyi2ItmB2IhsPGZOKqqpa3ByjwjtsDf1fzDfT1/gSOAK4LawvwWwOvCNKhXyCYlwLZar7CPs\nmZsHfIeNMpZzSKfUewl4T3XZkYQIl2FRbouA9YEPgbbAzqpML6GtJpiv6zvg3tIUkZM91aEQWmcy\nD4mIAMerVsyeXR7qkkIIC8m/A3TShP4sSVkV+BbYTRP6uSRlU+BV4FBN6OultNMbe0ubDMSa0LOr\nX3qnthBMQpOwlOQFYXsXSKjySxWfaxXgZOAWVeaHfUOBRaqcX0KdLsDLQNsU01TxsXxsbY2vMIWx\nUIQhmDLbRXVpDrRg7joIC3R4DTN5fQqcoMqfVXmdKyO+YlqOkaQ8C7ylCb06Zd852IN+FqYsztaE\njsyirU2xH+ppZUUgrXSIPAFcieqHuRalOhDhamBVVU7J0fm3xIId2md6WxfhXuB7VS7Lsj3BTM+7\nArsDbTCz1wHAusCJqrwZlNPtwNbA+ZgpqzsWzHKkKi9V9tpWJlwh5BBJSoQ5iTumduCSlMbYKGEO\ntq7w1ZlbcLJCZGPszfkmVAfnWpyqRoQ1MR/UZrmyu4cO/Cugd7qDWIS1gnwbqvJrOdscBhyL/R7e\nAl4HHksdZYRyx2JmrHeAIsz89CDQTZWJFb+ylQtXCDkipJN4CximCX0ow/GBWDqJIZ43qJKIXIHN\n0u4CtKG2P8DlJNejgxQ5LgMaqHJ22v5CYF1VBlagTQFWU2VuBer2By4Etk01Ozkl4wohR4T5BNcA\nm/qEr2pEJB/zq+yFrd/QD11x1m9OGR1sqsrUHMuyKRZS2jZlnkJD7PvfVZUvciDTUGBbbGJdG2y+\nxRrAL1ia/imqfFfTctVWqm0JTadkQuqIy4CLXRlUOxEwE9UJmEI4uDpPJkIDEQqDE7UmOBt4JNfK\nIPAZFnGUmvq+D/BxLpRB4Hys8/8cuAXYDwuVPQALl/0wzJp2KkhZC+Q4ZXNg+PtUTqVYOegHPBD+\nfxL4DyLnVaPZ6CZsNu+JIrwJXKJKNaVooSVmO9+0OtovL2Hy2kjgThHeA34GDgP+nUOZFgUZMiLC\nAGyCnodoVxAfIVSChfky8MCJXA9c6L6BakakMdALy+4KNj8jj2rqQEPnEmFRMRtgYZ//FeGoCrS1\nhgj9RHhShOki9Eo73gh4Gri2lowOihkKXISlTVmABU38N6cSlc4jQBcRNiupgAhri3BSMM+Vigjd\nRDhLhJtEeEqEFS6IIZ064UMAzVel9izxKCKL4aIF+Qz5bC0WdB1IM1cIVYsIjYF8VeaEHf2AQ1Hd\nL6XQNcDfqF5UxefeCngJi2r5ImX/dlg45kaq/JVFO6thE8n6YXH2zwJTsY7raFVGB0frI1iak74+\nKatyiHAO5oM5Mm1/Vyx/0z7Ax0BzoLsqv2Vooyk2N6IbMAr4EZskOhQ4S5X/VOtFVBEV8r+qaq3e\nAAU9udRykK9BuVX7BvLzKtz5TXPmth/ExH+EOQrr5Pp7qtMbiEJXhRsVWtt915tB3wSVUOZVhUPS\n6m2n8EVLrBqlAAAgAElEQVRVygK6Fuj3oIeWcPxp0DOzaGdP0Mmgw0FbpB3bDvQX0B6gF4G+C9oo\n5/dhBdhAm4L+D7RNyr4LQH8EPRO0OaikPF+rpNXvCToF9HbQxmnHtgKdCbpprq8zu+8CLXedWiD0\nnlhkxdfAuZkuCvRX0PYZ24BdFCYrPKRQUCUyFXI6hVxHIc1S909qQccP12bc++uwsN2pXEEh9RVG\nKAzK9fdYJzdT5McqfKjwncKTCm/cyfH1QKeGjrlvUBa/KjRMq5+nMFUtFXsln0MV0MNBfwI9v5Ry\nnUJn3rSUMlcE2XuWUmbn8FxPAV075/diBdpArwa9MdzTy0A/B10nrUwe6AOgo0E7gJ4HOj4o8dLu\nWx/Qb9OVfG3c6pxCAPKxVb/aYtPzx5P24w4K4fSgzfOXHIMGClcrTFc4WOFZhRcVVqmUTBcVbMjF\nebO4qN4DFPJTvYs44fumHPd1C779tRGLHtiM73cewDYpcuyj8Faub35d2UDbg24YvrtrFcYp7BU6\n9zyFMePZdBjoRNDtN+DrnxYhMxT2z9RezC6vj2aPiZV5GQBdG/Qp0C9At8ui/HDQS0s4dkDoVMrs\nMEC3Ae2Q63uyom2gLUFnhbf8j0H/VUK5eqBPhLf+YaDRMn1Mye1fA/pf0Hq5vtbS5UTLXSfHAm8P\nvJTy+TzgvPSLCtr8DWHx1aANFVoqfKzwtILdbChQeEBhrEK5tHd4kzgDdDRH9lzATpf/CTqG85pu\nnYj4ZsK/WHxGT15vdypbLVcf6iv8T6FVrh+A2r6BNgb9BnTmy+x+pcJXy90raDWXxn/24aEHFJr9\nSMvfbuOk10por8m6TP3fq+z691waf6eWmr0icr0Oeh1ogyzLtwlmibXS9q8H+jNoheTwrUqftXtA\nx4E2z6JsXjnbzgd9CfSGXF9n6XKi5a6TY4EPAe5O+XwkcEumi7qbcSfexEczNuLL6bNpOvMvGgxZ\nzm9gb5jXKXy6RFEUt1NIWwoZSyHfUMh9FDKAQuvEQc8B/ZC9T76Ei+p9zWEHNwR9aSO+vGMxzBy3\nLluXei1wn8LpuX4AanyDnRXKfKNaei/1LtD7h3L2mb+wxuIB3HtihjLSh4em/UWDqQpjZtHsnmBa\n2ThD2YtAR+Sz8OAB3DtlMUxTuLWcMq0fTEDlGmGA3gA6qni0A1oAOhZ0ObOnbzW/gdavzjd40Gag\nk0CPyfW1liwjWu46ORb44GwVQhFFo4oY88/PdJ11AZdOCLbXC0GXfTM3B+WlCp8prKmqUMj+FPJz\n83M5+8mNGfhaW179vhm/TWrBP9323vg56v05nVZvt6eQbymkZ7jhLR7l8LlvscMLZV4L7KHwbq4f\ngBrdzHejCrtnUbbXs+w77Bju+WUa6xylMONB+g7C7PX9lr3fugnolMVwi8JTCvlh9DYmtdPGnIO/\ngm4YRngvr8+3QxRihauzfwb1QtBby//sahPQoWFE8Crow+GtsVxvm77V3Q3zPfwCumOuZcksH1ru\nOjkWeLs0k9GQdMeyvb7Xm3s0fRYNYpP513L+5CKKBHTLYPebBfocFrFRHJEiCoWL4PMO/+bWDQcx\n7Ys1eFjNMTlW4WKF7Z5vvtGlvzSor4ceWPA/CnmYQp5acm7Yfj4FPzdl9q+gW5R6LWaumqnQNtcP\nQY1sdr0TFMYo3FdG2VaLkNl3c+yfP9D6v8HPc7TdW904dKhL7Pahg74p3MMQFq0FoM+GrWHYdwno\nfSn1NgL9dTDXd1L4RqF/2c+fCuiXZOE3KKWNBqBHgD4CumbO741vNbqB7gU6fbkX05zIQgQUpmxa\n7jZyfAH1sMyHbbFFvzM6lT/ktpPG8p/xC2i8YxFFk4oo6pFyQ1YFPSb8sN8F3QdUKGTd67fjh9kN\nWLDIbPzXKLRLq/dpf+4bOq8eM2/uyuQhu9FVVYujXz5S6AvaF/SjMq8H7lI4O9cPRY1scJbCSwqt\nFGYplGh7/4e8S0dy2PegV2Z+BvRA0O8IUTugH4JGGcoVgD4W3sbXCzb89dPKXAE6cj4FGyv8orBT\n6c+fbo35NGomZNm3FXIDLQR9JtdyLC8XWu46tUDovbDFQL4BhmS6qCcaP/F8EUWnqCpFFPUpouid\nIopk2XKaD3oo6Cd0ePpLLiqYSSEX/JXPNpoh8gj0xPDGKQrrqIU8zlSYoRb58rqChHZ/BW1d6rXA\nbsF3sWJ3LtA6jLTah8+vKxyQqezndCz4jSZ/duXd90Drl/wM6O2gj4K2xSI+Mtp+w70YDjoH9M4M\nx1fFQgev+4e8nuFebljKeW8ELcz5d+pbnd7CKPFL0Iy/g9zJhZa7Tq6Fzuainm347KJ1Tl3nYwq5\nqN6F9c54ptEz04/f8fihFLIxheSpKhSSTyF7kJCRnL/KbDYYPQv0VtBmmdvVN0CXDWU0M8V6Cr0U\n1k4p+zDoCaXKag7tjzR98tSKtsETCoUpn09QeCxT2cs4/8F36fpHSfcg5fttBDoBCy2+t4yyeaCD\nSYsrTzneAosuuW0h+ccp/KiwUXq5uziuEebD2DDlWpoqDFE4K+ffs291asNCVqeArpZrWZbKhJa3\nTp1IXTGGMe/vWrhrEltlqdGhbx+6ycHvHbztmM5jFgMNm85rOqOoU1HjD9p/MAXLt/IohSpY2oB9\ngc01ZTEPEdbD1pBdV9OWAMwsA32AI1TZv4yCe2AJ0ToT1qBeoRAZCJwDdEH1r7BvdWwt3Jao/rG0\nKEe+xY73NGXO4M464Y6ym6Yz8D5wiCovVE5MmgIvAhP/ouG7DZlfCOyO6kREWgOXLkaO+poN/+7A\nV7cBYzD76/HAaGAPYCdUJ1VGDmflQoThwG+qnC7CqsBgLJJy22z6maqXZwVdD+Hy7YuevOAKjtAo\n+gcgljgPOA1oNqvxrAZTVp/SutPUTt0KFhV8CJwdafTV0vrcCfysysUp+84F2qlyQnYy0ALLA7+m\nKn+XJiyWaXEEqveV+2JzhcgOwBnAIFRnlFDmdOw73x3Vr9OOvQA8gurDYU3df3fms4s+Ykst4J+W\nqGb1YxChDZbTvtIPZciFNBLY9kIunXABl3epxz+P1mNRH2BYS6Z22J9npwzj5N+B3YBPgGtRnYzI\nuUBXVKs2vbZIPeDKcJ6fq7RtJ+eIsAYwAbgLOA5bEW4roJ8q79a8PCtoLqON7ih6n6KiiRQV7VpS\nuSKKGhZRdG4RRb8WUXR9EUXFTsoNgg+gScrw7hPQXco5JHwLdI8yy8L2ClM0Pc1Cbd5glMJrwea+\nR9oxUUioTSJbr4T6Ryo8j4WMvg36xjTWGaGQcTZvzT4/2hb0rAHc+82NnLq4FVP+xNJizKWEGawK\njcI9rNoJZnC+WoDDf7UccyV8qzsblv7kedCtwuebQc/JjSxoeevUiRECRUV5WOrjO4D2GkV/lFQ+\nlngt4HJgbyyM9cHuRA8Cn6oyNJgmRgNttBwZVEU4H1hblVOzKDwKiFG9Idv2s5RBsIXKB2Gpnweq\n8lMlG22Dmc/WA7pia9c+BfwEtAI6AP/CRgY/i7AB0AnoiC1O0mh1fm3wA216tWXy3NX4/fKJdBzX\ngAWjgM1Rzcm6wJkI319joBmwQJWS39JF+mNveTuT6UcisgrQHpiAatnPkchmwKvYd3w/8Aqql5f3\nGpy6hQgHAwNU2bfmz72CmoyKL0rieCTwmUZRmT+kWOKuwK3AgqPZ5tQprPoisD6W372eKueUTw42\nwzrK9qplmDREOmPpjjdCdU55zlPK+Q8CLgUWY6tFtQaOAfqo8nolGr4CWBXV08Lnf2ErU83H0v5O\nA8agOleEQ4A7gfewhe4nAX8Ciyax0aAN+HaTfBYXAF8AT6I6tMJy5QiJ41WBvfMXLWp93AsvDHlj\n003HT2zbdiQwWqNousRx/V5vvXXKory8ZP2FC/NHXnrpnIJFi54DnsE6+eVXzROpD4wDbkT1fkRa\nYmsMHI7qGzV4eU4NE9Zd+ApYXW2Bnxo894qvEDYE3gE20iiaVVbd4Gt4GRjRnehgrJM+E+ilyifl\nkwNZnflT72fc141Z1D/SaHIZFe4A6qF6XBntbggo8G1JikaE9TGH6xHAa8XlRNgTe9t8AOuY1wPW\nAS5U5eMsLqoBMAXYhTIcqOHtehxwmSqjMhRoDrQAvs/qjbkWInEsmN+hNfBBt/HjV933nXf2u3/P\nPSd/ud56HRaLTG60YMEGm377bf02P//8wGO77tpq3Zkzf5p22GETsJW81gBuAIaj+ufShuVSYDOg\n15LRhsiewN3AlqjOrNELdWoUESZiL25l/yar9LwruEIAkDi+C5itUXRuyr51gMUaRcuZAGKJewHn\ndic6HRuyTwE6l/mWv3w7a82k/pcF6PxmLHw00uj0MgRfDXNUDkI1Y9RMSkf/F9AAeBu4U5XRaeWe\nBj5QZbmRkQitsWUD54Rr2whYX5WDyrwokb5Af1R3L7soOwHDgY1r+k2nppA47odFUW2jUfRXCBLo\nD0QL8/O3ea9jxw1b/P77R5v88MNhqP4gcdwcU5KXaBQ9GJzzZwK7YGsS53/Srt0qr2yzTYeTn3lm\no1X//ntZ857IVUAXYN+MZilnhSAEtnyhyk01e96VQyG0Aj4FOmMLbp+OdYj52Lqvb2DD99EaRRpL\nXA/4Hti3O9HVwBhVymXKCH6Jok9p+vE1dGjzIOM6AutHGs0tQ/hu2GpYm6L6v2UPkYcpqNGqXBM6\n9m7A9cDhqrYurAg9gdsxJVZyhNPSdhsDPwBbqDKljMJjgWtQfSaLdp/CRie3lVW2LiJx3A4zhfXQ\nKMo8ejS/wd+pIyCJ485YZNmeGkUfhnLrA21f3XLL1fa//PLbFxQUyKL8/Ps1ii5Ia68AeAuL0KrR\nzsKpOUToCxykStVGrS1/Ikl9sVgpFAKAxPF1QDvMRDILOAHrBLtgb2cnYkpgkEbRd7HEFwEtuxMN\nxpyJWZs0YombA2OBkd2JrgdmvMgbrzZi8euRRjdmcQHXA+ug2nvZ3ZwMHAXslPrGLUJ34DGgB7Zw\n0CfAOao8l63MItwI/KXKkFIKbY4t6diOMuZMBEfyu0BbVf4srWxdROK4HvYi8YRG0fUVqH8wZio6\nUqPojbCvETa/YQy2HOMbwHCNoquXrSzF321PVGvUpFCjiLTCfGBrAftl9LWsoISXvY+wsPXq6XBF\ndgJOJyVUuiIKIa/KBasZrsTyH90K9NQo+k6jaJFG0XiNopuBzbEY4HESx1cddzdN/2rIgCbPxC9S\nFO8KEEtcP5a4bRbnOh94O9LoElX+AG6+iM7tFE6LJc7Pov4FwOaIHFa8I5iKLsGiD5b5YYSRwSBs\nYtWV2PyH57M4Tyq3AceGxdtL4mTgzrKUQWAwcPeKqAwCF2E+mLIVfAY0ip7ERqoPSRw/KnG8HjAC\neym5SKNoJqbgT5I4PnHZyvotNr9jJCKNK34JtRSR1YIP5RMscq0JcEpuhapZVPkR+B3YuBpPcxG2\nDnilqJMjhKzr2g/zdOCP+49mr8878cM159B+9J7s3nA+TwJbYDbgG4AXIo2WGTnEErfEzFNdIo2m\nmzzkAY/cz7geLflrYA/t9lQWF7EtZsbqLOgc4BWCqajkKpyOLerdRZWSHb7W9gDgVFImgInwIvAf\nVYZnqNMY8zd0Im0imgj1gN7Y7ONxWJjmd0AnVaaXea11DInjQzEz3bYaRZW6vhChdB72zH2MmZ/m\npxzfABspDNYoWnahdpH7AfNZ1PYfZbaINMJMYpOA81CdgshGmK+sK6rf5VS+GkSE+4F3VSlz1n4F\nGu8KPAG0X7YPWElMRhUhlng3hZsG3ULBtWfRvOF87sDmKxyC/YAbA/unznKOJR4G/B5ptEyIqggN\n92X6x4fz46pH6rbribAaNiOxI9AQy9yaj0U1jVNFEbnxB9Zr35YfWmHhnAeU5ZwVYa0SY+VFmmKp\nOQ7CfCl3o3prSt29aDH/Kh5/51jdLfogre4xs2jeZ3VmDVTlu5Q6zTFzVZNwDe2DrB+q0q80Wesi\nEsfbAc9ho8wqM9cEP9ccjaLfMxzbAot8O0ij6K2lB6QxZpp8ENVrq0qWnGEO+fuwYIm+yyg5kbOw\neUK7rTDKrwxEOAbooUqfSjSyBjAMU67fpux/DnhJ0DuWNT+7QiiRWGIBvlxYj5Y3ncaPz18bdUw7\nNhB7u9sx0mh6LPEGmJOxQ6TR/9Lbayd/rHk1n067jE1++oRmzbGRxARgHhbDn49NplsI3Lc6v278\nEVv2f4Cjh17MpReWy5Yo0hCLftkgZe9uwAtB5jbYcHEjVOdaFfK44IvZRDOVetpWo+i34oqzpfln\nx3Lvek9z0EJgFKYY62M+hReAs1X5R4TVMZ/MOFWmZS1vLUTi+DhsQabHsfkkzbE31RM0irL2z1SR\nLD2xCYDdNIq+XHpAWmNh1YNQfbomZapyRE4A/g1st0wIrh3Lx777e1G9KwfS1TgitAdioHWF/Agi\nq86n/hsT6diqE5/PK+CfrqjORGQL4Pl1mXbKDNY9RlPyrblCKINY4h3mNWLePi/yH+AYjaI3ASSO\n84GD/9uD9vUW0RvrBG8Gvo40uqSk9h6Xd05anflDBYryLH/JS5FGqQ5iwRLyDQDmfsMGb23Ad0Ox\n5HDzshJaZFPgYWzYndpxfY7qBynlHsTmAFwcrqkt8/M+5+NmC2j75yg9Yvv+AAlJHnISwx7rwKS9\n59L0fcw/cDJmrjhblbqTgylLJI6PAK7F7Kz7Y7O95wFXaRTlJLpH4rg/kAB20ChaarYT2QpT7nst\nc3/rEmbGfA5LEPhVCWU6YR3kMag+l7I/H3vJ6QYcjOpyo6y6SOgLpmFBJCWbyizybADwG/AUqv8g\nUm8ejV54gX22PZzHPruFQW1PYtiMPLQ7MOJ72n7Wju9PBA5WZezSplwhZNdmHB+PDdn3kjhuioWG\n9sxbxJGv9aAr9va9DtA+0uWH/anEEq8GHI5lyswHuqb7IpY9uTwMzMQiAkr+8m1266lYSO2ZmCmh\ntPLFKSg6ozpD4viO/IWL5jQ5qf3vs6//8RIu6jycT5vdfTODXtuaD17YXt85TOI4D+jB/LxTWSj1\nabxoD42i2v1AlBOJ4z0wB28PjaLPwr4WWNjym7m8XonjIcDRQbapSw/IAVjAxN6ofpoj8bJHpD2m\n3Jph5sbOWEe//ATGZevtjN2bV7BnvDnwEDYbfwrQEtgH1fkltlGHEOF2bNLjYar8laFAD+xFdBpm\nem4F3DCHJtuNZ/P992L0tX+xyiXC4lFFdG/XjTdmK7Rvwtzv/2C1UapctWxzrhCyazOOG2CO0sFA\nEosjj4Ezi7qzA5Ya4uNIo3uybTOYnd4Drow0Knm4b6khXgVmACeh+n3a8WZYGO0gzAS1fJmS274W\naLzdbbddO759+09+OPzwWWv99lt+l5seeH5CvU57NjilU8vZNP9j1uqrbtPqiSd2x3wnv2N2yROA\nmzWKHsj2mms7Esc7AU8DB2gUjS2rfC6QOD4LG6H10Cha+uYo0gdLpX4uNvO59v5QRYp/J88Cc4EZ\nZc18T6nbBOsEdwJWwwI8ioMt/oOZXPuUkBKkGbAfZmb7tlZ/R4AIBVhmgVbA/qrMCQcKMEW4DZZ1\neJRlmpPtZtPs4s/ossthPH7jT7r2haGdFgUs+HASHWa+wD6/DeLWRcA+6eH0rhDK024cnwlchc1V\nuCOYjb4C+moUZUxVK3HchKV5fr4M22cpabkPwn7A20VaypunPQBnAmdhD/9UYJOwdcPs+NehOr58\nFyUtgEkDzzij0fz69Wc8cNVVfYGWfxcU3LXGqFH/nPjoCyOirz/ssd+VV9bDRikXAOM0ilTieGss\nvLWTRsv7TOoKEsctsTQSvTHfSn+NotGl18otEscnYc9VT42iiUsPyCZYp/gBcPJytvjagDnDfwQ6\nolrxRIu2lshMVD9K2dcQM59NwPwqqY7pFtjI4g8syeJ8zFmfrJQc1UyIUryJpabklqPY//Q2/LDh\nXow+fwbrjlLlDxGaYb/PAcAlqtyc1s7W2PX/CWypynLpT1whlKfdOC4A1tcoZe2EOD4N2F6j6IgM\n5btgoV3vYMPZjbH5DuOBw8Os6HwssdsJkUZx2UJIeyxSSEO9L4C3Ua2wA/epnXc+8LDCwvsX5edv\ntCSVh8j2I7t3f/GEM85o8k9+/m/zGjU6EZuEtczNlzi+GVhVo+jYlH1rA3M1irLzedQwEsc7YIq1\nbdgEGxU8ChQVK+vaTkibcQWWNiPVp7AqcA9mitmv1uWJEjkGy9HUq5rab4plJ14InIgtcrQ68F8s\niq84AnATLM3Iodj39Fm1yFMFBH/CBcCxe/DST49z2KbdeP268WyxLbA9Fpq8DeaHubCkjMYi7A9M\nVyWjr8kVQmXPZSOA74EtNIqmpOw/CotVP1OjaETK/obAh8DlGkWPAMQSHwscEmm0V03InCJLcUTQ\nQGCiRtEZqcfnFxRsdOf++w+f1Lp1r9tuvPHXEtpogiml3tj3cD5wLDBMo2hwtV5ABZA4boZFd12N\nzfadDPyvrvpBJI4vBnoCu2oULV1UyEaUbwBPoHpdjsTLjKU/ubpMf0HlzpEPnAQUYtl298FGA+ct\nZyYSOQIzQfVH9cVqk6kiWDLJhUuUuin78cAQVJ+wXTQH9gQmqlI+C8Fyp3OFUPnzxfENwAKNonND\nJ3szNsfg0GKnZFr5rbA3mC00iqbFssQ/sU+kUaVuaJbydsCc4htioXyvA7drFC2XdjuWuEGkUakO\nOonjQ7CZzvWAe7EIpyIsw2xGRZIrJI7vA/7WKDo517JUBcHJ/zTwo0bRv5c9KG2xiYL7ofpejQuX\nCZGOWGqO9VBdWAPna4lFi30FFJboMxDZHngS8wVeXSvSZJj56z2geMR3P3Ax0BjVapnj4wqhKs5n\nSc7GYTHU12Fx6xeUZjIJb3Y7AHsF09FZwJaRRhWfhJKdrJtgQ+dLgPs0ikr8UcYSd8d8BLtGGpXY\noYQU0IcDYzSKfgn77gJ+0ii6uKR61YnE8Y5AH+B6jaJvw759MWW9aWkLJtU1QtTbOOCK5Rz8Igdi\nI9UtUZ29fGURYHVgbSxKbg1sYlgBZkp7tErDOEWuARajem6ZZWsakfWwtPAF2GjhmyzqtMd8evWB\nRcDf2Cik8jOqLadZa0yhHY/NiZmLJb6skjVTlj+lK4SqOWccPwlsSspchTLKF2Bv568Cc5r+xrYP\nHsV+01oyZONJXFuqgzmFWOKGQINIl3+7z3DOTbFh89kaRQ+V0W57LIXAg8ABwBaRZt+JShy3x3wn\n7TLNvq0oYUbvGZgpbrnvSOK4MTZp7lBsBvVRmEPuHszR2lejLHw1dYyg6F/Hru+VZQ/KzVgSx5sI\nCxeFGaz9sUixNYDpWN6gmZizdQE2qfEXoHeVROOYGWsqWaylkTNE8rBovYuAs1FdPo3L0rLrY5GG\nd2Cpyy0kG1qjemAl5eiBjQg2W5L12NLjN05PHVOVrEzJ7aqbfkCXbJQBQHgzPwpbWvJfc5rxxBXn\n83KLWZwGjI0l3rqsNkLY6kjg7TC3oUQkjjthEQanZaEMmmLhgIWRRmdjiqFcGT01ir7BHHgnllW2\nnAzBfrBHpR+QON4G8w80AzprFJ2Ome62wkxyT66IygBAo+gL4EDgQYnjvqnHJq+11tmT11rrv1iC\nuGmIvA98gymJo4EWqHZCdTdUj0D1aFSPx+zunTDFUX5EBJGDEdkDkQ2DfF/VWmUAoLo4pBXfGbg2\nzNVZHhtNjAGGonolqs+j+izmsN4izJeoGBYNNRwYsEwKfNXfq1MZVBQfIVQTEse7yGJuHrMbN2Nv\nuU8AZ0caZVzTIJb4RMwh/DE2QeeQTBPcgknnVeBpjaJb04+ntZmPRSp8F6nZpGOJm2COrNMjjbJ2\nBEocb4b5StpplPkaSqnbAfhGo5RZ3Lao0eeYMrgH6FicXiMc+wA4XaPo8Qzt7Qh8pFG0/OSeFYig\n+EdjWVgfxjr847B4/dbavXt9bI3mT1EtcwXBlNnBO5W7Ixfph00++56lqef7o1rqC0mtwRYjaoLq\nyWn7W2KjsVtRXT7brc0HGYyl4ChfhJeZ8B4DpqJ6RlnFqxofIdQuxmoe63Qv4nUs6d062Ghhg/SC\nscQdsVzxfbBJSmtjET6Z2B2zRd6ZhQwnYE6sJau7hUV9jgLujCVune3FhEVjPsY6pawJnfsnwIVp\nh84ERmgUvYDlU7o0lC/AfkR3Z1IGQZaxK7oyANAo+hzYEVMCX2Ghzkdja3/sjuo8VOOslAGA6ufY\nfRgZIl6WRWQzRK5CZLO0/S2wSK4jUO2BajugQZ1RBsb1wBGIrLtkj0UvPQY8kFEZGCOxfvKwEo6X\nRi9s5FbSb7nW4SOEakTi+E7ga42ia4NJ6BQssuBs4JlIozkhKuldYFj3IsYCDYu6MwNzLJ4QafRC\nSnt52JvzlculT04jtvWkJwEDIk3Jqrn0+GlYLPQFwL2lpttYev7iHDX9NYqyCumTOL4Gm5kZAX00\niookjtfAOrhNNYr+3955h91ZFvn/M4SEpggShAVCCQLSDdIhMEgJ0qRFkCJNOgqaH7CCiKACwlJE\nAQURUClSBANEisoAIhtglxLpxBZXUJSii7ghML8/5n7DyVtPP+dN5nNd73XlnPOUee6c88xz3zPz\nnT+WbK6niHS7fQjpgx1dh7ZpXqBU1o/sCZ6L2dGEBtK+g+/Z38FEiNnqWCJl+jmihet+wDJEH46d\ngPGzg6kilwDe5+l6uCFyPgDunyuvTyJkarYd9OlfRCntY6uW0YiU0qeAA3C3+o2unwwqdxlFR+dU\nV920570STzibmOpPJ0SsXt3mbr7+9vxMAZ5x1U1MbBOiYvkW4EJ1fayItH2e0O4f9D/OxHYhgmkb\nDhTUNrF1iOWaN4FPq+vzVVzTpkT17DeIjIz5iCeh44A7XPWMim2XAJ4nAvRrED+q9Yi4wWhXPaJi\n27n53vwAABo2SURBVE8Xe98B1h/O1dKtRsyWJMZ1TF1B/pgdbE6kKq9CZCbdANyB+9uIHEl8zzYj\niv1uAdbA/bX+DzhMiOWhacRMawzh/NbHfUYV+04mtMqmEV0a/wDcPKCDEDmTCEjv1xTb+zuF2TKE\n3tM/y9/fK3836RC6DDEbRWR7rNW7+YqJjSIa9Hz4mG/y4pNr8V3gYCL3fzNXfcHEliRS1I58R/jd\n107mg/ePZ7+Z2+nPhzq3id0DXKqu1w6x3QgieLa3uq472LYV1zWGuEm8BKxGZLN8h6i03acn2Ctm\npwP/5qqHltdfJW5EaxEVub+tOOZ85dq/Nbs3cTIgYnYrcL2r/qA1J5DTgY8RN8Hzhtny0MCIXExU\nPU8gahmuq3K/DxAZekuUv3WBVYGvAlfOUYcRkiP3EqrGDclolCXUtQl5kJ46oM0J4cutiQSLhYCF\ngSdcdXbFeDqELkTMvg9MddWLyuv3EllMbxHCcosS6+efcFUTs28Ar7jqaT3HMLGR10/kko2mMnGF\nP/AS8WT+w0EC1OMo/ZLVB65NqNh+PqLKd2f1ARrM972uhYkv5X2u+qvy3gRixjGuXN90YjbTUzsw\nP5Gt9BtXPaia8yT9I2afAA5x1QmtOYEIkYI5luj33N03imqJAr/ngR81/PQusjEhjrkKURx6D5Ge\nfTtwU2XDqrpPYXYJIeC3CDEbf42oj/gmcNVgM8R0CF2ImO1KCOhtLWZLEV+Wlwi100UJmdszewT1\nSrrlNURlsJf33gs8N2IWO/5sW95HCOitChyt3le4zcS+Dzyprl+v1k4T+xqwoLpOauByEbOzieWh\nXwFruOp+vT5fAKCytWRSO2K2ECGTvOYc2kdNP5GM6IpK32YisgPwQNMKwqL/w87AVsTD0NNEi9CG\nxq20dz0LWM9VXxezxYmEk2eria+lQ+hCypP0i8QU9WqiOOy0gWIAJa30GeCACidxOiHENztf38S2\nJyQmHgGOU4+bgon9GxHMGquufatZB8DEPkQ84YxRr18QriyT3U/8MMaVTJmkBRTpjl+7ak11JUkL\nCfXXWbjXlJrd5zBmKxFSFzu66sP1mZJpp11Hkby4i8j/PstVvzxYQLh89kMi66MncHQ0vdI21fUO\nYm1xOvCciT1pYj8hZhdX1+IMyvGeIQJl29SyXz/2zySkL45PZ9ByZn9Pki7B/X+b4AxGEemuZ9br\nDOo+d84QWk+RmRjtqr+ocvuxxNPBMsDFwKuuesJA25vYIoQ0wQeJNd9r1bVmCW0TO5roKd1SDaak\nOZRA/B+Abefoo5AMa8qy6+rALo0o9+aS0VyEmP2SyPmfRMQTWp7yZ2KjCRmE5UsBW+VnixFZJ66u\n1WVmJC1HzC4mgvT/0YRjLUn0lrisyJUkbUbMxhOCmuu4ap+mNzUdq4575/yNnDBpKT8kWlt+rh3O\nAEBd/1rSVfcEvmdiCxOFYnsBGxE6SBub2M1DyWgnbeOnRA1IVQ6hdAY8ilhuvIuQQXmTqA35d2LG\nsXB5nbSRIuZ4JXBEo86gXjKG0L1cTwShL2nzeb8PHG5iZxM3h12KDcuo6w6E4Nz2gx3AxMTEFm+5\npQmEKNuGJRNtUMRsHFEVvzuRuHAwkd8+g2jduhkhWvfJksWUtJdzgPtdq9cYazYdmyGIyESiA9KH\ngA28spdqgqu+QmcChlOIJ8URRJVzby3464C9Cf2hgdgRuMHEPg98u1r576R2XPUNMXuQKFK6pffn\nJc6wMfFd2oP4v72yrE2fV278y7vqsxX7PEI4hmvacAkJs2t4diRmbh2jk0tG04gvXTUibUmbKEtB\nGw2yyU3AWSa2iLoO1PT9AKJ5zaHANib26VqznpKamALsQIVDKO1dzyJ6SbxC/L+t3dP0qIciEthb\n+fS7xLJSzQ6hLEktltIj1VNawV5G9F9pSbOcaunYkpG7P+Puzw29ZdJNqOvLRDXmTv19XoLP2xE3\no02I5YhHS31E0hqmADuUGpYejiH6H2zlqmuXdOe/9L97HyYDa5XGSIMiZiPFbG8xu1bMHieq7/8k\nZkvXehHzMOcDt7rqzzptSAaVk3roWTb6UT+f7QncXTEjOM7EZhFa+s1usJMEzxOd0dYCppUnzhOA\nLV215ocuV50pZj8gYgyzpZvFbGWiJeVMwIklqM8SejpXEoHtZ4FLCcHDnP0PQWkFuwWhjdRxWuoQ\nRORuotS6Nye5+601HOfLFS/NOyQnm8zmFuBCE3tfP+0+9yeeeCo5gyieO7caRdUETGwJ9eqWXVzV\nxaxn2WgakTp6W4O1CZcDPyv9wpckmvQooaUzqvzdC+zuqo9U7lha0B5OOoRBEbP3E2O0bzP6gkvI\ndGtDx+h0HYKI3ANMGiioPK/WIXQ7JnYz0dPhqor3ViSkNJZR15m9tj8JWFdd92rCuU8hnkRvmBsD\n1hZtT2cAn6zshzEYYrYDoXG1F9GJbpyr/qERO8TsASIbaRfiqf+r1TQmErNFCLmWFUtyRNIPZRb2\nqqt+tiXHH8bSFXnDH370LBtVsi9wfW9nUPgGMN7EPtLISU1sLJF3fwpwl4mt1sjxupRDiSfxE2vY\nxwj9qHMIFcyGnEHhfGAFQF315Gq71LnqG4Sqbb9xpnkZMZtPzLYSsyuJGNsXOmzSHHTMIYjIbiIy\ng0iJu11E+qh2Jl3NbUSR2sEmNqJ0hNufEO/rQ8lI+gpwZs97JrasidWaZncU7zbamUK0JT20ngvo\nRkxsJHAskR00pjRKGpKimfUAodl/5hCbV4Wr3uiq29SpSfVjot4hKYjZQUT70/OAXxN9TwbK1OsI\nHV8yGopcMupeSve3C4im71cQInyrDtKhbSShxPrfxNPs+4kOaaeq65AFeEWz6fdU1EeY2KqE1PY6\n6nM2IRqOmNgngSPUdUsTOwbYWl13q2ZfMdsFWMpVL2upkdXZshjxf7VsM9bHhzulCvn3wMdc9aG2\nnHMYLxklwxB1fQQYD5xOpDleMdiafmnWcwCRtjoR+AAxQ5xkYl8ss4zB2Af4VWWxnLo+R+TNf62R\na+kGyvVPAs4tb10BbFakyYfEVSd3gzMAKHIrDxL6V0kUBt7bLmdQL+kQkoZQV1fXm4iuUUMuVajr\nr9T1AnV9XF3fKTf3zYl01QtK97Y+lJvlZ4hOUb05A5hQZizDmfHEbOs2mL3MdhHhJIYjuWzE7B4n\nxwANd1BrNVmHkDSFRrJ91PUlE1PgTuB4oL9Ob1sQOfB9infU9e8l8+gCExs/jDOPJgHnq8/RDesi\nImX3u0SdwdZEH+tpREbXQ8DDXXrNPwHOFrMFXftv9zqPsCXx8H1Ppw0ZipwhJF2Bur5GLCN9foBA\n6meAbw1y47uSUOn8RGssbC1lWWgTQlxwNur6V+LaJhPO4OfE0+YDwDqEvMSNJVW1q3DVPwOPk9lG\nxwDfaqS3QbvIoHLSVZjYLoQO0jh1fbUsFX2KWFdfSX3gpuImtgWR5bSa+vB6IjWxq4Gn1LXfWIiJ\nSX/O0MQWILJWJgAT1fXRErz/MLAscFsjLVEbRcy2I3R61hqsIXwzMLEtib7Ga5a/y9X13MH3ai5i\ndiBRIDjJVWeI2RjCKa7Q6uvvY0s2yEnmBkzsAiL/fRLwbaJS9hB1HVIR18QmA79Q1wtaa2V1FIc2\narD+ESa2OlH1u/JgDm+I8+xNxFeeIlJyfwu8AbyPkLG4vVPLSqX385uuenSv95cBXmzGk3NJX/4F\nUUD3JPA6EZRfUV3/2ejxq0HMPgpcC1wFHESoOS8HLNKq4rNB7ckso2Qu4URgeeAxYolkw2qcQeFk\n4AsmQ/cHaDUmNoq4Kf26PMkPxKnAefU6A4DSxW4jIrA/Rl3XATYlYjJnAz83seXqPX6DTAI+LmZb\nAojZ/GJ2FpGGOUXMVmjk4CUR4dvAKep6srpeUyq8HySy2lqOmK1KOIO9S7vbLYmsuBOJNrjDgpwh\nJF2JiS0FLKyuv61j3x8AL6jraVVsOz+wUCM34wGOuziRZfM6sBDw0/5mLSa2FuH0VlZvTb5+ucYv\nEjGa8ertl5MoNRLnAtsA3yPqT/YnnqQnEanLF7nq27Ue28QOIaq7N60MyJvY+HKuD6nXftxqEbMl\niMZDZ7nq5RXvz0dIjj/eqnMPalcuGSXJbHmLh4kbwcu9PhPiqfF0YilqFDALOE1dz2ji+acAtxPL\nNasR0hKr9e4LYWI3AA+p6znNOPcQdp1DdEXbdpBeFi1DzK4mFFLPA07pufmL2WpEV76PAFOJJ/tb\nXPXRoY5pYksSVb8T1PWxXp8JcaM+U137NA9qBmI2ksiO+y9XPb4V56iXXDJKEqDUNlxHL50YE1ue\n6EH8WaLuYQki9Xol4LPlibIZfAe4Sl0nqevb6voUcDOxnFVpzzpEDUa7lhROBJ4jutmNbNM5KzkK\n2MZVT6qcCbjqs676UeCDRBxkAeAuMRtdxTHPBq7p7Qxgdir0OYT6a6s4H/gn0Ylu2JMzhGSupDTk\neZLIOlqUCK6OJ37A55Sq6crtdyRuzOMaWVIxsQ8SUhpjKgPJJrZ0sWcDIuC7ByH78RV1bZtMdHEE\nPwZeVNfD2nXeWhGzC4H5XPWYgbYxsc0Jx7/6QEt+JjaC6Bexr7o+2GQbDyd0pzZ21b8389jNIJeM\nkqQCE5tApB++RqzlP6au0wfZ/jxgLLBbvRk5JnY2gLqe0M9npxC1BhBB8yPV9f56ztMIJeD+KPAF\ndb2hycdejAio3tbIun1Zl3+a6PjWR1yv3OgfAb5eAuqD2XQMocx7CSGb/px6YzdwMdsCuAHY3LU7\ne3ykQ0iSBiiZQD09AF4jZCQWAv5EPNX/Dvj5QMHfsv8MIrj5Qj+fL0JUq95IVCS/1XubdmFiGxAx\njg3U9fdNON4ywOeILmuvA7cCxzWS6ipmxxJicNv3c74jgE8COtQ5TGwhIttqTWBVQmZlb3W9rU67\nViBiEwe46l31HKMdpENIkgYpcYZ9iBz+vxOtKZcl4gxrEHnlE9X7Zo6Y2D7AQeq6bfssrh8TO4Fo\nfqP1Fq+Z2IeJZZOPE1XW5xHj9ktC7LDuwrASsJ0GfM5VZ8vjm9j7idnDdv39P1Rh81bF1rX66fg3\nlE2LEA8NV7lq786AXUU6hCRpMSa2L7H2fxLw3cqnUxO7F/imut7YKftqoeTv3wk8oK5f7ufzE4AP\nEUWB3uuzLYHTiEDwt4DLKlt+mtgYIpZy/FBLOoMhZjsSvZrXdY3GSyZ2EYD6nIVutWBilwGz1PXI\nivfWAT6lrv0GoYtI3Y+IIPJB7ZCiKEtj7y3SLjWRWUZJ0mLU9WoiOH0scL2JrQxgYmsQyxE/6aB5\nNVFy9j8FHGRiX6iUHzexo4i+yOsDn67cz8TGEctelxJyImdpr/7P6jqDkHC40MSOL0H1nv1Hmdju\nJvadKjSYphDV1xeWfdcnMsROqeeaKzge2KUns8zEtiOEEw8vs8T+OJmI/RzRJmewADHOj5Qix5aT\nM4QkqQMTW5goqDqWeGp8D/BHdT150B27kLL+fzuhnHo0IRD4dUJhdiFCVmNzdX225P0/DJygrtdX\nceyeJaVdifqC6UTf56eBfwHPqw+cSQRw8ra28vSV+eVuN/OPpf7CaOAYdb2mvqudw7bdgLOI9q5f\nIhzNEcB96npp5bZitieRobahq77Y6LmrsG0R4BbgVaKR1E3VNJGqJJeMkqTNmNhoot7hEODD6vq7\nzlpUHyXz6HoiPXcs0antyfLZEcBhRM3EbcBUda2pF3C5we0GrAhcq67TSyzgSWBXdZ3aa/tlyvZ7\nAuNeX5T7zp3EFk+vzq4vT1Sr/0r72HUj0b1vh+Lw9gX21IoudWK2M9GEaYJr33qHZlMytaYQTvMw\nQqhwMrBKLbpM6RCSpEPYAGqkw4lSo3AKkTL6UMX7QhTWrUXk9O/ULCmIEog/EVhfXd8qa+ZfIZ7U\nbyOWTO5S13+J2Q6EcuqBwMtE8Pql0k+63vMvCIzoqdwuM6DngQ+o60wx+xghVrejqz5c73lqsGc9\nQtL8p8CkHikOE7se+G91PavaY6VDSJKkJZSZ0LlEKumrQ21fw3GFCGzfRQgBXguMINJCX+69fSkG\n25d3iw1nAau7Nk/i28QeAk7Y6h7mJ27OH3dtblFbWXJcH5hWZN7nI5YgjweOVddre22/GpG5tWq1\n458OIUmSYUcJzE8F/kEUe51UbRqsmN0LXOyqP2qiPaf/ZUmW3ut6dgP2cNX7mnXsinNcDGwPjAb+\nAvxv+dtvoGVHE7sU+Fu1y3WZZZQkybCjVI8fC/w/dT2hxpqI/wCOLymhTeHFpbnnXwuyPyHA1wpn\nsCahPLs+sDiwM5HGrEPEoE4HDiviib2POZ+JrdSobTlDSJJk2FIkpp8CDnfVe5twPBkxix9M3oWJ\nI95m7IT/0/9p3Mo5MbEpRFyk5iZOJjaJKCbcqpfU9+nAR9R1x573coaQJMk8hau+Q8Q2mqVoevjb\n87POqJlMXmAmfSQzGqXoa61C/Qq3FwBCzKh6jrkrEWg/uFH7coaQJMmwRswWJHSmtnLVpxs4zj7E\nDXfze7ZiEyKbamJzrJzdqOgx4IuN9GcoS0ZTiToRiDqRnSozwyCDykmSzKOI2SnA8q56aB37jiT6\nJuwE7O6qT5TK6qeBJRvQeVoceIEQR3yMyIhaEfhooynKJnY40SXuPYTi6xW9t8kloyRJ5lUuAfYQ\nqz6wKmYLiNl6RAvTVYANXPUJAHV9CfgNUYxXL9sT1d8HECq3fwOOaFK9yqXAi0Qsoo8zqJecISRJ\nMlcgZscR2kybueqbFe+PIVqZvodomboQoTu1MnHTvwY4s8QjZmNiJwJj1fXweuwxsWuBe3rLYDSL\noYohc8koSZJ5lpJ6ejXwFnCgq7qYrQLcTdQ3PAXMJDSUpgPPuOq/Bjqeia0A/BewjHoorVZLqfr+\nMyGx/ad6rqdR0iEkSTJPU/oVPEj0tf4loQn0JVe9vJ7jmdh9wLnqWpOKbem5cI66rl/PeZtBxhCS\nJJmncdU3CFG8U4mZwXH1OoPC1YRURq3sTHSNG1akQ0iSZK7CVacTTmGia8M9o28AJpjYotXuUPSZ\n0iEkSZJ0A676QDMql9X1FcCA3WvYbTUicP1oo+dvN+kQkiRJBucaos92H0zsvSb2pZ7Oa4WdCQnx\n7g7Q9kM6hCRJksG5Fdigsg0ozG67OQ1YG7jJxA4pHw3L5SLoYJaRiPRUBs4kUsAOcvfX+9kus4yS\nJOkoJnYVsAhRcfwOsAbRW/swdb2z9Cu4lShy2wdYWv3dWoh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MXkV6H9gNuLbglV4/6mFc4e2H1A+zd3JG7IvXUIrk4ybBI4DRSLdh9nWV7jQM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\nGl4vF0mZ7bOAt7343bLJ2nvxm2Dqt9mUkiORmvAcKenTtSTprjoAeKC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q0yKyi4hMAWYD\nhzfgkOuMQu4FcAGwPHBjWGnP08VQlr7Ae9EsKPA7MklEngE+ABYCw1R1sTMcBX4u/gOMEJH3sUX0\nGao6vcEGXUeIyH3AdkCHUHSdwFyWNZ43YwFgJBKJRIqiKbmqIpFIJNIIiIYjEolEIkURDUckEolE\niiIajkgkEokURTQckUgkEimKaDgikUgkUhRNpo4jEmlKiEh74IXw60rAAkxgUYHNg/BeJNIkiXUc\nkUgdIyIJYKaqXtnQY4lESkF0VUUi9cPiKqwYaYZEwxGJRCKRooiGIxKJRCJFEQ1HJBKJRIoiGo5I\nJBKJFEU0HJFI/RDTFyOLDTEdNxKJRCJFEXcckUgkEimKaDgikUgkUhTRcEQikUikKKLhiEQikUhR\nRMMRiUQikaKIhiMSiUQiRRENRyQSiUSKIhqOSCQSiRTF/wNKozsrafqH4gAAAABJRU5ErkJggg==\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -111,34 +137,80 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ "###Wiener Prozess\n", "\n", "Eine besondere Klasse ist der Wiener Prozess. Er hat folgende Eigenschaften:\n", "\n", - "- $W(0) = 0$\n", + "- $W(0) = 0$\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ "\n", - "- $W(t_2) - W(t_1) \\sim N(0,t_2-t_1)$\n", + "- $W(t_2) - W(t_1) \\sim N(0,t_2-t_1)$\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ "\n", - "- Differenzen sind i.i.d\n" + "- Differenzen sind i.i.d" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ "##Stochastische Differentialgleichungen\n", "\n", - "In der Bewertung von Finanzinstrumente spielen stochastiche Prozesse eine wichtige Rolle. \n", - "\n", + "In der Bewertung von Finanzinstrumente spielen stochastiche Prozesse eine wichtige Rolle. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ "###Beispiel Call Option\n", "\n", "Wir haben eine Aktie, den Aktienkurs den wir beobachten folgt einem stochastischen Prozess $S_t$. \n", "\n", - "Eine Call-Option gibt dem Optionshalter das Recht zum Zeitpunkt $T$ (OptionExpiry) die Aktie zu einem festgelegten Kurs $K$ (Strike) zu kaufen.\n", - "\n", - "Zum AusΓΌbungszeitpunkt ist der Wert der Option mit $V(T) = \\max(0.0, S_T-K)$ gegeben.\n" + "Eine Call-Option gibt dem Optionshalter das Recht zum Zeitpunkt $T$ (OptionExpiry) die Aktie zu einem festgelegten Kurs $K$ (Strike) zu kaufen.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "Zum AusΓΌbungszeitpunkt ist der Wert der Option mit $V(T) = \\max(0.0, S_T-K)$ gegeben." ] }, { @@ -147,14 +219,14 @@ "metadata": { "collapsed": false, "slideshow": { - "slide_type": "skip" + "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 3, @@ -165,7 +237,7 @@ "data": { "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -180,29 +252,79 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "###Aber was ist der Wert zum heutigen Zeitpunkt t=0?\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "source": [ - "Aber was ist der Wert zum heutigen Zeitpunkt t=0?\n", - "\n", "Unter gewissen Vorraussetzungen kann man zeigen, das der heutige Wert den erwarteten abdiskontierten Auszahlung entspricht\n", "\n", - "$V(0) = E[P(0,T) V(T, S_T) | F_0]$\n", + "$P(0,0) * V(0) = E[P(0,T) V(T, S_T) | F_0]$\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "**Vorraussetzungen:**\n", + "\n", + "- kein Arbitrage\n", + "- vollstΓ€ndiger Markt\n", "\n", - "Wie kΓΆnnen wir den Erwartungswert berechnen? Wir mΓΌssen dazu die Verteilung von V(T, S_T) kennnen. Die Verteilung ist abhΓ€ngig vom Prozess.\n", - "\n" + "** Grobskizze **\n", + "\n", + "- Replikationsportfolio aus handelbaren Assets (bei BS: die Aktie und Bankaccount)\n", + "- Da kein Arbitrage Preis des Replikationsportfolio muss Preis des Derivates entsprechen\n", + "- Unter einem bestimmen Wahrscheinlichkeitsmaß (risikoneutrale) sind alle handelbare abdiskontierte Assets Martingale und fΓΌr Martingale gilt die obere Formel" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "**Wie kΓΆnnen wir den Erwartungswert berechnen?**\n", + "\n", + "Wir mΓΌssen dazu die Verteilung von V(T, S_T) kennnen. Die Verteilung ist abhΓ€ngig vom Prozess." ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ - "#### Black Scholes SDGL" + "### Black Scholes SDGL" ] }, { "cell_type": "markdown", "metadata": { - "collapsed": true + "collapsed": true, + "slideshow": { + "slide_type": "fragment" + } }, "source": [ "Mit einer stochastischen Differentialgleichung wird die *Dynamik* des Prozesses beschrieben. Üblicherweise hat eine SDGL die Form\n", @@ -210,38 +332,87 @@ "$ dS_t = \\mu(t, S_t) dt + \\sigma(t, S_t) dW_t$\n", "\n", "mit dem Wiener prozess $W_t$. \n", - "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ "Die Γ„nderung des Prozesses von $t$ nach $t+\\delta t$ fΓΌr sehr kleine (unendlich kleine) $\\delta t$ wird mit dieser Formel beschrieben. Es besteht aus einem deterministischen Teil $\\mu(t, S_t)dt$ und einem stochastishen Teil beschrieben $\\sigma(t, S_t) dW$. \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ "\n", "Ein sehr bekanntes Modell ist das Black-Scholes Model mit der SDGL:\n", "\n", "$ dS_t = r S_t dt + \\sigma S_t dW$\n", "\n", - "mir dem risikolosen Zins $r$. FΓΌr dieses Modell gibt es eine LΓΆsung fΓΌr die Gleichung\n", + "mir dem risikolosen Zins $r$. \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "FΓΌr dieses Modell gibt es eine LΓΆsung fΓΌr die Gleichung\n", "\n", "$S(t) = S_0\\exp((r-\\frac{\\sigma^2}{2})t+\\sigma W_t)$\n", "\n", "In diesem Model sind die relativen VerΓ€nderung von S_0 nach S_t lognormal verteilt.\n", "\n", "$\\log(\\frac{S_t}{S_0}) = (r-\\frac{\\sigma^2}{2})t+\\sigma W_t$\n", - "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ "In diesem Modell gibt es sogar geschlossene Formen fΓΌr die Preise von einer Calloption:\n", "\n", "$V(0) = S_0\\Phi(d1) - K \\exp(-rT) \\Phi (d2) $\n", "\n", - "mit $d1=$, $d2=$ und $\\Phi$ die Verteilungsfunktion der Standardnormalverteilung.\n", - "\n", - "\n", - "....\n", - "\n", - "Was kann man machen wenn es keine geschlossene Formel gibt?\n" + "mit $d1=$, $d2=$ und $\\Phi$ die Verteilungsfunktion der Standardnormalverteilung.\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" + } + }, + "source": [ + "**Was kann man machen wenn es keine geschlossene Formel gibt?**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" } }, "source": [ @@ -250,7 +421,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "source": [ "Wie lassen sich Erwartungswerte schΓ€tzen?\n", "\n", @@ -266,7 +441,10 @@ { "cell_type": "markdown", "metadata": { - "collapsed": true + "collapsed": true, + "slideshow": { + "slide_type": "slide" + } }, "source": [ "####Beispiel 1:Berechnung von $\\pi$ mit MC\n", @@ -286,14 +464,44 @@ "cell_type": "code", "execution_count": 4, "metadata": { - "collapsed": false + "collapsed": false, + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [], + "source": [ + "def MCSimulation1(N):\n", + " x = np.linspace(-1,1)\n", + " y = np.sqrt(1-x**2)\n", + " plt.plot(x,y, color='b')\n", + " plt.plot(x,-y, color='b')\n", + " dart = np.random.uniform(-1,1, size=(2,N))\n", + " getroffen = np.sum(dart**2, axis=0)<=1\n", + " plt.scatter(dart[0,~getroffen], dart[1,~getroffen], color='r')\n", + " plt.scatter(dart[0,getroffen], dart[1,getroffen], color='b')\n", + " plt.title(\"Dartwurf N=%i\"%N)\n", + " plt.show()\n", + " print(\"Pi = %f\" % (4.*float(np.sum(getroffen))/float(N)) )\n", + "\n", + "np.random.seed(1)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "slideshow": { + "slide_type": "slide" + } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -305,12 +513,27 @@ "text": [ "Pi = 3.000000\n" ] - }, + } + ], + "source": [ + "MCSimulation1(4)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ { "data": { "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -322,12 +545,27 @@ "text": [ "Pi = 3.300000\n" ] - }, + } + ], + "source": [ + "MCSimulation1(40)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ { "data": { "image/png": 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c7dSEJVARXqse4OrdgTh/dY0WjtM201WY7xuBHg36xaCje3N5UPTDqesjqXsW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\ntK18rFQq5nU5Tq//ktxpjZB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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -339,12 +577,27 @@ "text": [ "Pi = 3.150000\n" ] - }, + } + ], + "source": [ + "MCSimulation1(400)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ { "data": { "image/png": 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VtJVwUSllxSqaXSWraLVeFMfQOG47Utp0LHCaZfuS4L5pMSDTwNAyJne/wCE7\nN/C97Ufyq3+6gG9/UCHLFbKFui9+WqqIMMZxV1stGhRl/EfSmDjsV8G2MAR4k1LqfqXUHUqp1xS8\n50LAPuo+77HpmWvp82UnOjdNnvdo88+uATeEmT6AUvQQH1gmGe45hPZbP0+E04F+dNCa7Ro19Aee\n9JEPRNtqwTjwZ5FrmInnacvxTwb3jHsuQTPLVvjMtxqHWbYtAY6mceKskZw23AZFMQFvCbB6mIPX\n/De+drJCdtzH6e9EBzu+GdimFN0AwQSwCf0O495DFT3eHirQpgWLoozf5aPfCRwtIqcCnwO+FXeg\nUurqEK0v2La5AlsQ13MkfFi9bGQDvdzPqajZ5S8dwDUBow/jsoRzHkV/YEPATWjGvhO7wfRJETZK\nYARER2iuwc4wHgsx9YeIycwZGAv/ifign71odeO11KOfd6ED0qKrI0FLp9djl4IBvgecH1w3DU+T\nbWIsA2mGZdskuhydBM8w0g7cs4AaPJ7x+DBqNI6BLvSY64psu/nD6vM9QUT8ZrQkfx72sbaEehK4\nbYHRd0FAKbU+zCtzXaSgbulM4K7Q/5djMfBGznkcOKQMPdW8obob56BoNzSJo6hev4NKjWSXyVZR\nuFZtkn427C8fLk/Yb9EdV9DGWpfShRMkG3adjYUxx7sYnKcix1XRwVau923He0uipFxELjmPwrSH\nut++ST6X1yBuG1/WtuwX2ACCDeFi7GkJ3rw7Z/icgjdcipaMjkVXTbIZdw+DmZxAZwBPlNX4eUMZ\nau/aCqK3mVFkDVYqMyDJxhBMvdnZyloZjTxtt7dPXjKeOjZjeZ5so0KzE0BaCc+4iXCAxgnelI60\ntmkDveENfaFvLMlzzjP+EBVS9YjINLq8Wy/wc+AWEXlIKfUhpdSHgsP+K/CAUmoX8JfARUXuOU/h\nnNelWq5Nbzda/ZJXX6SXydlwIuXlWrEZBDvRS/jZ8tU+lkaVaKscFYYoLyeR7doXSLw9ZB/53lk0\nD04/jbUnTE0DU6PhKuyqqGPQqrjtAV2LTgpoVYe+xIrmjVrvvzOmneNolauHQbtnqyKz1ryhFPcz\n46u/gV5Zx3/KEiqlSau6bxtSCA/klO7ipMipyP9ZI2SNumAgRlpMUsnE5covq/9mm6ZoTaqJcArl\nrZY+LOIOG46qtq0mRqinwk5apfWHxmps3MYSpuQ4HpVxloto9U6/1PNg9Uij33812L9gffjz8k4f\nuTs72EJKVTCRAAAgAElEQVSM1N3LRjaxje1sZDsbuY919HAtKxlOKzPogjVK0S1a0jPZNQ9GpyYu\n4rK4Dy2ZfYri3i1/hpbUHgeesexPMqI+Zrl/EYl8H+lRvJO0LjK3k+zeNM6I8c7qIN3VMg410ssb\nrgTuVIoBkrO2nqoUg8A3SFgdV1lSXcXw3v/B3zwfbDKZbe8C/oTG1VlS6cjFjXbPVkVmrXlFdmlk\n66nc1xQUcwiD0slknBRmkpC5Zrg0Cc6i27dil5hdAoeScq5kkfj3JEl3Ke2pUn5h9bSAsRESdM9z\nmEwUdtpqIk/Q2IyeP7hHKxMCGho6mKHJb/Eu1xN8krYIeYm/1VCqO3A/+0B019/zgSMe4HVNSv0X\nWE2FZXFS2DjwU7SE7KKXXY3dHfOdaGN8FC5Ghj0J+1xzA00CzzocH9eePC6HRudsW01V0CuzHSnX\nuJBy88UnoUbxXP3j6Pd1B+mrCdPXgvtKrgu4ObSytI2psnHIS6zovISbeI6Xuxy/HJ+TvwGe8bcS\n9cRsG9DL3HB/d3ydizbWshtzjW9yGRWuXIqr2HBJoDawqbBcVC1jaDXRbEXIjlAPHDsRe9H2pcA3\n0YbFOKwE1pbfPCtG0D7q9xS4RgXN9H+PbN+6ynj8auoBVrMSJVthuVrFCNfwSddTika0Lyh4xt9a\nJHrzDJY/FuN0zzfEbFtf4F6fQD/fSznO3R+4GjilwP3DSErXYCJ3Aa4PMnweYzlOBe1Km7ha9c1E\nJ1CzqhCHc+MCxTppFjgMqiT3W1Z0AdcHUr8tIroomp5vFS/yt3yQs7ib3noJ0izpORYtPONfOBgH\n/pHmD8RIqFdQj7C9QoTrC95vJXolY1O3uDArk26iDNhUGCY9xC3ANei2rgtorkl/k9RrFRssR0cQ\nuxRVfgX6WbMY2hXxKiuX92fD2kDqfzbn+UlQjf9UK49wEhWW8W+czSa2GeZ/APbypXMh/9KcgWf8\nrUViMqmDeLGMe5i8M9eia/ZGJdYONOPrF+HQgAzTb5Vv82wn5bON453o58uq5mgHHsSe8sC2MrFB\nAW8DPol+bhfGHe2Taeo1gG3eVS7oQK8CW81kh4QlD0yx38yGcfZni1bjd6Cl/miaFO/HH8Jc/yDm\nN+rJpBo+BFMs/ee8BpVYx8QJCi11f5x4Ca6DwADX2Dx6cQ8aKtzQFiMs7ZoPfTPJY3xnQK0KnMoC\nW04nl3xABh1ow/PJpE+8tpXBUnSCxR3A4Rnua0P0WSYpV61kci414F7WhVU+m6gHhG2intnTA2i7\nK1IRl6R5QyFXTkuOfZeAo7KCkibQgVx9IRc8l9zzE8xeioRBknPG1NBuoNHte6jXezXPlpbLfyvl\nFHkpSuHgJeNSaoLusvZd3L6B0LWTciBlydczTHMhdZOLaQDt2jsV/O4JnqdogFo4H1TTM3QxKndw\n7pQsYPfNMnhn2xtdpPHzgiJ5ejLk4jEVqKJURqGV6AeUdmyZ0b4ubUrK+ZI2CVUJEszFMYcQlVnA\npQhZ88gQP3GNYPeXT+qXERoLoccxYdc+qVIfPy4TyhT2MZTF778GsjXUP9YCQUEunwWbm6cM3rm0\nHauMRQbnPD0R7EK7xm0LnT+O1uVfQ7qaTtBL+jh/0S50lORAcGySeuAw7GqkMWioFJMVNeDL1Gs4\nGD3slQntWY7OnxMHk1q6X3T6503od7CeZiOwS8Tq02iDYVzMwDS6H5bS3Bcu/VNB16LtD/4fot4P\ncUboe4DjaHbpHUGrVGzvyjyrcQe2oQq2RDhNELQxejOwRUTrV4IU2XFjPc4A/2RCe6JQaFfiARHt\nQaQUO9GGe48saPdsVWTWmhekc4WIobvYKEuZcpFuzJK5SToM9s1GhGSaFDhaQjsaJLOYZ87bZqPW\n6kl4hjDZIle3pjxjUmbL/tC9K7ir7JJSTpt01bY2laFKiaMq9fKK4baFV45ZV1AD5Iu+HgyNl+4O\npmfa08G03MG5CzpStwze6Y27rUeDoaybPl7LzziA4bTzLiNG4hPtlXMeyRWIysSymO37oyVsKaMd\ngfHZVfpLw0rqeVyuI126r2JfZbyT5NVVklvkWgARVgE/iLm+DcsTrvkgevUSbZPp/1bl+ulAex4N\n0di2LrTb6W0k93GFRgNvBb1iyRp93QARemssueAwnt15Gv0vdFKpvZkfeWNuCryqp/V4WfifOziX\nn/FaVvCSkMwIVqNVDOGl+4xbmuhlLugPrtUpBNKun7fsnimvaJh+WK2V5Rp7Kcc3v2xBaEbtRHmx\nA3GuknkC6crC60iecEaA9wa/TeqEM1POScLeUIoI9N/De5U6vBv45gGMfhJFTdLLci5aeIm/9ZjJ\nEHgdl/MOvsM0nQxzsAujXE69duhOIkXQ0R/RbOWNyYsa8dGUu0LPk9cWUtYYjrOF3E7yaiZttWN8\n27PCds1J9ASymmb3yFXoFUar3G4nia+BnMbAlwOnU++HLTTm7c+KNYRr8NIgOOwP/GYH1du+pt7X\nj1J9C6nsYlnwjL/1uBW07/7/5hoke26e/dEfelnpDQzG0AnAyioAEk58ZpKL7UQHFdmSotWAW5Wi\nLzAKFpGInyQhUK4gjoTEhDDhvDYSc8xqsgc17aJev9gQ1KOPl9H83jqC9mR5n+F3dRPxk9iDIvQG\nE7XxkXdNILccrW7bENAduPn112J+Q3MRmAbBocaS5Vv5gFH1Laiau6Wg3YaJIgaKeUGBcbekkooD\njX2W6q6YRGFDY550vDbDYj+NRsdozdoGA53l2Dwuo6akYKsM3iYFtS12wPm9YXe1NIVrokbfKUIl\nDYP7Z/Hpd3X5DRdJsbpGRt5vOMagjBTMU0EbbKm8e2l0FbX1X39S260lGhcg5eGdXuJvPY4p8Vpr\nlGLALHGlLn3tREtfrpJejUad/BKSi4u4GG6NRBseU53ErypXW459Ci1JukrHu6mXFFyfcK+8mAS2\nBP1dJJr1eOzZVJej+/4foGm1cH2wGuoO7p8lK+gy7KusKO4RbSsyapK4VZcE9zcS+za0Qbdof3ei\nXVMftuzbENwDdDTxYbYLxLddeEtqhu1FjHbPVkVmrXlBgcR/LZeLYrosKdRIuSa601XyGgmkp1YE\nLsWVTsxCg3HSW0BbQxJgVCLO4wZqa28N7WJoXDH7EtpTFiVdf4xkaT9vn0/gHuGctc1ZKMv4jdJo\nUjsiEn9P23lBiygP7/QSf+vRcyfnVq7jypB+X4pe02Ru3IbW97q+xxVow9pA0QZYsIbiydn2Ypc8\na+iMoh8QYWNAUY+NHTSvTNJWDrb2KrQkeis6kGyDpT1ZUNTY2kXyqtG1z8coYeCFUIZdxZyflw8Z\n+5cL1ue8x4KEZ/ythkjvB9j6wHhDAGfit2qyJKYxjFeT3QvGeJj0UC4TcEWN+Fztxvsn+kxDaOPq\n+kD10WMMwsHvfqUYRkc0R8dzXh/xLnQcRR4voyi+QrIhsxbsT1Kn2ZK1ZY2biNYaCFel2pHxWqAn\nRmPkdRmvUQxhSWBYHoTNPiFnLFSwVGg7lFIiIrOdzndWEHitlBlWLuSXrvehXekOYG65gpqMi9F+\n2onOOFkGE84CW7qFuHQINlTRAU+Po6tgvRu94jK2FNt1auhUCEfTGLvxEMUC22rYhbzt6BVgXPxE\nlXg3V0GviPoTzk/C0+hgtNU0xgEY+1NBXiDcybmcSx/oPlywQV15eKdn/LOAwAB1V7vbMQuooeMW\nsubvqaEjkU+nOQ9R0XxAeWFjlkUmXAOTbjmuCI1hxmGf9+tJZ/xDNFZV2xH6bcvPY/p8M/FCyU3A\nJQn3FOA+y7Ur5A/OsvXxEDnUbSsYqZzCQyNv4N4bPi8fLlp4aM7CM/45DKV4ATi43e1IQA3tOz6I\nW9WnpOtkVSHuRKufZiMKea5jpwinB8LCZjTDez3NUfbToW3m3fVYbB+2qOhwcry4imQVdLqKG0mu\n72x737uDa8/2Ki0O4zQHPy4YeMY/F6FU9//khhv/kj9eM/uFqZxQQUuHRiGaZ9leBJPABbhJtWWi\nDOm9FZhEF6K/kuT3YGt/LIMLTSSg3/f/Jn2SNUFjp1nulQTzDOuD//eQvHIoA/uA/YhfaWwXKSTQ\nzFnk4p3tdkUq4pI05wm67+DciQ43N85qAde8Qu509XdQWmZMVzIFOvpoXVbJOJrt+2WhIq6Sg1jc\nXRu/NedgsCIBWibwLWtBmTzfQJV6jYHE9ixEysM7vVdPC/E1Lrr+Xdy2vJaepmGa+OyQrYTAjDTY\nDvwr9ULoSTphm8fIEMUygj5Z4Nw4uLRnN61LLwFabbOBUC6bIAjMeEJ14x5UWIQ/nBUkp7sd95Xc\nTuDRHPfqQK+Q4uBr7kbgVT0txEnqF8OPcFKcS6FQZ/Th3+2AKfByIdmX9VGUZYwVdMZJE3MQZR55\nDH5VdPs+jfZGuYPszC3OhlEBbia5uLtRa4FWbb2aevRu+Jin0NG+cddxHS82r51xtOolSW8PyR49\nUdQCKprtN86zyxW7ae63UeB6EbxxN4TCEr9S6lyl1MNKqQGl1MdjjvlssP9+pdRpRe85X/A4xyW9\nDBXzO4osqRh24xaqH0UXWvJeF9OWpHQOUWTOQhfBEJphvV2EVSKcTnm+3kvQLpUXBv/vynkdm7RU\nIZnpAzwldf37KegJMhzVtzv4vSa4Tg37asd1pbOa5qynXejqXWmIvsekMbCLclYxa9GTUhImiU8O\nZ/otjBXAlW1c1c5JFGL8SqklaKv/ucBrgPcppU6OHHMesEZETgQ+CPx1kXvOJ1RYliVCNm7pdQ/x\nQU9RbAUesGzfl3B9g7ixME02Zl7UK+cGaY7MjaYCHkerEPKgAz3B3QH8lOwMy2TAjMIUpUnC8QED\nupHmflJor6/wdlP8JArXifh12FdFWSfnSZIDtFbluKYNHWhPojgMoVdM92S8bjST56JHUYn/DGC3\niDwhIhXg69SXsgbvQiehQkT+AzhIKWVNuLQA0YNb+lmIl/rfCJzgeI0Po13/oijyUS5ldtVQFwIE\nUbmDSjEIXIQOYpqpS0C9Tm9edKAl9GvRK4w8K6U890xyj7RVOjsMvRIIr/pcJ9dONFMuKo0nVQQD\n/TxlxVrE2Xpq1O0yW3D/rjwsKMr4j0TrIw1+RfMHaTvmqIL3nRcIpNZrCl5mFe6M9wjsetZouP5s\noUL2UP61SrEVnb/dFB25hLq/+cnxp2ZSSYEe/x8Prp3HUFwh+4QRZ1idBJ6zbF+JZqx5XWyPQU9u\nszGxFcU48OuYfWaltg0d6JeVd+3I36yFh6LGGFfLcJTpWM9TSl0d+neHiOzI0aa5hvU5zysS/Tjb\nqKBVTKfQKBl2ko8Zvz9hfxfaiHoDcDaNQUn/CFxMtn5Lyuczhp649mGPtM36fiaxr74Enb4gqS15\nJ+5OtMfLzbTelz6MsBFc0GlCklYNVfSqLg1dwBVk7PvDePZCOHxBGHiVUuspmHSuKOPfg84rYnA0\nWqJPOuYoYgw4InJ1wfbMReR1m3oAuxtcFm+L2cIDoqNNe4Crafwo84yxNCa3Gs3M7qYeZWxUN2Wu\nbIz6Iq1QexhxHjeGEdr6Q9Ha4LUu4L+TL6o6inDEcBwmg/uYeyUVpDdYgu6DSRpzItnanFmt9Fp+\nthb1im4WQL6eQCDeYf5XSl2V9RpFB8G9wIlKqWOVUsuA3wG+HTnm2+gPEqXUmcCLImJb0i5UbCG7\n1HsTOvthVP0wCZwf7K+QPqk8TXpmyDDyqC5qwGlKUUOrtVwksSL+9wZdNKeWiDO8lnnfNMTdv4Ny\nV3BZVWhLKP69jwNPpByzD716yfusy4PzjWvnY/kuU/80uhjjo3wmb+3jBYlCA0FEpoGPAL3Az4Fb\nROQhpdSHlFIfCo65A3hMKbUb+CJwacE2zysEev6bM5zyNNpIfiWN72c3dcP576A/rDgmU0Xnrz8y\nOMfUbg1PAuPodMemetcI2YKahgIyzDZcezYNVfKl8i2KuRG0Ug6iq75JsgsYYRg32p0J+64lOd+U\noGMk0mIEXHAyemWX61qrGZo8jsc4jkfZxia6dZZOD4N2hxsXCTueLwTyA8fQ8ynqdUbjQuBd0iqE\nK3RF66Sa/02qhH7y1brtyxlenyUdQJE6t4uJ+smeGiH6Lsw1rGMPt/rOo+QbS2GaKPAcM9TJ5Mip\n7Kx+h/PMxjGB2DQW85ny8E6fsmF24Ko+MaqIIhWfQC+Xv4HOdhmuk4roRFU7qKdKWEd23/sKcBbF\n9OkuY+9BtForC6aJD/BpNSTDsTW0dH0FWprejnu8RhRFA9zCHjM7aI6ZMGmi0zyLktw+h9DPujtm\nv8FTlBCwV2HZyvtZ23Eb73oc3bcLNh9/HviUDa2GUj3f5byPv4vbVtXc7JwjaGNiuC+M18wQdaad\nZ9IeQqtz1uY8/2ngIGYnP75JNXwr2rfflHZcknJ/Id2DpAjK9LZqyBgZxCzkmfR30uxRlRcj1Jnz\nELBFdEH2IsWEdCEUjc3oyOE4FU4NrYIMZycdB24hn1dSDThPFmhKZvDZOeceQY9Ze17K5+RInpJl\nTORZCofVImPoouNFMidmoSl0xsetDkv9sqgVWUqnS+yPsto0TKMKbsShL2zPEacSq1EvUJ814+cY\noQyfuKl6bDQSekbX80eoqyJnMo2is7kmvd84FdGCzcyZl3d6VU9rcZn58QFu4mD2chY/ztPn4XO6\n0NG5u2h9UM4k8E4RDmX2Cms8TWuCzcZwi/ZM8/yJSvs13HMpRbEKnXrCqORsbqPRvrC58r4s5voK\neL3oVYXNaJuEhjQHoiXmTWR71hr1JHsuqiKDleh8/sehVVDXB6kuHk84R2H3hPOwwDP+WcLr+SmP\ncxx/yBfGadShpg1U2/616A8iyoQqNDK3IkwJ9Pi4Pljmu6ogpMD9dgOHOx6bJXkd6HY9lXqUzubo\nev/t6PKFD2doRxSdFFfRJOkQTwtUSHtoHHcV0sfemUHajP5QkjNXNZ/QaDvIqsJaglYHmbKRt9Ns\nfwijA/gE8Gxku0/JbIFn/K3FTCKxZVQ4lftZQu0RtORkDHpxGSKN8e+TNDPzuPe2hMY89R3oqkR5\n0Yn+6DYAp8a0MSpFF5HWXRKdGawkm61hFTplbxqew23yWkGg/6a87KF5kZYF1qS9uIW6+66LX/9K\nGhnvP+VsUxe6/+MmGpf+7kTbejYRb7xfSWOE9aMs4JKLReAZf2vRkLfoTO7hYV59+qV8/vRg02qa\nP4gwwx9CD3bX99SBHvgdkW1lLH9tKoYOyjWivsKybYy6f3lRb520fhT05OAyeUVXQy5qpDHSmdwk\nrVNXvBftH7+S7N++Sfhmwwjpk180T/7T6HeapfjKieg6Bq6R1C7ppxcliqZs8MiA/Zjgz/mYGmHV\ndQmHVdC6yriaq4aRZ/lwJ9CS/1yf6G0M9+FAR02MZ0kZaQjC98+yYgl7R5k88UlMab+U6w+hcxB9\nIuU6edEKb6wa8GdoASVOnWPUPmEcgZ7op3H3klpBttQWJlrXS/wRzHVGMN+xhUB662UjW7iMYQ4i\nxaa+HG0UTjKE7SKb3/dymt/1fMjWOAn0mNKBNEvWJvq4XaqWcJ8uRxsy4yR/2wQVluzH0Uz/Sooz\n/dlMWdyBLtz+uoRj4lY5imamP4J9xZM0wRexKy1KeMbfSuiAkU8CtS1sZtLdKcaWl91gHJ3nfwf2\njJE22NQ0D+Cep71G/uCiMAT3j9QU3QBtHDTBZqDVAzooR5dQnEtZTC+grpYKP6tN0jcTuHmW9TRP\n+HmM81nyM+VB9BrLSX4HWdSNFZr50hDxtrBx4lVF3rAbA8/4Ww2R64Hzhjkwi1RqmyF2oxnKQ+jl\n6xUO14nLhWMmj024S/42/XtWTKMlWpd73hAY5aJugMvROebNB30bzbrnCu2J3jU6aNBBZNHymlFb\nzq2iq42ZimM2VUkR47wN95Gf+Qt6DOYpWSm4TWK2SWsnerxGveFMUZ6463jDbhzaHXxQJAhhPhFI\ndwfTLsFbcYFGI2QP2rIFG40EgS6GygxIciGTJ8gpgIj43ERjCQE7Ju+Ma8CQy3spI6hslOZgPBOc\n1G1pR94gvaQcR2nvewB7INlIpK3hvp3I0NaKw7Hhvo72UUNQV7Dd1l4JH7OQKQ/v9BL/LEFQp3+X\n8zu70t3EH4/ZnscT40kapaRJtBppXYhmW02yDq2euSnlOFNw5TjsEmoX8dWshkRLeq6Vp1z8+x+l\nrsLJKzFH3VXDQVKbafaQyvt9HpTzPNClHqOrjEngPUGfIvVgLqOmugD4suP1l6JXDEkrYLM62keo\nOIsIvdK4QiKIL4hbFfk0zDHwXj2zAaW6gWsU0jEdX0Olgi5i0o9WXxR1k5xEp8yG+gdgfLLj7j+C\n9te+kHwGRhcPm9Vonb21GI/l2KTAH+P6GL3njoAhXInbxPZyh2Mel7p3UTf1nDNHoBn4XMozleS9\n8zw6SC7uPdne+1MSUZkE//cGfXE92sPJFS6lJDuCtqwDtinFLdQLsd+A/k7Mfb0AmxXtXqYUWa7M\nG4J+AdlAb9LyNpwWtz9luW6W13G5S6podUo0LXNSSufB0JLaJfXzXKaBjM8QpyoIU0/jeLWqZrJS\nWI3hov4qQqM0qnlMKuYe3PL4DATHh3Pn5M3fUwZVUvZP4FU98ee0u9FFGj8vCLoFqkIq45+KMJI0\nPehACrOIfhg19GSSdN0aOnFYO/PgV3FjxGl96cr4k2wFYZqZGPV4zT05mj42E3138C5bkZjO0EhM\neweDdmTt7zT7i3nOLH1S9jP3t/3bnyXKwzv9Eqn12EywFN3MFrriHRuiOVvS3s0RJBdcjqrxFPWo\n3jj3TIX2kLG5iZpzJKVdeVGlnvvmPbi7mtowgfb6SbvGEFpXHfUYsWE1WuXQnXJcGkwfvw64EbiT\nesrpJBRxwxyI2W5Uf1nVesb+EqeGG0er2VyjrW19X2icHdaUsscjDM/4ZxHd9LGNTZzNj8Y6qBZl\noPsDZ+Y8N2uqhQraIPf7wNuxl3LMAtukMzMjSt14mDfI7NPB34fQzH0k5rgbRBsMo8bKuJiFLuDm\ngPlvoVigVCduDN8gb+oNQU9sLhNhFqxG1wAIv6MZF0sRrg/u6zLOOy3Xyd3WDqrcxCVrUaon7zUW\nPNq9TCmyXJkXpFU9Y6E16ITAxPv5kkDNpqIwvyfQetksy9sq7nnns+ZnN22K5mjPU74xyY3UqBGM\nCiRrG3uD64dVWnHuhmHbR1h3nabGCbexP+jLouop136Ly9sfR1tD72og6PeyahOYMdc0PshuAzHu\nxYXqTHQwLddyudlQlQVabrEo72x7o4s0ft6QZv59ASUZes2HnbcObg9uRsKJ4Lg8H1m/fl8NTN+Q\nq20gLX6gn/KNhjZmGb1PmKGn3b+huIfDOcMZ2rqVZiYYNtjbxkackDAWnFfUEO1K00Fb0oyvUXI1\nyNve45NLmar9Nt+Tu9gY3bmgi7Dk5Z3enXM2oFM3aHc4pfoSjjyGun9zVBUzRrrb4IUinK4UJ9JY\npk4CCqv2+tHqmzj3zt1Be6LukMcEqo5tNLvkuagiJtE67iQ3Sxd3v6wYQGemDJfzI3Ifo7u+GK36\n2YxWaZxIsx58dZA/CHT6jPXUfc6H0O6qxv3wduB9Dm2cBq4SrSZBKXqol9nsCH4/RvPYMEVWbO+y\nC537yabak+BvFlfUsPuszZU2rTRmHA4judAKaHXQp4mUZTyIvZMf5TO/6uFPj85x38WJds9WRWat\neUla+p+4i43SxWhIMGlS+9ik0zSprYo9ytHmtWLUHDap30hftvuZVUleiXAiRmoLU9leHmFJPrxK\niVN3RcsO2lZRiaqq0DtwjY5ucj+MeW9xYyPp2DxqvaQxFn5PZZUAHaV51RQdBxOR99h3Nj/80vHs\nrk3SabvomHhVj/2cdje6SOPnFTWqewYE5C42ytn8SJT29kz72Fz1uk1L2xim0Bfsi6p8JoifYKZJ\nd+NzIRe1RxkMxfiqh/Xxrqkv+kL9l/V5zaSaRb0ySrOPvGsfjJDM3F1VcGntbaXLaS3ynuKevcEu\ncyRPjd7Ce6IHjYj+zhY808/LO9ve6CKNnzfUbOBtEO9fyRNlfkAN/ssxDCjOSBu3Ogh/nEbPXIQx\nF8mNk6Q7rlIvLt6PXrkMUjdqZmljEcafxoiTyMVHfjZpIui7EZKN7WVMCv0O/d1gl1FU5c5mvb6I\ntqV5xh9D3p1zdhDNMNmgU72Yr0B57vGnRHzNbTlgHpRQCH7w22S7PCbh2gq4Du1//hiNja7hnhff\nxZU0Tu88lXBOB7oQ/WrgNOo1W9eQPSfRjtDvrK6QpmRhHjQUOY9BkpvrdM77xsGkXF4JnEC8Hec+\nitV3BnhdSpyEuffMtyR0sIX/ZTt2HXCHd+m0wzP+OYB7OYOcqV6G0EbYMJaTzjgaGHTIWLsBN4a1\nJiBBB+jsRgdgRc9tRQnBuFTTBqYQvWuHxrVxvfkhdT//uHiAogwvitWEivhY8AA63sA20bbSYUNh\n5xmTuAXBVUmWcDrR43AHzTESAnzyVfyiqfyjxF9TG8R1riyPEDzjnx1EJcYyql+No71PHrfsW6cU\nfaFAo2ge89URyep68nnRmERaJ2CXqIuOr+gHPYkuhp7WJleY2sapE1TA/OPeW1rfZV3OmWpWj6Uc\n9yTZJtdW1fI1SdyeTDimApwPfC/lWl3oSfdTNPbbFNB/A5e9vDM0J3Qxxkf5i6RJ3pRf9AjBu3O2\nGlra2Eyjq19DlszNbOH7nEMtPnNnGENohrVFhF6lh/zZNDKf1Wjp/c3Ag8G9V1EveG0yHpoiFlky\nK9qQZbkiGY6/L/h7YvD3OZJVUSPEFwSPYrcIpwMEfWjcJiG+ctOT2FdEac+zL0O7QE+i16Mn9TWR\nfYKOmM0Seb0PuIfk7KxFsCZwPb014fpm0tlC83i1YT2N/boc2Hwcj9f2Z5zX0M8BvMRmttA941Ub\ni1j1wwkAABzvSURBVLxqt4WLAgaFQ9DLzUeAPuCgmOOeAH6K/oh/UqaBYs5Ts1F3TOrePRKmk3nQ\nJUq3wc2w3nczxsCsBkVj0E07rqxiLVNo43A/btGjw2iDoqt3zFbcA7/6I/1n3DtNxsqw26cpHuPq\nXhmlgQztMjRIs3tjlXzRzGE3yLxG+TTjrUvb+iL9bWtLknH7e69gz9N/wwezNn5BJ2zLwzuL3OzP\ngY8Fvz8OfDrmuMeBQ1rR+DlPFgYvxs2scUKYupPu2jLGbeO2Qt1LpCfpfjEfS+KHSH5mlofCUb9l\n+X9Hmc8obpNKmAlFq0mVXZXMTCZZoncrwTvfintqbZe+zxuxbVyKszxDlEx1rynq2WX7gt/Dkeft\np9GDawrkWyD/XEVtFYfAlxAt6Ojd2Wb8DwOHBb8PBx6OOe5xYHUrGj/nKZnx9wsMivbprwrar/80\n7pUDGJYOKjWQvZbBH+uiRrb86CaUfzZLLw604Z5xtJXZKz1ZpZi7Y0/kHeedNEcotnKRAveO6+c9\nuK/o9v6EN1wojUJTGk3JAnfrzMM7VXBiZiil9orIwcFvBbxg/o8c9xgwjLbof1FE/i7meiIic6mK\nUXFo/X44tcEkWkcfTntsrVr1PIdWX8HTHVU6o32yW2RG52255Ux1KJM90aYLNtW+PkCzDjkPhOIV\nqHajhYQzyVf9KytcqoXNFQyJcCjMvN9/IpvNIIrdlPPew4jrTwFeQH8DeVI5hDEsqJ+g7VdZcAUi\n1xe895xFHt6ZaNxVSm1HS/NRXBH+R0REKRU3g5wlIs8opV4GbFdKPSwid8fc7+rQvztEZEdS++Y8\nRHpRahPaUHcicADNue6tzOdlDC7B4gyiqB0XPiXE6KHR4LsZbdgNG3UNOoH/TfHyjgaPAmbSvx34\nXbI5Dgi6Xw7DbvST4B6HUd6kMF+YPsAhStGPNp66vrekydil1GQWDKHLIV5Ns3eXoiTjaidTed/Z\nNSjVj86ZNe+hlFpPci2OdBRYXjwMHB78fgUxqp7IOVcBm8tarswLatbn28ias2Elw02bu3hput5n\nTaodo76JGgS3ki+S1MXgHM3XUnbaX0Ot0L3npWjWzHbctwhNl9zucPqPUVqS2qEmn+KKKYEeyabq\nMbRg9fx5eGcRqefbwPuD3+8HvhU9QCm1v1JqZfB7BbARHXyymBCN2rXhy1gqFX2cT0OD1C98jD+f\nRKm+kJtoNLvkZZFtHcDvkexjHYWg1QHXkR6UEw3q6QQ3v9SMMBGkRVDBPbo4CbuoF23ZVcL1bBDL\ntjzfqy24bEnOa8VhT7AiuQatzolbaeyOaY8QE9zVwTSHMMi1XMEnua4TuBDtnhwXU2HrN48oCswy\nh6CDMRrcOdFL9u8Gv49Hfxi7gJ8Bl5c5a80Lsht4o2Tz9BEBuZbL5RCelxUMyyr2yqMcZ/aNHcgL\nNve5OMk+mt1zykHqMyuIPtpbh7csKppZVGjOc2TL3LmVuidOHvfLGuVJzX1Be6ZKvGZe6gn6zClh\n3hIqNUsentSMhhZa0Fk68/DOtje6SOPnBbmpegZFe/kMBL/3RAZ4VWDkr/mQHMUv5UFOlmu53JbV\n07jJ2W4T/chc1SbGjbBIYZTZVIckUVqRFZd2miR1iSmv6+M6V9+VxaBtqr92vsNo8ZrYSXgpk9XP\ncNlWyafWCdOgLGCmn5d35vbqKRsL0qvHoK6WWUd+Q9cIsOofuZg/4q94kYOQlmhUmrA9+JvVk8Kg\nBgitUf9kwdMwU4HbFJQPe5nU0CqKtDG4k8aCLmI5ZzfwEepG9x1oY9zbHK5fFrajo2Q3k//dlY1w\nmyAhkvht9LGd7nHgFrR6J69RfzsiG3OeOy+Qi3e2e7YqMmvNO3KT/uOoJsEq4FR2ZpEci0iPVZKD\nhiaol1ycK1J9lKYp1yjscq1R7CUdi9TlzdK/e+rflfXdmWI4SWMj7X5Z+9SUjgyrG2tQa2pDJ+O2\nEop5aEGreIrwzvnk0jY/oVR3YIw1CUU2UTcMZsnqOGNEfTnPZzkna1m9MEwmzB2WY8fQ7qK3ol05\n5+pYGqO4UTgMl2t10mx034wuG5gXCr2SSEvwVwU+HyTp60O/u+g5y4F/J35sCNrhICmp2wMp+6PY\nhZbcw66oSmdKqqKosZwJTqOf27nAJf+OC25hgbhwlo52z1ZFZq05T80SflW0O5qJ3JU8lKNsowvF\nle8rmtahSnJkposkmyZdphVnKaN/spDNDdbYSoqswEw1sajOfpDGPEPR1YaLW270PklGcCO9uxr8\nje0p9tlP5meteBFV8RK/leaqlLZQEHW37ACuBW6jQJbEbvrYxiY20MdZ3M3L+TVLimd6HiLe1fGY\nAtcdBS5A68bFsv/L1FdAV9CYh70G3ISWLoewr5B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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -359,30 +612,16 @@ } ], "source": [ - "def MCSimulation1(N):\n", - " x = np.linspace(-1,1)\n", - " y = np.sqrt(1-x**2)\n", - " plt.plot(x,y, color='b')\n", - " plt.plot(x,-y, color='b')\n", - " dart = np.random.uniform(-1,1, size=(2,N))\n", - " getroffen = np.sum(dart**2, axis=0)<=1\n", - " plt.scatter(dart[0,~getroffen], dart[1,~getroffen], color='r')\n", - " plt.scatter(dart[0,getroffen], dart[1,getroffen], color='b')\n", - " plt.title(\"Dartwurf N=%i\"%N)\n", - " plt.show()\n", - " print(\"Pi = %f\" % (4.*float(np.sum(getroffen))/float(N)) )\n", - "\n", - "np.random.seed(1)\n", - "MCSimulation1(4)\n", - "MCSimulation1(40)\n", - "MCSimulation1(400)\n", "MCSimulation1(4000)" ] }, { "cell_type": "markdown", "metadata": { - "collapsed": false + "collapsed": false, + "slideshow": { + "slide_type": "slide" + } }, "source": [ "###Beispiel 2. Calloption mit Monte Carlo Simulation\n", @@ -396,9 +635,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 9, "metadata": { - "collapsed": true + "collapsed": true, + "slideshow": { + "slide_type": "skip" + } }, "outputs": [], "source": [ @@ -407,9 +649,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 10, "metadata": { - "collapsed": true + "collapsed": true, + "slideshow": { + "slide_type": "skip" + } }, "outputs": [], "source": [ @@ -428,9 +673,12 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 11, "metadata": { - "collapsed": true + "collapsed": true, + "slideshow": { + "slide_type": "skip" + } }, "outputs": [], "source": [ @@ -440,16 +688,19 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 12, "metadata": { - "collapsed": false + "collapsed": false, + "slideshow": { + "slide_type": "fragment" + } }, "outputs": [ { "data": { "image/png": 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q+w/y1ey1uG6eoPY1FXYo5CaeOOLZEZg0+n/f1q9/dFdMzNigdtGb6PDhQSrs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CIUna3lHuqhLcb14119bxSlwckWsb7S5g8Psm2F1qUgU/v20OeDRMcIhZgdoEZBAS9oyS53VA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PPTrCvnRHz4NXcWd2hLjdHRBFx8GT45Q+TPS/rG8MdwHwr/CkebQL1RYdSYfwJn5yvE2yv1\nXF4t+MSfQ4OPUFSX5u+Wy3Tz3++5L01VMzaeuj+epcnpCMqGx+FW1pvx7b1Zn209X4TWwMeelfKG\noS8oRUegl9Y8rWbNysS8hYvZernBjMweF+YE7M20wa+0Gx+vd3uURv55oInWuof197uIwXoFqKe1\nvqjEL2dqrUsmOfaxGnkAZZo2YmJ6MHbsVE6cOE+bNpX1xIn3LLUT768NEceyZuAfAdWxVgJKqRqI\nHO++DnR4tyc9FyM39XLg0nMLY5vdPLR1KCNGbCIurhligKYD63U6LoCpzNqIIFMTQxvJBvcAmg4z\n3Z5dwStl95LXleu1bUTXiiGrTeO0F2ynEXbPZu4wHBK2iN3lGDBwKuWAFmjdgmtbsnJsZiRR5/3I\nXv+icayj36DPcngtfY5jc7qyKt6JLcB3BARo5AHtAxT1xnv2XOZe8MGnFxKIngzMD8Dw6suRAW05\n005BuaS8+0RjnhlhNpTWhpFibMIaFw+E8jbI0MYyZZq+yIvXBaFiHkltbC3phjFANDBZa65Z/VDM\nmBHAhQvD6datEvnzhyBuqL/uaaNIkcLcvLkCf/+8jBx5hhw5LiOU0B7aMBLE6LxCCe3fiU6DSxPV\n/C95EfyEtoTJTLMIGzf+yujRuVq5uzdbHBo6DAlYdkTrY8o0+yP881qJ8zcSMNUsX20sby2qHLvd\np/+UqXHPrff0yfLSC/bwds1D67Dhk7CgiTv++F3PA04DbbWlqW4NQkXgma053W6ty/TmpNyhJW/O\nn3D1rXVF83/2DL85rQr1vhS1/6Pcw0OqH2v+W68inpxckZ3f2+RmWV7gt3N+fntjnGhU5VXsoa7h\nc4iPGoislqohK6D2wDlgAdFsZhKTyYK5zt3FbW3dti8Ed+vmQeTpMzh7F8Dm4uF3ZuvVHW9Mcduc\n3/WbF1pfH6yDiENWYYvQetxdYy/3y/cqPp7IJk3C3Ox2O9BxYSmaNjrKovqBPWiyqk10gw3hR+vG\nvZTLRux3kc7M9hpOded4Jq2eh7t7LCuuzeHNWM3adZfx+mh+n+OxKz/KR6Uf53PN6E24kzPVVirc\nj8KK17BV/I5A14/ot+k8B3K4X7/kdvPAkSwc3JaHXfmew7VxboaFXCYsUxwu2SLJUiyWa97xuAPe\nKpZrQIXK/fVZi4SxB3idzz73Z+bMGVy+HMEzdVu55p3fu/pZ2sxZRLvCN/jpURr5kgg9rCbiC1uD\nzFg6a62zWPso4GrC34mOfexGPgFq7txcTJy4CQ+P7IwY0REvryWIq6k8orOesBVCAkZbeestf3bv\nLkStWoZes+Z24NEqSxgIvFKNau+PZWwjO/bCdux53XDzjlYxRLjFRflGue9xwWUzsAIwDW2kXNcx\nEUxllkKYCF0MfTdjxxLrKopkrTZCjLgTYly+RanMdjLPDKdg1WP0mn2TMr8aBGy2vldl4CkNlW96\nepbcV7DgvM9athw9d8yYCKvt4kAzQjfl5NiMEkScqOcZ5bn57SVv5at1oNYZG7a+COuhVMIWQ0x5\nG7YCe9l7LT/5O2cl68qk1FRTmcuBEEMbyVL7lGm+CjTRhtEmuf8nMz71ET51GUMbaaPzpROWcmFP\nJLawH3kpmIgbojfQj9jYvbzzTiQHDjSnXr2NbNw4mtBQDbj1pGfVVrTqFUlkpmMcOzJUD62AUjmQ\nld54tJ5hqU+ajZ577vz8kJDS0S4u0/KEhg5F61hlmpWA1UDN+724RMdHr3Yj2rfUmpDc+z7+LHOV\n5xpEjG29+K/4LLrQuXNk9/FhY6ZMfAcsNgLIjdy71WPxWLaH0S210j4l1XD3635R2tXH5dRv+WtG\n7s1feMnUXPO/u+Fm/6nR/ryr3/9u5AuuXNMlmHgrhmy/2RWt+jzro2N8nz3n7lXXr9Fq5hY+zpeG\nNg4AqGDlhBj7EUABruHKF06KMSMo65qZlss+ZnSVE5HAQQ+3ktu2TQt9dmvp8tGvvDU8e5Zbty7H\nK/VHydOnj/w2ePDAHcWK9as1ffp32jC0FWdZDmyNbtDgjGtcXHOgHgk1U5VqfZge87bnbOYxbegZ\nW4V1IydMr3TtR2Cm0hRe+B03mxxhl2cszdE6zjSVT7xmwcUoivT5rpX/jcULfOi/ew37J7uyemI5\nwnL7gt5Lns0/0aZbVEGPq83Kng+vUeWcLfrpo2E38+fH5/xg3FwmE517B2FZI8ly3ovjRa+xAGHt\nbMKKY0n3aAO8zWczFjMqOBhn50XYTnWhG9OAYscm82Gh60xRUPBR++RfQVwO4Yj/KRphhGRJtM9V\nrXXWJMf9a4w8gFLKFT+/3ylRogyjRimcnG4hfuHE2zGgOO+8M5KDB+vz2WcnyJatOGL49yIRfSnY\nMGxYJrZsmYDMIrMBC5xwmtXppRWhZiPXPxv+ykcdv8EJcatU0vDHudxsmtuF06sb3xakckF06V0A\n10LHyDJ5EJ2XNWfdzFfZxR2apoheCZ/XFTEAqxERs4KIi6CcNozLFk+7CzDR6nNF5NptB/4K6to1\n95+lS7cInDvX2+/GDc/3O3c+Mb9BA1PbbNutfbZow4hVwSoz0AlNX99w3xztN7bPVP1w9eOFLhfa\nBRxAjN/+v/n7SF/6fon4rtsmTt4CMJVZHIlXlDP0vTN1ZZrbgHe1YaTsq00CU5mzgWuGFjdGcrAm\nKD2AEfp+hZPvAyvWkiDOFon4mH9G4hZ52bx5PoGBmujoTECoL76HhjK0cClK+X3O55u3sOXIQhY2\ntGEbaKmOFgXWmTD8GQgMhGMjoUjvwYOXzWjZsi3irtmGuAYDtWEsSEMffZAM1QV5Oneuef769VUN\nmjSJXr5oUav1a+M3Ao18/6JH3h9p4LMPfbUqP50uVCUobM4Hn1LkqD9TBvrfjLJ/O3oF1SqeJXLK\nL/zqFsdzQO4rnixv2456W3O6rwkZO71wFHnrunDV7VSWW9rFnnWXX5jfkYPFyXa4GLUa/kq0Wwx7\njuQ4snRwt8HlwzzCngU+Jn/H3ynQdQDLV9ZlxoyIGXZ71R7R0RtO+vDFr4XxbnaYHh6x+CqNy1VP\np7hVT5W/cS17cZfJL7T/ucb+/WW+mDChfM3p08OP5c69H0l6mx77zDOHnLSWVXYiZtoWNeudeNyD\nKtE3ouDX72++4ONag52DIC5c/fgtq1ofoIiCuiQup2gqpxhcxt7U3v2Gbxp5+fB7HfLiEhlLWM7v\niHcZhCREdQKaAOvJHHWegA3Nq5Wa4/12wA/uH2xsdG7PqkuembJHq7DyO+bGuDIoOfqqrCD1Ntpv\nOMGGl5oTHT2dvpeHICv98kATHajDUGqukkn042HXKKU+QGRQByLUrQtKqVxASHLuGiRglwBTa20+\ncCceAEopd5Rahs3mR1zcEmRlsiVJVmk/hDb2tNb6rMWXL4/MXgsj/nnZYmIys3XrcfLl66q7dNl2\nuw3TrIdwqacDJb1vUPapvyhR80/iq23B2TmW+HgbcUouiVaWr9rFjuv6Ovz+3kjWIXSqxElXoUhC\n0P6kDB5lmuOBAtow2if6skWQItE70PqitV8F6zvX0AEBxy75+LTwjI6edN3LSw3q12/vj/XqFUWu\nb6sEyQEVrBQSSOyK3OjhiBvqF8DUgTpcScr/EiQJqmtSxpCpzHFADkMb3ZL0+yngR6BIeqQLTGVm\nQ15gLQxt3KNoqZTqhLja9iP++tZa62TdRWmBRXVsgExyznLixLvMmfM2GzbcwG7vAaxvTevFnej0\ndAQRS/KS92VDG7esvtZBAvUVAwiIKgXjL0KP1yD0Pbmer6H1dWWaDRCZhDPATm0YPTLU13z5Gqnr\n11c0b9o0esnChV/btPYHqse58+HG2ZzcdlZ18Zkc2DRO2VWxL8fOxDnufcPQZ1SwckfuWQ9g8uRf\nsPfdwtMKOpz3Ivc3T/mdy+xZOcePpdFrazUdRZZKp0motgb5naPjhtU2r7s2WRGdudxh//idFdXx\nJa2dzmyrQgUUU7l6dQJt284FTmphgH2LrEIzA/2ueLAh+zCyAnUH/sm4EetwOpqF2tXO8Yzdyal7\nUMPFX5XdmTmgxIV9e4vzYR8PzjZ00lG3XWlHVJ/xF2k4pIDTmHHhvtuqhXpQuO7Y13PEZ31KfTh9\n+ozByze2BWom5r9b19YVWNSO7/x681nRNRsaTR89ZdJhfans/Lv263gyF2Vuvo9/VKXqWVcXG5bp\nA68R4/zYt11rcr0I4Ysht/8xatWsrD/88OY918U5/ll8Ls4jc00Prl2bSeWbgzD4CKjBVEZxlWoA\nWby8/K6FhfV71DN5f631JYtlsQqogVQ7CtVaj1NKvQX4/tsCrylBKeWKUBLrIw9uESRJYy1iUAch\n8YbjKTaS0JZpeiGSCBOQpenniVL0uwElEUNzADigDeOGFaDMZZ0rKWINbVxM93cyTQ+Eo/y2NoxF\nKezjicwQR2vDuFM1XthDnYD34pXaXnnGDNuuokXdEEN/1wzYMvjlEWPfBMkM3gxcIg53TvI0Htwi\nJ4cQvfJQ4Kc2m9qE9F/ZfzPQxtDG5kR9SlPANTmYyuwEvAFUNbRhVedSnogGS13u6K/8iDChOuoU\n9L3TCvXtt26sWrWGRYtq4OQ0i2vXXg8hxBVhGpWfxaw5c5mboCwZlPBiWaFWjL/EpVYv83IW4OeZ\nsLIHkJBZebt90yyI0A3f0oaRLpLAXe3kzduKmzd/eLpdu8OLFiz4qrKLy/pTYWHNiYt7vgMd/FvS\nwv6yrevNyPiYEVrrb28fF6ycEZpubaA4kENpjleKKB9xrNa7FbNeORp3zN/nCMoWB9qGxgbaRnyM\nOzFXcuBbOQwXn/iiR2zfv/IVuSvupHasM79nDuNFQxvxSsrq7QI6a5HiHQvUIEmeiApWWYN/Y1mP\n7VSbWN3n1e5rB7xxjaoFbURP8mXX4FBqhsXh6QJscOXyruxO3ze8ENep2qyGo3bNqr29iIed7fum\nk8+m+XVyx9cOvztv3vgOI0asWV21andtGHeSnCSA+y2SuPVCCAHlkVjYOmQiszaAkAuI62k8+/bt\neWbTaP/+bc9VGj+e9X8WeHEdPXv2ItLpbdqVakLets1RV+w827S2/vDD3bfPM3uLCyPynSO6vhe3\njs8mKuo1ghiNuFzr60BhJlovnF8JCHj6URv5dYiOsh0YrLUOsSiU3yPL1xP8yyiU6YF14yUY/VJA\nr7Rmet5uwzRLIMyBQ0BPbRg3UjnkH4cyzdpIZmk5bRj36Ggr0/wM8NKG0emegwGENjhIw+D+AwYc\nmN66dSTJGPq7DglW3sgs3wewcwMXfmUUmdlAYxHzQoK0ATmv5Tzeaksr7wV1FlS/Pv76pUQB1zIJ\nHPv0wHpZrgJ+NbQxwXLP/KBQez7l049KUMIAqsYSe2sZy56JIupsO9pNtmG7gPDrjyWuQJUalGHU\n4MKFlYSHa2y25vrkyQ0WPXUNQqcbbFFOcyDsm3yI//4ZZ5wHz2KWLZbY6d10txS54v8kVO7cnQkP\n/xIfHxeio8EwdLk8TXXQrAK2vp8oLoTuPc/rr3tit5fRWp9Nto1g5UH5Sb3xKRvEqQU/c3L2RcQO\nxFo/E34PA76jXshVJJHofWBASABLgTXO3NpRh5aLgPJfQtN3od4+CMsKAWi9O7lzAyzNV2d+9rM9\nOhzNduP085eDb3pwzQ/4VAXxQfvNNZ9/aWe2oCIXC5U46Vlcz6qzfOOPNX75BFipA/U15LneDGS5\n4u3dJ/vSpWUQt/MEhBwQi+jdZ0Xu82i4XaqxHVA/DluDS/g77YovH37o2+27rh+5UnXoUFx276bT\nsGF6MYAyzdLAIuIIY1gpN6LfLsKVX9xo27abnjZtnjJND1a7/8aE16vBX7OIjelJEAOQe6OODtS3\nn1Vlmp8CeQgIaOFIhvoXQpmmO+KrbQK8qI173QgP4Zy+yIwjMzJrzoP47xNKE/6iDWOXMs02yM1d\nKYHal0J7Bdf361e01r59MxbVrWvvPHz4qUh395b3M/T3tCFGbj0wRWs9DUAFKy9bvK1Z1SNVp/9V\n+C/PWOfYzeRudYTCvbPr+o1bpaFNhQTG45Ekm+vArRBCCgFb3uCN8R54vPMSL+0oRalCQqBhORLo\nzBxBRL6NbOyRj3w3ilP8skIVRBK/XkotIK6GD6/Db799zN9/V6BevTXcutVMm6bdesl8gRiJtveU\nLBQG2njk+owKIUQjsYmnDW38ndbxfBAopWoh+QlbpD4C24AehjZ+Uab5DJ999gM7d9qZMqWKbtw4\nqRtDIavTHsBz2jB2pPm8plnJ78aNhbPHjo1stOnv7Dv4OFt21h0vzJe/ALuLQOMzEBetdfvkjjeV\n6YJ4C17zcVn8UwHb1A4vt+J6l91s69iG8Fd20iIoBLcjfhx8q0P9H/9o9FY/bM57gTfueu6UKg5U\nIKGcomkWQQx8Ke4Ua2mSdNWkTDMb8L6NuFb1z8z5tvD6eS0qVMC3eHEinZxoZxiiCppofzegKvHU\nZGzJnlxYUITTo2wMHnycatUu0/qDikSFb4MddQniecRO1NGB+mSiNnojOTE1CAi44TDy/2Io02yL\n0OXGAJPvlwH7D5xrLhIQ/BhZbeVC3BXLECXHLkhCz1TkQd2UQlMo06yD8LLjyhw/PnR39+5tjuXK\nVa/NqFF/7ylSpFE6DX1BxAUWqLX+KuFzU5lVo52jl3Yc0HFoaB7/Twk/GkN8zIfADB14r/yEZSwb\nImnn/siM0RfI4oqrR13qRjahiVsZyrjEEbfdC68FCJtp/z0CdEr5IQyZBSGETESosAURv/5dmizK\nNL24cqUzP/zwNr/8koeSJbdTr15fPWHClkTf5XVkfOukleVjKrMnMpusYeh7aZEPC5bRXAusNbRx\nO06mhg71Ys6cQ7z4ojdt2gwHpmvDiLPcf18hrKzW2jDOK6V6W3+/fd8sbblmneKVmrCobt2wt3v2\nDH11as5uVbc6r0LKXn5tudV2AW9orZcm6WsZxGd/BehuaOMsSr0Y5cSnE2pz6LWt5PW0c8ol3tbf\nZe3a+oh7tZPVt0O9RlsAAB76SURBVGDE1fKONox7pcCFQTYECXBHISSGTMik4WqirQrwDZ07/8GZ\nM9OQEoGj0+LuswKs75JjRT+iO2XDyzeGCxXiie3gR1D7msiKv4EOvLOCUab5NLIKr6MN47Cj/N9/\nAMo0CyH8+Qigd1IVzH/oHK2RmWJFbRjhiT4PQJah5RDtj1XA9ym6aeSYZohuSEfEkM5R8fGbzr7w\nwj7PqKigYb16Hfy8ZcsaCYbeikXksdp/HqlsvzDxg2VRBBcjAdL+Cfo3pjK/PJsb507zqcf6Z1sT\nF9kfSWr6AZiiA/W+RFnCo5CZchDwQwghIFWSOmt0Wzv23cc5vjwPeXJ44dUQCEhqsO/6nkIS+AOY\nHELIJ4j+TzOgSUAIFwADu709y5e/yKxZNjJl2kpUVA999WpSFdNmiEumhqGNUymdLyms2f9i4FBC\nEt2jgKnMD5H4UPOkLiqlVClsto3MmHGIIkVsyOx9FHAY6E5AQCwy+30GMYCHEWmHew2euMw+Rfj+\nvVVIyF9IQlmVSa8zsPIOliDU4FVKKQMJNJfRWt8wlZkfoXe2tPrweeIXdV+lxh2E7mOhf+OlS5dd\n9faehbjE2ib42S3q6+sIMWQu8L42jKvKNGsgmjR1EIM9PUFnypKj8EXuM9kuXjzBiy92QupHdNRa\n35MAlRqUYhBu+9+g6hRv3nwhnrCVozk08Q2gvQ7Ut1VrlWkWQGSKb4scOoz8fwRWUKc/suz8CJig\njeQTgjLQtj8yE2qrDWNjMv//FKFlHkM0V/IAQ7RhzE9m304I3bJVwkzfeljGAK2HffPNhEELF37w\nW6VK8RNefPHKzmLFsiMzoFiEBnoOeUhisFxEyGx6HQEBCgnk9cLNbSRLl/4UsMGtxJBJrPizJusa\nrOUFQxs3VLDyR3yUfYjBzg2yEIUTXhzElyNeUV5xpc6WKlTibIlivhG+kVEuUav+zvv3DxtLbtwN\nXAoJColFDPYzQANDGylqFSmlCrngskmjL8Q66ahOrt0LPBfTNPuwkl/FH3M7e5MjR+KIijpETMxg\nre+tRWsqsyxSt/S5pEJwaYGpzOxIkLynoY001d59EJjKbIeMzVMpvQCVUoOAdqxaNRNX12BEq2gM\nAQE+yMwTxE8diyWrjLCo7FYDHsh93gt5QXySIEZmuX3GA42n9uftcnuZBTQztLFVKfV5TnK6L2BB\nKND1fE5+eO0TCl3PQjzibltOQMAJxED3B9bg6lqDefMU/v7rgP7JFZ5RppkDeWE8z9WrJzl7Njfr\n16/k55+PExmZl+yUIhsFiSOSKC4TyQVucZYoziNaSN0RHZ4uWt8pYJ9eKEV34E1mBr3LpY/nU6Dz\n3+Rp1SZhMmQ9Z+uBudowPkp0PRxG/r8E6009HXENvJqcUU5newpxqxzUhvFWCvt4I5l1HohUsjcy\no/9AG8anifYbgDBUmmjjXj+xpbD5VY6rV0N+HDnSu8yJE42O5snjMr9+/UuL69YdfyJXrq+0YUQo\n0+wDjATeRmZXTREBp0NAVo4cycHEiZ64uNh5/fXjJaMKXZw0hMuekRjhhH/1Bm/sOcCBajjTED88\nyM9XqqFaX39//adKnCvR2i3Wrdxx/+PbtxbdevhM1jMKRU64vWUHvupqdu3dzex2O0M5gcKYGL+6\nmIWj3JkRGX2jxt6s5xgz1K5iw6/v7rQ087VOO8rU/lYvmDCb2b+TQoayZaA3AyMMfe8LM60wlfk0\nYjznW21liMefhvOUQtwXje+XOW3Jd6xFJBzGWp8VQdx+axDCRaz1uYeCH6uAswk/eEqiZCPEWL1O\nMoql1j07EujwaW/GljzI6OMcb+ePf9M44oaFOt1YFvxFvhMnC9IBCdqeBZoRE9OU0aPd2b8/mr59\n+1O7tp25c+exaFE4YWHVtdYn7vOdcpLZay4+EXXJG3+JvNjJiRu++KGJII5DaFxR+GHDBycyAxBD\nDBGcIgvjsLFIB+oUY1hpgRqeOStuYetRzl/w9K/OyAtrIpBQWSwS6JbYresw8v9BWDf5C8iydwlC\ndcwQA0eZZhcs6mBy6e6J9qsCeGjD+MP6uzDywM5AZnbBSPp5I20YJ+/Tjg9yQ3ZytdsXzh4zZseL\nISG1lDCSliCaOBtUSEiCAmYfbRgLlWn6ITQ8qeI0aNAtdu3qj8zspwHOBSnY7HmeLxNAgNqW+dC+\nGX1sV67XLnPsh/ZOWz2ieA1Rx/wYmJsg7XxP/4KVl/W9zJCgkLeR5XhZRBIiXJmmf/ZLPNv9SwbV\n/JNyi9pw6/t2LJ3Zk6LZL3PS1U5HQxvayqRdgMxEtyByAJcMLfx9U5lu1nnWGdp45z6XKE2wXhjT\nEbdaV0MbW1I5JD1teyC+/zeBYYY2Zqd2jFKqABKYbYCwpb5HJG8/sXbIjuRK1NFQ+xJk2g5Xn4Zx\nmeA3tN6X6jk+/vh9tm17rdXX4dH94vvk3MrWc18WNv86ErGrKTNnLsLLa1CCtIXFeluEr28cs2Zt\nwNe3IfJC70BAQFVEBbWx1vqu8nuWq68zMJHW7KU8hVGsQ1a+O4FdycV/AFSw8kRiW7UQ12U9JHA+\nH2HtpGslroJVFiR57k8dKKURLVfudCQD/QSidXXXasRh5P/DUKaZBTGwTREVwpBUDkl6fH6E695Q\nG8bODJw/D5IpG44kojRNT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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -473,17 +724,20 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 13, "metadata": { "collapsed": false, - "scrolled": true + "scrolled": true, + "slideshow": { + "slide_type": "slide" + } }, "outputs": [ { "data": { "image/png": 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Au9Zj3twY1bC0GX/DayS3vY4KXcKXN0h2LnS74FAE16UrVAyc52OBZu6p9JGU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\ns+fwwjaD1IX0+m24+j9C8L9DM07v83FSUL1NmvW/ucbah3+fv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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -498,20 +752,34 @@ " plt.plot(t, S[i,:])" ] }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "###GΓΌte der Simulation in AbhΓ€ngigkeit der Sitchprobe" + ] + }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 14, "metadata": { - "collapsed": false + "collapsed": false, + "slideshow": { + "slide_type": "fragment" + } }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 10, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, @@ -519,7 +787,7 @@ "data": { "image/png": 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4.006122\n", + " 4.208557\n", + " 2.992062\n", + " 4.055670\n", + " 3.890020\n", + " 4.035907\n", + " 4.014299\n", + " 4.006577\n", " \n", " \n", " 18\n", - " 2.949070\n", - " 4.360241\n", - " 3.992905\n", - " 3.943630\n", - " 3.997630\n", - " 4.011300\n", - " 4.005711\n", + " 3.912931\n", + " 4.234620\n", + " 4.081956\n", + " 3.962941\n", + " 3.987279\n", + " 4.006018\n", + " 4.007529\n", " \n", " \n", " 19\n", - " 4.911999\n", - " 4.364572\n", - " 4.002349\n", - " 3.997426\n", - " 3.994726\n", - " 4.020332\n", - " 4.009137\n", + " 5.831976\n", + " 4.371024\n", + " 3.875515\n", + " 3.999242\n", + " 4.013709\n", + " 4.016788\n", + " 4.007910\n", " \n", " \n", "\n", @@ -789,29 +1060,29 @@ ], "text/plain": [ " 10 100 1000 10000 100000 1000000 10000000\n", - "0 4.906548 5.294698 3.639421 4.067554 4.011043 4.008300 4.010495\n", - "1 4.045447 4.193208 3.675234 3.950394 4.000466 4.003294 4.009961\n", - "2 3.781044 4.069883 3.812419 3.991908 4.019511 4.011926 4.006118\n", - "3 3.181005 3.404997 3.846536 3.881560 3.997962 4.007517 4.009783\n", - "4 2.862575 4.165775 3.925851 4.019147 4.033484 4.004791 4.007811\n", - "5 2.151565 4.371950 3.762102 4.052336 4.005776 4.015000 4.006264\n", - "6 2.355832 4.016829 4.024916 3.995432 4.047429 4.015476 4.006216\n", - "7 0.709642 3.167480 3.697572 3.996979 3.963552 4.009702 4.008771\n", - "8 5.179126 2.954287 3.999991 4.097551 3.998149 4.002971 4.006549\n", - "9 5.829153 3.938499 3.873002 4.006437 4.028177 4.008132 4.005116\n", - "10 0.933170 4.066512 3.959150 4.001901 3.982441 4.009084 4.011994\n", - "11 2.624002 3.190735 3.886773 4.089420 3.968595 4.002560 4.009763\n", - "12 5.581318 4.418143 4.216188 3.998958 4.009083 4.005293 4.006704\n", - "13 4.535982 4.206602 4.237326 4.014739 4.005382 3.997234 4.009190\n", - "14 8.195545 3.122013 4.033490 4.001259 4.011972 4.012704 4.006725\n", - "15 4.146682 3.982844 4.011506 3.957476 4.040843 4.000705 4.009913\n", - "16 4.491857 3.696805 3.974811 4.035294 4.030621 4.018620 4.007754\n", - "17 7.630725 3.880077 3.996466 4.064760 3.986851 3.998750 4.006122\n", - "18 2.949070 4.360241 3.992905 3.943630 3.997630 4.011300 4.005711\n", - "19 4.911999 4.364572 4.002349 3.997426 3.994726 4.020332 4.009137" + "0 7.002519 4.920343 3.760139 3.981517 4.024967 3.997949 4.010126\n", + "1 6.995833 3.949006 3.919908 4.004953 3.993682 4.013823 4.008804\n", + "2 6.970454 4.173934 4.147529 4.010374 3.968703 4.006621 4.009861\n", + "3 0.437431 3.266483 3.711490 3.971027 4.010516 4.011429 4.011645\n", + "4 4.377311 3.298525 3.722210 4.075284 3.999880 4.001456 4.009976\n", + "5 1.486307 3.734809 3.958199 4.089549 3.982037 4.006475 4.005380\n", + "6 2.165392 4.272690 4.009487 3.951646 4.024424 4.011966 4.009139\n", + "7 1.308562 4.370478 4.006764 3.927404 3.992059 4.007220 4.005435\n", + "8 6.029680 3.742611 4.408471 4.081101 4.010679 4.009382 4.009448\n", + "9 3.598355 3.492616 4.096478 4.071650 3.977139 3.995654 4.008295\n", + "10 1.830120 2.739179 4.074707 3.998869 3.997334 4.011498 4.006894\n", + "11 3.984010 3.787820 4.096035 4.076278 4.007272 3.999398 4.010719\n", + "12 7.682964 3.190652 4.009614 3.997602 4.012674 4.013603 4.010228\n", + "13 1.725964 2.843935 4.052156 4.079586 4.001027 4.003518 4.006891\n", + "14 3.384860 3.360019 4.088791 4.065781 4.050036 4.005878 4.009252\n", + "15 3.635062 3.658024 4.142389 4.014601 4.035410 4.001456 4.007769\n", + "16 2.704679 3.800666 4.037882 3.967375 4.000176 4.005432 4.006744\n", + "17 4.208557 2.992062 4.055670 3.890020 4.035907 4.014299 4.006577\n", + "18 3.912931 4.234620 4.081956 3.962941 3.987279 4.006018 4.007529\n", + "19 5.831976 4.371024 3.875515 3.999242 4.013709 4.016788 4.007910" ] }, - "execution_count": 26, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -821,27 +1092,41 @@ "pd.DataFrame(V_MC, index=N).T" ] }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "###Varianz in AbhΓ€ngigkeit der Sitchprobe" + ] + }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 18, "metadata": { - "collapsed": false + "collapsed": false, + "slideshow": { + "slide_type": "fragment" + } }, "outputs": [ { "data": { "text/plain": [ - "10 3.765250\n", - "100 0.314827\n", - "1000 0.025343\n", - "10000 0.002690\n", - "100000 0.000500\n", - "1000000 0.000041\n", - "10000000 0.000004\n", + "10 4.730080\n", + "100 0.328129\n", + "1000 0.025969\n", + "10000 0.003326\n", + "100000 0.000432\n", + "1000000 0.000035\n", + "10000000 0.000003\n", "dtype: float64" ] }, - "execution_count": 28, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -849,6 +1134,15 @@ "source": [ "pd.DataFrame(V_MC, index=N).T.var()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/StochProcess..Basics.slides.html b/StochProcess..Basics.slides.html new file mode 100644 index 0000000..3e26d56 --- /dev/null +++ b/StochProcess..Basics.slides.html @@ -0,0 +1,6557 @@ + + + + + + + + + + + +StochProcess..Basics slides + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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In [1]:
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import numpy as np
+import matplotlib.pyplot as plt 
+%matplotlib inline
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Stochastische Prozesse und eine kurze Einführung in Monte-Carlo Simulationen

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Was ist ist ein stochastischer Prozess?

Wikipedia beschreibt einen stochastischen Prozess als

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die mathematische Beschreibung von zeitlich geordneten, zufΓ€lligen VorgΓ€ngen.

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"Defintion":

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Sei $(\Omega, P, F)$ ein Wahrscheinlichkeitsraum, $T$ eine Indexmenge (ΓΌblicherweise T=N oder T=R). +Eine Folge von von Zufallsvariablen $X_t$ mit $t \in T$ mit $X_t : \Omega \to R$ einen stochastischen Prozess.

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Beispiele

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  • Aktienkurse, Zinsen, Fx-Kurse

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  • Warteschlange an der Supermarktkasse

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  • Arbeitslosenzahlen

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  • ...

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Eine Realisierung / Beobachtung eines Stochastischen Prozess nennt man auch Pfad.

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In [2]:
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dt = 1./100.
+T = np.arange(0, 1, dt)
+for i in range(0,5):
+    X = np.cumsum(np.sqrt(dt) * np.random.randn(T.shape[0]))
+    plt.plot(T,X)
+plt.xlabel("T")
+plt.ylabel("X_t")
+plt.title("Pfade eines stochastischen Prozess")
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Out[2]:
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<matplotlib.text.Text at 0x6f385f8>
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Wiener Prozess

Eine besondere Klasse ist der Wiener Prozess. Er hat folgende Eigenschaften:

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  • $W(0) = 0$
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  • $W(t_2) - W(t_1) \sim N(0,t_2-t_1)$
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  • Differenzen sind i.i.d
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Stochastische Differentialgleichungen

In der Bewertung von Finanzinstrumente spielen stochastiche Prozesse eine wichtige Rolle.

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Beispiel Call Option

Wir haben eine Aktie, den Aktienkurs den wir beobachten folgt einem stochastischen Prozess $S_t$.

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Eine Call-Option gibt dem Optionshalter das Recht zum Zeitpunkt $T$ (OptionExpiry) die Aktie zu einem festgelegten Kurs $K$ (Strike) zu kaufen.

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Zum AusΓΌbungszeitpunkt ist der Wert der Option mit $V(T) = \max(0.0, S_T-K)$ gegeben.

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In [3]:
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K = 130
+S_T = np.linspace(100, 160, 50)
+plt.plot(S_T, np.fmax(S_T-K, 0) )
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Out[3]:
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[<matplotlib.lines.Line2D at 0x768ebe0>]
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Aber was ist der Wert zum heutigen Zeitpunkt t=0?

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Unter gewissen Vorraussetzungen kann man zeigen, das der heutige Wert den erwarteten abdiskontierten Auszahlung entspricht

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$P(0,0) * V(0) = E[P(0,T) V(T, S_T) | F_0]$

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Vorraussetzungen:

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  • kein Arbitrage
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  • vollstΓ€ndiger Markt
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Grobskizze

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  • Replikationsportfolio aus handelbaren Assets (bei BS: die Aktie und Bankaccount)
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  • Da kein Arbitrage Preis des Replikationsportfolio muss Preis des Derivates entsprechen
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  • Unter einem bestimmen Wahrscheinlichkeitsmaß (risikoneutrale) sind alle handelbare abdiskontierte Assets Martingale und fΓΌr Martingale gilt die obere Formel
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Wie kΓΆnnen wir den Erwartungswert berechnen?

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Wir mΓΌssen dazu die Verteilung von V(T, S_T) kennnen. Die Verteilung ist abhΓ€ngig vom Prozess.

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Black Scholes SDGL

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Mit einer stochastischen Differentialgleichung wird die Dynamik des Prozesses beschrieben. Üblicherweise hat eine SDGL die Form

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$ dS_t = \mu(t, S_t) dt + \sigma(t, S_t) dW_t$

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mit dem Wiener prozess $W_t$.

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Die Γ„nderung des Prozesses von $t$ nach $t+\delta t$ fΓΌr sehr kleine (unendlich kleine) $\delta t$ wird mit dieser Formel beschrieben. Es besteht aus einem deterministischen Teil $\mu(t, S_t)dt$ und einem stochastishen Teil beschrieben $\sigma(t, S_t) dW$.

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Ein sehr bekanntes Modell ist das Black-Scholes Model mit der SDGL:

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$ dS_t = r S_t dt + \sigma S_t dW$

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mir dem risikolosen Zins $r$.

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FΓΌr dieses Modell gibt es eine LΓΆsung fΓΌr die Gleichung

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$S(t) = S_0\exp((r-\frac{\sigma^2}{2})t+\sigma W_t)$

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In diesem Model sind die relativen VerΓ€nderung von S_0 nach S_t lognormal verteilt.

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$\log(\frac{S_t}{S_0}) = (r-\frac{\sigma^2}{2})t+\sigma W_t$

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In diesem Modell gibt es sogar geschlossene Formen fΓΌr die Preise von einer Calloption:

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$V(0) = S_0\Phi(d1) - K \exp(-rT) \Phi (d2) $

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mit $d1=$, $d2=$ und $\Phi$ die Verteilungsfunktion der Standardnormalverteilung.

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Was kann man machen wenn es keine geschlossene Formel gibt?

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Monte Carlo Methoden

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Wie lassen sich Erwartungswerte schΓ€tzen?

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Mittelwert

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Sei $X_i$ unabhΓ€ngig und identisch verteilte Zufallsvariablen mit $E[X_i] = \mu$. Dann ist

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$\hat{\mu} = \frac{1}{n} \sum_i^n X_i $

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ein erwartungstreuer und konsistenter SchΓ€tzer.

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Beispiel 1:Berechnung von $\pi$ mit MC

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  • Dartwurf auf ein Quadrat 2 x 2

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  • In dem Quadrat ist der Einheits-Kreis gezeichnet

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  • $X_i = 1$, fall der Dart innerhalb des Kreises landet und sonst Null.

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  • Wir nehmen dass wir beim werfen nicht zielen und somit jeder Punkte auf der FlΓ€che mit der gleichen Wahrscheinlichkeit getroffen werden. Die WΓΌrfe sind unabhΓ€ngig und die Wahrscheinlichkeit das ein Dart im Kreis landet liegt bei $\frac{1}{4}\pi$.

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In [4]:
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def MCSimulation1(N):
+    x = np.linspace(-1,1)
+    y = np.sqrt(1-x**2)
+    plt.plot(x,y, color='b')
+    plt.plot(x,-y, color='b')
+    dart = np.random.uniform(-1,1, size=(2,N))
+    getroffen = np.sum(dart**2, axis=0)<=1
+    plt.scatter(dart[0,~getroffen], dart[1,~getroffen], color='r')
+    plt.scatter(dart[0,getroffen], dart[1,getroffen], color='b')
+    plt.title("Dartwurf N=%i"%N)
+    plt.show()
+    print("Pi = %f" % (4.*float(np.sum(getroffen))/float(N)) )
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+np.random.seed(1)
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In [5]:
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MCSimulation1(4)
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Pi = 3.000000
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In [6]:
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MCSimulation1(40)
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Pi = 3.300000
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In [7]:
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MCSimulation1(400)
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Pi = 3.150000
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In [8]:
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MCSimulation1(4000)
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Beispiel 2. Calloption mit Monte Carlo Simulation

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  • r = 0.03
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  • $\sigma= 0.2$
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  • T = 1
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In [10]:
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import scipy.stats as ss
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+#Black and Scholes
+def d1(S0, K, r, sigma, T):
+    return (np.log(S0/K) + (r + sigma**2 / 2) * T)/(sigma * np.sqrt(T))
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+def d2(S0, K, r, sigma, T):
+    return (np.log(S0 / K) + (r - sigma**2 / 2) * T) / (sigma * np.sqrt(T))
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+def BlackScholesCall(S0, K, r, sigma, T):
+    return S0 * ss.norm.cdf(d1(S0, K, r, sigma, T)) - K * np.exp(-r * T) * ss.norm.cdf(d2(S0, K, r, sigma, T))
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In [11]:
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def payOff(S_T, K, T, r):
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In [12]:
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def pathGeneration(N, S_0, sigma, r, T, dt):
+    t = np.arange(0, T+dt, dt)
+    dW = np.sqrt(dt) * np.random.randn(N, len(t)-1)
+    S = np.zeros((N, len(t)))
+    S[:, 0] = S_0
+    S[:,1:] = S_0*np.cumprod(np.exp((r-sigma**2/2)*dt+sigma*dW), axis=1)
+    return t, S
+
+t, S1 = pathGeneration(100, 100, 0.1, 0.03, 1, 1/52.)
+
+for i in range(0, 100):
+    plt.plot(t, S1[i,:])
+
+ +
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In [13]:
+
+
+
N = 10000
+t, S = pathGeneration(N, 100, 0.1, 0.03, 1, 1)
+
+for i in range(0, 100):
+    plt.plot(t, S[i,:])
+
+ +
+
+
+ +
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+ + +
+ + +
+ +
+ +
+ +
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+ +
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+
+
+

Güte der Simulation in Abhängigkeit der Sitchprobe

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+ +
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+
In [14]:
+
+
+
N = np.linspace(100,2e6, 70)
+V_MC = np.zeros(N.shape)
+V_BS = np.zeros(N.shape)
+V_BS[:] = BlackScholesCall(100, 103, 0.03, 0.1, 1)
+for i in range(N.shape[0]):
+    t, S = pathGeneration(N[i], 100, 0.1, 0.03, 1, 1)
+    V_MC[i] = np.mean(payOff(S[:,-1], 103, 1, 0.03))
+    
+plt.plot(N,V_MC)
+plt.plot(N,V_BS)
+plt.ylim(np.min(V_MC)-0.1, np.max(V_BS)+0.1)
+plt.legend(["MC", "BS"])
+
+ +
+
+
+ +
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+ + +
Out[14]:
+ + +
+
<matplotlib.legend.Legend at 0x98d2e80>
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In [15]:
+
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+
N = np.array([10**i for i in range(1,8)])
+V_MC = np.zeros((7,20))
+
+for i in range(N.shape[0]):
+    for j in range(0, 20):
+        t, S = pathGeneration(N[i], 100, 0.1, 0.03, 1, 1)
+        V_MC[i, j] = np.mean(payOff(S[:,-1], 103, 1, 0.03))
+
+ +
+
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+ +
+
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+ +
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+
In [16]:
+
+
+
import pandas as pd
+pd.DataFrame(V_MC, index=N).T
+
+ +
+
+
+ +
+
+ + +
Out[16]:
+ +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
10100100010000100000100000010000000
07.0025194.9203433.7601393.9815174.0249673.9979494.010126
16.9958333.9490063.9199084.0049533.9936824.0138234.008804
26.9704544.1739344.1475294.0103743.9687034.0066214.009861
30.4374313.2664833.7114903.9710274.0105164.0114294.011645
44.3773113.2985253.7222104.0752843.9998804.0014564.009976
51.4863073.7348093.9581994.0895493.9820374.0064754.005380
62.1653924.2726904.0094873.9516464.0244244.0119664.009139
71.3085624.3704784.0067643.9274043.9920594.0072204.005435
86.0296803.7426114.4084714.0811014.0106794.0093824.009448
93.5983553.4926164.0964784.0716503.9771393.9956544.008295
101.8301202.7391794.0747073.9988693.9973344.0114984.006894
113.9840103.7878204.0960354.0762784.0072723.9993984.010719
127.6829643.1906524.0096143.9976024.0126744.0136034.010228
131.7259642.8439354.0521564.0795864.0010274.0035184.006891
143.3848603.3600194.0887914.0657814.0500364.0058784.009252
153.6350623.6580244.1423894.0146014.0354104.0014564.007769
162.7046793.8006664.0378823.9673754.0001764.0054324.006744
174.2085572.9920624.0556703.8900204.0359074.0142994.006577
183.9129314.2346204.0819563.9629413.9872794.0060184.007529
195.8319764.3710243.8755153.9992424.0137094.0167884.007910
+
+
+ +
+ +
+
+ +
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+
+ +
+
+
+
+
+

Varianz in Abhängigkeit der Sitchprobe

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+ +
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+
In [18]:
+
+
+
pd.DataFrame(V_MC, index=N).T.var()
+
+ +
+
+
+ +
+
+ + +
Out[18]:
+ + +
+
10          4.730080
+100         0.328129
+1000        0.025969
+10000       0.003326
+100000      0.000432
+1000000     0.000035
+10000000    0.000003
+dtype: float64
+
+ +
+ +
+
+ +
+
+ +
+
+
In [ ]:
+
+
+
 
+
+ +
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+ +
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+ + + + + + + + + + + + + + + + + + + From 0f890b113d958c5d039e9980cffedaea59c11cc2 Mon Sep 17 00:00:00 2001 From: Matthias Groncki Date: Tue, 19 May 2015 19:42:16 +0200 Subject: [PATCH 11/25] Some small bugfixes to slides --- StochProcess..Basics.ipynb | 96 ++- StochProcess..Basics.slides.html | 1062 ++++++++++++++---------------- 2 files changed, 574 insertions(+), 584 deletions(-) diff --git a/StochProcess..Basics.ipynb b/StochProcess..Basics.ipynb index 0416843..ea5d527 100644 --- a/StochProcess..Basics.ipynb +++ b/StochProcess..Basics.ipynb @@ -106,7 +106,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 2, @@ -115,9 +115,9 @@ }, { "data": { - "image/png": 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MXkV6H9gNuLbglV4/6mFc4e2H1A+zd3JG7IvXUIrk4ybBI4DRSLdh9nWV7jQM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Py7WJpqqoOMpCWM/4Dujo1H2Xb3kiVUtQFgVYAa5DsRrbx4ciRZEqRrw/G8sy\n7Yoz+4n3KwF3AT8AA9W5aWn7dwWGAEeoc2+K99cCG6pzxy7XrqYqDhHZD7sBdYH/U9Wb0/Y7bKo1\nKWwaoqrXZegnKo4y4MVfB6zp1J2Sb1kiVY94fwOmMAYBL1b3okm1nZCKZQQwD1Pg/2Ro0xiLDZmO\nrS8dgZWw/RRz910fS+dydKFTQlA0X4c+l7r+1kjFISJ1sbiAvYBfgA+BY1R1QkobB1ygqgeV0FdU\nHDnixbfEHqZtnbqf8i1PpGoR7/fGSo9uo879nmdxIoFQ/bAfFu9xcGquK/G+IVY46y8s/mOxeL8K\ncDywHjYDmQR8VcQsZcWxrgC2K1wor6mL4x2Aiar6I4CIPA0cDExIaxcVQuXQC3g6Ko3ajXjfAhgI\n3KnOvRG2tcJmGSdEpVG9COnkzxDvTwfGivcPYTExMzGT4mygW8jES4g8v6cUXT+HuVKfhtWMLzP5\nzlW1DjAl5e+fw7ZUFOgkIp+JyHAR2azKpKvFePFrAT2AG/ItS6TS6Y8NEp8U7+8W71fGlMYj6tzo\n/IoWyYY69wA2kF4EtMYKRH2FxXvk7CYf8oCdCXxWXtnyPeMojZ3sE6C1qs4Vkc7YNK19poYi0ifl\nT6+qvtwS1l4uBx516n4prpEI9QBVZXHViBWpSEJ8wA5YeotGmAlkMmai7JM/ySKlQZ17D3ivwjos\nKGgFOIFy5ZrLt+L4BdOkhbTGZh1LUdVZKd9fE5H7RKSFqs5I70xV+5T2xF58e6CuU5duFivumALM\n17qH05ob6BSq9R0LFDt7E6ET5l/eUoR5WLDX1aoMqHQhI+UmRH7fCxymzs0F5gInBDfNCercwrwK\nGMkD2hqYpcrtACLSuyy95FtxfAS0E5G2wK9YXqRjUhuISCvgd1VVEemALegXURpl4GHMk6tTSQ1D\n4aI+wEnAfGAfzPOhptIHuNfp8i58qYhwJGY37YYV2GmCLdbdCFFxVHdCNtr7gcHq3Lup+6J5asVE\nhLbAHZgzUrnIq+JQ1UUichbmSlYXeFhVJ4hIz7D/QczV7AwRWYSNmI4uy7nE+w2wNBqnJAXUA9YC\nxIvv5NS9k+04L34DrOjMDMzF7QAsX0+1Uxxe/LrAbcC52ZRCiBDvTDD3ibAjts7RCngrfNbHFtH2\nVl1qD50lwghgoAgbqlIkyV2k8glpuhsVl6U2KI1LsCwAx1eVbJGyIUITbMD8LfCFKn9VwjnqYqWe\nb075P10X8E9+AAAgAElEQVT2/vIdx1FRFOdWJt63xZKyjQPWGFPATLG1kvrAXk7doZmO8+IPwUbX\nNwD9nDoNyf4mYwFz1aYkZFAIw7EArg+dutOztBsKvFmAewmbPXTCUjx/BuwaPk2Bk1SXNxsCiDAA\n+FaV2yrlQiLFIt6fj6WkuAfLsvpn2v6GwIPAfzBXzilFe4lUJ0Q4FquxPg1LJPo74FQp028XFNFJ\nwLOq/B62XYata+ylypJlbWtgHEdFku0GiPetsSpcdwL3bTqeCXeez2oNF7A2NuP6EdjZqfu28Jhg\nmroRi+A80ql7P7VPL/4moKFTd37KtouwCNyeTl2RF25l4sU74FkstcAIbOGzwKn7Kq1dR+CZwbTZ\n8hHW/xa4G7hTlbmlPZcI+wFXqbJzRckfKT3i/Shs0LM5Ztp9DHMgmYTNigdg6e+7F6bujlRvRBgE\nvKfK/SLUwUyMU1QpEuhcyv4eAnbE1oyfBf6Hud9ul66MYunYgBffwIvf2ovv8vzq/pz9X+HDBv9y\nrzrXX51bfNW1/PbksSwqSFjs1M3ByjumKoBWwBhs4XjbdKURuB/o5sWvHI45AjgXm9F84sUfk+GY\nSsGL3wF7OI526p506mZgM6Tb0toJVl+jzyOs3wX4VJXrc1EagTHAZiKsVQHiR3JAvG+CvRAGqXNn\nYPUxpmMjyZswk+8o4MioNGoGIgi2ZjoSIMwGHga6hX259ncENoDdGdgYmIo5SJxd1hlMxvPUthmH\nF38bti7yzedbsmazf1ij7U9MBc4AZiiMPWgY42c35Ql1bkBQFF9jN3k9LI30I0Df4jynvPghwGjg\nA+A1YF+n7hMvfjvgcayucnenrlLdWL34Z4CxTt3dKdsaYP7eZzp1I734ptiLZXdg6wLc68ADqjxX\nlnOK8CTwpioPlP8KaigixwELUX22ks9TuF4xRJKkPXCROldQqeeMVBkibAkMVWXDlG2CBUGfpMq7\nWQ8u2ldrzOHoQFU+KN0xNTNyvDLYAzi2IGECMLH+ArYfue9St9I5AvfObsorwDDx/hkS1rv7bL5p\nPpN31ptCM+B0p+6FUpynP5ZauhFwmlP3CYBT97EXvy223nAZlG26WRrCYvjewKmp2526BV58L+B2\nL/4SbJqaALsW4NpgdtRh5Tj1C+Gc1VdxiDTGYhbWYFnmgaHAo5R3tGSpcm4AGiHyOqozSzqkHPQE\nrgJ2woJlX6/Ec0Wqnn1J+01V0WC+6g6lUxwpi993lVZplIdaZaoKpqONsdH+OcCwBfu4H5y6JzAP\nk0eAfurcR9i6x+/AwAGnMXFOE9qM2pPjSqk0CMf/Bdzt1L2YusOpm4d5s5wVanZXFqcDTzgtmggN\ne0nOwMxqPZ26k5y6v4CTgcdUKU/q9BFARxFWKUcfZUdkw5IbcRemNAZhv/vj2DPxMiLlNbMdgLmP\nD8dmA5WDyE7YQvhOwHaN588/mGrozRcpF0vNVGk8DnQVoVHhBhF2EWGiCL+LMFOE+SL8I8JUzGFH\ngVuqRGpVrRUfQBOSPROSsSRJM5LkD5KkXdb2SbISSbJayt//R5JclMs5E5I6mWXRlUHvfJT3u40h\n+WGVF5LXSJKBJMmGZb2+hGSLhERS/m6UkExLSDYu5phmCclK4fqa8cqbnWDJr6Cblf9+6zDQ46r8\nt4auCqqwfTFtjlP4VqFZ2vYGCtcoTFM4uBwyjFQ4XmFdhelqmQ0q+jrXUJiicJCqMrxDh0tWGzr0\n397du0uFnyt+8vIBbQw6C7R5lv2jQI8M39uA/gp6KGgr0FVAVwJtDrom6AagDXOXAS2L7LVqxoG5\nlr6D5WMZqS57fQl1bq46Nz1l09OYl0qpKWYN5ALggBNlh2uSnUWuupYtZAmTgfc3Hjx41GcbbZSx\nup4Xf6QXv1vaNvHibwQ+B24Ki9xgpTvHOc2eBrsAt0cB7mj53xt7AZ9TT1/mzk8bkfjcZhsi9RA5\nAJEXESk0i72ArSVVHRYoei/wENA3S5tNsdlGV1SXn4mpLkD1auAQ4GFEcp8xibTHFqWfR/VnzFxX\nseZIM4U9DQxCdRjAwdddN7tg3Lh5fQYN2rdCzxXJJ7thTirZTJ2DsEXylbHSEreo8qIq01T5W5W5\nqsxUZaoqk7R8VoScqHWK469V+AhzS70+x2M9sJ54XxozSFZEaIWZRPbhrk8/ublrk3U2+rTekjF7\n0ubsu+nQ4euv1zr+8ssPm9+gwZ7LnVz89phv/lNe/O1efKPgFvwo5iWxKTatvTUoj3OwdZaiMngv\n4v1KtJx/E72+vp3Z9V7hvRbXctDOY5lTbzTwvnjfJxTxKemCzsRclq/EbOyFAWVDgU1FeEKEVXO8\nTSWdc3VErgrKSsK2+lgg5i1YAOaWwZSTetxKWAbQy1DNHuSk+i7wCnYPc+UMYCCq88PfNwP7IrJ1\nGfrKxuFYpP7SdBAL69ffp8n8+QOBGxGpbf9vaz0iNBThJRG2TdmczUxVyIuYd9TzmMt1v0oUMSdq\nlVdVQjKzx/9x56QN2VydOzLnPry/D5iizpW5Nq8I9wHzSfwEoBdXbnF6k7GrDLqLcR9vxOydNuLO\nlTo9dfyMqx4fvOTUV1/dCNXFXnwT7MG4GvPUul9h8wUN+LPBAmaKudrO8eJbYA/ar5i7cHunbklw\n09wXS7m8H9CCJSxgTr36NFk0mON3nMBvjXth0fmtSfyqmK38InXutWIupgWW3383VD9DpAkWpLQG\nqnNFWAl7cR4MnKzKqAz3dHssuOm0IsFophRaYjmw5gOrAhdiazdDsRnkZMxd+jhgO6ALqksQOQ04\nHNV9Q18NsP9o04AelPRg28xhLLBhkZlJ9mOaBHm2RfWnlO1nYsWQ9i7xvIWHWM2FPsC96lISTdo9\nGQdcieorKW3/aDJv3oazu3QZjq2tjMFyvf2SosQi1RQRzsUcHVbFgvu+EeFLoIcqmVz+C48bCGwC\nFFTGjCLGcQBLhD8nbUhPcp9tFPI0ZUxpAiDCxkBXbvt0COZ100XfXn30HOqdcyo7rLUOz13wGwfV\n7d+z5T+P7nbiWnMaNuweDr0VFn/oKNjMUbDOXv/jujvPp8GLh7LD3v9j34KE8eL9GwUJl546gNsX\n1mPN7zfgsYKEs8T7VzFFcgb2ItwaqMee7nYO2uV23cOdpL82vgVLl3KUKrPDC/x+Sk5HcRgwcuno\nXXUO9lLbxf5kripns9a8/3LVV8PkivG3i/eCSCtE2gSl8SrKb8Ab4v36af3fAXyPLeIvwGY0LbEX\ncw8s+nk45ohwItAd1ULz4KNAe0R2Caadx4GFQM9SvbxVv8W8Wc4sse0yjgXeXk5pGAOwlC2Hl6YT\n8b4R8AKqZ1P0N+iCeYG9mrKtE/DN7M6d/8RePpthz9coYHJQ8JFqigjNsGzUR4d/R4qwE5b26KMS\nDj8b2LMqzVCloVbNOB5on3xw+oNMVecOLlMfZrqZAuyZrVi8CHU0JWQ/bd8QRN9nzBt7A8PUuf6F\nxwBfvUPH93bkw5leRn0zdyW9e0Grjxa1mtTu1IU0vWFnDvthYcN/Nz3xkkt+eq6goA32gA3E0qKs\nA2wAdAR2qbto8S4NFi+aO79+/WFap84I5tYdpV12/StFDgEmAl1V+STLta4e2rRW52ZlaoPIaOBe\nVF9I2dYXaIjqpaGfRsDL/NmgAXPrdlpp1b9H/dDt6HbftG69cLd+/VowYq37uGWT4xn5Rj/qay9g\nL3XuW0R2wQIXt0B1Rpgx1EO1aECiyOpAc1S/T9t+MvbinQS0BQ7IafRt6yEem3Vkzf0U2hbOBC5B\ntahLrMhugHnvFdNXqIXxUrM5c2YPuvHGjmede+78n7t2XS/lHGOBfqg+k3LMjcBCde7qDOcdCPyA\n6rXFX2wkX4hwDdBGle7h7/Mxxf+yKjlbRipWtjJWTs23Z0HFeSigh52VzCBJdihXP0lyF0nSO4uX\nw9mgf4Luk2Hf/qCTGfrWYSTJeJKkftr+7uPY6m+FfVWVTtclh9zY8fWFCWN0Otv9+uQeewxqNGLE\nTwdef/0skmTNrDLC5otE/lgMHyhMHsUez67Hj+NSPSpAdwD9FrRYDxySZBhJ0j3LedZS+Euhcdr2\n3RU+CMc3JEleI0meIknqtmo85dHuZ18+r9krr/zT4qWXFp580hUDQKeBTgXtSJKcTJL8Un/kyEFd\ne/ee2X7w4DdIkiPL/HtBfYWJCu8qrFzGPp5WuLgU7XYNnloZPelCm8cUbirmfjcmSd5uPGLE4IV1\n6nwxp0GDm1d96aXF/9t22/NS7u13CnVD+/okyWUkyZ8kyX+ynHPT4CW2UkX/n4qf8n+Cx9N00DZp\n288D3Sv/8qFlOa5Wmaq+3oTx6tyH5ezmaeDokGEUsOAaEe7GzEFnAYNFLOhOhPoi3AA8xCoLTqL5\nohuBC9JrHbzJrq9sxMRm7fh2HsDYK9zQ665a/O5nBz76zyH9un1w7FVX7bugfv0Lh11xxSwtKGia\nUTKRlYHn6qpeXEe1w5dsftQvrNNlDHtsIiy5IqXl0cBTqiUWynocOCHLvq7AMFTnpW1/D9j09R12\naIktRM8BumlBQcMp89Zv36n/H4tmvdru6iNfe7P31Y881r0Js2/AvEM6q3MDgWPOe/75Nbb+7ruv\nv23degRWA7lsqC7EUkTvQ0kzhuxcB1yIyNoltDsbuIdlprJMXAz0CDOZTJxUZ8mSWbO6dFm73pIl\nb6y0YMGl9Rcvfmlcu3bXILI/Nsu8CdXF4v1OWDzSrsD26tznGXtUnYB5Ep5cgvyR/HA18Kgqy5k3\nVblLM6wJ1hjyrfEqUnM2GJHsWu5+kkQYk3zPqORlXnj7Bm76rAcbzxzDBrPGcuNnW5Ekq4K2A/0O\n9G7QD0CHg7YiSc4nSYarvbGXHwHCYd+y0XjQoSnn2pEk0TDyXzO0e1jhnCKygSg8oTAwZdRyrbD4\niX+pP2E/hv8FujVoHdCfSxOrEUbAM0iSdTOc7x2FzpmOm1+//qi2Tz31DknyEknSILR/XuGxBsw/\nEPR70GfGsdVvi5HTQB3oh6FdhzBCXoMkqRtG0xUfB5HLB3opTFbYKsv+ddXS1TQrRV/nKozSYAZO\nudf1GDPmhxd22eUVhWEps4oDVhk27BOFP4MMDUiSDiEO6SiSpOS4DdhJ4UeF+iW2jZ8q+4CuHywU\nq+VbluwyomU6Lt+CV+YNAG0IelB6YAzodqAfg34NenihSSe0P5vV5k9lj6kLOPPbOdzz8TyGvP0X\nY5KJJMkkkmQ6SdIadHXQl0DPARWSZPXwn31ThU0U5irsvPS8MGA2K/UKZpvNl8qSJJsu93KAwxVG\nFLlGOF3h80KFBNo2PJTrKvT8hnYfg34KuifoZxmOr6+wt8LBakF0hyrUC4GPF6e1bavwR6YXEUlS\nb+sBAz7fesCAH0iShqF9Z4VvFBoE2Z4E/eRH1ttF4edBnNBcWPz3RDY4Q+FXhaNS+nucJOkZApmu\nAN2pJBNbpXzgqHDNXTLsu07hnlL2U0/hI4UbUpWHjB59zMaDBv2s8L5Ck5Trb0SS/P1qhw6HKOxH\nktQhSd4jSU7MUf43FKo+IDN+sn5Ae4Hel285ipcRLdNx1UDw/bAkg98Bl2Rpc3fY/xmwTWlvAGgP\n0Bmgv9Hwr75csuoznNdmKue2XcSVDWZz0i4DQceFWcMloJNBXwbNeA5VhSTpQ5I8n7ZNSJJnSJI7\nVBWFuxRGK/yisE6YLfyksCnopaDPZL0n0Fxh1nIzFmgTRqTtU67tWdCrw/6Vl8D0TRg/JqwpXJbW\npygMUvgyjHafD9/7kCS7kySfp7W/VOGBDNdelyR5crWhQ9+d07DhF6FtfYUJCgekyNYAdKWw/3mF\n+z9niz9/Z/UfFDql9XkMr/sR4TcYFtZmxoNenC2ittI+0FHhN4WzUrY1CjOkTXLoZ3WFzxRuUpDe\n3bvLOs8++/vTzn2nsEqG+/oUSXJa+N6NJHmfJHNWgmLO2TkMLHJTujab2rpK7/MK8gF9A7ToQKQa\nfcqqOPKa5FDMjfIezE79C/ChiAxTs9sWtukCbKSq7URkR8yNdKeMHRalJ3Acjf76hWMPHMqPu7fh\nkx4j2ebR3qz64yq0eftOLOHfkcD+mBdSVp/qwM3AF+L9fupcYd6gHpivdffg638CsC3mvvlCkEMw\nBfkTMEGEAlWSIr2rzkTkYyzor9Al80bMvv6t3RN2x9JrnxiOmS0ijyUU1F2LqRth6zSpXI25cO6I\nudQS8jV9Mm+ffXzjkSObi/dHYwkbNzusb99TJq+xxosfeX845i67GZZZdw/gx45ffbX3Sv/++zMi\na2CVyyanyIoqCzD3WrDAwZc/ZrvXTuWhugu1/vLVFh9b732O+Plxmiy6lzn1zg1bdw73bIIIFwNP\nqpa4XlN+VN9FpBPwaojzOB97Nj5DNaOXXZZ+/kRkD8xdtl6LWbNaN5k/v2nnDz7YBNW/MxzxAnCK\neP8U9lsfri7nmvYjsAzIB1KaBJb2nPbCygFMQ2STwjdJpPyEoNhtIMP/8VpAvhfHOwATVfVHtYXO\np7FgslQOwhZXUdX3gVVCHfJiEWE7YA3a+pFc2uJU1hs7mQazmuq3B3TWZ57/APtP3YI+sqEqT6ty\nQimUBurcPMJCqXjfSLzfDPvPfrQ6Nx9bmB6L+frfhL1UXwFGoKpq9S/OA+4RoX6W0wzHyruC1Vnf\nHbg1XNdqmJvu+bp8LY371mTaUVNYt70qP6TciO6YgjlwqdIAUP0N6NFo4cLBLf/6awBwiSxZsvfh\nb7yx2Z6ffMLH7dv/jtUbfwJbKB8f/t335csvnw28iaUcuQI4P+tLR/VrVNudxKNXLKL+3iGLJ0uv\nZeAGQ5je8DeGvf1ayoDmbVVOwOJILgZGhxiZykf1ByxuYjMszcN5ZInQL6Gf6cCeQMETe+21//wG\nDc5rNmfOjCytX8NcrW8FRqtz75XhfIop6RsRKX5AKHIw8A1WPnhrYCUsM0H5EdkWkeqbNbnq2Bcr\nPZDuXFI7yPM06QjgoZS/jwf6p7V5mRTzBvbC3y7TlGvb07ibPmwapokDkEWX04fL6MPn9MlgIujD\ng/Qhp8SGS49NkiEkyU0kyeckSQ+1IbEofKyw39K2sLKa6+x+y2RVAR0BemHG/mELhR9Cf28p9AjH\n1QMdDXpLluNeV+gWvtdXOEbhd4XsC+Vwh8KLCtsofBhs5SWbZeA8hfma9nsV/3vrV6AdwvcGoG+C\n3s6Y5FKSJOMaQrjmc0H/AD2xytY/7P49pOaCW7cUz0OjTNvXefZZJ6NH/0iS1Cvh+KEkyWySZO1y\nyCzh9zulmDadFabq8utv/RWuqIB7VldtfWeeQrnc4mv6B/Qx0DPyLUfJcqJlOS7fM47STo3TA1Qy\nHnfBu2wPjJY+Mp+r651K73rXY26K+2nvjCaClyg6w1n+xH3lJOkrfaSv1E3bdR428xiPzQAAdgBW\nITX/jLmJ7ojqiGWb0HDsZSJkcgP9CksPcinQHIuSBqvqtwCr85GJezDX0n7Az5jr8KGoji/mEi8D\n2mDlJe8HHKUzy4zEIr77lKJtIa8BnUOA4n3h+IsRXgX2T3WBLkSVRar0Axw2+xgUkr5VLqoLUT0V\n2BLVYotxiff7A7+L9wembd/ul5Ytn9Q6dS5R5xaVcMbbgFPVuV/LIbNi96hvMEWlCSodgMHYMzE2\nZc8LWMqU8tITc8++Eksds0ISZtX7sXz0f60i34WcfsHq4hbSGnvhFddm3bCtCF98weZ1v6Df4jr7\nbI+e2ITexzrtrcUppzHAU9JXWmpv/SN9p/SVM7B6C5OA16SvHKO9dTqAOjdFvN8d+FqdKzzHf4EH\nSPf116IyqPKdCA8Ct2PrBMu3F3kNiy7dF9XFIpyMpaPYUZVsL7LhmFnqb2BnVCcWc+2F5/oXkb3D\n9+kltE49bjwi66Fa0gsxldewuImZmJLdWZUl4vkSM5tuhinNDKfjKxE6YGajD0TYSZXS5ZgqD6rF\npnoI2QZuxNKnPCDeb05h4kMrrHOauuXrtWQ8jXNvV4C0oPoBIm9hGZqXRZPbms1LwMlYksdU3gLa\nhN9zcsZ+RbbDBiLnkalwlZmP+2DrYJOByxFpQ9H0LCsCHYCpqmS+l3lERBw2CCtfPxneaVWGmC32\nG8wW/CtWhvUYLbo4fpaqdhHLhnqXqhZZHBcRVXh/MXWuq8fiG7A1gNFpjbYANgQahs9b0ofbgFe1\ntz6yXNO+ciZwEfYfYQr2Eu8KHKa9dVyGi1kNS+HRDtU/S3f9NAnX/BzQN8xEAJghLfb8g5aXb8I3\n47Eqf82wnDUTMvdW/RGhIfAHMBvoqClBUSHB5I/qXImFaEQYgKnXnhUuo/cdgXnq3KelbH8MtsDc\nEUsN8xKmuLcADlPnxhZzeOUgsgHwIaaIW2HrhD2Bvqj+X5ZjHgE+RbVoBlZzYvkA+AdYGzi4yKxU\nZBAwDdVe4e/bAEG18mYelqKlHrY+Wm0Q4TqgniqX5luWkqiRSQ7VRqtnYcnmxgPPqOoEEekpIj1D\nm+HAJBGZCDyIjeozcifn/TaRja4HGpPuzSCyGZaXqAdBAQAjWs1mOFafYVnTvnIOpjQKtLf+oL11\nkfbWXpjpaKT0TYsMtrxLLwNDSqs07NqYgymmI4BrC4vTi7DDasy4axO+aYjNwI4F1q3JSgNALVHb\nTcDhmhZJi03r9y9lVxdjJq89S2yZA2H28CgwSrzfoRTt62MV+i5X51Sd+xmL9H4fcHlRGgCqkzCT\n1PdYluHVgGOyKg2jOHPVKcBc7Fm9FXgTkUMQ2RCRHRE5BfMCvCblmLuBExFpXr6LKZabsOusbuxP\nLTZTAfmP46jIRZ7V+OPOmTRduCtvLL/gbQudHyqclrb9yZkNeIA+/EOfEFjXh/PpwyT60CbjefrQ\ngz6Mpw9NFTZTeFktRqN7aRZRM8uuLUE/A70V9KYQi3FsXgLh8vX7WUXG2SRJydHZds86g04CLVuO\nqswyHEySfECSHESSTCNJti2h/Wkkyah837uMH2islseqdM+Qxav8rdAybftqIY5lq5RtnYLTwKTg\n+PGawh4Z+nxKIbMDSPmvb021aP7pChtU5b0FbQraNsu+dbDcVMU6Q1SXD2VcHK9V2XFVVRC5GzNH\nHUuhLVbkKiw2oDOpF2zpqD8//Ej+eGEz+mD1yk8F9tDeOoUsSF95qNFCVp17PTuKFVfpTwm28JLl\nZzVsDeAn4CxVppWnv5qIeD8GuEOde6VU7YVHgX9UrSBTqA+iWkYXSPH+bawm/XPi/SFYdb/9Mpmt\nxPvGWFDq4epciW7cNQKRZzG38YEp2x4AFqCae9Erke2xmcxGqC4oqXmOfd+OZY6eC6xUJvmK7Z7m\n2AzrS2CiKipCYywN/8XAYmBzVf5KO87eH5q2bllNqZGmqkriYqxi3YeIbIHINpgH0ymka0nVGUCP\nR4bSdtW59Mem5K44pRE4u8c4tvt+Veahelt5lYaJwnRVOqjSdUVUGoHRkJP56QLgcBHeFeE3YDrw\npQgtcz1xWNtYG3vRoc4Nxcyor4j3mUrMngt8WGuUhvEiheYqkcYhiPEQLIA0d1Q/whI13k1hJceK\nQGRN4CTMVNUfOB6RclWhFEFEaCTCLiIMwgZwZ2Lm7SkiPI2tYXbEFMpL4fypfTTH3j9PlkeWGkEp\npzPnlmZbtZpyQTe13EOTFE4o7tg/GzNodFum9jyAjUt1PpD5dfl6/2OYQZ8MU/SqvO4+bB/MZxvm\n+zco97UkyU5F0p+U+LvrhqC7BhNBHdDrQN8mLT9ZKc79AklyVobt95MkD6Vt+0/IS1alJpJK/0Az\nhdnBZPWvWtLFsqe9X9bnF1qR7wuLPeqX8vdghUtzft7smRkZ0hItBF0QYo0uBF0jtBEsqenJoNun\nHNscSya6S/i7TkiZU7qcZtXkU+S9WcpPqUxVIjJOVbdJ2/apqlZkneVykXHKZXWg9wduoLgLNZ/3\n/li6hrsx81NzbNG6K/AFcPrSPkQ6AzfVu5pzF9fhGWB37Z1DSopyIn2lCVZt7kzMa+ZdLLp8HrZI\n30t7V7BpoAoQ7+ths4Z26tzvZerDimY9g5Wi7WbvlRLP2x54G1hfnZuTtq8Z9vufrM6NDoWrPgRu\nU+cGlUXGao2N5v8F/i72/0xufbbFntGTUc1eqrh0fa2JOdJsjmU/AJGtMFf09SmlSUyEAiwrwn1Y\n/NJszbHKngiHYe7l22Bu+/tgZqoa83+vrKaqYhWHiByDefTsivl6F9IUWKyqFerVUh7KXMlq+U7a\nYdPyg4BFmEfKkLBtOKrXhHZjgIGoPi595UQs4KljpliQikD6imDlSbfH4liaYV5j9wLDtbcuDm22\nwNKzXK29tVTrBNUN8f5l4HF17pkSG2frw9Y63sCU6HWapWJjyjkfBKZlrLBn+ztj93pLrCzx2sBR\nKfE7kZIQ2Rkzhe2LZnBnL30/dwGgel7a9lGYh1WC1ac/BLiF1OqVS5tyEeY1eYIq/yu7KAh2TfWw\n1C07qJVJrjFUluJoA6yP2fIuYVkE9z/A55pb8FelUiGKY1lnawHTl45ebJTzPma//AFTJhsS/Mel\nr1yPjfj30t45lC4trTh9ZQss39XhmHvuH9o7c0Eh6StnATtqb81WoKlaI96fB2ymzp2W43GCJSXc\nDuhOgWuJJf5bC1s7GQUMUeXv0H5dbFB0PNAE6FjcLEe8H4zFabQHtlLnsuWdimRD5HDM4eBO4FZy\njb+wmK6HsJr009L2dcZKES/E4qLeCufZG9VPlzVjO2x9opNWQICeCOtihbSOVuWdktpXN8r63iw2\nclwt6vMnSshGKyLvqmrHXE9ebSmcAi/7eyoiB2Evn++BO9Me+quwBI3XYSOZimYfYIT21o9L0fZ5\n4FrpK421d5HqfTWB0ZgzQ6kJHk4DgM2x4LvbVTkX2EqENtiCe2fgOhEuYrT/mzo8DjzPhKZXcs42\nDRUqXBAAACAASURBVFhU5+8SjFrnY9UPT4xKo4yoDkHkQ+y3OgKR04EPS2USs8j3R4FDiigNYwRm\nlv6AwrrzIv8CQxHpgGrhoKAncH9FKA0AVX4WoU1pTKK1iYryqmpUQf1UX1Q/w4IH1wGWC6QKo/9L\ngROkbwmZScvG3lC6KbX21qnAJxRm1615fAmsLN63LU1j8b41NrqsC+yCzcr2Fe9DACk/qTJQla7A\nIdTRi5na6BmebP0wBa4V/91uMIvqXAs8UBiAmQl1bjrQXp0bna1NpBRYSpPO2Fric8CviDyDSE9E\nGmQ8RqQZNku4AtXMo3pbtX1zqdKwbc9haV+GINJAhGbYmuXAjH2UkRVNaUDtdMetPFSHAW1QnVVk\nV2+d9P/tnXecXGX1/98fIKA0QapAMARDLwaQAKGMIBAIHaR3RGlfxS9IFS5XEIiUnyBFCb2DdBBC\nQllpgoD0IjUKUr5UqUrJ+f1xnoVlM7s7szszd2b2vF+vfZGZee69Zy+79+xzyufgGj1r1PKSyjUD\n3oNyWxWHXQpsVUs7GkXKG9xGl7JcdXTMrY6OlbuLIKqjYyM8UX0psJ2VSh9ZqfQOXuTwK3V0rPmV\ncxv3cdMdJzCtvcGZwxfHS2+H4rmjZeg2/1xipMRO3WwLBoo/5M8FhuES9jfhM2zGTbVWmga4ELgd\ns/H9uFqGF1wcjYcmb2u1PEQzEo6jWroLGH6Vy6n9A3sV4EnL7J0+V37JVcAY5eqXimxKtBfJF45D\nHR2L4zpJlwJ3qaNj3TQH5Xf4X62bWal0fNeHupVKz+LCkZeoo+OLyj91dAxhesuY57+72BSNNeNc\nM943l37ZEPiRxPYS35Q4DQ9/nCCxbKO+8UGFO5AXkxPZCNg0hYQddxp/AGbG1aj7c40p+Njl3Rbg\npb3T+YIB0qvjkLRgL5+tVntzWp7LgU2Vq6cBTf1hHSoMU3WSFHzvATbofE+5tlKuycr1lnJ9pFyf\nKNfpyr8ciqVcw5TrMuD5VPJbFLcCa6qjY3W8AeswYDheMn0i8BpehvxdK5XKhi6sVLoNL1eeoI6O\n5dLbuwAvWqnUMdV6/yt0bDr/U8AUfLjR4cCJvYWxghrgzbhbA+ORFkwNgyfjQo0bVVpm28O5X3uB\n4ZN+wXEL4HnKYID0tePokHRgGvEKgKR5JV0I/LbLuh3rYl2LYZn9A0+er9nX2iqoOL/RjUvxX0SU\n6yd8Kd++KDAnXlL6MfCkch2mXEfjXb5P4DmSX3Q/oXJJeQ9x6BpipdILybar8RDUBVYqfW6l0qV4\nSexovBy23IyVrue5AtgTuEkdHaNxB3Roj+uNJ3A59LXN2MeMt/FE7rfo4oSDOmF2Lz6X5FL853UU\nsD4+02ZAbMZVU3Zn/AxGXUUXBw19lePOjpfiroJvFZfGq0uOA06z3sM2DaWm5bgDsSPXz4GlLbNd\na3CuOfBZIHNV29CnXN/Acy4n43Xta1tmz5dZtzCQ401fh1tm/1KuBYGHgO92yq+kpP81uFO8Cw8n\nXWuZ1UWxN2lFvWCl0qM1ONfG+MPoZiuVNulr/VTHi/XwP5SWbqXmrpbEw1M34M56rbQTGeAp+QYw\n+d/MOmFW3n/qi36soD59HF1Ovi++hX8FWNmsTy2nhtNEjmMB4BHgWwPt3lauLYEdLLMN+1xc/vhr\ngBHAOpZZ2eFXvRybAyMss21TzuM0PJm5A/4X/1p4D8Qwy6z+A5UGiDo6VgFeslKpXz+7EhOAm8yn\nEQb1RJoBmAbrvZw8hQ/VW4NnUhL4PTCLoSNICgG12MW0A3UROZQ0u6Q/4LHh9fAegZskNU3HeLNh\nmb2Mx8jXrsHpqs5vdGM3YKVqnUbiN8BqyjUaHwO6CrCVZfamZXatZfZTPBcxVZhSuWZTrm8NwO6a\nY6XSPf11Gon9gUMl5ulzZTAwzP7bl9NIHAi8JnGSxPLd81ASQ/By3EVxyaC/453lNR8ANtjoK8fx\nIK4IubyZ3Wze5r89cJSkS+puXetyGf2orlKuGZVrxvRv0f/8BuBJcsumLh2u8NgP8d6US3Al2LFl\ndhanAHuVqcI6Fw+RtQ1mPI7/5Xp5eiDVFWkQ9EYNgLST2APPYb2L94Q8KTFOYrU0l/4qXHNujBmd\n426PBv63x56RoCL6chxrmNlxXaVFzNv3V6H7hL0qkfRNSZMkPSNpoqRy0tVImizpUUkPSfrrQK7Z\nQK4ANqymHFa5ZsNDXG8o11240OI0QMPEE8twMS4et2HaSXXnDrz66PudbyjXanhSc03lXxZVtAk5\n8CG+G6sbEiPwv6QXqud1WpwfAG+ZcaUZGT6DZ2fgE/x3523gfWBT6zqfxZ9fT+ECpkE/6dVx9JTL\nMOeMAV77IGCSmS2Chzx6ms9rQMnMRprZigO8ZkOwzF7FRfa2q2S9ck2DC7TdBMwNHIHrgR1pWY0U\nSvuBZWaW2R6W2VSDjDo/x3cd+8AXu6Tj8bDOK7huVNtgxuf4/9MNJbat46U2xEU2T4ky4B75EV0U\nHJLa931mHGbGcsC8wPZmlNPD+h1Q08FPg43CJgBKehrf0bwuFxHsMLPFyqx7EVjBzN7q43xNkRzv\nRLnWxh+i3+3r4a9ch+DlnqVWk0NPu6p/4OqgqwAHAN/DK+/escyOKtC8uiCxDGnolBkDrvoqc/7b\n8GKEHDjMjKkUXgczaVDXs8C3u4SgqjnBtHgIfivMWiWKURdacQLgPPalWNnr0GPS0YBbJD0gaffG\nmFYTbsU1vEb3tki5foD/xf7DVnMaAJbZB/hcg58CxwD7J+2uiXhyv+1IzuJw6hCySqWj38N3n3sC\nJ0nMUuvrtDg7Atf0y2kAmH2O75SrEtMMvqSujiPlMB4r87VR13Xm256e/iofnYZIrQfs3Sod6+nh\neRrevVwW5VoD1+HZrp+VT83CaXjl1ZOWWWfu605gpHLNWpxZdeVsYGmJkX2urI51gLvM+NCMO/Di\niLJ9BxIzSBwqMXeNbWhaUujuK2GqfnI2sEEamRBUSV0dh5mtbWZLl/m6DugMUSGff1F2FoIliXMz\newPvJO4xzyHpiC5fpZp/Q9VzHq4Z9ZUfTuWaRrkOxKuvduzysG1J0vTDY3Dn0fneR7gMeakgs+pK\nmhZ3Il4SWkvGAn/q8voAYNs0R6I7e+EP0Uclthkk+ZBV8LlAdw/oLGbv4L9/g6o0V1Kp63Oy3+cp\nMMfxG+AtMxsn6SBgNjM7qNuaGYFpzex9+XjXiUBuZhPLnK+pchydKNcZwEuW2ZHp9Xx4WeeceF9E\n0zVT1grlOgBY0DLbp2hb6kEKIb0ArGzGczU43zS4DtcoM17s8v5OuHLDip3JXonZgb/jjnkm4Bw8\nbr+3Ga28e50KiWnxgXJL4uGlCWYcX4MTL4nv6IYNSAurhWnFHMexwNqSnsFlLI4FkDSfpM6/uOYF\n7pT0MD6B74ZyTqPJORX4iXKtr1xX41pQT+KJ8LZ1Gom2zXMAmPE+Pq96Kl2vfvI94I2uTiNxPr4j\n36/Le4cCV5vxpBn34xVsDwOPSPxComX7FCSGShwgcb7Eg3iF4S347uABXD9s4Jg9gZfmbl6T8w0i\nCttx1Jpm3XEAKNdt+JzwM4BL+tuU12qkMuNX8e717g/DtkBiTuAZYMmBznmQOBKY3mzq8JfEMPyh\nuQo+HvWBdM3Xuq37Dt58uRBelXU38HKrDBtKu4t78N1UB+kPreSk63HBzYGfYbZ6Xc7f5NRVq6oV\naHLHoSL7MYpEuS4E7rBswH0/TYvEScB/zTigimOWwXcQtwEXmDFF4m/AvikpXu6YnwGb4c74SbMe\nk+bC51vsju9iOodj7dRDX0PTILEHrk6xem8aVDW84BC8nHzttAMZVLRiqGrQMFidRmIitdHtamaO\nB3aVWLSvhRLDJM7HY+vP4gnueyQ2xkUky49GdU4BZgBWx2XHy5Ka4a41YwM83Lsi3lldF2n4lJup\nxXnmwSvI9myI0wAw+xSv0NqjIddrE8JxBPVmIrBWwYOh6ooZL+FhoXNSqKUsEkvi+m+TgRFmHAWs\njBdLnIGr737W0/Gpc30rXEbjwwptMzP+iTudmlUQSUwjsarE6cAbEtd119eSWEziMYm7JY6X2CL1\nqfTE8cC5ZjxWKzsr5ExgO9S+P6O1JhxHUFcss9fwno56SnQ0A6fiOkk/62XNgcDxZhxuxnsAZkwx\n41x8R7BXXxcx4x9m3NcP+64AlpcY3o9jv4LEynhS+Q/4zJdRwH+AayS+ntYsh+vZnQz8Ep/7vQsu\nRLhxmXOuie+kGj8rw+yfuNz61g2/dosSOY6g7ijXOniX9ch2Dtulh/J9wKpm/L3bZ8Pw3cbwfnc8\nDxCJE/FczMFd3lsXWB/PrfQujeO7qQNx57gH3r1t6bPp8L6lefD/1xcCe3SXS5FYDW++ewDXZFse\nr7zbANjNjGsH/p32A2k94EjMVijk+gUROY6gmbkF7zVYuWhD6okZL9BzyGo/YHxRTiNxBrBLZ6mu\nxLfxUt8NcGXZryAxm8RoiY0ldsPzMusAy5txdVdHk0JsOwIv0znyt4zGlhl3AssC/8IrvrYC/or3\nrhTjNJyJwBxIg8px9JfYcQQNIY3UXcEyq0gxuFVJieIb8cqn3VK11Ny4PP4S3ctnC7Dvdrz35Bpc\nFv8K4Ga86molM55P6xbFH6av4j0kb+J9IqemXEtP558GmMuM13ta07R4I/IoYDPq/WB0oUWj4PHb\nUY4bjqOpUa7Z8S7rxSyz1nuoVIHETLjzeAZPSP8KmMOMPQs1DJDYGi/TfRTPq2xshknsC2yJ5xmW\nwed+H2rGOYUZ22hcqeJW4HbMDuljraZyLj4v/RRgEmZX93LsfLiszIOY/agK+xYGdgLGYVZRcUTf\np4xQVdDEWGbvAFfi2kptTap42gBYAg8P7YHLzDcDVwNLA5vgfR2dD7+TgQ/wPMUEYJ9B5TQAzD7C\nZ6FsgdSzk5d+DDyB1L3Q4Bi8f2a/qQ/64tjF8BDddUAJabOKbJOWwmf8rAbcl85TGOE4gkZyGrCH\nck1XtCH1JnU6r4c/pG9O+Y/CSeKMewKbmPFOl/en4HmOpYBtB+0MELM3gTHAYUhTVX8hDQN+jTvg\nu5C+l97fB9gYT/aPQFqkzLGr4N3wOWYZsANwOi7y2rlmEaTrkI5M1wJpRTxPeAAuz3QScCdSYVVg\nEaoKGopy3QP81jK7vGhbGkFKRE9jxn+KtiWoAncINwK7YHZDek94gcAkzMYlx3Jm+toJGI3Zi0gn\nAP/9SrhLmgd4HNgRs5u6vJ/jeZX1gG1wp3AcMD8+bfJvwEhgV8yu73LcSHym+q8w6/fOMHIc4Tha\nAuUaiwtaLptmlgRBcyKNAq4FDsTsvBSi+hGwCmafdVkzHn+wP5DeWwoP9307DY0C6TTcmfy82zWG\n4D0kU4A5gC3TXHSQvg5sCjyP2dS9O9ISePhqDGYP9u9bDMcRjqMFSHPJ7weOscyuLNqeIOgVaXHc\nCVyMO43vY/Z4BcfdDxyG2YSUj7gTWIxyI7Cl7wA/xvtIqhNzlLbAO+5XSGG2Kg8PxxGOo0VQro2A\nI/GGwNh1BM2NNBQPW12I2bgKj9kLWAOzrZCuBe7EbOAzRMpfaxywHL7z6LFUuvyhUVUVtA7XA5/j\nFShB0NyYvQQsU7HTcC4B1kXaBC9vPqUutjmHAgL2r+M1vkLsOIJCUK5NgMPhi5Goo/FpdhdYZv8o\nyq4gqBnSpXjZ8y6YXVLnay2L994Mq2bX0XI7Dkk/lPSEpM8lLdfLujGSnpb0rKRaz3cOiuNa/Odv\nPN5VPR5YEPibcp2aRuzWBOWaQbk2U66htTpnEFTAqXhH/mV1v5LZI8AreClx3SkyVPUYXjFQdmgN\ngLwt/xT8ZiwBbCNPVgUtThI7/F88ZLULsIRl9mNgMeAj4HHlmkoGXLmWV667lOtk5VpTuYb0dA3l\nWkq5TsZ1kU7AVVqDoDGY3YnZ+g2UFTkDT7LXncJDVZJuB/Yzs7+V+WxlIDOzMen1QQBmdmyZtRGq\naiOU6zt4QvIK4FDLzJRrPVyU7xBgLjwMsDDwv5bZed2O3xA4C5f+PgeX/X4cGGpZbeQagqCpkGbG\nZe6XxuxflR3Sv+dms3fwzg+81OX1y3izTNDmWGbPKddoXJrhAuW6E1ee3cgy+0tadrRyLQlMUK4h\nltmZAMq1Ou40xlpm93eeU7nuBrbAZTWCoL0w+wDpMmBXvGqxbtTVcUiahI+u7M4h1rULsmeq2g5J\nOqLLyw4z66jm+KC5sMzeUK41gYtwuYXVLbNnuq15Qrm+D9yapEzuxXcp23Z1Gomz8PBYOI6gXTkD\nuBrp6HJJckklvAhlQDR7qGol4IguoaqDgSlWpiwuQlXtS2oaHGKZfdLLmuF4InIW4MflmgtTPuQl\nyjigepF2TZ9ZVqbzNwjqQdfmwz6XtmgDYHIc+1uZlnlJ0wF/B9bCKwb+CmxjZk+VWRuOY5CjXAsC\ni1tmN/ey5jjgc8vsoB4+Xxz4JvCIZfZBL+dZF1gBz8M83HWyYXJ0JSADhgLT407tgHaXlA+aAGl3\nYCxmm/S9tMUch6RNcSnnOYF/Aw+Z2XpyrfrxZjY2rVsP+C0wLXCWmR3Tw/nCcQR9olyL4bOwh1qW\n9Ib8/UXxUaZr4gnGJfCc2lXAId0cwyx4CfFNwBrAjMA9wBB80uG8wNeAo/Aw29fxnpWdgV9YZufW\n8VtsOZTrm/jQqEMss4lF29PyeJL8cWBfzK7pfWmLOY5aE44jqJSUJB8HPIQPLhoLrA2cCPzOMvsg\n5UsWAy4Ffm3Zlw1cynUUsKBltmN6vQi++/gYn2nxHvBgV8eU1i2NO5h5LLOP6vtdtgbKNS2+a/sU\nl80YGbuyGuBh/uuAFTGb3POycBzhOIKKUK5d8PGp7+N9RH8GzrfM3i2zdgV8Wtt3LbNXlevbuNT1\nspbZy/24dgdwnGX2pwF8C22Dch0DrAisi+/4lser4ULDbKBI++NVhKth9mn5JS3WOR4EBXI+PrBo\nbstsc8vs5HJOA8AyewCvVDkj5S7GASf3x2kkbgLW7+exbYVybYHPoNg67c5y4BvAz3s9MKiUE/FZ\n8UfX+sSx4wiCPlCu6fHCjLtxYcbF+ttEmMJV1wILd82btCvJ2S6Fj9LdAO/N+hBXB1gIWNeyLwtj\nlGsYfq/XT047GAjSnMCD+C75EuCGNCI3fRw7jiCoC6kMeCdczuHgAXaeP45XWU09WrQ9uR2PtX8L\nD0V9H9gS2AsY1dVpAFhmk4F9gbMGw4jhuuMzOpbF/x/sBry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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -206,7 +206,7 @@ "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "fragment" + "slide_type": "slide" } }, "source": [ @@ -226,7 +226,7 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 3, @@ -237,7 +237,7 @@ "data": { "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -286,12 +286,22 @@ "\n", "- kein Arbitrage\n", "- vollstÀndiger Markt\n", - "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ "** Grobskizze **\n", "\n", "- Replikationsportfolio aus handelbaren Assets (bei BS: die Aktie und Bankaccount)\n", "- Da kein Arbitrage Preis des Replikationsportfolio muss Preis des Derivates entsprechen\n", - "- Unter einem bestimmen Wahrscheinlichkeitsmaß (risikoneutrale) sind alle handelbare abdiskontierte Assets Martingale und für Martingale gilt die obere Formel" + "- Unter einem bestimmen Wahrscheinlichkeitsmaß (risikoneutrale) sind alle diskontierte handelbare Assets Martingale " ] }, { @@ -394,7 +404,7 @@ "\n", "$V(0) = S_0\\Phi(d1) - K \\exp(-rT) \\Phi (d2) $\n", "\n", - "mit $d1=$, $d2=$ und $\\Phi$ die Verteilungsfunktion der Standardnormalverteilung.\n" + "mit $d1=\\frac{\\log{\\frac{S_0}{K}}+(r + \\frac{\\sigma^2}{2})T}{\\sigma\\sqrt{T}}$, $d2=d1-\\sigma\\sqrt{T}$ und $\\Phi$ die Verteilungsfunktion der Standardnormalverteilung.\n" ] }, { @@ -455,7 +465,7 @@ "\n", "- $X_i = 1$, fall der Dart innerhalb des Kreises landet und sonst Null.\n", "\n", - "- Wir nehmen dass wir beim werfen nicht zielen und somit jeder Punkte auf der FlÀche mit der gleichen Wahrscheinlichkeit getroffen werden. Die Würfe sind unabhÀngig und die Wahrscheinlichkeit das ein Dart im Kreis landet liegt bei $\\frac{1}{4}\\pi$. \n", + "- Wir nehmen dass wir nicht \"zielen\" und somit jeder Punkte auf der FlÀche mit der gleichen Wahrscheinlichkeit getroffen werden. Die Würfe sind unabhÀngig und die Wahrscheinlichkeit das ein Dart im Kreis landet liegt bei $\\frac{1}{4}\\pi$. \n", "\n", "\n" ] @@ -501,7 +511,7 @@ "data": { "image/png": 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c7dSEJVARXqse4OrdgTh/dY0WjtM201WY7xuBHg36xaCje3N5UPTDqesjqXsW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\ntK18rFQq5nU5Tq//ktxpjZB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VtJVwUSllxSqaXSWraLVeFMfQOG47Utp0LHCaZfuS4L5pMSDTwNAyJne/wCE7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\nAtcdBS5A68bFsv/L1FdAV9CYh70G3ISWLoewr5B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wKvB+tCppi4Ty+GdFF2Mso/LoMAdN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\nv5lZw/wLu3WN6PB/0xoAAAAASUVORK5CYII=\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -689,6 +699,26 @@ { "cell_type": "code", "execution_count": 12, + "metadata": { + "collapsed": true, + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [], + "source": [ + "def pathGeneration(N, S_0, sigma, r, T, dt):\n", + " t = np.arange(0, T+dt, dt)\n", + " dW = np.sqrt(dt) * np.random.randn(N, len(t)-1)\n", + " S = np.zeros((N, len(t)))\n", + " S[:, 0] = S_0\n", + " S[:,1:] = S_0*np.cumprod(np.exp((r-sigma**2/2)*dt+sigma*dW), axis=1)\n", + " return t, S" + ] + }, + { + "cell_type": "code", + "execution_count": 13, "metadata": { "collapsed": false, "slideshow": { @@ -700,7 +730,7 @@ "data": { "image/png": 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q+w/y1ey1uG6eoPY1FXYo5CaeOOLZEZg0+n/f1q9/dFdMzNigdtGb6PDhQSrs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CIUna3lHuqhLcb14119bxSlwckWsb7S5g8Psm2F1qUgU/v20OeDRMcIhZgdoEZBAS9oyS53VA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PPTrCvnRHz4NXcWd2hLjdHRBFx8GT45Q+TPS/rG8MdwHwr/CkebQL1RYdSYfwJn5yvE2yv1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\nG8bODJw/D5IpG44kojRNT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KcD9d4UzuWGSFj01YF6VQb66oPMtl9HClGM1oH6/sZSqSoI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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -708,14 +738,6 @@ } ], "source": [ - "def pathGeneration(N, S_0, sigma, r, T, dt):\n", - " t = np.arange(0, T+dt, dt)\n", - " dW = np.sqrt(dt) * np.random.randn(N, len(t)-1)\n", - " S = np.zeros((N, len(t)))\n", - " S[:, 0] = S_0\n", - " S[:,1:] = S_0*np.cumprod(np.exp((r-sigma**2/2)*dt+sigma*dW), axis=1)\n", - " return t, S\n", - "\n", "t, S1 = pathGeneration(100, 100, 0.1, 0.03, 1, 1/52.)\n", "\n", "for i in range(0, 100):\n", @@ -724,7 +746,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": { "collapsed": false, "scrolled": true, @@ -737,7 +759,7 @@ "data": { "image/png": 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Au9Zj3twY1bC0GX/DayS3vY4KXcKXN0h2LnS74FAE16UrVAyc52OBZu6p9JGU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\ns+fwwjaD1IX0+m24+j9C8L9DM07v83FSUL1NmvW/ucbah3+fv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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -765,7 +787,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": { "collapsed": false, "slideshow": { @@ -776,10 +798,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 14, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, @@ -787,7 +809,7 @@ "data": { "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -811,9 +833,12 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": { - "collapsed": true + "collapsed": true, + "slideshow": { + "slide_type": "skip" + } }, "outputs": [], "source": [ @@ -828,7 +853,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": { "collapsed": false, "slideshow": { @@ -1082,7 +1107,7 @@ "19 5.831976 4.371024 3.875515 3.999242 4.013709 4.016788 4.007910" ] }, - "execution_count": 16, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -1139,7 +1164,10 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": true + "collapsed": true, + "slideshow": { + "slide_type": "skip" + } }, "outputs": [], "source": [] diff --git a/StochProcess..Basics.slides.html b/StochProcess..Basics.slides.html index 3e26d56..ab02bcd 100644 --- a/StochProcess..Basics.slides.html +++ b/StochProcess..Basics.slides.html @@ -336,7 +336,7 @@

Was ist ist ein stochastischer
-
<matplotlib.text.Text at 0x6f385f8>
+
<matplotlib.text.Text at 0x73286d8>
@@ -346,524 +346,461 @@

Was ist ist ein stochastischer
@@ -944,7 +881,9 @@

Beispiel Call Option + +
+
@@ -955,8 +894,7 @@

Beispiel Call Option +

@@ -980,7 +918,7 @@

Beispiel Call Option -
[<matplotlib.lines.Line2D at 0x768ebe0>]
+
[<matplotlib.lines.Line2D at 0x7ac8898>]

@@ -1122,17 +1060,28 @@

Aber was ist der Wert
  • kein Arbitrage
  • vollstΓ€ndiger Markt
    • + +

    +
    + +
    + +
    +
    +
    +
    +

    Grobskizze

    • Replikationsportfolio aus handelbaren Assets (bei BS: die Aktie und Bankaccount)
    • Da kein Arbitrage Preis des Replikationsportfolio muss Preis des Derivates entsprechen
      • -
    • Unter einem bestimmen Wahrscheinlichkeitsmaß (risikoneutrale) sind alle handelbare abdiskontierte Assets Martingale und fΓΌr Martingale gilt die obere Formel
      • +
    • Unter einem bestimmen Wahrscheinlichkeitsmaß (risikoneutrale) sind alle diskontierte handelbare Assets Martingale
    -
    +
    @@ -1157,7 +1106,7 @@

    Aber was ist der Wert

    Black Scholes SDGL

    -
    +
    @@ -1171,7 +1120,7 @@

    Black Scholes SDGL +

    @@ -1198,7 +1147,7 @@

    Black Scholes SDGL +

    @@ -1213,7 +1162,7 @@

    Black Scholes SDGL +

    @@ -1222,12 +1171,12 @@

    Black Scholes SDGL

    In diesem Modell gibt es sogar geschlossene Formen fΓΌr die Preise von einer Calloption:

    $V(0) = S_0\Phi(d1) - K \exp(-rT) \Phi (d2) $

    -

    mit $d1=$, $d2=$ und $\Phi$ die Verteilungsfunktion der Standardnormalverteilung.

    +

    mit $d1=\frac{\log{\frac{S_0}{K}}+(r + \frac{\sigma^2}{2})T}{\sigma\sqrt{T}}$, $d2=d1-\sigma\sqrt{T}$ und $\Phi$ die Verteilungsfunktion der Standardnormalverteilung.

    -
    +
    @@ -1251,7 +1200,7 @@

    Black Scholes SDGLMonte Carlo Methoden

    -
    +
    @@ -1283,7 +1232,7 @@

    Beispiel 1:Berechnung von $\pi$

  • $X_i = 1$, fall der Dart innerhalb des Kreises landet und sonst Null.

    • -

  • Wir nehmen dass wir beim werfen nicht zielen und somit jeder Punkte auf der FlΓ€che mit der gleichen Wahrscheinlichkeit getroffen werden. Die WΓΌrfe sind unabhΓ€ngig und die Wahrscheinlichkeit das ein Dart im Kreis landet liegt bei $\frac{1}{4}\pi$.

    +

  • Wir nehmen dass wir nicht "zielen" und somit jeder Punkte auf der FlΓ€che mit der gleichen Wahrscheinlichkeit getroffen werden. Die WΓΌrfe sind unabhΓ€ngig und die Wahrscheinlichkeit das ein Dart im Kreis landet liegt bei $\frac{1}{4}\pi$.

    • @@ -2579,7 +2528,7 @@

    Beispiel 2. Calloptio

    -
    +
    @@ -2593,8 +2542,21 @@

    Beispiel 2. Calloptio S[:, 0] = S_0 S[:,1:] = S_0*np.cumprod(np.exp((r-sigma**2/2)*dt+sigma*dW), axis=1) return t, S +

    -t, S1 = pathGeneration(100, 100, 0.1, 0.03, 1, 1/52.) +
    +
    +
    + +
    +
    + +
    +
    +
    In [13]:
    +
    +
    +
    t, S1 = pathGeneration(100, 100, 0.1, 0.03, 1, 1/52.)
 
 for i in range(0, 100):
     plt.plot(t, S1[i,:])
@@ -4784,7 +4746,7 @@ 
    Beispiel 2. Calloptio
     
 
    
 
    
-
    In [13]:
    
+
    In [14]:
    
 
    
     
    
 
    N = 10000
@@ -5954,11 +5916,11 @@ 
    Beispiel 2. Calloptio
 
    Güte der Simulation in Abhängigkeit der Sitchprobe
    
 
    
 
    
-
    
+
    
     
 
    
 
    
-
    In [14]:
    
+
    In [15]:
    
 
    
     
    
 
    N = np.linspace(100,2e6, 70)
@@ -5983,11 +5945,11 @@ 
    Güte
 
    
 
 
-
    Out[14]:
    
+
    Out[15]:
    
 
 
 
    
-
    <matplotlib.legend.Legend at 0x98d2e80>
    
+
    <matplotlib.legend.Legend at 0x9d55d30>
    
 
    
 
 
    
@@ -6160,11 +6122,11 @@ 
    Güte
 
    
 
 
    
-    
    
-        
+    
    
+    
 
    
 
    
-
    In [15]:
    
+
    In [16]:
    
 
    
     
    
 
    N = np.array([10**i for i in range(1,8)])
@@ -6181,13 +6143,13 @@ 
    Güte
 
    
 
 
    
-
    +
    -
    In [16]:
    +
    In [17]:
    import pandas as pd
@@ -6202,7 +6164,7 @@ 
    Güte
 
    
 
 
-
    Out[16]:
    
+
    Out[17]:
    
 
 
    
 
    
@@ -6442,7 +6404,7 @@ 
    Güte
 
    Varianz in Abhängigkeit der Sitchprobe
    
 
    
 
    
-
    
+
    
     
 
    
 
    
@@ -6480,8 +6442,8 @@ 
    Varianz in Abhängigkei
 
    
 
 
    
-    
    
-        
+    
    
+    
 
    
 
    
 
    In [ ]:
    
@@ -6495,7 +6457,7 @@ 
    Varianz in Abhängigkei
 
    
 
 
    
-        
    
+
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z^oxR{@RRcTc>EMDy{5c=bN!1Vul8HYZ*Ly2bLTILi0Mztt0w`kpA`IjGVprS{=VG( zg#-W^KLG%*7r(Eg|Gq@{Yjn58U!wn9FuV?bJ;nP4;J5y{M}M2}y^j9R literal 0 HcmV?d00001 From fc06efbd0fc77035af019b4b65f538d7fdbb6914 Mon Sep 17 00:00:00 2001 From: Matthias Groncki Date: Thu, 26 May 2016 17:14:55 +0200 Subject: [PATCH 13/25] Added AMC Pricing for Bermuda Swaptions --- ...ng-Swaption-Part_II-Copy1-checkpoint.ipynb | 834 +++++++++++++++++ ...oPricing-Swaption-Part_II-checkpoint.ipynb | 830 +++++++++++++++++ ...onteCarloPricing-Swaption-checkpoint.ipynb | 667 ++++++++++++++ ...eCarloPricing-Swaption-Part_II-Copy1.ipynb | 841 ++++++++++++++++++ MonteCarloPricing-Swaption-Part_II.ipynb | 830 +++++++++++++++++ MonteCarloPricing-Swaption.ipynb | 20 +- 6 files changed, 4018 insertions(+), 4 deletions(-) create mode 100644 .ipynb_checkpoints/MonteCarloPricing-Swaption-Part_II-Copy1-checkpoint.ipynb create mode 100644 .ipynb_checkpoints/MonteCarloPricing-Swaption-Part_II-checkpoint.ipynb create mode 100644 .ipynb_checkpoints/MonteCarloPricing-Swaption-checkpoint.ipynb create mode 100644 MonteCarloPricing-Swaption-Part_II-Copy1.ipynb create mode 100644 MonteCarloPricing-Swaption-Part_II.ipynb diff --git a/.ipynb_checkpoints/MonteCarloPricing-Swaption-Part_II-Copy1-checkpoint.ipynb b/.ipynb_checkpoints/MonteCarloPricing-Swaption-Part_II-Copy1-checkpoint.ipynb new file mode 100644 index 0000000..53bd87c --- /dev/null +++ b/.ipynb_checkpoints/MonteCarloPricing-Swaption-Part_II-Copy1-checkpoint.ipynb @@ -0,0 +1,834 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "# Swaption Pricing Part 2: American Monte Carlo for Bermudas" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Copyright (c) 2015 Matthias Groncki*\n", + "\n", + "\n", + "Redistribution and use in source and binary forms, with or without\n", + "modification, are permitted provided that the following conditions are met:\n", + "\n", + " - Redistributions of source code must retain the above copyright notice,\n", + " this list of conditions and the following disclaimer.\n", + "\n", + "THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n", + "AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n", + "IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\n", + "DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE LIABLE\n", + "FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\n", + "DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\n", + "SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\n", + "CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\n", + "OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\n", + "OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n", + "\n", + "This disclaimer is taken from the QuantLib license" + ] + }, + { + "cell_type": "code", + "execution_count": 455, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# import the used libraries\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import statsmodels.api as sm\n", + "import QuantLib as ql\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 456, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Setting evaluation date\n", + "today = ql.Date(7,4,2015)\n", + "ql.Settings.instance().setEvaluationDate(today)" + ] + }, + { + "cell_type": "code", + "execution_count": 457, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Setup Marketdata\n", + "rate = ql.SimpleQuote(0.03)\n", + "rate_handle = ql.QuoteHandle(rate)\n", + "dc = ql.Actual365Fixed()\n", + "yts = ql.FlatForward(today, rate_handle, dc)\n", + "yts.enableExtrapolation()\n", + "hyts = ql.RelinkableYieldTermStructureHandle(yts)\n", + "t0_curve = ql.YieldTermStructureHandle(yts)\n", + "euribor6m = ql.Euribor6M(hyts)\n", + "cal = ql.TARGET()" + ] + }, + { + "cell_type": "code", + "execution_count": 458, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Setup a dummy portfolio with two Swaps\n", + "def makeSwap(start, maturity, nominal, fixedRate, index, typ=ql.VanillaSwap.Payer):\n", + " \"\"\"\n", + " creates a plain vanilla swap with fixedLegTenor 1Y\n", + " \n", + " parameter:\n", + " \n", + " start (ql.Date) : Start Date\n", + " \n", + " maturity (ql.Period) : SwapTenor\n", + " \n", + " nominal (float) : Nominal\n", + " \n", + " fixedRate (float) : rate paid on fixed leg\n", + " \n", + " index (ql.IborIndex) : Index\n", + " \n", + " return: tuple(ql.Swap, list) Swap and all fixing dates\n", + " \n", + " \n", + " \"\"\"\n", + " end = ql.TARGET().advance(start, maturity)\n", + " fixedLegTenor = ql.Period(\"1y\")\n", + " fixedLegBDC = ql.ModifiedFollowing\n", + " fixedLegDC = ql.Thirty360(ql.Thirty360.BondBasis)\n", + " spread = 0.0\n", + " fixedSchedule = ql.Schedule(start,\n", + " end, \n", + " fixedLegTenor, \n", + " index.fixingCalendar(), \n", + " fixedLegBDC,\n", + " fixedLegBDC, \n", + " ql.DateGeneration.Backward,\n", + " False)\n", + " floatSchedule = ql.Schedule(start,\n", + " end,\n", + " index.tenor(),\n", + " index.fixingCalendar(),\n", + " index.businessDayConvention(),\n", + " index.businessDayConvention(),\n", + " ql.DateGeneration.Backward,\n", + " False)\n", + " swap = ql.VanillaSwap(typ, \n", + " nominal,\n", + " fixedSchedule,\n", + " fixedRate,\n", + " fixedLegDC,\n", + " floatSchedule,\n", + " index,\n", + " spread,\n", + " index.dayCounter())\n", + " return swap, [index.fixingDate(x) for x in floatSchedule][:-1]\n", + "\n", + "\n", + "def makeSwaption(swap, callDates, settlement):\n", + " if len(callDates) == 1:\n", + " exercise = ql.EuropeanExercise(callDates[0])\n", + " else:\n", + " exercise = ql.BermudanExercise(callDates)\n", + " return ql.Swaption(swap, exercise, settlement)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lets start with an european swaption" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a forward starting plain vanilla swap using the helper function above and create a european swaption which allows us to to enter the swap in one year from today." + ] + }, + { + "cell_type": "code", + "execution_count": 459, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "settlementDate = today + ql.Period(\"1Y\")\n", + "\n", + "swaps = [makeSwap(settlementDate,\n", + " ql.Period(\"5Y\"),\n", + " 1e6,\n", + " 0.03047096,\n", + " euribor6m)\n", + " ]\n", + "\n", + "calldates = [euribor6m.fixingDate(settlementDate), euribor6m.valueDate(ql.Date(5,4,2017))]\n", + " \n", + "swaptions = [makeSwaption(swap, \n", + " calldates, \n", + " ql.Settlement.Physical) \n", + " for swap, fd in swaps]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pricing of the underlying at time 0 using the QuantLib pricing engine" + ] + }, + { + "cell_type": "code", + "execution_count": 460, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Swap NPV at time 0: 0.0091\n" + ] + } + ], + "source": [ + "#%%timeit\n", + "# Setup pricing engine and calculate the npv of the underlying swap\n", + "engine = ql.DiscountingSwapEngine(hyts)\n", + "for swap, fixingDates in swaps:\n", + " swap.setPricingEngine(engine)\n", + " print(\"Swap NPV at time 0: %.4f\" % swap.NPV())\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Setup the Gaussian Shortrate model (a.k.a Hull White model)\n", + "\n", + "Don't worry about calibration, assume we know the calbriated model parameter" + ] + }, + { + "cell_type": "code", + "execution_count": 461, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Stochastic Process " + ] + }, + { + "cell_type": "code", + "execution_count": 462, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Assume the model is already calibrated either historical or market implied\n", + "volas = [ql.QuoteHandle(ql.SimpleQuote(0.0075)),\n", + " ql.QuoteHandle(ql.SimpleQuote(0.0075))]\n", + "meanRev = [ql.QuoteHandle(ql.SimpleQuote(0.002))]\n", + "model = ql.Gsr(t0_curve, [today+100], volas, meanRev, 16.)\n", + "process = model.stateProcess()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Calculate the swaption price using an integral pricing engine" + ] + }, + { + "cell_type": "code", + "execution_count": 463, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "swaptionEngine = ql.Gaussian1dSwaptionEngine(model)" + ] + }, + { + "cell_type": "code", + "execution_count": 464, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Swaption NPV : 17068.93\n" + ] + } + ], + "source": [ + "for swaption in swaptions:\n", + " swaption.setPricingEngine(swaptionEngine)\n", + " print(\"Swaption NPV : %.2f\" % swaption.NPV())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "###Pricing with an Monte Carlo method" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "####Create a swap path pricer in Python \n", + "\n", + "Convert all Dates into times in years (using the same DayCounter as in the yieldTermStructure and store all fix cashflows in a numpy array." + ] + }, + { + "cell_type": "code", + "execution_count": 465, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "mcDC = yts.dayCounter()\n", + "\n", + "def timeFromReferenceFactory(daycounter, ref):\n", + " \"\"\"\n", + " returns a function, that calculate the time in years\n", + " from a the reference date *ref* to date *dat* \n", + " with respect to the given DayCountConvention *daycounter*\n", + " \n", + " Parameter:\n", + " dayCounter (ql.DayCounter)\n", + " ref (ql.Date)\n", + " \n", + " Return:\n", + " \n", + " f(np.array(ql.Date)) -> np.array(float)\n", + " \"\"\"\n", + " def impl(dat):\n", + " return daycounter.yearFraction(ref, dat)\n", + " return np.vectorize(impl)\n", + "\n", + "timeFromReference = timeFromReferenceFactory(mcDC, today)" + ] + }, + { + "cell_type": "code", + "execution_count": 466, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def getFixedLeg(swap, t):\n", + " \"\"\"\n", + " returns all future payment times and amount of the fixed leg of the underlying swap\n", + " \n", + " Parameter:\n", + " swap (ql.Swap)\n", + " t (float) \n", + " \n", + " Return:\n", + " (np.array, np.array) (times, amounts)\n", + "\n", + " \"\"\"\n", + " fixed_leg = swap.leg(0)\n", + " n = len(fixed_leg)\n", + " fixed_times=[]\n", + " fixed_amounts=[]\n", + " npv = 0\n", + " for i in range(n):\n", + " cf = fixed_leg[i]\n", + " t_i = timeFromReference(cf.date())\n", + " if t_i > t:\n", + " fixed_times.append(t_i)\n", + " fixed_amounts.append(cf.amount())\n", + " return np.array(fixed_times), np.array(fixed_amounts)\n", + "\n", + "\n", + "def getFloatingLeg(swap, t):\n", + " \n", + " float_leg = swap.leg(1)\n", + " n = len(float_leg)\n", + " float_times = []\n", + " float_dcf = []\n", + " accrual_start_time = []\n", + " accrual_end_time = []\n", + " nominals = []\n", + " for i in range(n):\n", + " # convert base classiborstart_idx Cashflow to\n", + " # FloatingRateCoupon\n", + " cf = ql.as_floating_rate_coupon(float_leg[i])\n", + " value_date = cf.referencePeriodStart()\n", + " t_fix_i = timeFromReference(value_date)\n", + " t_i = timeFromReference(cf.date()) \n", + " if t_fix_i >= t:\n", + " iborIndex = cf.index()\n", + " \n", + " index_mat = cf.referencePeriodEnd()\n", + " # year fraction\n", + " float_dcf.append(cf.accrualPeriod())\n", + " # calculate the start and end time\n", + " accrual_start_time.append(t_fix_i)\n", + " accrual_end_time.append(timeFromReference(index_mat))\n", + " # payment time\n", + " float_times.append(t_i)\n", + " # nominals \n", + " nominals.append(cf.nominal())\n", + " return np.array(float_times), np.array(float_dcf), np.array(accrual_start_time), np.array(accrual_end_time), np.array(nominals)\n", + "\n", + "\n", + "def swapPathNPV(swap, t):\n", + " fixed_times, fixed_amounts = getFixedLeg(swap, t)\n", + " float_times, float_dcf, accrual_start_time, accrual_end_time, nominals = getFloatingLeg(swap, t)\n", + " df_times = np.concatenate([fixed_times, \n", + " accrual_start_time, \n", + " accrual_end_time, \n", + " float_times])\n", + " df_times = np.unique(df_times)\n", + " # Store indices of fix leg payment times in \n", + " # the df_times array\n", + " fix_idx = np.in1d(df_times, fixed_times, True)\n", + " fix_idx = fix_idx.nonzero()\n", + " # Indices of the floating leg payment times \n", + " # in the df_times array\n", + " float_idx = np.in1d(df_times, float_times, True)\n", + " float_idx = float_idx.nonzero()\n", + " # Indices of the accrual start and end time\n", + " # in the df_times array\n", + " accrual_start_idx = np.in1d(df_times, accrual_start_time, True)\n", + " accrual_start_idx = accrual_start_idx.nonzero()\n", + " accrual_end_idx = np.in1d(df_times, accrual_end_time, True)\n", + " accrual_end_idx = accrual_end_idx.nonzero()\n", + " # Calculate NPV\n", + " def calc(x_t):\n", + " discount = np.vectorize(lambda T: model.zerobond(T, t, x_t))\n", + " dfs = discount(df_times)\n", + " # Calculate fixed leg npv\n", + " fix_leg_npv = np.sum(fixed_amounts * dfs[fix_idx])\n", + " # Estimate the index fixings\n", + " index_fixings = (dfs[accrual_start_idx] / dfs[accrual_end_idx] - 1) \n", + " index_fixings /= float_dcf\n", + " # Calculate the floating leg npv\n", + " float_leg_npv = np.sum(nominals * index_fixings * float_dcf * dfs[float_idx])\n", + " npv = float_leg_npv - fix_leg_npv\n", + " return npv\n", + " return calc" + ] + }, + { + "cell_type": "code", + "execution_count": 467, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.99726027, 2.00273973])" + ] + }, + "execution_count": 467, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Convert call date to time\n", + "callTimes = timeFromReference(calldates)\n", + "callTimes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "####Test the t0 Swap Pricing" + ] + }, + { + "cell_type": "code", + "execution_count": 468, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Swap NPV at time 0: 0.0091\n", + "Error : 0.00000000\n" + ] + } + ], + "source": [ + "npv = swapPathNPV(swap, 0.)(0)\n", + "print(\"Swap NPV at time 0: %.4f\" % npv)\n", + "print(\"Error : %.8f\" % (npv - swap.NPV()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Monte Carlo Simulation\n", + "\n", + "#### Generate time grid and paths" + ] + }, + { + "cell_type": "code", + "execution_count": 569, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def fixingdates(swap):\n", + " leg = swap.leg(1)\n", + " n = len(leg)\n", + " fixing_dates = []\n", + " for i in range(0, n):\n", + " cf = ql.as_floating_rate_coupon(float_leg[i])\n", + " value_date = cf.referencePeriodStart()\n", + " fixing_dates.append(value_date)\n", + " return fixing_dates\n", + "\n", + "# Define evaluation grid\n", + "\n", + "date_grid = [today] + calldates \n", + "\n", + "date_grid = np.unique(np.sort(date_grid))\n", + "time_grid = np.vectorize(lambda x: ql.Actual365Fixed().yearFraction(today, x))(date_grid)\n", + "dt = time_grid[1:] - time_grid[:-1]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 570, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([Date(7,4,2015), Date(5,4,2016), Date(7,4,2017)], dtype=object)" + ] + }, + "execution_count": 570, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "date_grid" + ] + }, + { + "cell_type": "code", + "execution_count": 571, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Random number generator\n", + "seed = 1\n", + "urng = ql.MersenneTwisterUniformRng(seed)\n", + "usrg = ql.MersenneTwisterUniformRsg(len(time_grid)-1,urng)\n", + "generator = ql.InvCumulativeMersenneTwisterGaussianRsg(usrg)" + ] + }, + { + "cell_type": "code", + "execution_count": 572, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%%timeit\n", + "# Generate N paths\n", + "M = 100000\n", + "m = len(time_grid)\n", + "x = np.zeros((M, m))\n", + "y = np.zeros((M, m))\n", + "numeraires = np.zeros((M, m))\n", + " \n", + "for n in range(0, M):\n", + " numeraires[n, 0] = model.numeraire(0, 0)\n", + " \n", + "for n in range(0,M):\n", + " dWs = generator.nextSequence().value()\n", + " j = 1\n", + " for i in range(1, len(time_grid)):\n", + " t0 = time_grid[i-1]\n", + " t1 = time_grid[i]\n", + " e = process.expectation(t0, \n", + " x[n,i-1], \n", + " dt[i-1])\n", + " std = process.stdDeviation(t0,\n", + " x[n,i-1],\n", + " dt[i-1])\n", + " x[n,i] = e + dWs[i-1] * std \n", + " e_0_0 = process.expectation(0,0,t1)\n", + " std_0_0 = process.stdDeviation(0,0,t1)\n", + " y[n,i] = (x[n,i] - e_0_0) / std_0_0\n", + " numeraires[n, i] = model.numeraire(t1, y[n,i])\n", + " #df_times_temp = df_times.copy()\n", + " #df_times_temp[df_times_temp <= t1] = t1\n", + " #dfs[n,i] = np.vectorize(lambda T: model.zerobond(T, t1, y[n,i]))(df_times_temp)" + ] + }, + { + "cell_type": "code", + "execution_count": 573, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "\n", + " \n", + "pricer = np.vectorize(swapPathNPV(swap, time_grid[-1]))\n", + "\n", + "cont_value = pricer(y[:,-1]) / numeraires[:,-1]\n", + "cont_value[cont_value < 0] = 0\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 574, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0. , 0.99726027, 2.00273973])" + ] + }, + "execution_count": 574, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "time_grid" + ] + }, + { + "cell_type": "code", + "execution_count": 575, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "\n", + "\n", + "pricer = np.vectorize(swapPathNPV(swap, time_grid[-2]))\n", + "\n", + "\n", + "exercise_values = pricer(y[:,-2]) / numeraires[:,-2]\n", + "exercise_values[exercise_values < 0] = 0" + ] + }, + { + "cell_type": "code", + "execution_count": 611, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "states = y[:, -2]\n", + "Y = np.column_stack((states, states**2, states**3))\n", + "Y = sm.add_constant(Y)\n", + "ols = sm.OLS(cont_value, Y)\n", + "ols_result = ols.fit()\n", + "cont_value_hat = np.sum(ols_result.params * Y, axis=1)\n", + "npv_amc = np.maximum(cont_value_hat, exercise_values)" + ] + }, + { + "cell_type": "code", + "execution_count": 612, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "npv_amc = np.mean(npv_amc) * numeraires[0,0]" + ] + }, + { + "cell_type": "code", + "execution_count": 613, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "17127.228006267218" + ] + }, + "execution_count": 613, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "npv_amc" + ] + }, + { + "cell_type": "code", + "execution_count": 614, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MC NPV: 17127.2280\n", + "Error : 58.2987 \n", + "Rel. Error in bps: 0.5830 \n" + ] + } + ], + "source": [ + "print(\"MC NPV: %.4f\" % npv_amc)\n", + "print(\"Error : %.4f \" % (npv_amc-swaption.NPV()))\n", + "print(\"Rel. Error in bps: %.4f \" % ((npv_amc-swaption.NPV()) / 100))" + ] + }, + { + "cell_type": "code", + "execution_count": 615, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 615, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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EREQ62I4dO7j44oupqqqisLCQKVOm8MMf/rCrmyUSQ93BKVB3sIjInrG3dgeL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XtLF4EEBhIYEBCwiMgrJ8fpNx5Ir1dHk7PN+rmMXBPnrF1utMnMnEOeqsyaOUSdk8kbD1L1FUY0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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,10))\n", + "plt.scatter(states, cont_value)\n", + "plt.scatter(states, cont_value_hat, marker='x', c='red')\n", + "plt.legend([\"continuation values at t+1\", \"regression\"])\n", + "plt.title(\"Regression Bermudan Swaption\")\n", + "plt.xlabel(\"State at t\")\n", + "plt.ylabel(\"NPV\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/.ipynb_checkpoints/MonteCarloPricing-Swaption-Part_II-checkpoint.ipynb b/.ipynb_checkpoints/MonteCarloPricing-Swaption-Part_II-checkpoint.ipynb new file mode 100644 index 0000000..4df7972 --- /dev/null +++ b/.ipynb_checkpoints/MonteCarloPricing-Swaption-Part_II-checkpoint.ipynb @@ -0,0 +1,830 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "# Swaption Pricing Part 2: American Monte Carlo for Bermudas" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Copyright (c) 2015 Matthias Groncki*\n", + "\n", + "\n", + "Redistribution and use in source and binary forms, with or without\n", + "modification, are permitted provided that the following conditions are met:\n", + "\n", + " - Redistributions of source code must retain the above copyright notice,\n", + " this list of conditions and the following disclaimer.\n", + "\n", + "THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n", + "AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n", + "IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\n", + "DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE LIABLE\n", + "FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\n", + "DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\n", + "SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\n", + "CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\n", + "OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\n", + "OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n", + "\n", + "This disclaimer is taken from the QuantLib license" + ] + }, + { + "cell_type": "code", + "execution_count": 455, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# import the used libraries\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import statsmodels.api as sm\n", + "import QuantLib as ql\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 456, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Setting evaluation date\n", + "today = ql.Date(7,4,2015)\n", + "ql.Settings.instance().setEvaluationDate(today)" + ] + }, + { + "cell_type": "code", + "execution_count": 457, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Setup Marketdata\n", + "rate = ql.SimpleQuote(0.03)\n", + "rate_handle = ql.QuoteHandle(rate)\n", + "dc = ql.Actual365Fixed()\n", + "yts = ql.FlatForward(today, rate_handle, dc)\n", + "yts.enableExtrapolation()\n", + "hyts = ql.RelinkableYieldTermStructureHandle(yts)\n", + "t0_curve = ql.YieldTermStructureHandle(yts)\n", + "euribor6m = ql.Euribor6M(hyts)\n", + "cal = ql.TARGET()" + ] + }, + { + "cell_type": "code", + "execution_count": 458, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Setup a dummy portfolio with two Swaps\n", + "def makeSwap(start, maturity, nominal, fixedRate, index, typ=ql.VanillaSwap.Payer):\n", + " \"\"\"\n", + " creates a plain vanilla swap with fixedLegTenor 1Y\n", + " \n", + " parameter:\n", + " \n", + " start (ql.Date) : Start Date\n", + " \n", + " maturity (ql.Period) : SwapTenor\n", + " \n", + " nominal (float) : Nominal\n", + " \n", + " fixedRate (float) : rate paid on fixed leg\n", + " \n", + " index (ql.IborIndex) : Index\n", + " \n", + " return: tuple(ql.Swap, list) Swap and all fixing dates\n", + " \n", + " \n", + " \"\"\"\n", + " end = ql.TARGET().advance(start, maturity)\n", + " fixedLegTenor = ql.Period(\"1y\")\n", + " fixedLegBDC = ql.ModifiedFollowing\n", + " fixedLegDC = ql.Thirty360(ql.Thirty360.BondBasis)\n", + " spread = 0.0\n", + " fixedSchedule = ql.Schedule(start,\n", + " end, \n", + " fixedLegTenor, \n", + " index.fixingCalendar(), \n", + " fixedLegBDC,\n", + " fixedLegBDC, \n", + " ql.DateGeneration.Backward,\n", + " False)\n", + " floatSchedule = ql.Schedule(start,\n", + " end,\n", + " index.tenor(),\n", + " index.fixingCalendar(),\n", + " index.businessDayConvention(),\n", + " index.businessDayConvention(),\n", + " ql.DateGeneration.Backward,\n", + " False)\n", + " swap = ql.VanillaSwap(typ, \n", + " nominal,\n", + " fixedSchedule,\n", + " fixedRate,\n", + " fixedLegDC,\n", + " floatSchedule,\n", + " index,\n", + " spread,\n", + " index.dayCounter())\n", + " return swap, [index.fixingDate(x) for x in floatSchedule][:-1]\n", + "\n", + "\n", + "def makeSwaption(swap, callDates, settlement):\n", + " if len(callDates) == 1:\n", + " exercise = ql.EuropeanExercise(callDates[0])\n", + " else:\n", + " exercise = ql.BermudanExercise(callDates)\n", + " return ql.Swaption(swap, exercise, settlement)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lets start with an european swaption" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a forward starting plain vanilla swap using the helper function above and create a european swaption which allows us to to enter the swap in one year from today." + ] + }, + { + "cell_type": "code", + "execution_count": 459, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "settlementDate = today + ql.Period(\"1Y\")\n", + "\n", + "swaps = [makeSwap(settlementDate,\n", + " ql.Period(\"5Y\"),\n", + " 1e6,\n", + " 0.03047096,\n", + " euribor6m)\n", + " ]\n", + "\n", + "calldates = [euribor6m.fixingDate(settlementDate), euribor6m.valueDate(ql.Date(5,4,2017))]\n", + " \n", + "swaptions = [makeSwaption(swap, \n", + " calldates, \n", + " ql.Settlement.Physical) \n", + " for swap, fd in swaps]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pricing of the underlying at time 0 using the QuantLib pricing engine" + ] + }, + { + "cell_type": "code", + "execution_count": 460, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Swap NPV at time 0: 0.0091\n" + ] + } + ], + "source": [ + "#%%timeit\n", + "# Setup pricing engine and calculate the npv of the underlying swap\n", + "engine = ql.DiscountingSwapEngine(hyts)\n", + "for swap, fixingDates in swaps:\n", + " swap.setPricingEngine(engine)\n", + " print(\"Swap NPV at time 0: %.4f\" % swap.NPV())\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Setup the Gaussian Shortrate model (a.k.a Hull White model)\n", + "\n", + "Don't worry about calibration, assume we know the calbriated model parameter" + ] + }, + { + "cell_type": "code", + "execution_count": 461, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Stochastic Process " + ] + }, + { + "cell_type": "code", + "execution_count": 462, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Assume the model is already calibrated either historical or market implied\n", + "volas = [ql.QuoteHandle(ql.SimpleQuote(0.0075)),\n", + " ql.QuoteHandle(ql.SimpleQuote(0.0075))]\n", + "meanRev = [ql.QuoteHandle(ql.SimpleQuote(0.002))]\n", + "model = ql.Gsr(t0_curve, [today+100], volas, meanRev, 16.)\n", + "process = model.stateProcess()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Calculate the swaption price using an integral pricing engine" + ] + }, + { + "cell_type": "code", + "execution_count": 463, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "swaptionEngine = ql.Gaussian1dSwaptionEngine(model)" + ] + }, + { + "cell_type": "code", + "execution_count": 464, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Swaption NPV : 17068.93\n" + ] + } + ], + "source": [ + "for swaption in swaptions:\n", + " swaption.setPricingEngine(swaptionEngine)\n", + " print(\"Swaption NPV : %.2f\" % swaption.NPV())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "###Pricing with an Monte Carlo method" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "####Create a swap path pricer in Python \n", + "\n", + "Convert all Dates into times in years (using the same DayCounter as in the yieldTermStructure and store all fix cashflows in a numpy array." + ] + }, + { + "cell_type": "code", + "execution_count": 465, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "mcDC = yts.dayCounter()\n", + "\n", + "def timeFromReferenceFactory(daycounter, ref):\n", + " \"\"\"\n", + " returns a function, that calculate the time in years\n", + " from a the reference date *ref* to date *dat* \n", + " with respect to the given DayCountConvention *daycounter*\n", + " \n", + " Parameter:\n", + " dayCounter (ql.DayCounter)\n", + " ref (ql.Date)\n", + " \n", + " Return:\n", + " \n", + " f(np.array(ql.Date)) -> np.array(float)\n", + " \"\"\"\n", + " def impl(dat):\n", + " return daycounter.yearFraction(ref, dat)\n", + " return np.vectorize(impl)\n", + "\n", + "timeFromReference = timeFromReferenceFactory(mcDC, today)" + ] + }, + { + "cell_type": "code", + "execution_count": 466, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def getFixedLeg(swap, t):\n", + " \"\"\"\n", + " returns all future payment times and amount of the fixed leg of the underlying swap\n", + " \n", + " Parameter:\n", + " swap (ql.Swap)\n", + " t (float) \n", + " \n", + " Return:\n", + " (np.array, np.array) (times, amounts)\n", + "\n", + " \"\"\"\n", + " fixed_leg = swap.leg(0)\n", + " n = len(fixed_leg)\n", + " fixed_times=[]\n", + " fixed_amounts=[]\n", + " npv = 0\n", + " for i in range(n):\n", + " cf = fixed_leg[i]\n", + " t_i = timeFromReference(cf.date())\n", + " if t_i > t:\n", + " fixed_times.append(t_i)\n", + " fixed_amounts.append(cf.amount())\n", + " return np.array(fixed_times), np.array(fixed_amounts)\n", + "\n", + "\n", + "def getFloatingLeg(swap, t):\n", + " \n", + " float_leg = swap.leg(1)\n", + " n = len(float_leg)\n", + " float_times = []\n", + " float_dcf = []\n", + " accrual_start_time = []\n", + " accrual_end_time = []\n", + " nominals = []\n", + " for i in range(n):\n", + " # convert base classiborstart_idx Cashflow to\n", + " # FloatingRateCoupon\n", + " cf = ql.as_floating_rate_coupon(float_leg[i])\n", + " value_date = cf.referencePeriodStart()\n", + " t_fix_i = timeFromReference(value_date)\n", + " t_i = timeFromReference(cf.date()) \n", + " if t_fix_i >= t:\n", + " iborIndex = cf.index()\n", + " \n", + " index_mat = cf.referencePeriodEnd()\n", + " # year fraction\n", + " float_dcf.append(cf.accrualPeriod())\n", + " # calculate the start and end time\n", + " accrual_start_time.append(t_fix_i)\n", + " accrual_end_time.append(timeFromReference(index_mat))\n", + " # payment time\n", + " float_times.append(t_i)\n", + " # nominals \n", + " nominals.append(cf.nominal())\n", + " return np.array(float_times), np.array(float_dcf), np.array(accrual_start_time), np.array(accrual_end_time), np.array(nominals)\n", + "\n", + "\n", + "def swapPathNPV(swap, t):\n", + " fixed_times, fixed_amounts = getFixedLeg(swap, t)\n", + " float_times, float_dcf, accrual_start_time, accrual_end_time, nominals = getFloatingLeg(swap, t)\n", + " df_times = np.concatenate([fixed_times, \n", + " accrual_start_time, \n", + " accrual_end_time, \n", + " float_times])\n", + " df_times = np.unique(df_times)\n", + " # Store indices of fix leg payment times in \n", + " # the df_times array\n", + " fix_idx = np.in1d(df_times, fixed_times, True)\n", + " fix_idx = fix_idx.nonzero()\n", + " # Indices of the floating leg payment times \n", + " # in the df_times array\n", + " float_idx = np.in1d(df_times, float_times, True)\n", + " float_idx = float_idx.nonzero()\n", + " # Indices of the accrual start and end time\n", + " # in the df_times array\n", + " accrual_start_idx = np.in1d(df_times, accrual_start_time, True)\n", + " accrual_start_idx = accrual_start_idx.nonzero()\n", + " accrual_end_idx = np.in1d(df_times, accrual_end_time, True)\n", + " accrual_end_idx = accrual_end_idx.nonzero()\n", + " # Calculate NPV\n", + " def calc(x_t):\n", + " discount = np.vectorize(lambda T: model.zerobond(T, t, x_t))\n", + " dfs = discount(df_times)\n", + " # Calculate fixed leg npv\n", + " fix_leg_npv = np.sum(fixed_amounts * dfs[fix_idx])\n", + " # Estimate the index fixings\n", + " index_fixings = (dfs[accrual_start_idx] / dfs[accrual_end_idx] - 1) \n", + " index_fixings /= float_dcf\n", + " # Calculate the floating leg npv\n", + " float_leg_npv = np.sum(nominals * index_fixings * float_dcf * dfs[float_idx])\n", + " npv = float_leg_npv - fix_leg_npv\n", + " return npv\n", + " return calc" + ] + }, + { + "cell_type": "code", + "execution_count": 467, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.99726027, 2.00273973])" + ] + }, + "execution_count": 467, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Convert call date to time\n", + "callTimes = timeFromReference(calldates)\n", + "callTimes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "####Test the t0 Swap Pricing" + ] + }, + { + "cell_type": "code", + "execution_count": 468, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Swap NPV at time 0: 0.0091\n", + "Error : 0.00000000\n" + ] + } + ], + "source": [ + "npv = swapPathNPV(swap, 0.)(0)\n", + "print(\"Swap NPV at time 0: %.4f\" % npv)\n", + "print(\"Error : %.8f\" % (npv - swap.NPV()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Monte Carlo Simulation\n", + "\n", + "#### Generate time grid and paths" + ] + }, + { + "cell_type": "code", + "execution_count": 569, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def fixingdates(swap):\n", + " leg = swap.leg(1)\n", + " n = len(leg)\n", + " fixing_dates = []\n", + " for i in range(0, n):\n", + " cf = ql.as_floating_rate_coupon(float_leg[i])\n", + " value_date = cf.referencePeriodStart()\n", + " fixing_dates.append(value_date)\n", + " return fixing_dates\n", + "\n", + "# Define evaluation grid\n", + "\n", + "date_grid = [today] + calldates \n", + "\n", + "date_grid = np.unique(np.sort(date_grid))\n", + "time_grid = np.vectorize(lambda x: ql.Actual365Fixed().yearFraction(today, x))(date_grid)\n", + "dt = time_grid[1:] - time_grid[:-1]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 570, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([Date(7,4,2015), Date(5,4,2016), Date(7,4,2017)], dtype=object)" + ] + }, + "execution_count": 570, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "date_grid" + ] + }, + { + "cell_type": "code", + "execution_count": 643, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Random number generator\n", + "seed = 1\n", + "urng = ql.MersenneTwisterUniformRng(seed)\n", + "usrg = ql.MersenneTwisterUniformRsg(len(time_grid)-1,urng)\n", + "generator = ql.InvCumulativeMersenneTwisterGaussianRsg(usrg)" + ] + }, + { + "cell_type": "code", + "execution_count": 644, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%%timeit\n", + "# Generate N paths\n", + "M = 100000\n", + "m = len(time_grid)\n", + "x = np.zeros((M, m))\n", + "y = np.zeros((M, m))\n", + "numeraires = np.zeros((M, m))\n", + " \n", + "for n in range(0, M):\n", + " numeraires[n, 0] = model.numeraire(0, 0)\n", + " \n", + "for n in range(0,M):\n", + " dWs = generator.nextSequence().value()\n", + " j = 1\n", + " for i in range(1, len(time_grid)):\n", + " t0 = time_grid[i-1]\n", + " t1 = time_grid[i]\n", + " e = process.expectation(t0, \n", + " x[n,i-1], \n", + " dt[i-1])\n", + " std = process.stdDeviation(t0,\n", + " x[n,i-1],\n", + " dt[i-1])\n", + " x[n,i] = e + dWs[i-1] * std \n", + " e_0_0 = process.expectation(0,0,t1)\n", + " std_0_0 = process.stdDeviation(0,0,t1)\n", + " y[n,i] = (x[n,i] - e_0_0) / std_0_0\n", + " numeraires[n, i] = model.numeraire(t1, y[n,i])\n", + " #df_times_temp = df_times.copy()\n", + " #df_times_temp[df_times_temp <= t1] = t1\n", + " #dfs[n,i] = np.vectorize(lambda T: model.zerobond(T, t1, y[n,i]))(df_times_temp)" + ] + }, + { + "cell_type": "code", + "execution_count": 645, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "\n", + " \n", + "pricer = np.vectorize(swapPathNPV(swap, time_grid[-1]))\n", + "\n", + "cont_value = pricer(y[:,-1]) / numeraires[:,-1]\n", + "cont_value[cont_value < 0] = 0\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 646, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0. , 0.99726027, 2.00273973])" + ] + }, + "execution_count": 646, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "time_grid" + ] + }, + { + "cell_type": "code", + "execution_count": 647, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "pricer = np.vectorize(swapPathNPV(swap, time_grid[-2]))\n", + "exercise_values = pricer(y[:,-2]) / numeraires[:,-2]\n", + "exercise_values[exercise_values < 0] = 0" + ] + }, + { + "cell_type": "code", + "execution_count": 648, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "states = y[:, -2]\n", + "Y = np.column_stack((states, states**2, states**3))\n", + "Y = sm.add_constant(Y)\n", + "ols = sm.OLS(cont_value, Y)\n", + "ols_result = ols.fit()\n", + "cont_value_hat = np.sum(ols_result.params * Y, axis=1)\n", + "npv_amc = np.maximum(cont_value_hat, exercise_values)" + ] + }, + { + "cell_type": "code", + "execution_count": 649, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "npv_amc = np.mean(npv_amc) * numeraires[0,0]" + ] + }, + { + "cell_type": "code", + "execution_count": 650, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "17127.228006267218" + ] + }, + "execution_count": 650, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "npv_amc" + ] + }, + { + "cell_type": "code", + "execution_count": 651, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MC NPV: 17127.2280\n", + "Error : 58.2987 \n", + "Rel. Error in bps: 0.5830 \n" + ] + } + ], + "source": [ + "print(\"MC NPV: %.4f\" % npv_amc)\n", + "print(\"Error : %.4f \" % (npv_amc-swaption.NPV()))\n", + "print(\"Rel. Error in bps: %.4f \" % ((npv_amc-swaption.NPV()) / 100))" + ] + }, + { + "cell_type": "code", + "execution_count": 652, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 652, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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EREQ62I4dO7j44oupqqqisLCQKVOm8MMf/rCrmyUSQ93BKVB3sIjInrG3dgeL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XtLF4EEBhIYEBCwiMgrJ8fpNx5Ir1dHk7PN+rmMXBPnrF1utMnMnEOeqsyaOUSdk8kbD1L1FUY0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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,10))\n", + "plt.scatter(states, cont_value)\n", + "plt.scatter(states, cont_value_hat, marker='x', c='red')\n", + "plt.legend([\"continuation values at t+1\", \"regression\"])\n", + "plt.title(\"Regression Bermudan Swaption\")\n", + "plt.xlabel(\"State at t\")\n", + "plt.ylabel(\"NPV\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/.ipynb_checkpoints/MonteCarloPricing-Swaption-checkpoint.ipynb b/.ipynb_checkpoints/MonteCarloPricing-Swaption-checkpoint.ipynb new file mode 100644 index 0000000..25b20e8 --- /dev/null +++ b/.ipynb_checkpoints/MonteCarloPricing-Swaption-checkpoint.ipynb @@ -0,0 +1,667 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "#Swaption Pricing : Monte-Carlo Methods" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Copyright (c) 2015 Matthias Groncki*\n", + "\n", + "\n", + "Redistribution and use in source and binary forms, with or without\n", + "modification, are permitted provided that the following conditions are met:\n", + "\n", + " - Redistributions of source code must retain the above copyright notice,\n", + " this list of conditions and the following disclaimer.\n", + "\n", + "THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n", + "AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n", + "IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\n", + "DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE LIABLE\n", + "FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\n", + "DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\n", + "SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\n", + "CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\n", + "OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\n", + "OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n", + "\n", + "This disclaimer is taken from the QuantLib license" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# import the used libraries\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import QuantLib as ql\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Setting evaluation date\n", + "today = ql.Date(7,4,2015)\n", + "ql.Settings.instance().setEvaluationDate(today)" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Setup Marketdata\n", + "rate = ql.SimpleQuote(0.03)\n", + "rate_handle = ql.QuoteHandle(rate)\n", + "dc = ql.Actual365Fixed()\n", + "yts = ql.FlatForward(today, rate_handle, dc)\n", + "yts.enableExtrapolation()\n", + "hyts = ql.RelinkableYieldTermStructureHandle(yts)\n", + "t0_curve = ql.YieldTermStructureHandle(yts)\n", + "euribor6m = ql.Euribor6M(hyts)\n", + "cal = ql.TARGET()" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Setup a dummy portfolio with two Swaps\n", + "def makeSwap(start, maturity, nominal, fixedRate, index, typ=ql.VanillaSwap.Payer):\n", + " \"\"\"\n", + " creates a plain vanilla swap with fixedLegTenor 1Y\n", + " \n", + " parameter:\n", + " \n", + " start (ql.Date) : Start Date\n", + " \n", + " maturity (ql.Period) : SwapTenor\n", + " \n", + " nominal (float) : Nominal\n", + " \n", + " fixedRate (float) : rate paid on fixed leg\n", + " \n", + " index (ql.IborIndex) : Index\n", + " \n", + " return: tuple(ql.Swap, list) Swap and all fixing dates\n", + " \n", + " \n", + " \"\"\"\n", + " end = ql.TARGET().advance(start, maturity)\n", + " fixedLegTenor = ql.Period(\"1y\")\n", + " fixedLegBDC = ql.ModifiedFollowing\n", + " fixedLegDC = ql.Thirty360(ql.Thirty360.BondBasis)\n", + " spread = 0.0\n", + " fixedSchedule = ql.Schedule(start,\n", + " end, \n", + " fixedLegTenor, \n", + " index.fixingCalendar(), \n", + " fixedLegBDC,\n", + " fixedLegBDC, \n", + " ql.DateGeneration.Backward,\n", + " False)\n", + " floatSchedule = ql.Schedule(start,\n", + " end,\n", + " index.tenor(),\n", + " index.fixingCalendar(),\n", + " index.businessDayConvention(),\n", + " index.businessDayConvention(),\n", + " ql.DateGeneration.Backward,\n", + " False)\n", + " swap = ql.VanillaSwap(typ, \n", + " nominal,\n", + " fixedSchedule,\n", + " fixedRate,\n", + " fixedLegDC,\n", + " floatSchedule,\n", + " index,\n", + " spread,\n", + " index.dayCounter())\n", + " return swap, [index.fixingDate(x) for x in floatSchedule][:-1]\n", + "\n", + "\n", + "def makeSwaption(swap, callDates, settlement):\n", + " if len(callDates) == 1:\n", + " exercise = ql.EuropeanExercise(callDates[0])\n", + " else:\n", + " exercise = ql.BermudanExercise(callDates)\n", + " return ql.Swaption(swap, exercise, settlement)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lets start with an european swaption" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a forward starting plain vanilla swap using the helper function above and create a european swaption which allows us to to enter the swap in one year from today." + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "settlementDate = today + ql.Period(\"1Y\")\n", + "\n", + "swaps = [makeSwap(settlementDate,\n", + " ql.Period(\"5Y\"),\n", + " 1e6,\n", + " 0.03047096,\n", + " euribor6m)\n", + " ]\n", + "\n", + "calldates = [euribor6m.fixingDate(settlementDate)]\n", + " \n", + "swaptions = [makeSwaption(swap, \n", + " calldates, \n", + " ql.Settlement.Physical) \n", + " for swap, fd in swaps]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pricing of the underlying at time 0 using the QuantLib pricing engine" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Swap NPV at time 0: 0.0091\n" + ] + } + ], + "source": [ + "#%%timeit\n", + "# Setup pricing engine and calculate the npv of the underlying swap\n", + "engine = ql.DiscountingSwapEngine(hyts)\n", + "for swap, fixingDates in swaps:\n", + " swap.setPricingEngine(engine)\n", + " print(\"Swap NPV at time 0: %.4f\" % swap.NPV())\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Setup the Gaussian Shortrate model (a.k.a Hull White model)\n", + "\n", + "Don't worry about calibration, assume we know the calbriated model parameter" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Stochastic Process " + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Assume the model is already calibrated either historical or market implied\n", + "volas = [ql.QuoteHandle(ql.SimpleQuote(0.0075)),\n", + " ql.QuoteHandle(ql.SimpleQuote(0.0075))]\n", + "meanRev = [ql.QuoteHandle(ql.SimpleQuote(0.02))]\n", + "model = ql.Gsr(t0_curve, [today+100], volas, meanRev, 16.)\n", + "process = model.stateProcess()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Calculate the swaption price using an integral pricing engine" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "swaptionEngine = ql.Gaussian1dSwaptionEngine(model)" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Swaption NPV : 12898.40\n" + ] + } + ], + "source": [ + "for swaption in swaptions:\n", + " swaption.setPricingEngine(swaptionEngine)\n", + " print(\"Swaption NPV : %.2f\" % swaption.NPV())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "###Pricing with an Monte Carlo method" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "####Create a swap path pricer in Python \n", + "\n", + "Convert all Dates into times in years (using the same DayCounter as in the yieldTermStructure and store all fix cashflows in a numpy array." + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "mcDC = yts.dayCounter()\n", + "\n", + "def timeFromReferenceFactory(daycounter, ref):\n", + " \"\"\"\n", + " returns a function, that calculate the time in years\n", + " from a the reference date *ref* to date *dat* \n", + " with respect to the given DayCountConvention *daycounter*\n", + " \n", + " Parameter:\n", + " dayCounter (ql.DayCounter)\n", + " ref (ql.Date)\n", + " \n", + " Return:\n", + " \n", + " f(np.array(ql.Date)) -> np.array(float)\n", + " \"\"\"\n", + " def impl(dat):\n", + " return daycounter.yearFraction(ref, dat)\n", + " return np.vectorize(impl)\n", + "\n", + "timeFromReference = timeFromReferenceFactory(mcDC, today)" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "fixed_leg = swap.leg(0)\n", + "n = len(fixed_leg)\n", + "fixed_times = np.zeros(n)\n", + "fixed_amounts = np.zeros(n)\n", + "for i in range(n):\n", + " cf = fixed_leg[i]\n", + " fixed_times[i] = timeFromReference(cf.date())\n", + " fixed_amounts[i] = cf.amount()" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "float_leg = swap.leg(1)\n", + "n = len(float_leg)\n", + "float_times = np.zeros(n)\n", + "float_dcf = np.zeros(n)\n", + "accrual_start_time = np.zeros(n)\n", + "accrual_end_time = np.zeros(n)\n", + "nominals = np.zeros(n)\n", + "for i in range(n):\n", + " # convert base classiborstart_idx Cashflow to\n", + " # FloatingRateCoupon\n", + " cf = ql.as_floating_rate_coupon(float_leg[i])\n", + " iborIndex = cf.index()\n", + " value_date = cf.referencePeriodStart()\n", + " index_mat = cf.referencePeriodEnd()\n", + " # year fraction\n", + " float_dcf[i] = cf.accrualPeriod()\n", + " # calculate the start and end time\n", + " accrual_start_time[i] = timeFromReference(value_date)\n", + " accrual_end_time[i] = timeFromReference(index_mat)\n", + " # payment time\n", + " float_times[i] = timeFromReference(cf.date())\n", + " # nominals \n", + " nominals[i] = cf.nominal()" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Store all times for which we need a discount factor in one array\n", + "df_times = np.concatenate([fixed_times, \n", + " accrual_start_time, \n", + " accrual_end_time, \n", + " float_times])\n", + "df_times = np.unique(df_times)" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Store indices of fix leg payment times in \n", + "# the df_times array\n", + "fix_idx = np.in1d(df_times, fixed_times, True)\n", + "fix_idx = fix_idx.nonzero()\n", + "# Indices of the floating leg payment times \n", + "# in the df_times array\n", + "float_idx = np.in1d(df_times, float_times, True)\n", + "float_idx = float_idx.nonzero()\n", + "# Indices of the accrual start and end time\n", + "# in the df_times array\n", + "accrual_start_idx = np.in1d(df_times, accrual_start_time, True)\n", + "accrual_start_idx = accrual_start_idx.nonzero()\n", + "accrual_end_idx = np.in1d(df_times, accrual_end_time, True)\n", + "accrual_end_idx = accrual_end_idx.nonzero()" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Convert call date to time\n", + "callTimes = timeFromReference(calldates)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "####Test the t0 Swap Pricing" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "t = 0\n", + "x_t = 0\n", + "# Calculate all discount factors\n", + "discount = np.vectorize(lambda T: model.zerobond(T, t, x_t))\n", + "dfs = discount(df_times)\n", + "# Calculate fixed leg npv\n", + "fix_leg_npv = np.sum(fixed_amounts * dfs[fix_idx])\n", + "# Estimate the index fixings\n", + "index_fixings = (dfs[accrual_start_idx] / dfs[accrual_end_idx] - 1) \n", + "index_fixings /= float_dcf\n", + "# Calculate the floating leg npv\n", + "float_leg_npv = np.sum(nominals * index_fixings * float_dcf * dfs[float_idx])\n", + "npv = float_leg_npv - fix_leg_npv\n" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Swap NPV at time 0: 0.0091\n", + "Error : 0.00000000\n" + ] + } + ], + "source": [ + "npv\n", + "print(\"Swap NPV at time 0: %.4f\" % npv)\n", + "print(\"Error : %.8f\" % (npv - swap.NPV()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Monte Carlo Simulation\n", + "\n", + "#### Generate time grid and paths" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Define evaluation grid\n", + "date_grid = [today] + calldates\n", + "\n", + "date_grid = np.unique(np.sort(date_grid))\n", + "time_grid = np.vectorize(lambda x: ql.Actual365Fixed().yearFraction(today, x))(date_grid)\n", + "dt = time_grid[1:] - time_grid[:-1]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Random number generator\n", + "seed = 1\n", + "urng = ql.MersenneTwisterUniformRng(seed)\n", + "usrg = ql.MersenneTwisterUniformRsg(len(time_grid)-1,urng)\n", + "generator = ql.InvCumulativeMersenneTwisterGaussianRsg(usrg)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%%timeit\n", + "# Generate N paths\n", + "M = 100000\n", + "m = len(time_grid)\n", + "x = np.zeros((M, m))\n", + "y = np.zeros((M, m))\n", + "numeraires = np.zeros((M,m))\n", + "dfs = np.zeros((M, m, len(df_times)))\n", + "\n", + "for n in range(0,M):\n", + " numeraires[n, 0] = model.numeraire(0, 0)\n", + " \n", + "for n in range(0,M):\n", + " dWs = generator.nextSequence().value()\n", + " for i in range(1, len(time_grid)):\n", + " t0 = time_grid[i-1]\n", + " t1 = time_grid[i]\n", + " e = process.expectation(t0, \n", + " x[n,i-1], \n", + " dt[i-1])\n", + " std = process.stdDeviation(t0,\n", + " x[n,i-1],\n", + " dt[i-1])\n", + " x[n,i] = e + dWs[i-1] * std \n", + " e_0_0 = process.expectation(0,0,t1)\n", + " std_0_0 = process.stdDeviation(0,0,t1)\n", + " y[n,i] = (x[n,i] - e_0_0) / std_0_0\n", + " numeraires[n ,i] = model.numeraire(t1, y[n, i])\n", + " dfs[n,i] = np.vectorize(lambda T : model.zerobond(T, t1, y[n,i]))(df_times) " + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# plot the paths\n", + "#for i in range(0, 1000):\n", + "# plt.plot(time_grid, x[i,:])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%%timeit\n", + "# Calculate NPV of the underlying swap at expiry\n", + "index_fixings = dfs[:,-1, accrual_start_idx][:,0,:] / dfs[:, -1, accrual_end_idx][:,0,:] - 1\n", + "index_fixings /= float_dcf\n", + "floatLeg_npv = np.sum(index_fixings * float_dcf * dfs[:,-1, float_idx][:,0,:] * nominals, \n", + " axis = 1) \n", + "fixedLeg_npv = np.sum(fixed_amounts * dfs[:, -1, fix_idx][:,0,:], axis=1)\n", + "npv = (floatLeg_npv - fixedLeg_npv)\n", + "# Apply payoff function \n", + "npv[npv < 0] = 0\n", + "# Deflate NPV\n", + "npv = npv / numeraires[:,-1] \n", + "npv = np.mean(npv) * numeraires[0,0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "print(\"MC NPV: %.4f\" % npv)\n", + "print(\"Error : %.4f \" % (npv-swaption.NPV()))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/MonteCarloPricing-Swaption-Part_II-Copy1.ipynb b/MonteCarloPricing-Swaption-Part_II-Copy1.ipynb new file mode 100644 index 0000000..8e2e0d9 --- /dev/null +++ b/MonteCarloPricing-Swaption-Part_II-Copy1.ipynb @@ -0,0 +1,841 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "# Swaption Pricing Part 2: American Monte Carlo for Bermudas" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Copyright (c) 2015 Matthias Groncki*\n", + "\n", + "\n", + "Redistribution and use in source and binary forms, with or without\n", + "modification, are permitted provided that the following conditions are met:\n", + "\n", + " - Redistributions of source code must retain the above copyright notice,\n", + " this list of conditions and the following disclaimer.\n", + "\n", + "THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n", + "AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n", + "IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\n", + "DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE LIABLE\n", + "FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\n", + "DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\n", + "SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\n", + "CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\n", + "OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\n", + "OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n", + "\n", + "This disclaimer is taken from the QuantLib license" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# import the used libraries\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import statsmodels.api as sm\n", + "import QuantLib as ql\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Setting evaluation date\n", + "today = ql.Date(7,4,2015)\n", + "ql.Settings.instance().setEvaluationDate(today)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Setup Marketdata\n", + "rate = ql.SimpleQuote(0.03)\n", + "rate_handle = ql.QuoteHandle(rate)\n", + "dc = ql.Actual365Fixed()\n", + "yts = ql.FlatForward(today, rate_handle, dc)\n", + "yts.enableExtrapolation()\n", + "hyts = ql.RelinkableYieldTermStructureHandle(yts)\n", + "t0_curve = ql.YieldTermStructureHandle(yts)\n", + "euribor6m = ql.Euribor6M(hyts)\n", + "cal = ql.TARGET()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Setup a dummy portfolio with two Swaps\n", + "def makeSwap(start, maturity, nominal, fixedRate, index, typ=ql.VanillaSwap.Payer):\n", + " \"\"\"\n", + " creates a plain vanilla swap with fixedLegTenor 1Y\n", + " \n", + " parameter:\n", + " \n", + " start (ql.Date) : Start Date\n", + " \n", + " maturity (ql.Period) : SwapTenor\n", + " \n", + " nominal (float) : Nominal\n", + " \n", + " fixedRate (float) : rate paid on fixed leg\n", + " \n", + " index (ql.IborIndex) : Index\n", + " \n", + " return: tuple(ql.Swap, list) Swap and all fixing dates\n", + " \n", + " \n", + " \"\"\"\n", + " end = ql.TARGET().advance(start, maturity)\n", + " fixedLegTenor = ql.Period(\"1y\")\n", + " fixedLegBDC = ql.ModifiedFollowing\n", + " fixedLegDC = ql.Thirty360(ql.Thirty360.BondBasis)\n", + " spread = 0.0\n", + " fixedSchedule = ql.Schedule(start,\n", + " end, \n", + " fixedLegTenor, \n", + " index.fixingCalendar(), \n", + " fixedLegBDC,\n", + " fixedLegBDC, \n", + " ql.DateGeneration.Backward,\n", + " False)\n", + " floatSchedule = ql.Schedule(start,\n", + " end,\n", + " index.tenor(),\n", + " index.fixingCalendar(),\n", + " index.businessDayConvention(),\n", + " index.businessDayConvention(),\n", + " ql.DateGeneration.Backward,\n", + " False)\n", + " swap = ql.VanillaSwap(typ, \n", + " nominal,\n", + " fixedSchedule,\n", + " fixedRate,\n", + " fixedLegDC,\n", + " floatSchedule,\n", + " index,\n", + " spread,\n", + " index.dayCounter())\n", + " return swap, [index.fixingDate(x) for x in floatSchedule][:-1]\n", + "\n", + "\n", + "def makeSwaption(swap, callDates, settlement):\n", + " if len(callDates) == 1:\n", + " exercise = ql.EuropeanExercise(callDates[0])\n", + " else:\n", + " exercise = ql.BermudanExercise(callDates)\n", + " return ql.Swaption(swap, exercise, settlement)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lets start with an european swaption" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a forward starting plain vanilla swap using the helper function above and create a european swaption which allows us to to enter the swap in one year from today." + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "settlementDate = today + ql.Period(\"1Y\")\n", + "\n", + "swaps = [makeSwap(settlementDate,\n", + " ql.Period(\"5Y\"),\n", + " 1e6,\n", + " 0.03047096,\n", + " euribor6m)\n", + " ]\n", + "\n", + "calldates = [euribor6m.fixingDate(settlementDate), ql.Date(5,4,2017), ql.Date(5,4, 2018)]\n", + " \n", + "swaptions = [makeSwaption(swap, \n", + " calldates, \n", + " ql.Settlement.Physical) \n", + " for swap, fd in swaps]\n", + "\n", + "calldates = [euribor6m.valueDate(d) for d in calldates]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pricing of the underlying at time 0 using the QuantLib pricing engine" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Swap NPV at time 0: 0.0091\n" + ] + } + ], + "source": [ + "#%%timeit\n", + "# Setup pricing engine and calculate the npv of the underlying swap\n", + "engine = ql.DiscountingSwapEngine(hyts)\n", + "for swap, fixingDates in swaps:\n", + " swap.setPricingEngine(engine)\n", + " print(\"Swap NPV at time 0: %.4f\" % swap.NPV())\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Setup the Gaussian Shortrate model (a.k.a Hull White model)\n", + "\n", + "Don't worry about calibration, assume we know the calbriated model parameter" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Stochastic Process " + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Assume the model is already calibrated either historical or market implied\n", + "volas = [ql.QuoteHandle(ql.SimpleQuote(0.0075)),\n", + " ql.QuoteHandle(ql.SimpleQuote(0.0075))]\n", + "meanRev = [ql.QuoteHandle(ql.SimpleQuote(0.002))]\n", + "model = ql.Gsr(t0_curve, [today+100], volas, meanRev, 16.)\n", + "process = model.stateProcess()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Calculate the swaption price using an integral pricing engine" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "swaptionEngine = ql.Gaussian1dSwaptionEngine(model)" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Swaption NPV : 18662.14\n" + ] + } + ], + "source": [ + "for swaption in swaptions:\n", + " swaption.setPricingEngine(swaptionEngine)\n", + " print(\"Swaption NPV : %.2f\" % swaption.NPV())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "###Pricing with an Monte Carlo method" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "####Create a swap path pricer in Python \n", + "\n", + "Convert all Dates into times in years (using the same DayCounter as in the yieldTermStructure and store all fix cashflows in a numpy array." + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "mcDC = yts.dayCounter()\n", + "\n", + "def timeFromReferenceFactory(daycounter, ref):\n", + " \"\"\"\n", + " returns a function, that calculate the time in years\n", + " from a the reference date *ref* to date *dat* \n", + " with respect to the given DayCountConvention *daycounter*\n", + " \n", + " Parameter:\n", + " dayCounter (ql.DayCounter)\n", + " ref (ql.Date)\n", + " \n", + " Return:\n", + " \n", + " f(np.array(ql.Date)) -> np.array(float)\n", + " \"\"\"\n", + " def impl(dat):\n", + " return daycounter.yearFraction(ref, dat)\n", + " return np.vectorize(impl)\n", + "\n", + "timeFromReference = timeFromReferenceFactory(mcDC, today)" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def getFixedLeg(swap, t):\n", + " \"\"\"\n", + " returns all future payment times and amount of the fixed leg of the underlying swap\n", + " \n", + " Parameter:\n", + " swap (ql.Swap)\n", + " t (float) \n", + " \n", + " Return:\n", + " (np.array, np.array) (times, amounts)\n", + "\n", + " \"\"\"\n", + " fixed_leg = swap.leg(0)\n", + " n = len(fixed_leg)\n", + " fixed_times=[]\n", + " fixed_amounts=[]\n", + " npv = 0\n", + " for i in range(n):\n", + " cf = fixed_leg[i]\n", + " t_i = timeFromReference(cf.date())\n", + " if t_i > t:\n", + " fixed_times.append(t_i)\n", + " fixed_amounts.append(cf.amount())\n", + " return np.array(fixed_times), np.array(fixed_amounts)\n", + "\n", + "\n", + "def getFloatingLeg(swap, t):\n", + " \n", + " float_leg = swap.leg(1)\n", + " n = len(float_leg)\n", + " float_times = []\n", + " float_dcf = []\n", + " accrual_start_time = []\n", + " accrual_end_time = []\n", + " nominals = []\n", + " for i in range(n):\n", + " # convert base classiborstart_idx Cashflow to\n", + " # FloatingRateCoupon\n", + " cf = ql.as_floating_rate_coupon(float_leg[i])\n", + " value_date = cf.referencePeriodStart()\n", + " t_fix_i = timeFromReference(value_date)\n", + " t_i = timeFromReference(cf.date()) \n", + " if t_fix_i >= t:\n", + " iborIndex = cf.index()\n", + " \n", + " index_mat = cf.referencePeriodEnd()\n", + " # year fraction\n", + " float_dcf.append(cf.accrualPeriod())\n", + " # calculate the start and end time\n", + " accrual_start_time.append(t_fix_i)\n", + " accrual_end_time.append(timeFromReference(index_mat))\n", + " # payment time\n", + " float_times.append(t_i)\n", + " # nominals \n", + " nominals.append(cf.nominal())\n", + " return np.array(float_times), np.array(float_dcf), np.array(accrual_start_time), np.array(accrual_end_time), np.array(nominals)\n", + "\n", + "\n", + "def swapPathNPV(swap, t):\n", + " fixed_times, fixed_amounts = getFixedLeg(swap, t)\n", + " float_times, float_dcf, accrual_start_time, accrual_end_time, nominals = getFloatingLeg(swap, t)\n", + " df_times = np.concatenate([fixed_times, \n", + " accrual_start_time, \n", + " accrual_end_time, \n", + " float_times])\n", + " df_times = np.unique(df_times)\n", + " # Store indices of fix leg payment times in \n", + " # the df_times array\n", + " fix_idx = np.in1d(df_times, fixed_times, True)\n", + " fix_idx = fix_idx.nonzero()\n", + " # Indices of the floating leg payment times \n", + " # in the df_times array\n", + " float_idx = np.in1d(df_times, float_times, True)\n", + " float_idx = float_idx.nonzero()\n", + " # Indices of the accrual start and end time\n", + " # in the df_times array\n", + " accrual_start_idx = np.in1d(df_times, accrual_start_time, True)\n", + " accrual_start_idx = accrual_start_idx.nonzero()\n", + " accrual_end_idx = np.in1d(df_times, accrual_end_time, True)\n", + " accrual_end_idx = accrual_end_idx.nonzero()\n", + " # Calculate NPV\n", + " def calc(x_t):\n", + " discount = np.vectorize(lambda T: model.zerobond(T, t, x_t))\n", + " dfs = discount(df_times)\n", + " # Calculate fixed leg npv\n", + " fix_leg_npv = np.sum(fixed_amounts * dfs[fix_idx])\n", + " # Estimate the index fixings\n", + " index_fixings = (dfs[accrual_start_idx] / dfs[accrual_end_idx] - 1) \n", + " index_fixings /= float_dcf\n", + " # Calculate the floating leg npv\n", + " float_leg_npv = np.sum(nominals * index_fixings * float_dcf * dfs[float_idx])\n", + " npv = float_leg_npv - fix_leg_npv\n", + " return npv\n", + " return calc" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1.00273973, 2.00273973, 3.00821918])" + ] + }, + "execution_count": 117, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Convert call date to time\n", + "callTimes = timeFromReference(calldates)\n", + "callTimes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "####Test the t0 Swap Pricing" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Swap NPV at time 0: 0.0091\n", + "Error : 0.00000000\n" + ] + } + ], + "source": [ + "npv = swapPathNPV(swap, 0.)(0)\n", + "print(\"Swap NPV at time 0: %.4f\" % npv)\n", + "print(\"Error : %.8f\" % (npv - swap.NPV()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Monte Carlo Simulation\n", + "\n", + "#### Generate time grid and paths" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def fixingdates(swap):\n", + " leg = swap.leg(1)\n", + " n = len(leg)\n", + " fixing_dates = []\n", + " for i in range(0, n):\n", + " cf = ql.as_floating_rate_coupon(leg[i])\n", + " value_date = cf.referencePeriodStart()\n", + " fixing_dates.append(value_date)\n", + " return fixing_dates\n", + "\n", + "# Define evaluation grid\n", + "\n", + "date_grid = [today] + calldates #+ fixingdates(swap)\n", + "\n", + "date_grid = np.unique(np.sort(date_grid))\n", + "time_grid = np.vectorize(lambda x: ql.Actual365Fixed().yearFraction(today, x))(date_grid)\n", + "dt = time_grid[1:] - time_grid[:-1]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([Date(7,4,2015), Date(7,4,2016), Date(7,4,2017), Date(9,4,2018)], dtype=object)" + ] + }, + "execution_count": 120, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "date_grid" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Random number generator\n", + "seed = 1\n", + "urng = ql.MersenneTwisterUniformRng(seed)\n", + "usrg = ql.MersenneTwisterUniformRsg(len(time_grid)-1,urng)\n", + "generator = ql.InvCumulativeMersenneTwisterGaussianRsg(usrg)" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%%timeit\n", + "# Generate N paths\n", + "M = 100000\n", + "m = len(time_grid)\n", + "x = np.zeros((M, m))\n", + "y = np.zeros((M, m))\n", + "numeraires = np.zeros((M, m))\n", + " \n", + "for n in range(0, M):\n", + " numeraires[n, 0] = model.numeraire(0, 0)\n", + " \n", + "for n in range(0,M):\n", + " dWs = generator.nextSequence().value()\n", + " j = 1\n", + " for i in range(1, len(time_grid)):\n", + " t0 = time_grid[i-1]\n", + " t1 = time_grid[i]\n", + " e = process.expectation(t0, \n", + " x[n,i-1], \n", + " dt[i-1])\n", + " std = process.stdDeviation(t0,\n", + " x[n,i-1],\n", + " dt[i-1])\n", + " x[n,i] = e + dWs[i-1] * std \n", + " e_0_0 = process.expectation(0,0,t1)\n", + " std_0_0 = process.stdDeviation(0,0,t1)\n", + " y[n,i] = (x[n,i] - e_0_0) / std_0_0\n", + " numeraires[n, i] = model.numeraire(t1, y[n,i])\n", + " #df_times_temp = df_times.copy()\n", + " #df_times_temp[df_times_temp <= t1] = t1\n", + " #dfs[n,i] = np.vectorize(lambda T: model.zerobond(T, t1, y[n,i]))(df_times_temp)" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "last_call_time = callTimes[-1]\n", + "\n", + "call_idx = np.where(time_grid==last_call_time)[0][0]\n", + " \n", + "pricer = np.vectorize(swapPathNPV(swap, time_grid[call_idx]))\n", + "\n", + "cont_value = pricer(y[:,call_idx]) / numeraires[:,call_idx]\n", + "cont_value[cont_value < 0] = 0\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 131, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "call_idx" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2\n", + "call\n", + "1\n", + "call\n" + ] + } + ], + "source": [ + "for i in range(call_idx-1, 0,-1):\n", + " print(i)\n", + " t = time_grid[i]\n", + " if t in callTimes:\n", + " print(\"call\")\n", + " pricer = np.vectorize(swapPathNPV(swap, t))\n", + " exercise_values = pricer(y[:,i]) / numeraires[:,i]\n", + " exercise_values[exercise_values < 0] = 0\n", + " states = y[:, i]\n", + " Y = np.column_stack((states, states**2, states**3, states**4))\n", + " Y = sm.add_constant(Y)\n", + " ols = sm.OLS(cont_value, Y)\n", + " ols_result = ols.fit()\n", + " cont_value_hat = np.sum(ols_result.params * Y, axis=1)\n", + " cont_value = np.maximum(cont_value_hat, exercise_values)\n", + "npv_amc = cont_value" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "npv_amc = np.mean(npv_amc) * numeraires[0,0]" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "18789.378507421919" + ] + }, + "execution_count": 134, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "npv_amc" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MC NPV: 18789.3785\n", + "Error : 127.2350 \n", + "Rel. Error in bps: 1.2724 \n" + ] + } + ], + "source": [ + "print(\"MC NPV: %.4f\" % npv_amc)\n", + "print(\"Error : %.4f \" % (npv_amc-swaption.NPV()))\n", + "print(\"Rel. Error in bps: %.4f \" % ((npv_amc-swaption.NPV()) / 100))" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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RERGRGKSFISIiUmGkpqZqjzqRCIXfCnBvaGFIBLQwRERERCoLLQwRERERkV1S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\ny+dWfoDp6/py0sKlNCxfnk6wSijlU1L5q/S+P8iqf1OnGgA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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,10))\n", + "plt.scatter(states, cont_value)\n", + "plt.scatter(states, cont_value_hat, marker='x', c='red')\n", + "plt.legend([\"continuation values at t+1\", \"regression\"])\n", + "plt.title(\"Regression Bermudan Swaption\")\n", + "plt.xlabel(\"State at t\")\n", + "plt.ylabel(\"NPV\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/MonteCarloPricing-Swaption-Part_II.ipynb b/MonteCarloPricing-Swaption-Part_II.ipynb new file mode 100644 index 0000000..4df7972 --- /dev/null +++ b/MonteCarloPricing-Swaption-Part_II.ipynb @@ -0,0 +1,830 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "# Swaption Pricing Part 2: American Monte Carlo for Bermudas" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Copyright (c) 2015 Matthias Groncki*\n", + "\n", + "\n", + "Redistribution and use in source and binary forms, with or without\n", + "modification, are permitted provided that the following conditions are met:\n", + "\n", + " - Redistributions of source code must retain the above copyright notice,\n", + " this list of conditions and the following disclaimer.\n", + "\n", + "THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n", + "AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n", + "IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\n", + "DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE LIABLE\n", + "FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\n", + "DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\n", + "SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\n", + "CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\n", + "OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\n", + "OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n", + "\n", + "This disclaimer is taken from the QuantLib license" + ] + }, + { + "cell_type": "code", + "execution_count": 455, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# import the used libraries\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import statsmodels.api as sm\n", + "import QuantLib as ql\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 456, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Setting evaluation date\n", + "today = ql.Date(7,4,2015)\n", + "ql.Settings.instance().setEvaluationDate(today)" + ] + }, + { + "cell_type": "code", + "execution_count": 457, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Setup Marketdata\n", + "rate = ql.SimpleQuote(0.03)\n", + "rate_handle = ql.QuoteHandle(rate)\n", + "dc = ql.Actual365Fixed()\n", + "yts = ql.FlatForward(today, rate_handle, dc)\n", + "yts.enableExtrapolation()\n", + "hyts = ql.RelinkableYieldTermStructureHandle(yts)\n", + "t0_curve = ql.YieldTermStructureHandle(yts)\n", + "euribor6m = ql.Euribor6M(hyts)\n", + "cal = ql.TARGET()" + ] + }, + { + "cell_type": "code", + "execution_count": 458, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Setup a dummy portfolio with two Swaps\n", + "def makeSwap(start, maturity, nominal, fixedRate, index, typ=ql.VanillaSwap.Payer):\n", + " \"\"\"\n", + " creates a plain vanilla swap with fixedLegTenor 1Y\n", + " \n", + " parameter:\n", + " \n", + " start (ql.Date) : Start Date\n", + " \n", + " maturity (ql.Period) : SwapTenor\n", + " \n", + " nominal (float) : Nominal\n", + " \n", + " fixedRate (float) : rate paid on fixed leg\n", + " \n", + " index (ql.IborIndex) : Index\n", + " \n", + " return: tuple(ql.Swap, list) Swap and all fixing dates\n", + " \n", + " \n", + " \"\"\"\n", + " end = ql.TARGET().advance(start, maturity)\n", + " fixedLegTenor = ql.Period(\"1y\")\n", + " fixedLegBDC = ql.ModifiedFollowing\n", + " fixedLegDC = ql.Thirty360(ql.Thirty360.BondBasis)\n", + " spread = 0.0\n", + " fixedSchedule = ql.Schedule(start,\n", + " end, \n", + " fixedLegTenor, \n", + " index.fixingCalendar(), \n", + " fixedLegBDC,\n", + " fixedLegBDC, \n", + " ql.DateGeneration.Backward,\n", + " False)\n", + " floatSchedule = ql.Schedule(start,\n", + " end,\n", + " index.tenor(),\n", + " index.fixingCalendar(),\n", + " index.businessDayConvention(),\n", + " index.businessDayConvention(),\n", + " ql.DateGeneration.Backward,\n", + " False)\n", + " swap = ql.VanillaSwap(typ, \n", + " nominal,\n", + " fixedSchedule,\n", + " fixedRate,\n", + " fixedLegDC,\n", + " floatSchedule,\n", + " index,\n", + " spread,\n", + " index.dayCounter())\n", + " return swap, [index.fixingDate(x) for x in floatSchedule][:-1]\n", + "\n", + "\n", + "def makeSwaption(swap, callDates, settlement):\n", + " if len(callDates) == 1:\n", + " exercise = ql.EuropeanExercise(callDates[0])\n", + " else:\n", + " exercise = ql.BermudanExercise(callDates)\n", + " return ql.Swaption(swap, exercise, settlement)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lets start with an european swaption" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a forward starting plain vanilla swap using the helper function above and create a european swaption which allows us to to enter the swap in one year from today." + ] + }, + { + "cell_type": "code", + "execution_count": 459, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "settlementDate = today + ql.Period(\"1Y\")\n", + "\n", + "swaps = [makeSwap(settlementDate,\n", + " ql.Period(\"5Y\"),\n", + " 1e6,\n", + " 0.03047096,\n", + " euribor6m)\n", + " ]\n", + "\n", + "calldates = [euribor6m.fixingDate(settlementDate), euribor6m.valueDate(ql.Date(5,4,2017))]\n", + " \n", + "swaptions = [makeSwaption(swap, \n", + " calldates, \n", + " ql.Settlement.Physical) \n", + " for swap, fd in swaps]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pricing of the underlying at time 0 using the QuantLib pricing engine" + ] + }, + { + "cell_type": "code", + "execution_count": 460, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Swap NPV at time 0: 0.0091\n" + ] + } + ], + "source": [ + "#%%timeit\n", + "# Setup pricing engine and calculate the npv of the underlying swap\n", + "engine = ql.DiscountingSwapEngine(hyts)\n", + "for swap, fixingDates in swaps:\n", + " swap.setPricingEngine(engine)\n", + " print(\"Swap NPV at time 0: %.4f\" % swap.NPV())\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Setup the Gaussian Shortrate model (a.k.a Hull White model)\n", + "\n", + "Don't worry about calibration, assume we know the calbriated model parameter" + ] + }, + { + "cell_type": "code", + "execution_count": 461, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Stochastic Process " + ] + }, + { + "cell_type": "code", + "execution_count": 462, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Assume the model is already calibrated either historical or market implied\n", + "volas = [ql.QuoteHandle(ql.SimpleQuote(0.0075)),\n", + " ql.QuoteHandle(ql.SimpleQuote(0.0075))]\n", + "meanRev = [ql.QuoteHandle(ql.SimpleQuote(0.002))]\n", + "model = ql.Gsr(t0_curve, [today+100], volas, meanRev, 16.)\n", + "process = model.stateProcess()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Calculate the swaption price using an integral pricing engine" + ] + }, + { + "cell_type": "code", + "execution_count": 463, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "swaptionEngine = ql.Gaussian1dSwaptionEngine(model)" + ] + }, + { + "cell_type": "code", + "execution_count": 464, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Swaption NPV : 17068.93\n" + ] + } + ], + "source": [ + "for swaption in swaptions:\n", + " swaption.setPricingEngine(swaptionEngine)\n", + " print(\"Swaption NPV : %.2f\" % swaption.NPV())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "###Pricing with an Monte Carlo method" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "####Create a swap path pricer in Python \n", + "\n", + "Convert all Dates into times in years (using the same DayCounter as in the yieldTermStructure and store all fix cashflows in a numpy array." + ] + }, + { + "cell_type": "code", + "execution_count": 465, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "mcDC = yts.dayCounter()\n", + "\n", + "def timeFromReferenceFactory(daycounter, ref):\n", + " \"\"\"\n", + " returns a function, that calculate the time in years\n", + " from a the reference date *ref* to date *dat* \n", + " with respect to the given DayCountConvention *daycounter*\n", + " \n", + " Parameter:\n", + " dayCounter (ql.DayCounter)\n", + " ref (ql.Date)\n", + " \n", + " Return:\n", + " \n", + " f(np.array(ql.Date)) -> np.array(float)\n", + " \"\"\"\n", + " def impl(dat):\n", + " return daycounter.yearFraction(ref, dat)\n", + " return np.vectorize(impl)\n", + "\n", + "timeFromReference = timeFromReferenceFactory(mcDC, today)" + ] + }, + { + "cell_type": "code", + "execution_count": 466, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def getFixedLeg(swap, t):\n", + " \"\"\"\n", + " returns all future payment times and amount of the fixed leg of the underlying swap\n", + " \n", + " Parameter:\n", + " swap (ql.Swap)\n", + " t (float) \n", + " \n", + " Return:\n", + " (np.array, np.array) (times, amounts)\n", + "\n", + " \"\"\"\n", + " fixed_leg = swap.leg(0)\n", + " n = len(fixed_leg)\n", + " fixed_times=[]\n", + " fixed_amounts=[]\n", + " npv = 0\n", + " for i in range(n):\n", + " cf = fixed_leg[i]\n", + " t_i = timeFromReference(cf.date())\n", + " if t_i > t:\n", + " fixed_times.append(t_i)\n", + " fixed_amounts.append(cf.amount())\n", + " return np.array(fixed_times), np.array(fixed_amounts)\n", + "\n", + "\n", + "def getFloatingLeg(swap, t):\n", + " \n", + " float_leg = swap.leg(1)\n", + " n = len(float_leg)\n", + " float_times = []\n", + " float_dcf = []\n", + " accrual_start_time = []\n", + " accrual_end_time = []\n", + " nominals = []\n", + " for i in range(n):\n", + " # convert base classiborstart_idx Cashflow to\n", + " # FloatingRateCoupon\n", + " cf = ql.as_floating_rate_coupon(float_leg[i])\n", + " value_date = cf.referencePeriodStart()\n", + " t_fix_i = timeFromReference(value_date)\n", + " t_i = timeFromReference(cf.date()) \n", + " if t_fix_i >= t:\n", + " iborIndex = cf.index()\n", + " \n", + " index_mat = cf.referencePeriodEnd()\n", + " # year fraction\n", + " float_dcf.append(cf.accrualPeriod())\n", + " # calculate the start and end time\n", + " accrual_start_time.append(t_fix_i)\n", + " accrual_end_time.append(timeFromReference(index_mat))\n", + " # payment time\n", + " float_times.append(t_i)\n", + " # nominals \n", + " nominals.append(cf.nominal())\n", + " return np.array(float_times), np.array(float_dcf), np.array(accrual_start_time), np.array(accrual_end_time), np.array(nominals)\n", + "\n", + "\n", + "def swapPathNPV(swap, t):\n", + " fixed_times, fixed_amounts = getFixedLeg(swap, t)\n", + " float_times, float_dcf, accrual_start_time, accrual_end_time, nominals = getFloatingLeg(swap, t)\n", + " df_times = np.concatenate([fixed_times, \n", + " accrual_start_time, \n", + " accrual_end_time, \n", + " float_times])\n", + " df_times = np.unique(df_times)\n", + " # Store indices of fix leg payment times in \n", + " # the df_times array\n", + " fix_idx = np.in1d(df_times, fixed_times, True)\n", + " fix_idx = fix_idx.nonzero()\n", + " # Indices of the floating leg payment times \n", + " # in the df_times array\n", + " float_idx = np.in1d(df_times, float_times, True)\n", + " float_idx = float_idx.nonzero()\n", + " # Indices of the accrual start and end time\n", + " # in the df_times array\n", + " accrual_start_idx = np.in1d(df_times, accrual_start_time, True)\n", + " accrual_start_idx = accrual_start_idx.nonzero()\n", + " accrual_end_idx = np.in1d(df_times, accrual_end_time, True)\n", + " accrual_end_idx = accrual_end_idx.nonzero()\n", + " # Calculate NPV\n", + " def calc(x_t):\n", + " discount = np.vectorize(lambda T: model.zerobond(T, t, x_t))\n", + " dfs = discount(df_times)\n", + " # Calculate fixed leg npv\n", + " fix_leg_npv = np.sum(fixed_amounts * dfs[fix_idx])\n", + " # Estimate the index fixings\n", + " index_fixings = (dfs[accrual_start_idx] / dfs[accrual_end_idx] - 1) \n", + " index_fixings /= float_dcf\n", + " # Calculate the floating leg npv\n", + " float_leg_npv = np.sum(nominals * index_fixings * float_dcf * dfs[float_idx])\n", + " npv = float_leg_npv - fix_leg_npv\n", + " return npv\n", + " return calc" + ] + }, + { + "cell_type": "code", + "execution_count": 467, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.99726027, 2.00273973])" + ] + }, + "execution_count": 467, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Convert call date to time\n", + "callTimes = timeFromReference(calldates)\n", + "callTimes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "####Test the t0 Swap Pricing" + ] + }, + { + "cell_type": "code", + "execution_count": 468, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Swap NPV at time 0: 0.0091\n", + "Error : 0.00000000\n" + ] + } + ], + "source": [ + "npv = swapPathNPV(swap, 0.)(0)\n", + "print(\"Swap NPV at time 0: %.4f\" % npv)\n", + "print(\"Error : %.8f\" % (npv - swap.NPV()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Monte Carlo Simulation\n", + "\n", + "#### Generate time grid and paths" + ] + }, + { + "cell_type": "code", + "execution_count": 569, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def fixingdates(swap):\n", + " leg = swap.leg(1)\n", + " n = len(leg)\n", + " fixing_dates = []\n", + " for i in range(0, n):\n", + " cf = ql.as_floating_rate_coupon(float_leg[i])\n", + " value_date = cf.referencePeriodStart()\n", + " fixing_dates.append(value_date)\n", + " return fixing_dates\n", + "\n", + "# Define evaluation grid\n", + "\n", + "date_grid = [today] + calldates \n", + "\n", + "date_grid = np.unique(np.sort(date_grid))\n", + "time_grid = np.vectorize(lambda x: ql.Actual365Fixed().yearFraction(today, x))(date_grid)\n", + "dt = time_grid[1:] - time_grid[:-1]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 570, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([Date(7,4,2015), Date(5,4,2016), Date(7,4,2017)], dtype=object)" + ] + }, + "execution_count": 570, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "date_grid" + ] + }, + { + "cell_type": "code", + "execution_count": 643, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Random number generator\n", + "seed = 1\n", + "urng = ql.MersenneTwisterUniformRng(seed)\n", + "usrg = ql.MersenneTwisterUniformRsg(len(time_grid)-1,urng)\n", + "generator = ql.InvCumulativeMersenneTwisterGaussianRsg(usrg)" + ] + }, + { + "cell_type": "code", + "execution_count": 644, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%%timeit\n", + "# Generate N paths\n", + "M = 100000\n", + "m = len(time_grid)\n", + "x = np.zeros((M, m))\n", + "y = np.zeros((M, m))\n", + "numeraires = np.zeros((M, m))\n", + " \n", + "for n in range(0, M):\n", + " numeraires[n, 0] = model.numeraire(0, 0)\n", + " \n", + "for n in range(0,M):\n", + " dWs = generator.nextSequence().value()\n", + " j = 1\n", + " for i in range(1, len(time_grid)):\n", + " t0 = time_grid[i-1]\n", + " t1 = time_grid[i]\n", + " e = process.expectation(t0, \n", + " x[n,i-1], \n", + " dt[i-1])\n", + " std = process.stdDeviation(t0,\n", + " x[n,i-1],\n", + " dt[i-1])\n", + " x[n,i] = e + dWs[i-1] * std \n", + " e_0_0 = process.expectation(0,0,t1)\n", + " std_0_0 = process.stdDeviation(0,0,t1)\n", + " y[n,i] = (x[n,i] - e_0_0) / std_0_0\n", + " numeraires[n, i] = model.numeraire(t1, y[n,i])\n", + " #df_times_temp = df_times.copy()\n", + " #df_times_temp[df_times_temp <= t1] = t1\n", + " #dfs[n,i] = np.vectorize(lambda T: model.zerobond(T, t1, y[n,i]))(df_times_temp)" + ] + }, + { + "cell_type": "code", + "execution_count": 645, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "\n", + " \n", + "pricer = np.vectorize(swapPathNPV(swap, time_grid[-1]))\n", + "\n", + "cont_value = pricer(y[:,-1]) / numeraires[:,-1]\n", + "cont_value[cont_value < 0] = 0\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 646, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0. , 0.99726027, 2.00273973])" + ] + }, + "execution_count": 646, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "time_grid" + ] + }, + { + "cell_type": "code", + "execution_count": 647, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "pricer = np.vectorize(swapPathNPV(swap, time_grid[-2]))\n", + "exercise_values = pricer(y[:,-2]) / numeraires[:,-2]\n", + "exercise_values[exercise_values < 0] = 0" + ] + }, + { + "cell_type": "code", + "execution_count": 648, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "states = y[:, -2]\n", + "Y = np.column_stack((states, states**2, states**3))\n", + "Y = sm.add_constant(Y)\n", + "ols = sm.OLS(cont_value, Y)\n", + "ols_result = ols.fit()\n", + "cont_value_hat = np.sum(ols_result.params * Y, axis=1)\n", + "npv_amc = np.maximum(cont_value_hat, exercise_values)" + ] + }, + { + "cell_type": "code", + "execution_count": 649, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "npv_amc = np.mean(npv_amc) * numeraires[0,0]" + ] + }, + { + "cell_type": "code", + "execution_count": 650, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "17127.228006267218" + ] + }, + "execution_count": 650, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "npv_amc" + ] + }, + { + "cell_type": "code", + "execution_count": 651, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MC NPV: 17127.2280\n", + "Error : 58.2987 \n", + "Rel. Error in bps: 0.5830 \n" + ] + } + ], + "source": [ + "print(\"MC NPV: %.4f\" % npv_amc)\n", + "print(\"Error : %.4f \" % (npv_amc-swaption.NPV()))\n", + "print(\"Rel. Error in bps: %.4f \" % ((npv_amc-swaption.NPV()) / 100))" + ] + }, + { + "cell_type": "code", + "execution_count": 652, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 652, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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EREQ62I4dO7j44oupqqqisLCQKVOm8MMf/rCrmyUSQ93BKVB3sIjInrG3dgeL\ndAZ1B4uIiIhIqykEioiIiGQghUARERGRDKSJISIikjZKS0u1Rp1IiqK3AmwLTQxJgSaGiIiISHeh\niSEiIiIikpRCoIiIiEgGUggUERERyUAKgSIiIiIZSCFQREREJAMpBIqIiIhkIIVAERERkQykECgi\nIiKSgRQCRURERDKQQqCIiIhIBlIIFBEREclACoEiIiIiGUghUERERCQDKQSKiIiIZCCFQBEREZEM\npBAoIiIikoEUAkVEREQykEKgiIiISAZSCBQRERHJQAqBIiIiIhlIIVBEREQkAykEioiIiGQghUAR\nERGRDKQQKCIiIpKBFAJFREREMpBCoIiIiEgGUggUERERyUAKgSIiIiIZSCFQREREJAMpBIqIiIhk\nIIVAERERkQykECgiIiKSgRQCRURERDKQQqCIiIhIBlIIFBEREclACoEiIiIiGUghUERERCQDKQSK\niIiIZCCFQBEREZEMlBYh0DnXwzn3d+fcH/zHRc65F51z7zrnypxzBVGvvdo5t9I5945z7rio42Od\nc287595zzt0ddTzgnFvgv+d159x+Uc+d77/+XefceXvq84qIiIh0tbQIgcBlQGXU46uARWZ2EPAy\ncDWAc+5g4CxgBPAN4H7nnPPf8wAw1cwOBA50zh3vH58KbDSzLwB3A7f55yoCrgPGAV8EZkSHTRER\nEZG9WZeHQOfcIOAE4DdRh08BHvV/fhQ41f/5ZGCBme0ysypgJTDeOdcP6G1my/zXPRb1nuhzPQ0c\n4/98PPCimW0ys8+AF4Gvd+RnExEREUlXXR4CgbuAKwCLOtbXzD4CMLMNQB//+EBgddTr1vrHBgJr\noo6v8Y/FvMfMdgObnHPFzZxLREREZK/XpSHQOXci8JGZ/QNwzbzUmnmu1ZftwHOJiIiIdEu9uvj6\nXwFOds6dAISA3s65x4ENzrm+ZvaR39Vb479+LTA46v2D/GPJjke/Z51zrieQb2YbnXNrgYlx7ylP\n1tCZM2dGfp44cSITJ05M9lIRERGRPWbx4sUsXry41e9zZh1ZZGs759wE4GdmdrJz7jbgEzO71Tl3\nJVBkZlf5E0OewJvIMRB4CfiCmZlz7m/ApcAy4E/AvWb2gnPuEmCkmV3inDsbONXMzvYnhrwBjMWr\niL4BHO6PD4xvm6XLfRIRERFpjnMOM2ux57OrK4HJ/Ap40jl3IVCNNyMYM6t0zj2JN5N4J3BJVDr7\nETAXyAZQjUd7AAAgAElEQVSeN7MX/OMPA48751YCnwBn++f61Dk3Cy/8GXB9ogAoIiIisjdKm0pg\nOlMlUERERLqLVCuB6TA7WERERET2MIVAERERkQykECgiIiKSgRQCRURERDKQQqCIiIhIBlIIFBER\nEclACoEiIiIiGUghUERERCQDKQSKiIiIZCCFQBEREZEMpBAoIiKSgtraWpYtW0ZtbW1XN0WkQygE\nioiItGD+/IWUlg7n2GN/QGnpcObPX9jVTRJpN2dmXd2GtOecM90nEZHMVFtbS2npcOrry4FRwNuE\nQpOorl5BSUkJtbW1VFVVMWTIEEpKSrq6uSI45zAz19LrVAkUERFpRlVVFYHAELwACDCKrKxSqqqq\nVCGUbk2VwBSoEigikrmSVQKXL1/K4YcflbRCKNJVVAkUERHpACUlJTz88P2EQpPIzx9LKDSJhx++\nn7q6uqQVQpHuQJXAFKgSKCIi8WP/WhorKNJVUq0E9toTjREREenuSkpKYsJduEI4deoksrJK2bmz\nmocfvl8BULoNVQJToEqgiIgko9nBkm5SrQQqBKZAIVBERES6C00MEREREZGkFAJFREREMpBCoIiI\niEgGUggUERERyUAKgSIi0m3V1taybNkyamtru7opIt2OQqCIiHRL2rdXpH20REwKtESMiEh60W4d\nHUfrHO59tESMiIjstaqqqrRvbwdQNTWzqRKYAlUCRUTSiyqB7ad7uPdSJVBERPZa4X17Q6FJ5OeP\nJRSapH17W0nVVFElMAWqBIqIpCeNZ2s7VQL3XqlWAnvticaIiIh0hpKSEgWWNgpXU6dOnURWVik7\nd1armpphVAlMgSqBIiKyt1I1de+TaiVQITAFCoEiIiLSXWhiiIiIiIgkpRAoIiLSAbSFnXQ3CoEi\nIiLtpEWXpTvSmMAUaEygiEhmSzZ5ora2ljfffJNTT52ipVYkbWhMoIiISAdIVuULHz/ttEupry9G\niy5Ld6NKYApUCRQRyUzJFlRevnwphx9+lH+8P3AQsBhVAiUdqBIoIiLSTsm2VquoqIg6XgI8AHyJ\n3NzRbdrCLpVJJZp4Ih1NIVBERCSJIUOGsGNHFfC2f+Rtdu6sZvz48XHHR5CdHeB//ud2qqtXMGXK\n5JSvkcqkEk08kc6g7uAUqDtYRKR76chdMObPX8jUqZfEbK02ZcrkpMdb286W9u/VHr/SWto7WERE\nMlI4nAUCXhWvLeEs2pQpk/na145pEiqTHW+NcHdzfX3TSSXh86XyGpG2UCUwBaoEioh0D92taqZK\noHQGTQwREZFuo6MmPSSbyJGuy7WUlJTw8MP3EwpNIj9/bMJJJam8RqQtVAlMgSqBIiKdp73dt9Hj\n/4BuWTVLZQxjR45zlL1bqpVAhcAUKASKiHSO9nZ1JgqQQLsnbIh0Z5oYIiIiaa89kx5qa2uZOvUS\n6uvL/fe/zdSpXoCsrl6hqplICzQmUEREukyydfjCXbvNSTT+r76+iDlzHqKkpIRx48bt8QCoBZ2l\nO1EIFBGRLtOeSQ+JAiRs5Kabbu+SEKYFnaW70ZjAFGhMoIhI52rrpIcbb7yZa6+9CW/v3mrgfvLz\nb2XRojmMGzeus5rbhJZxkXSiJWJERKTbaGv37cUXX0R2dgD4ObACGJFyd3JH6m5L04iAQqCIiHRj\nJSUlPPLIbEKhH5Off3yXraHXnrGNXUljGDObZgeLiEi3VVtbywEH7M/y5Uupq6vrstnA4bGNU6dO\nilmaJp27gjt6ez3pfjQmMAUaEygikn7SMcR0lwWdNYZx76bFojuQQqCISHrp7BDTXcJcWy1btoxj\nj/0BmzYtjxzLzx+7xyfUSOfQxBAREdlrdeZEjL1hqZeWxvp11zGM0rEUAkVEpNvpiBCTKChF70Ky\nadNy6uvLmTr1km41cSKVENue9Rll76EQKCIi3U57Q0yyoNTdl3ppTYidMmUy1dUrWLRoDtXVK7p8\nPKXseRoTmAKNCRQRSU9tGbvX3HhCoFtPmNBYPwGNCRQRkQzQlkWmm6v2dfduUo31k9ZQJTAFqgSK\niOw9UplZ3J1nB4eXzoler1BdvZlFS8R0IIVAEZG9y94elLpziJX2UwjsQAqBIiJ7HwUl2VspBHYg\nhUARERHpLjQxREREpJtoaXFnkc6gECgiImmrPeEo/N533nmn0wNWe9rZmh1KFBalIykEiohIWmrP\n9m3h906YcDYHH3w4EyZM7bQt4NrTztYs7tyW6yg0SnM0JjAFGhMoIrJnpbKMS8vvfQY4Hei8hZ/b\n005IfXHntlwnPAM6EPDWDtzbZkBLchoTKCIi3VZ7tm9rfG8u0LZz7Il2QuqLO7f2OnvDHsjS+RQC\nRUQk7bRn54vG934OtO0ce6KdkPoeyK29TnffA1n2DIVAERFJO+3Zvq3xvaeTnZ0PHEkodGinbAHX\nEdvMTZkymerqFSxaNIfq6hUJu2xbex1tHyep0JjAFGhMoIhI12jPgs7h9+bl5VFXV9epi0LvqYWn\nW3OdvX1XFElOi0V3IIVAERHpjrQrSmZSCOxACoEiItIeCmOyJ2l2sIiISBpozzqCIp1JlcAUqBIo\nItIxMq0i1t51BDPqfu3eDT17Jn8sKVMlUERE0komVsTas1RLRt2v3bvhqKNg8WLv8eLF3uPdu7uy\nVXs9VQJToEqgiEj7tLci1l219XNn5P1avBjOPBN+9CO47z546imYOLGrW9UtqRIoIiJpI1MXL27r\nOoIZeb8mTvQC4PXXe98VADudKoEpUCVQRKR90qWy1Vlj7Fo6b2uvmy73a49SJbDDqBIoIiJpoyN2\n1mivzhpjl8p5S0pKGDduXMqft6PuV21tLcuWLUv/PYN374arr/aC38yZ3verr9aYwE6mSmAKVAkU\nEekY8RWxPbnTRmdU1jq7YldbW8ubb74JwJgxY1p1zvCOIYGAt4Vc2u8YotnBHaZbVAKdc0Hn3P85\n5950zv3TOTfDP17knHvROfeuc67MOVcQ9Z6rnXMrnXPvOOeOizo+1jn3tnPuPefc3VHHA865Bf57\nXnfO7Rf13Pn+6991zp23pz63iEimiq6Idebs1/gKWGeNsevssXuLFr3MqadO4ayzrm7VPaqtrWXq\n1Euory9n06bl1NeXM3XqJeldEYwPfAqAnc/MuvQLyPG/9wT+BowHbgV+4R+/EviV//PBwJtAL2AI\nsIrGaub/AeP8n58Hjvd//iFwv//zZGCB/3MR8D5QABSGf07SRhMRkY5TU1NjoVCxwVsGZvCWhULF\nVlNT0+5zz5u3wEKhYisoGGuhULHNm7eg067XmZ+j8dzlBhUG5Smfu6KiwgoKxvpt8r7y88dYRUVF\nu9sl6c/PLS1msC4fE2hmW/0fg3jhzoBTgEf9448Cp/o/n4wX4naZWRWwEhjvnOsH9DazZf7rHot6\nT/S5ngaO8X8+HnjRzDaZ2WfAi8DXO/jjiYhIAokqaD16DIp0fbZVsgoY0CljEjtzrKNXTSwETgd+\nAJyOWX5KVcYhQ7wuYHjbP/I2O3dWM2TIkHa3S/YeXR4CnXM9nHNvAhuAl/wg19fMPgIwsw1AH//l\nA4HVUW9f6x8bCKyJOr7GPxbzHjPbDWxyzhU3cy4REelkiULK55+v4pRTJrerW7i57tkpUyZTXb2C\nRYvmUF29osPGx3XWefPy8qivXw+UA8uBcrZt+4i8vLwW35sOE3Ek/fXq6gaYWQMwxjmXD/zeOXcI\nXjUw5mUdeMkWB0qKiEjnCoeUqVMnUV9fBGwEHmHbthFMnTqJr33tmJjAkuoEkthw6U3UiK6AlZSU\ndEoQSuW8rZ0EU1dXRyh0APX1jYE2FBpGXV1dSm2aMmUyX/vaMZmz7Zy0WpeHwDAz2+ycW4zXJfuR\nc66vmX3kd/XW+C9bCwyOetsg/1iy49HvWeec6wnkm9lG59xaYGLce8qTtW/mzJmRnydOnMhErV0k\nItIuU6ZMZp99ijjttEv5/PN3AS+khCt34dDSmlmu0eEyK6uUnTur06IC1paZul5wXUt0oIV1rerS\n7azQK+ll8eLFLA5vudcKXbpEjHNuX2CnmW1yzoWAMuBXwARgo5nd6py7Eigys6uccwcDTwBfxOu6\nfQn4gpmZc+5vwKXAMuBPwL1m9oJz7hJgpJld4pw7GzjVzM52zhUBbwBj8brF3wAO98cHxrfTuvI+\niYjsrVpaYqU92651ZQUs+vpAm5eRCYfH6ECb1su8SFpIdYmYrq4E9gcedc71wAtiC83seT/QPemc\nuxCoBs4CMLNK59yTQCWwE7gkKp39CJgLZAPPm9kL/vGHgcedcyuBT4Cz/XN96pybhRf+DLg+UQAU\nEZHO01LlLjzGL7pLNL5SmOy8XVUBi6/6XXPNz9r0GUBdutK5tFh0ClQJFBHpXMkqd91t+7Rk7TVr\nYNu2JXSHzyDdX7dYLFpERASSb6vW3Wa5JpudPH36Fd3mM0jmUCUwBaoEioh0ra4e45dIojY1V7kE\n0u4zyN4p1UqgQmAKFAJFRCRac7N903UyRzoGaekcCoEdSCFQRETCUhmnmG6Bqy1L1Ej3pRDYgRQC\nRUS6VjqFqmXLlnHssT9g06blkWP5+WNZtGgO48aN68KWJdbdJtdI+2liiIiI7BXmz19Iaelwjj32\nB5SWDm/XtnIdobvty9vcVnqS2RQCRUQkbdXW1jJ16iXU15ezadNy6uvLmTr1Emprazv9usuWLUt4\nndbMWG7uPJ0p+rrdLbTKnqMQKCIi7dZZYaczqljxbY1/HF153G+/A7nxxpubfK4pUyZTXb2CRYvm\nUF29ImZ8Xfh8c+Y8RGnpcCZNumiPVjDjK6eLFr3crZbZkT3IzPTVwpd3m0REJJF58xZYKFRsBQVj\nLRQqtnnzFnTYuWtqaiwUKjZ4y8AM3rJQqNhqamo6pK3Tpl0a83j27AejrrfAoMjgAMvOLrRZs25q\n8brh8/fuPcYgZHBrpN2BQEGb251ITU2NVVRUWE1NTeTnysrKpPersrLS5s6da5WVlR3WBklPfm5p\nOd+k8qJM/1IIFBFJrKNDWiLhYJWfP6ZdITNRW72gVh55HAwWWu/ehxrUGDQNg81dP/H5i/1zmcEw\nKysra8+tiIgOs1lZvS0QKLCCgrEWDOZbKHSofz3vKz9/jM2adVOnBXVJP6mGQHUHi4hIm+2JSQfN\ndb22RqK2wiAgN/LYW0KlGngJGIK3xf0lwGJgZbNjEpOfvwpvPN76NrU7Xuw4yRfYubMXO3a8wqZN\ny9m+/Vnq61cRPf5vx47/cPPNd+7xcZWS/hQCRUSkzfbUpINk28rFa25sYl5eHtu2fRDTVlgDfB55\nvGvXh9xzzx1kZ/8IWEFjGGwMdvX1RcyZ81CT8ye6F7ASuBCYSM+ejjFjxqT6kZOKDZtVwNCo9k0k\nO7svweCEyPi/6dOv0OxgSSyVcmGmf6HuYBGRpDqqu7aj2pGoyzP8XCg01CBkodDImDGB8W2vqamx\nWbNusuzsQoOcuC7eAuvZMycyti56bF74OjDMIN8g1+AAg2ybPfvBDvmcsd3ONX5XdWx3fGVlZcx4\nwc7uspf0gsYEKgSKiOwp0UGoq66fLOg0fa7cgsF8q6ystJqaGisrK7OysrKEba+pqbGzzprsB8Ex\nBr0NCgy+YMFgoU2bdlmT4BkdIPPyRlogkGdXXXVNp42TzMrKs0CgoNkQni5BXfaMVEOgdgxJgXYM\nERFJb83t4gEkfO6KK87g5pvvbHYrtfnzF3LBBd9jx47dwK+B6UB4543FwAnA3wjvxBEMTuDNN19j\nxIgR1NbWMmfOQy1eI7wbSl5eHnV1dUl3RYnfNSX6MdDijirvvPMOFRUVjB8/nhEjRrTi7kp3k+qO\nIV1eZesOX6gSKCKS1hJVArOzC62srCzpsileV2/yLtLGc5Yb5PlVwAOjZt5WxD02v0KYH6kIttQN\n29hNfajfTT00YaWuvcvwdOYyPpJ+UHewQqCISKaI7oL1ukgbl01JNPZv1qybrKBgrD+mrsKgxvLz\nx1hZWVmkW7uiosJ/jfnLxOTHjQ8s95eYiV8SptxCoWIrKyuLer9FlmupqKiItDnxkjLlMWGxvWP6\nNCYw86QaAnt1aj1SRESkleK7PVt63ZIlf+WXv5xFIDAU53pwySVf55575lBfX86OHV437cMPT2L5\n8qWR7laAG274FXAQ3uza/1BXt41TT50S6bq97LKLo2b7Tgb60rPnCWRlTSQQGMLOndVMnXoRDz00\nge3bS4BPgPuBiWRllQJEvd9rR/TM6fAs3/r66CVlSoHcyOzdkpKShK+Lfr4l7X2/7MVSSYqZ/oUq\ngSIie8S8eQssO7vQcnMPsuzswqTdluHuzezsEU2qcY0LPieuwJl51bGsrPy4KlzswtGQY7165TaZ\ndBG9O0dFRYUtXbrUgsF8/701Bk9YdnZhzGzhRBMy9upK4K5dzT+WToW6gxUCRUSidfUM3pZ4way3\nv+TJWIMiy8rKa9Le2FBTYTA6JvDl5Y32Q1lsMIzeLm3WrJvMW7rFor6+4J8v/HhMJNDFzx5OtP2c\n1/YcgwMsECiIWW4m2X1vHBM40iBk2dlDmh0T2NbZvXt0dvCuXWZHHmlWXu49Li/3HisI7jEKgQqB\nIiIR3WFiQFlZmTVdky+nyVZrsWP1ord3awx84T2Aw+EqesJFTU2NPymkyBJXAr1qHhQa1Fhe3kib\nO3dus5W57OxCy85uul5fKoE7vrKY7D3tDfF79H8CysvN9t3XbMYM73s4EMoeoRCoECgiYmbdZ2KA\nFwLjq3NN99tt+nlu9QOcN8M2vChzZWVlVDdt4+dunLCxwA+Qh/nv/7Z5M4BzrHGx5+MNgpaTc2Ck\nezo2hHpfubkHWm5ubEUyvgs648yY4d2IGTO6uiUZJ9UQqG3jRET2cntif9+OMGbMGAKBWqK3XcvK\nqmXw4MExryspKeGuu35FMDiB7OyRwEyCwX0JBquZPfseLr74IgDq6urIzj4AmOi/c1TchI0ReFvD\nnQb0A+4GsoDXgVXAX/0vx9atO9m2zXH++d8jLy+P7ds/AOYBXnt3766hoWE1nb19XrexeDHcdx/M\nmOF9X7y4q1skCSgEiojs5fbU/r7tVVJSwty5cwiFJhEMHgIcSa9e+3D44Ucxf/7CyOvmz1/IT35y\nFYHAYMxWc/vtN/LXvz7D6tWrIgEQkn9ugLvu+hWh0CTy848nO/vXZGV9Quw+wbXAdmB/4GFgE/A/\n7NzZwJVXTmf37t3AlUApWVlf4ZFHZvPww/f75/T27H344fszc/bt7t1w9dXw1FMwc6b3/eqrveOS\nVrRjSAq0Y4iIdHfz5y9k6tRLyMoqZefO6oQ7V6SLd955hzFjjmT79mfxqnhvEwpNorp6BQClpcOp\nry8nvOxK+LlEgSv6c2/b9gFmu8nJOZAdO6q44YbplJTsw/jx43nmmd9z7bU3Ag6YAdwKDAbeA+4B\nHgDmAGcB64EcwkvLZGXtZO3aD5rs4tFRAbAzztnpdu+Gnj2TP5ZOleqOIaoEiohkgClTJlNdvYJF\ni+ZQXb0ibQMgJO/GraqqanXXdvhzP/XUr+jRw7Fz56ts2rSc+vorueKKX/LjH9/D4YcfRUlJCdnZ\nQWAKMBNva7h/4G0JdyXwH+BzoAboibdl3HJgMTt3NvDmm28CXjVz3LhxHRbW5s9fSGnpcI499geU\nlg6PqYimtfjApwCYllQJTIEqgSKSKdKh6lRbW5u02getqwSGxe4tXAscCNwHHAusJxSaxA03TOea\na65j584BeBXAsAOALcAu4Ft4AXBVzPNlZfdz3HHHJfwsqewL3Nr70G0qgtIlVAkUEZFWSZeqU0lJ\nSdLxdfHPZWdP4JprftbiOWPHBz4E7ADuBIYD72BWENl1BNYQPzklENhKXt4AsrOfoVevmpjnA4GP\nGTNmTJNrhu/nhAlnc/DBhzNhwtRW3dfuMqFHurFUphBn+hdaIkZE9nLpuIxMTU2NlZWVNVmoOfzc\nrFk3pbz24bx5CywrK89fCiZ+LcJCg+wES84Miyz6HL3G3rRpl/rPH2AQsmnTLk3Ydu9+ljdZx7A1\n6we293eS7guES+dA6wQqBIqIpCrR2nftWeeuufCRajBpboHrlgJSZWWlzZ071yorK+NeW+avJ2hR\nX0OsZ88B/iLR4WNDLbxtXHQ7Y8NdhcVv8db0flaYt/tJ2+7r7NkPWjCYb3l5IxPeg+buY3sXCFeA\n7L4UAhUCRURS1pGVwObCR6rBpKWw1RiyavznayLhatq0y/xK3YEGITvzzMnN7DDSWPXzdhBZYNF7\n+MbvWFJRUWGhUGyIDIVGNgl1NTU1FggUtKsSGL5XvXuPieyCkup9bO/vszvsMCPJKQQqBIqItEpH\n7C/bXPhoTTDxwtb+foAaa1Bs2dlDImGrsrLSevbMMSiw6H2Gly5d6oe62O3gvG3iYoNfbu4hCV6b\n43cPL/AD40BbuHBhpF2VlZUJzx+9L3H4Pnjdz0UGQyJBM9X72t77WFFRYb17j2lTBbJVAXL79uYf\nS5dINQRqYoiIiAAds4xMc5MZYp/zFmPu2XNAwokOeXl51Nevx1uqZTlQzrZtH5GXl8f8+QsZPfpI\ndu/ujze/8RfAYpzryd///ne89f0arw+DOP/8KVETTW5l9ux7+NnPTk/w2v7ALf7jg4AQ5577febM\neQjwlq8JhfoBk4CxwCSys/tSV1fX5D706jUUeBd4ElhOMGj87//OT+m+pnYf+wPLgP5NJowsWfJX\ntmxZQVsWCE95QsqOHbDPPnD33d7ju+/2Hu/Y0eI1JE2kkhQz/QtVAkVEkooeO5ZaBetWv8I32qL3\n+o1WVlZmweAhTbpdy8rKmpzfO1eN9e59mC1cuDBppS5+jFuyql4gEN4/OHy83CBot99+Z8pjAlOt\nGDZ3T5u7j1lZvf0qY2MVNNyG2bMf9K8dvs+jkt7n1l67ibvuMnPObMIE7/tdd6V0DelcqDtYIVBE\npLMlGjvWXLdyY0BJHjDmzVvgd9/mNHldWVmZ5eaOjgmHMMbgCQsGC62mpsamTv2eQdC8yR3e7N1k\nkxwaZ/p+IfJaL0ge4J97gR+kDowEqVS6zcvKyvw2FPntKzII2L333tvqcXm5uaNirtM43rDx3gQC\nBZGAGAzm+wHbLDxmMje36bjFVK6d0tCACRO8X8SECSmfXzqXQqBCoIhIp2qpWpUodCUaq9a792E2\nd+7cBJXEcEAbZpBjPXvm2OzZDyaoBOYYhCwrK8+mTbvMQqFiy8s7zAKBArv99jtbnOQQPZPYzOyq\nq67xz1lu8ZM6wkGzpZmzXgg8wBonrjxokGM5OaNaDlW+cBjOzT3IsrMLI+9pbia3d38PTdru1v5+\nW5wdrEpgWlIIVAgUEelUbVlWJlFwhJD17n2ohULFNmvWTVGzfgvNm/jxhP/YC5mzZz/oV8KG+SFx\nmv98ecIqY+ykkOZnydbU1ERVIYN+BTA2sKY6uaKxWlfjVwJTn6nb1okhTbvcW9cV3Crbt5vl5TUG\nv7vu8h5rckiXSzUEamKIiIikpLa2lmXLllFbWwvE78IBqUw+iN7xo3fvMcCRwEy2bHmb+vpybr75\nTrZv/wB4CegLDAO+DZQAo+jRYxBjxx7GmjUruffey8jJ2R/4b//5XOInevToMQizfWKOmfXn+eef\nj3yO6M/25ptvEgzuDzyCt0fw6pjPt2vXhylNrigpKWHu3DmEQpPIzv4yENuGlnb+aG5yRmo7qtxK\nXt4AgsEqZs++h4svvqjFNsffi+j7k1AgAJ98Apdf7j2+/HLvcSCQ8rWki6WSFDP9C1UCRSTDJetS\nbeuyMjU1NTZ37ly/6zK2kjhr1k1+NS7UbAWtaUWsaSUwO7vIYncD8SqPubkjIlXF+J1HvKVdwhW8\nqQYhy8sb3eYFlxNNZmlPJTD+3OEdVeIn6LRloWetD7h3QN3BCoEiIh2hpUDS1sDRUrfmrFk3+YEs\nx8JbuM2e/WDMtaJDaFZWb3/twMbXewFvqN81Osb/vp8/Tu8tPyDGTkAJBAosO7swEmzjr5nKZ45/\nvrmwnOxcLQXs6MAWCBRYVlZeu8JbOm4dKG2jEKgQKCLSIVt/dfSWctHtCk/0SBZ0oqtd4dfGB53K\nykq79957o8b+1Rg8YdnZhbZ06VLr1SvX4PcWXtIlvKSM91kOsMaZtI2fraysLOa+Rd/H+GpZsmAa\n385Ev4tUdv5I9PurrKy0YDB2rKNXNa1pc3jrjN+zdA2FQIVAEclwHdW1l6hCFAwWtrjmXUsVrvB2\naLfffqdVVFRYZWVl0opbZWVlwipVOBjm5IzyK3oLIgEmK2s/Pygd4HcTh3fuuDUqOBVaS9u6xd/H\nxu5iM7jWIGi5uSNj2pNKNa2tlbd58xb4y8DETlrxKp0VbQ5vqgTuPRQCFQJFJIN19F7A0d2yELJQ\naGizwbK5alii2cEXXvj9pOsNFhSMtWAw3+L37O3d+zA/DCWqhpVb08WaCw3uNG+plkOjAuEC/33D\nmuzRG9ter8Lova/GvGVfQn4l0ZuRGwwW+uMcm+5pHC9R5S03d1TMXsXJf6/lTcJreyuB0b+39mwd\nKF1PIVAhUEQyWEd17cWGoEqD3n4ASR4smwugidYJhIObBLamS7s0DXXBYKHl5o6MO9cwg4EG+eYt\nAB39XHhR6Xy76aab7NJLL4uM/evVK9d69cqNLFUTvSaf197wotFj/TA81Q+VsTuX5OaO9MclJt7N\no6X7BDkxawIm+r023r9wm7yFrrOy8mLCW3vGarZ3CIF0LYVAhUARyWCN693FrrHXvnFiFX6waT5Y\nNhdAvS3P4qt3TQNbbu4oy809KOZYMDjYsrLyLSfn0EjXa9NxccUG+xvc1iQ0Qo4f9nqb10WcY1lZ\neXbVVdckDa3eDifZFj9LGbL8wBkdMkdZMJjvj0FsvO/h3TwSCVfevHMV+cGu+S7k2M9cbpBvubnD\nY28eWYYAACAASURBVMYxpjIUoFVhb9eu5h9LWlEIVAgUkQw2b94Cf7HixrDT/hmjLS963NKSKDU1\nNX5ICpm3kHGxwU9TqATe6r9mqEHQevXKtXnzFkRtQxc+162Ratq0aZf6Yw8Ps2Cw0K/8Rbe/3KC3\nZWXlNqlO5uaOsoULF/qfY5o1biNn5lVEA3GVwHKDoE2adIx5lcKxfnsWtFiB9bbCO8gaJ6s0X7VN\n9JmbXzqn5XGOzf7Z2LXL7MgjzcrLvcfl5d5jBcG0pRCoECgiGaojxwOGl2ppXIYlz7Ky8pvsZ2sW\nGyyysvIsEChoMrascVuzawzyzFuuJWQ9ew42b6zhyCZjAvPyRiao6hVZdnZhpFoXDOZbXt5Iy84u\nsh/84IeRSSvh53r3PtSCwXwLBA6yxm3cwnsC51jPnrlNqoaBQL6/vEyNNY6/W2DeLiZf8Ntf4AfT\nbINB/vfoiScFFgjkNTuJJtFM35Z+X9GfOf730NJQgFb/+di1ywt+++5rNmOG2T77NAZCSUsKgQqB\nIpKhOmo8YHSoy84utFmzbrLZsx9ssp9tsupfdnZhZCHjsMYq1mi/ktY7ppoWDObHBKbwotLe7F+L\n+hpjubkHxgSb8CLT0Uu3xLbp2qgKWuws4aysfL/yGNst672uPCr8xa4p6HVlx44B9MJhjf+eHAsE\nDm4y4ST6fgSD+Zad7YXh7OxDkr42XrLu3JZCXqv+fERXAWfM8F48aJCqgGlOIVAhUEQyVEdUApOd\nI34f3vDCyl535gHNBoum53zC4sfVJQojiSdQNFYCk73GqwCGu3mjq3nhczSuF9i792F2/vkX+NW8\nxm7ZUGikBYP5lp8/xgKBPAsEDo4LowMSBMMcg4XWdBxh7B6+sYG42OB0i15upj0zc1tanLpVfz7K\ny80KCsxCIbOcHLP8fFUC05xCoEKgiGSw9i71kWz5kvjJGl6IC0+CaH68YNNz1jQJUMkmUTSOcRxm\nicY4JmpvXt7IqG7WCvMqgNFtH2WNO4eEomb1llt4YelQqDiyfuHtt99p8d3S3nviJ4gMs0BgQJNQ\nHJ44Eh4b2XRSS7jq2Bhi77333iZdyalO6Gjuda3687Frl9nAgd6HmDFDYwK7AYVAhUARyXDtWeoj\n1UqgF+LClTOv+zPReMFk52ycYDHGwsupJFo0Ovz+6L1yU2lvuEs4GNyvSYDzHh9s0RNK4Fz/+IEG\nITvzzMmR63qf/Vb/9aOigmN8MMyzhQsXJpy5nJOzf2QWb9Olcr5g4cWe4bKYdkybdqmZdezevi3+\n+QiHvPB4wGuv9b6XlysApjmFQIVAEZFWS7Q9WnS1KP5Y/HIoicYBRot+fzBYGDXxwltYOTt7qAWD\nha0KOeGQFl7qJb661TjxIjbAQf/Idb3gNdggGFWNK7eePXP87u7R1rgjSbi9Q8ybORweL+gtNh2+\nbuws3gKDXIMDku4s0lgJrEwYWJ977rk9t6NHeCzgokXe97vuin2sEJjWFAIVAkVEWiVRlSlRtSh6\n399Ey9C0VGFKvBVcjcEDTcJPc+vlJWpDr165NmvWTTFjBefOnRs3NjAc4IJR1wovQbO/eZM9vu8H\nxviJIo27cng/F/qPayw398Amu3147ctL+LnCQTC8hM2FF17kV1v3s6YLXR9gPXtmN9k1JdmEjnYv\n+Lxrl336+9/bjsJCq/vpT2NnBCsApj2FQIVAEZGUtTRZIDpUNLcWYDjY5OaOTqmSN3v2g/7yLDnm\nTcpofnKJWfTew4c2CVfhCSOVlZWR2cLJXuctE1NosXsKFxsckuD1xQZl/msPtPAagF5IvKnZwOqt\nAzg64eeKD2s1NTW2cOHChJVAmJswTMZ3n6ca5pPatctqDzjAjgv0tluC/czAPi8qVvjrRhQCFQJF\nRFJWUVHRpMoUCo20ioqKuPX/elsgUJBwNnBe3kh/N46WJ3qYeWHFG2cXnhzSdHJJMOgFusTVwwrz\nZtY2difDGAsEBvjdvwdEVfG85WECgRHW2K27wA+BffxwF39ei/oaZuHt2bwt4yrN6wYvMG9pl+Rb\nvbVmNm74c1544UX+tQ7wv19qYJadPcSCwcJIl/e0aZfFBL5E3cxZWb1bNY6wZv16+zc9bAshqyNk\n9QRsJ9inTz/d+j9Y0iUUAhUCRURSVlmZeBza0qVLk+wY0jSwJZspG99FahYdjJ6w2K3ovMklwaC3\nn3AoNNQCgQLLysqzgoKxFgzmR4XVGvPWGYxeoy/HvAWboz9HgR/29resrDx/HGN5VOgLn2d01Hmb\nLknT2A0cigpoBRYI7JfwM0ZLZTZufAVvxozrrVevkHlb4NVYeB3FpUuXJgjE4dCcHzfhpOkM7JbG\nEVa8/rp9M+cA24UzA6sjZFcGB9iWkSNVDewmUg2BPRARkYxXV1dHKNQPmASMBSaRnd2XVatWEQgM\nAUYBVcBQ/+cS4AHgS+TmjiYUmoRZA7AeeNs/69v+46aqqqr8844BVgGLgWVAX3r16oHZh8Dz1Nd/\nwI4dr7BzZxabNr3A9u3PUl+/yj/3x/7ZFgPL/e8G9PfbiP99X+D/s/fuUXJe9ZXo7np8j3p1dfsh\nW7Kklrol69GSJTMYQ0gCWRDyAhw8xmCIYdkGYyLkxCRY1ozl2IomEYog0dyJtCSS9M1FojuZxLnx\nLJIK3FvJpAdIk0BMJhUGCDEJgdzyMDzM2JFoa98/zvnVOd/5TrVKUtuWWmevVatVVd93vkeVV23v\n32/v338C8Pf43vf+GwqFEqLotfp1uZaDAL6g170CwP0AbtTvv1Rf6xX6+XIAHwLwRQD/FadOPYmV\nK1cueH9f9aofwh/8wUfwu7/7y/jKVz6PN7/51sz7Tz75JO6889145pk2vv3tv8Izz7TxS7/0AQwN\nlQAcBTAG4DUoFFbj1a9+Hb70pS/ju9/9rvXZqGstl1fh1Kl/sD6Dj+nztbdZjSeeeMJ/oqdOYdtP\n/zQ2nPpn/CtiAECCZ/D46e/gmY99DCgWF7zOgIsMgzDFS/2BoAQGBAQscRhlrk03I2+h2cHiBlZ9\nbxupHLAjlMiXYrHat+xZLte1QjeilTVVbh0aijzxKdsp8SlJMqb7CGPmDRTjzBo+3Bgb1Y9nIlzM\n9apzb+g1UqoScksrif3Xk7J5P7gKn21cEfhyDk0GY16NzH82RuU7cOAg47jJen0bk6SpjTMDKIHz\n8+QNN5CrV/PZoSH+K8B5/fjW8uVBBbyIgFAODiQwICAg4GzQr2Rpv95vJrCJQ1lJe47u6153U18S\nqMjJo5p4tTMkK4oafcuxSdJkkkios69se1T/HddrZ+cCCwnasUOy+Nbpc1bEMorWWs+3678p6/Vt\nTNNRlsu1gcur/mzESq6HsN92Ntm0ibAYS9zPbMeOnT3TTBw3eOTI0cGDoU+eJJNEPfRBTwN86qGH\nyJe8JJDAiwiBBAYSGBAQEHDWWGgere0Odh2tWQLT1uTrUS/hIZXyVS6volICbbctCYxnMv+iaNjq\nN6ywWFQzdk0P4agmchVNAOcIzBK4gsAxArfRVhp37NjpnPO0RRqHac8NrlTWMknULF83P1GiXRaa\n8+tX+LYTON4zvQhcsuaSTZcISx6j3zSTJagDu4M/+EFmTrZcDuHQFyECCQwkMCAgIOB5QX+iM9cj\nPK5aNjs7S38MS7vnKLbJjXIRSyj1G5x9lWFiaCjRREkiYZZpYhjRlH0fZRw3eOzYMR3b4jOBDFPl\n/k3yve99L48dO5YLwD5y5Kg2YWzpq671i9IxM4vXsVyuZkikTdbkGLXaZM8co9RY5dB23b6+z6Ff\njqB7jq2PflRd38c/bnYuFMhqldy4MZDAiwyBBAYSGBAQEPC8wF/KlP5CRXhcMrJv3z7mncQTLJWq\nPVIjhGhmZkZH0nRppmlkp3889NDDud43cw5SVrVVw0S/7rqTqc/rTmYjWq5iqVTnrl0PcHZ2NjcS\nzs1UlIxCFatTY7ksvYZ28HRKySR01URRBWu1bYyiYR44cHDBfEYhzWczUeTEiWmWy3UWkPITiPnD\nxQqfuuwyngY4H8c8DZDLloVS8EWIQAIDCQwICAh43iCkJU0nLRXOEB6bqOzdu0+bQrLRJUDKY8eO\nOeuttcjYCIF7qMrHpMkHXMM9e/b0USNbBFZQxaz4eu5qnvPwxcwIoUwJlKlGzpljCckV9Q5Yk7n+\nJGly167dutdR8gb399aP4+YC5fXHe0TxTGrfoP1/3W5Xq6vK6PNKfJxdDPNfAT4F8LWVCf5cKeWp\nKCaffvr5+RIFLBoCCQwkMCAgIGDRMEhPWafT4dTUFGdnZ3tKWK02mTEoZMOh7V68JoeG0h5RVNu9\nX5O0doYMnTnPUF4f1o8JKsdwNtxaHfcDBIpUPYzK0Vwo5MezmfL2JFWmYJI5L3HlqnO7TpNGUSuN\nEtrpdFguV6kUQLN+vb6tR+Tm5uY87uitjOPGgn1/Z/NZzc3NaXX1ehYwz0/gRv4W3kgC/C28lp/A\njSzgM6xb5DTg4kEggYEEBgQEBCwKBul/840qU/s1Wa/bRge3/NqlchRXWS7XekqhIooTllpnTCPF\n4jLapdodO3ZmzsGokXZUTNtDHmO9tloniq7umUAWLm9PaqWvQSlHSyxLfp9Juj2RxkntH//W6XS8\na1Uqa9lqtQZ3+y4AVwm8Fx/ksxhiGwmfxRDvxQc5SE9hwIWJQAIDCQwICAgY2BXabzufuuVTnlzS\nlCTNPpEnM8yXZZsEOmw0trPVaun+Ofv9ht7mUU3cHibQZrlc4+zsrPc6VB+hO/pNyOMmTf6y7tso\nGu6td+LEtO4xlMzAq/Tf2zRxUucMzLFaneTU1JRXvQNirzvaEORt3vFvZmzcVio1s0pgTU9VXcit\nPShUT2Ct1xP4fw6V6CqBZ5ouEnBhIpDAQAIDAgIucfjUOR9h8G1H+hUrYJS1WjYc2denVq2u95Aw\nmb9boyqpZo0SaTrKmZkZ+su2o7RjXoB39Eijez1ioFBZgubcFbm8QpPKa5g3pqxjHDd619/pdBhF\ndU0+JVBaegJNP1+/4GYg5c6d9y6YIbhQtMuBAwcZRTWq0vMOTTyvo20k6ffZDYT5eXa/+lV1D3/5\nl/ns6Cj/5qafZBdD/PF0/JxVxoAXHoEEBhIYEBBwCcOnzkXRcM+xapNCHwFRJUkpd9pESfWm9VcC\nu1QZeA0PKbLn7yb84R/+ESZJM1PWbLVa9BlGfH2AhULKKKqxUhnvqW3SdxjHyzVZU/mC4rA16+Sn\nn0jJV9QvVZZ2CekWAvdqQraOcWxUPiFkKsMwZRyvOqM5Y25ujq1Wq6/Z4/77H6ApjQ9T5SA+3ssY\nPBs3cAbz8+SLX6yCoX/lV1QMzJVXktUqv/k7v8OnJifZ/frXF+W7GPD8I5DAQAIDAgIuYQwyhixN\nR3XkSNYEkaZS3tzCfOk2H2dCmrgRISzlcqM3vUL16NlBzl0CE9y3b19v5JztjFW9gyMUowZQ8pCx\ncYvkTRBosFyusVSqaoImbuKDBB5hHDe4a9duZtW/aWt/E1Zdr2/joUOHWChUPESxqc+/3TNq2DDk\nub0gMXMVPN8Ukn7qInCU9fo2Tk1NnXUuYA8yHSSK1I6Fgvr7/ver90MkzEWNQAIDCQwICLiEMcgY\nMpmh61PZjh07pve3p20kfM1rfmSBWcDZXj7psVOZgGVNqK7XRKpIIGWa5s0mR44cZRTVmKZrGUUN\nXnfdizznmOh13Ouzt9uvn2+nmjTi26dBEyZtrj9JNur1dmqCqNYoFOKM49m9B4Y89ydmC6m0tira\nzyUM1JkkfiXQniSyIJ5+2hBA+3Hy5Pl98QIuCAQSGEhgQEDAJY6suUEmZzxI6W8zSuAai+gME4hZ\nrV6nVbVUE6R9FIXNV+JUZdy8WhfHDVarG6kMHVmi5aplnU6He/fu0yPZtjOOm3zooYd1pMpKmhFz\nDQKX0R/yvILGdewjwQdp+hFTKpVRXWcUbaSb36eIa4dKQU15661vyjie3VKwIm0pVQ+f2i9Jmpyd\nneXU1FTPAexT8GZmZnrbkIos+h3HY9y7d1/muAtNEslhfl4FQN90U54EfvCDz90XMuB5QyCBgQQG\nBARc4jAxIDJuTZQx3wzdNlWwsj1147gmdnlC5ZY4+/fyPUj/fOB1moyqX6JyeZUOUvatsUaTyDqV\nMaJOoyy620rI85ze1uY4E5rwVQncpc9LRbwMDcVMklUEVtGUrIVYrtfHirRRQ+6nUt5mZmb0ffad\nywSHhqSncT2BlHfeeddA/Zqkbc7ZyoXc2QtNEslhfp7csIEcGsoSwJERNSYuqIEXPQIJDCQwICBg\nieFso0CyilN/IidqUrW6nlk1r8t+o9WkxCnnpEwXEcWIoYjalbljZidvtPXr8tw3wm0rFTl1CV9D\nkzlzvGJxhCrKRfL5fLOJDxMY85zXMJVK6IY9p1TTQRKaHsTr9fs7CVSsnkchuEIehSzmyeqBAwcz\nWX++nkA7V1BmCPdT+M5qbvD8PNlqmQ2FDJbL5FNPndd3NODCQCCBgQQGBAQsIZxLFEhW5ZuiO6XC\nJgn91CQV55JX6NJ0tBeqrHrgbNftYQJlFotCqGxSJ2qcEMTtev119JdwRzUJ7DcSrqVJ2h5N1Eb0\n9c5RqZASKj1KVdLuUKmK7nldp8/DVfMiAvcTmPWSuWyJ23Y/S//lnOfc13HPnj29EvHMzMwZCdyZ\n/gdg4LnB8/PkjTeSBw6YgxUK5D33BBVwCSGQwEACAwIClggG/oFnnizs2HEvs/l6+xdc48SJad2H\nto6mfNtluXw141gZF2QOrimBSum1q4nWKIH1LJVqzKtx0odXscjao9Z29ig5OV+fmpYlXMWiuHyn\naZTAWD9SKqXver1f0XNew1TxLzZZG6cqDwuBdMncBO2StulJFJWyXxRNyjjeQGWMWcMkaerezXOI\nenE+uwUniczPq8fHP04Wi0r5q1TUY9OmMCN4CSGQwEACAwIClggGLfW5aqF//FmaKyu6xHF2dlaX\nJ9uZ/Q4cONgzbqggaCmBdqnKv8N0FUNjLjE9ber5nfq56sm74453WHEyiX4v0mtup1IkUwJraaZ9\nbCIQ841vfJOOZRGydVTvJ4YY+5zaLBQk8Dm1yGaV+X5Em2g2mXcWZ5XAcrmhyegaihEnTSf1a34i\nLuXxcrl2XqPg7OBpr2IoZpCNGxUJvOwy9UVasUI9f8lLQizMEkIggYEEBgQELBEMogT6tlHzfrMR\nI5IvJ/vaxDFJmrzlllt12PIYzYg1Rd78o+DEPdugr6evWt3KSmWcttkiSTazXK6xWs1Grfgy9hSZ\na1GVdhMql3BC4Pv1exOM46Z2OE9rouaSuYo+R1Epl1ukTEjqYQK79f5CHoXgzmlip4hjmioSfcst\nt/aIW5KMsFisZo6bJCO9uJZOp8M9e/awWp3M3B+lWM70nX4yCAZuFfjoR8nhYRMNUy6r5+12IIBL\nDIEEBhIYEBBwgeFcZ7yS+VLf3r37cu5cd0ybyrPLqlc2ecwSx2mLAI3o521N7joEyDRdq5U6m8SM\na1et30VcLje8ztlqdWMua29ubq7PqLmrmS3ftukvM7epZhNvdta4ikqplBK0fY6iTArxK1G5eJv6\nvVHKqDZlHGmzWKz0nLxCnJVreL1FGrsD5QOqYyYsFJLc92KQ78vArQLf+pbq/fs3/8bcmKEh8o/+\nSPUIBhK4pBBIYCCBAQEBFxDOa8arhowyc6NEZFTaQuYNX5nRlJn7GTK6VOXaOU2IEu8xdu6UvkO3\np6/JUqnKXbse0AaSbezXl9hv3q8Jlr6GJrZljr5MwqEh6f/Lln+N0nctsyaTflmCMfu7izu5e6C2\ne78mmiOU3sNyuZYrz6rPSu6PkG21hj19xP2+uKQ//xmae5FrFTh5kqzVyMlJZjYslYIKuEQRSGAg\ngQEBARcIzsbYcS7rGKXNELCFev7y6/miWbZTApKr1Q3Mh0aPO32H2ckiwE1UylnMSmUzk6TJu+++\nJzdNo1ab5Nve9nZGUY31+hY9+k3CnCssFFIWizWHNNlGEiFiI/q4co4VGuexmDtk/q6UbX1Zguuo\n+g3LzM9N3kLlFPaNsJPewjYljLtUqjOOG6xUxhlFtZ7qeejQIX1O3cxxp6am+n7OQKU3H9n+PAf6\nbp08Sa5alSWAhYLqDwy9gEsSi0ICAVw1yCJL/RFIYEBAwPngrDLcznKdanUrq9Vrrde6rFbXs9Vq\nDbRmVkU0BEZIx5EjRzk1NcU4lqkiauxbFF3d62FTfW6jzv51Tco2ayL2INN0VPf8ueHVUoqtEXiQ\nUVTjsWPHODMz06d8WiWwzCJ64mIWt25XE9XjVNEuvtJxQpUX6FP72vp1X2RMOdf7pwhoiyZ/UHIE\nl9EokxOUucudTsd7TjMzMz2Cl5/7rEi5lNddJbivqeTkSUX0PvjBfDj0u94VCOASxWKRwH8B8HEA\ndwJoDrLgUnwEEhgQEHA+eP6UwHNbu9vt8pZb3kR7qsUtt9zaW6MfaZmdneWuXbvpz90bpxqdZnrq\nisXL9XzhCZoxdvaa4gBexzhWymGtts1Zd4Kq769JpeqZ6R1Zt64QzPX6XFxCdVifd4EmSzClCoCW\n44iDeJMmjTEVqU21sWWrfn/fAmSznnktjpvsdrvcsWMnjXKaslBIPK7utiaXh/X1dmlCqLOftVft\nnZ8nb7hBGUDcOcE33qjUwDPEwpxPH2vAC4fFIoFFAK8B8FsA/j8A/zeANwFIB1l8qTwCCQwICDhf\nnDHD7TzWOdPaZ4oPORNJ3bt3X45IRdFGPdO3QhPK7BIgOyvvMM1IN3ubln5fppO47/czgEwzOzFk\nmKXSFdoII6Xjo1Qh0u7cYul33GStJwrmqPN32CKDZpJIHDe5c+fPWK83NOF0ifC1mdcqlS09BbjT\n6fDQoUOWI9rMdL7jjncyq5JWqIithFCr9RZUlOfnVck3SXoncBrgs6USTyeJMosM8F07nz7WgBcG\ni94TCCAC8HoAH9EK4fFB973YH4EEBgQELAYWS1XxrdNvbfkhT9O1VPEmWxYwiDBHLrrdrpX15yN5\n4ogVwmbnAVY0ERM10B6tNq2fb9Hv72C+124bszODR6jUwwaVmjes35/pkaeZmRnu3Hkvo6jBYvFK\nZlW+ZcyaMapUSpx9zAm9vpynjITLlowrlQ2O87rd5x5lTSpx3MgYQObm5vRnY8rIcbwy5+o2PY/Z\nUvSCqu9TT5GbNvF0qZQhgbvi5fwUCvzIh08s+B1bDPU64IXBc2IMAbAOwB4AXwDwmbPZt8961wD4\nfwH8LYC/AbBTvz4C4E8A/A8ALQDD1j4PAPgigL8D8MPW69cD+Jw+t1+1Xo8ATOt9PglglfXe2/T2\n/wPA7Quc5+J+OgEBAUsWF0r5LK8y9f9Bz/7gdwkcZ5I0e45do0KZcGejrsk83lUENtLOAzQzhH25\ngiOZ4ymC4yqFNf3aWk3+Xk+V19eyHmOsVtczioZZLteYJKsJxCwWV/YhZVWLQBY9x0ypjCfyvM48\nOd3KcrnKNN3kXO9V9Adjp/q9hHE8zjhu9Ny+/Urtam37mNupCPcOAinr9W0Lq3NPPUUWCvyXiXU8\nbauAAP8Bq1nCpxckdYvVxxrwwmDRSCCAlQDeB+Azmiz9AoANgyw+wNpXAdim/13T628AsB/A+/Tr\n9wP4Zf3vTQA+C6AEYAzAlwAM6ff+AsCL9b8/CuA1+t/3APh1/e9bAUzTEM2/BzAMoCn/7nOei/8J\nBQQELDlcKOUzMypuvf77ak0izA+6LzS6XJYsvQmWy3Xu3buPMzMzFgmSDLyrrNfkWPaYN5tQjTlk\nxs4VlBFv1xOo8NprpTy7hopkluk6ktX7N9PuNbzrrnfqvsj3WiRM+u3sY08QOER7tJ0htDJf+FYq\nJVDKsFX6DCJvfvNP0S0Rq+fX0g3G3r17tzaTjFApn4oUJskI7777HiZJNnsxSTZr8u6S57Rn1lnw\nfzTm58kXv5inh4Z4WpM/IYFfB/gdpCzh5IKkLiiBFzcWqyfwEwC+AuBXALxokAXP5wHgDwC8CsDn\nASyjIYqf1//eBeB+a/s/AvASvU3Hev1NAA7rf/8xgJfofxcBdN1t9PPDAG7tc16L9sEEBAQsTVwo\nP5p+ZUkFErtEpl43peF8cPQIZRqHyt9r9/YtFissFBICU8zn7CniVyhUWSz6cwVnZmY0aXMzAVMC\n92nCJ4TQN84tO66tVKqzXF5hvT6nyZ5PCRRC6b5eopkvrPoAo0hNNSkWqyyXG6zXtzGOmzxw4KB3\nHJ9xRNtmlYrVO9nPhZw9TzGHKBPNuF6jlskKXFBxPnmS3LiR8+VyhgA+jZj/HREj/NlA38/F6mMN\neP6xWCTwB/TfywdZ7HweWtl7QiuC33Te+1/6738EcJv1+ocAvAHAiwD8ifX6ywH8of733wBYbr33\nRQCjAN4LYLf1+r8HcF+fc1ucTyUgIGDJ4kIpn01NTTFvUNhOKSPGsZCrbGBzq9VaIDhakcgoWqXn\n7orKKAqafawtBN7DKKrxscce47vedQ+TZCRDJLrdLt/2trd79l3HvOo2rNd0t5uzno9TuXxX0lYW\njaFDegLF5OEqhOP6WkzvXqlU5ezsbCaPT/7t+6yTZLOeESzlY1ERp6nK5T6H8lzms7FLvBLdU6ms\nZxSZySoSGN5XcZ6fV1NBVq3i6UKhd8DTAH8fr+AnMMRGdfPApO5CaW8IODssFgl8LYAugK8B+CqA\n7xtk0bN9aOL3lwBeT4v0We9/g4tHAr8USGBAQMBi48JWApUbtlqd5J49e3KBzTK39kzB0eVyzVn7\nUeadtw0qpW2dJp2rGEU17tq1m91u1yo7u6YJUdPWOMfeRtcM4SqBinhF1rl1qPoJ2/rfj9CodI8y\nT3LrVD2NpClRr2ccN71EyR/mnHryA8WF3KX/M2n3tqnVJnvl+X7fpSNHjuoycfaeZ75n3/iG/GXH\n6AAAIABJREFUunFWJqCUhJ8F+KFfPRRI3SWAxSKBn4Pu/9Nl1z8bZNGzeUD19/0xgHut1/7OKQf/\nnf63Ww7+Y6sc/HfW64OWg49Y+xxZqBz80EMP9R7tdnvRPqiAgIClgwulfGYy6LKjydJ0lJ1Oh1E0\nnCEYUTSsy49C8lxyNkygq3v5REUTsmQ7b6W/ziVs1xKI+dBDD2sSI8e3R8yJ+9XN/pMJHyNUZHSE\nqnRrx6dEep9JmhnI62lCpKm3vazPuUto9IM5gugaaGZmZnjo0CE+9NDDzJpAfO5mGbn3OPOqZNap\nbB/HpzTW69u00jicu7ae4jw/r4KhrUgYIYEnR0b4bJqqUnHAkkO73c7wlMUigZ9Z6PliPAD8NoAP\nOK/tF7IHvzEkArAGWWPIpwDcAGAIyhjyI/r1d8MYQ94EvzFE/u0NxA5KYEBAwKC4EMpnJ05MM44b\nLJUu18TK9JUZsmdIVblcsyZ5PKaJXNMiXUpViyIhie0cWTLzc10VcZPef6smazGz5equfn6IygU8\nTFNOja3txZQigclXUSl4u6nMGTIlxO0zFMXN7Yv0qZh5JVII1okT05lxdkCZhcLlPHPO4RoqddTO\nAnyUxWKVcdzw/g9Dt9vNEXV1bL+a2COQTz+dywUkQMaxev0MwdABSweLRQK/CuA+65F5PsgBzrD+\n9wF4FsBfa3L3GQA/osu1H4dyC/+JTc6gImK+hHxEzIt06feLAH7Nej0G8Dv69U8BGLPee7t+/QsI\nETEBAQEvAM6HNPbLC8zGvWSVtThu6nKwkKoOS6WraTL7UgJXO6RrHaOoxu///h/QRMsXjDyhCZFN\nDtuaaD1KlRfYj0COsP/EjceYN3KM0CiFo9b7+5hX4yY0WTxKpdYJKa4y36c3nivpxnGTs7OzTBKf\niWVMH9vOHUyZJJs1aZNJKg26fY31+ja2Wq3ew/0MXaJeLIqK6F5bzHK5xunf/jBZqZBXXklnI0UK\nb7ghjIi7hLBYJPChhR6DHGApPAIJDAgIeC5wPpEy/fbNlhLn6CpzyuEq5otpTRIXdt8Wi1VHBes3\nV7dtrXmZJlwrNVFKqUauzdEESEtptEZFKl2Ss4kqCPoWvZYEN09b20zq92Mq8uojkjdbRKxC4HK9\nbt65/NKXfj/tsm2xuIJx3GC5vNE5N7vUW7HI4BhVudpWHdu587LHw/k+wzS1iXqXSbKBxWLNubYq\nVc/j4xwpN3janQ1sP77xjefkOxxwYWJRSGB4BBIYEBDw3MAodm3a48IGUQQXMqHklcARL/lQES0V\n+o0gk5o0rWMUNXRUiavEiRFDxcioyJgmlcFC5uyO6LVHmB+99iBNubdNpSIOO8eJac/XVWvYREiy\n+SSkeZxDQwmzvXq3Oc9lH1WSjaJh1mrXMY6bvPnmW3rvmfKtn8QZ04dNVo/TTDHJZv8By1gs1nsO\n4AMHDuamgshn2C9AWk1usXsjawS6LGCef1Gs8Bs/8ANZ4jc0RK5eTV5/fVABLzEslhK4Z4HHg4Mc\nYCk8AgkMCAhYbPjGhSXJ2ECRMmeKo7ENKuVyjVE0nOs9a7VarFS20h8JM0rgUcZxgzMzM6xURMET\n0rOdqn8vZamUcnZ2lidOTGvjQkoViZIdl+ZX6O7Ta9Q0kYtoZgEnffYp6r+raErN9vnvoFLjVKnb\nf21jVFNFKozjDQRiRtFKKgVvOVVZebN1f6f1seTcfKHYoh4OE7iCqgSdLcPL7Gbj8s2W1OUzVN+N\nNfpctxMYZRRdzWpVeh/ls1BqZAGf4Q9HdT5bqWRJYJKQGzaQJ09eEL2qAc8fFosEvtfz2AMVIP3d\nQQ6wFB6BBAYEBCw2+qk99lzZfhgkjsb+0fcRgAMHDtKocLZLV5yrykhyxx3vZH4yhlECZRbxkSNH\ntXFkDZXJwzZYzDGfzbe59/q998rUEVEvj9OMd7P3WUelBr6FRiFsWsdyzSFz+rztNbbq63bnCMu0\nlJhZt/CMPpe2Xv/9VJmErjvZJoVx7v3bbnur89m12c+J7FOJk6TpjaVpVDdzbqjIx3b+jCkHy99i\nkbzhBn7kwycuiEk2Ac8fFr0cDKAOlaX3D9q9e+Wg+17sj0ACAwICFhum78sQlDSdHDhcWsKEq9X1\nTBJ/np3AJYFHjohJYzONqpZw5857LZdwl/0dtHmVTpWER5gd2ybEqJ3b3p6YUSr5RrxNsv9kj349\njEL6JAJm0nPcVJO/Kes8OhZxE7J7G7Pq3zCBnfq9dfoe7KAxqDSpStod6/y6VKqoKnvLuDej4k73\n1nMzCX1xQ+5rR44c5dx//s88DfAUTBbgaYD8sR8jCwV2v/zlCyK/MuD5xaKRQO3U/UVN/n4BwMgg\nCy+lRyCBAQEBi42zCZf2KXlSUqxUFp7+4BpI1H4uuWqyWt3Aqakph6BI6LOdt7eRwNvoc9b6MwLH\nWC43rJ7BTZpAjRJI9ASSPVRqmk02Rwi8g9lsvaI+H9dAIscWBc9VFMWEIuPbEk3WSKU2vqcPWXR7\nFN2QaukLnNbrXkfjOp6mURfHCdRYLFbZ6XR0P+ZxfQ6HGUU1rwI8OzvLPXv2cHZ21v9d+MY3eBrg\nN5GdD/wJgKde9CLyqacumEk2Ac8vFqscfEDn590PoDbIgkvxEUhgQEDAc4FBwqV9LmCj5JkSra8c\nbKaAmN68crnKatU1LWxhHDc4OzurCWKb/l66tiZQdebz+PIxKMA4b731Vna7XV1WFvIXs1CQOBZR\n24ToiclD5g63KSVRRSKP0UwDscnZQU2sxIAyRhMN09bHiaiUxNfQKHjyuktqJ6hCrt3X7HF1W6nU\nP/tevN8ikO49qvD229+uJ6aIk3mCUTSc++x37JAS+XoCKXfs2Jn7bnT/8i9700BsEvjawjWc++Qn\ne9+Dc1ECQw/hxY3FIoGnATwD4CkA37EeTwH4ziAHWAqPQAIDAgLI8/9h7Jfr129N3w94kjQ9St4o\nazVTShaVMEnGaFQzU3rMl2pjvuxlL2eajmpDQsR8eXZCE6YyjdIlxoUKgQc8xLHCYrHCH//x19H0\n2NnkteIhSooIlkpVRtEm5xyuYtacsYqmT1FUuY005hG7Z+8uFgpXslCo0g58VmTzLp6bEpgyitx7\n3NTrXsO863pcv95k1uWcJWYD9Yt+61s8DfBbDgE8DfAbAGf/7M96m57tJJvziS4KuDAQImICCQwI\nCFhEnO8PY7/9FyKBppRnHKHV6nrt6rXJxVbGcYPdbtdSCceYVdN8Ez6uYbY38EEC77T2s7cfpipf\ntpkvuSbMG0yG9Xb3UJHHtcz3+ElAtUuUIkuVtB3GCbMj5Zr0l3Wz4/LUcaQ0a46vcvfkWvfr9aS0\n/CPMupUlosY8j6KVjKKajtCx7/FRva5LcJvWuVdo3NbZEu3U1BTzYdzrODU1Zb4cTz/NkzfckFMB\npTQ8XKzkppDY37N+37sLZQZ2wPkhkMBAAgMCAhYJ5/vD2G//fmHB9n6qdGjy9gqF1JPbl3LXrt16\nLnCDyowgBEp603zKnhs+XKcJObYJXTagOY7H9Dms02RPiJREuwi5EmPGg1TlYpe8jutjuuQ05m23\nvZWve91P6ueTFgG7Xl/bPqr+wjlrP5/5RIwavjzEcQKP6NdF2bTNMikVKbZ7BsXsIUHN+2nG4dn3\n+CjzsTLTzrGP575PMp94QSXw6aeVA/iOO3Iq4GmAl6NE4NG+39GF/ocm9BAuDQQSGEhgQEDAIuF8\nfxh9+9dqk33DggX5GbISfKz6ycrlDSyXGyyVqhwevl7n9IlBwSZutmPVJlubmSdFtumiS6UWRs6+\nsdXPJ+cnqmCD2WkZQsYe9hIb9XBLvCqCRplJ7mPWjSzkVHoIr9Hr38J8X992TfLG6c9DrFCNq2sy\nr1Ia93K2Z/AojWHGLuv6xt4lVD2Id/a59uxc5x077u2RM3XtJig70xP41a/aF9kjf6ICVnAtgSbj\neGXuO3qm/6E5l//hCf2DFx4CCQwkMCAgYJHwXCiBcdxgvb49Q1pcYtlqtRjHQmCkT6xNpX6p+BaV\nzSckrP+M3XK5lukLMyqbj5hkX7vxxpfq19dSkTxRIn2zejcTWEFDIudoRsdJ+Xmt/vtevc2dNH2I\nDSrlUHr5qnq9dX2InJgnxLDikrwq8wRSCOdV+vlL6SeQcwS2slyuaoJ9n+ee2WXdncy6mUuM4zH9\nvj0LuKnPN6Ipb7dza0dRjW95y1v42GOPmS+TdgSfrFZ7KiABnkoSrQI+lrk3Bw4czHwXB/kfmrPp\nIQz9gxcmAgkMJDAgIGARIdEqMvbrXHsC7Yw3H7FstVrsdrs6B1AIywhVb91ymgkjTQKXsVQa1fvn\n5wSrfdcQSHnkyFF2u13u3buPSdJkvb5FkxAhJiOaqAjRUZM0VDCyECkxlayiURh9WX5iPLHLq3Zv\nX6yvJaa4hRXxG6VSMdPe9Sli9z792nH6ewhlVu8OfR3r9faXUZWRRbFcr9dT5dKsYtdPCVS9jcXi\ntXodn3p6mCZIOiJwL8UlrMrzQkCHqQhtymIxZZJspJlsMkVFjmVdiZ2xlMCTJ3mqUMg5ggnwX266\niaNF181sekUFg/4PzSDqXugfvHARSGAggQEBAYsEIXD1uopSOXLk6Dmt4/6w2sQwioZZLtc4PHw9\nk6TplIGFqPRTtB6kb06wIhG1Xlkw+6Pd0gSjQzOGzJ4YIgaLfuPbhLStZlbd209VMpV9fFM7NlEp\nfpLrt6bPMRICV2riJuHNvu3K+nGF3meFJlxHCdyv1xCCOUqlALas615HpWraAdMrqIhhlVkC61MC\npexrE+WdBMa5a9fu3mecJE3u2rWbrVZLT2wRA0+q74ncP/uzNOab2T/8Q28kjDzqJVcJzbrG3e/z\noG7hfgj9gxcuAgkMJDAgIGAR8FyrHZLnpwKE5RjHmS+zTlApWf6SaK02yXJZnKquOzbtza1VP9oS\nZXKNJkuy3YgmZ+65LBQMLcYTIWBHqVStbTRqobhnbeJmz/z1EcVt+thCEkk1wm0l7Zm6iqyJKcOe\n4rGfJo7GjsR5nEYZvJ5mBF5bn+sj+nlRr+1e+yoaF7Hc40fpI6elUtU7tm+h0XFAyjheo9c/qM9z\nG0tI+PTQEOcdBfA0wJO1GgnwNz7wq/o8trJffqT9vTvfPr6gBF64CCQwkMCAgIBFwPOhdmSP0aVS\nqXxGDimJuiaT6zg1NdUjlKqP0Ch8Mo6u2+1qsin9aUKCYk1spjWBsMuKXc+5DFMpWF2a4GibUG20\niJddEhaFcRWz5WsfsZXcvy00JLKlz7NNEyAtfZK2YWSY+Yw/Wc93PWL+kNBqiZPpMKuutvW9WqEf\n0gs4RZ/7+u677+l9xkK6Op0Op6amWK1u1Ptl+0Lr9W3ct28fjbKojt3AxxdUAbuf/Szn5uZ44MBB\nxnGDtdpkX5Vv0FLvICRxsVTFgMVFIIGBBAYEBCwCng+1wxxDSNN1BCKWy43ej+vevft45MhRHRlT\n6Xs+2ViZ7QSGWSpVe0rgi170Yg8JqlCpbI/T75QVB6+ob0f188N91mrRBEP7SryivNn3VZQ7V8Uc\nphofl1CpgGVrO5m84ZJIn5K6lYo4+pRNIZruue6m6c2TSSZi+rCvza8EzszM9Po703SUcazIcKm0\nUm+/Mrdfmo5ydnaWimwqdTTC05zHEL+MYk4FnIeaFzwcDWdGA/YjcIMYOc7W7BHcwRceAgkMJDAg\nIGCR8FyoHe4Pp+kRM4QgSZo9o4i9365dD7BUqtOOF7HDp/P9hKo0acwgvtm7l2miVaXpUxNCdlST\nMTuouUYzJ9dda4Z+dWw7szEyQoKkH+41VArYMI1ZRYKebbWxQmVYifU6jzBrGOmn9o3RrxI2qXoB\nXdIoCqSvF1F6AeXchZwqkjg0lLBS2cooGubQUMz8zGEhwUKWx1kuN3jixDTvvvseqhK4IrYNdPms\nRwWU1xoo5IjkoBNofLFEocR78SOQwEACAwICFhGLqXa4SsuOHfcyjht0p0T0Kzubsq4hZfJDPTc3\nl4ueUURF1D5xyLoEScwZR2kcq1frbcdpXLZCTvbRn41Xod8VbJdkt+u1y1REMKHqfztM1adom1Vk\nVJx7jBU0pFUMLW7fX9MiactoSrgSDC2q3s19znWNXueV9BPnVTSj9Nr6/r6G+ZidCk25XcjwnPV3\nrHcfpJwr11NBnacBfslTBp7HEL+DIZYyruL+35tBWhuC2WNpIJDAQAIDAgIuQOSVljZNX9tgCsze\nvfl8PvmhVuVgu7QpZdZ1mtBFNNMwNnjIU5OqnNvW5CWmmcW7haY0LKpcQqWsjTObyWcf2y3xjlAp\niceo3LsxlYNXlDR721iTJCGFQsCkh88ls5PW+rNUat6jmqDJeDuTswhAvy6BzsZUoRTUZfqejDBP\n7CoEDjKKrmYUqT48u4ybV0BtBbCt71ubbp6jIvE7WUDCT6PA7yFvBvkSVvE0wBo+n7sPQQkMCCQw\nkMCAgIALEHmlZY5GARSFah3juNm3qb9UEmNH9oe60+mw1WrpYGM7gsUmL0I42pqwrGKWsIzTlGlv\nJbBSl559azxORSxnqHL/CsyXgMdocvxkBF1E4CaazEN5zSVZdb1dQhPvIsTyMP25iEJwd9CobNJn\nKWVdyRFsUhFcIbV1GlW0qY8VUxFD+Wy206idD+jtJhjHTd599z1M003sb3KZoCG1YshJaY+TS9ON\nvUkyE/i/+ppBngXY6H0GinzXatcNHO68UGvD+WZiBrzwCCQwkMCAgIALEAsrgUqhiuOGmRPrYOfO\nezXZyZKS1772JqbpKKtVGRlXpeqV28YsUZISpJCmmP0JnhCVftM0xDQhCmHE/r1z99GoijJT192u\nax3jaiqlzFUGUyrC2c9RfJhmfrGUex+nP0dRVM1HqErQooSKijhHk38oaxzX55W/hnK5rgO+xeCT\nj+oBHqOomlG0kT7yWypVeTkSbyj0aYDfvuwyzgOMMEYh0EmyauAMy4VaGxYrEzPghUUggYEEBgQE\nXKBw1ZhXv/pHaStU9pxY+wf7yJGjmuAI+TBBwu4cYilV9lcChZTI1JCtzM4bFpLoI3YjzMenuOuJ\nsaNGRXCbVApYTF/MjTrWcWaJsU9N26jX2K/XrtD0BEp/YYkq5kbeI/0TVUaYVQbfQdU3+H3MBke/\njqY3ssJiscq77nonFcns0pSqx/i2t72NhYKYa8rMmkeyhDtNR3UPoJs7+Di3AQtHwvzt33JmZkaP\nDWxn1jzX0m0oBS8dBBIYSGBAQMALgDMZSOy8OPnrunmjaDgTLSJTRFSvX5vKlCF9eMMEiiyXN3hI\nVVU/hJQ1NEkTcnOnRWQkg88ldSUqc8aotUaZSo1zzRJr9cM2dohqOKnPZQv9Kt6wPv529lcfj2ti\nZ2foCRFuWK9L6fhB9lcC/bEu6rp8r0dUgdgx43iVHgcX07iWawRSpqnc2+/Tr60jEPPnfu59vc+z\nVpvMqGytVovV6rUUJfQa/G0v/sUXCTMfx+TJk4tu4gimkKWDQAIDCQwICHiesVC+msztdd9vtVoe\nMjXOmZkZa6rEHFWZ82qaPrpRqliXiEbJconL1XpbW63aROB2ZsOeXSPHFpr+uyaNocImn+LMFYey\n7OuWb0dpTBj/Vq93nEallJLtfiryeD8XLiuvolHl7Hu2mfnS9qgmbqIIyrrb9Vpu/+I6AnuoVFH7\n9a167Y006qZNIvuVmg/q+76mR/p27drNKGqwXjc9ea4CN4H/Y2EV8M//vPedWkzlLiiBSweBBAYS\nGBAQ8DxioR/QEyemdaRL3sU5MzOTex2o8NChQ0zTtZo0XUtRmvJE41FNQmzFb0Q/zxtIgArjuKHN\nHm1rG9kvolIFpT9vkiYDzz5P2/nrhifbpO1KZzsxo8RU6l1MpdjVafrsJizCJmqn9E2S/nKxGDpk\nKsgcFeGtU5HL43r9KhWh7nc/Z2kUUVEZm1RO6jnr2g7RkEhfqXmrPsZhfZ130UeS7e9Imo7yGizL\nqYA2ATwFkCdP9r53g2ZYhgkglxYCCQwkMCAg4DmG/cPar5TWarU0OfSNe5vkoUOHdJiwxLikLBQS\na2qElBt96teEJiBCQmzFb5yqtClRKiqMeNeuBzgzM8Ndux5gsVjVx3T3k948IUZtKmIogcy+CR2+\nczvGfIlZyKHkCNb185R5wlqjIowzVETYNo7ICDqbXE7ofYZpZga/w9pnnKpkLLOGd2buu8r4IxUp\ntQlpg4o82hE17+fCSqBMdpHzi5gN21Y9jvIdmZub4+zHPsZ5gN/DUE4F7JHCr399we+hD8btOxix\nCxNALn4EEhhIYEBAwHMIt/R75MhRrxLYarU0OXSJk1LSarVtmiQ8SJmHG0XDHoWwTb9y1dZru2PP\nxADSJhDx2LFjPHDgoI6PEUWq3GdNmY8rIcsu8WvRkEd5z5fZd5jubFw/ydxD1Uvoqmnr9DnW6XcJ\nC0GUa/eRMdsIM0JjrKnqe9OhcgcnNDE2w1Rk0lfeVUQ2SZq85ZZbacbpSUbiJBXx9Y2hkzL7NKXH\nsVyu975HV1nkz1UBCZC6DHw2UGai7GccSrxLH4EEBhIYEBDwHKFf6VeIoF1Ky247rYmIL79P3K9K\nQTx06BDzvYLLGMfN3vq33fZWRtEwq9XNXNjlO8Ebb3wZs+XIo/rvfpo+PwmHTqlKqTLerU1DXEUh\nG9PEZzezo9VkHTGV+MrEtqJn9+q5BG6U+RFvsrZM6tijz5VU5NQNah6nIrTD+p5cxmysjcSsXKGv\n9Qa99npnHQmCjvjGN76JrVZLq7UJFal9QK93DY0qae8vs4uFjKaM40bPFJTgKZ4GvMHQ8/L8y18+\n6++pmj6SvSf1+rZg9ljiCCQwkMCAgIDnCAu5KH2ltBMnpnUMyDoqlege5hWyTVTzdtu94Gefa1hc\nxbJ+t9vl1NSUnjLRoTJviMpFihKYJWNtKqVroyZkYtbwxb5I+XaF3q5KRQbdcqfE3NglT1E4RzUJ\nSvX+rkImYdFlmhKsjKZzZ/pu0GTvcZrZwRUaE4urSDapFMkm/Y7ghr4WOee6PkdXyRvVn5E6Zppu\nYRw3WSpdznx/YdtzHEPygXHeffc9lkpMXo6vLGwG+S//5Zy+p2pedPZ/WOK4GZTAJY5AAgMJDAgI\neI5wLi7KTqejVZk2s2XLLtV0C3GrmpxAKTlXq1vP2PRfLtdpcvPEcTtCo3aJMmWmkpjJGkJofQaH\n7ZrYpXq/MfrHqDWojBmyX5cqt09MJnM0kzJsxTDS+8p5J1Rq3TD9OYcpFVm1S9D2dqK2CjmVucTX\nUCmTvhnAN1n3pp8x5UF9nitpytm2OcZVX5exXG4wSaT3cX/uuyLfowr+K+dR4DdRzqmAzwL8K8Ss\nJiNnbdIw31MJr1ZEPARAL30EEhhIYEBAwHOIc3FRqgZ9NWO2XK5pY4aoaXnHKKnI49TUlHeCiKiO\ns7Oz3lFypmfwoPVvN5/Pdvb6eupEwdqqCZWYKnxhzxLRYhOxBpWi16ZRFcUk0WHWYGEfs63XW0aT\nUThK1Qc4R2NGmdbHsMu3XYvIvdciahJr4/YMClF0x+OlVOrkNXqbtfqcdtPMJW7TqH/iRH6chUKV\njz32GOfm5nqB0O4YthMnppkWK3wK/mBoMYPIeLhz6eXrl00YsLQRSGAggQEBSx4vtIvxbI5vxnFt\nZxw3eeDAwZyaaDtG5+bmFnR1CqGM4w2arFzuIWZrCLydRvGTXjfbDTxBVZ4epRoxF9E4kW0jxihV\nKVbUNZ8SKM7bWJMiV5XbSVNiPkoTC+Pm9W3X5yeKZptimsn2K1aoVEkfuR3V+7vuZHFLC6mc1udX\noCp5u/2Kr6BxacsIObkeyW28l0Zdlb5KFWsjqq4dEt5qtTgzM8MkaXItfnvBMvBfYB0LmKfdcvBc\nfk8DlgYCCQwkMCBgSWOhYOYLDb7ycRw3dB+fS36O98aJ9XN1GsenTMaQsqTdQyivXUMzFUSiTkao\nCOOIJkVtmky8Mk1/nl0O3U8zrUOInhuBIlEv6/R5uQ5bURVlmsnIAgROCF/ROo8qVe7gPouUjes1\n91lErKnPt8F88LMYUezeRdvUYhNfcRFXFjjPfpNHZnv/np2dZavV4v33P6DL9qrk3ESh73zgbyLm\naYBN/FLvPIOrN2BQBBIYSGBAwJLFxTbZwGckUeW5fIZekjR55MhRPRPWjmEha7XreOjQIc9+tkon\nzl0hJh0qxa1Npb65hKWmiY64fW1ThBBDKRmXaXLzyvpxWB/jLZ61XSfwVprw5OU0yqX0KYryJxmA\nEU0Zt0BDNO3zk1zAa/W5Fwms1vtLzI3P6FKlIbg+B/KwPt5yvV2/fskp5pXMjXp7EljHQkFIZtYB\nvQEfWlAFXAb07kmxGHr5AgbHoCSwgICAgICLDE888QSiaAzAVv3KVpTLq/HEE0+8cCe1AMbGxnDq\n1BMAPqdf+RyeffZr+LVfez/S9JVoNK5Hmr4Se/f+O/zjP34BTz75JE6dOg2AAK4FMAPgT/Hd734e\nP//zB3Hy5OWwrx24BsDXADwM4HsAvgpgGYC/A/ByAGMAfgzAvwew3Nl3GYAVALoA/hXACIBx/d4V\nAG4DsBbAaQAVAL8I4C79fIXe/mX6HN21rwbwsd41A18C8LMAXgfguwD+Qb9+K4DfA/BPAEYBfEtf\nx6f1Pn8BINbHm9TnJcchgAKASJ/T5QC+CeBmAN8BUANwI4AJAK8A8Jt6vdMAngHwO9b1ynlfqc9v\nB4C/BvC/APxvANnPEPgKgGF9v+3X/0Uf9wSAf8Lp0zGADwFYBWANgK24Bh10cBdO672GkMdJPKDv\nd4Jnn12Bn/3ZXfjIR2Y8Wxo8+eST+PSnP40nn3xywe0CAgAEJXCQB4ISGBBwQeFiUgKlH8uXIWi/\nb0e+5HsFpQ9vI/sHEZepSqXS95d4tvO9NkJTFpV+Np/B5C5rm5Rq4oesdxdVmdgXGC3Cj0E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XtLF4EEBhIYEBCwiMgrJ8fpNx5Ir1dHk7PN+rmMXBPnrF1utMnMnEOeqsyaOUSdk8kbD1L1FUY0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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,10))\n", + "plt.scatter(states, cont_value)\n", + "plt.scatter(states, cont_value_hat, marker='x', c='red')\n", + "plt.legend([\"continuation values at t+1\", \"regression\"])\n", + "plt.title(\"Regression Bermudan Swaption\")\n", + "plt.xlabel(\"State at t\")\n", + "plt.ylabel(\"NPV\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/MonteCarloPricing-Swaption.ipynb b/MonteCarloPricing-Swaption.ipynb index 3e157a3..8fdc089 100644 --- a/MonteCarloPricing-Swaption.ipynb +++ b/MonteCarloPricing-Swaption.ipynb @@ -247,16 +247,28 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 1, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'ql' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Assume the model is already calibrated either historical or market implied\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m volas = [ql.QuoteHandle(ql.SimpleQuote(0.0075)),\n\u001b[0m\u001b[1;32m 3\u001b[0m ql.QuoteHandle(ql.SimpleQuote(0.0075))]\n\u001b[1;32m 4\u001b[0m \u001b[0mmeanRev\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mql\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mQuoteHandle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mql\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSimpleQuote\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.002\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mql\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGsr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt0_curve\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mtoday\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvolas\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeanRev\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'ql' is not defined" + ] + } + ], "source": [ "# Assume the model is already calibrated either historical or market implied\n", "volas = [ql.QuoteHandle(ql.SimpleQuote(0.0075)),\n", " ql.QuoteHandle(ql.SimpleQuote(0.0075))]\n", - "meanRev = [ql.QuoteHandle(ql.SimpleQuote(0.02))]\n", + "meanRev = [ql.QuoteHandle(ql.SimpleQuote(0.002))]\n", "model = ql.Gsr(t0_curve, [today+100], volas, meanRev, 16.)\n", "process = model.stateProcess()" ] @@ -659,7 +671,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.4.3" + "version": "3.5.1" } }, "nbformat": 4, From 1e19ecc3499d655edd9c75fe2af3394cd5a5900c Mon Sep 17 00:00:00 2001 From: Matthias Groncki Date: Thu, 9 Jun 2016 07:53:53 +0200 Subject: [PATCH 14/25] Added a draft of a presentation --- Introduction_Pricing_German.ipynb | 1374 +++++++++++++++++++++++++++++ 1 file changed, 1374 insertions(+) create mode 100644 Introduction_Pricing_German.ipynb diff --git a/Introduction_Pricing_German.ipynb b/Introduction_Pricing_German.ipynb new file mode 100644 index 0000000..e37da21 --- /dev/null +++ b/Introduction_Pricing_German.ipynb @@ -0,0 +1,1374 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true, + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt \n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# EinfΓΌhrung in die Bewertung von Derivaten\n", + "### Matthias Groncki, RC_x\n", + "### 13.06.2016\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Was ist ein Derivat\n", + "\n", + "Wiki: Ein Derivat ist ein gegenseitiger Vertrag, der seinen wirtschaftlichen Wert vom beizulegenden Zeitwert einer marktbezogenen Referenzgrâße ableitet\n", + "\n", + "## Beispiele fΓΌr Derivate\n", + "\n", + "* **Put / Call auf ein Underlying wie z.B Aktien oder FremdwΓ€hrungen**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "Eine europΓ€ische Call-Option gibt dem Optionshalter das Recht zum Zeitpunkt $T$ (OptionExpiry) das Underlying zu einem festgelegten Kurs $K$ (Strike) zu kaufen.\n", + "\n", + "Ist der Kurs des Underlyings $S_T>K$ wird der Halter der Option die Option ausΓΌben und erhΓ€lt entweder \n", + "\n", + "* das Underlying zum vereinbarten Kaufpreis (physical delivery) oder \n", + "* erhΓ€lt die Differenz aus $S_T - K$ (cash settled). \n", + "\n", + "Ist der Kurs des Underlying niedriger, wird der Halter die Option verfallen lassen. \n", + "\n", + "Der Wert der Option am Ende der Laufzeit ist mit $V(T) = \\max(0.0, S_T-K)$ gegeben.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "K = 130\n", + "S_T = np.linspace(100, 160, 50)\n", + "plt.plot(S_T, np.fmax(S_T-K, 0) )" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Weitere Optionen die in der IKB im Bestand sind (oder zum Handel zugelassen):\n", + "\n", + "* **Zinsderivate**:\n", + " * Caps / Floors : Call / Put auf einen Referenzzinssatz\n", + " * Swaps (Libor, CMS, CMS Spread): Tauscht zukΓΌnftige variable Zinszahlungen gegen einen fixen Zinssatz oder anderen variablen Zins\n", + " * Swaptions : Call / Put auf einen Swap\n", + " * Forwards\n", + "* **FX Derivate**:\n", + " * Call / Put\n", + " * Barrier\n", + " * Forward\n", + " * TG Chance\n", + "* **Credit Derivate**:\n", + " * Credit Default Swap\n", + "* **Inflation Derivate**:\n", + " * Inflation Cap/Floor\n", + " * Inflation Swaps\n", + " \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "**Derivate sind auch in anderen Produkten eingebettet und mΓΌssten eigentlich auch als Derivate bewertet werden.**\n", + "\n", + "* BGB KΓΌndigungsrechte bei Kredite\n", + "* Implizite Floors bei Bonds und Kredite\n", + "* Equitykicker bei MezzFund" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Wie kann man ein Derivat bewerten" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "* Wir benΓΆtigen ein Modell zur Bewertung (Risikoneutrale Bewertung)\n", + "\n", + "Allgemeine Modellannahmen:\n", + "\n", + "* ?\n", + "* ?\n", + "\n", + "Spezielle Modellannahmen:\n", + "\n", + "* Modell fΓΌr die Preisverteilung (den stochastischen Prozess) des Underlying" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Beispiel: Binominalmodell\n", + "\n", + "* Es gibt zwei Perioden\n", + "\n", + "\n", + "* Es gibt eine risikolose Anlage (Bankkonto) mit einer verzinsung von 2%.\n", + "\n", + "\n", + "* Der heutige Aktienkurs $S_0$ betrΓ€gt 100\n", + "\n", + "\n", + "* Mit der Wahrscheinlichkeit von 20% verdoppelt sich der Kurs 200 und mit einer Wahrscheinlichkeit von 80% halbiert sich der Kurs \n", + "\n", + "\n", + "* Wir wollen einen Call mit Expiry in der zweiten Periode und Strike 150 bewerten" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Wir sind der allwissende Investor \n", + "\n", + "* Wir kennen die tatsΓ€chliche Verteilung des Underlyings\n", + "\n", + "\n", + "* Was ist der Wert der Option fΓΌr den allwissenden Investor?\n", + "\n", + "\n", + "*Erwartete Auszahlung: * \n", + "\n", + "$E[V(S_2)] = 0.2 \\times V(200) + 0.8 \\times V(50) = 0.2 \\times (200-150) + 0.8 \\times 0 = 10$\n", + "\n", + "Probleme:\n", + "\n", + "- Wer kennt schon die tatsΓ€chliche Verteilung (wie kann diese geschΓ€tzt werden)\n", + "- Jeder Marktteilnehmer hat eine anderen Meinung\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Stochastische Prozesse und eine kurze EinfΓΌhrung in Monte-Carlo Simulationen" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "##Was ist ist ein stochastischer Prozess?\n", + "\n", + "Wikipedia beschreibt einen stochastischen Prozess als\n", + "\n", + "die mathematische Beschreibung von zeitlich geordneten, zufΓ€lligen VorgΓ€ngen.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "\n", + "\n", + "**\"Defintion\":**\n", + "\n", + "Sei $(\\Omega, P, F)$ ein Wahrscheinlichkeitsraum, $T$ eine Indexmenge (ΓΌblicherweise T=N oder T=R). \n", + "Eine Folge von von Zufallsvariablen $X_t$ mit $t \\in T$ mit $X_t : \\Omega \\to R$ einen stochastischen Prozess.\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "**Beispiele**\n", + "\n", + "- Aktienkurse, Zinsen, Fx-Kurse \n", + "\n", + "- Warteschlange an der Supermarktkasse\n", + "\n", + "- Arbeitslosenzahlen\n", + "\n", + "- ...\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "Eine Realisierung / Beobachtung eines Stochastischen Prozess nennt man auch **Pfad**." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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Py7WJpqqoOMpCWM/4Dujo1H2Xb3kiVUtQFgVYAa5DsRrbx4ciRZEqRrw/G8sy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\ncBxBEARBVYTjCIIgCKri/wNy61LHNgKTWgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dt = 1./100.\n", + "T = np.arange(0, 1, dt)\n", + "for i in range(0,5):\n", + " X = np.cumsum(np.sqrt(dt) * np.random.randn(T.shape[0]))\n", + " plt.plot(T,X)\n", + "plt.xlabel(\"T\")\n", + "plt.ylabel(\"X_t\")\n", + "plt.title(\"Pfade eines stochastischen Prozess\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "###Wiener Prozess\n", + "\n", + "Eine besondere Klasse ist der Wiener Prozess. Er hat folgende Eigenschaften:\n", + "\n", + "- $W(0) = 0$\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "\n", + "- $W(t_2) - W(t_1) \\sim N(0,t_2-t_1)$\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "\n", + "- Differenzen sind i.i.d" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "##Stochastische Differentialgleichungen\n", + "\n", + "In der Bewertung von Finanzinstrumente spielen stochastiche Prozesse eine wichtige Rolle. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "###Beispiel Call Option\n", + "\n", + "Wir haben eine Aktie, den Aktienkurs den wir beobachten folgt einem stochastischen Prozess $S_t$. \n", + "\n", + "Eine Call-Option gibt dem Optionshalter das Recht zum Zeitpunkt $T$ (OptionExpiry) die Aktie zu einem festgelegten Kurs $K$ (Strike) zu kaufen.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "Zum AusΓΌbungszeitpunkt ist der Wert der Option mit $V(T) = \\max(0.0, S_T-K)$ gegeben." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "###Aber was ist der Wert zum heutigen Zeitpunkt t=0?\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "Unter gewissen Vorraussetzungen kann man zeigen, das der heutige Wert den erwarteten abdiskontierten Auszahlung entspricht\n", + "\n", + "$P(0,0) * V(0) = E[P(0,T) V(T, S_T) | F_0]$\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "**Vorraussetzungen:**\n", + "\n", + "- kein Arbitrage\n", + "- vollstΓ€ndiger Markt\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "** Grobskizze **\n", + "\n", + "- Replikationsportfolio aus handelbaren Assets (bei BS: die Aktie und Bankaccount)\n", + "- Da kein Arbitrage Preis des Replikationsportfolio muss Preis des Derivates entsprechen\n", + "- Unter einem bestimmen Wahrscheinlichkeitsmaß (risikoneutrale) sind alle diskontierte handelbare Assets Martingale " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "**Wie kΓΆnnen wir den Erwartungswert berechnen?**\n", + "\n", + "Wir mΓΌssen dazu die Verteilung von V(T, S_T) kennnen. Die Verteilung ist abhΓ€ngig vom Prozess." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Black Scholes SDGL" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "Mit einer stochastischen Differentialgleichung wird die *Dynamik* des Prozesses beschrieben. Üblicherweise hat eine SDGL die Form\n", + "\n", + "$ dS_t = \\mu(t, S_t) dt + \\sigma(t, S_t) dW_t$\n", + "\n", + "mit dem Wiener prozess $W_t$. \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "Die Γ„nderung des Prozesses von $t$ nach $t+\\delta t$ fΓΌr sehr kleine (unendlich kleine) $\\delta t$ wird mit dieser Formel beschrieben. Es besteht aus einem deterministischen Teil $\\mu(t, S_t)dt$ und einem stochastishen Teil beschrieben $\\sigma(t, S_t) dW$. \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "\n", + "Ein sehr bekanntes Modell ist das Black-Scholes Model mit der SDGL:\n", + "\n", + "$ dS_t = r S_t dt + \\sigma S_t dW$\n", + "\n", + "mir dem risikolosen Zins $r$. \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "FΓΌr dieses Modell gibt es eine LΓΆsung fΓΌr die Gleichung\n", + "\n", + "$S(t) = S_0\\exp((r-\\frac{\\sigma^2}{2})t+\\sigma W_t)$\n", + "\n", + "In diesem Model sind die relativen VerΓ€nderung von S_0 nach S_t lognormal verteilt.\n", + "\n", + "$\\log(\\frac{S_t}{S_0}) = (r-\\frac{\\sigma^2}{2})t+\\sigma W_t$\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "In diesem Modell gibt es sogar geschlossene Formen fΓΌr die Preise von einer Calloption:\n", + "\n", + "$V(0) = S_0\\Phi(d1) - K \\exp(-rT) \\Phi (d2) $\n", + "\n", + "mit $d1=\\frac{\\log{\\frac{S_0}{K}}+(r + \\frac{\\sigma^2}{2})T}{\\sigma\\sqrt{T}}$, $d2=d1-\\sigma\\sqrt{T}$ und $\\Phi$ die Verteilungsfunktion der Standardnormalverteilung.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "**Was kann man machen wenn es keine geschlossene Formel gibt?**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "###Monte Carlo Methoden" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "Wie lassen sich Erwartungswerte schΓ€tzen?\n", + "\n", + "**Mittelwert** \n", + "\n", + "Sei $X_i$ unabhΓ€ngig und identisch verteilte Zufallsvariablen mit $E[X_i] = \\mu$. Dann ist \n", + "\n", + "$\\hat{\\mu} = \\frac{1}{n} \\sum_i^n X_i $\n", + "\n", + "ein erwartungstreuer und konsistenter SchΓ€tzer.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "####Beispiel 1:Berechnung von $\\pi$ mit MC\n", + "\n", + "- Dartwurf auf ein Quadrat 2 x 2\n", + "\n", + "- In dem Quadrat ist der Einheits-Kreis gezeichnet\n", + "\n", + "- $X_i = 1$, fall der Dart innerhalb des Kreises landet und sonst Null.\n", + "\n", + "- Wir nehmen dass wir nicht \"zielen\" und somit jeder Punkte auf der FlΓ€che mit der gleichen Wahrscheinlichkeit getroffen werden. Die WΓΌrfe sind unabhΓ€ngig und die Wahrscheinlichkeit das ein Dart im Kreis landet liegt bei $\\frac{1}{4}\\pi$. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [], + "source": [ + "def MCSimulation1(N):\n", + " x = np.linspace(-1,1)\n", + " y = np.sqrt(1-x**2)\n", + " plt.plot(x,y, color='b')\n", + " plt.plot(x,-y, color='b')\n", + " dart = np.random.uniform(-1,1, size=(2,N))\n", + " getroffen = np.sum(dart**2, axis=0)<=1\n", + " plt.scatter(dart[0,~getroffen], dart[1,~getroffen], color='r')\n", + " plt.scatter(dart[0,getroffen], dart[1,getroffen], color='b')\n", + " plt.title(\"Dartwurf N=%i\"%N)\n", + " plt.show()\n", + " print(\"Pi = %f\" % (4.*float(np.sum(getroffen))/float(N)) )\n", + "\n", + "np.random.seed(1)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "data": { + "image/png": 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c7dSEJVARXqse4OrdgTh/dY0WjtM201WY7xuBHg36xaCje3N5UPTDqesjqXsW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\ntK18rFQq5nU5Tq//ktxpjZB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VtJVwUSllxSqaXSWraLVeFMfQOG47Utp0LHCaZfuS4L5pMSDTwNAyJne/wCE7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\nAtcdBS5A68bFsv/L1FdAV9CYh70G3ISWLoewr5B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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pi = 3.147000\n" + ] + } + ], + "source": [ + "MCSimulation1(4000)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "###Beispiel 2. Calloption mit Monte Carlo Simulation\n", + "\n", + "- r = 0.03 \n", + "- $\\sigma= 0.2$\n", + "- T = 1\n", + "- K = 103\n", + "- S_0 = 100" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true, + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [], + "source": [ + "np.random.seed(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true, + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [], + "source": [ + "import scipy.stats as ss\n", + "\n", + "#Black and Scholes\n", + "def d1(S0, K, r, sigma, T):\n", + " return (np.log(S0/K) + (r + sigma**2 / 2) * T)/(sigma * np.sqrt(T))\n", + " \n", + "def d2(S0, K, r, sigma, T):\n", + " return (np.log(S0 / K) + (r - sigma**2 / 2) * T) / (sigma * np.sqrt(T))\n", + " \n", + "def BlackScholesCall(S0, K, r, sigma, T):\n", + " return S0 * ss.norm.cdf(d1(S0, K, r, sigma, T)) - K * np.exp(-r * T) * ss.norm.cdf(d2(S0, K, r, sigma, T))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true, + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [], + "source": [ + "def payOff(S_T, K, T, r):\n", + " return np.fmax(S_T-K, 0.0)*np.exp(-r*T)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true, + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [], + "source": [ + "def pathGeneration(N, S_0, sigma, r, T, dt):\n", + " t = np.arange(0, T+dt, dt)\n", + " dW = np.sqrt(dt) * np.random.randn(N, len(t)-1)\n", + " S = np.zeros((N, len(t)))\n", + " S[:, 0] = S_0\n", + " S[:,1:] = S_0*np.cumprod(np.exp((r-sigma**2/2)*dt+sigma*dW), axis=1)\n", + " return t, S" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false, + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "image/png": 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q+w/y1ey1uG6eoPY1FXYo5CaeOOLZEZg0+n/f1q9/dFdMzNigdtGb6PDhQSrs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CIUna3lHuqhLcb14119bxSlwckWsb7S5g8Psm2F1qUgU/v20OeDRMcIhZgdoEZBAS9oyS53VA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PPTrCvnRHz4NXcWd2hLjdHRBFx8GT45Q+TPS/rG8MdwHwr/CkebQL1RYdSYfwJn5yvE2yv1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\nG8bODJw/D5IpG44kojRNT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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "t, S1 = pathGeneration(100, 100, 0.1, 0.03, 1, 1/52.)\n", + "\n", + "for i in range(0, 100):\n", + " plt.plot(t, S1[i,:])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false, + "scrolled": true, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "data": { + "image/png": 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Au9Zj3twY1bC0GX/DayS3vY4KXcKXN0h2LnS74FAE16UrVAyc52OBZu6p9JGU\nVINStYgFNJHQb3lgTe1WuQpWZfkw3Iq7BsPUDI6TWhgkXJ2Ea4mfq/kBjtYm83KOn4PAidiDcI4n\n7+DnDlOo9r2I175Po7T1dzuR2S+xZdczEqilbjcjF43PICUTTwLfnSrbPYcFBxByjRZXtyKk/hRw\neLH8ddNU6QiZRzdMk4j31eM6Yq3DP7GVdmXEV9odWahKO+v5Aoh/HkvqI8So9FW/tr45KTj+2+GW\n7ocGL3eVNzf0+ocGI1QXpOgaf4kuUrUtVdfvfqaoZelPEJXuJ35jdE0H3rY3SGl6DZfvHAk5vWTm\nwbgH3ta4LlyjpP8iH/E1sb00gdTUSovQLxG/KXr0VoSudis3UOF3sTbdy/aIZn3vOOWAtzIRSkOM\nLAlwoSjIW8uSeTnk4TBSMzjjhdIheQc/N7iFao8tu/Zjl13PmBW3suwrscf39iGK/TuGcRsF00pl\nYhdX34u8E4gWazTM+3FngGUrbcFW69WI8o4S+4lYXz2m0s5ASL0ayXpHlfqhhay0szZmY2OMy5Ed\ny/3AfuVSB+7+k013RVo7Hxu92r2u/VJ32rXGEZWf66U2ZYku81QPFA+uObT87L3f9mjPQeRiuZYY\nld6LJ/I2KZdewa1OkZDZSVqeJsEPhzTq/A2K+i9yf8I17s+HrIxylFrG1IQ+ow2ndqsgsCw1gc2J\nHupHwqzsHqMgNYFIVRLuqN1Snsgb1Um84lYcM4w5zt0Xe2yZgpccknfwgUWMat+FWC+xqt2FTFDc\niRDV0wi5v3YrdWyaqhI7y+7HzrIfv43FVmDbMCuttUaLq2/p/c8V1juPFdi++haENKO++puGYb97\nsSrtou1H9cgewNvYpP6WXiC7yIoxriBepYeIVem/srozORT5rfHm7vv7L3UVNTb0JSigNi+dal/Z\neGFk2ZWaJuN7OR1FLyCx1rj44hCuzKMkX3gF79i7eDJaSckJkxiCo6DOtJPfewnDc42P5YyTlVmK\ny1WHvDuL3RSdDaFnuWBVtg/D7WLr4Dg1/eOk5wcIVyXhyhO75VhNEi8XBDkAnJzT3CFJUtUBy5AL\n33Lrcz9wXMFWh+QdfOAQo9p3ITZDNCFzA6VqkEz7F5Cj+9Gy676ZHtM0VT5y8vRRoAAp79iDkOHc\n/0NI0mM9NrGnE19cveB1cdZs+ahS345sIkeVuhk7+8Y61bkFW6mvRA7wREn9zYWqtLP8+9gY4zqE\nUPcD+xNhYjx+AAAgAElEQVQSfW9v2rXCiLR3Pzp6pXtF6+XuxBvXx1RpfoDapAJd7KnuLunZ+Grt\nhc173LgPI3sVNzdGx1AVJ0m69Cr+wSO4k5tJyhkjLRlOgjrdTV73Fe5yNfFQ+gj5OcUWoV9hMqFP\nO4xN7VYuoNTvYn2mj+1hzfq+ccrDEfwVlt2SF+BiYYC3V6SwLymBQ4jdMrvNZpknVMVkMl+CHII6\njhSZHAeOp/wpA71+6nic1xySd/CBgKXaNyB2TFS1fwV4SaMSEYLeCRQjZdffuFXZtZX3/gyi2FcA\nP0KI3ZyuRegWi/Qj9kvUX+/Gzq+/tdDF1aap0hCCjvrqqdibpXsNwz76b1XabcJW6muQcgoTIfX9\nWt/+ZEsrxriUeJWeh+x/iEr/4sqxpOD4znBTj9FzuSv3SsOAOzHkpjYrU1f4KkYLx5afWd5w77+l\n9GeZQD922mVDGFZeIHTjdYLdb+EJNhLMHiYrFS6AOt5PTlcjG1UzD6UOUpKdj9tdh5SJxG6KHtWG\nMe1FX+1WPqAu3ctdITf1IxFWdo9RmOiB6kRcBWK3nC4N8cayFF627JbZVTTKBnIhNolHCb0KOe8Q\nR+Yb/xOtbxWwFFHzsR9JwCkeZ4ND8g5+pmGp9miuPQmxY76hUW3IRuBjCKnuQ06izlh2bXV/Powo\n9q3Isf89wLOx9sUcFpiOXVx9P/KfM+qvn5/z480A01Q+RAlH1XotsvkZVevHo8rRKqiIVtrVI6r3\nJPGVdrc9RljJ4LMN2IS+CejAIvRQXtLRNY9U3Bdp6/3UyJXu6uaG7mB3V5jKgkRqQ8W60L20pbxj\ny7NVl9f8EPG/y7GHdK2/QmDkAImtb+L2NhDI7CcrHa4peHeI7M4m1uprfDxxkIrsHDyeWoQoYwn9\nnVsQeroLVub6qXcrtg2Eqe0dI3OJn3BFEuT7aVoS4Fh1EmZpiDcQu2V27V3yb2MimS9D5uAcJ4bQ\n//ABGv/3ZkqxSXyZ9WcqcBr53cV+NOova+1svDr4mcQE1f5phMCiqj0fu+x6ELFjvs0M88ItcvwI\notgfQDYco1n2GW2caRZYgm3DrEVI82ngmZnWMVdYvvoybF/9LqSmLuqr749emCyfex02qW9C3uab\n2O1Ht1VpZ8UYS4hX6ZXIxvEBYP+KR2v9KSH9uXBTz5aOy10ZVy4NubIzEqhNz6HMW9lfMLry6Kqz\n278RGE08hmyQr0Oiixtu4Es+SNLV/Xjc5/Cl95CVoelywTujZLQ3s0Y382Cgj7qsTBISapFMe+ym\n6DvaMKZ8jWq3UkBxwM26LC/3hWFD7xgVoxGC5YlQGmQ418/FwiBvL09hX7qXt4GGWdktkgSqZTKZ\nJ2Gr8hPA8e/W0fDIL5DDZGWehfxuT1q3jZL5Ff1lHVGKBOQCWItk8K0PtcYheQc/M5hBtfdgl12v\nQ2a/PMEMEUNrUFY9otg/gVgTe4AfGMYcNzrVzYmRUWLPw/bX97IANkfMuguJ99V7sS2Yl6M5fKvS\nbg32QK+7kD2IqFJ/9XYr7SyLZw3x0xgjWCo9tSztbO2HC7ertr4HB690lV5t6PWNjmhq8lOoCpaM\nF6iaq9XN235QfL3mWWAUscQ2AOs7SSg5RFLTfhLCp/GltpOREWHEA4fDpLVdZ8X4dT7s72FNZjpe\nbw1SvBIl9EPMTOgJQG2ml7tCHupHIqzqHqXI78ZVJXZLR56fMyVB9q9IZZ/XxVHDmMXFWc4xVDCZ\nzIuQk6w3yfzQEs5t+RUSx9yTyDwPufhOVOaX9Jd1WCmCSJIpSuRRUi9F3qWcBk7jjpzhIy1dPJP/\nI4fkHbyvMYXX/iJC7i9p1Erssut3EGJ/arqyayvyuMm6/WeBqwix//tU81RusTAvQp7R4upBomME\n4MBCFVdbtX6xvnoGYj1FffXLshzlRjZHo0p9K/L6Yivtbiulo2Q0cWyMcSVCKvuB/cs/uzQnJRR5\naLyxZ23b5e6Ua00jqjDXR03KEl3irewpHFrz+spT9d9K0N5WZG9kPbChD8+yYyTdeAPv6HG8iTdI\nzRjH44XDEVJbWqkbb+G+hB42piXj9y1FqWbiN0Xf0YYx5QRNtVuluGBFXoDtHsW2gXFqe8bIyvaj\nKxMhz7JbKhN5tTqJV5mN3SIX9SVMtlqWIoPIbpL5xTTObt2JqyWJamyLpQ7IR068xhL5CaBBf1mP\nK0UacYr85keedb/TwCkyRy7yyNVhPnLdTyhcbq1hKfIOqov6+nyH5B28LxGj2nchaYmoah+3vr8T\nmS3zDW5Rdm2aajmi2D8HDGPPZZ+bJy4HfqLF1Q8gb5+fQvz1GTdxZwvTVF7kQhS1YJYhVkfUVz9m\nGDpibWAuxyb1uxGCiW0/mt1m3xSw3gksJ956SbbWsj9zaUZTxV3ZhqdjsL63sbPgysV+j8etqM1N\npyJQOpJP3YW6y8a/5nYUHyNmRvoQrrUnSRo8gK/vHbzBaySnjxIKwFFN8rV2lo60sN3bw9bkEAF/\nFUq1MNlDn/QOxLJb8kNu1mX6+FBEs6F3nIqhMEmlIXRpiOE8PxfzAxyqS2ZfrpwOvXRLu0V+58uY\nrM7HifHN24KcfuhRxg4WUEa8Mi9G3kHFWiwngQs8rseR8Q4TVXmN9TMTVa70aVZ2N/MLTWHWd6aQ\noKuwyTwHIf0z0Y8l12j48m7CVecpqKf+Bw7JO3jfIEa1R3PtLwBf/Qq/Zv4a/xwtu74fKal+AjnK\nP+V/UtNUZdhZ9iTgO4hqPzanyKPMEo/aMBuReSvR4urbbmWy3l0sw1bq25Cpg1Ff/Q3D0MPKngUf\n237Uie2pm/o2irSVbAJuwlbq65F3AvtdCa6DNR8rL00L6ntHr/Ysa7ncndjaOkZFfpDqxIJIkbeq\nraRnw4srzm17CrFraoH1o6gN50n0HyDQeZgEbyOJaYOkhuCkJqmxm8qRFu7x9GCEfCQFqlDqBvGE\nfmQaQvcA1dk+tiZ6uHc4zOquMYo8Ck9VIqogSGeen9PFQQ6sTGVvwM07hqHbb/ED8CHkOpHM0xFS\nPg6c6PVy6rc+yuC3V5JHPJmXIbHPiTbLOYvMi5nkl1MLjBFV5f7wWe6/0ctHm6GqPwt1k8iXWre7\nSeSeMc4++iTdn/83Qt4xKhALp8r6M89ay9l66j++4CSvlHoCGW/aqrVebn3vF4DHrcWu11ofibn9\nnyH/ecPA72qtX5jiMR2S/wDDUu2/iFgyIaKnUVHJyAbqLyGbaE8A/z5d2bVpqjwkKrkD2QD8HkLs\nB+aQR46eYo0SexF2cfULC1FcbWXuo776fcAAtlJ/2TB0h0Xq1cQXZfQT3340r/Ym611AFfEqvQA5\n3LQ/e1lWZ8nq9C3ezsGtnVc6sy43DLhTk93UZGZRFigfyA8vO7763P3fSu7PaEcIZUMYNlwimP8W\noda3SXBdJJTaR0YSnIfEy72UD7ay1d1NfTCB9EA5SrURvyl6RBvGpKFqardKdMGKggDb3S62DYxT\n1zVKTqYPXZGIzvNzbUmA45WJmFWJvOJxcdIwZsjvy2svI95qWYZ42g1YZD7k4eTf3UXv32wjbdRD\nLTaZlyPed6zFchI4y+NaI578RGVehSSKRJkXDF7hY9eHqG9NIHukAJvIi5EL600yrz1J02/+E7ru\nFDnEE3kF0IP492cRYRD985KhjXHrd73w6Rql1DbkH+O3Ykh+KXKF/wrwR1GSVzLn4UlENeQj/9Cr\nJk6jc0j+gwdLtW9EiP2TWKr9AuVvldPwaeTCXw38KzIYbMpmJCsL/mmE2NcgZLwHma8yuyy7JE/u\nxvbXw9j59Tdut7jaGjFsYKv1HOJ99QaL1MuJJ/UxbE/9ZT3PkcFKTkVOjDH2APs9Ac+RqvuKKtMD\n3DVytbuy6XKPv6cnzNKCJKoSi8IF7uqmyra7flR1ec1h5AK8OgLrm/DXHiGp/SAJkXMEkrvITNY0\nKYIXBigdaGOzq4v7fG6yg2Uo1c5kQp80qE3tVnlJHtZn+bg/rNnYO0Zl/zjJJSF0SYjhXB8N+QEO\n1yXzYkGQN4HL01685eeZw2TfvBYp9T4OHB91cfIbq+j+i+3420JUY5N5FbKZO1GZn+Fx7UL+bU5U\n5qVI5v40nshp1nS18kDLOBs6gySGS7HJPEAMkSf1cmHHHgYf+jG+xAHKsYm8CggST+DRP88Zevro\nZ8yPYXEilEoiZD+OknzM918mnuT/DIhorf/O+vo54HGt9ZsLsVAH7z8oRSp2QiYEfDVE/zf6SapE\niP1TwGvYZdeT5p+YpgohZLwDsS1eQIj9pzOquPiFJAMfRoj9I4ivGSX2k7cz+Ms0VQJyAYsq9RXI\nAKyoWj9qGDqslJpYaecivv3o0lxnqlsXi2LiN0iXIlVDB3JWZI8UVidt8HUPrW5r7ExrvDLsys1M\noCYtVxcHynsLRla+uebEh54KhIMRoFLDhha8a98lqf8g3tFT+ENtZKZE6HQRODtMcV87G+jiPp+L\ngkAxSnUQn3KZROjRYVw5PrYletg+EmF11yjFQEJVEqogQGeunzOFAQ6sTuOFRA/vzJh4kiz+VL65\nC4vMw4qTP66i/S+24z6ZTSn2Jmg1MsdmIpmf5nEdtXAm+uU5CNGeJmX0Ave09XJvq6amNwWvrsTe\n+GzHInIV4cyW/bR/7jtEak+R6dJxRF6IKPiJRH4WuG5oY97/Ft8PJP9/kOPR/2Z9/TXgWa31DxZi\noQ7eH5hOtXeSdiaN7i8glkwEu+x6ktdtbUh+GCH2jyAbgHuAp6aqj5tmIUuwi6vvQg4KRf31uaVr\n4tcW9cujSv1u5KIRjTa+bhh6SClVRPxMdT8x9gtwfh6k7kM6U2OtFxfwhi/Fd7J0c25lpo/1A9d6\nChsv9XrD45qlealUJJaM5qulF2ubjB8VXa+6hhDXuk4SNpwgyfMmvoET+PzXSU8JM+zW/lNjFPa1\ns1Z3c18ClPkLLUKPTbkc0YYRR8ZqtwomKJbnB7jPrbh7IExd5yi5qQlQadktuX6OlydiLkvmZY+L\nU9NeqCXRFE2pxJJ5NmKFHA8rTr5WxI2/uhv9UilLIq6byrwG2cOIz5kPpp/m/+lIYmq/PEB047Oi\nv4n61iE2d3goGszERXTzMxuJR54BzuQ1c/mTP2Lk/hfxpvZQjE3kVUgCayoibzC0sSg1jO9Xkv+p\n1vqHC7FQB+8tplLtG3nz395k8yaE2O9CfPOvM0XZtZVlvwch9k8ig6b2AN+fZW5ZIf/Bo/56BdLG\n9DTwPLdx+Mc01RIkpx5V66OISn8ReMkwdLuSi0p9zEcy8ZV2Z+ZB6jnEq/RVCFnsz1uV7ckvDq1K\n6BmsudHYldR8bVQV5/lZmpoXKfBVtpcMrH111Yl73/DgCQHLe/FsOkNi1kH83cfweZtITR7FlaB9\nJ8Pkd3WyOtLFvV5NdcIS3K4uJm+Kxm1mqt0qK9nD+mw/HwpH2NQ7TlXvGCmFQSgJMpQXoCHPz+Ga\nZPaWhdiP2C2TX7/45sVM9s0rkJkyxyNw4mguzf+wkfCe5aTH+Oa1yNmBeGXeWnuGfzqZzmRVXoMk\nrk7jC59lXWc797SPsbrLT8bokpjNzyEsIveOcM4w6fnoT9B1J0l1R26SeDWS+DrPFBaLoSdvIi80\nrBk6uci+QxmP8833muT/FEBr/bfW188BX9ZaH5xwPw3sjvmWqbU257pwB4uPGNW+CyHm54GvDhBs\nCzL0GEL6pxDV/oOJZdeWKt6AnWW/jp1lj5thPs0CPMQXV7uxbZjXprJ/ZgPTVEnIBSe6YZoHvISt\n1i/W15ONbb3UA5nYlXYmcHLiftPML0W5EXKLVenpwIFARuBC0aqMyowEtbz/enfO5Uv9Hl+CoiYn\ng7LEkqF8XXtq9dkHns/ozRsAygZxbzxHqOIQwd538Louk5Q4RNCnvaci5HV2sTLcg+EOsyIhD5fq\nQqlYQj8cS+gWkZQt8YvdMhxmTdcYJeMaX2UiWHbL2YIAB1al8kKal8PTlqXISNyJvnkdsmdwXMPx\n8xk0PbGa0a+tIbkjSBU2mQ8xMWd+9qMX2PNMFpNVeSVSdHKa7OEG7mrvZXOHpqY3RGK4BCHyIuQi\nckZFOFN1juaPPMvo1tdJyOikEJvISxC/fiqvvMnQxoLOIJr0I9utQoj/Xzbpo4EydVFFEkYTBgPD\ngdGe4z157wXJ/19a68PW19GN1w3YG68VE9WNo+Tf/4hR7buQzaKv/hH/40f/gz+ORh9zsMuuJ81a\nN01Vhx15HMce33t2Fk8eQnLr0eLqRmxiPzYff93y1ddjK/XVyBCtqK9+pL6eNOLtlzxkPyGq1N+d\nI6mnEh9j3AA0K5c6mLs805+XG6jz9A+WXb/SHWxrH6cyP0R1SkE4P6GyubJj8yt1F7acB7JGcK29\nSHDVUUIDh/DqiyQm9pHi1wnnNDmtvSwf7+Jud5i17hw8qhulYjdFD2vDuPkuSe1W/gTFssIg97vl\nMNGyjlHykjyoCttuOVEawlydyj7Lbpk84+cWI3E1HL+aTOO/L2P4q2vxXciIy5uPMzFnfuhXL/PM\nV7OZrMyFqN2R09T0XWdTxyDrO12UDKTi1RUImfuwVHlqFxcNk776l6HmNCkJUtQRtVjCTJ1euWDo\nWxdpzxfWRTQPm7zLb36uKQNSA6OBG2kDaT35nfkjpa2lrvKW8sTS1tLM/M78YHA0eAWZ83Opnvrf\nWIx0zR5E8WQCN4AvI17Y/7G+1wO8o7X+iHX7P0dIYBz4Pa3181M8pkPy70NYqn0Tttf+vI/hr/aS\n7PYy9kVkMNdziGrfN/EUqFUp9zmE2NOws+zv3DLLLkXQDyHEfjeysRktrm6c6a5TwXoHsRRbqd+D\nROqiSv21+nr8xFfaFRNfaXdUz/Kkq7VBWkm8Si8GDoWyg1fzl6aVpnt0dW9Ld8bly4Ou9BQPNZlZ\nuihY1lc0tvzddScefD04muQdQy2/THDjcUL6EL6xswSD3aQFtOcyOruln9rRbra6xtjoysSnemMI\nPeqh3zwwpXar9LQE1mf5eCCsxW7pHiUtPwAlIYZy/VzO83O4Oom91Um8zlR2yyxG4t4IcemZKgb+\nZTUJbxaSr9XNTVCYaLO8+qVmXvpvUTKPVeaZwDmSxs6xtquDjR2jLO/xkjuchftmxPAGcMY9zrlV\nR2kzTEY2vIUvq41cJURebT3ORabwyg0dv7+wkLB6WiercSHxEk/EM5A8mNyR05MzWNRepMtbyn1l\nN8rSitqLMjP6M1pc2nUJIfIG68/ox/XYdxLOgDIH84Kl2qO59gDw1W/ySy//Ev/6EFJ23Y747E+i\n49+mm6bKQWyYHQjJfR8h9tdnzLKrmyQctWGWEl9cPWe/0zRVLvZm6X3I5u9NX72+nhHiK+0qkCP8\nUVI/rGcZr7Tms68nfs5Lv8vjOpRTkx7KzfBVuwcHlzRd6fX19YepyU+mPLlwLN9TfWX51XvfKLlW\n1xmG4qsENp8hMfUtfIOnCPjayAjhbiGSeXWQpSM93OUaY4tKI0ivZbnctF2ihB4dxlUY4O6gh/tG\nLLtlJEyg3LZbzi0JcGBVCi9k+zk0yW6ZxUjcTj8XXyql79srcT1bQVaMb+4l1mKJuE/y/P/s5K3f\nzkS7J/rlXtCnKRq8wvquHtZ1hqnqC5E2FvXLMxBSPpPfROPW1+m/6w2oPE+yf4Qyaz3liFUz1aZn\no6GNBeuavfnjkQTREqayVDTlCpUUHAm2ZfRl9BZ0FoyX3ij1lN0oSyppK8nK68obCowFJpJ39OtG\nQxuznobqkLyDWWMq1V7GxW+cpTrTQ/gx5D/4k0im/WjsfU1TpSLRyB3IALEfI8S+1zBm8MjFk96M\nTewB4our55RIsMYI342t1gsQst4LvPjQQ7T097MN21dfirxDiK20m9VzKjkpG6vSa4F3E3NDbUtK\nk4tSXJGSntae5MbGYZWf7aU6PTdS6C/rKu9f9+6qU/e/69bezGZ8G86RVHQY/+BxfJ7rpIci7l4V\nSb88TOVQD1vUGHepFFJ0v6XQYz30GwBqt/J6XdQWB/mQS3H3wDjLO0bJC7hwlycSyQvQnOvjeFGQ\nV9em8aLfPYXdcouRuH1ezh0opOe7dfDDGlK7Ajfz5iFiVflo8DTP/kM/R7+YjnbHqvKlwCDe8Bnq\neptZ1znAyh4XJQMpBMPFFpkPAGcCg5xfd4iOu95gbOUxvDk3yFG2V+5maiK/YOiZG5zmA7VbJTOD\nGk8IJ/SlDqR25XbnDhe3FauyG2WBstay9PyOfF9Gf8ZlhZqSyA099WC1ea3RIXkHt8JE1e4i/FUT\n4/g2Xv8kUnb9JmLH/Di27No0VRCxU3YgpLkPIfZnZhwAJYr3PmQy5MeQjdeov35kLv66aSoPclGJ\n+uprESJ8Edj7h3/I6XfeiSvKWGb9fZTUD+pZVNpZs9lXEU/qCW6v+2h2ZWooO9lbytBA1tXGvoRI\nGGqXpFGaVDRUFKm7vPriA29ldRWqVhKWnyex9gjB4WP4XVdJDY67xlyRtIZRKgZ62cgoW1UyGeP9\nuFwTCb0FQO1WKele1uf4+PC4ZlPvGNWdo6Tn+m27JdfPkcpE9i1P4RXgSpzdcouRuIMezryTR9d/\nVBP+Xh1Jl9IoR8g8BdlMFzLvzz7Hc38/yIlH0sEVq8wrgRYyRy6wqrudNV0j1PZ6yRtKx6vLkXcG\nl11hzlRcoHnTmwysOwTlF0kODt2MI+YhhDjVpmfr7WTKJ/1eRY0XMAWJK60qgGDicGJ7Vl9Wf0FH\nQbjsRpmvtLU0paitKDW3J/e6f8zfwGQ7pQG4sZDrNJUZQn52RdbHzc/rqb/XIXkHkzCFan/uQzz/\nvZ/yYJmbyBeRt9vRsuub+XJrs/JDCLF/DFHBe4AfGYaeckqg9YRZ2MXV9Qh5Rf31S7Ndt+WrV2Er\ndQNJS+wF9n7nO7z1la+wClupr0KmV8ZW2t2y8EHJemNjjGuAC0m5oe7cgsQlSSq8pLO1J9TSMkZp\nXoDKtLxwoa+yraZ9y/GlF7Y09+IvO0fi2uMEw+/g5xLJgRHl8eiUi2O6rLePDYywVSeSOz6A2/U2\n8Zui16PDuIqDGCE39w1HWNM1SulgmGBZCPIDdOb4OZ/v58DyVJ4vCPC2YeiumBcw40jcETenT2XR\n/lwFYz+oJXgkj0KtqEOsEbugor26gb1/PcbZj6ehPUuxlXkh7sglygausKq7m1XdYSr6g2SM5OCm\nCvAAp9M7aFh3iK71bzO27AS+7FayrENCFUhr1lREfvPI/kJA7VYpTGGpqIiqQFHgHff2pfWn9eR1\n5Y2WtJW4ym6UJZa0lWTmdeX1pvenNyjUVL54o6GNBSkxN5XpQS5sceQ94esgEjZoRA5VyefJPS31\nvZ/4iUPyDm5iomoPMvC1Q6y7VsOZzyAbjk8h5P56VFFbxRV3I8T+aeQ/4x7gezM2zCtViW3DLEfU\n9dPATyf6+DPB8vi3Y/vqLuuxXjx+nNd/93eJPVW6FjkBGSX1/XpChHPyMpUbIa5YlZ7l8XtOZpUk\nhzITPQWRwYHUxsYBdyjgpjo7Q5ckFg+Ujq66tOb0h0+q4ayMc4TWnSYxcBjf2AUS/QMqmEDSpXCk\ntLuPtZERtqoQRSODuF1vEU/ozWq38vhcLC0J8WEXYre0j5Kf4MJdESKSG6A5x8eJwiCvrUvj+UQP\nJw3DspRuMRJ3zMWpi2nceKmU4aeW4nu1hLwR8c1ziJ1p3rj5Kq//2TjnH0xDu6NkXgOkExo/T13P\ndVZ197OsB4oHk0kaz0dRDrR4Rzhbe4ob6w4xsOooquQyScFBiq2NzwC3cWR/NrCGmRUykcS1Klda\nlQO+pKGkzpyenMHC9kJd2lrqL20tTc/vzHfndOc0+Mf9sQR+83NDT9/3OluYylRI4GAigcd+noPs\nJ9jkDY0k9d5g84FhPvKsYvnxZNyRQqBAa1UwPu4pVUoXuFyRxO3bIx6H5H/OEaPadyEWyXO/x98/\n97/5gzolhH8RIfbvRcuuLcW8FiH2R5CN1j3I+N6p56vIAZcN2MSehvjrTwEvz7a42hppsA1brRcj\nGfS9zc2YX/gCqZHITVLfgNgIUVJ/Q9+isFsplYLk/KNKfSPQmpQb6snNCeUGGMvsaOv1d3aFqcpP\npCI1f6zYtfTG8mv1p3OvrR47R2jlBRKzD+EbPkvI102yVyVejYSLOwZYHR5mKwFKh4ZIcL0Vm0W3\nCD0x28f6LB8fHo+wpXec6vYRMrJ9qJIQgzl+ruT4OFKRyL41abwMNN60W2YYiRtWnGxMofmNQoae\nqcbzfDlZ3QGWIheA88BJIq6TnH+whbd+J0zDvamWMresFu1myfBFlve0srJ7mOq+BPKG0vBHSlCk\noTlXeJUrq47SteYI49Vn8WW1keUJU4nYHYtyZP/m72y3SmMKS8UdcVdGXJEl/lF/b3p/el9+Z/54\nSVuJp7S1NLmovSgptyu3OXUg9YIL10Q75RLQdrtrM5XpR17/VOQd/XycWPKGq4T6r7P6nRHuflVz\n1xsBgkN5QEEk4ioaH/eUuFyRAqUiwcHB5N6urpyh1taiSHNzmbupqTLQ3Fye3NmZ19PWVtDU2Zlz\nJRJJWPgplIsBh+QXHpZq/wKi2v0FXP3mfrYMFNL0WYQ4v4lk2m/m1E1T1WBn2cHOsp+e5kn8iMqO\nFld3YG+cvj2b4mrr1OtabF99PXAEeLG7m5c//3n04ODNBMxmhLSipP6a1tPbRDFDwWJVepkn4LmY\nVZAUSAu6cscHB0NXm4ZcWakeqrOydUmgrLeib92l2tMPNF3VGdUXSSw9QmDkFAFPG6k+FWzR4cK2\nAVaNj7BF+6gaGsGnDsYdLrr8jWaufDO3NMQ9ITf3D4dZ1zVGad84iSVBdEGArhw/53L9HKxL5rny\nRA4ahpUemmEkbgROXU/i6qElDDxbgevZStIaU6lECOUicJJx32lOf6qdI78S4crdaUQSoi1DS/GG\n+0hohjAAACAASURBVKjob2RFTxfLesYp7w+QMZqFR5ej6E3s4/yyE7SuOcJg3UlcRY0khwYoVOK1\nL9qRfavJqYgJRO4Ouyu10mVKK0/KYEpXTnfOcFF7kau0tTRQ0laSntud25PbnXvRN+67yGRfvOl2\nbB9TmS5EZc+kwlOQ6am2jRIcaKHm9DCb3gyzZX8CS65nAQXhsLtofDyh1O0O5ysVDgwMpPZ2dWUP\nt7YWRa5dK/dcu1YRun69LNjZmdfR1pZ/taM3t0mnjPeSNzxE2UCY8n43VX0BigdT8EWWIOeOsqmv\nd5T8zxMmqnY348/+T/7o4O/yD2uUzHTZi0Qfn49OXjRNVYSdZc9GqvX2AIemOZKegRxIehgh5WPY\nxdUXbrVG611CBbZSr0cieXsHB3np13+dnqtXb26W3oX8p42S+qta60njau2lqQCyERuNMG5BMZqU\nHerNzghkevVoakd7X8LQkGZpfgplyYUjpZG6lhUNDzT0d9VmNJBY/S7B8HECrmbSfMrfSbigZViv\nGBtii/ZRPTBMSL0Vd7jo6O9fD/Ufqy4J8QBwj2W3FAAJFSHCuX6u5/g5URDgtXVpPJfqteyWGUbi\nRuBSR5DLx3Lo3VsGz1WScjyb0oiLEuvncZLhlDOceKSH45/XXN2UQsRr2Sy6nLSxG9T0NrOip4+a\nXigcTCRlbAku8jxjNJQ1cHXlMXpWvMt4xQX8me1kecKUs0hH9q09hnQmkLgr4qpwaVdlWIWzg6PB\n3sy+zIH8jvxISVuJr7S1NGVJ5xJyu3MvxajxWGvl8u0kakxlJjM9eRch74K6ibVR/EMtVFwYZPU7\nYTa96aHqXDKecMHYWEJxJOIpcbnG812uSKC/P6W3qyt35MaN4khzc1lCU1Nl6MaNYm9nZ+6NtraC\nqx3D2a06bbyP/MERygagsj+B8v4QecPpeHSUwF3IBeQakbFmhq5203d+iP5zY/RfdDHU5GW0K4nH\nI7/ikPzPAawqsajX7tvM/u8+zcPuLNofQcZCPwH8W7Rk2jRVNpKc2YH4tz9AiP01w5jisI9SZdg2\nzGrkuH+0uHrmsgZ5viziffUEYO/YGHv/8i9pOXjwZgPSNoTwTez2o2kfXylVQPwG6bKEgOd6Rm6i\nL8VPxsjgoL+lZVQVZPuoysiJlHirums6Nl9NuvjA2HmSl54mlHAMv25UaT7tHVDhJddHdN3IEJt1\nArUDYyRHDuKK2Rjd/8mOHFf3uhw/Hxmz7ZasdC+qOMhgjp/GHB9HykLsW5XKPp+bq0Y9MM1IXA3t\n3X4unc6i85ViIs9XkHQwn8LhBEoRcjlBb955Tn6uj5OfgeZ1aUS8VUAN7kg+hUNN1Pa2sqxniKo+\nN7nDaQTDhYArq40Ly07QuuJdBmtO486/RkpogHw1/ZH9s8C1+R7ZV7uVF3mHGOuLl3nCnuqwK1zi\nirhcaQNp3bnduWPFbcXu0tbSxMKOwmBOd05zbnfueW/YGyXwWCLvmI+lYiozASHKmTYzPcRaKL7h\n6xRf6WfZiTBrjrhYeSykQwO5Y2O+0kjEXeJ2j+W5XBF/X19aX2dn7mhra5G+dq0i4dq18sS2tkI6\nOnJb2joLmjtGs9p09ugABUNhygYU5f0+SgeSyBrJQpGPHM5qA64RGW9m+Hon/RcG6D8/xkADDDYl\nMNoeIjyS6dKuAqVVfsQVSfOOe/tDw6GB9P70kazeLJ3bnevO68oL/NOb/5TjkPwHFJZq34wQ+ycS\n6Xv+6zx29tP8YKMSNfsdRLUfRmttzTz/JELsm5CijCeBF29u5NkPrpDHiBJ7NvHF1TMmVKx45VZs\nC6YMeGV8nH3/+I9cfuopSrAr7VqJL8qYstJOyUz4lcRaL4pQUmawNzPVl+rRo0ltbQNul1LULEmn\nLLF4qHxkVWvBmY93NI6UlFwgKekovvBFleod94y7dG7zWKR2cIhNOoHawVEyxg7idkcJ/RCvfXi4\nIjhSH3TzoeEwa7vGKOsZI6koIOmWbD/nc30crE35/9l78yDZrrvO83vOXfPeXG/uay1v19NqbZZs\nS0+WZVu2wTYmGGAaehqizTDBRBNB0zDR0RHt6OlpoBlipmOCAU80mGYdDAHGxljeVJZkCcnPWp7e\norfXmllZud99P/PHzXpV72nxw3Z3Q49OxInIqozIrMrK+uT3fs/39/vhiaMZPHfiBJu+WUtcBlBT\nxKVLGobPthF+5QCUpzqoTxQcALAFRs5geOQqzn3CxPkfIOjdlUcsJjaLGuZw0NjArfoYt+gBFi0J\nJa8EgbVSDnqHL2Dr1tOYHj+DePkKUtoYJT7EMkm84DcC+eXvpmR/rsZLuMEX5yP+MAE5GNKwlPbS\nenlWdlqjFlscLKYWB4u56rQ6rU/qlwt24SJe74tv/V0LleaHmSW8Obw78/u3savCRa+H5paJI+d9\n3PEKxR2vSKyyU/YC6QBjtMPzfp3SWDIMzRyNan6/v4hud1nc3DyUHo0a3nhc3e5P2/1xrI1YzXfR\ntmMsWxyWLQUdO49cUAVBA0mx3RZYtAW3P4J11YB12YN5hcHZ4OENFIRmjotoC0AzpnFRCAUr42bM\nglnw98FbqU/rWc3QQs3U+iWjtMHHfJfAH0gYWAo2/TQuxRmc5yp4+l+9Dfn/xtZ+1U4QSz+GP/7r\n38T/lMlB/xgSL/t3MR92vbJCUkislR9DAtsnsZdlt254YAnXD642sZdff/6tBlfPffV3YE+p3w/g\n5TjGV/7kT3D5M59BIQjwMJIEzxTXj7TrvvHvSUpIPox2oX63kOJnxZIipmXkHMsWh8MQB5opHCo0\nwmV6dLa4+YHt6fYDxVUUSi9DCi/QrOgSjqLaC6Ojpot3xhyOWwHK3gsQ+BcAnERovJh6/uPSQSX6\nIAhOWCFuH3poRQzichpRXUavIuFMXcbTdxfwN1UZp088AoI3aInLgIrL48paHv3nm/C/vgTpyUVU\nN3NYZgR9xPQMeu/YwGsfNXHpQxQ7xwuIpMMg7BgqHocj+iaO6zqOGgxtW0U2qFMGtbWFq7ecxeDW\n03AOXwBf713zyov4Ppbsk08RCYnSv84X52LucEjDDsc4ppma0Rg3os6wwy/tLGXrk3pUnVXX6pP6\nBTES93vjVwCsnWAnvmNkdf9aISsK3txGac+3g10VLvg9VPsGDl7ycPwMcOtpKVpcLfiMX2KMLAiC\nXyVkF+D1oN9fIFtbB6Vud1kZj2vmaFLf6RvNnTHRpqzu+ejYwJIlYslKo+kUoEZ1JFZTHyzegjcY\nwF4zYF1xYa1GsNcpvL4MX8/yIeogaEY0Kgqh4KbdtFkwC35Fr7DatMbXprVUfVLPaqbmaKa2XTSK\nG2LE93gYMxl9R8F6kMZFksFFQcUVRcSsiKTj5O4WkHx4XdsE+B/fhvx/A+tG1d7G+tf+E36y+zC+\n8RBJ2tv+LpJh12vzLPujSMD+g0iU6R8hybJPbnjgPJL+Mx9Fkn8/iz1//S0bh62skAO43lffjmN8\n9YkncPa3fgspXccDSD40bFyv1F/XaXI+ru4YrlfpjYyWsrSsmCaxlxoMXZpVORytlthyatlZmN0/\nZBc/hm7caJxCKjpL07yBFEfLvSg8oru4N6Y4bkVoWC9AEp8HcBL6udP1136uXJPiDwUxHpwFODrw\nUM4KoAsKnJqMtbKElxYUfP1+DV++699gq/KN17fEZcDBgKK7lUXvxTrslUWIK4sonStjKaIYIhTP\nYuPBLi58xMKVRzkMjxURSYcgRUfQsS0c0/u4Rbdx0KSouXkoUTM/gX74IraOn8Hs2DlEC2tQChOU\n+RAd8n0q2Z+r8Qquz4sfEEPxKCNsOeTCQtpNG9VZ1WsP22RxsKi2hi2pole69Un9kmZpF3G9nXLl\nBHv9SL83fc+QFQ5vnAnff1vFrgIX/B5KwxkW1lwcOQ8cPyOEBy/nPTFcIARtQfBqhMSCrhet0age\nbG8vkW53We52l1OTSXU60mvDbbM5mPB5gzW9EAs2waIlo2NnUXdL8wNMD4xtwR8PYK9PYV91YK0G\nsDcIvL5EvGmGD8IqgEZIw6IQCV7aTVsFq+BXZhVWm9aE2qSWqs6qqmZqpmZqvZJR3EiFGAqYmin0\nPAWrURpXaBqXJQVraR52GXvg1pAEFvbDu3fD17tbv7FY8O2K13/ga79q5xFI/xy//tS/wr+pKHAe\nQmKf/A6Ab6w8CSA5pPwxAD+MRN39MYA/PXHihgEdhCxgb7DGfUhU9a6//qa595UVUgLwXuyp9VQc\n4yvPPYdTv/mbQLeLu5DAPsT1I+1WX/97kQxuGFcnyHysaSkuJcaqbTq8YcY40srgYL4VLAa3zZSr\nP+wO9dvK56CSVzmVjlmap9pOHB2aeezeiOC4FaFlnIQiPgvg29j+0qUj3V87qHDs/U6Ee6c+lkc+\nsq3EbplUZFyqyXj+cBpfuiOPb554BCJu8M0ZcDwiMPtpbL5agfnMArgnF6G9WMeiK2AKL30Oaw9t\n4+KHHKw9xGN0WEMkHkIhWMayOcIxfYRjho9FS0TRL4ks1hbXsHnsHIbHzsE9eAlcbRt5xUaLJAdt\n33PJPvkUkZGo8aSzIcOyFEpHCSOHAi5o8TEfFY2i1Rw344XBgrQwWEhXZpVpdVa90pg0zguRcGMV\nZ/dmfPq5jZLHWx9m1pAAbR180EVhMkVr08XBSxGOneO9pdV8UJw0CI3bouhVCYmF2axkD4eNsN9f\npFtbB6Xt7QVxMq2Oh0Z11Heb44mYs1jLjbFg81iwFLSdPMpeBTzLgbFtBNM+nM0J7DUT1moAZwNw\ntgXqTdO875cZolpIwxIf837GyViaqQUlo8Rq05pQn9RT1VlVyZv5iWZp2yW9sJUJ4omEoZ1C11ew\nylRcFVRclVPYynLwq9iDt4ebA/fwexk/+Tbk/wGuuWp/EIlq/+g9+NbT/w/+6ewOvPIISRTO7wD4\nf1eehI7kEHQ3yz5Dotj/5MSJfVWkib9+J/b89RaALyAB+1du7Pe+u+ZWz7uw56sfAvDUyy/jxU9/\nGt65c9eGUHO4fqTdlf2tpOcxxiVcr9KPpHOyU8jwKRb50nDsk6om4Ei5Ei/yh+1c74OW0/1A+jI0\n6WUuhT7L8VxuxMIDU5/dEzAct2N0Zi8iIzwDxN+WVn975+jss7cT4IQZ4faRh7YbQ1pSEdVlbJcl\nnGnIeOb2PL744f8Vl0vPXW+1sESdp0YK1s+VoD/XBlYWkX++hfYkBQtW+TxWT/Rx6f0ONt4lYLJc\nAhEOoe40cMjs4xZ9isNmjKatkkxQqwwRHbmArWPnMDtyHnF7A2p+ijIXoUy+x5L9uRqvYp8aF0Lh\nsBAJR0MaLoZcmMvZObM6rQadYYdbHCyma9OaX5lVNhrjxvl93vguyNdupiHWClmRsJcJfzOQxwDW\nwYVbyM2mqPdsLF8J2aGLnLe0Vgjq/SqR3KYkuRVCmDCblazhsBn1eou02z2Q6vc7ZKJXxkOrOtn2\nmtOpkrFZ2yXo2CI6dhpNR0PBr4EwC6Heg9Mdw163YK+7cNZjOD2ec6Zp3nc1xoJKyIUlLubCjJOx\nC1YhKM/KifKe1pSKXhHzVn6kmYXtqp7r5/xgJmHHSWErVLBOVVwVFawrMnp5ingX3AqSzpffCdx9\nMGZ/p9f0+7Hehvw/oDVX7T8B4JMZ6OK/xb988ZP49KIEfxHAH2A+7HplhRzGXpZdxF6W/fS+BxOQ\n+N+7/nqApCjpcwCefSN/fV7Zehf2lPo7Abx6/jye/8xnYL7wAtpxjIeRvNGvKXUAF26Auowk957E\nGAnezQtcqlCQiSww2TAcLooYjjXzOJDu+NrsPTZb/yHa9Q+qL3EptslyPE3riJYmfny3z3CrHaM9\nfgVF8RsIzVcqq7/qtZxv3hUmdsuxHQ8VlQPtKHCqMtYrEl5upfD1hwN85dH/AQqNXgfzhi6he0nD\n+IUm2FOLSD/bRmszA5fpCxex+vAOrrzPw+b9ImYLJSj0MNp2FkeMPo4aFg6YFDU3m46iyvJV7Bw5\nn6jypasQKjvIpxzUyfdYsk8+RRTs88ZpTA/KvnwLI+yAz/sNIRTCslF2WqMWFgYLqda4JZT0Uq82\nrV1ujpvn+Ji/Lqlygp1485YTuJYJr+CtDzMLALbAhV1kjDEqOyY662F88BLnLWxkg+ZWmRZmdVF0\nyoQwfjotO8NhK9reXuS3tg6khqNGMNark6Fdm26HdWOSTntouxQdO4W2nUXDLUMNFETmNtzeCM6m\nDnvdgb0RwelR3pmkec8pMOYVA84vcjEXX4O3XsbcNlFKRonLW7lh0cz367PMIO97uoy+n8JWrGCN\nU7Eup7CRkTDUCFgNQBnJ3+utoL27J9/LbOCbWfPzLW3+c5WRHCLv2jstJNHOCpLzmPwjjyD/NuT/\nHq/9qp0g/ujH8Jff+vf4xWAZVx4gwFNIVPsXV55EDYla/zEkf+TdLPsL+yois0hmo34UyazUi9g7\nOD37Rm/Oeb/3XV/9vQAGa2t49g//ENOnnkLR8/BuJJfgK9hT6udugHoD+2KMhOAuJSOFWZUT48jn\np9OQdKoiDhfrcSW82+E3P8qm0/coL3NqvIocz2QHbHEcRu/wIhy3GTqjV1HlV4i7dmZp89flgnv2\nTifCvRMfB4YecnUZaCmYVCRcKkt44WAKT/z0/44rxeevDW/eBfphW8BoNY/hyQbCZzpQn22jfqFA\nwnBy9DLWHh5g9YSH7j0yjFYZpfgIFm3giD7AEcPDoi3yWVdr9iEfuoTesdegH7qIuLkFNaujwkUQ\nyXdZsv+6EW4Myyk/dQsXc0cCPugEXJAumAWrPq1HnWFH6Aw6Slkvjyuzylpr3Hotb+cv4PUNsd7U\nUlkhKxm89WFmC4AOGm1CtUYoDU20Nv146Sp1FzZzYXOrSKr9sqSYZULAT6dlZzBoxb3eEtfrLaeG\no4Y7NsvToVvTe3HdmOaUEG1XQNtR0bTzqHpliLoBd3sApzuFs27D3gjh9IhoTRTOs/KMuVpAPY0w\ngoybsTVTC8p6GdVpVahP62rRKEY5KzcsmZlRQ1eGRdc1ZfSDFDaJgnVBwXoqha2sgFmZJK8tsAft\nt4L3znc7Texm1vyK+EZgl+evexPJz1pG8iGaRdIKwkdiezIkNp6A5IrZRHLFPkJyTtN75BH85NuQ\n/3u49qv2DtbkX8O/OP8J/PkRHpGPBOx/sPIkQiT++o8j6QT4F0jsmG9cy7InOfFdf/0BJAMudgdX\nvy61srJCNOz56o8BULtdPPPZz2Lna19DxjBwLxKV8A3sqfXTu9OPSDIw4nbsAp2ShyglpXxehsRH\nwszwqCQQ3NIooskf9cXhB8Kg94PSaa6Ki8jSiI8JFoZReKcb4VaboTU6g2b8JG+cvHJk8NtFyV2/\nzQxxx8hH2wohL6iIGondcrYq4ZnHVvHM4/8OhLdxDHOYx8BtAYdgM4v+yzV4z7aR+mYH1VeLPOzJ\nHZex+tAIaw8F2L5TRlCtohkcxJJt4ag+wyEzRMNWSn5YWlyDefQChkfOw11Yg1AeoCB5KALYIN9F\nyf6NI9wkXzoqhuKxmMbLruDWpEDyK3rFa41adHGwqFanVbeklzYb08bF5qh5lmPcfl98/c2qSueZ\n8Abe2kYRQeJNKPYQhYmORteLOuvU62xlwvZmgbQ2SlJ2VgQYN51W3cGgxXq9Rb7XW5bH06o1sqr6\nwK/qPdScWT4Vo+1JaNkZNJwitKmIeHsHbncCZ8uCveHD6UIyxinOM3MsdnI+5xYIQDNOxi6YhaCs\nl0ltWhNr05pSsApuwcqOy0Z61JxJk7Lr2TJ6UQqbVMGGpGBDldHTeNgVJBDcwXdW3Ntg7HvuPfO6\n1zq52s3jemCXkMC6jeRQ+ZrKBpBGAmcfyRQqIMnni0jmzupIriIGSGygrfnuAxiGMYZ/O4b7l1uI\nX5pCipPHTXYoVeGrdfza+Kfehvzfk7VftYvwPvpJfPrVf4l/y1fRP0Lmw66f+SucCTP4GBLF/iCA\nv0Gi2J84cYJ5c3/9ViRQ/xgSiOwfXH2dglxZIfL8cXZ99SODAZ7/3Oew9eUvQxwMcAeSN+iNI+2i\n5GcmGvZijO8ilDyQSgksrVAhigLOMCMcaqawlFmMUsbDAet9lL8a3kleo2nqgiekPYzCO5wYt1gM\n7eFraHtfz+lf3Fmc/FmdeDu36AGO9l1UBQp+Xky0UZHw8nKIp3/6M7jafg5lwvZgHhPk+mlsn67A\nfq4F8ZsdlF/RFH6gv+Mq1t4zwvq7Q+zcqkDWGmj4LRy0pjhqGFi2iJJxsu1BnDl4EYOjFzA7cBms\n3oOa1VElDCZJxsXddMn+XI1fGxrBR/xBxVNuA3DQ471WyIVK0Sza9XGdLQwXpOa4yZWM0nZlVrnS\nGXbOZp3sdUmVN1L/88PMIt5ahVdB4h3I7g5ysxlq227Y2qJeaysddTZzpLVRlCq9AgjhJpME4N3u\nstjvLwhjvWqM7Iq5E1TNHq17uiYCLU9By8mhMi1A3dIRdEdwtgw4mx6xe0zSRzLn6Zk4tnIB52YZ\nYj7rZO2CVQjLswTe1VlVLlh5O2+lJ1VDnbRm4qzk2I6KbaSwySvYkGVsZWXsaBy8ChKF+p3BDYxu\nplXGza6VFSLienVdQqKsO0j+L6rz72nYU9kRXq+yY+yp7DH2Pog2kBSeDYceps+OEH5zBJyagnNj\n5AEUEYoV+OkGYqEORsogsQYuKIDzMuBdBbEQwc0FcPMx7DJglTmYNQF2kcLLmnj2l7JvQ/6/8tqv\n2u/Bt9K/gl9efwRP3kLBXgXwOxs/gi9c/lmcQKLY34/EpvkjAJ8/cYKZ83Fr78GeYifYs2Ge2X+p\nOVcad2BPqT8wneK1L34RV594AmRjA0cYwwKAb2LPgnmJMRbOY4xHMFfplKfvBWOtbEZiIh/xMyMg\nWpbicKXGcsF9Idn5EPrm+7mznEbMSCK0OY7D26wYxy2G1uAC2jvfaJp/Zlf0JxuhNzk88XFg4CFf\nlubpFgmXyzxOvv8UTn3s92FIYxwCcFtEcDuAzjiFnddKsJ5vgnu2A+2lfE5aM+9bYxvvnmDzwRDj\nYyoK6RZanoZD5hiHTZdvmkIt8LWFDURHLmBw6BK8hTUI2hgFIUCKMFx4I4vlzUr2yadIBnM1TmJy\nIO2lj3MRdyzkwgVHdMopPxVUp9WgPWpznWFHLhmlSVkvrzcmjfONcePs3BvfBfnrDlZXyEoKe9B+\nA5CzDgAPktdHxpiw8sAJm13qNbtK3N7Kks56QWqt5RllZDKp+js7bdbrLUn9nQ6dmBVjaFfNnajq\nbAuVQC9KHFquivq0gPwWB2lzCHdLh7PlUKvHZH0kct5MjWIzE1A7G5NImNsmYXlWJtVZVarOKlLe\nyhmalZ5WjJTennF61bZ9BT2iYFNIYUuV0ctLGJYoQh43B+4+bqKn/3da83YZGVwP7N1zhl0vu4zr\nVbaAPZVNkKhsDkkOf4bXq+yNIMbORRPuy1MEp2bAeQPcNEAakVCGl2kikupgpAISl0CjPDgvC95N\ng4YcvKwPNx/BLjHYJQ5WlYdV5uBlHXhZA15uqtjSpGayWcfy9CXLMg6EPb+DddbGBqlhmy9iJGah\np0T42Ri0yCO+923I/1dY+1T7zxQw/ugv4t9f+Fn835kcZgoBPuOV8PvPfRZLSBT7RwG8jESx//mJ\nE2wMQtK4fnD1VeyB/dX9/vrKClnAnlJ/VNcx/drXcP5LX0J46RKW4hjLAJ7D9SPtApI8x70AHiQc\neYiAPCSKlEsrHBcEPvV8hiPNLCr8bRE/fR/T9R/gXmMHMQkUwtUmLDxuRexWC6jvXOY7rz234H42\nyhjfrruecWDko60HUDoKwrqMfknE2SNjvPqjX8LqrU8jRSMcDwnuIMBRU4RxScPsZAPk2RZyL+Yq\nynnnvvWg+54pNu+P4R/Moiw30PJSOGxOcMAINcVJtWdhfvkSZkcuYbp8BahtQ1UtaIygx8V4DTdR\nsj8fGtHEXI0rrnJMCqRbYxofcAW3HnGRXNJLbnPcRGfYSVWnVbtoFLvVWfXywnDh9FyN71oqG/t7\njM8z4TW8qQpnHQAZCEEfqjVhpaEd1Prwm9tK3NpMk856Tlq4nGWyT+dl9KTbWxaHw0Y8MqvmyKvY\n/bjibYvVyCiJApqzLMpbWWQ2dJCNKdyuzRu9SNKHAvUmShybqk+tTEQDIeNkXM3UwopeIdVpVSzr\nJSFvZY2SpepVXdLbOrVrlu2r6HIpbMkpdNMy+pqIcY4gHuI7g/sNM91/lzUfCFPE9dbIri2yq7J3\nvewMknx9jD2VzWEP4rsqe4i5lw1gY+Shf8GEf05HdMEErlggA4dT4OaaCOUGGK2CsBJoqIEGWfBu\nBrwrI5RDuPkQjhbDLlNYZR5WhYeXc+BlLc5RZyWL6g071FuWbyxatrXkDZ02ttBAl1awIxQwEdMw\nFYo4H4MWCViBQ5QjYJwL2bahuDqy/gSFcIQiG6JE+qjyO6iIfVTlAcqKzqnRU9Gj0tuQ/y+45qr9\nJzmEn/wQvpj91/jXgzvx8gEK9kVG8btP/zXsWMaPIukbs4ZEsf/piROsC0Lq2Btc/R4kYN4drLG5\n+xwrK6SAJLr4PgCPWRbyTz2F0088AffsWdSDAAexN9JuBcALSNI1C5irdF7iPhgH8VI6LTCeiznD\nDFHVBCzkDjLZfnfs6B+hl4MHMfDzhJZ0RLeYEbvVZKhur6rtp17pRJ9nonWubrrOwo6HGgH4BRV2\nVcJGI8TZd5/B5U98AePCFloRwR1IfHOymsfkxTri55ok++1MQz3j3b9l9B+eond3DGlBQ0msYcGJ\ncdg0UnWTtEI30+4x/tAFjA9dhtvehKCNoREGnzC8Rtl1FssbluzPR7gtA1gWA/Fw2k3fDuCwz/tt\nR3SKqqf6tUkt6ow6YmPcQNEo9stGebU5ar7WGrdO7xuovLq/x/gKWcnhTfPgrA2gAS7Sodgj4Jr0\n1gAAIABJREFUVphYQXWAoNGTotZWmrbX0+LilSzLGmQ0qfn9nQ7d7i2Kw3EjGFkVe+BXnR1Ugu1U\nmZklkkJ9I4/cFkNqbYJo0xD17VAyBjz1JnIU6apPTTXkAjHjpF3N1MKyXqbVaVUsGyVasDJm0UoZ\nNV00OzPiNCwjVNETZHSVFLazEnY0ATOXgN0MuAdvVfn8VmveQno/sKvz92QL13vZOSQqW0QC7P0q\nG0jGBOoAJkigve3H6G46GF02YV0yEV+1gA2bcEM9mw29dB0xVwVQAQ01cEEenJeG4KgACLxsAEeL\nYJcIrAoHsyLAy3nEzVgZWzCqFsyaHRkt27M6tmUtOTO3xbZZFX1awlDIQpcleFkOUQ6AxiHK8QhT\nHiTXhuIYyPhT5KMRivEQJbKDCpeAuiKPaCHlpfiApEOPy7i+VDDibG7EypltrpAecPn0kM+oU15R\ndE5WLU5QHcqpHiFqSOJ0DCYAJx4D3ob8f+a1X7UfwKWP/S/4d2s/jj+qyHA3QfA7L/8fODW7HR9G\n0unRwm5f9kdwGUmV525+/QgSD/5zAL6EeQvdlRUiITlUfQzA+1wXtzz3HF574gnop06h5Dg4gKSq\ndVepP49EybwDwIOcyD1CgEcoIKspSnw/JCAMB6o1ZOP74tD4INn0P4ie2yIkZyM+ZsTxcZOhtrZe\nbn3uUg1fA+y16tT1WjseCpoINFOYVnlcuaWHKx/8W2y+5xkQ4uFYRHEHZShsZjE+VUXwtw1OPam2\ns6fDe7e3B++dYHw3RbZeQImr4IDt0AOGXZcssWUGmcVVuIcvwlhaQ1TtIyO7UGKKq1yEM+R6kF/Y\nX7I/HxrRQhI1PJCzc7fzEX9LyIVLtmjXYhoLlVnFb41btDVsCSWjNCkaxY3arHZxYbBwOutk9ydV\nhifYCbZCVkS8aSacJbcJ45ByBnFuZgXlURw2emLc6KZIZz0tLlxJx9qUjIxSmAB8SRjNqt7IrjqD\noOLt0ErYTRWoXZmkUeqqUDcMCKszyeoFkj6g1B1LUTxL+dRQAs4T027G04xCWNErtDqrSEWjyApW\n2iybKatucHZnFntNy4xVti2l0FMl9PMSBhIPc5vcjOq+iWlZ+9fcFtRw/eHj7mu038veTYwoSEC9\nX2VzSA4fDcwTIzHDjh5gsOlgcsWCvmohWreBrp5WxkYm6/lSCSAVkKh4zbcWHBWcJyJQQziFCE4R\nsCoUZkWAWwgkV7ZLJmdVLFh1O7Aatm+1HdvuuLrXCodRGUO+gImYgpMW4WcEBDkOUVZAkInAhTYU\n10DGmyEXjaHtA3VZGJCSbEiqHCokQDrw+ZwTiHmTqYUZtMwOLaQHfFadcKo641KqyYmKTfm0S4ga\nEKQjxBIDbyLZFmLeQszZCDkHHufC5VyY1IHO+RhTHzvUR5f4WKMhLvEGLmbPoifNYL4N+f9MixBo\nAH5Chfkz/z3+MP8v8GvWEq4WKNgfrP04vnr1n14rVFIwz7Lf8fM4U3gFD2IP7BL2+q9/A4z583+g\n2zBX6q6Ld33729j8ylcwfPFF5A0DS0jsnV2l/iwS9fMAgAclVfxY4ATLSooDR2NqOzFqRRUV+TZG\nnEcxcD9CNu27QNQA8RE9jm81Ga2d7jXbf7pZoM+ywN6ujt2gOgmgtlII6xJ2Fm2s3ncBmx9+GpPO\nFZRDgjuFCO2BitmZMrzn62LqpNrJnPHvGV6ePDaNwzt55MoFVEgeB00zXzWCduQqrWEkHbwI48BV\nOO1NCLkZ8jHFiDCc4yOcw5uU7JNPkTwSX3w542aOp/zU7TGJD7qi27RFu5BxM359UmftYVuszCpO\n0Sz2yrPylfaofaY1bp2hjF5riIVEGd6YCd/zwAnrgJEiJG8SZw0jKI2isLrDx81uirTXVWnhihKV\nxhg4+ai/0+F2+gvcSK+6I6fi7oTVYJvX4u0Mk5ziNI3sZgRpw0gFG45o9UHdsRDHM9mjesrnXEl1\nVb+YeN60NqtKBTMfFq20VTYlu6Fz7oIe+U3TJCrblmX0sjJ2siJGUx7Od/K6ewCmN2uXzA/o9wO7\ngSSn38T1XnYOiS0iz1/HGHsqO8Seyh57EUZDH5Oug9majdmaDX9Tl4W+nlGndirnBEIu8a2DAjg/\nB8FJQ7BlRAKDlwthayyxQqo8Z+figik6RZuzyxaz6nZo1x3PaTm223YNr+nP4iLGXBqmKsNVJXiZ\nObAzBIxzkHJMpP0ZcuEYWjxCETsocwOUxJmYkY2UIjCFBcj4oZB1QjFvQdZmyObGXE4dc2l1ShXV\n4CTVooLqEC7tE6QDMJmBt65BmnE2It5GyNnwORcudWFxDnTqY0w9DKiPHg2wRgNc5hxcyL+CTUGH\ndTN/p3ktQxrJh+a1/QgeeeJtyH8f11y1v4sg/uQDeO7jv4Rf3X4cf1PlET49vRN/+eqvIBdL+O+Q\nQOOzAP74wR/Cy+IE78eev97Fnr/+0rxDZBtzpe77ePTUKfhf/Sq2v/UtpMZjLAI4jX1zSpHYDw8K\nCv84jcmJKIxUJUWJ54WQRA6N3DKTo/dg5j1OtqxHEYky2OEZi28xmNx+atBs/NmOyr0ExxmWB25U\nDBmETgpOC+gd28bmQ6cweO/zYIKFY1KIg4YE97USnBcqKemk2s6cCe+enLM+MHOFO0UUCnnUIkVc\nNIy2bJGW46kL6yQ6cJlZS2uIKgOkuQgs4nBJCHD6Bovl4gl2wp4PjWgDWE55qSNZJ3snYeSIz/sL\nlmRVGGF8dVYNW6MWX5/U46JZHBSN4nptWnttaWfp1L6kyioShfgGh5msDRovgpEW4yKXZUw9LEzD\nsNrnokZPou2NlLhwRQmLEzZkctwfdLh+v0PGZsUdulW/HxfDniTQvhrKXmEiEXnDSsUbruR3Q+KN\n+Ciaij6dpTzeEVVXDYqmFpX1Eq3MqoJm5oKSpTplU7QbOvEXZmHYMg0uzfqqjH5OwjAUMd7m4L0V\nvHtI7JK3zHTPDyBz2AN2FQmwd73syvy+PPa8bA4JpIEkMUKQHD4aMcNsFmA68DDre9C3HBirBu9t\nTTPyyE6puitm7IDLMBIlsObdDAQ7BS7g4WVDOIUYdokQs8yrRo4VLcEtmpxbtZldsQOn4Xhuw3W8\npmsGtcCM87EpKbAVBbYqwVMleGkBgeRB8k2kPR3ZcAwtGkEjA5S5KZ8VdTktmXKKZ2oU0KwfcVkn\nFgsmkzWDKLkpzaRnnKrOqKyaVEzbhFc9QjIBkIpAHUCYQ/qakk4g7XEuLOpC51xMqI8BDdClPjZo\ngMvUw4XMeaynejDeCtLziGsGN8D5ZneIMGvDztqwVQuWa8K0DRiODt0zYPi/hd+65W3Ifx/Wrmpv\nYvNnfxr/Ufs5/F9xARPTb0Z/fOpXYbhNPI4k1fKXAP74vp/EaWXjWuOvEwC+hT1/fXVlheTn33+f\n7+Oxc+dQ+frXsfH88xB2dtBh7LqRdq8CuJVw5D0pVfoRz/KXRYEQSmPi+QzVfAk54X44/gewbT0O\nD23g4IzFt45YYfEL00rtryYCdwaGOyv2nTibFUDaImbLBrbvuor+o9+GdfgyqrKPQzGBcLEI82RJ\nFU6qnfTZ8C7j1fiD+iRzl4R8JkeaHl8pGXaHOUJrGspLl4l94Crzml3waQtqIGCLxjgrBjiNfRbL\nZx7+zOD3Hvm9ApKo4YGiUbyLj/jjIRcu26LdsCU7m7NzQWPcIK1xiy8axYlmaltlvXy5PWy/2h61\nT3OMu4qk86CMN7JQuGgJjHQYIDHF1sPCLAgrAxLXewJpbabEhaupsDhmA0jx9rTODUdNNjSr3sAr\nB1tEZttSzI/SYSqUh6GCDUeMuyENdxDFE9GjM8nlTVH10qFmFOKKXqblWZnXrKxfshSnaghuUydh\nRw9Y29T5dLyTljFICRgPJYy7FMGbwbuHJF3yppnuecO5EvaU9v6rjxoSaGvY87JlJAp7V2Xv5rQt\nN4Ix9qEPPRg7HoyuTe11PRX3DIUb25Ji+HzajUg6QpgF72Ug2CoEW0KYihMrRIOkl7jCLEM0U/RK\nJvEqDpyqHXo1x3PrruNXPScuBw7JRbaQhqWk4Cgy3JQMV4nAxSbSnoFMMEEhHkPDmMvzMzEj6pIq\n2opEoUYhyXixkLOZUHAgagYkzaRqZkZTqkEl1SJC2iU07YGoIYhHwBuAYDHGW4j5xO4IOAcu58Km\nLow5pIfUR4/62CAhrlAf59VVrKavQL8xljmPrkr4LsHswcvZsLMOnKwNWzRhWibMa2DWoQc69MiA\nEZswmQmTOnCoA4d34YoBAjFEmIoQKQxMQGJp7W79hq/fLob6bteuapfg/syH8dcf/+f49ck9OJln\n2fAvzv8SrowewD0geA+AJwD80T0/hcvpq9cqTo8D+DLmg6tXnoSFJG/+WBjifefP4/annsLWs8+C\ndbtoxvF1I+16AG5L5eQfQhCf8L0gLUsEnh9DkWUU1eNg8WMYOo/D8u4BPeCA3X6F1Q7+uZEvfcVg\n3AUydaz8yGdKQ0K4GGJyZICd+89j9uArEJsDLIgR8mt5mC9qWe6k2lLPsDutU+IH7G7+boGVlGy6\nasULshm0PE9sbzO6fIk4ixssLg+hRByMiMMl0ccrfHQtxXL+Nz7yG1ufv+fzTRKTZc3UblN85Y6Y\nxIddwW2bKbMEBq4+rUetUYuv6BW7YBb6RaO4Wp/Wzx3sHXwp7aUvIckY7/rhexYKHy4D6LCIKzHJ\ns8O87kalIeJaXyCtDUlYWJVDbcqGHM96ZpEbTBvR0Cr7W0E67nKU7MgRbwq+IJAdV8BWQNk2izDi\nPDIVHcEUFU+JimYhrszKtKSXOc3KeCVT9mqm4LV0RJ2ZR9qmIWfinbSIiSVi3Bcx3qSI3gzc2wDG\nr4NHorLT2AN2HYnK3p1EVENii+wmRhQkVsjuYScHIIoYHD2ANfFhDn1YAxdW1xL9rpGKt02Zmzii\nZAVUdWOmhiRUITgqBCsFwijcfMiZBeRmGi3M0rRoiH7Ron7FhldxQrfm+H7Fc8Oy5xEtcEk+dIU0\nLDkFJ6XATnGIOAvqHNT5eErzZCZk+JmYEQxZEZyUAJIJQ5L1GZd1IBRsiJpJhKJF5bxJZNUkomoT\nLuOBqgEQEHAmgWAyJthxzFuI+ATSHnVhcS5M6mLCJZDeJj42aYAr1MOlVA8Xs69huvs6z+GsYA+4\nOdwElBlY1oWbs2DlHTgZB07Ghg0Dhm3CdHXong49MGCEu2C2YBEL1tw6dwUfvhQilCNEKQZGCIgJ\nQGdgu1DeD+fvcFs0gA8EwE8x4HEKSGkkV1zp12/ya29D/u+4dlX7nXjpf/4kPl34Cfy+wIn+2e4/\nCV7Y/AQaTMD7AHwTEf7k7p9FN3PxmhWTwdxfd6t48m//BIcBvC8M8djFi3jo2Wcx+eY34a+voxrH\nuMoYvo7ET7c5ibtHVeV/5BjOIiWghDBEEaBlWhD5E5i5H4LhPAJ0BEj3nWS1A3/uKNrTdkhW6dB1\nMm4EYZGHe9DA+PZNTO4/C3b3BeRLJmp9lTgv57PkZLqVOkvvcE5lHvUvVe7jSUFWmzk97BCHNc1A\nXLxKgqVV5jd74MUA1JOwRmOcSTk4RYDzIQ3P/4fH/8Po8/d+vpq1s8fyVv4dAI75vL9oS3bVkqx0\nwSpErVGL1qa1SDO1oWZqGyW9dHFxsHiqNWyd58C5SNTlvN0s60AIlkHYIgv5BuNiFmV1JypOWFTd\n5klzSxA7q3KgTdiQE1jPz9KBWYk27EywxURsU0oNLhRCZkaU7PiE9uKIDqlHJ4It6ILip+KiobGy\nXqYlvQjNSvtlU/ZqBh+2dcY6usd1zFkqHU2oiOlAxKgrYrJJEL9Z98Cd/ZnuG/qMVJAAewGJLbKb\nGNktpNlV2UCisiljIE4EdxbAmQSwhx6cHYdzdywx7NsS61sSmTiCaIUk5SFSQhKkINgp8I5APDVW\n9UJcmOapNs1QzRTDkkX9ks38qhP6ZTcMym7ASr6HfOBy+cjh08yRVFiyBE90IQcG0oFOsvGMy1Jd\nTHO6mOaNlML5KmVIh4xkfMblXcYXbMJrNviSTYWCSaSsTQTVBU17IBEBNSk4C0y0YibYcThX0y7n\nwuEcGNTDdK6k+9THFvVxlUQ4L41wufAiRit4EngDv/mtdow4a8POW7DyNuysCzfjwlVNmKEBwzFh\nejp0fw7myIABCxZs2GSumAUfvhgilEOEMgGJ5mA2GNjs5uFctIG7Q+BeBtzDklk7dQHg3gDIeAtY\nX3efiqSLpYnknMN8k20B5BfehvxNPX+i2ivo/9wn8Oc/+M/wf9odusrGH/a/vPpPIAYFvBfAac7G\nZ2//RYxzZ/EIkrjjAHN//Zm/wnaYwaNRhPddvIgPnjwJPPMMzMuXUYpjbMUxvgrgVRBE2VL648wN\nH7IsTxUFwA8Y0qksFOkdcKOPYGY9BjQryL/nq6y89HlfyL3g2ejSvuOrCgGWI1hHh5jcdQX+O88i\ndccGSjZPolP5bPxttSWfke8MXs0/5J+p38unc5LckYywHXqkNYi5pcvE62wyFGYQPQmDkMdF2cXL\nko8zhmxc/r2Hf8/54l1fzJaM0t1CLNwW0vCgLdpNQzE0GlPamDRYY9ygRaM41Sytpxna5fqkfvbo\n1tGrSqC4SDzdIsDaEP2DoPESi7gmIk6N0pYbapMoru5wpNEVhPaqGGhTNuJ59MIUWfPT4YaXinsx\nj2kM3oNHfMwCRgdxxO3A48e8Lcx4OZBYAu8KLRlarFlKUDHloK5zUVuP0TEcsW2YUjrSZyKmOxKG\nmwKmm/tigjfaJ8b8bGS3z8ju5KElXJ/L3i2k2VXZIuYqO4zB6SGCaQB34sEZecQfury/YwvhwBYx\ncgQy8QTBCiF5CFMR9SQmWJIYUhQm+VibZElhlqUFU4pKFg3LNvPLThyW3DAquz7TAp8UApdmI1fI\nMFtUYYkheGZCDUyaZrqQIbqgcrqkUkuRuUChDJmAkWzAuJwDTnPBazahZZtwZZsIWRd8OkmbEpNL\n1LTFYtEOI9FmwTzh4VAXJudiSj2MqI8+DbBJA6yRABejSfVK9+T/5llYVnGT/rIFS9sFswMn48JN\nu3AlA4ZvwHBNmL4OPTRhRibM2IQJGzZ14FAXruDBE0OEUohQIiA+BbUwT+jEiN8CzoIJND3gYAgc\nioDDLNmLADockJbx3QGZmz/HTQD5pu+zGMNNRVXf7kL5HZ8XGo/gHz+Gr/yzn8Z/LHwIXxCn97GT\nm58MxtYy7gPBljDBXx3/FPT8K3gIyTCOlwB8bnIXvv7Kb2AhivDYpUv48CuvoPr005icP488Y9gJ\nQ3wVwJqcEQuqLH/CnFmdKIopIQDAIa0cAMPj0O0PgWvWUXrvV1i+/eUQ6VcCne1wQzeS6gTRQQvm\nrV3Y91wEfdd5pMsGEc/ksuGL6ZZ4Jn1H9GrxgfBi9R1cJq0Ibc6K2nbAtddJvLSKsN5nfEzhejJW\nCcOZjMFe3Ciud5+44wn/Wwe+VQbBbTGJj7iiu2DKZtmSLLVklKLmuMmV9bJVMAsDzdTW6uP65cO9\nw1sVo2IA4ACWheweYly0zCK+TXyxGEteEBamYVwZUDS6gtBe44P8jI0EQq4yHldAok1fYhNfIFYc\ncy6s2CfjKOCHzBNGnC1OOCmQoJkFVp5VSNHMs5Kp+hVTihoGjdt6RBd0R26ajpcJzaGAybaMnQ0B\n+ibeANzdj2B04ReQRQLsOpLD6g72ctm75eq7iZEUAMIYYisC1QPEUx/BNIA38qg/dPhw5PDx0BXY\n2OXJzON4M4LoI5BiakvZIOC0WTrSplkUZhmqmRIrWlxQthEWnTgquWFc9AJovk/yocvlIodPM1sQ\nEFKTqJHJqbHJqzBEhZqyQq2UTMM0GNIhQ84HzXvgNAec5hBasQkqLviCB8JHgCGAWhSciVi0o1iy\nw0Bw2LUYHudiNof0DgmwCUfuhrNWz9x8uD/pPe5FUfFNIR0gyBkwNBNm3oadc+FmHThpF67iwOF3\nwWzACAwYkQkztmAxC9YumHkPnhAgkCJEAgX1KKi5a2fEiKcRoimuwZgYQMYGGh7QCoF2BCxEQJsB\nTQY0CFDlgIIACApuHsgKkgE23w103+o+nzH8lwXmvvU25N/wuUAAvPs4Tv/8j+BPP/Qz+O2QHTIn\n/Z+2X5vcg8Pg4Erb+JvDvwG3+C08iKQX+1cjEV949VewM74D9126hB84fRq3PPssZqdPQ2UME9/H\nUyDoF6u5eyIneIduOCrHEUQRQ0ougeMegOF8HMpSHsUT30Km9Y0okM+F42jKW0HMHQjhHx3DvmMN\n8QPnId29DnlLyoQvZdvC6dxt8dniPdG2djvHZ/O0E7msOYnp4hUStrcYVAfEUtELeVzgg+BUL3Op\ne3L5JD3TPlOZpCcHfd5ftiSrPlNmBSES0Bw3SXVaDTVTGxWNYrcz7PQWB4vbzXFzwscch5RdZny0\nFMfcIvHEMgAuys+CqDwirN7lhdYG5xcm2BRCXOEiXKWIB47EZiGozXziED1y+THzhCG1pAknRAIr\nGhpKeokUzXxUMpWwZkhRwwDaesi3DY82jWCaDY0dEaOujP4aD3sL+8BtdTC68AuIZrcji0RhL2DP\ny64ggfZ+lS14MZgRAHoAMg0QTn0EY48GI5ePRi4fj10eE48jekB4O4x5PgjEbOjwmiGxwizNND1N\nC4aMksWFRRtR0WFx0Q3joh+g4Ps0Hzo0Gzu8yhzeJanY5BRmCgpMSSGmnCKOIpIozRhLhyB5HzTv\nMaK5hCu5BBUHpOKC5UIwSwQxOVCLMMFikWhHoewEnuAyZ57wmFEPY+ryY2Yr49jUhv50cWhtPzA0\nh/f7cZy7DtIOnLwOvThXzHkHTnaumFUPnjL3mz0DRmDCDAwYsQWL2bCJDXsXzLwPX4oR8xw4hwNn\nERADczsjQjSJQGZA0QEqHlALgFoINEKgHgNVNo/LU0DjgRwPKCJAbwbIu71gvhNw/y5AthnD963v\nzd+X9Tbkr3sOaAWMf+rj+Iuf/8f4vfxtjefp1o+yM+PHgnwsQUptYGX50/BL38Q7CVBiwF+N78dL\nZ34Zyms7+MjZs3jn88/DOXUKYhjCDEO8kMqITJXle82ZU/X8kDIAHCdBFG+BG34YmcMZlN51HlLj\nZOyIl+Md3+TEGOSIDe/WHvy7L4N78BJk1VKiV3IL3On8cbaavzMe5o9xyJTR8CK0u8DCGqLKEJyT\ngu7J7KorOJfG0tXNC7Wz8vnG+dKGttG0U3ZbT+klW7Ll8qwcNyYNUtSLs8akMegMO4PWqDVqTOtT\nlSMyE/1yFHMLxBcr1BfUKGMFYWlIUO1TvrXOTbUhW5UcskbDeCsGG0aU6HFILWLFLjdhtjAmRmpC\n+YhD0dRYSS+TopmLy6Yc1gwxbuqEtvWAr5uh0zSDUcE3ehJGmzK2Vzm4W4xi21qCPnwXgp0TEO0F\nlEGvedm7iZEC5io7YpDNEJweINYD0FmAeOqTcOLTcOzSaOzxbOJxmHqUOB54yQ65tBvwOTempZka\n52dpopkyNIuL57BmRTdiBT+AFng0HzlcJnY4BsosPgG1JaWIlZKJq/CI0gwsGzLkAtCCC1r0CIou\nSMVDXPQREBHMEUAtwjiLxIIdh7IT+LLrO7wLi9rUpA5vwk7NmJ2ZRUZ5GkyXZvbwTt0e3eGHLK3a\nsLUZZpoFS7Nh5xw4ORduxoOnunAVF65swQoNGL4Jc9fO2AUzceBwPnzBhy8yMMKDtymondgZVGdI\nGTFyZoi8GaPoAuUAqIRAJQbKMVBmgEaBAgVyHJAWgJQIiDJA3gzIIq4H6fdDHVuM4Q2bwv2DW0nf\nKXm+U/tu3/j1d3ufTID/f/euIQSER/DuR/G1X/44/uJ9P1z8A2x/GNvTj9l+kEdeWcXfLv4nROVv\n4D7CMPNz+NrVf4TRynHcdu483vvSS+BffBHE9+GB4Ewun65EXtSZ6a4MEMQMEMUGYvGdyB4uoXj/\nJvjGa8wkG6wfeLQWgh2bwL9zHeydFyEeHIisxy+Qs/lb0M3eyqaZQzRSG6xiCaSzBtbeAhgQmmnW\nMxVrfSxvjTezr9FL9QuFy7XL5Ul6Upkq05wU/H/svVnQbdt13/Ubs1nNbr7+dLeXbqOr28iWHcmx\nY0e+cWInfsAVUiFFuaiiKKpSPBAKXgihHKKHPCTwBLwFKKAqhDikCEkoxymwnQSMI7lBqLEkW1dX\ntzv3nPO1u1vNbAYPa+3vfOfoSJbkKxPsrKrxjTHHXGuuffbZ6/8fc8xmlTx+9rjcOr21efru0xdP\nnj55+vjpY+c3w04/zWWVMdek90e2qXa1CBoPT5Ubd0x48nVz+/C2vuVbeYekd0h6ItEspdGNuWBd\nnHNRn4mocLg84Gh5xOFqJ19f1vnm0uUnFrjHVjndWuXF48v+7mG3fKfk+M3Cv/tG88G4vHiFcPb9\nlKtnmfcH7OO4yZVctirzNlMvAsXFEGGziHDRk856k886m886o+eNQVcifqm23kQ766I5XBV6sJiy\nt6w5WFk9bCQfNqr7XeIwdHIQO7MTWylyko2rdF3UbMqadV1KN3HkeUZ3AuwFZL9HDls46tBrPZ0r\nCHi0d2rXom6jsWhSqJrQVk1s7cZtZFOsWU/Wut5dxcWNVXfx9Pru6TPhvfObdpHZHSPmLTDPWtpJ\nR1e3tH7FKozAHMeZGTQ00tDYMWIuBEkO1xiqjWGnEfY2md2Nsr9J7DeRg0Y5THAY70/A2RfYGQF5\n4qEqoCjBTB8C5in393B5PwG5+f8yVfEtHSKW9xdYv506w7DeoB3l0lZoE3QJ+gChHyR2EFtIm1HW\nkJegq0GzZNg1bQGyAPkf4M//gQR5EQ5e5nP/zk/xv/y7Pz3/b/f8j74Tj/9Md697Ih9Mv8Znnvwf\n4fov8BFRfvP8e/jNf/zj7Pxyww9+/vMcffrTpLYlTef2TmGro9Wy39m0UQCMnWL3XmA0S/nrAAAg\nAElEQVT23HUOvu8Mf/NNPde7nOckzzbkV94jfex1sR9924jtb/Fm/Sp3Zh9mMfsgyT/B/vmuPP01\n8s4SznZ0cTHf3D2b3VnemXw5f/Xal2ZfvvXl/bcP395viqa4eXaT528/H56+9/T6qdMnL55oDprD\nfoeyn9a2L3dcW84lOpcOznRz6y25ePrLcmf3Hu+antu202PbybnZsLRLXZQXcl6fkyVzbXnE4eKQ\nw/Us31jW+dbCyeNLMTdWurm1SudPLsPtfVm+q4/fvtt8z5316iX1m6eouiNmccYOjgNgP2Z2FpH5\noqdYRNwioIuIjFF2PmuNtmujnBnsClOtstRtsgcb4WBV6t6ylv2V14O15Gtd5qgPsh962Q1BpqGn\nc451UdFUFZu6pJ9Y8k5GdyLsBdjv4KiHo56udPRS0ItHk8l2I8lvNFabFKomtuWa1mzKJq2n7Wq5\n058v9sLJxWG+d36Ne4t9t8kya2lnPf20pa07uqqhsWM6I65Z53FmBuOUOdcTnWUnOnY7w15r2GuV\nvRb228xem9nvEntdYj8OgLwH7BiYOZg4qP0AyL4GW18B5e3LIX63YHy1fq16udjp9/4YgLbkuw+y\njzp3u6tkq6Nk6DL0cZAQBrkE2C3INpA3oGvQEWhZcTlwICswSzALsKtB3HJYAOs34AMUOnyOcpSr\ntmMg3o4B+Lsr0n4D+1Hl//gPDMiLILucf+LP8nf+8k9Wf/+Hv/+H/rF591+Rk+6lbj55iy8//nex\n1/93ns4TPvPLH2f9Dx7nuc+/w9Of/jRxs0H39su+b2R6sexdzsNMV3/jJvWzBxy80lFeu8Nds0Sz\n8vIZ+XvfEj7+Buba5kBT9zL3pi+zqp4RlafYuXtDH7sjrGoNx7v94mzn3vrO/Cv5a7ufLb9860vz\n37r5W9PD9aG89PZL+oG7TzfPrG5ubrYHaafdM3UzK30zqf2mLtZ7J3ry1G9zdvMNuTc/5b1izT3b\ncOJWXPgV5+UFZ5MLokkcLg85Wh1wtJzlG8uKW0snN1cm3lixuh7T+fV6czz5wPEyfvhO339gSX9E\nHedMYsFsDfPzwHwZqZcBv4jIGGXrcoOmC6OcG7ULxK+QapNlZ5PlcOM5XHkON16vNcJBq+x3kd0u\nyrwLqMC6KmirgmZSEKZ2SH3sRtgPcNDDYU9fW1pb0JuCiNOcTbatxGKj0W9yMKvUh7WNm1UVL5bT\ndLGYcbKcc7zYNafLmW8SdUdXd3TFmG+Oa9ZpGADcSEMyLc5GCufYD5aD3rDXG/Y72OuVvV7ZDZnd\nPrGbErtJ2VGYW5haqDyUY5TsajATYALS8P5Hx913JToetpKueP+B9ZvWjSDnGAC2y2P0mkaAHUE2\nbqPYDvIIsrkB3YyyHL+k9QCwZgWyHrRdgluDXYPbDCBbbMA3UASo9D6wWr418PwmdbaFaYRZgp0I\nOwl2E+xm2Euwp5Z9JuybKfumZG5rZrZi5ioqV1L6gsI7nHdQWXLp0MqipUVLgxYWLSyUFvUWLQzq\nzWB7i7qr8ld5ef/3PciLcPgn+bn/4CfdP/zzf+pjf3Pv7E927eYHWjd5h3du/X3cjV9g/qVrvP53\nn2XvU8JTv/KrZFVs4UtdLPBN24NX7GMTqqd3uP5CQI6W3C56rnfwyh3Rj72BPHs2YW/9LDF/Dxv/\nASQ8w+zOk5hk9c5+au/tXnR3919Pb+1+1r9+9IV6US/8B+89oy+ePRWeWt0Mh+sjnW/2TL2eezal\nO9m9J8e33uDs+tvcm51w7JccF0tOywWn9YKzyQWdCxwuDzhcHnB9NdMbi4qbK8dhpDsq+83RbrM+\nvLFqpk+chfTkBc1e708t03NlepGoVpFiETGrHsIS8jnKQtRcCH6FVCuYbrJca4UbjeVaY9nfCHsN\n7DSZeZsoYqKpPU3t6CaeMBXSHHQ3ofsBDgJhKrSuoLMFQZwGvPZqcupN0rWmtEoprnLarKwuljVn\ny0rOlrU5W9b2ZFUW65jdmnVeskkrVNcYWozp8DZRi+MgWvaCYT8KuwF2g7Ibld2U2YmZHc3MNDMV\nqEdQLkrw1RAlA5fT3OT9GszbfKvT3K78WA33I7rvSvSqo633fbUM9/Q6gGuXR3C9EsWmfoxiR5DN\n7RDB5ob7U1I2A7BuwdWMwGpXI7huxgh2DUUDvoWyAQnQ6TcFUumg6KAKMNlKhGmCSTLMY8GuVuxK\nwY6WzE3JzJRMTMlUCmpTUFlPYQtK6/HW453DOo91FmMdxlvE2UsQVW/AmUcAp0GtRa1DjR1s48jG\nouJQ8WT1KI6snoxDcai4wRYLBEQTaEQ0CTnCoIWcRDWJ5iiao+ScTNZLbVKONmowMUcbNdqg0QUN\nNmhwHcH1BNdp8K1E3/G3P/WfPvX7EuRFkI/wmT/xU+Z//uRPfM/Pfnz+Y7+t55+Iubqn57d+Dh//\nGeHv79H+4pyjf/4WRUpiQl+zXPWSqojcMuw8VXPwdKa/1nFSZp5fCB99C16943hs+Rj7i1eR9CJm\n9QHs4hluH1Tpzt4m3tl7O9/d+ZI9L9/wOxh5cnOUH1veSgerI4rN1GxSMhduI/duvMXpwbucTE85\nqS44ri44nVxwMj2n9R0Hq0OOlnscrabc6DxHRtNhmcLBrO0PDjahOFqnuL9y55POLUwuF0ld22Dy\nBcLFkJAzC7RYCNUa2WuUww6uN8JBY9jfwHwjzJpM3WZCYWhrRze1hJmQdnQE6kicQeuLQcTTGq+N\nWt1kmzc92q/RbpV0s0wsVpazpTMnq6m5t5rbu+s6nfVeGywdTnoKo0yx7CfLXhR2k7AbGaLjlJmr\nMtPMDKUWpbJK6UdA9mMANwKpLMag7XcVHT8wkDe8XWsLtO9L9KpQ50EmOshlvRkGx0qB0oDLENIA\nsDGOedh+ANmrAJvbIYJlC7JrkBFkzWaMYDeDuPUVgG0G0Q76FulainZD2fVUQalHIK2iZx4LZqlg\nnj1TLZjlkqkWTCmYUFDjqKWgkoJSPIXZisONYp3HGjeAqLXIpThwFszDwHkfNAcx94GTK+C5BU4c\nWRzDXtkJ0QgaRTRBjoLGATA1iuZoVKNkjSNoRpM02UQwSaONGmzQaAPBBqLrR8DsJbiO4DsJrpXg\nWxN8a3rfmOAbG4pBomuJvg3RdTHaEKONIZoYsw0pSQrZxJhMjNnEBDk7leSSUZclu2zUJ9EiDdon\nQ5EMRRKKaLRIRspoKJJIEY1UUaRIImU0oxaz9RUJW27rE+bP/faXnv99BfJzWR7+G/Lf/ZUf+/Df\n+zdffO2fTk9fS7lY5a74h7hf/xTNP7T43+httWxLs14r3aTB3YRrj1uqJ+DieiJZ+Mgdw0ffgQ9e\nHPDE2XNcP/4eyvPnuVN/gDf3p/l8eqGb6VvGlXdlLwjTMFGNBU1EFtJysneH0733OJ6dcDI552R6\nzvH0nE3ZcLDa57CZcS0WHIlht0i5nna5mLXZTltM1Ynre0uThQViluAXMNnAfD3MEN5r4KCF/Y2w\ns4HpBiZrRYB2aulmhjhjBOpM2lGa0tO4gsYVrI3XNZ5ltiyy1ZO+0MXGyWLluFg6OV1N5GQ1z8er\nGaeruaz7qQg7atnNIyhn2Mkwz8pcM1OUiSi1yZQWigI0QryStmQ5pCjdBbgF3wR0LXF1i9vdh/nN\n+BJfCK/y2fx9/Hp6kS9KTfstAbBCFe+He9N8H1xrGc8dgbUyUBgo7CAuQYoQwyg95O4+yOpWNgzR\n6wi0ZguwG6xd49wGZzc41+BdR5Ujk5CYhMA0JaYxMY2ZWUrMUmaeYJosNQWVeiotKPEDgIrDq8cb\nhxM/AKhxWBmiUDFOjLEw2GANGAfGgnGKGSPIUW+BcwDMh4HTowQgDtEmUUSjoBHVK8DJAJaq0WQN\nJmk0iWgT0UQNNhEGwJRowxYwTXC9BNtJ8J0ZALMzyfQ5uk6T7VPyXc62S9m1WW2bs+uSmjZjY1aJ\nGZIiKQtJhZiFqEYzRTb4JAMwJsEnkTIZ8QlGINyC4KUuI+ITpkwiRdLRRoqk43lqiqxSRJUiZzP4\nVXzOUqY8nJeTFCnhNIuQERJCHr9NuxUdtNFBnHJp26wYBZP1ioBJQ52koU4S2DiWk2K2dgSJYzmC\niYoJIEGR+Bg//6ffd5AXkf+GYTfFu6r66ug7AP42w9zlN4B/TVXPx7r/CPi3GFYG/gVV/cePaPMb\ngrwI8mf4n/70n3r+Z//q9/3Iz3+o+bEVy1XOr/8s8r9+yaTPpLk/76CZLDm4npnfhPyYcPe6ctgI\nH70NL55Mefb4ST749ivsnb3K2zvP8N50j74+I9a3CWbJxnRc+IbTyRknu3c4np9wMj3jZH7Gqlyz\n3+6wF0vmBqY+MbFBpwRmKbLbBTlsMofLAainI2DPGtjdDDLbwGQNvoN+KvQzGYB6V0lzpakdTelZ\nu5KVqVhKxTk1Z7nWu3HKe2EmJ82Ui1WlZ6taLlZ7bFZHulnd0q69lmFXYa7KVAdAnkimsmBF6MPY\nG28grxRdCXlZkhY7bFa7tKs9ls0Bp/0+Z/GQk3DISbzGvXTISR6FPc6oOXcFSw9xcgVcp3oFXM2Y\nIjBQDrnFS4D1DrwdItoUrkSwPeQW0QajDYYGT0MpLZW0lLahlI7KtFSmpzQ9lQlUmqhzosqJScrU\nmqlVqVOiVqVCpVKlVigFKYASixUn1jiMODXiEBnAU8SpiAOxw/w3caqDBrkPkvkB0CwYRjUjwhhp\nMoLmoI0SRTVKJppMMJkoA2AGk4gmSrCRaCO9DRJckOB6iTYQbdBkgiYbSDbkbHtVE3TQnarpVW2n\nmE6xLcb0KqZRY1s19BgJWE2DEDEacTlJkZEigU8MgJeRIo46qfikbEFv8GUpUqbIKj5lypSlyJki\nJYqs2GTwyeKyxQ1afTLqslVRp/cB0GSweh/wbOYS+OQq8CUdZfCbEeAG4FMkgEl5AL2omJgxMWFT\nxsaEjRmbEjZGbEi4FHEx4GOPT2G0O4rYUcSWIm2o4ih5QxUXTOOCSTqXKp3aST51Xk9smRduWGqr\nNnhs75HksNEhyWGCw6TRTg7JV217WSdqH9TJDHYezrsUtUiyoGa4PlvQbb3h//hLr343QP5HGCKy\n//4KyP914FhV/7qI/IfAvqr+RRF5ieHtRx9jmAP9vwEv6EMbNz0K5H9Cfv7Wn3r2b//nH/vDP/9T\n3R9+1/+T34Jf+EeHfLG12Mk5O/s97iasbgl395UXTuDVuwUfunOd5959np3NB7lbXef1XcP57JyL\n+pSzyTnHu/c42TnmeHbKqlwz7yv2gmNXM7v07MfA9TZzcwO3FvDUBTx+BrsjaE9WUG4gFRDmkEZp\np4ZN5ViXBUtXsTBTznXOie5yHHd5L+7zXn/ESXuN1fpI16trrFY3tF1dIzdzCsRMWeSaZZqyCBNW\n/ZRlP2XdTVm2U5bdnGWYswpzlnGHVa5ZSs1aata2YmNLGlvQ2ZLee4JzRO9Q75DSYLzFOYv3Dmst\nzkac9pS5pdCOko5SWwr6wZaekp7SBCklUNJTSKLSJCWZUpOUminJlCPUFaoUDG9d86BOwCM4BCtG\nDQYjVg12AM4xyrwKnoOtQBhAkyjCCJhEgTBEl8Qh2hxA0yQJZgRNmwgmkkwi2kgyUZOJJBNREzWZ\nQLY9KoEBJPshc2w6FdMhpkOkw0iLs41YadXS4GUjjgYvUZwGsTkwjEAEfB5Az6dMlRSXBZcMPptB\nJ6suG3XJ4LJVl6y6EQRtsjr4TBrO89klmyWLZnE5GJOjSI7GahCTo5gcjGgwNkcR7Y1oFJN7YzUY\nyUFM7o3RYIz2RrQ3Rnux2htyEEu39Q0ylMVob4TOGO3F0BlDb4TBFu2NkV5EemMIIhJE6IyRYCCI\niIpawCDZjOC0tc0IUOYRPnnQlwWTBVRGn4xiEB23l8ky2ox13OeBkQskj0Fv3vpBMld83PdtJQkm\nASpjnYx1giiogBodRAbNqO/78n2fjPZWmzzav4Nm/IBkVMYPSRrr0li33cs/8V9+6U98V9I1IvIM\n8A+ugPwXgU+o6h0RuQn8kqq+OEbxWVX/2njePwL+iqr+yqNAXgT5z174t//9l17++Z/5B6nZ+6fv\nVJR6htlraW9k7t6EpoRX7wjP3Ztya7lHGeZcuCmfv9Hz1tEpx/NTzquGvd5y1Buud8r1JnNzk3l8\noTx5Ds8cw9P34MYa8gziHPrpkAppKsum9GxsxcrUrHXKKu2wCXts4g7rfpe2nSBr1K9U/Uq13KhO\ntM8VHRWBikBJ0oJMQZaSLAOIYYaI0IhFsIhkPB0lgVKDFAQtJUhBpCBREhlaSVKMwzzF2OEe4BEc\n6PD+YRnFIBgMBrl8BQ8wRpsQREZ9CZpEo6POBEl0Y2QZTCDayBhVEk0g2UAygWR7ku01m4Danmw6\nUdOTbQsSENOiZhhCxfQjePYY02EIiAlqpcfQISYNPzzR8UeICgJqQEXBgBodklZGVQ0qoqghj1Fi\nxqAIGaNZzEAOIkQzEoUR0hBlP2QjWzuJkAwSRcmDT7JAEiQZZQszWSCLSho/aJZMNuMTPT7r98Hk\nKtBcAoyyfYZNkiv2FaBRyGb4NrLlEliyVfg60MnfGHQkP2B/a6BzFViu+LYAswUbSQMYXb5fddSS\nQLegFAd71CNnb7NFSI7bdDuSh5+pSaM/B0yMSBq0SRETAraPuC5gw/ae6YpO75Nva2f9T36Pc9jf\n4vFdW/H6CJA/U9X90RbgVFX3ReS/AH5FVf/mWPdfAT+nqn/34Q/6Z3/o+bwu7pjV3prTo8TXjmDe\nw3MnwtHaIsC9aeR46hEMj68jTywSj5/DjX5ct5fgUA07Ysne0zlP5wo6KelMSasVjdY0qWKTpvR9\nTWw9fVcS8UQxgxgzPOxiSAayDICQjaLDw002SpZElESWPESNJpMkE01iyGne9wU71tkH67PJZJNI\nZnimsomoyWRJ6OhTk8gmDc+tpMtn9tKWfPlMX9qMz7koeQTOPJYVHjgnS37gfESHYGkA2EFjkAFA\nkC3AjMALg3/MPaoO5StddDui3ghSY/2DPslX6jNyWTdcK0Ne8355PF+2odjWHq/9entIpoodeEBM\nRsZrxGSMpEt7OG/0s63PGJMQoxhJGJMxohibMCZhZWjP2oQhY93YvlOM00tbLIN2itixzoK4PIQA\nVhE/+iyIEeDhh/iqb6vNI3wPXyt8/bWPumZ7mG9w3je7zzfzfbM2ech+1HmP+pzf6ef5brbzqM/6\nfn+e4Xjtte/7TkDe/c6nfONjDMm/GUs8su7vfC/G3Pkh/L3HqL98xP7pLvutYOggKteT8KpR9uyE\nHTtlbip2TI01BmV47qNk7kjmPZSht6MPyfBc1gKlKMkoyUAymXhpQxbIZqyXIZjKV+ovRYZzk5HR\nHqLGNJJCGqPFJEKygo5tqxHygJ9kkSFIE8hGhrrxnGRBRQa/QDZmaMPI/XOv6sv7bP1mbMuQrRnu\nacyVdszYlkGtIct4zWWb3P+c5rITvfXLFf/Y9tbWr4elb+O38Ht4vN/3199Fk4+4UMcA+HfTxsOH\nAFeiuG/94/5OZ347//BvFx/G7t3ln+/k/t+43W/vmkf5v8F58qj6b+17+uYY+u219YjjOwH5OyJy\nU1XfE5FbwN3R/w7DVq3b44nR93XHrZ+7kbNGk9O7VPUOxeEtzpoptzcHtJtrNN1NQjzAmhMw76H5\nXXJ6F3F3seUppl7gpiuK+Ypqt6GaeSZ1yazyzEvLXmE48HDoMvuFZS5QpoSPw+DRLCZ8zrikWE1j\nPKlDODFiliggMoyoi4AYFDPYGJAR5eRKwKSCsGUOiyaDZotmc2nnbFE1g08NWYd6skHVKlnG8oCg\ncp8hdOxWIDrqwXdZHnIKI0sNKK1jO5f2/fbG8lUWUhG5auvAOKIysIqKDFG/mKFPJiIYGb42VRVV\nRTOiWUEVVWTQ2zoVVeCB8mjrA3WjL0vOCA+co6KaJT9QVpQsWVU0j0SvY1nHHkxWUTJ5ez1X23qo\nPLRlHqzb6mTSts0H6oY0Tr68Jpv7dcmky3NUVLMZ7pFMethHkrS9D1vflbbZXrMtZ8mSzZV73G9H\nHrjmwbbIkmX0a5YsD1w79CZFRR8VhX4r5ffrnH/R2n2/P8vDh17KVxHeeMD/HR3fSbrmrwMnqvrX\nROQvAnsPDbx+nPsDr8/pQzd4OK8kn5SjvTf4cy+/eesvHJx/6Nn63ku2Ca9wsv84bV0ynV1QzlYY\nlwid0KxqVotdTk9vcXHxGJvNIdatsMUx+Htkc4/EXbLehvA1aN+A/DbMVjCzMJsi0wkyn2DmE8xs\niplPYTaH+Rzd3SftHpD2j9DZjCJHfN/iQ0vRtRR9S9VtpaHuGuq2YdI2TLqGutkwbdfM+zWzfs1O\nu2Heb9htWnb7jp2up85KIcOKFSc6CiQH0QvBC8EbojdEN+jkDNFb0mgnbwddDDp7Q/KG7B8uC9kz\naKdkB+oVtUO6V2xGrWKuZDKMZKxkLBFLwm21JAzp0s4qZAxJLTm5kbC2RGZH4hrTPCOpKFbJojIi\nIMkw5qzySFpZshkxxyTJZEmSzGibLMlEM2bK7Jj/splkE9ENSJZsuiwPOl+x75e3dvBbHQl+KAev\n9MX2XCVZSFZJVq7YkIZ/56iHbhUy5Lzui32o/Dv5v5NrvhttCYxd5QclPcL3nfj/ZVuQk6QM5Gyy\nJpM0mZQVzWmY87/V+tNnP/3r343ZNX8L+ATD1q53gL/M8OKMn2XY+vUNHpxC+ZcYplBG4N9T1Z9/\nRJvfdPBAPim3SLzml/yrHzqXH/vQW7fmR198xZjjl2XRvcLneZUv8iKH17/GzRc/w/7TbzPd3VC4\nhE0G2XhSU9KuZty5+xT37j3JyckNVssDxAZsdQ7FOcldEN05+GNwd8HcBn0T0ldh8x4sF7BZQ1nD\nZIrUk2wnVbbTKhXTIlW7RSrnnmJeiZuUxu5Ulp2ZzTsziXu7pq9n0tuK3lQEUxGkJpqaZMphfCkN\nsxxl3EFDQouJLbZvKUJH2QXKrqPqOqq2pWwHXbctdbehbhuqbk3dNUzbNVXf4XXooXjNFDnjc6bI\nShWEKhomvVDFQcooVAnKCGWCYphGN1yTMl6VYAxBDME6grEE44jGEYyjt47gPaH093VR0lcFoSgI\nhacvPX3ptC8tXWEklJauNITKalcIoTIaSiEUQl8JoUSzN6hT1CPZCeoV7JDSthLFSsJKGrQmnEZ1\nRJxGHEkHHdURhjJxJKmIlyukJWlLYkNbJDHkS20kDxolI5eDvDraOgZh9/Xw6778HQ9pEgUdp5Bo\nHmzN5lLnJA8O/D0s8ZtIeEgiw/4oYdT9Q+XuId1eKW/lwXsEFwk+sZkkVrPMcp45OVTO9jOnB5l7\n1zJvP5F4+4nMxd63SzD/ohDZ/1/asq/x2od/Xy2Gujz/k/IU8BqJPy6ZH//gCfXHPmPSE7/1wfn0\n9GXb51f4Eq/wGfkIb/JBpvXb5BufY/30l3jyyds8/txSr19T3bFI1ai4tcVuLHnjaZdz3rn9LO/d\nfoaT48c4vzhkvd5DVbB+gdo10a2hXEC1gMnFoMszKO6CfRfCGfQX0C5huYTFYtCqUE+UqopURSeV\nXdmSi6KSi6oyF9PaXBzs+O5ot2J+MK+qw72qPNitZWc6aZ3ba8TOWmMmrfiyNd4NUpjOlNKail5q\nemp6qYmmQsViU4tJA1lIaiC1aGzQ1JBzQ0prct5gYocNHbbvcaHHdAHXRnw7TGJwXRjIpm0oYqbI\nWX1K+Jzwqric8JopVXGa8WRxOvgKHUjGa8ZnpQ7CpLfUwVCPZFPHrf0g0ZSRgZxGshlkIC6AYCzR\nOKI4onE6kI4n2JJoC4IrtXeFBFfRFSWd93RlIZ33tN5r6wuawmszrvhtikLXRcmmKGRdFGyKgnVR\nyLIspXeFdq6gcz7HwmjwVqMXTd5oKo0ak9RJUGeSWhPFmahWIs4ErEk4E8SaiDFJnASxJomVKNYk\ncRKNkSxOorEktRrVD2SVPVEdUb2G7IjqJKgn4IjqCeqIeLnUWCJOEo4olrSVkaySWLKYSwLLImQx\nqBiyjCMzo0AepzspMs5senSKQC7T5pfXbp/nbdT/qOj/UTNbHpbAo8ntKpE9TG7dFbt/SB4msv6h\ntr4ReX7LdT/6o793APoHYj95+aQIw1t/XgNeI/PHJSBP/Tb3Pv4b7Dz/ZnHzVv+CET4iX+Nl+XX5\nKL8pL3NPr1HP36C9+Vn6x38D2fk8uvcOxWN1eP7oyXhrf8ccFMbOtbN7ciHTrsFvFFaO9WKfN99+\nntvvPMfdO09xdnaNxXKPZjMnZ4fICtUlcAFuiRbNsGxopsOUoWkD04thQbqsgGPIpxAuoFtAMxLD\nYgGrFTQbKEulqgKVbyjMEmcvMOYeSW/T5TdYb96uuos7N29o89xz9K++Snju++diH7+1c5H3rjX9\n7vU+TG/0qT4MFAc9fjeIm/fip53YSSOu2hhXNlK4xnjbSmkaKaWjpqWmk/vkYXLApRabOkzskNgi\nqYXYDuSRGjRtSGlN1A0pr7Ax4EeyKNo06C5dkojtMiYbNCmaE1mzJhJZkw7pvfHPdkaQURUSHhWv\nmUKyOEniRcWTxZMpNG+Xyw+SlWlnqHujkzCQzKQ3WgcjdTBaJ0MdRLY9myoKZUKGXg1Db2YkGZcz\nTgeSczpIEksUp1EcyXiNxms0BdGUGqwnuILeee3dQCid99IWBW3ppS0LNmVhmsqzqQsZCMjp2pd5\nU3rd+JJNWejaFbSupjOFdlJIa0rppKQ1JZ0tTSeltFKYZJyqSB5mJYwi5BGrt/Ywe8io3tfbCVGo\nDMSkzgWxNoozvVibxNlgrInG2midicaaZKyJ1kq0VtIgJJzG6IjZEZMjZE9MnpA8IXtCdhJyIX32\nA1nhCOqJXBIXA2F5ogy9sChu6HWZK8RlrhCZDNMK8tfpYSqBPkJvJ+1ekhBbLYkWrkQAACAASURB\nVF8/YPowRn2jnPw2h/6NSO3hqZrxEfqb9dIuy6+9xr/++x7kv66tAfQ/BPwx4DWUHyWx4R6v3/oN\n8h/6LE9/uOHpjzBPM17yd3nJfJqP8Xn5Pr4oz7Kixu19hXjjcxoe+zXR658B+zlgmSlutVx/Zblz\n80ObF/Zrd6Na7+y45WSe125fL+TAnLKXzrENnJ3c4I03X+LtN5/nvdsf5Pj4FhcX+6xXM7puRs4l\nIiuMOceYc0TOUV2TUyJlBxOL7BpkL5N37LCAv+iHbZ/cBcgd4BTyOcQL6FawXsLFBSwWynIJ69Xw\ng6vLntK3FG6FtWeIuUfU2/Txbdbrd2iau0NjnAJno774xV9EGd6yNGN449IsYufHHB2ecnC0ZH7Q\nhtm1Pk4O+1Qd9pR7Ab/TSzHtxU068VVvfNEaX2ykcI14s6GSjU611ZqWWjqp6UxJFotPHS72uNhh\n430CYZQ8EkhOG1Jek/KaqCuirrCxw/cR3yXKNlG1St1C1TnKUFF2BT54TBgWJ5ok5Jw0aVLVqElT\njoSciDkSNBJzkJCCxBwlarBRo0uSXJbk1WSrJjsVdWqxGDXZeFFTiEqJipcHCaZKUHeGSW+1DkYn\nvWHaO530RifRDD2baJgEQxWFOhipkhnJBtkuzS8S+JwoclaXEz4ndXqZphKrEatJBCWJ0yRes/gc\njRtJx2uwg/TO07tCW1fQ+YK28LRFQVt42VSlbKpiFC+biTfrSSnrqTcb73VT+Ny7Iremyp0ptDNF\n7ii1k4JW6txR0EqlHaV0lJKzGxYuJBnyfcNUNDfO782obufdD8A3zKEf9xHQNJLQg9pqxuigBxl2\nFHBZhsEtlWFVmgpWzQNi1I5ijCTnTHRWonUSnZXk7DCyhSUFT4iOGJ3E6IjRE9JAXPftgj7f1yEX\nBPX06glajARWEMQTGAlNPBFHEEcSPyQ+GfqlydihF2YsWSzJmit6O1fOkM2ffK2//gcO5L+u7U+K\nAV5hiPT/GPBHUd7jnM/xOS72P8XBR5f8oVfgxg8h7TNcq5a8WHyaj/E5fpAvyst8kSdILuH2v5Lj\n9c9quPWrkh//dUP9hSFP30wi7C2YPX2Hxz/6Ds9//+KxXWYfyLef3Tdnt3bcRbWrC7OnFxxxzHV7\nhz17yp17T/H6G6/w5ldf5N23nx97BQcsFzOaZkIIE6BGZI21Z3h3qt6dYUxD1iQhOvq+JCWHMwtM\nEdC5VZ1XpPkEndbCXIYN/opjcLfBnABnkC8grgZyWC7g/EIHcljAegVdZyiKQOkb/EgOyDFR3yWE\n26zXt0npmAeJ4RQ4U9X2G/1//NIvief+yyzmW71iursljjXTgyZN9/s0OexytRe02O0pZr0U04E4\nXNlJ4TvjXSulbXSiK53oRic0WtNKLa2U0psSmyM+9fjQ4+OYigo9EjskdqrxfvoqpQ05bYh5Tdal\nRFZEVgRdIrGl6BNFFyl6qEJBGQqqUFCFiiqUTLqJ1n1N3U+o2yllX1H0pZggqjmmlENOGmLKIQdC\njPQx5D510ved6fLGN2ntW22KjtZ1tD7Q+WCCiybaaJJLLttssTixYnE4LFasmMFC1KpRNwyeexmW\nzlURJp3VSWe0DlanndFJZ3UarE76QU9Hkhm01UkwUkeRMZUm5XajrHEbBJ9UfE7qNanPUV0exj62\n2ydYjWI0iCGKJUrGahaXB+JxORmXk/EpWKfRuhys19567Xyh3ZBG07YopC28bsqSTVnIpi5kXZay\nrr1ZTUpZTQqzrgvbVoVpS2/aqrSd99rZQnvjY2eL3JoydzJIa8rcaamBAg1WiQZ6gWBkWN57KYZh\naa8dF84MmKuSUCLKuKBLhgVdgx6icMNgm3HRl9WE0XHgJw8DW0UexGfwWSl02OijyIJT8CMxuTwQ\n0lWSMuoGPfxv8xM/+qE/8CD/dff6pFiG97YOkT78MPBVOv5PPs8d/glFfcFHX4Uf/H6QH4bmI7jZ\nlOvTT/Exfo0/Il/mY3yZ5/mKHGH9Su3BV2K88dnU3fpV0cc/XbD/m8JFC2dG6ecdeecYOfxtjl76\nAh/+yFu8+qpK4T70dPPOy4/373zgwJzt7fpzv2vOOchnXM/HXLN3OCiOOV8f8PrXXuaNr7zE2298\niPfeeZaTkxssFnPW65quK1EtgQkiHc6eaOGPtShOtCg6nFNRnIRQyXpd0zQZa5YUdoWRoEmNpqIk\nzSfk2Ux0ZyrsTGHuoLwN5buKPwZzCnIuxCXENTTrsddwnllcKItFZrUSNq1FyJTFQA7GnKFyl6x3\n6fs7NM1tvr7XsLUXD2958Tsdv/RLIgx75lwSxlYnzGzBzsE5ewcrZodrpnttrvb6XO/1udrrKGcB\nP+kpJp3xZSdF0UnhWinNRmd5oxNtdKqNVtJQ00ppWlNKFEsZe4oY1IceH6L4PmAGAskSOoid5tig\nsdGcNpLymqRryXkgjshKoi4lshxIJLf4aPDJUUQ/SkEVPHVfUoWKuq+07msm3YRJP9G6mzLpZtTd\nTOp+KmX0uYw2u95EG7Q3QYMEbVPs2o6uX/pVWBSreFGs0sKv06JYpWXRsvQb3fiGzve2c50LLtjg\ngosuumSTyy67bLPPNjssDovH4sSIEytWnBgsRpyIOkWdqjoV9YpkwQTRMpg87axOO5snvcvT3uq0\ntzrrbR6IxjHd6mCY9o5JNNTBSh2N1ONOjFUy44Zj2CKp8UnFa8o+p+xyzF6DDr2ZqOMUAQxBhGDM\n5bpnlYzNWVzKYgfCEZeScSlal6N1KViXe+dT713uvc+d97m5TzhsqoJ1VbKqC7OsK1nVpWnK0jZl\nYduyNJuqdG1Z2KYobFd52xaFa8tCOutTZ4vY2SJ1pkidKXNSN2xqFCUTho2OCEbpzbAsPRhG8mEk\noPtE9F8/+/t/P/nf9b0/KR74Q9yP9H8A+CLKL/I1Ps/fQznnez18/AX46B+B8x+B9iPI7APYw9/i\nefllflg+zw/xVb5PvsjjvMMOVXWS5OCrTbjx2dA+9mnLrU9X3Ph8wRq4m2BZZtLOmrz3Lvnws9i9\nX+GFF77AT/7kiqJ4EXjV5f6FD2zeeuFGeu/6gT2r99yp7LkzOUyn3Ej3uGbvMvcXnG6ucef4Md5+\n4wXe/MqLvP3Gh7l752nOL3Z0tSqlax2qgmoFzBGJOHeci+JuroqzXJVryiJIUVgxdmr6uG+WC6uL\nCyNdiFTuHoVZqNDnnNGYCwIT4mQmeWdHmE8NO/PhxUc758r0GMo7SnEC5lzQhZA30G+G2UlnZ3kg\nh/PMYqGs1rBuLH2wFL7F2RXGnqIco3qPvr9DCNuU0sPEsO09dO/Xb2LsbUx5BHEAs5Zy95y9gyXz\ngw2TvYZ6r9Vq3udqt9dqp6OY9BTTTopL4mh0omud5oE0JjTDWIc0VNKawrSmEJczRQxahqhFHyn6\nKEUXxXYBE0KW0GdCpzoQiGZdkfOKqEtJLCXqiqQriSxM1JVE1hJZk2xEBVyy2OTwyeGTpYx+7ImU\n1P1Wap30tVZ9rdNuyqSbckkm/UzqbmaqUOYy2lQFF6rouklXtFXwGyE3PV2zoe0aumZF0565ZX9S\nLOJJuYin5SIvioVdFkuzLtZ24ze28Y3tXOd73/tgg08uueRSmWxyWEoMJZYCgx/JxbPttwx/zdiT\n4cGdPlQki0oUNclkG222aZAi2DTtXJ4Fl6e9zbPO51nvmPVW58FfIRsrs97KJFqZDGRjqmhMFcWU\nSWyRxBQJ6zO2SNm4HJPXmJ3GbOmzoRs2iaPXkWgwBLPdbMMQjRDtOAiSsgySxMVkbErGpWRtisbF\n4FwKzqXeudR5l7rC56b06RNf+Oev/EuQ/zYP+aSUDEC/jfS/H/h/gF+g45/yN1hwzPcCHxf4gQ/C\nMz8Kb34Cuo/A7geQm4HK/zM+zv/NHzWv84O8wUvyJdnjlAmT+k7H/hsX7a3PNf1jn7Lc+tQOR1+c\nkRFOjHAnQTcN5L1z8v4b5P1fx5W/wmr1S/zMz2yoqqeB54BXgReruHn2meatJ65zb/fAnpg9d8a+\nO5Vr+R434j2O7D0q03Jvc5N7Z7e4d/sm777xLG/+9it6+50PyenpgS6WJSHA8AoDi+oE2AEs3t9N\nRXE3VeVZnFQXeVK11FWWovRO7NR3YWJWFwd6drbL2QIp7D0m7j31nKton1LWHLLPfa4JOjPRzq3u\n7Bp2dkZiaGH3QtlfZeZLqI/BnQlmYchrSI3StcJyoZyfZS7OEhfnieVSWa5htbG0rceYiB17DnBC\nzseE8B45j/mpR/YeToHlw+s23u/jSm/j6whjqzMyXzHbW7BzsGa611DvNdQ7nZbzTqt5p+W0p9wS\nR9lRmo1O09DjmOpGJ9rqZCANKmlNaVopTDRGqpi0DDGXfaLsEmWXpGiS+C4hfcoSQqYPidRkzWvN\nea1JlyRdMPY2JLKQwIVElibq0kQTTLRJkkkSxy03bLbYbHDZ4qOjiF6r6KhCKXVfMEhF3dda91We\n9lPqbqKTbqqTbqp1N5NJN6fuZ6bu5mZIg7lQ9b6vo+0stJAbuXxhlGwUWWVYdaTVgrZZ0LULFvGC\ni3hcHHNanZqT6sSel+duWS7dyq/8ptz4zne+c10RXSyTSyWGGkuNobpCLiWG4iFyeYBgHiYXtttI\nJVSSZJNMNskkk0y2ySYb7chjLvnkUxF8mgSf5r3TeefzTu+Z917nvWPee2bBybR3Mg3OTIMxdbRm\nEowtk9gqGveHVz/7LyP53+0hn5QJ8EPcj/RfBX4V+AXgF/lbfIEv8T0MC75+QOAHHofij8HrPw7N\nq3DwJDxRIbMvciv/Cj/if4tP6Ft8XF7nCfmKmbJRr5PpnbXuffXu5rEvHMfHP+W59X9dY/eNI1xf\nsfLK3SwcA3G3QffukQ++hJn/GiK/zNe+9k/4G39jyfAi0adHeRl4CXh2HhZP/r/svWmMZGl2nvd8\nd4sb+56RkZm1dHV1Ld1dPb1Mz7CHJmfxUBIJi4Ro2AIkmDAM2aJsA/4hAzYMGD3tPzQN/bAMC/rh\nBQIEUgtsi6RoeCiRnJrpmel9qd671qzcY7tx7431bt/nHzeyMrOquqe7OT0cDvMUApGZlZGxZOT7\nnvOe95zvZLjdaKquWdMHVMyhqGsD0ZJ7LMk+Db2PUoLutE3fXWbQadPbWlE762fYvHWJbuek8Ee2\nlDIEFSBloiWJgVI5hCijVA7D6CaZTC+y7UGYtf2wmB3JYj4U+azSLdu0E0xrFmYNz2nJodMSQ68h\ngjCgnLlBVt+RhnITknkSSxWHiSEDmVUheS2iaCSUDTIVg1JZUMqlpFD2JdWRojpRlGeCvA+Gp8FE\nQ80V0VwxCzS8ocRzYtxhgucl+COJPxZMphqzuUmcGOj6eNH9dpCyt6ga7iaDu4liqJQ6OIzkJxyX\nLwuD+/Q27r6OMIo+pdqYQmVKrjwjW55jFwIyxUBlcil5WHYorMyUvJykxJFM5X7FkWOmsqlrh4w2\n1yw90ExNU0rZcSLtMFHZQCl7psjMpchMlWbNpNACmWhBHAsZSJnMYiWnUqqxTNRYJconUSMR4xHh\nighfj/BEJEZ6ZER6qMdabMRaokmRpPNwQi32KelKQ090zERXVmJgxUYqaUUW2cASuSizkLVslQts\nlUtJRGbDvMoHBXJBYUEmJeywoOWCom5HOc2KtdBQBIIkADUX6aFYiwNsxFShjRTaWGH4CmMMYgpM\nA4K5hxc5OMmQYeLgJF2tq7p2V+/muqaTdUzf9jNTa2oGVpBVhsqjk0cjh0EOjdwdgklJxV5ULpkF\nsexXMCnBGGjoaf8FE8X/ijgG+R9ziOdEkVTH38/0zwMvAN9ZXF7lWyyzAP3F9VNN6P4VuPXvwewx\nqC/DmQI0NshFb/OQ9R7fYFN8lW31mH5dy3ObIkpIaee6w7i8uTlpvb+uHnhBsPb9U2T3TmLMKsQa\nDHXBTiQYZyRJZYSqbkP1HUz7NcLwu2xsvK7W1yMAcflygQMSeJCUsB4CdaIeOq3VaCe3JLqyYgy1\nijHUWqpDS3Vo0qemDZgnWbqzNv1hSgT97bbqbp1Q2+vn2dm8oHl+MUFMlSZGoEItSaRIElNImUOI\nGkqV0HUnsax+aNuDeS43nBTyflApTGS1GGr5nMxphsrF6LnRtG4M+y3lOksM3SXhTeoibwyomB9i\ni91Yl8NIJrM4jGUUJHoyJ6sCcnpI0UgoWZKyiaga5KpQNqA8SKh4B8RQm0NlrlEMNPSxhgggCiRh\nKJjMBJ6T4Doxw2GM7yW4fsJoDOOJxnRuEkb2YqWmB6TkIGWfj64aDn88/ryrh08bi2oj1fN+BHFI\nRGFGtjymUJ2QL8/IlmdkiwGZQkAmFyorF5DJzrGNCfloKgvxZFFxzFSOmcoxJ6vNsbW5yGiBsPS5\nZmqxpmuZOJGZKJF2oGR2rsjOIDfRRHYktMwE3QhlrCVhTBxEyHks5SyWapIkaixj5SeJ8omES6Rc\nEeFqEa4emhM9NBb/9HjxL9EWI9MiXXeahqZEWo0kujITHSsxsCND2ZEpsqFFLrRELrTIpUQiF812\nmQ/yKj9PySQ3L6lcUFz0S0rCDou6HeUNTZmaQEaQhAIZgJoJ1OIoWyYgJgp9rNBHCn3EnfNq7lzu\nfB4Szjp04t/gN/74GOQ/5xDPiSrwixxk+qeA75MC/p8CV/gWABc5AP0vAw+V4b2/Brf/fZg8Bc0K\nnC/CiT3M4Co140OetG5rfyXZU89oe2pVv6pbbMsKpjGLrGyvG5R2r86aV69x5vkRD/7pOQzvEcS8\njRbbjE1JX2jshoJ5PkLVhsjaTazSW+j6KwwG3+HKlZuHwUZcvmyT7ho6vbhcBC4Ap1FyuRX2yqvJ\nTlTX+lrVcPS6NtDaya5aoifqok9F9/CjEt15m8GwxWBvmf7OiuxunZB7t8+I7Y2HteFwSem6F+u6\npzQxFlKGi5MdLKTML8igjqaNpWn2g0zGmWazw1GpNPRLpdG4WZ1SL0/yGSuoaJoszqJC3h03rOGg\npdzBknKHLeGOGlqcmNSsDjVzXWbl7VAkwzCJp/NZJMM5ejLFEnNyWkDRjChbkrIN5QzUNPQ6FANF\n2UmouZKqL6lNFLWpohZoVCKNUmJghAIVS8JIMY81RmMYDmJcJ8IdRnhejOsl+IfIYR7YSGmiad6i\ncuij1EdVDHd/7iqlos/v3fzjjUW18ZG9jUPXBaAYoxfHFCoLuaq0Txxz7PyCPLJTcslEFcIJhWQi\nC8lM5dRU5ZmpnJiRE3Nl64HIaHNh6aFm6oFm6EKBHSdJJlApecxQuYkmcmNN5EaanpsKIxOoWI/i\nUCRhSDKPSIJIqkks5TiOGSURbhLiqUAMVSQcAuEagTU1AnNuBkZgRkZohkZkxHpsxFqiJ1qiJ5rS\nlOCedX26FOhSU4bUlBUbZGJ9UY0YZCOLfGCJfGCJbGjLXGjLXJBV+TAnc/O8zAV5mQ+KMhcUVH5e\nVP9R978rH4P8TzjEc6IJfI394SxoAd/lAPTfVc8qJYTIA09yNOMvZeHVX4HbfxtmPw81Ex4vwEMO\nYv4hRW6ymt/mq8me9kt46mFzXRTEdS1DN6lgW/5Mz/a2pvnhG3F1/UPO/aHHI99eRSXPoKKz6PMa\nka5wTcFeotFJFLIyQ9W6iOqH2Nm3UOqH3LjxPbW56dzz3C5fNkl3EJ0iJYGzpETwILCqq6i2EuxF\nbbWnavrAqBhDY1nusSz3aIq+qGsD8toEJ6rTmS8zcFo4nSUGO8uyu3kq2d04I3c3LuhO74RumF5o\nmU6s656AiSFlqMex0KPIQsoimlZXUraEpoXSMAaBaQ4ntu15+bw3rFbdQbs9GLeWXKtke7WMMauH\nkV0dz6slb1Kzh4NlmVYJLeH6Dc2fVUXR9GiaHSqiG2XlxtyIu9MwnM4mKg4niGRChgl5fU7BDCll\nJeUslLNQMaEuoAoZT1JxYqpuQs1PqI8V9SnU54JqZFBODIroZDRJLCVzKZglOt4wwe1HDIcR7jBk\n6EYLclCMJoLJzCQIssRxHiFmwBClBnwyYtj/ePLTVj182jhUbXxspcEh4pCIQohVHlMoL+SqI8Qx\nxzYnKh9OyYVjVYgnqphMVV5NZUHNVE6bkxVzldXnZPRAWHooTCPQDT3WdN2KZZIJZZIJkdkZ0p4K\nlZ1oIj/WtNwUPT/ByM6QVkhoBTLQ4nhOMg+UnIVSTaKYURQrLwpxZaANZMCAieWJaWZqTDMTIzBn\nVmgGVqgHVmREZqxHVqwlRqJJQ2rKUKDL/wH9GOT/nEM8J1Y4AP1vkL4JL7Ov6cPV/QMJFgeuPM0B\n6H8JGFrw8l+D9f8M5l+DagJftuHRCSQfYIUfspTvc1bva78cjsTPWW6yan5gmKyLgvLiIrY9GKms\nc3NqzV8k37vCE//nlIu/V0e3f45YPo4ITkCSYZKRDDSN7UDDMyWq6qPqm2SKH2JZbzCbfY/19VfU\n3t59nSzi8mUNaHMgCT1AWgmcJd1rtJRJZslqvBu36BgVfWg2RF9bSXZUS3VoiIFW0x1MEdKLl+gE\nbYZOA6fTZLDVTjqbJ5LdjbPJ7sZ5RoO1jBBBnMkMAsMYKl13NRibSRIbUSS0MMwIKctoWlMptSxA\nU4YxCAzDG5vmyLHtUb9UGndXVnrOQw/dDOv1bjFv+TVNqvZoXGuOppWKN6llh4NlNey3pOu0hOs2\nteG4qQkFTatDXe/LinKCgtyb6POdUaj88Ygo9BGJj8WInD6llAko52XqS80vqgUjXf1kg3Bjik5E\ndZhQ92OaY0V9omjMNGqhQTUxKCqLvAEZOyEyFHOlMQn1lBgGIa4TMHRTchh6Md5IMhofkEMUFdPt\ncJ+IDO7+3FVKxT+mP4efurh8Wej86N7GPV+L0YszsuWFXFVc9Dnyc+zcHFuOKQQTlQ/GFOKJKiRT\nWVATVUgrDpXTAmVrAbYRYOmBsMxQM4xANwxNKZWJVGKFKrEDkgVxqNxUiPyEffIwc1OwQoL/43e+\nnj0G+Z+yEM+JUxxk+d8gXT60D/h/qp5Vt+58rxAa8BBHZZ5HgA9ECvw3/2sIvwpLAXxZhycTsD5A\n+O9QtjdZK87F05OJ9k0t5Jy9K2vGB5bBhiyreWIrK9tz4qz7fqipH5CYL/GN/37Oxf+nia4/QmI/\ng4wvoM0bRGaCZwl6SmdzJpgXIlRtgFFbx86+h2G8TK93mffeu/ZxPndx+bIgRbfTHBDBQ8C5xdfa\ngFZIRrPVeEc0RD9TMYbWityVK/EOTXqirqUOIoVGN1kQwaDOcK+h+tst2d08Ee3cfjDaWX9YzkdL\nGRLDNE1vksn0QsMYCE0bmjCx4jg2o0jXgyBLSgbLSso2kEXXB6FheGNdn/YNY96x7WC71XJ758/f\nmJw794a2vHwrr2tJczysnfS9ens8rdT8SS0/dFo4veXEHSzhukuaO2po43lZK5tDlowONeFEFenO\nSuHQNZNdL6I78pnPHYgdMsolZ4yoZOdUiopiBYoF0k6zlVYLTSBWCC/CHIZUhxENP6E5SmhOBI2Z\noDE3qEYmJZkhpxvY2RirLFFZwVzq+BOB2wsYDgKGwznDYcBwGDL0Y1xf4o1gMrXukIOUBdJD2D9N\n1bD/8fQvevXwaWNRbWT4hJXG4WuJKEaYxX3i2K845tiZCfn5hHwwphCOKURjVYgnsqC+/c3fPn0M\n8j/FsVjB8CBHQX/OgbTzHfWs2jpyGyFs0mGuwzLPEvCagJe+Add+G6Kn4HQETyXwJQ2qN8F5A0v7\ngFZtxvkA/auh0J60p/Gp3DU9w1XDEjtRQ0mhEiPb2wvs0ZsysX+Av/oDfv03Rjz8f68AFxD2EyTa\nkxCeRqkMUztmqOvsxDo7ISSVOaK+R658nUzmbaR8gevXv0en0/skf/Di8uV9h9DpQ9cXSKuCVSAL\nalyNh0lb7lk1fZBt0tPW4u2kLfdEg55WNYZaxXCZSZuOatENlnEHNZy9hhpsN5POxlq0s3423Fm/\nGMWTuqmiXEHXg3km40wymb3INLumprlWkkytOI6tMDSM+TyHlDU0bVUp1UbKktB1LzQMd6xp04EQ\n8ZYQ4natNt09e3bDv3jxFXXx4kvGqVPv51y3eWLYXz45cmsr/rhWH02qpaG7JIbddnJINtLdaUPX\niVkyOzS0vqzKYVCNvVExHg1tek7CluvjTbqoqIulehTNIc1iQKUM+RoUymm1ULFTGampoCJgnCC8\nEM0LyA8DGl5Ca5SwNILWVKcxN6iHJqXEJits7JzELMeYZYWW1wiFiefEDPsBQ2fG0JnhDAOcYcDQ\ni3C8BG+kMZ1azOdZoqhAkqQe3E9PDPvVw6c6LeVnORbVxn17G1//On9wDPJ/gWIB+hc4cO58jfSN\nv5/pf0c9qzr33E6IOkdlni+TksXLwEtfgfd/B+RpuCDhySjN+ld3YPAaRG9QLvd4IKuLp4ZV8ykj\nkRcLTrKcedfSuKnl6AZNNGMekB1shObsNea17zFuv8yv/+1dHvvdU+ljFhfA/iJSPoIIm0RWjJ+B\nvmawMdVxNAkND7OyTS5/Fct6k9nsea5ff1kNh9NP/BpdvpznoAo4vbg+S1oRrJEa/MdCJUEz6est\nOrmq5thryU68Fm+rluxoNc3RK8ZQKxgjRrJEhyV6YQt3UMXZran+djPu3F4Nd249ON/ZOB+qcd0g\nLhQEQjfNsZvNdueZzJY0za4Fri3l1A5DaQWBacxmBaRsoWkrSqkVJWVd0/VRpOvuWNenDiTbYWhf\nsyxz/fTpjvPIIy/GX/jCd7l06XmrWBw2XLe51u+vnvCHjVXfqy2NJrWK6zU1p7MSLdxGwnWbujdp\n6JOwoFVNR7W0PVHDiaqxOy3Hvldi5BQYdhU7gwk77h5hsEFGbdLMDmhWIsp1KDagUE0PTSjlUhmp\nIe7ISHgxwgvQvTmGN6fmRrS8hJWRojXRaM1M6kGGYpwlq7JYOR2rEmNW+EM05gAAIABJREFUEsyS\nwCjoRBh4gwB3MMNxpgwHMwbDOY47pz8MGHoSf6QzHlvM5znCsECSFBe/wxGfzKl05HOl1OyTvpd+\nFuIvxRbKn+VY7N25xEGm/1XSk7X2M/3vqmfV4J7bpefsPsBR0P8CcAt4CXj5i/DWn4JZhMdIgf+L\nGpwbgHMFpi9gZK+z0oh51D1hfCEoao/lg/jB0m1R1N7NIDZkWTlBQxgZf5xknWuJlrzKaOW7hKU3\n+epzt/j6t06QEtZ50B9BmU+hogcBg2k2xrU09hKT9YnGPBchGgPypQ2y2ffRtFcYDL7LBx988Gn1\n4EMOocNEcJqUBE4DdVL5YaarSC4nnWxd9AstupyMNqPVeEc06RtV3dHLhqvZ+pyhrLAnWvTjJdx+\nmeFuVfW3GtHe7ZVw6+ZDk97WmZBJXRCWciirpOuRl8m4o2x2J7Lt28I0Oxml3Gwcz7NRJDOzmWnM\nZjWSpI2ur0ilVkiSltC0eaLr7kTXp45SyXYcZ24kSfnDQkHfevjhF8Onn/43yTPP/KG+unqjDLSC\nwG73emsnhk5rdeTVWv6oVvO8pjnorkTD3rJ0B0u4wyXdG9d1d1Y3LBGqJWNPNOnLajKcV2NvVML3\nioz6Zbw9i+5ewM3BLh3vHQriKqfLPdpVRbENxSbk66QyUj71CFSstFpYyEj4IcKfo/szTHdGbjhn\n2U1Y8WFlrLE8NWjMsxSjPLbKYebslBiqMWZJYZQ0jLyJVOA7UzxnhuNMcJwJg+GMvjOjPwxxXIXn\n6YzHGWazLGGYX1QOtcXb4LNIS95fxOrhGOR/xmKxd+cJDjL9nwducODR/556Vnn3va0QJimgH5Z5\nTgJvsMj4H4M3X4WCmbp+npTwpILHxuC/D973QX+N4pLLQ3JVe2LwgHHe0LlYGUUniu9bhvzA1PXt\nuJ6Mw4rQbceJbf89JfWX8U88jzTfBm7zLVEjnS24AFxAmJeQ4jGIlogyAeMsDHSTrdBgYyKQtSl2\nrUuhcJNM5h2UeoGrV7/HYLD7WfRecfmyQSr7nOYoETxISoxt0ipoDMQZNTdaslOsi37uZLw1Pxlv\nxe1k16gzMMuGa5RMT+hazCCps6cv048beP0Szm5Z9bfq0d76ynzz5oNjd/fUnFEdgkoGla2CUKY5\n72ezvWkut5lks9d109zNSunn4jjIz+fKmk5tczpdJklW0PVVqVRbSbmsgZIpGUyGkGxHUeaGlKXr\nUL1ZLg/6zzzzr6Nf/uV/oh555Id5XU9aQEtK0XLdpbXBoL3qu/W279frnl+3nd5K4HRWErffwnWb\nuuc1dHdaN+ZxVqsbfbUkOqIunagWu5My3qjIyCnhd8t4OyUGm5Lr3S7vDb5PVX+Nh8sO7RaUV6G0\nDIUGFGpQKEFpv+msw5KEsgaTGEZzhD9DH82wvCn2cEZtELLqKU6MNFbGFvVZjmJYIKsV0LN5zBJY\n1eigaijq6FkTlcRMRxNcZ4IzGDFwJjjuhJ4zo+eEOENwXYPRKMN0ai/IoUxKDgXA55PJSXdXDx+5\nkO/zjmOQ/xmPxd6dpzm6d+c9DuSd76tn1eQjby9EmXRvz+GMX2MB+sDLT8Frr6Y20CeBJ1QK/E8G\nEF2H3g9BfhdRWWelVuWxnUfMR6O2frYQxw/VOrKRectW6rqe0faCJRnEOURusJuY07eJ8j9g3H4J\neAfo8C1hkgLtIvsXF8H8Aio+h9I05rkQL6PTxWJ9YrAXS8ymT6G8Sz5/FdN8k9ns+1y79rIajfzP\n/JqmDqFljspB+06hB0kloYQUEEKAvBzlmvQqbbUnHwg3ZifiLZZk16xqjlUyPKNoeSLBoC/r7Gpt\nBkkNz8mr4U6Z3lYt3FtvT2/fODOadlZn+A3JvGqhCmUwypom+5Y1dnK53aBQuKmy2Wumrm/nlfIL\nYRjm5nPNnkwq5njcXlQGqxJWVJIsC7DQdXeqadMhxDtRlLmpVOE6VG6Ctlur7fb+5t/8B/Gv/uo/\ntmx7tkT6e24Brfk8t9Lvr6y5bnPZ9+pN36/nBr2V0OmsRMPeMq7T1DyvaXiTuuHNq0ZWm6klrcOS\n6qlqPJxXccclfK+E3y/h71Vwt5forle4evs673l/gM1lHq/MOHkCamtQXIHi0kJGOtR0rlqphLQv\nI/khjGaI8RRjNMH0pmTdKYXejDVHcsrTWBtlqE2KFOdFMlYJI1fAKFiY1XAhKSmMosDIm2gZExnN\niGYjfHeEMxzjOCN6wzFdZ0avHzEYCIZDA9+3mE7txa7wMlAlJYiEzyAt8RkW8t3zfj0G+b9cIZ4T\nNkf37jwJvMlBpv+CevajNcuFzLPGUdB/EthhAfrAS2fhyrX0+55Y/P8TMm3yZjZg51UI/gQKr5Bd\n0TjvXhJfGJyzzhkVcaY2C0/Xr+k2b2eVtq4K9GZtlSBilXVuKz26wrTxPGHpDeAdpXAXfYol7oA/\nF0B7GLRLqGSJ2J4xySkcw2QnyXDD0xjbEYWGQ7G4STb7AUK8Qq/3PDduvPtnXUVwyCF0d19gf4r4\nFOkGE5d0QlGCssrSrSyJXvZUuDF+INqIVuIdo0nfKmtDq2j5Rt4aibnM0lMNdvU2g6iK7+WUs12g\nv1UNtm8tjzduPOBH/dYMrxkzreuochmsJjDX9aiTzXpeobAR5fPXtHz+akbXbxeSZFwMgqgwmWSy\n4/GScUAGaxJWpJRtIWVe03V/pmmTIcQ7cWzdUqpwA0q30jMw2V1bu9r77d/+ZbWycrPBITLgoEpY\ndZzWiu81mp5fLzm9djjorIbDblu6gyXNdZuGP64b3qxmhomlNfS+bKk9rSGdqKqG0xK+X8J3yni9\nMt5uDWdzle2bj/Dm9RdwRv8I5Hc4UZRcOAPtU1BZS6uFYgMKdSiWUmIo21A10mqhIVIZaTSHyRQx\nHmOMxlj+mOxwQrE/Z2kv5IGhYNXN0RgXKcZlrEwFPVfCyOcwy8mCHCRGGcyCjp63EJpAhhOSyGc6\ndhm6IwaOR3c4ptOf0etJej1wHAPfNxmPswRBHqWqHJBDHkgnpj9lc3p/Id8xyP8lj8XenZ/nINN/\nFHiFg0z/ZfXsx4OeEMIg3YFzWOY5C7zNoYwfuK7SDPhJDmX9Ehpd2HwTxn8C1ndguUvLvsCljcf0\nx4LT5gOFjDq9NAzb5bdskvcywtiMq8lw1tbQg6m0vevA60xa3yfJvAW8rxTTxfOzF4/lAgf6/6Mo\neQGlS4J8gG/r9LQMW4HFDV8gKjPK1S6FwjqW9Q5Svsi1a9/Ddbf+rFnVndfswCF0NxGcJq0ICiwG\nlEhP+TGESvI1nNpKshueidYnp8MN1U72zBqOXTS8TDHj67Y5E+OkQFctsWO0ccIKvp9Rzm5O9TYq\nwfat1mhr/aQre40pbitisqShKkWwGyAKwK5pBr18vj8qFm/KYvEDI5d7zzbNm6UwnJbnc1kYjSq5\n0ahtjEYpGRjGagKrKkmWkbKia9pkrmkTF+LdOLZuQe4mFNdB2wF2gd1f/MX/q/vcc/9BmfT90Lr7\nMp0W2sNha8V1my3fq1cG/XbodFbng25buv2W8IZN3fMbljetmX5YMQpiLJdEh5bsipoazitpleCW\n8Qb7VUKLzvqD3Lh5jqvrHxAN/xOQr2GW4NxJOHMamiehsgql1kJGqkCxCOWFjNQg7S+UNZhGMJrB\ndIyYjjDGYyzPJ+eOKfdmNPfmnNhVrPUL1P0KRVXFzFfRc2WMXBGjpGFVQ8xKhFXZ7zUY6LkMKomR\n0QgVe8SRg+u79IYe3f6ITi9kb0/S7QoGAx3XNRmP08rhgBhqi4+jxXto7Rjkj+NOiOdEiaN7d84B\nP+Sgkfu6evZHNzqFEAVSID+c8Rc4CvovK6V6CFEjzfjvZP0KTrqw9S44l0H7t1C/grm6wtnNJ3ii\n97D1sNnWV+sqPrm8KUuZN/KJ+tAw9d1gKR7NWjrWaKis8Yck1stMll4A7R3gmlJEi+cpSLX1Q+DP\nhQUBNEmyY6Z5yTBj0VE2N0cmWzNFqTmiVNoll7uGZb3JZPIDrl9/SU2nwx/Pb2Dx+qUOoZPc2xfY\nl4XqLPbrk0pCGmDrKqo06GdPRRve2eDW/FS0abRkN1MRw2zB9DN5e6ybRogbV+iyxLbRxg3KeH5G\nDbqW6t0uzbZuLfl7t9suveaYYStgvAyqXoBcHbQWiJEQcieTmfZLpb1JqfQhxeI7mVzuvbyuXy9G\n0bQ2HmdKvr+U9f1lYzRaUVIuC11fTWBNStkWSVLXNW0eatrEVSreSxJzHbI3obC+XxnsX77zHTFf\nPN97yABoJYnWct2lVddtLntuo+Y47XDQWZkP9laTYW8Zz2nqntc0/UnN8uZVUypdNERPLauOaKh+\nUsGdlvH8Mp5Twu9VcHcb9DdW2Ln1GG/dqODt+tC9BMkG1FPJ6NEHYPVUKiOV2wsZqQ7F8qJayELV\nXFQLQEbAKIDJFKYjmLkYkxEZ3yc/HFHtTWjtzGjdhJXdPC2/TM5oYhTrGPnqomqwMav7J3MmmBUw\nizp6zkKzTFQ8R0Y+Sg5RcsBkOqDnDvm7//l/fAzyx/GRIZ4TNY7u3TlBundnP9O/op79ZNmtEKLN\nwZTul0l7BQMOyTzAG0qpGSlJfIGjwH9hClvXofs8JN+G0vNwWqc6vcSljSe1JycPWg+WarTa03C1\n9Z6pyyv5WL9BTnRn7Xge1HVlux2M+bsExRcIKq+RVhu3lbpzdifiOZEnddocNH8R6bUyQoL8nHFO\nY2Bm2QqzXHU0JkZMrT6kVNrEtj9A016j232e9fW3P4+mm7h8OcO9DqHDvYE2aZnvkjaJFWABJUvN\nGytyd3wmXPfPBreSE9GW1VD9bEn3snlrnMnaY11oimFcZU+02DFWcIOS8lyLQU9X3c3cbOPmkjvc\najp062Oc5RmjFYlaykGxBvoyCBvYFiLZyeVGTqWyOa9U3tFLpbey2ew7RcO4Vg6CWc116xXPa+U8\nr62NRm2kXEHXV6QQq1LKFeJ4SRciTjRt7EK8lyTmbbDXIb9+iAz2K4TRd74jBGkWe19CAFqTSbHt\nukvLnttoOs5y3O+uTge7a5HTXcYbtDTPbZj+uGb5s6o1ikpmSfPlkuqyrDqihhOU8cYlfLeC2y/h\nd6oMt5fZu/0Q1248xPUNoAP0RJpJ14EGtFfgwmlon4TqWlotFJupRbVUglIBSguLaktCTYNEpjLS\ndAIzDwIHfeJhjz0KQ59ab8zKxoylD3Ua121aowrZQguj0MQo1NDzZYx8kb/zL4/XGhzHJw/xnFgi\ntWnuZ/pNju7deW9/BcOP/FnptO55jso8DwPvczTj/0ApJREiQzrN+yQL8FdwKYLeJmz/EOZ/BNk/\ngbUuWu00p69/kS92LxmXtDVrbSmfNFZ7UaPyWj5O3s1I47asJs5kVYVxXmC7G2jJW0xrPySxr5A2\ne/eUOjioeWFXXeMI+HMexMNAjSTnMcsneFmLrshxe2ZzdSCwS3Oq1R75/DqZzDskycusr38fx7n1\neVnyFg6hFe5fCZwirRImpNXAHUmItNqq59TYOhVv9s8GN+cPzW+qlXjXrgknXzD8XDYzydjZiRZL\nEyepsae12DZW8GZF5boWTk+ozq41vX2zPphsV4d06z699oTRWoJqZ6FSBqMNYolUTtjWtHi3WBy6\n9fr1qFp90yyV3shns2+XNO1adTIRDddtVV23nfW8tub7baRso+uryT4ZJElLB01p2sRTKupIqW+k\nZJBbv7syABylUIvJ0zJHSeCIfJQkest1m23Pay45g2XV766OB3ur4aCzIt1+S/ecpuH7dcufVTNe\nULMEUjTpy2W1pzXp7VcJozKeU8a7UyWssXXrUd65WWCyB3QBR6SHdueBBhgNOHsSHjgNzRNQXYHS\nUiojFctpf6G8sKguJemfYUmHWQSjKczGMHchGsATv3gM8sfxmWOxd+fwNG6eo3t3rn1S0AcQQmRJ\np3UPyzwN0v38dzJ+pdTu4gY6KejuZ/z7Td5JB269BqNvg/FtaN2Es1lyW5e4dPtLfGl8PnO+1NSb\nq3pQP3lDs/XXCiHvW5qxHSxF7uSEJpUWkfFvgvY60/qLKP1t4F2luEeaWayXPscR6YcLwEMoc0pU\nmDIpaAysHLtJjhu+ybYPtfqYcnmXXO4GpnmF8fgH3Lz5EkHQ/zzH/RcOoRb3gv/hjxOgRzp0FJFK\nQlmgAqpRlc7wTLTunZ9djx6cr+vLSSdX0dxC1hrnbHtqZbIzbRZnGci62tWWxY6xgjstKG9o0u+h\nOntMNtZL/XC37LBXd+m1R3inIljLQr0EVgvEKumGxm1g2zDCvUql67da78W12st2qfR6MZN5uwq3\nar6fX3KcdtV1l7Oum1YGac9gLYHVZJ8MlLI1TZv4SkUdpfRNyKxD9vZdZLAD9PYrvAUhFPmYCkEp\n0ZpOC8uuu7Q8dFpar7fmDfZWg/7eqnS7y8JzlkzPq1ujSTXjz6uZSVIwKsJNqwT2tDqD/SrBq+D2\ny3idOoPtFp3b57h64xQbm6SE0EWljjiRvv9rQAPyLXj4VCoj1degvJwSw7e+fAzyx/FjC/GcOM0B\n6H+d9A9037nzp+pZtf6pf6YQDY7KPF8i3Zl9WOZ5TSk13r8BqWRxR+ohzfoND66+DYN/A+r/hdoV\nOCdBrLDywZf4UveL4ovJCfvEckmVHwiD6vKVrIxfz4f6dd3WOrN2NB6fMJQeTTCnV4kzrxBUXiGV\nfO40e+96PXTSrPmu7J8LIMrI3IB5McHPWfT0PNtRjg96OqGS1GouxeIWtv0hmvYq3e7zbG6+rdRH\nW15/XLFwCNW5P/jvf2yRShMuEJBKQhnSadQlTcVaS3Y75+Y3xudn19WD81tmQ/WLRX1UtDOTnJWd\nW6YdiHFUoC8bakdvs6O3hTfNS3doMugodrtqvLVl9eRufsBebUhnxcN9IITTGVgqgt1cEEGdlJC2\ngS3LmnUbje3Rysobql7/Yb5YfLViWe9Uo8hd9rxc03GWK8PhcnY4XNEON5CFWEukXFFJsqxJWTCE\nmI0h6iolthZksAHiTgN5cens93r24/JlUeBjCAFoxbHR8rzm8nC4ZPW6a16/szbr767Fw24bt7dk\n+G7D8sc1ezSrZLyoYpvEqkFPttnTluiqu6qEbgV3d4nu5gk2bz3Me7dsgg7QFbB7DPLH8bnEorl5\nlqOZ/pSje3e2P/XPTUH8DEez/cdIh74OyzzvHpmGTXsCd7J9UuCvBfD+Vdj9E4h+Hwo/hAciOGli\nfvgoj65/ha/4jxqP5upW/XQ2yp91kkL+5eI8vpJLrNuqKHuTE9F80rYwpwP08H3C4ovE2TdJJZ+r\ndwPAodenzH3BnzNgesSFCZOiwM3m2KPA+iTH9T3I5UOq1T6FwjqZzHvE8Uvcvv0DXPfGT3KXvLh8\nucz9HUL7HxdJScAh/b0ngMlCEgKapgo6J6OtwcX5teDC9Jp2KtjM1hiUcuakaNmznJmbm5qZCD8q\n01MNta232dFXhD/OyuHQoN9R7HTUuNONO+xm+uzUB+ytuLhn5vCQCcsFyDcWRLBKWpFssagMbHvc\nW1m5OT1x4iXq9e+WisWXakLcbE4mcsX3rYbjLFdTMmjjeW0hZRvDWI2FOJFIubogg4opxHwKUU8p\ntiBzG+wNEHfLRLtKcU9/5vJlkeX+RHBHOlJKtCaTYst1l3K93prb66yN+3trkbO7ittr6b7TtHy/\nZo9mFdsPK/ZM5owqw6RFh3e5ZByD/HH8RGIB+hc5unenz0Gm/x31rOp+pp8thMXRad0vk/5Rv8HR\njH/ziAxyH2cPcDKBDzZg43sw/VeQ+WNYm6T9gEGN2rvP8EzvK3wlPJ053cpr+XOZIH/mmkXySjHg\n3YzKbEXNcDg+KaNZw8Ia7yDkW8zLL6KMt0nBf/1ws/eu18ngYOna3fq/jcr1CEsRo4JF3yqyHRe5\n4Vr0HKhUJpTLHXK56xjGW0ynL3Dz5kuE4Wea/v2zhLh8OcdHO4ROkQrJHdLm+2FJKEfaPG0DMqum\nmw/Nb/iPTD9MLkyvmyfCrXxJ8yq2NSvpuSBr5Oem0gReVFFdmmzpq+xpLbxxVg0dg95uwlZH+a4z\n69Glw06lx86Kw/ChGVw04EQudcaIVQ52G+2xIAJQ28XicHDy5PuzBx74rtZo/Ek1m321EQT+2nhM\nezTSG46zVHGc5azjtPG8FaFUSgawFiu1ppJkWSRJLSNEPFcq6qc/19y4iwzuVAhKMb7fa3r5srBJ\nZ0I+tkqIIqvleY1Sv786/Ht/75XmMcgfx59LLBqZj3GQ6f8iaZZ1eO/OPQeTfOKfL0SFe6d14Sjo\nv6qUcu+6YWHxuO5k/MAFBdcHcP1FcP8V6H8AS/0U+EsCceU85299la+OnhZPmzW7dsaU5iMizDXf\nKITzV4sz86phmrvz9tz3TyPDooE1voXS3yAovryweN7T7L3Pa1YnBf598N+/Po0weyRFj1lJw83n\n6GolNoIitzo6s7miXncpFrex7asI8Rq93vNsb7+t1P3XXHzeIS5ftjhwCJ3mXiJYISWADqlVdD8L\nznDQMK0Du5XE2X14dnV2afI+56c3Mu14r5TXR1UrE5REIcrp+UCPpYEbV9UeLbVlroqOWMIb2XLY\n1+nsJWK7k/iT4aSHM9tmq9Ble2XA8OIEHtXhgSxUaqDtVwQrpGsttg8uaqtW2xs+9NDrwdmz37aq\n1X9b0/WrK+OxOjke0/Z90RgOa2XHWckNBm3l+3cqg0iIE7GUqzJJVrQkaVhAAmEf1A6Ym2l1cG9l\nALgf9X65fFlYQPPrX2frGOSP46ciFhns4b07XwGuc3TvzmdfR5DKPCe4d1p3k6Myz1v3TL3ex9lD\nuhhubwrvXIHe74H8l1BdT4H3HHAjT/6dZ3im8zW+Fl80LlYM03gsE2Uu+GjGS+Vp9EY+tG+KPL3J\nifnEO20izQRzeo04+wpx9g1S4H/nfs3eu147k1TCulv6uQBCILK7RJWAccliYJfYVWXWRwU2t8Gy\nIqrVAfn87YX88zIbGz/A96/tT03+ecQhh9BHyUEnScF/l8VuelKHiklqAGiSZuYBqI12tNv/wuTd\n8NL4A+Oh6c1cK+lWMtaspmWjEsU4q+dCLYhtnLim9kRLbS6IwPdt6fQFe9ux2OnGfuiNewzdLXbM\nXTZaXdxLY3hCg3M2NCqHiGCVtCrZ4ZBEBHK73b7lP/bY89G5c79vl0p/3JxOx6d9n1NpZSDqw2Gp\n7Djt7GDQVguZSBnGiViItViptSRJ2iJJmpZSpoBgAGoXjA3IbN5FBjsgrhyD/HH8VIZ4Tlgc3bvz\nJeBdDpw7P/i4vTuf6D7Sad1HONrUfRB4i6MZ/8175I57nT371+MY3rwOW/8agt+F/BV4UKWuoZlA\nXDnJyWvf5JujX+AXjLpVP4PgcTuyT23YQfBCacJbuSS7EddDZ3QqCv1TNloyTvX+wstI8y1S8H/v\nfs3eu17D/RUL95F+OIEwtlHFIfMqeMU8PbPCVlRms2/T7UKxOKNS2SOXu3lH/rl162Wi6Mc2/ftZ\nY+EQWuKj5aDTpNbQTdKKYMyBJJQnlYRWSN0p25qKb58NbvlPjN5Wl0YfZB6crxeqyqmZmbAui7Kk\nCrGt25GYhnkcWZc7osWmuSa6qoHv28mgL9jdjrTdbugrz+/hulvszjbYaO7hfmEMXxbwqAnL5buI\nYJmUpLYPXzQt3jl9+t3RM8/8YXzx4j/LJcn7J11XPjgacXI0oj0eU3fdfHkwWLZ7vRXpeW1NyrYy\nzbUoJYMTSZKsiDg+UzwG+eP4CxGLFQU/x0Gm/wSp5n54786fefBICFHk3mndLIspXVLQf0Up1b/f\njUnB5UiDFzAUvN6B638E43+arm84IdOBrzbwjoFx5Rme2fur/NXoCfFEWVryCV3ql/TYqLxdHk1f\nKk+N9y1h78zbM88/Eyej1Tx60EfItwmLL4PYl3w+stl75KE+JzKkjfH7NX9DtOwmSXXOuGrh5Ep0\nRJWtaZntXYPRSFGr+Yfknzfodr/P3t4VpT67xPbjjIVDqMbH20QzwP4Ak0/qEoJ009m+JHSSlCA2\nMnK2+ej0g/lT3hX90vjD3Mlgs1wSfp1cUo9LlChGljClGAdF1ZcNtaMts2mtiZ6s47tW0usLdjdD\nrd+f+ewTQddZZ724iffEGP4dBU8ZsFYCfYUDIlgjPWBl+67LlmEEu48++oPJL/3S76izZ/9Fqdeb\nnHMcHvR9TozHtP/5P+fhY5A/jr+QsZhMPbx35xFSEN7P9F/5UXt3PvF9CbHCURvnF0kte4dlnjc+\ncrr1wNlzOOuvA1cm8M7z4PwT4PehNU/7AY+QlvlXmjTf/xV+xf8lfslc0pZOhWb4JSuyzk+NUL5S\n9aJXi3P7umYbncnJydh7ECbNHOZsE6m/SZx7nYXkA9z6qGbvkYeaZv8t7iv9sAzaBlqpS1BXjMp5\n+naVHVljd1hicxOEiKlWHfL5jYX88yqbm99nNPrwp+3ADnH5comPt4mWSSuBHQ4kIUUqCRVJJaGT\npM3aTWCjnLg7T42uJE+7VzIXJ9eKa8FOJWtMm2FRa8QlVRTF0FRC4IcV1VMNuaMts2Gd0PpJTXmu\nmfS6sLMV6n5/PMLzugyH2/T7N1mPbuI/NYJvJPDzOpwpgLFCSgD7ZNAgNTPcIQEQ/8UxyB/Hz0Qs\n9u78AgeZ/lmO7t1545Ps3flE95VO617gqMxzkXSN853dPMCHHylrpM6exzma9Z8C3k3gzSuw/U8h\n/idQdVPQf5wUXK4IxJtP8uTWr/Pr0dM8XUmM5AmpyScyUWZ1L++OX6iOeD0fFDZkJemNHpiGw7MG\nYTGDMb9ObL+GNPenet8hdXN8sinl50SWdOXDYfDfbwRP0DK3oD5iWrcYlip09Rrbszp7nSy7u5DP\nzymXu4fknxe5fftl4vj2T+OBHIccQh9FBEukBLBJCq5jUolo/zhQ8ypJAAAaOElEQVS+GmkfaI10\nzcRtgdxYDvf6X3FfFU+7V/LnJjfKrbhb0+xkeV7W63FJ5bVCaMSJiRdVZFc11La+wmbmhDaIKsod\nmnG3q9jemBtzxx/h+10cZ5te7yYbvauMvjCEX47hGxo8kgPzHx6D/HH8TMZi785XOcj014DnOcj0\n3/qke3c+0f2l07pPcFTmqXEwrfsS6VK2vY/5Ifdz9pwnnQF4Ywuu/S7M/hHkN9KvP04KNleBN4sU\n3/01fs39VX7VaNB4cG7On9aUdkkokb1a6Y1fqIysd7MyvxW2pkP3bCCHZ3MkpkSP3ifKvgravsXz\nXaX4xLLLIvtf5f7STxPEdfTiLslSzKiWY5Crs0uTjltje9tgOIRq1adU2sG2ryHEm/R636fTeQvo\n/qTtn580Fg6hNT5aDlol7Qesc1QSEqQSYHnxPSdJm7QbwIZAbpyfXvO/Ongx86T3duHM9HatLp3G\nvGisBGWtlpRkVuQjPQyzuHFFdmiqrX0iCCvSHRpJd0+xuzkxov/5f9GOQf44/lLEYu/O1zjI9Osc\n3bvz/qdZwfCJ7lOIJvdO6444qu+/9rFTrAfOnsNSz2OkXu7Xp/D274H7D8B4I20aP774f5f0rIAr\nF7l48zf4jehLfKmphHosNMKnrdg6O7En87dq3fDF0iR31TQye9OTo9HwXIx7ugRqjFBvE2f3JZ/9\nyd5P1exeyGr3X/kAQ4RxA6PhMG+ZDCsVelaTvWCJXj+Vf+JYLuSfTSzrfZLkVTY3f8hk8v6dKeef\n0hCXL+scOIROcy8R7Ov9t0mrgUNnDGCRSkJtjlpKN4Dbpgo2n/beDL82eCH3Be+9ysn5dt3Wp8vT\nsrUSlLWKLCW2sBPt3/2m5Bjkj+MvZYjnxCpHp3GzHN27c/1zAP39KeDDoH+J1Cp697TuR8sXH+Ps\nAV6X8MYPYee3QP1/sKbSBu/jpI6St4ArJuaVv8HfGPwt/lamTPl8qIdPKqGe0KW+1C323NfqfV4p\nzks3Kcief2YUOhcEo9UyWtRF6m8ircOSz1Wl+FT9j8WcxKFzfo9UABXgKnp+A7E8Z7yUwyk12BMt\nel6LTifL1hbYdkC53COXu4VhvMN0+gKbmy8Tx7d+ktO/nzUOOYQ+ri+QkJLAbdI+0GTxNZ00+99v\nDp8klfM2Ft+7UUz8ndE3f+1bxyB/HMfBkb07+5m+4ujendufy/2mmfpjHJV5VoDXOWrj3PpY2eKo\ns+fwwjaD1IX0+m24+j9C8L9DM07v83FSUL1NmvW/ucbah3+fv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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "N = 10000\n", + "t, S = pathGeneration(N, 100, 0.1, 0.03, 1, 1)\n", + "\n", + "for i in range(0, 100):\n", + " plt.plot(t, S[i,:])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "###GΓΌte der Simulation in AbhΓ€ngigkeit der Sitchprobe" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false, + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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4.013603 4.010228\n", + "13 1.725964 2.843935 4.052156 4.079586 4.001027 4.003518 4.006891\n", + "14 3.384860 3.360019 4.088791 4.065781 4.050036 4.005878 4.009252\n", + "15 3.635062 3.658024 4.142389 4.014601 4.035410 4.001456 4.007769\n", + "16 2.704679 3.800666 4.037882 3.967375 4.000176 4.005432 4.006744\n", + "17 4.208557 2.992062 4.055670 3.890020 4.035907 4.014299 4.006577\n", + "18 3.912931 4.234620 4.081956 3.962941 3.987279 4.006018 4.007529\n", + "19 5.831976 4.371024 3.875515 3.999242 4.013709 4.016788 4.007910" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "pd.DataFrame(V_MC, index=N).T" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "###Varianz in AbhÀngigkeit der Sitchprobe" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false, + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "10 4.730080\n", + "100 0.328129\n", + "1000 0.025969\n", + "10000 0.003326\n", + "100000 0.000432\n", + "1000000 0.000035\n", + "10000000 0.000003\n", + "dtype: float64" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.DataFrame(V_MC, index=N).T.var()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true, + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Slideshow", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} From 1736c59d56e5aefd00ecb5516158bacab1cf687f Mon Sep 17 00:00:00 2001 From: Matthias Groncki Date: Mon, 13 Jun 2016 12:12:19 +0200 Subject: [PATCH 15/25] test --- Introduction_Pricing_German.ipynb | 619 +++++++++++++++++++++++------- 1 file changed, 483 insertions(+), 136 deletions(-) diff --git a/Introduction_Pricing_German.ipynb b/Introduction_Pricing_German.ipynb index e37da21..789d08c 100644 --- a/Introduction_Pricing_German.ipynb +++ b/Introduction_Pricing_German.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 5, + "execution_count": 145, "metadata": { "collapsed": true, "slideshow": { @@ -51,7 +51,7 @@ "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "subslide" + "slide_type": "fragment" } }, "source": [ @@ -69,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 146, "metadata": { "collapsed": false, "slideshow": { @@ -80,10 +80,10 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 8, + "execution_count": 146, "metadata": {}, "output_type": "execute_result" }, @@ -91,7 +91,7 @@ "data": { "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -112,7 +112,7 @@ } }, "source": [ - "### Weitere Optionen die in der IKB im Bestand sind (oder zum Handel zugelassen):\n", + "### Weitere Optionen sind:\n", "\n", "* **Zinsderivate**:\n", " * Caps / Floors : Call / Put auf einen Referenzzinssatz\n", @@ -128,15 +128,14 @@ " * Credit Default Swap\n", "* **Inflation Derivate**:\n", " * Inflation Cap/Floor\n", - " * Inflation Swaps\n", - " \n" + " * Inflation Swaps" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "subslide" + "slide_type": "slide" } }, "source": [ @@ -162,11 +161,11 @@ "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "subslide" + "slide_type": "fragment" } }, "source": [ - "* Wir benâtigen ein Modell zur Bewertung (Risikoneutrale Bewertung)\n", + "* Wir benâtigen ein Modell zur Bewertung\n", "\n", "Allgemeine Modellannahmen:\n", "\n", @@ -186,18 +185,76 @@ } }, "source": [ - "# Beispiel: Binominalmodell\n", + "### Was ist ist ein stochastischer Prozess?\n", + "\n", + "Wikipedia beschreibt einen stochastischen Prozess als\n", + "\n", + "die mathematische Beschreibung von zeitlich geordneten, zufÀlligen VorgÀngen.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "**\"Defintion\":**\n", + "\n", + "Sei $(\\Omega, P, F)$ ein Wahrscheinlichkeitsraum, $T$ eine Indexmenge (üblicherweise T=N oder T=R). \n", + "Eine Folge von von Zufallsvariablen $X_t$ mit $t \\in T$ mit $X_t : \\Omega \\to R$ einen stochastischen Prozess.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "**Beispiele**\n", + "\n", + "- Aktienkurse, Zinsen, Fx-Kurse \n", + "\n", + "- Warteschlange an der Supermarktkasse\n", + "\n", + "- ..." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "Eine Realisierung / Beobachtung eines Stochastischen Prozess nennt man auch **Pfad**." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Beispiel: Binominalmodell\n", "\n", "* Es gibt zwei Perioden\n", "\n", "\n", - "* Es gibt eine risikolose Anlage (Bankkonto) mit einer verzinsung von 2%.\n", + "* Es gibt eine risikolose Anlage (Bankkonto) mit einer verzinsung von r = 2%.\n", "\n", "\n", "* Der heutige Aktienkurs $S_0$ betrÀgt 100\n", "\n", "\n", - "* Mit der Wahrscheinlichkeit von 20% verdoppelt sich der Kurs 200 und mit einer Wahrscheinlichkeit von 80% halbiert sich der Kurs \n", + "* Mit der Wahrscheinlichkeit von p=20% verdoppelt sich der Kurs 200 und mit einer Wahrscheinlichkeit von 80% halbiert sich der Kurs \n", "\n", "\n", "* Wir wollen einen Call mit Expiry in der zweiten Periode und Strike 150 bewerten" @@ -211,33 +268,43 @@ } }, "source": [ - "## Wir sind der allwissende Investor \n", - "\n", - "* Wir kennen die tatsÀchliche Verteilung des Underlyings\n", + "### Der allwissende Investor \n", "\n", + "* Kennt die tatsÀchliche Verteilung des Underlyings\n", "\n", "* Was ist der Wert der Option für den allwissenden Investor?\n", - "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ "\n", "*Erwartete Auszahlung: * \n", "\n", - "$E[V(S_2)] = 0.2 \\times V(200) + 0.8 \\times V(50) = 0.2 \\times (200-150) + 0.8 \\times 0 = 10$\n", + "$E[V(S_1)] = 0.2 \\times V(200) + 0.8 \\times V(50) = 0.2 \\times (200-150) + 0.8 \\times 0 = 10$\n", "\n", - "Probleme:\n", "\n", - "- Wer kennt schon die tatsÀchliche Verteilung (wie kann diese geschÀtzt werden)\n", - "- Jeder Marktteilnehmer hat eine anderen Meinung\n" + "\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "slide" + "slide_type": "fragment" } }, "source": [ - "## Stochastische Prozesse und eine kurze Einführung in Monte-Carlo Simulationen" + "Probleme:\n", + "\n", + "- Wer kennt schon die tatsÀchliche Verteilung (wie kann diese geschÀtzt werden)\n", + "- Jeder Marktteilnehmer hat eine anderen Meinung" ] }, { @@ -248,11 +315,9 @@ } }, "source": [ - "##Was ist ist ein stochastischer Prozess?\n", - "\n", - "Wikipedia beschreibt einen stochastischen Prozess als\n", + "Pricing über ein Replikationsportfolio\n", "\n", - "die mathematische Beschreibung von zeitlich geordneten, zufÀlligen VorgÀngen.\n", + "Wir stellen ein Portfolio aus $\\Delta x$ Stück des Underlying und einer Geldanlage oder Kreditaufnahme zum risikolosen Zins $r$ in Hâhe von $B$ zusammen, so dass wir die Auszahlung des Derivates in jedem Zustand replizieren:\n", "\n" ] }, @@ -260,19 +325,28 @@ "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "slide" + "slide_type": "fragment" } }, "source": [ + "Replikationsbedingung:\n", "\n", + "(1) $\\Delta u S_0 + B (1+r) = V(uS_0) $\n", "\n", - "**\"Defintion\":**\n", - "\n", - "Sei $(\\Omega, P, F)$ ein Wahrscheinlichkeitsraum, $T$ eine Indexmenge (üblicherweise T=N oder T=R). \n", - "Eine Folge von von Zufallsvariablen $X_t$ mit $t \\in T$ mit $X_t : \\Omega \\to R$ einen stochastischen Prozess.\n", - "\n", + "(2) $\\Delta d S_0 + B (1+r) = V(dS_0) $\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "$\\Rightarrow \\Delta = \\frac{V(uS_0) - V(dS_0)}{(u-d)S_0}$\n", "\n", - "\n" + "$\\Rightarrow B = \\frac{V(dS_0) - \\Delta d S_0}{1+r}$\n" ] }, { @@ -283,16 +357,53 @@ } }, "source": [ - "**Beispiele**\n", + "Preis des Calls ergibt sich aus den Preis des Replikationsportfolio zum Start:\n", "\n", - "- Aktienkurse, Zinsen, Fx-Kurse \n", + "$V(0) = \\Delta S_0 + B$" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": { + "collapsed": false, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Anzahl Underlying: 0.3333333333333333\n", + "Nominal im Bankaccount -16.33986928104575\n" + ] + } + ], + "source": [ + "def V(S_T, K):\n", + " return max(S_T-K, 0)\n", "\n", - "- Warteschlange an der Supermarktkasse\n", + "S_0 = 100\n", + "K = 150\n", + "r = 0.02\n", + "p = 0.2\n", + "q = 1-p\n", "\n", - "- Arbeitslosenzahlen\n", + "u = 2\n", + "d = 0.5\n", "\n", - "- ...\n", - "\n" + "S_u = u*S_0\n", + "S_d = d*S_0\n", + "\n", + "delta = (V(S_u, K) - V(S_d, K)) / (S_0*(u-d))\n", + "\n", + "\n", + "B = (V(S_d,K) - delta*S_d) / (1+r)\n", + "\n", + "print(\"Anzahl Underlying: \", delta)\n", + "print(\"Nominal im Bankaccount\", B)" ] }, { @@ -303,12 +414,12 @@ } }, "source": [ - "Eine Realisierung / Beobachtung eines Stochastischen Prozess nennt man auch **Pfad**." + "### Test des Portfolios" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 148, "metadata": { "collapsed": false, "slideshow": { @@ -317,35 +428,74 @@ }, "outputs": [ { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Wert des Portfolios im Falle des fallenden Kurs 0.0\n" + ] + } + ], + "source": [ + "print(\"Wert des Portfolios im Falle des fallenden Kurs\", delta*S_d + B*(1+r))" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": { + "collapsed": false, + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wert des Portfolios im Falle des steigenden Kurs 49.99999999999999\n" + ] + } + ], + "source": [ + "print(\"Wert des Portfolios im Falle des steigenden Kurs\", delta*S_u + B*(1+r))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "#### Wert des Portfolios zum heutigen Zeitpunkt" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": { + "collapsed": false, + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ { "data": { - "image/png": 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Py7WJpqqoOMpCWM/4Dujo1H2Xb3kiVUtQFgVYAa5DsRrbx4ciRZEqRrw/G8sy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\nD0V9H9gS2AsY1dVpAFhmk4F9gbMGw4jhuuMzOpbF/x/sBry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"text/plain": [ - "" + "16.993464052287578" ] }, + "execution_count": 150, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "dt = 1./100.\n", - "T = np.arange(0, 1, dt)\n", - "for i in range(0,5):\n", - " X = np.cumsum(np.sqrt(dt) * np.random.randn(T.shape[0]))\n", - " plt.plot(T,X)\n", - "plt.xlabel(\"T\")\n", - "plt.ylabel(\"X_t\")\n", - "plt.title(\"Pfade eines stochastischen Prozess\")" + "V0 = delta*S_0 + B\n", + "V0" ] }, { @@ -356,63 +506,115 @@ } }, "source": [ - "###Wiener Prozess\n", + "**Arbitrage:**\n", + " \n", + " - Kaufe n Optionen vom allwissenden Investor für 10 EUR\n", + " - Verkaufe das Replikationsporfolio\n", + " - Risikolosergewinn von ca. 7 EUR pro Option \n", + " \n", + " \n", + "Das würde ja jeder machen also kann der Erwartungswert nicht der faire Preis für die Option sein.\n", "\n", - "Eine besondere Klasse ist der Wiener Prozess. Er hat folgende Eigenschaften:\n", + "Faire Preis ist über das Replikationsportfolio bestimmt. \n", "\n", - "- $W(0) = 0$\n" + "Generellen Annahmen für die Bewertung:\n", + "\n", + "- VollstÀndiger Markt (Existenz eines Replikationsportfolios)\n", + "- Keine Arbitrage" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "fragment" + "slide_type": "slide" } }, "source": [ + "## Risikoneutrale Wahrscheinlichkeiten\n", "\n", - "- $W(t_2) - W(t_1) \\sim N(0,t_2-t_1)$\n" + "Bei der Bewertung spielen die tatsÀchlichen Eintrittswahrscheinlichkeiten keine Rolle.\n", + "\n", + "Wir verÀndern die Wahrscheinlichkeiten so, dass das abdiskontierte Hedgeinstrument ein Martingal ist.\n", + "\n", + "\n", + "$S_0 = \\frac{1}{1+r} E[S_T]$\n", + "\n", + "$S_0 = \\frac{1}{1+r} (p uS_0 + (1-p)dS_0)$\n" ] }, { "cell_type": "markdown", "metadata": { + "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "source": [ + "Beide abdiskontierte Hedgeinstrumente sind Martingale, so ist auch das Hedgeportfolio (als lineare Kombination aus beiden) ein Martingal.\n", "\n", - "- Differenzen sind i.i.d" + "Also gilt:\n", + " \n", + "\n", + "$V(0) = \\frac{1}{1+r} E^{rl}[V(S_T)]$\n" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 151, "metadata": { + "collapsed": false, "slideshow": { "slide_type": "slide" } }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Risikoneutrale Wahrscheinlichkeit: 0.3466666666666667\n", + "Call NPV 16.99346405228758\n" + ] + } + ], "source": [ - "##Stochastische Differentialgleichungen\n", + "# Berechnung in unseren Beispiel\n", "\n", - "In der Bewertung von Finanzinstrumente spielen stochastiche Prozesse eine wichtige Rolle. \n" + "p_rl = ((1+r) - d) / (u-d)\n", + "\n", + "print(\"Risikoneutrale Wahrscheinlichkeit: \", p_rl)\n", + "\n", + "V_0 = 1 / (1+r) * p_rl * V(S_u, K) + (1-p_rl) * V(S_d, K)\n", + "\n", + "print(\"Call NPV\", V_0)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "fragment" + "slide_type": "slide" } }, "source": [ - "###Beispiel Call Option\n", + "## Zusammenfassung I\n", + "\n", + "\n", + "- Bewertung von Derivate über Replikationsportfolien & Abitrageargumente\n", + "\n", + "\n", + "- Wert des Derivates unabhÀngig von den Eintrittswahrscheinlichkeiten\n", + "\n", + "\n", + "- Konstruktion eines \"alternativen\" Wahrscheinlichkeitsmaß erleichtert die Bewertung\n", "\n", - "Wir haben eine Aktie, den Aktienkurs den wir beobachten folgt einem stochastischen Prozess $S_t$. \n", "\n", - "Eine Call-Option gibt dem Optionshalter das Recht zum Zeitpunkt $T$ (OptionExpiry) die Aktie zu einem festgelegten Kurs $K$ (Strike) zu kaufen.\n" + "- Risikoneutrale Wahrscheinlichkeitsmaß stimmt mit dem \"physikalischen\" Maß nur über die Menge der mâglichen AusgÀngen überein\n", + "\n", + "\n", + "- WÀhlt man den \"falschen\" stochastischen Prozess, hÀlt das Hedgeportfolio nicht" ] }, { @@ -423,18 +625,27 @@ } }, "source": [ - "Zum Ausübungszeitpunkt ist der Wert der Option mit $V(T) = \\max(0.0, S_T-K)$ gegeben." + "## Zeitstetige Modelle & stochastische DGL" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "slide" + "slide_type": "fragment" } }, "source": [ - "###Aber was ist der Wert zum heutigen Zeitpunkt t=0?\n" + "\n", + "** Grobskizze **\n", + "\n", + "- (VollstÀndiger Markt) Es existiert Replikationsportfolio aus handelbaren Assets \n", + "\n", + "\n", + "- (Arbitragefreier Markt) Preis des Replikationsportfolio muss Preis des Derivates entsprechen\n", + "\n", + "\n", + "=> Es existiert ein risikoneutrales Maß unter dem alle diskontierte handelbare Assets Martingale sind" ] }, { @@ -445,9 +656,10 @@ } }, "source": [ - "Unter gewissen Vorraussetzungen kann man zeigen, das der heutige Wert den erwarteten abdiskontierten Auszahlung entspricht\n", "\n", - "$P(0,0) * V(0) = E[P(0,T) V(T, S_T) | F_0]$\n" + "Der Preis des Derivates ist dann:\n", + "\n", + "$P(0,0) V(0) = E^P[P(0,T) V(T, S_T) | F_0]$\n" ] }, { @@ -458,11 +670,24 @@ } }, "source": [ - "**Vorraussetzungen:**\n", + "**Wie kânnen wir den Erwartungswert berechnen?**\n", "\n", - "- kein Arbitrage\n", - "- vollstÀndiger Markt\n", - "\n" + "Wir müssen dazu die Verteilung von V(T, S_T) kennnen. Die Verteilung ist abhÀngig vom unterliegenden Prozess." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Wiener Prozess\n", + "\n", + "Ein besondere stochastischer Prozess ist der Wiener Prozess. Er hat folgende Eigenschaften:\n", + "\n", + "- $W(0) = 0$\n" ] }, { @@ -473,11 +698,8 @@ } }, "source": [ - "** Grobskizze **\n", "\n", - "- Replikationsportfolio aus handelbaren Assets (bei BS: die Aktie und Bankaccount)\n", - "- Da kein Arbitrage Preis des Replikationsportfolio muss Preis des Derivates entsprechen\n", - "- Unter einem bestimmen Wahrscheinlichkeitsmaß (risikoneutrale) sind alle diskontierte handelbare Assets Martingale " + "- $W(t_2) - W(t_1) \\sim N(0,t_2-t_1)$\n" ] }, { @@ -488,9 +710,50 @@ } }, "source": [ - "**Wie kânnen wir den Erwartungswert berechnen?**\n", "\n", - "Wir müssen dazu die Verteilung von V(T, S_T) kennnen. Die Verteilung ist abhÀngig vom Prozess." + "- Differenzen sind i.i.d" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": { + "collapsed": false, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 152, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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Bf39g/nyZpjsysnLbuG4dUL++7PwUtgkLA3x9zbd99RVQrx4wb17Rtptvlu61ZZ1ZFhQA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\nuDm7Edcrjg1HNzg0DmvRF+gdsrKjNErS70sn6sWoZnO1aftr8ZnoQ+4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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dt = 1./100.\n", + "T = np.arange(0, 1, dt)\n", + "for i in range(0,5):\n", + " X = np.cumsum(np.sqrt(dt) * np.random.randn(T.shape[0]))\n", + " plt.plot(T,X)\n", + "plt.xlabel(\"T\")\n", + "plt.ylabel(\"X_t\")\n", + "plt.title(\"Pfade eines stochastischen Prozess\")" ] }, { @@ -501,7 +764,7 @@ } }, "source": [ - "### Black Scholes SDGL" + "## Stochastische DGL" ] }, { @@ -541,12 +804,14 @@ } }, "source": [ + "## Black Scholes Modell\n", + "\n", "\n", "Ein sehr bekanntes Modell ist das Black-Scholes Model mit der SDGL:\n", "\n", - "$ dS_t = r S_t dt + \\sigma S_t dW$\n", + "$ dS_t = \\mu S_t dt + \\sigma S_t dW$\n", + "\n", "\n", - "mir dem risikolosen Zins $r$. \n", "\n" ] }, @@ -560,14 +825,32 @@ "source": [ "Für dieses Modell gibt es eine Lâsung für die Gleichung\n", "\n", - "$S(t) = S_0\\exp((r-\\frac{\\sigma^2}{2})t+\\sigma W_t)$\n", + "$S(t) = S_0\\exp((\\mu-\\frac{\\sigma^2}{2})t+\\sigma W_t)$\n", "\n", "In diesem Model sind die relativen VerÀnderung von S_0 nach S_t lognormal verteilt.\n", "\n", - "$\\log(\\frac{S_t}{S_0}) = (r-\\frac{\\sigma^2}{2})t+\\sigma W_t$\n", + "$\\log(\\frac{S_t}{S_0}) = (\\mu-\\frac{\\sigma^2}{2})t+\\sigma W_t$\n", + "\n", "\n" ] }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Maßwechsel in die risikoneutrale Welt\n", + "\n", + "$ dS_t = r S_t dt + \\sigma S_t dW$\n", + "\n", + "$E[S_t] = S_0\\exp(rt) $\n", + "\n", + "- Abdiskontierte Preis des Underlyings ist ein Martingal" + ] + }, { "cell_type": "markdown", "metadata": { @@ -602,7 +885,7 @@ } }, "source": [ - "###Monte Carlo Methoden" + "## Monte Carlo Methoden" ] }, { @@ -648,7 +931,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 153, "metadata": { "collapsed": false, "slideshow": { @@ -675,7 +958,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 154, "metadata": { "collapsed": false, "slideshow": { @@ -685,9 +968,9 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -707,7 +990,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 155, "metadata": { "collapsed": false, "slideshow": { @@ -717,9 +1000,9 @@ "outputs": [ { "data": { - "image/png": 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lh1QpdZjXaIgb3D8AbwLeAHwWONMzapbDid/GRGIc8D7geOAO4PMR3FhuVNYr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\nAAAAAElFTkSuQmCC\n", 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -739,7 +1022,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 156, "metadata": { "collapsed": false, "slideshow": { @@ -749,9 +1032,9 @@ "outputs": [ { "data": { - "image/png": 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c7dSEJVARXqse4OrdgTh/dY0WjtM201WY7xuBHg36xaCje3N5UPTDqesjqXsW\n2UVKq0vf9Zw3vaz+0JSQbLI/mOAvFmeKZ82RIR37AD9T5QIR3oE7mDLQg1Y5RJV0kKsx3XXooM2A\nj/1E4vXARcL2ABvHPHtu41yMR+v9ASBoz9xeZPOGTcApwH6ML3fnDyW2BdvcbQiBy9i9gctgdUdv\n5c5S6jm5W+QUcRf9uvvE9sHg5rueU9MbnODFnUYunU4fEfM9PQTc0CpTHYYJ/uawNTAZeMHgeD2A\nVrsYY4BCH3LLg86wIXrBDbjv6kjf9ZyWXsS1QXQVMq4/aL13E3QFi3ATLUMc2tZPVdFT9YlA3wJ6\naV2bam3rzJvezOsKBWUpdTWmZz7idNy9u0zdY/9bAfqrtvlqpuxo0Tg2428OewObUpOKQytcq+fD\nyZcmzEbj0k4KWybfthHw/XFKOC9rAAtS+sOwqdui5VkDeFVLvHQeEowYrUNEVFWH9uo0Ef4ErAns\nEXk1V93ViK0gZqN1KZ4EcZ1p142A94EgPCWL56LhfaNoXYswn4l7ATerskN9XPpFpM43ZmJ5VgLP\nUmVFo4w1jFKys+1lSpXlSl8IdA3QJ0Bn1bG8TlPzZKmA6jSD66uJXV/VIAXNJ+PcJKxMi1eXOrFM\nujFttIyJ/oD+Drpn2+1Sf7ujheN4yPQ1uI2Um4CjE8KcGry/GtjdF/N9IUL+Q3x/PElCinhXunFX\n5Zng7ynfVfpSQhkTy1nXYFg23QT+B76A5gd0F+hX226n+vsBWjhOxQwnAzcD2+F0aguBnSNhDgbO\nDf7fG7jMF/N9IWr0GJjyASSdDp4fiV+b47K6hEVN9RgWonGz4Tm+B+0265aCm9J1DYZl002KF1Mv\nz7TdVvX3BbRonKqbu3sBN6vqbar6NPBT4NBImNcD3w+4uxzYQEQ2r5hv37A3cHmD+e1K8hWJ24Z/\n6HjTvlV4vJVKe7KJG3PL0wtx1zoOsBR3+1TmTVA+NspzplFpc1bjN6WXE3MuIDA9jjuo1iaSTJij\n9TKZfm9a14Kqgn8r4M7Q77uCZ1lhtq6Yb28QXAO3P8HBrRoQ/QBgvPfPKCZ4zAyEwBLG9wcvVh5a\nwdqoQcR5kryO0IAFvIkMQevjDmQRZgPnVkmjAOYDTyfwkWcw9GG5VOowWcFJRdo90SOJqi4bNGe4\n6I5zbDwROSH0c56qzivBU9cguDt24w76VIY6k8lZOCE0nfSZ2XJgdh18FEXbVjA5sEQDa6uA191y\nxKnkKiHiG8GEAAAeL0lEQVTI59PED8DRNKIuCsoI4iNxg9wA03Auwhfg+lF0MBwc+AJPbRbpv4XS\n1Xg3D3GuG7rWtypBRGYAMyolUlG3tA9wfuj3sUQ2eIFvAIeFfi8CNvehp+oLgZ4HemgD+cTppp8k\n4QKMSNzG9PE+8vKpb8/iJ0GfPMECpqoePCF+1oarb8dm4fJ1foM7o14uxFnTTWqbp3rLixaOUzHD\nKcAtuM3dqWRv7u7DaG7ungB6YoHwpT7oBMGf+/pDn8I0I5+qAtL7IJVW9gR+J9RrVb7yDjC+2jKG\n3zThH3U33Qmzzoz0DunLYFWtnGjhOB4yPQjnDOlm4Njg2RHAEaEwpwXvrwam+2K+LwR6EOgfEt6N\n6+xVhEdVgZrEUw31UcfMuO7L0iv7wS+RTyn//SX5jXMTPj9aljoG3brSBf0M6Kfr6hddoVYEf5vM\n94VANwZ9DHRy5HlcZ481JcyZz+mReFGVRZlZYKXLQ3Lm4WNmXOvMru7B0Gc+Zeonb5vUVfd1pAs6\nF/S1dfaLLpAJ/g4T6I2gL4o8S3KsFfcs6yTm7Jh4p4feV/mwS6kbMvj1OTMuNTvM4qEpYV9DXytr\nG59nYtALwQ86CfQR0E3bbo/62xstHKdtpqsw3ycC/QHov0SeFTl8lXWrVuyAkZFX3O1NhTYYW6zP\nqhubqSsbX4NL3eXIWTbfXmA7r+oB3QX05rb6Z5Nkgr/DBPpB0G9HnqW5Wyh0NaNHwZ90ojP3JnHD\n9Rq3R5IpSBPqY/XKJm99eeC9d266I2nP9pWPT55B3wP647b7ZxNkgr/DBPoS0Gtinsd29qKCh3hV\nz+xQ+vOZ6MQqaTk/O0b4ryCHWWiesnms0zyOupKc1j0aU1+r67ghwd85n0BpbZYwyHoZuHz3FdBv\ngn64zbpsrs3QwnHaZroK830i0Kk4m+J1c4Yv/FEFAntxQLNj0liWV3gTv8lchJfaVSUJgnPCSoXx\ns9M088Ww4G+L/9YEf1KZA4o6/KtkiFB3XwFdCLpXW3XZbLuhheO0zXQV5vtGoJeAvrJA+DoO6OS1\nEMoSqllWIm3NmKO0MuH/zAGtav3nbN+4gXlAjW4qJ9RnmsO/QurIpvoK6Nq4g4vTmqq7NqmM7LQb\nuJrFvcC38jrw0nb93MT5AOoaojwuZ7w/mYHTuQHS+vsSIv5e6q5/He9vZkHweHqIcvvqKeocrkD4\nbUn2dXM73by0/SXAX1XH9QVDGG2PVlVGrT5RMLtb7nM5mzPPqq57C+0P+Mq7BI9xm7tp6qrEmX5d\nvGWETePViw1+VviY5ytJ3gsJx6l67sC3Rc9RoF+q89vqEpWRna0zXYX5PpHv5WyBfL2oK8qk4yvv\nijwvi6l3DQRabeqUIsKMZEuqWtRqaeGJ1+dHVWde7muoq6+A/gr0MB989YHKyM6q3jkNHYfGezBs\nJB1fedeEy7Xeu46LeOo8kmQ1VKPqE3XeMuP4WYJTR9XiSdVXXxFBcD7BzAd/CkzH3xxK+R3vGnxc\nNFJnehFE3Q4P0Ie6f5z8l9cU7Vtl+uIC7fadCgNsjXMeeVvLfHQbbS9TqixX+kbBcvZG0Nt8LpWb\nUqkkqC8GB3jGWaLk4akG3W5U35/kEqN2tVMJVU+jbqrTwvtulyYJ9M2gZ7XNR7NlRgvHaZvpKsz3\nkUBfCXqJx/Sa9KOfdNo1+izzIFVKeqX2PVIGpdYEGGP68sVk6MWbGrwL8t4ZfgrwfTLocW3z0WyZ\n0aJxTNVTN0RmIjInoJnAlcBuIkz1lEOlu1c9IK4PTWO8iqUJnuLqYQbt3/m7M+42q9R7jLVjV1RG\n+alZJecT+1DfNadDAxP8dcIJ+nF3sCryMtzlNe/vyYcURlQ3vCopYMn0EnXNZQVPywK19KBct6At\nkr6Pu4SbgAhrALsDf2mbl86j7WVKleVK5wnmxNjDzcFdxbjChwoiSdVT11I9km6SC4Rcqp6Y9Err\nwLuol05QZU243KRMeT20YRG7/065lkjhczrodW3z0Xy50cJx2ma6CvOdp3jBv3g9Hrkl74dUQDDW\n4jwru93GHfIqtLlbII8inkU7o5eOaYfa9j4otp/gze6/S20A+t+M0MGtsXKjheO0zXQV5jtPMFPh\nqegX8yrm5rrIuqwA7+IMrYpA6GJ5SpY7l1OzEoI57qDaspSJQpn081zi09qqC3Q70CWgW7Td5s33\nMbRwnLaZrsJ8L8gJ/8XhL+x8DtQ1WB57kfX4Oikn8LomKKsKhDYFiodyZ5mXVh7wE9LNmpUX9fya\nZ+XZWr8D/SHoJ9tu83b6GVo4TttMV2G+NxSj8jmDw+YHM5S/+f6QuiYofQiEPIKnS5TQBrnNS4uU\nN0XwJ+4n+KpPslc0mXsaHup6N9D7QNcNJlpzAup8P/FTfrRwnLaZrsJ8b2iiyucphZmge4DeA7pO\nfJ2UF+BNCsqsvNqcCbZFSWVOqqsq7UW8qmcFKfsJPvpHTP+M7mHE7mn47pug54N+KOk7a7sv1N/X\n0MJx2ma6CvO9ooSZCOgZoJ9Irpduz3RjPv5xd9cmhOmFqqZivRTdEPVxcje8uZu4n+CrPRLKGJ7h\nJ60A8u4XZPZ70P1BbwadqglWdG33hfr7Glo4TttMV2F+GAj0ucHHulkDeXkfRBI+/pXR9H3l3fWB\nMMJnXrWO9xVRWppV8ovUf+pmdUI+mZe35K070EkBD29V1UTz6bb7Qv19DS0cp22mqzA/LAT6JdCv\n1JxHLbPuhI+7suDKWYYJq4uCadWte847a61D8Ce2d9n8YtJMNU9NCB/n33/cPkBe/kAPA70CVFQ1\nUaVaR9t2iUzw95RANwlmQruGnvnWgxbSORdIN8mXfB2CP9fqIifPnVE91Tgox9r2l80vof5TN29D\n/St6mU/a4JFp9gq6HuitRK8ytc3dfHHaZroK88NEoIfjLHzWrkMQpHy0Sd42V3/IWYNDECfTPNVD\nHXlZXSQNgi23fx1quLRZf5mLdaqoiJLUPoX3AUAF9Meg32yzzbpCJvh7TEFn/hHot+sQTAlCIO6j\niwrwXCaIdQiuhDwqry66KPhL1EPjKqS0gaRsnac8T3Mb/W7Qa0Gf1XZbdIFM8PecQNcFvQn06joE\nU/RjSvjo4mZlnRGSeFhdVBFgvtugZPw2N41L8Z/Ed9G2AH0B6IOgL2qrD3aNGhX8wEY4V7c3AnOA\nDRLC3Qb8FbgKuMIn88NIONv+R0CX1i2YYj66uNl0pwR/iG8fNuiNWhn5GHCKCPM2B7gidRUzIUkK\ntyboVaBHtNn/ukZNC/6TgKOC/48GPp8Q7lZgozqYH0qCmcfy2Ruew21PbMa9g42zCXp3f50m1dtm\n65eZdIkSBFSjXi6LptHGAOehjpP2JU4F/QUDKx6joM7QwnEqZLYI2Dz4fwtgUUK4W4GN62B+6Cgw\nR1uJ6EGcox/hC09/gNMaFbxxH3hTH32XKUEg5XW6Nqi/yqunNmbxTeaZNLCBvhF3ZemGbfeFrlHT\ngv/h0P8S/h0J9/dAzXMl8D6fzA8dhQ6gPMjGugM36HO47YmqwiJfe46ecC9S5gSBVOYwUpjKnpht\ntK18rFQq5nU5Tq//ktxpjZBZZxnZOYUUiMjcYDYfxXHhH6qqIqIJyeyrqveKyKbAXBFZpKp/TMjv\nhNDPeao6L42/YcYmLOECZvICblgrO3Q1hG5YGuS1n0gr1xQ2Bk9lvh14ViiNuBvEordwASwBFgCn\npOUX8Di4sWt12ODvsLbNKcB+jNXZMmBH4G2qXJkrhbGb71a3LSKzUB2KOhORGbhrRcujwiizCNgi\n+P/ZJKh6InGOB470NWoNHU08ebjyRI6+A1ZVniWmt0u/zRtTypVmElhGVx7dCJ9Pxv5Lnnzi+Cyj\nXqlrJdCkqidSjotA7wd9V6E0Rsx1QxnZWSWzk4Cjg/+PIWZzFzcbWjf4f23gEuBAX8z3ivIuPV24\n+QorB532RI5eOYlnngG9tI4PbhgFf5awKlNmxk7C5jYnzcFHkplj1YHJqxqp7KBSOF7wndzPpn9Y\nj0duBT2qcPub4M+OUyGzjYDfETHnBLYEzgn+3x5YGNC1wLE+me8NFfUhEu+/f9WaPPUg6Pb+O063\nzP48lSlVcJYtc4UBo9DKo4Tg92Ex5LUfFEkPdObm3Dt/f+au/DWv05czT/+NLz/9NJNn5p40DWjE\nfPY0Kvi7wHxvqMgMJObGrgF9muP+Bnon6C7+679/m7tlBGrVMpPgYqBCGdJOrnbaVNRXetGyTuIZ\nPYALdCWi6la/49SfwbP09rLN3fQ4bTNdhfneUF7Bn3BHbzgO6DtB7wWd3nq5GusbxXXgvmevoXR9\nC34v/nR8lLdFwT8h3Ku5YPDjsYRvYahn8cXqGS0cp22mqzDfG8q79IwfICbEAX0D6AOg+9XXHrU5\nDSs6wy6tA6+pDKWEY4iXwSUp3s9JFE0nGt73YJk3vbg6PcAJ/mUa2uvKNXkaQTLB32XKs/SMF/yL\n4+KAHhAIf+8O03wLgCppJgnasgK4ej8tbW0TZ8vf2l5KyoCapj4rM7BMcAsdpUP51RfC8n0aS1ee\nwWHzFW5KEfpqgn9Qz2jhOG0zXYX5oaOCm1Kg++Iumf5A6FnnlvxV0kwR/GUHksb9/CSUobHBykd7\npNV3XH3kbZ8jOfmjm3Ovfpgv6gFcoPszd+UHOG128C2kzfZN1bO6bdDCcdpmugrzQ0kFN6VAnwd6\nHejXQaf6ENptCv6IEJmN81YajTc7Jmxeoe/NP33FsnsV/J4Go8JWQ0l1mid90P+7IUuWz2X/aMA5\n6jZwo88HG7tDv2FbrO3RwnHaZroK80aDutP1QM/CHXi50IPgb0XVExPGq7AsKrw8tk+tqp4y/BeN\nk1J3hVVxoGuAfg30bwv5hz/GNPC4cyzjnkd5GyHrneS2QQvHaZvpKsz3kmrqqLiLpz+LU/1UdulM\nDTPgrDQThEUTgr/2/YJQ2Sds7lat77L8F8kzaaBIqdOk8FuAzgP9Leh6Gq/eTJrtj+dxxOz1k9sG\nLRynbaarMN87aqCjgr4V58//Bp9COyEvr4NDTsFfepacIoxyedmsuR5LrziqDFwlhH9uXX40POjB\nOFPkT4FOjnwXY5OheCOHuNn+SJ3QTW4XtHCctpmuwnzvKH4mU4enzeeC/hn0fNDNU8JVmWU2oQ4K\n08A3TiV3ATHCaCYTLwFfVueAGcNnpRVH2bbw1YZZ/Qh0GugXQe8AfUVqejEuSxInSHkHiCEnE/xd\npmQrhfAsx5v6B6dH/Uwwwzoo5n1rs8yMdKObu7UOTAnlaFR4+KjLMoN42XyL5AW6E+7WrF+Bpl/I\nFOOkUNNO6brwyyIFWObrG+oLmeDvMsXPTlYqzNYa1T+grwC9HfT08IfnYZbZih29b/7aLgdjqqZK\n9wgXyCu82ilc9lwDKsxcxtT5J/CJJzfgoWcO5rdfJs+tWWVUNw2tortMZWTnJAxtYiHOr3bYX/ta\njPlgLw+RmYjMUeS469jl34HHgWtFeKsIUjl95zd9aej3KmCeh3SbRrQccT71a0HoToDpwCRcHS4A\n/3chhPI6IKAzce1VtOzR+wXG91eRmVew51l7c/n0P/PSZ13F7pPP4ZAjFDmweilisSTm2XRE5gR+\n+Q1xaHu0qjJq9YqSNnbr2KBKyAv0paB/A/0N6DvyqELS20xn+56pllFZpKST2zOkjzxL8NjYaiMp\nr6JlT+MZdJ3DOf22zbhPf8g7dFXRPl3G+CHdv9VIWPmUkZ2tM12F+d5RnC6/DkuflMEEt9F2POgS\n0B+A/q6swPMtuIoI6wLpNS7Q26q/JvJKaKPXgL4L9K6D+e0997PphIxyT2bS9ruS3o09j/NqO/Rq\nHxP8fSXfm7s5VhGg24H+L07/fxh5dLAT2sy74O/svkHSIFJlcPE90DWVV6TM/w56GehfQF+mdW24\npk2QxiyBVpjgzxmnbaarMG+UQAVWEaAvB10A+icKevusYYbeScGfVE4f5W9yVeIzL5yrkB+B3g16\nOOikSP+bH8zAs33n56GkyUz8QDMgU/UkxWmb6SrMG6VQgVUE6GTQd4PeirP93zN/u3kVJp28CSxp\nQEp4vrgJId5iXTwH9FtBOY8HXbeRvJMFf5Ir88WjIPRdm6BF40gQsXWIiKqqD2sTQ0mIMBV4L/Ax\n4ErgeFWurjnPmYxZhQwsSlb/Vs/WLWUgwhycJUwYc4O/0ecDLKUG65y2IMKWwNHAO4FvASer8lCD\nDAyskgYWRUuBWbi+EtcGc1Gty5KoUygjO82c07AaqqxQ5RvADsBFwHkinC/CKz2ZgK6GCDNFmA+c\ny3gTQ1Q5MKBSQjNIe05APkz6kkw+o8/D8GOW2zJE2EmE/8Hdmb0K2EWVY2sT+oEZ8gRzTNULcIJ+\nAc6E8/rgzSnA8kgqy2nIJLe3aHuZUmW5YlR3m+g00H8GXQR6BeibQKd4SDfNNUNV9891XbmYtbm7\nuIv7EyXLKjjT3zNB7wf9BOjGteedtTeVbBLtf0+hR1RGdrbOdBXmjZpqG52Eu+7xUpwV0GzQzSqk\nV5t/+iR9fE31EnUvUcUFRuump6Brgb4H9ErQW0A/BPqs3GlUtU7LskYzp2wJ7YYWjTOl2fWFoY9Q\nZRXwa+DXIkwHPgjcIMI5wNeBS913WRmNnZqtitBJ2IHOeT/gM7iT2JCyP5GwrzEuLZGG9gdEZl7F\nbh8/lQ9vPZW3b7CCaZcAnwDOD9o9dzpE60NkFk5FkxR+rA6SwsHGiMxZ/b/BD9oeraqMWr2mnl8g\nAboR6EcCNdCNwYx3m5xxo+qYwp43C6Rdl9+bKg7Oovw17hYadP1DOOuLL+WSlZtxn/4nJ+lNPG9p\n6b5YZDaerLKZreMdGS7T8aaa0d8jYa6Z3ZZo4ThtM12F+d7SEF0gEeiD98Zd/bgEdG6gLtgwI16d\nVx3WrjapIPiTTEDjnvmul2mgrwM9A/SRV/L7+8/iEF3BlHDG5QacYoI/3p3yRM+ccZetD65e7OWE\nqZ6+iBaO0zbTVZjvLQ2prhKnI/4nnAvex0DPwR3u2aBt3mooa1kf+LFuoInf7Pbh+2ga6GtBvw/6\nEOjFON39Jl77YZHJTHy+ce4WRtIFQ/E2RovGMXPOphA2UxtSXaUqS1X5mSpvBLYCfgS8AbhDhD+I\n8P9E2KFdLv1Anf49zrwwC3GmobODtKKeJkuZhIqwmQjvFuEXwP3AMbhzGS9S5eWqnKbK4gReyu2x\njJlbzg0oWb8fn+/tMeFu98afYRzsAFcTmLjxNbA7nhb8dYdRkj+UbiH/xlwQnLWB/YFDAnocOB/4\nPXCRKo/WyG1tiNngzXVoK+XQ2nQmTgrmqpJ6EEmEacA+uDo+ENgJ+B1wNnCeKg+kRC7Ult4Qzdch\n7oAWrfDXI5SRnSb4m4Cb5UdPFw5mitCnDp10gjIn/yJMAnbH1cercQLrGuAPwJ+AP6vySDnWxgvU\nLAFcFUknerMEdSSNaH2GETuQiLAmsAewL/Aq4GXADbiB9HfAxaqsyMtDZ9DWINRzlJKdFfRKbwGu\nA1YC01PCvQZYBNwEHO1TT9Ubqs/n/pxgA+yxQB86u29lCfYF9sddE3kh6OOg14B+A+c/6B/IcWis\nrM69CpXd4M2RxuLNuXf+GRw2fyUy50e8/XDQt4CeBHoJ6JM4W/tTQd9I1pWG9feJ4q6UjbxRGdlZ\nJbOdgB2BC5MEPzAZuBnYDlgDd+PUzr6Y7w35tuJJv3yiXuFf88Y07q7gPUH/A/THOHPRJ3Guf78K\n+q84j6IbR+JNEKCbc69XCxAmXl04O6YJZseFTUlzAt/r8uitR3Ly0/szVzdkiW7JXau25o5LQT8O\n+irQdVrv08l9MeoueSis17pMjQr+UKZpgv+lwPmh38cAx/hivlfkc+aT7JFQFRY3UI5GP2bQ9QJh\n/x+g3wH9M+ijuIvkLwT9Nu4swbiq2J+5K33xmbCiiLW/T1t9gK4DujPoIThf9r9h/C1mq6Zz5UOf\nYbaew0F6H5sNXnTTmiVtIjCk1mtdozKys+6Tu1sBd4Z+3wXsXXOe3YTTV/ZfZ6l6ASIDr4jQgC5W\nlceAiwMCIHAatzXwAuD5wAbA9rhVJqAsYqdJr+L3bMk9bMqDawl62heFk4AHgUeAx0L0BLBclZUJ\nbMTdNbttTLhNcJuV0bC/EGElMBX3HdwS0IU4i5uZOJ3+F+bzkiSPkwaDF6QKfhGZC2wR82q2qp6d\nI30txZUhC6fgXATEbQj+d+25d2AQcxNK7gzod8A3wpu73+GfN92XS3e7i625hy15kE25np2n4DaT\nNwXWB9YL0brANBGewVldLYdxg8D6MWxsFPm9CniG+G/mGpxF08MB71F8evV/bpsu3L5dNmOM9sUw\nr2nvDC2islWPiFwIHKmqC2Le7QOcoKqvCX4fC6xS1f+KCavAJ0OP5qnqvErMDTPGLCCeC2wOrAD+\nG9UTW+WrKyhhfRSsItbAmdlOY7zb8hn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ykX7X1+t5NrkvJu/CVVeRZ5yRbJ2zThwhIIbhMjJ/PjBlijZS+hl6g4x49oVC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\n5yk6pQl5UUc4cQrH9nZzYZb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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -771,7 +1054,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 157, "metadata": { "collapsed": false, "slideshow": { @@ -781,9 +1064,9 @@ "outputs": [ { "data": { - "image/png": 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VtJVwUSllxSqaXSWraLVeFMfQOG47Utp0LHCaZfuS4L5pMSDTwNAyJne/wCE7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\nv5lZw/wLu3WN6PB/0xoAAAAASUVORK5CYII=\n", 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d7b/gkvGy5ctTHQbfR9zby7VlqmO82jMTe2k/bCeGyYrtOeyuVhfFZZpuI3HYmXk+z8+L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\nDi0Pvm1HX9o2qXG4YUKgFtS0QiDMdL1Q4F+rmOG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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -821,7 +1104,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 158, "metadata": { "collapsed": true, "slideshow": { @@ -835,7 +1118,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 159, "metadata": { "collapsed": true, "slideshow": { @@ -859,7 +1142,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 160, "metadata": { "collapsed": true, "slideshow": { @@ -874,7 +1157,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 161, "metadata": { "collapsed": true, "slideshow": { @@ -894,7 +1177,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 162, "metadata": { "collapsed": false, "slideshow": { @@ -904,9 +1187,9 @@ "outputs": [ { "data": { - "image/png": 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q+w/y1ey1uG6eoPY1FXYo5CaeOOLZEZg0+n/f1q9/dFdMzNigdtGb6PDhQSrs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CIUna3lHuqhLcb14119bxSlwckWsb7S5g8Psm2F1qUgU/v20OeDRMcIhZgdoEZBAS9oyS53VA\nh85yWmC04mwKJU4SF2Gw+qpD6gh6sXNHL4mv79emRqv465NkR+vMV0PNL1/e5sc5xFW7HYKXBA11\n6apQ70dNqvgZ74SffsQc+ggdj/ti4akjqGrfHrqY7eoIbntDBG9HLQNSzBCUEi+mHyqmfgCs7UAL\n1kLQFdjAfCpD+kxnLyoUKng+bDGaXGqCgz02rCRDH7cWLca4Nv3IjBuHpxlHjuzTU+BKBc9+eq1f\nciu8G2OEYksR/JxfWjudON/7QA/g80JKo24EBhJR1cxVpVbXKtcFr6sdVzvMGZw3Kfb2vwxRAVgD\niKcCW6xA2wCgoxX/oTL6HmUzH8B2ANUAWoBBBLp17IZ2r+V4xAsog4Bh4CQn3BzW2vtWV9dXBevX\nz7EHBsKnuhor/aSuV2wZ0buEQt8xNY+6CNpyH1G5+kul/+E1Rs61vlNsLC39H0yD5t9v9QYw7elT\nUgTgCATihCnHWVS6v4w19ptCCNwArMB3B/Mp+BnvYXeqjnG2NDqNcVsrshpZxm5ICckKMdOeSYLN\nwSPL4YAFaDonMPDSzPz8qCn/pyCvo5Rq/70nAGoppVpCyD4Aryml5/4tOwbgLqXf0739Z8gzi1h0\n9ByFdzondLQkQ2qUIq8kEhXu9yEV5+HaxSCMHFuF8IROGPbxR/ixLDSuU7BUFw8p8cQUeR00xgK8\nth3AE7xHOoB6iCGgFdrGVKKpVyEUgRr7gOpqVt5XYKwXFLabJQNFkWUCGVF/AyVKFkmGYXhJ2+CV\n0ApZCAUFD0AMkchC7Y5q48wZRwu7dLnmsXLl7DyzRd+8snKx0KLxOu5O3m4yRgK70AqOhQtRLpXi\nGIA+Sy/h3Klr2F8pBSPW+/BuPcvYb4NYunWxkk8u0XEuUi9eZ+l+h+JAbwvKREPdEumk3QuJ5VlL\nm+3sWP1809zsUTA2nyTWcoHQcZkqN75e94OwZVdAeVZEaz9jrRTmeXPh6OZN0aQQ+GYw475nESrW\n/wKMTsT0XmYMmfIO1upQfFw+ldgbXKa/g3rZS/QMMtfwxAAAIABJREFUrEPjTIRf+gHPxjXufUGy\nYv4S5eMCtSVR35Azt4nVrzgV6u2fkatvQuqHNOvUZLpTsmz4hmZljMquUXdIF0REkLpLkF9sYmWl\nXnO9BJeWLF7vBWMtgpuHmtZ/bGOnZWWMbMZsbt7582lnb93qUevt/dqgVDo5wqNliAmWwVcEGFMg\nrv4AG18CWAWKMpaiciRgyK6K1ckb+pb3dfrgYTfc+r11EN26Be2jOtMLF/aRQmkc1ZnHk3qzzZwj\n5JiynMrVt/+IFTV+ZxWGHLHbpTyns0g4a0YHGfN1TLlLepCrXEX11FXHwy4CdAY/Uudsg51w0JiN\n1jonOXOqbFp9SGGZIkHeybb8+QnCKczGIYHnPIa2O8qIRBa9hqtjGNBDBFgdG0vNCQmkqcOB5I22\nlars4pYVs33/cI+WpRJUG+2z8kVsLd/uo3FFwgsPtnziSbfR0s6GJFrV28E25BPiVyiyqmst4lIH\nIIaL0QGpXAlD1clJsTpjsOC3Zs1VWUcu0dCu+b3C82mzXaMsmc4fEWEpO/iZQj9TEoB8yYYIENQF\nUbtdguvXJvF77m6zJ2gM3EIOjkY85bbXCksjeE1Xi5Zt/u5PxvvF+/51pw9sK6lyWBrDoRKr4eC3\nYjHbH39jKVhiQgCcUYMwrhQtHEALEFgAKgWlL8HWn2ilEJUzoVzym7Em6sd/RhevFhBafQ7JktBu\nX8qiT0wZw/V6F2xv+jHWul2/QqzCPuqEVVw4DGuSoF7jq4n89OnCKl9sb5GEZ7U+EBY2AhbeHzQo\nO2TshN+aM0TQOzvVvQWwoSful9j/avwAH5yU2JvaHHFxlQAuqAYGxZpm7XM+ws2rzGcDxFJYAn+N\nfVX/n1gSDUQmAKUUqE3B95ANNQAogC747juikMGA4WopmCiJNrXmizWuMNTriNeMVhH99l5sc0cy\n3h71ZvSQ61Efb/tn55+MFbd2aRTuXxvDmEQ8zeSdCx/ModL8QKz4NjX2ZEICWQFgLIAusbG0+n9m\nG7oDOAbgSWzs38vXrBn1FXolT2cd0NR2Sao5oWshf/2637naWq81lKI8IYEQfD/ktwzAUAAFdRbJ\nrXEX+k4wnLulBM8bAERSSstJ3FqChHXC/3HI//tcSyl1/n+A/B1K6bX/0h6VKKZCbHQDAwoZOkDO\ntgVYB8ptLuiBcvQTfcI3nyfYP2w/pv79KzoVx+EJ3HCH8UK3wR8g/mbhV6ePJe9QzUSCwBkqfIQn\n0mJMyJSI8CmHx0dbIeRKoK0W6F8mQjsQXGwm4EKYAweuKZEZ6U+5WpCqb0V4bTLhOaVwUCqERFLB\n3Qcko0xKF2/QGl1kYuml+kjx+/cfCPtoH232Weq4aBmJOGoWlk/mRR2/okGbC4miEDIrJ6ZvJ9to\nfVcwdglQ9Q5C58eMdU3nXhy93Ju9XTiWoWQyNFIBcsN+WKGGDLvMxdhuorC7aCJU5rqBOtny8vaG\nH83lUp+SXFb7ASTPW4XkvoRuF3Ekw1oLiarXZ0uLn2sxbFhjAM74HqlSAkIs+P13Gb5+rcepU98A\nbAFwlQJRABbVyyWjzSoJV6JQO/p1nVA/MeKhNsL1LbN6oxdt2qX7Z3H4Zy6JloUK/svsdeOXiNCs\nWZOad8nVF2PYpFKR0je9aoQoh+1U4ze2QPWibZSyW8OfwuP3TxlH0XAoBgzFL7t+p5IhapOuvrIu\nN/eb8607C/T6sTfdEL2sDApp4fQjuWFmjVPN6VlBz8BwI5C5X4Zk2zd8fLyOzWAl67F+vBzyusdD\nnfLjp3ouwOE9tL323pEXJ9E724V5Nie27ZBR7xZ/3bDWqWXoO3NtadJdbebA/QRXcAUZcPj7w3//\nftIWrJSdf+waDHUay3KdQdLM9zfin5vbsCpqnqVpzxeSC36DNbkI5jnWajhJpnAZ9TEFTYUvIXVV\nHg6pWm/nJUT9h/gXYw6CG7o2vPg8VHrREaL53P6euZ/ujHxMwCHHdKqvdK/b6L3wbp690aCZFX9I\nr5c9pHIOVzcFa+MWbzyW6JMq6xnHPFZ+s0fTbDaE+crsBVFowGB1yqigE417WB7Lu+a8wbX+7fAt\n1INYBSk0mhp7Tk5M2YkTa2TjvBa+eDXy4IDC0E7cFvc6iE53tgXO2s/9mc0xvyXY4VMkRfe1qw25\nkmYs5W2cZ9p6drM2paLPOagVyc5Vl83XXb1gl02XTWZ2iQtnD6nH8iyEfTmFU119kMxWw0I+w93y\nPDzVVt46Wc2dSnB0wbMiDg5wPUt8J49ZC9m08LxwVZpbxwN/Os07eLBy4dOneRzQUI323E3trC7z\nps4hp9/Ww+V5Z8s44bStGNPFSgTqNokHWed1+MMXrz7ysC2UAsMAeLwHVAnoscTMtt89k9P7aq0v\nFw6kyXPvEAItQLOw+eN5tCj/GatWEQ+dlRiG5Ukc7U9/cagVtt/wu18Yvqjuos+RvzBpOY2NNXzn\nSUMokJUIZHwA1l4FCv9DOeYCIAQsojABDKqnSHFv17KntvenV45aWS+tamyqy5+uaNltCfXLaMl0\nSu2uI5Q4od1rVu9T+nnVwzkB6eUv8rGhIQJmcxU2btwE4BKltCohgWzC911719jY7/7I/8E3qABs\nGTRo/6jRozcXsSzvdn3PFtW41z4SumpDivSHZ2EAEvDdddECADUaIbp2DY9OnIABQF/IZSXYvSbc\nY+/RCk0FG+QteAquejfZFdvV/6PmmlhKaTkhxAvA03/NNcsBgFK65d969wCsoZQm/1fIs2PFmHJx\nKsZrvRHkuAmxwYEGuQ+MdQTbnafjen1rmIRURPc6DLFTJdokTsOZ0hHAplTwb1ygSaaoq3TBem48\n9lqXgHUz4dSMEQhINsA1keCbUoNsFyNcc+w4rOVwj0ihq7PBy4VxTDRY+I9hQNI3TiIIDodHczeH\nb3QTqaeoKRZfvoF0aRWuBwKvUjjoDRwiWghwbSrgy6eFKHm3XLDxatNE+1+KvZhPGqBAfi8O0Wnl\nqG0nIG8uBErAGKzA5SLgYTUgZgHZ13607MFSyBsWkdDIWoSHDKFV9zsRv0pj9jV/RSP32HPswO7/\nQC2zQlzlAsUnP2SKPlIfX+1XT/fqDTteYtpXJdqtSyRcnAV0xReKl2qpRd+4hV74kL4ZBsMMfLc9\n/um6e3drvb7d9PA1tY7PwlW9BSdfju3W5uAkR+2lJpk1qu3RMbaolFRxN7sef46QYP81O7WP2fBF\nbW0pN9b4+dsml/Dk8IrC1g2vgnp11t6euVHU7ip+VCfDv2QZtmlDoHdK91fTS4OjK/7ov1Iq/vb1\n/6LuPaOiyr5232ftvSsHipwzCEoQEREVFcw5tjm0qdXGnHMObc62OdvmnDOoqCiKGECQjORQxMpV\ne90P/M/7nvOecz/cM+65d/xrjP2lRo2q2nus9VtrzTmfZ/I+AoH1bxdXkJ3elabA8rA6f2k/NjQ0\nVfqC+UJeeIz7BccO9S73ZjicPHjYmhLq1cvUW026lZ7A5OzhkGUUIW2FPQwVlwH8BZk3C//5SbCY\nxeB8pLh6dTc9f34XgP0ZLvadIg/ssA9/p87yumTn2bdOyJn9vll+mmldwPeex7kj0/8QMCbrZ28H\nfwuJeOotWbyElThckvYtfUA+TmuL+tgMJHPBfEvB59o47qB4KK5/+g03fCmFs9Eo1Oh0CtHDh0Nq\nCwtVloiIRPu27VNIrcUKUomRKbE4YzHdhd+LLxj7Od4UPn5sP23/kJPHUJ++rOPDTS1aKUuG3bGD\n0EMK+qoKn2ESp+Jnv+e4evVWs2bEPi+P+XHsmDXj4VEtMhtYs+mzstb3k97W55GOFA8C4h3a6u//\nHJ01YPjfrqnI/7Ev1xQ83ZtRQjkSzy19ye6KFZRnWeg0Rr7jujq2zdGDDeEO77/NqjgS9FbaNnen\nbG7YuGdJfNA/AUy3ikvUoCykO+r92WW8DiL2B79atE3dqdbbphoisgEhsIap4jSSC0oYVjB+9tgQ\nvrTVBFy+pY6IeOy2bVuvTYSgV+zydmWYN+8dnjyRMMePyxyBD8nATldA8h2jw++6d1iyedxCPL6k\nM68sfJqRYrZ9qUePmSbRLfCewRr8PAHgFQvc1oNBCmLBIhLe/b2wr/JXi5HvjeUtqbG+J/bEjwHy\nHIBxV0DIeA/O19V1qJP39yD9i4bo7S8BrAGQ0B5vWsXhb5cc+NJD+DO3DM7XANzEX4G5eOK0Gk2r\nyTwAl7GWYKALDt15hLH0jkgA89yfgDgUWOs40enOo2sTxrQ8cmo/HMW06n5gvJWu9QddbIjm8NYd\nJwo/feqxFkAc4hOcwfO/Y8iQzairG/IvsB+TSLDjwQOsRlOuuUdsLG2Lpo2UFwAvljV5njsXEPbX\nX6chFBrK/vqrHxGMuDgTNTZ70ftBD92s7edzcuDx/j34r19hm5UFxssbjTIforbXtVO0KeuialkY\nRhiWR4ZNKrIk34zd/8j4MGpuVvT/V5DfBqCaUrr1X2BX/ZfEayT+M/HqR//LDxBCKGa4gkk2oGV1\nJ2zLnwiLTQbM7R5A4NYA4ZEADLU8RzfVTjykbdAwrh9ESTMxpKYzwlu/R8hTA86a1DRdOIj/UteM\nDeu7CmmGHfCMISgyAQ61Hhj5Q853tipnrvfrD/I2rKJnj00Oe88somHuG03fvtSVubqirmVLZ9sN\nmz59bDy8KxTE2kleE9QAa5H9qoOXMT7xAWY272m6HXNT4JKthEtjBTIzzGAYICiQoI0VRVSBgm+Z\nJ2OkEi0MXTX4PlqG9B/NeMnZX2T8zwpyoC3F2k4xvCx+PDjP26TO5w6xNjEY6i5HUW0j6q0tsJcA\ng10BKRXiyZNh1FNpoBGeqab9t7INT54SCfw4Ad/XRFsqYa65BK4fDSOTNdbUtj6eGOqA27bAWU5F\n01z9bim+fo2XGlsuqcQGV4ssBHadc4vtnsocu4ofYoVlO3ezgw/d7WlZmXH8y9zerRXpo/mGzl2+\nAAMYG90nGTFDW6+AVAqIVTqUl0sC2X6mp5Y3gl2IpRcFqVVdTemKl/CvdPDWugzWlhgHlFNTg6en\n9HyPHpzDuXPGDWb9e/oHEuCIyTIDvk17Yy86OK5tJ4H3BE3j+ymGk3t3pXtXeCfEILb0LMYtn4DT\noGF17bD7yxRQfhaKb35E7qFoNF8thsQlG+XPwpHhPhIdOhyHWj0EPj4pjmr1t6m3rsmiEq+yq0KY\nen2O0ZCZAXt3NwaRQQ60Z4yFeu7ao6NlriKLWM+ZvbItEVUr+DWzRsHZ5wOJtk3jCAhqqpxy81xs\nnLcziyV/a+N4IW9iZLIGVFbKLUqlkaSnh5qLisIELVvGE0CnvXbdOelB7MZY1pMllBUiJufyzzif\nw55nymnUnXI8BXD1sDtaOxoRPrsO8FPg3erFd8dCI48FcGmyT+xpfz/ErJwBi+QBZ6/paxaaJQTZ\nP4OgfhuIaS9vw+Jmgv6XEiMCI0zpnV8J1oeYESwRoLHOiv6p2E7MAjECsg04vmk5Hk3yhzCiBC2q\nchAxl0eZfRtkiqagutEb54br8Eycgf1HN+CPika8l/XDV6sZcNQIca2P1nLRNz8LZ9tYUHz+otSi\nyx2DpSekrQ9ZzsWczr3eTx1FKeJ//kT69D+JCMuXD4O9fSnmzx+P6Xw5/sYGAHYABlFK60aQX5M9\nmp85emzgJnireUaVshTiRiN9VHRKj8ZzLNBdCJg7Yb7AHnk4hiQ0MOWwc3GBWK+HVuMMha4XwDy1\nonyq/j1gyItEpHCI95BOK8auMFlYiz9dQ7UkIWEmgH40JqbX7IQhVoHI+NsTBb+dwe/0Oh1s4dX1\nPPT63ZjR4iXqm+9B+Fmj9eA4VyuhxWYIF6rbt/gCzOYuVWL4Khdjk1W0fRV7btIammWfy/RsGFB9\n7dFIJi1qgQ0S1tbiy+8ZAOYhPoFHUx/aKBoTk/svdrmjKeY/nGFw6Pp1+Ny7J29/8mQrJ0q1pYCg\nAiDFbdrkSyIjK3wPHnRaBCyc1a3bsejy8sJ89TcroYHRetaQGsZiAQVwIW4JOryRsZnun3p0GPdy\nvJDIdI3CVl9eWIWm3Z3ftY9fqjlwEO3TvwoWyw8Acf8nqmsuoim2ZQegHE0Cg9sArgDwwP9cQrkc\nTckPM4A5lNLH/4vvpPL2fo2NbbVy4S0pWAc3bDQMQ8QXd5jEZizi50PMx1Ib2xkkR2aGvs3fyPe/\nBSfFOBxd0x3zZk1GmaIaRh4w8wSkIhhWRje0aVuDr5WLUO1oj9E+x9He+AHfNy0wDU+0E+QMTeTt\nJx6mi+c9Myyty7CUchLyqrR1ynsbc4caiQtrLpKbYZ3DshKrGnIoxTrm9UZy8uQbVHrZomGuCYnf\nKYJbwqg8rrFk5vLie+4g7wuAhmIg1I5DHj0Fi7EjOna6i8TXLWGoOWBwW+BoyTy7TCq2vQ6rgg1o\n31cLr95AiUWDSBuKFkrgVx5obW54abE+0uFDwhD2y4/2ZPCw3ZbYAbtPT3mqDhbZkcjFzYUmxwaD\n2c6Bka7aZ5u2OHFT8zcO1xNZ21ctepbr7XqxFMJqBlnUBZk0DNWoKfwm+64t4I2B66VSQGKNqXV/\n0+RGrp5jZwnCwvia9esL7d8naYw1G4FdPJEvjVxMd/SwNmPnoQboo1RK+pHJQgWWOXryL60ykVti\nYqiJM/l7eaFPr4KyrCxTxwcPUBkCfO4O6c9d0IYBCKCAQ50IK46FY8yaIZ1FbOBsZgc7B7d2zc7/\n81k7l+lo8SoaTzs+Rk/mLMbfGIg7owD4od+r2YJJedNZuZIz11UZzN/nloNop9M19CHp0eM65s7t\nI+K4uukn78oPJ9bxC2uuKubr9bjv4s3PKXuS36rPLE7JPvJIfA2IxRzcXL1LLLGDizYlPQr1EeYY\nCpZpDbyZ7p82lVSO8O27o9ucBHlmWgidz88z6Otrc7Fl0aoHD+qGSySQADgLYCnPM6Jduw5r7t+f\n6Kb4Pd1W06tezE8cb4T7zkqM9LeeHr1a6mv5ZFryQ7r7eXhGAoBjoYvwZ0MpTneYAfnq62v0zb5H\naRrFVbajdZNFjyNQr0gyksM8pHPkiod1Oxt6agT4kpsJS7AjIkUPQWRfgM09gH4DAIVEDA0jtCwk\n23hZNltZ4O1od3n9hrygVKlnyrnCouzP0U7dd8nFv6TdLIpGA1fv8fFd4cbH6Xfs+k5JNrchio2L\n+S2ZRQtaqOcsrEBvtztwMu7t/Oocula6ICAgAuWVRixqp4ThpiWYuSUvm1PKLXKSWwLDq9i6Uo1h\njynux2ePQRz9PHU8tAWrAToQVuH7MP8zB0qjAfSOj0fFsQXX7vVMs+5Z5nHV9CDsh/CV32eoahvg\nVkPr2/n2OXVM8MCWAu1RqNwsfCYdTququlMXV605v5h1tG2zp23/0IVJPkfZaItD5YzD2wt5E+vW\n94+JdUaBcTVdQ68A/2HDnA7gCYAXAN6OOBzb0mDA3++SiJu6UcCaGAGBtwFobwYsAPOGAf/DSs+S\nDCyIWyv0eBHW+CLtmeSl+AG7Yr2J4T2DShZ9yxLQjL4bce3SdSyxdoCk9gmAXugcnwsgBcBCGhNz\n/X/BsFA0xd5b+/pKSfcewvw3ibVbv30DzzBw7N0b8z99wueyMlAAWoUiRDJmTHbXqsrArN4ZMf41\nmQ4ZizouT7QOYcZEF3cVT3g4vd5skpfvN4XVvIZ9awBHKcVMkpBAAJxATY0NhgzxARDybyOG8vFB\nj9zWAU9g7gTX3Fco4yUIHLUY7J1jKNblQPD7MpS5hAD2eohMgOuX5yis34spL/qir9gb/NJtxncV\nyoJt7Cxf3i6KYYwaWBsyUJ0sRYy5ALM7noFB1widKRAuSWKIDi1EvrOQ7hH4kJ/5gfBAPsJRCV9R\nI1x9i+DZ+gNKQhNxXpiLLwYfqg/eQ9ZuNGDM25OwQTIKyUiLteiMdkczd/5wiJ3S7P6BBNd48gEB\n2Xz8MyNX/pyBnak5lN4jUKmegsZGCUwmOSD455uSmRbUdxBlWgUHmjxDv1JGCGHOM2h2n4dBXR6o\n74s1DvHoRf3JT6ZA5IZGr5fE2H8u01IuapwT8Eu7c7PQ/mu+oL7PLDd2SutMadX+UYfd4qe3HYww\nzwBBhLjB+g95o3YUJnfcaon0vGnUf9aIJF/q4E4Z0xmFRXhdDLItRmLZ9T3O/OXbYlFw8G3q4XGf\nv//AnfDREyp/K/5oeyh7CTcHS+kFzKfOCCErUEYue1zWvh/kgt39Rkkfq3ug5Gu+qUXNLe7iLeSY\nSuEAwBCCEKMOOpdwOJ26jESiEwoHLv1tRvrfXQa2NTvV8y0T1mrvffwiyvpTJfg47erdutpEv1z4\nVD5Gz4TBuLXkEjO0oeXYZYr+/Y5Lg1SM+Qk/IL2jMdFn5d5lbFboVhOK233F/WUf4DtvNufQR8DH\n7ya+fnoMHCCo+VbcomB+YrJ9dCFRar2pnCgMpIKCl1SQ3J9E6KqpMUhCzMAwT7zLLMVJDw+8PnwY\nMyjFoPS/+95qcW3BqHftyLQVm3EUQId4xP5Ck4KURVN44EZsLEWXQ9uKPvv5Oxq3hCzRPP81E+gi\nBbZvZHuX/7Vn5nnpT0k7fT9yTyeEaVBMDH0FQlrnKZwep5mP2J5ePdYoOq5iq3NcyCKsZvJQaDYj\nSMNBrLBVlJpt9swUfv/QQndcXyaoUJVz+wt4dE+thLyQotzGylLUi9Oz4dXShkDkOT4h3zxPsf0/\n+i0rM6d0dIJtFaO2K7Ko3Y1boyLXZ7ifpAfbVaYY1bby3UEr5k75GDTB68+sh+j5xFjGVLpONr7u\nfn4JQulnWK9EfMIJpCs/QmTxgLXR95qsZ8tDFZ7331QLxCKvXZbo2q/HEpwUE1vnz8SHKj3PufYv\nb7SOVSNtTUvr8C35PfIuNQTXvfVvF2kxMQx+QStxNo24oZJqtNwiUa4mKepXPvz2BcAdnJ08WF91\nT25ESrpSOaCT0T42uCDn/k9nhIdLsX6j0c+4lp0nC645MGa+ta1XCQ12bRS+roZuXxh2MAQXY2Lo\nDwAgCQl+qKiYDK12AOzsAppU/xVZfWzeKj1EiQ4P8opYLeSNNd7zuTZWhKRmts3QnwoI7Z6agVk8\nbxC2+66tmfqPKq/0O7t1O4OoXlaNfoNE0iO5BqO59emZECivkpex3QDsoyHbs2ET8YXGxMz5L+zy\nAjAXwHjIZA/Q/4AbiSRtuWA78W7zbL0Xn5sikyEJTerjdjEx/wnX48flqW5u5paTl+0s3lqgsst0\nzaiPLAu005jY+r113cuSYEsA7EDT6SEZwBhK8ZIkJIjAmxKQvITF0s//W+3//n9TvNr6cWXqYUpH\nyYd18LacQHpWFUT1eoxePhgvX42H9lsiKl1vw3XOUINW7qCv2rNcRtpUcQev7sDH/s+KTnbt54q0\nHNP8nVbUe/pt0f0Ke+Toium20d/J4iRbZBk1QE0nIOk36prVSrdYWypyZHTsbtVKlMozUFbGwyEA\nYFqI+Ho3MzULKLpkONHh3+TcrTBf88NegWx4cjWvleXo3UqSTPFeAlVAiS/f7WtP2vUHKXUz3lW5\nk3z529kKyxIbM02/buDwFfDyM2PYqCicvbK4uLq4hSvLStC69RMIhY9QW/CMWnKqabLEHa30B/N+\nGNr52mK/JQn7kgZNrY37bm27VSLSdfdNm8Xmxc8B4d7y1dxsguoyAjk1Ri+k/Gjb5tz71Ye51DJb\n5IqdoVBdoULxJrJxQ5HOwQXVWshUHCyyWq2AqNgGnDkDevsmABNIs4HrKhw0tqL4Z35KqtQAoU4E\n768bOxnzUx7jXqs08OgHsygXyBCAFZkI43nXyWKYy/OS7pBgrtnWbBBUaopkBunnUheapY1kGpn3\n6okt1thfHd9QdDesuZOv6Rs3jTuEFlwhadBKUXh8qLmlVyVX5/LDsvnACT6lrH2pQlFjM3jgXvTo\nc0peq5Oa37/v/ZIXqzGk260ulVWuNfZWauXWrafIm6+RBAF3+KjSS9pa+8+iaVM1IjtHq61uNjUr\nY39/5cz2mJO6R1IujfQvZdVp9PLhegzu6dOW+W5oY1xx8p8Nh9Q1XzcCfqGhGDB/ProVFcG4ezfu\nVFfjZRd0ceyBHvMOrbJ7WtDWyRlDhw7v09VgWLQINTExTdqO3glLzibTNqPM88NT6lJ9wgB0AIgH\nCP7GTCJyeB5lPLY91f4mN0J3esa+3shWJFMK3Qdy+pqBaxxyuMNzcjn5DkTDRLRdfmcMTZlq9Guw\ntjDA+Wf2VY75ze/00bgUClpo3X72etXxIGaf2621KBiuQI7mN2shNtVCwpdBYqkApSzS5PPra7rW\nNYpDnjjro3IJlZpRUuKjB0i1jaDKWWpdR0xAZW2WvZ3I6G95FOBtPC0eKehMXvFDEsvrfbYOU+xv\n2VjzMj3mY02tY6Tg5ou77a1ej8pAoMSaLzPlvF1iCtAvzc6zz2ylq7mhUSlVD0eGtOrRX/BSKILh\nyd4sRNYYYTckKLT8mTnG/v1ro6WyxDzpd6uY5Q5VbMh3j3JTrmswl+XFEf75qwoc2GDNMYTvNVxh\nGTegPrFRbeImT0agPewXd2q/PPb6WPFk6byVvI1hqHnNubtvdlaUds5oADPeE8ZR7kgXs3AFUJqZ\nifvz5yNCq0VrADfBMBdw82KlUv92hRnC4VrraEs73bXUDbLzYR8+9mKP3NpgrkttTrabv9fApd76\nJL/GIvRPEi1dwqOceNS+vVXU9+ghfqlCiQj3JXCQOzdr/Oq9WxqOzyZDeaIkJectT82aQ6CW5XQN\nbSCE+ABYA0L6onfvp5g+nUCm6IXPKinklqWQpEmITDq366mxwoWzDDKGgY4Q7OM4HI2NRUXXrrg+\nfBp6nCmD6WOFzOxw9Ri21EjryPgzjmN2LASgmQd0/5vS/9CR9QewB0Ao1hJbEMF1WIUEY27Kv1dn\nKHt7MkzTyv6yztOf9NZGopBmIT+8HQYcjkWj5jD5AAAgAElEQVSakMdZ1QC0T/8dWs+jIM0ZXqAJ\nI8YR0UT0Yz/mJezG4Y6M7u7Wr1UL7Ke6rTnRgZB5W3XswT8lm7Yq+Ff640BRBAOny1Te/GCdxqVc\nxVbw6P+zBTqXjsPjnqfoR7efpNZggaIO0HKAUQigFiBmcaNLg6tc5CCH2sMRY+KTef+KBiboxzTK\nNAwhtkj6GIIVBwGkTcKJKuOY58aBgy/kz3lpbSrT1guCr7ZifupSmN+G82gTqrL8c+18SUpKj71e\nluyJpXANMsmyYbJ4goQf461C99xpyClvqfCEl86N1fV15ySuLuGWs8ccDF45w2Te3mo8vDuJV1pS\nShwgcW6ENaMRyEl015too6/jxXGr6LJ0UzZ/rlczccVj4tme6tNKUY0iSEk9FMJRg84bogf6YeGc\naMZsolSvIw6eDMqrBYCJUJhhgPmOAej+OwX53B9I+GWDXP8uUOspBn1MB2PIBruuBbC+CPAbN5xX\n1L0jdvlqjPq8F2UL95jPtRsmSCVhGFr7FIO5K5DLKlBjZOjPeqYyws5sw+rFXMmDCLNfm0ROlEss\nVR04lifUGJ/ngLwHkaWuCaENqnUPXHycf6lOHlhT6NflqsvQDq+pgDUbyrL8z18/Pfz3ngv3yLRm\nQuOf/j7q9vEDl1nW3HVaxF/XJwzfIDV7mAS/vkIzkoMWdp1s4TeLuGTONomNpcV5GryL84VXH2e0\nul2MJ8eeIIumwxUlcEYjqJXWSmXPOIfknFhO6bkzGjx/LkZTLimuQ/yG+RkInN0dT7tfij2SDKAV\npXhF1hEG8fiJZMhhVHaU9Wn3VdNupBFVbhQ7O4na4GfJdhT7WGDQbcTBnHjkNYd1vR59nf+BX6q/\nV7lX5Kg3oxpEZpHKtdotw7ve4sIGVzoUaXxMTooigc4sV8uLFa+FZbJMd1z5jYEhrzjY54Q05twS\nMVPhLilCI1ePhIzqHXYWr/wO2j8PWD3IdtB1scX6NhcaZlQiyLnQzq7ctm/SLVjX9i2C69lJOGlg\nwE+ccIJx6fRRw9TsXM0+NvbOfKNq62+PSpMn8kgMXmYt/0Y+QP3hD6UoVm2nmG7FsXb1PwMxLR6x\njwEMLdKheNwHHAIwE53jvwTezz3RqcCzl1Ujg4dRhRVt2j/H4DWVEjZlpu6t3lt5EMumy5Fy/dxO\n+531eTaT/zz22dLHMKR6DMYoSyH+tiTC4v1r5k97yfI/iVllJKrhNE0uxRMbEWak18PU1wl66wxw\nl45CNnAgTMOHY9OAFGwDMAbACncJpMPc8HJX3ucDTHDdPdE7ucr5JsP/1upmaY9XzV1LO2VT8R/7\nSLHRmlz7Kwtp3/F9716cdnfHYp7HmYcPMfDYVfhrx4HsCyMlD23nXHuDDgEbjDMjFn0of6qpRidc\nwFfUi9tg3rzP6NYtAAyjBnAOYyM7okT4TbBWcFfGkW61YWf6OpMqul0733XDCqj79EFAjx5gflWA\nPDdCdLsUxihbvFkcANc/fs+YtGtXt3vfv0dv3rDhUiOAiQA6UErN/8lJnEPbPVboPa8tgB2IfhiP\nbr2T/60gTwhhlCqUNUz1tqdJs3B0+ElaLnEkq+bex7qIbY2tJm2Ub7lvg7dOApDyFqDNPwIk0Ey8\nIjnrL2exuLgfVl48gfG/r0VPv/0moY+Aij52tJzSveauPrbjMCksG7vkfqTmCulu0wZWUdl4G1CM\nEqUZUr0MnmpvGhKaVeFibcjav+SDwVwa9vgl7xBwBm6TrqOINnCSLLv56/xDrbTM8bXXLDK2iNnW\ntd/R2Cd9Bnz1/Jq4bOzS10NdESFJRZitEDbt2xO3OfX9iiuLnztsPtTcfLZULy5FNRmA4Kcbxc8+\ngMO8juNE5tyizkX49T4QfeuM4CAARaX1PTjuGgPyTGZV+s/JOgW+wgR7dtj03nOW+fh97frX8gu0\nh/mZdgpOiJ+iW5n9xleW1i3eeWzdOUj3+FNGDTGmSUF5lYDTwEmMTKbBa24f9Ik+iuOzGUSxAitD\nwfkbX512nXezen3xFwOTCXBmzCg1VYNreQ3BcaPhvqkOjwq9mGl4N/w1fF+mwbozD8H4ljBt9MX+\nQUUYvTMbTtyuLVh+rrjRavID+ULrVejHX0fbujdwlhhAH3Xnn2XLGvZHXhxjWWe5n2B3tQtWbbiH\n0G+S6s/SclsfvWPlNqf6ST1K0hsJps9zRmSUK46WVaN8wlw8VtZg/Ds3bBgxC3H7QlChEqE5KCzJ\nn3rsXLr89qOlS6a8F1fy9ztF3+wshh6sFiZZHl4Fboen/c1bU6uVimtI+VPgbPoZt0KKde7ucDdJ\n0bg3G/+8rkI1ABmaTKts0FRD3SwiO0IzJGOd9Zp1MJk+/L4CO6qcSZe+cdI/J0h/5/4ZdbDHrWto\ncjQcCWCTaCWURg6/YQOTAle3YRg+nAOlclhZCd1OnzYcy+0jJOhN5sDjYiaihgC6BoBvTSktAgCy\njlgDaAfgFV1DG8VPnnS/fWDebeGcH5LHr0Zrtq4//wXAAwAPs+Gb5Yvc2wDKAPwJ4CeAGgDNgya3\nsJ/9YHaWse/dBkunBBvvX80lKgtvtrJ85urDaEKjUI76epsWjo6FdoTAQoHKND64Qr5wnd83H6n0\n3cxPTC88Mu3CPIsFArEeYouZsu/RmPUIigAr+3LM8M0Fs3o9+vbWxTz7b/PXO857cKv8VkcnJ8wo\nNQnZ4Gwf3uL1pbbAjrexhlj3QDnjz4gWRyo8L2oe/7SiTHMla+DNNsXCmeo1Dc29xKYFM4UVFVQ0\nfMSS57fbLuxUlyZPD29kmwHbaoxQm34B5e2fP8ehf15DfTIRfWALJ1stqqKUkDm2guSOEUYW5JdT\nQf9HIZo2Uy5fXphqNguCOve9yH35w6qIULH+5AxdC0XXm19ueQpr3JR5HcNaJwiW8RvUKUM230Z9\nfbtly7CqRw+0ARC/di2+vbTCXUaB0DXNoO/UCatLSxF05gy6P47nrBE3gEPv8SwY7ik42Sqv5Nhi\nSWnbzVpGN75a9RU2QrAhVqDftMqM4hZHgvisfWZa/U4NA0pktVAK3eDjJUP2LD8kesvgC6A9mkKC\nBMBdoxFxPXviDIAn/yEkXUeEMCj2QK+cirQRs+jjnYf+e3b+P2bu/58ulHI5mWYXITxQ0N6KY+8d\nMXlmdmLMDiYUht9iZK3mkUE6CyyHltFLxlnAikkA84qwySvQvMSC8k47YJ8xHxtnrIG+ztaSo2rG\nOM5a3eDa+k5d3Ptz0gKjyVbQXKlz8uMkjVYVsCqJtFSrY2hiynZTtvMwVvVlgolhzLXYuVCgda7I\nGzw2r5mb0VR0HG+aB2JpxgaUfItnRSNzty0lihIrBGWaaHvbJHI7MMogFrkKqj10pMqaJZ7l7+mw\nwm0IayVCndqFXHsdxD9RPWOYhDV8K/3VH1lZmUEmzgTandc3NNdxIOBhRAGS4IkIWNBiYXoXlV4y\nSX+gxdSzQlg/8tCv4QO+9MTXBjcUKe7OCGxV4GMnXLrsKm8wKgzhES8/2dpeb5Ec/5ah0mKrkFCf\n7Kqq38sJCW8b7fyQefvxHCupdeGnYeXbM7gc9RoXuU5MB2QwmWi0aPUtqVNZNOptc0il5AEBZwQg\nsiJ12nqJgpDV78dxa4MeG/XKTa4wHu0C04c3yEUumrPWSBpqh6joCTLOyd8ZkxRrjRvYbQL32gpy\n/X2b6lvyu0pJvY3+yJEjjTKD7I7EJIkD8NIG715JB62LK5hpVhkNogarBi3behKST26B2DkQkZWJ\nuPPHevRDkwfNbgqcPXwE5x08sERahXKJKxQg0OnUincSgbavKstChMVMiTqaX2q2xvWYGKqtlcsT\nN44bF7pzxIgheBnbJkiIzsd80cU+HhaH56jJnwSlOhIvweAMgDsxMVQPAGQdYQB4TnsybURG12Eb\nshy//irhnjmIvCZKBYsXF5q+5hUnASSsSfW6TSPA5mwbtDysUK4/7BU5BfHxrrAoamEry5DYK9qy\nag0rrNCgBjWUghI0edkHUkrz/6d5kJDgCGAngOjNjSvvxDS8mWqyhvn6w7jN+/YddEBTuZ5Kgfpn\n3xEc4oYiTwb0DoBW711xNuoPjHerdnt1dv/ZAQAkt6OvFF6JPRZyU23h3S7b1nnVlNSbLcKKiRNX\nuY8atU0tEBhvmsD2ofXK1uy0QwR97pstYy8VccTkwgCHYhF/FxWiK7jv3Ai1kMdr25eKvz4PjP5p\nsOL3+OeXQ8JORW5VIBr8U31+cDd7FEhbqHslj7yCqTE0JjWBJNgAmAPQGUr7BKGd+xndBs3BZ/0X\nzxo8abG1qbrajQOuC8TKCq243QGFNuwYE+xcZRjhjpSV9MIdk5FZh0nbGOhT9ADEADgAVWCQAUdI\nYU/8kUVk7sGzaPmXv6iHxw+Eh78g/v6fv7Rrd9dGJmvYbugZX3vsDxy73xP6qXmnRV29bnMKRa2A\nEAyMRXwLAKNx9GgybGxGICYmAXZ2TwG8R/b+H8i7kcHcBOtjAltawdgE/NmVFHWfCCemTGdTdKz+\nY+EPBxcxTL90EDUTSXlPVvEjyrP8cDtb3PjXf32VipaqeXS3HoXne8VqToaFWGG7jMO5zSPofABI\nSCCD0OT7xaGpiCUOgKPRiPSTJ+HD8xh4NQg5//pMOY5+uIqSNqsBtKQgOtLkKPDvBXlCiEgiQbl4\nYaC85ns7Fp/HUHboGAsr1HFC++a4EJCCZ0/tcLDFUGqx7UZ6rSmmH/psgdSs+tAnvXX4nYirgu3N\npbA22ljq1k/UyRqy5ddD9PRe1ANiNhHa61ss6ZUlp987ccbXYb7sz889TfZRP9llz08wA29W6dIs\nf6GUlbywW7G2f7WVjmhXbsEnnfP76TjqWYtOzuh+V5fY8k7ZJts4d2rhWW+dtWlERopQXeoFZXl3\nqGu0mqPzqMBopRJY7XYip/ZGYMn+1dlZhrqH9l239A1Rar3c3oH++OGqzshosPcmbvougujZHt0M\nx+eFXPmAz6qWTHCp4J92oIe/2JOivCoENYjUI64PTHuDju5fEWqbx+SmTli2qL2/v4ldvz6S5ubm\nkyZ32QFGEX4TWrjmfNfOIdoWAVTiqWnJOoptG25+fSm/+85CdDDA3gF8bT1IO1mnLfM08yoz3DMm\nuVW7tVDoJaYKq7dlZ4xn3N5UV7McADEHODoBDW1Flop0A4t8Anj5U9h0rItVnbCaM5Ulz17a4Ubn\nzXSpYidcqmrI6SODtbw0/2tsWuzeX3a/mCS/pLETX0zsLjVJy6w1Ki4jdBjZlPXWKPfJKj+8ZZSv\njIXKYgJEWhBlGupENZCYFCgqVjlYfQv1qAst+2lrbaiXFT7Hi+E+CF/5BSv6hUvWybQGJ0uSXalM\nXt/T957u23+Mp4QE95bZ2R/ezZghlRiN3ufuw8vRjI+uN5ETdBotAIykBEvMCijzx6GhZCCcqAB3\n0NR8owRAMYCST9v/CV83zfk0x+qoV8pfucqsRGX3fyDfzIMYgLUWexxFOJLtE1lrRsvbRlm51T6v\nvfS1Ef6RAjDiOJyi0ZJwolZpzXNMI5YM76NtFhWFgj//pH/9D+M/IcEOTc1MVgE4tRDb9/bFg492\nLxEnumF38u0UZ5FNrr48+JiuRKHROsugcSGgAgrCLsNfDxvCrp+J7/zmAq9qdbmg885ax3Iyxsyh\nsvrtnVNI3T2XaS8ytNr1wj4o+D07YeuC9CNHNntER99ROrgVZB1X/S5PpaF3Qw6fe7T85uxVrH+2\nPbYsdYaikVy9Ojf+76M7wtGsMRuZivuwMFVw1epVW5PX/7WOOvpnQfcTcuP7UY021wdVG7Tpo4w8\nMUXTNfTbf39/CSRBxZLGhYRalpPwZLKc7kt89bkBmILxcGGnAXSqwmiXvyaiStHaln8HYFJMDDWT\nhw+3oLh6PmYvMkJXlr17NyrDwvAoJobuJAQ9AJwH9r/juIXdly4Vqrt2bZSgaSF0goXh0O15Cwuh\ncdc75VSWjcwNfB7QVjRNt6uiUWy/4Wr9WFsAfauVynCx0WiOvHev5n1hoY15zJj7ZkdHXwB+MNX9\nQuVrR2SKHyO6c7hYYG7chBU+4fh8AcDorEZ8K9HB2h8OqqULXub8+hUYQykoAPxLuZoEQLYL89i3\naK+/guGuDOj8mBh6PiGBSADsR5OdwR0AvwC4ATiMplPdQrUaNV91cNuVD9rFAe9n++EmQ5C/bdvx\nmUplmWVm2MrAjsvg+m8HeQAQCsmy8I5k9fvO1mLAQGHdDfAeayZSa0EMnuN3/jwOf5sGn/O1uJO6\nBttFCfzxEZsJYQzayOxw6dUeJ+HzK/BKjqImslFc79mypCsz+kUPOIvF1KmQUC5ia/n61QOzCn/4\ntK3llaRMHUyG0at5cY67/D3XBiG/ahX5pKq72yruUDjq7V3ZY+NhTT4gY9YJFJspFSR1h0Nha7xT\nPiU365/Rac7i5BC+ecvAnFomrfYvYhEouL9Wi1HrWoN9bzYZxR2+ky9HDebASiKQdSOGhiGoHXeX\nb2ASkOeUZhWbThvEXsQTXR1GQOFghKrnK+ocnEe2T7n07G/n4Svm9a1JdGnA/Yfn6WBCescCCTeB\nP78HB//tUlRk8I7saFf28tVjlcHjKTPBkFP76udKB2vhfVO9dGOGmtU2q6+vEzk6Uuh1QO8+oIIO\nqD65AIdhxjjMBQsJdKBwDyj307s+d5F8yv4iWi5dggJTAVJdXvCpuvwGrd6kULri8LE1eCyXSzrp\n9fzQhjqzp+HvtWTFfCe6gllNgioVMK/cTfUWy2vrRkU9mkwJFQDkFmJRAbAzswb6UmqwXOQ8q/On\nd7jO1TRzW96yoUd7n19SicFi4HRUQClg5jiikYoIxxnNFobliu62sYz9+/1pyhv7VErhdI8fZKxp\nDJqxkm488T+MpYSETgCOADhBY2N9K6Nhlb4G3X12QB7bDOk5toigayhFk/1GTwCLKIPAqg74UBGL\nuup2YHkxnAG4AHD5WNRfYXO1W5bfHX8XMYqliYFv1TtbPVaX3C5phA5h8JU3YvR8izBp9AvuiVuf\nOPxNVmLj9TK49J6Jx9wfKLJa01NJZYqxqPz4rb6uhpfHdsE/t/588hcEgv4ABgAIBRAPYBViY7IX\nLZp06ePHHu7x8SN5FuYAO6ZSb5ZDYaICoUZjZbLwnACATgRD3XHmd9uNS7RcSYuReolUKtKJhDsa\nZLL9MJvDnGvSbsSIksozt46ykrBG5e6hIywWO/6r3hU+Gy/vL4jruSF03c8BP7/qjtsCqBKYOeWp\n9zNtnF/2rtrqoKr6ztuELl8+zhgUlLQLwOaYGNqYIHja0Qz2/KuOsFfb8TVPu7KmApXAZJjRRoLQ\njUXosI0DZ4yka+j/1MREQzxGPSCjT6k5L7JkxtI7daq6bgDOD3PDtThfHECT6nPOf0tyk4QEggZ6\nDnm6QVjUBUH+n9K2b+/ZYujQkgM6nXIigKGU4g0hpKNCgedz5qCha1fYY8cCEZ8Z+NSslba2bFkm\nqtJLi92+5b7beFvV8kVZbTOxlxf6tY0y9yqtKgpO/ZU5auuKEEkZLf1txUzRYVQ36wpEX4yP/wGg\nNX5d+RtWwQaiDFz1FN3Bgj8JQAggOiaGponFWuehQ/dmTZy4Wstx5pExMfQFACQkkC4ADgFoaQb7\nai72RAip4eTnLgvlDg5gLl1COCFwBrClBx6fFsA0V4XaccH4bohGIteaJttcKTCw90rA/umMiq7e\nUPM8PCiFtKQEyMwkyMmmuHQZ/2cUr/9vv/4r5AkhVgIBikdssmk8/zn4W6fuPldGeb9rRsD3W92w\nI2CReDM6JKhhV1FAsvzt9GoXTmTvVEjOFgJuh+Yj0T+Jfxv4AfD+Qwvnvnuurd/6Ivh1m9sV6CR2\nlf9hDpld+VMrxFSsvfAD8MkHFFqghSMrNNRGrzxC1j68JWXeLZBqoQXhCuEpOGG55RZaYFs+xM1D\nayUUebyvQ+Svp/T2YO96Vh123HjaOJJJupjWq/v4uU+ec2tHLzW6ZEQcSYpi2yW3Ia22121g665Z\n00dvq81J0nJLQ1222MaWh9ov0tAloIRXqUSWu8cLZWYDT6gRtFM04FcxGO2z+1Nro8rwTHbx3j+D\nLg5RvkR6XhH8wcAokkA0dCgEda2h7+eNBk8xIz5yeGf2y3uTPXxdkhI/VYR1w3w3SizC7+LDKe26\n/N7hc5R9ZVhoOHgFC7bSCGzcC5TkwTLEbyg/XObAX7U6prl8zSw91wmZ7kWe3hedWgtb1nsIHVqU\nsYhI1jOehcL6XDtj2Sc3Af0SalQU9KvasUPrutJqBfPrjQmXb1Cs2GMyblbMy7VD1aURuJzphmIl\nmoDpzNzs1bboqX/g+x8zNL1RzuZDyqZB19gOx+9Gi5/2XN9rqsupKbFqVmTKOrV167vRz54NJIBv\nVQTM6SsZYd7XlqaL9+ZfeVIn7UlHDGfAWHrSNfQjAJCEhFYANnMmGrDwUlFSj1NuN4Vj9wXzQ+6u\nctvnUiiPd/kndE7BgCpl1UbTetPl/zIAWwOYgqYdlD+AL2jahSVpPJApK8QKCsb3K7bsUCOio5Ez\nji63+yVtaPeOzXZubrl0Y2RNQ52y2s0j84+MzMhcCjL7reRcW5OKtDBYNdhOto9UF6cG22FQsc7B\n5uJH7sblzvWSZubGNnGv0eifBZk5FByNwBeVhcmQcc18U1Co9T6lLbQ/A558oBRGAEhIIN0ocLza\nYpu6qGrf3fxktyE417qPU0AOldtVvV784VhUUEiSujqu9h4htb14Hk67T5+tTPkR48Csv3V7z+aF\nfcZ8MQp6LR1Q+rzzeLeBH09afvPKYJbuWze96P3Yo6uukAd7nnaMbnfumHCOoRy8Tc1SxfVBgwC0\nhUEoxInJPJ535TBnrz63U1HjXOyxbYXPOYNwK2p+7GcDiHkVJneYj4qg67hzciylMAMAWUcIgAD8\nwlTWTGYvSp3DtM1rrpcptg0XnM+pN5u5axcuLKs6dWqtHGAK0KS1KQBQAKn5F668mwkhz2Bo+2M9\n2/9zLDFxkEqjUWnRtCiUiMWNgs2bnUevX68TRQR2KJ2RPdda6PpL8sK63DAx+QK9r8mv/Auw8wPz\nQ4V9ymTU+tRhNwFG1QEbc2EtqcPhTxF44qAZeHKKLIW+lMZB0X0prYsn64gPA3ya5oOi39wgYAgW\nAFjA1eF19CDI76Fv/xXY9GRvfNgNAP+gqQLmAZoAfxJABIDOmWiWPEe/bbxy89Ja28oMZWBzWBxm\njthylJneHEA3AOfQtKNvC97cB9QSRTRZmk5MRnZI1sNWWR9r8SNHhJJqEVWKxFQidNdUyDsS0+vV\nin9LyAMAx5EdvXtjyoIFqEJTPO4GgGv9cadCAt37CfS0JCKBJUHJaQyyzfxC27Vljyabpa5qnc3+\nTUEYvmqZyaz5aAIwmq7FZx7cSAukaznU64oVKF/VBc43muNK/cOYXfgy6h3g/EaA7h2nYD06oLmV\nUADe3mwHKfu1XmuOhFZC0p53Jt8PU+9W+kTHNhhdAM+YT40Tn2RKQp84cupSEa6QC1XyxW1enN6z\nu/fuYcNOrBo9uqMgIyeAenvK22+4jIhf1hYPgx/x1nozElZAiXUNKeUa+Jfen4wvAp7pdK/zdDIz\nHD28/Jn4dxXloHWOcou8mjfydmJOgDpSh8guwNQxgJsLqJ5HoZRDCQCqtcBVzMC9sNyVrF78jKh1\nIoMu+Ew6YteJOaMyoGV5X/Jb74s3HpYhK6ka0yYKUN5ZCr9DVziuhtqg0ScEpfHf4LU9DoyzPYwQ\n8kYqtFAjBCq1ES6FUuqSaUttS1m9SxlDbWoZ4Zm1OYIFDsuQctGJ/6avMzy8XS1qMVKLxeNElnrG\nGpWMK1dr5gwNmro6m1e25g5HFroKOEDjl2QRlH0wmhQBQlNdZ4u43pa71w+m8/1EyQ27gv2Ran0Y\nwPrXFyS2wvcBpwXispNCU1VxSaT47TNVJ+zLX35av+FCCIS1YWgXehiToxxAaMf5t3PS+l2RdSY2\n1QShXyow6pKdaP1UHfexhVwDn1wTY3Z81+wdbrS9YZN6OtXyfzMY5WiamFH/utqgqU55DmhT3D7h\nH1f7hiLf7KPnt8gS0luTseIM02+GCiHrXEbhVmSEhdWiRqWE2rYO9pUzYw8HXkGOLBl7/UXIkbuJ\nGF2jTP7TWS1VAK4KikZRKjKVr1j/2ozTG1svfyTv/usfjPUAIALwCk2uif4A/CXQOs3AQV0Hmig9\nVdsKARr1s8QdW6LKa9xtli0bXxtSkylJ+d6ROfjlgzY9/dwZSvv1jeh/a7R9h8wrH5v5u065c828\n4Po9cZmVeFf10cozlVqnZx8/drcrKAi6x/NM58tX5uvRbckrH9+bsXtO7zHJ9bLbJPrNc3xutRPB\n31WYvysJDpXTYmJo3h8JY5RjcGErgO4AhsfE0BTSa94AhJ26hlOJBjh/rEXAXQaeL1VgLBZkagWo\nDnm4vb5kQpcvy+4bVFyU4eBScm7vCv3JD4tXAriLJkGl5/9wcXxL7EupxXkvVpQi0Z87G2B76NCO\nyPj4kZEAbBcunNI2PPx5xNWZi5KTatdN6NvSjXZpcLp3LvvB/UfAbNoUy1wA0DhCeAmlzDiAOKDJ\nJbIzgDl4/DgVAuE5NHKOQVO32JnKHqnaCpceGX17V3WWQRe3IR21lUbUWOmxLPW2qkvR5tpF5vUh\naTkSL9+uvnepOgq59YEQgYEfmqzY76NJMCoC8Ob9e4zZ8KaLm2L2BAwxXdYeKh/CUZWNBirVWgBn\naExMPQCQdSQUwA0IrJ8i4uwzfMtcCmfnVrBSGlnCNHqo1VYWgUBd6OCQjZ8Kf0xv4/hvC3lCiCvD\nIH35coxlWdxfu/Y/j38kISEAwDaBkbruWGxpxXHv0D/lCJPh6pke1y/0xsQ7kcuM6qc1P6Mu2j5t\nA3L1EniRBmloxLppRiSvB/rZMxhLGeYCueQAACAASURBVLS+4wP+o5FN8M6P7dkJ00kKgugd2L7L\nV6jcQsz17sHmev75b0ZDxaSyanA0CUCSzWVJmflIwN8DW50+/3S2dUHg05ULfmM8HYQXJhHGytpU\n6amDT2oFx1I3KKDE/W619NQca2b2s6/VFZ8zBZ7lno/8IGOt3MoH3Zfm0ov+LzgTZ6S2UtPW6Fz9\nogf3wbaLo42tHPG9vgKRHgqOblwpYqaSybRxTCpj4/mqYM925DdwqMIclAIYASDHSYxrJ0KErUVC\nvtfRY5uUN2/Oohcu+J790Wjus/adhxPn8rnGCL50kheON2MlI7F5WWSzkGz9qp8XxLp6M7ZsEvIS\ngy8M1c4mU5k7q/kSxOWY6jR1YXa3iyMMdQYbja2qmmkjgNmD2KjpCOFZy5u9K/SxLwOsHtQnGs5A\n+7cZZ+YuCZToOaHIImItorzmzbk0326Yeiia7BpnRG1AFt1yihBhuQ12xIk/vmhj/aJDIioW7kCU\nqg7RZRDtmoDwwSvbnXHqUEncSbmjkVIiqQ7Mq8/Pr6eyhfeEvLJSumZ9LapreMDqOprpAs0rBanU\ngdXygthnW5hpR0vA0IPCKtxtNwwdSJNZ27ibshvv9QJ91Wfvz6e3pWyL+98Zq0cTmnES6DIPnNrh\n8uHSbz9gZAdTioIEkiAGa/bnPQo7PHP5/KdtjeOD1unRPQG0jqExlCQktEATsIcBmMDBNKh3/WX2\n6dRr3/XldYI5/nOyegRQobRdsjfWr+4Jg7gyNh6eaIKQFZqqaLIAFEie9LEKU9FvcwL4Kkcr4zOL\nhUm6NWLrqH+qJ/X3R9aO0Wg/cweGCBpwTHAPfXOi8dYdgPFNUBDzsG1bLDt/vlxmNIoAZJR3xYyv\nS4Svd+w4okpJ6Z5aVeU6ID6eVBzPQ39NrWzPyHMr7B3SwlliEkzG2fHX4Fa8AMACALsA7IiJocaE\nBDISwAEAJwB8WZOGKW+qEC02WX3XFIUnIXl6AdIPDAIYKyCBY1nTngP72/dxv967N3kdpu9inKQW\noy4LwEJQmvLfP+8EkkDWoUVQArF/Av8GByzOePnUt72ag+V9TAzdkZBAGFCkY/6up0htNbx2wiJm\nXvxHu5JS5FAKpn9/XJ4xA7pfvwL7MAzfys3tJwhBPIANMTH0DSEkFsDfAHLAsqtx4UJLODhscN+0\nRCTNTrabNyHIvP3c+UM5OUHG6LCpQwo6nfN2qrKiAWyPsvHjrtkbDNIf1qa6d873ECbLhV+jD879\nGoswEHREkwOmKTcX96ZNw2Cexyjn5+dm1MHKvjG38iCmTFkNSk9SSrcDAFlHRgPYC2Au1uIumnb3\nNgCG0qZ4/V0Af4HSA02MhD1AKv5tIf+v97ehqUxMjKYH9t9sQ9Vo6vJy4JHz1Vpi4vKuRmj4kPi3\n8jGC8zDrfSyZWM6FWsZu69fV6PB/sffW0VEl2x7/57QkHTcCgSQkAYK7Ox3cGZjB3d1lcBJcBnd3\nh8EtQE4Dg3twCZAQiLt1J91dvz9OmMfMnTv3vrfW77fW777Xa53VWelzqurUrtq15bv3vlmLHqRg\nxoqOnSRiIgcw2oKlnlS+cGmxzv2u5MsTdUEsLtGQ5gtNp6dRY1MOKSXtOblFRXYBMy3HyZQ8Hw0Y\nefVDRd43s+PtSAsZpIaG8jYyUtXowPAmxT0srg46ddKTcdKL6rM6xydcKO8+QlN7/x4Xa6JDpspd\nYyvlvq7Hb34Po97lZH296jarlEeq59jZrkt7TJVcPNNomJGbtXUVDk268bmQinHbtrFp2DCMP3jX\nSMxcMKVYuPdzl+h+q40579Js8jLK3Gj229jQgK8lA1Akz2KUe24R0xea32UUdtuxM9harVpYo/MX\n+07yqbOj2cwBa2Pjv7oZzWtGlfIevD89vUSaeXvqgHc/d3I+hI9fC7y/BBH4LsfsE+kSXyLc6u6d\nqbHk2ZCb7pKbl+Zuk5enzcoW6uT3MaULX7jQX9Wr18Kv1X2ennjVc2adIhSpfICjV25xLbArE/dG\n4xMVjbeqL9atUZJNzqIFulGiduI7h4eDi8x7H7it0uGhLjGFIuW97y/1vWS5VOUyl5M0WssBYVX7\n5hWKt+6VisYe/1K7aSGb1ENtdZ/KNcg0arK1n4ntNt9SoWdE9oVngZbqH+1cvb00pPp8tdq6pWNr\nmyOlp3vEXb3aLWTO0zULNJq8iAI3WSYJpgPVuzft1r/7ze6bzCpz2U6Znd7+t9apwaCaz4yHya+L\nBq4YccCIkCoLQfQf7gmRBgE/b9i6wS/LNmvGpIhJy757fhYwAVgJrJEJ6my18vPChY0yht4fXtml\n1iNSn5ZLKBDvo1GhkoA3+VcmUBjwEgivPHWeH2C2sdhkAw9RJNJQFbM3Dmd9nzfEaTXUzl3Gz8/6\nsrfmQbo/cSZ9SHcOvUeRjjegaCs3gAqferHqQ39pHHBMpRKVUXwEiVbB01dyqfKe/l+Ln47Iit9/\nz7qTRDbLKwFYA5RBgW+WQEGHqPP35xGzFa1GRVPA88IFXqxdS/nRo6l34MDrcikphY6NHj0m5+zp\noZ3XvsxrA9bqtei9V0vcnN+oP7kDl1925nOT8qQ1DiCrmj0W12RsUuM0Nje2jbC2jA7MiD5arpmj\nWrIWI96zLavGred2neiCbcZdSesTPi4qnuDXSQTXrUukVsuLmBj/2N2753QLDHw84Mcf11xGEYqm\nAR+BeT17cvPrVyahOEH9dHZYfHwl3Zd0Z0mTlkszU/HIH6xJNkFkWp7YlZvTedrdAhqVWFDXA+Pg\nAIwFbHEAhMpEsn0kztpkyPHhjtGHRYsWcSc0lDDgvIAlwECUmrxhvhAXrfhilhFMUj4d2xFMDnBK\nBdffwfpiija5GBjGX2Tw/f81k//udxuUE83ju+/qwDDgcht1y4OTdCMPra2Qk/n2bUBiwuYol/lT\n3R1/q8/5Xbv03aUQqR2KlHELRX1qJwfLmlS0i5ZTclg4rjEZaGYKatvBvWDsz8+ksLSGHu1BnbcH\nIVXhSd/qXF6ipfjlKFpMOI/IkYivNgzfa2a04qMkcWeAPzV8bbgTHM5HdIwb+oCTG8/SbmVt5k3s\n4DUDtQ2dqra4X1f9sO3ezzbmp9HvLVRe+VljV8ivbtzb7DHT3VwOlD6ed6bqmfQyWXl74g4xPj0N\ny8DBGOMq8ORoNGWr63QxAw39Ar2vtdQ6WNRYXFPRlnuJvdMr4Zb9UhT8FGFNKuP2Psq2s1+s0VVt\n02+HzatHjcT7r6UWnNs/acKUsgdOVah/qau2+RXSwzp97Nukd8DWgZrcggncA/ZtJyD0DPfn5lK1\nlweeUmteo9LYS8IrwuoQ/cn4hLLqazSyVSFQq/NE9TqXePCg2WujUTu6Hk2XT2ayTwwx9h54vLfB\ny84KJZwxW1VICZLisOqlF/rzUogkzXIpvLb+mf4jUm+U56b5Tl4zqYnWWuGpWWunPvfwTruaoXjZ\n3cLDzaJLy6Dp5EzXpN25NV5W8pkY0FbYOqoQte6qn6YlWm+kPY3Iamw6fy+ZnqlXZiwQ8nxfF1J/\nsKAuVqL8w8wiRd89mHJ+r1st7q21Czbt7Cf3e9vuQTuVe5Z7ab3Q5wFIBoNG6PXmv1h+5P9u04u9\n15qbLlfq1zbis9WsnScE+/5wT4jkD9yfcXzG+Lpv6i5rN7WdsKqsQWKOeAMgzbPzxNbDW0yJfpJ/\nv1TfVXVz6KJttXUdDkdcc7rksc6eI6jpXCS5yO7gI8GhgbGB3oAdEAPEjhw4ckCkZ6Rfli6rhRws\nq1Gw+pMAYvi6xcTgxb8xP6MSH16OZp3XQ6r2qc7DHij5zqcCuwQSKPnKF5hR3c7EqcylRlWcb5as\n+KJFh10THBzSH+n1InWHtKNZMMEXqhXxih2w7nHhBZvNWXev4YggFw3virjyvktn9nbowHUU5m4L\nrEDQXGWiW8OW4sHYsVKlLVsw/PwzTxs0UFcxGu2dtmxZ8u706WG5INWQMZiAtV/R/bgdf8+GxKiq\nkSFSsMn8gEg08o6SGOybkrInglHmPEn9Q449ZSIqp1DY6fE1r+eBNZHEg7LZi0d9XPHqscMnZpSf\nLZYYDJJarxcWScIBuAcsF4Id32hlMEhalACq6SjmnHlAqtXKjpgYIpctY/1TY6mSUkZGiGQ0Orql\npcUkWSxukkRUzZo4ezm7JL/P8dFF1Hnva6/VbE62yZot5ojUfIf+DygM+5kXJMdBwRxYrVPs9bEo\nVakaAz5P4UE9N2rmDEe94ioh4i4VgqHLTEiapPC6L/n3L0GIW3/BG/8zmPzfPOcMDAXGu+MWOdNm\napVtPs3y3piLpzdp+DK70z2j/8j11DU31d+XQqTmKImm8mq8qxE+fP/aUtOo5JmO5lwOmgFCYMxv\n82d86E8/m8LsP2flY9MfhOCaFCJp+dioNdfmLCQxsBT6cfC8SyZ53rfwuXsO16jSGofEik5OqfVc\n/R98ahcYN2HLBz60D2fr+nPUnNam0KptIw78MFVaEvvx41X/y7EWp3WVSSlsR//WnHtSw/jwU3h2\nPafGYarEGpfuquZ2DtHYanOels+kzhN3ruRYOHmwFt6etgwDRk7YWbncJ8/IESmGlEde0SQc3s1p\nq4YmqAjSfaVQ6WVcefb03JAxTuVvDu4dXDKw2QmVKrSZUOkNUsarknlO+/ou6BkS2Nii5sm0bq4T\njpPRLAe72a8oVdPMOfUYjt6dYXdr2dPik/0/NHh645Ha9cDevTPc0tIKugKHvQu+n791TbULV6aP\nd930YbJ9DrpcwafnNlQI7E3/PU95Maw0S1T9SRMq1E1AHYFSyHI6UEUv9AKgfEVpkt/XWosH16kj\nrY75YDY8uW6D6AgFu5or6bJyBn51t79fZ0fK3td7bxKHf5CGDgcb8uzVcGd7LLr7hpz4q3MnM1WY\nGcs4woGDHtnsTlhKt7vUmtap8H5dxYo3Vjy51VRjzNA456DbbW47OXTZizLbKkZWXNPU0nSaZDC0\nREmy9wDF93PiW5ZBAMlgcKjBvcuzmVttYM/nB+O/+jsBP32Dy4GCr1fDlQEBxPRYtqYlvp8vN661\n7KJQSp42BIqjqN82KFlZPwHzu9/9qe+gSH0B1bpRWe3acyMzkxcEswWYCXREgdmtFXNEshQi9UGB\nWdYUc0TKt74NkkHKIKPNZz7vK0pRqTTnP3lx6a0NSf1sRGqWsq6pgoI6ykXBY8v76Dl7NWNnupDG\nPnq+E6j8x7My/BDdgy5w0W0xi99HEPHbBjb85FTl3SsWT3W8n57XfEooDUinN18pwS0s7hbGJynw\n0zpAbYsNepUZF7OWnA4mzIUgbSO2D/arugf5+b+8GfThwckSvO9tRRUdif8YDeYWw/mwtgOfw725\nHBfAjlY64m1RpNxjKNrMAhSk1piFdeTE8HKWG7Xv5Xpk2X41bzDWbnOwUdOdkfUcTdvjRo7q8NvN\nTq4ZGZ6/Vaw4dMeypYtRgoz6fU+v3+fOIGmALih0KoRiOTjwLceMJElaata8TXh4RclkPN+3ZcGy\nzs/quX+MNjs8415WipTpGFArkBdNX5jy1HnzkVgn5ogcJEk3E3buhy734V0B5QCcAJzkG4OVpEIZ\nNnSqPoDlMU+w+/EOlpOQOxWW/6ysw9cIkfM3DNBJgvT/aCb/3fM6oJ899nMDVEXdildubL30eql2\np/WxdfnPlviHehEo9Pqsxj819jJpTeceF31R1XRngtl6f+R4cjzW/2GzzpICyOM5V3jNw6w5YL8B\n+ADGX3Cyd6GUmE9FVy+8cm0x60ASEmoTSFaBygIqs6TKKGKt55tsruuVo/HQ8qDTIUKLHWPIpwGE\nzKnP4gOfibcIGsmNqAxsMpl0H6OjS9ZZf2v73PigzJBUF2KWjEtKW9tkhvPbIm9NOo142LEIFcs4\nY/70tWj/7WMjISCzLs2bROB1bxO3ccRAT2ES5/InxBG4CeySEKESQj4euN1k3+asz/scD6tD7ds5\nYUmtUo9UbW5Xe0P8D0nHKu/PxNG7ue2pV28rLfFs1TH2ac3aaK1WVc3U1AJmSRJOTk4pUmamK3Z2\nmXG2tsZwQAWIGv1onRzpM24LQ+YsY7KNic9Af5UPWWItMyQX3H8IEo3PAthI1uLHuRV6iiKXt1Ms\nDT6UhBpt4HSaSlXb1mpVX4KXV6FONaT0H/Bic/3q9V9JZ6TVUURRjWqVR8u/FityirN+u3h9+zi/\noKLvy7c0nTQRzfCh9HmfzK2OjzB4ZuPQtif15EZ8IbxC20cXCm1bEhruIAkfkSYuGx387mTu+aKx\n+xRo03vkBjaghJFLKIz1BxTJ+VfHDK46Osas2iwNLXt016R5+3bPHgtUFIKEb2vGYJBsz8aws6Yb\nHT3v13svLZ5WkF87RWOb+7nXPe5/yWEC+X4BoByKVH0xEIcum1fuC5CmLbpArXvu798TNngww4AW\nQojHUohUHIUhdwQiUJyvfYET30MU86uxbQdcLnFpoQ0244EgFKb4AngOvMhF9XIEVQMjcByLoh04\nTWbJo0VMF2qsSXmoW6Xi5nCPWrmXcDMe57x1B8k1WsAHA3JN2p65zNjVSWgs1fRB+H10ZdID+GlW\nKjYlIWkLHPaCa8Cdu3sw77vAqTNhVDpdVZq57cKqLsX4kDWO1TeAInEU9KvKowY/szhvHrPtz6tb\nxlYVj5xTbXn8oAhPysXTJE2HtVF/QpPsMXlkMz9xKW2sSMt+YdKXaU4hATYb7roai2BLjgqbXDMu\n2Tm5hTMyNOpcB6NZZWPzvphWUyBWJUq+kXKcwx2k7Kce6sgYd+0oIqzlSP+OyQnQ5GlwyDJT9LMV\nW9NdIorvI8X9NvA6SEZb6WZmRK0NR52uxd6y/2z9mt4Y8/1V5FZ4DZZ+aJ2K2lbA1MGU/ar0K2FV\nWWeXXcKTyBwMBrBUV5BCTmcpu34uDvVccU1ZQZHD5djTcVJz0fe6H3GVtjEuTPDzVQgMgHEIsfdf\nMD0VcFxSUjz/5zP579rRdKDDplvc6uqicte2FktsvYU/r91Vlh668Eeqr4Vdl1M693yB5EIMaOiE\nfUoqSoTZCTFHCClE8gRuYmYN82kHvMdhxGS8xT5KJran3CU1aa6RuCVOxTb7hJgjTJIkruNzO5QK\nh8qQXqQFX6vZo86zpciDPG2tjZ+EY2xAgAPZLbNw0N5He6gCGa180Pcqy2O9Xog1axp4xMX53bt1\nq/1tWe7S66SLYdfCGXS+Xx267RNv64Y9L367Srj93SJxedEuUZpc2wgNGjtwCgTzGCvv3abg0KIH\nSZQjkUritGIaQJL8gNv7KjBzbNyDOc1SbX3PdhtxPmuvXF3f5PCq56PtZhS9k6V7v76lpkfr5bk/\n9vvlnQVzqehoKTkjo8GtU6dGlnz4sKlLRob7FqCyWp1beOHCdiE1a4buQbHpfgBC9XoRm9+feyyF\n5q9ibN91DNOt4Uj2fZ7ab+JhCoR8BL0X2KrbEBPbkyjfnlRfKSjTEXSv4dkM4Os3yF0+LSugSLEV\nVagenuBElLPGTs+mYc5qrw+9qrdldRoc8QJTgj8VO1ajU8JjNFvnQsGnJDyoRYzNhwrlnM61lrbf\nfCWFEWYd19st58Y9k33yCy9qsFq8UfmpRqi+iBGjnQ8lrKjY8/e+DQZ19fu0ah7KjNp3RE06/Zpl\n6nBsZecfY3oCE4XgFIDBIDkCQ8xWfn6cipspV9OvfvfLk4GFyEGnUnNZZ4E+C1+R+CiVdJT6sCnA\n88ou+K64PUwtPan8gs3DaqHYyu+PGcOqZ8/4CagphFBMSSFSAIrJ4TZK6gVn4BIK5O4EwYzlv3Kd\nZH57D4NkcEM5VMoD5QWUsyBV3Ytf7j783luRXG0xZkfiZ1eAxMNqrHOzsC+3jSF3Q9hqfxbVm7pk\neKCUznv+hvEOCaOeVbAvdUVUHk+SysyO927c0ndnUsJ16ti/xLrPyuNGPpLfB6vWXPtrrtfoAcxr\n0NFl+oMHzUyurgkVxo0zxH8bX4ECX9olJRU+2eKH+ZFD28zxT7FHCjNhlON5lZLB63P7qRCYhFtQ\nP+6996Aoe0JnqD40DvHmS9F99HKorPq0KUnUb5ioLl77hKWdOCMCpY84WoqTdV2N5bGX+vHYV4Fe\n5k8VnCWpRkqaKJuuAyQMnuke1eIaHusuojjZoS722UfItRlH23NhNLjekQKJQ0h2L0d4xUxS3OyB\nmJwCmV57e6Mua3lUtNqa4CXHoP0uQILkACgSD2SisvF09ySlQ7LJ6pBn3/285tWoD6sXn+Bz++vI\nrZ/xwq4pZbIjiNdkgU2TIvrcwz1PWlfvXL3IL9EvFIjTE+QAnMyn7RS+y13zJ2Y3y4RNax25tf9X\nMfnv2lMD3WxtXZYczNvr/bOHxZJmrm3xdovmTWzg8+xslzYEKwXHUWyJMSjohbYoNrBH5GHDW9rg\niTN25JLOQX47cpVXnfujOJx2oRRCaQZ4ATdA3KV4aAwZhY3EVxwCIlCyTbVxq3LE6lj5iI1G9864\n77hZXTBLZbOhcnfLQ0vNtEePGtvn5dkKF5ekZLNZ4+BgVNutzXpnd9AvmrcTM3kf4INRsrMW+qzO\nDPxota38zGLj+SXG9KKAHL2/4dEAUXyOmjh9OPcamgnIKo0drZSx0NArg7kWSdVg+obTqVOyW2jz\n0PgCpWkee4WuUSrfQQWlFNxUQS0PLcnJW+ZuMAzpbDZPEqB6gaIm56Lgx6OBPkKQYzBI1VHSDbTX\n68XtP899uFSx/H62XvBHU3g0aSYL3bQw6x7sKQoPzSqkk6c53f4c505sZGNroKoQwvRP6CihmDae\nyDL2nPjhpdg01Gl/7rGTe9neUgPO9eHZcDhxT0/y4ihWls7yvLqhSn8rN+tVemx6YzNHNc8tp0hO\nmrmiuaU8lmizmdfTpmJNf+idsFS1qmSK1VWKw92yGP8HJnQhp7j/VouYAHRHbT7B9IUlWD6x1rjs\nuiee4mYSgn4ABoNUDbhgFRjGPqHU83S2y8FyGoq6P7nruK4F413j13f35eLgAFqGxvF+6Rs0VrjY\nsQjD+9oV8nAYuCtPk6sL1At9dH6b/YRgQtOmfLVauSGEWCCFSLb56/S8mCPmAUghUjGgFTCQdPLY\njD9Z1BRCRCrzhjtKPpRif7oCfMhOWMWTNA9yjUZUA1vRsERRIkPuUqvsdBbeSKOCyzXaVvFhWNJB\njrjHUDjlOg2ezmSBSo219mPVCpG7aLnaLT5aVXI5SRK4WCFyQXOMD2pQrmkhSXXm9Hbx5GmQuoXt\ngKgd0Tc8jtt3UBVcfUlWFc+sgsRsoFC2WWq/fPGe6q8jyksa+5S8qQubvijjYp2HorlUANKwsqJh\nCzRfzUVG9rBfLd2zNHBXuUaE5MTVnf+OEfXiaH7JUfvcNrvt02zTk1ZBzT72SzejWoWS1lcrYb24\nn56aHzjpaYexpSTL6WRoSiJ7/karWFsv7den01hUriLPuuqDuAsUQoh3+bTwAyaQo+vNh2JfKPUm\nuZ35ZOrVcZOK13zzJgXoIEE24F8R3JbBqltguxB1wRJ4e5YuVlZcKnZNI91WmTVZmlRvvLf78nXZ\nRVK7WcDiXgB1ei5rPaweb+dnzn9YmtKtUGzwiWoyd1ehd9UwUp17w910xX7/BkW4uiKgDbDRj0/r\no/Bf+L+SyX/Xrnpc4Ibn2s8xgcs8Tgs/p66/bV0zp7xWmzdQrxenpRCpNYqauxgYDKQCu1EkF0EK\nTkSRSSV2/FE9pgrQA8UO+QDl1G0lBOEABoNUwWJRD1y6dMfQyMgy2Xr9kdW7dgWnm0wOJdWYc5Yx\nufgQtjTdq+5pmucwNaNtt00PPTxishy16V/bXHlQ2OFu647pqmLW+QtypJ8u/vbS3mgK6LhgQQ7w\ni1syO3/9EVegy8OAh1Mm95nlKLRTHSnb2MqvSzLwPwsKdl4DrHowf2odzL6dG9Hn1ywcP+GSm8GW\nBwvt5vuJ8896rGvGlc1mjBcAX5AegmMSSiFhC8phcRnY9S3laf77tQJ2Ai30evH0u3nxrE9C2BTe\nlBxB1efR2D8CKQ/FDj0YBbXRoTnNB3Sko98IRpwXiB0oiZgy/hkdDQZJGxtL2MaN2MZdL1VyIQvV\nJkwnznBmqQnTdA88OlSlarar5OoyghGa/qL/1VOciosgoikqxjIbb2Aa2cw915grWhXPho3WxH5y\nq1BwKBWMne/0dYgi15qIm6YURjKI+trIbvGv6aNeBmX58yVs7P33LmYGLaBM4UjhkGwwSDb5dF/S\n4oqNb8XIip2X7FtyXkKaYsVKslNyTrpdut3heoe7h/4a+qvBIHkLwb6P2ZR8m4FD04IkZnff++V0\nKUP17U22VxdzlPzo+aHwx9++JXboUDqjohGzmYwiuXf+cxSp5C6Vowb3qEk2GnqKOSJUktACd4E0\nlKIaESha1wfgoxBkGCSDlE+PBcCqT9gvTeTIiJ6Ezm+D1i4KR0MS19Y6kNlpFvM6ted07gF65MRR\nqE0PSibgkPmYI52zdOnGXRYd9/Nc6QG0S83l0aTVk6pHhPa3q1R+WULkpyWFtKpU0zxtz5O1xIO6\naYUbOeSJ1o5Peqwybr3f3jEudLzlbLHaL4ebzlbp8vlMzqysJc+BdfsWVHwYkVdyxefPJRs/e1ZP\n9Ty8vrWJx4U3c9XD7YvFpvinqH2fRuesKlVUtc8adexEui6OudWG8wRF2CoUTvlqn3S+3u2NF3rl\nz8MaFIRKS4RIkSRaOxRM2NbzwBjHfdbe2V7xKWLurl1OXWQ5U2uxxKAENR1EiC8Gg+QBtA9cxRX3\nCzaHL9WoUW3WgAHVnw0Y8Cx/0RdA8UXUA/oZwXMBiGVIUkO1ZIlobk2IrkpKrpZuYo54Dr/nRwrF\nyB3Xxa5SHnlT6lP/fRnKDLRgWfia1/XvclftiXNeO4TRRMzwjVjtNTDWATxngOsPak24xlKkWjGi\npP/VTB7gkJehoHO6+NK3y/moTvzB3gAAIABJREFUxGMbvZfOyWpbowa7UWBsy4OuMSz/78tARzHn\nn6hHf/ORJGYBvrIsLUcxMZQFdqamFtjdsWPCQhQm11kI8vLvtylK5InNDK3YnFCzCjEGJXBiBhCd\nhd+S++zatmUwS863z+z2uUuXEmqrtZ+90Xj++34NkmHee6/3bQcPHeZOxkAXKvRzIeOFGfPFn7FN\nXCWPDHYFXpVn+vk40sqU53lV3ZAXifV8L3sUmFX46GG61IQtMdCyHnjGgsMFFMZ+A/j0V46q3/s2\nSF3z33UrMD8oSBQtS9qVXwh312Bt3ZxGT4BHEBsMhecDg4QQlyVJklSojh3ikP4sZw/tYU8JlE3y\nFEWLivn+UqnIqlGDHeHhFDca2SIEy2XkNBQnehBwL5fc+0c5qvuVX5snOyUXIwMt8AInXmGDPxZ8\nMeGBEQ0+5E2frdla19c6fMAIu9h4bZa5dYnWhyefm9xdYNXcx1W7Vyok4rRa9/6Om1IGJu8z1+Ku\nbicvP7mStwvYwdhVq/niXSvlQpNErdmmfqYuMzndPt3WpDHppveYLmfZZl098csJrUu2y3Cgq17o\nbxgMkloIpguopBq0bRMfiu/qPKHz/ETnxNEojtSs/Hn1BJ5OmMDBxx78RD0SkWj47Xdl/Ui2KOkQ\nFgJLCeYDinBykHnGDCy2dVGEjr/dyAbJUBTYAhQEJu5gw/kXXLa2JdU9JF+7kiRGouDitwOjgK4y\nBme8ozexp48Glfjyre+gIFEJrPsZWvUunk9bNrUrqo49uk31+Wsh81TrS4u3MEmRBZ/lOX1oKk0z\nNWCXpm9IkePH+7+7WSt04NJbPSvw7JYVVYMIirt4khCTrbI716L7Vk3HjuuDPDxifBFYrV8Lapiw\nUrKpd9zsVPtXUKGuOJnPQGyuO+asYjhnlqCoTTIOHrchPoiHkT3ZXH0odW1SqQI0D7us6jNx0LUl\nXWLOREw2/1JgeZcu10L69fPJsbGpZJebm+oTH68q9fmzm73JFJ/q4HA/zdHxUeikSd2dc3IuaS9f\njjRrNO2ApiIoSIE1/pcp7RVKdai4HGhoByorjN5cneFTm6Iplcim+z5MQEE2tTq9+PRYJ6PTiQgi\nfh3EIHeU/EXXtGjPLWaxpipVh4K1mJpslSPvclVkq57yQb1HfY9blghVGXVt8yOLrPlfz+QBzjgZ\ndp1rw4+bM39R8+jRBflAzDiUwIIHLJw2cZ8xfmSkZ+SGy8cvpwIYDJIz0AEFnqZFkQTOf8up8edP\nrVoX/J88afTq6FHfbGfn5HnAOr1eOSwkCRsUG1sSitNMg1IqEaCLQGqU334MEIIQ1wAMkmEQioMu\nSE9QUxRmWoHvJF6DZFABB2JcY2x7ju3p45FQz7NUwhK/8I45ZDiKtJrP1F/q35Y+PNU/7Hpl0qRD\nF7waefXcPLH6lF9Ojpx2Y0QVKDMILHnwqQtUOP2vmMKfPwaDVBhYde9ei3obpx9y2mgJt+qw9tcL\n/cn8d68GXIC+P8PeYIg7DYvsYGWdc5xbYI/9WKB2EEHOKAWQC6NIY4WBwpJEYXd3yur1iORk6oSF\n/TFzo0EyqPRC/0cHZAOPqSQmL8BJCBxUSdha36DjMQ7cohCPuM4jIiX7OUt0ueU8ckyDBpKa7ocH\nejzl5bKRLoe7kKfd9v5cF/bmljL+hodbdy6Lbnaxzx1yilYUksVMmdfq+9r4dxcDb5R6VvTZG5ds\nl+Obt2weneyYXKNLWpe3342vBcphNEcv9Jt2StKgcKTGtTlY7xKX3u5kZzruNCAHB3JQo0jevzVr\nRk7J9gxYH4kTB1kkokVI/vuVRcFZ90Zxpm4UQhwFkEKkAnyuc4T9ZxtQf3ETcXnp9X+LhopU3+8m\nN9cuYpH1GVkPAuAuQkz7r3llBYqJcjUKQ58tYyiHylIEq3qAXujTJYkSKM7+rkJgkCTW1SSpwsLB\no4sczSgbsPPw1GyzUA+xw3JBizWqL58+/XC+0k7sjB2BoKAg0QhooNNl3rpcrwplst4PFipaZwby\nKseLZyXWUjympmelD8/XOCS3OyE21jhirePOvUYF2K5W0whoh2JW/BU4Abz22007z2sE2yZR8mNf\nzLaJkFYBu4Iy1jdhDWLbWM47j2dliXliVhIoKCoUhE1hl8xMX/2TJ008U1MbxHh4lLhVvnxKipNT\nR5R0F/cGnT17d+vy5a2AXgjxW/5EHUXRmBJQkDR7UPb+y6sBTJrWlClGDXnv3LFu3LZyqn9c5bnA\naL3QH8qnr0qIP/IYg2Qol+bwZbfKYl9e67hHY9HkJIQ5NT/17F3j3hn8fOMOoS3+j8kDBslQIk/D\ngw770lU5Q3rbVc0s1XS544xA/D/N5W3JguTaRALRbB66kxIRbVHs7AbgEAqKZDwKUmEVsEev/13q\nklAkqtUTJ4aKz59L74mP953zj++HPYod+w0KE7OgbIbcvxmzGkW6na4X+tNI0k4gAyHGfHePU37/\nq3K0OY7Te0w3xjumZ404tNorxdGZ+DpCfaCeTZ6lRC7qBO3zYtmRRb9EFkvIXlTeH04IMEzFdWYQ\n+uDSVNy3XISYt/1351aS6OOlyVi7yzHMwbb9yXv039VBrxcJ3/0+ChgIXbwgLQ8eeMHxTzY0GHSJ\nGxuBCXqh/0PdX4NBclGeYSwKMqGnXi8+/8uxKPUvT5AdlY6Nezoaxx4oBT/WADeFXi+kg1NGsGf1\nOtWNXNPKFdi+gKzts1BZHLlzaSO/2NiwGxhOkHwyrFzY4Axhu7JisjGnavoT+6MlysbVsxb2XtVt\nfFboCex4SxYZnNnABj8PPCK60nUgiGooOUtUkPuyDIeZg2PLZ9zR7GSFbW8amqszILsbfedasETj\nQiJ92ICWjazgPlAfB9pgpZG9FmN2OhJKDdNygCuKKeY3FKHAiMLYomDQV9hyjco73/HDwOYoUNVt\nYs7fb2ZJklxQMOLdJbXUyWoRr1FqmQ5BiAv5NNQCV/PHsR84ZYf5xll+K6aCupmozUOppgsiIaYW\nSfERODoVIce3OimpKgjhctNij8Ibdp406bKHEGpHCfE4VHcpRxMUVpPxK6qjtZQC+qDEvhRCcU5/\nlfKId3yHk9mZsup4h3uZE8/4gbQNOWj/62RGpW5hkrcDGQF9WGx24Yhe/4/pm/NfskZKJda/mkEV\nXSwJGaWxR6KOvpmYCzwUgsV/v7AkSZLlDig28UMDzp17caFmzS2R3bu/1losviimWzuUGgNVEMKI\nJFUBfkJBarkBp7K0nOn+o9Rl6JWBjd0SW/iUZPmFAtxZCcgI8ZdpNsqPlPSDH3Ku9vPuX/KyuvqD\nqnUD2v0EZArBpP94nPx/52OQDMe/Fsbti01CULkoR2EndKclteUk+3pVxTPhRzYN8+BaIwtj1syn\n/s1Nev13OGSFmdcHJmCV6rN+5EPOtSlDr32p9Nxvg8TIoCCRjbIBSgrBPxBMkvBDiU7MA8oLQdK/\nMebmKBoHYM22IdnZjGO0FV1ifjtlUQK8rgKjcjW5s1vMbFEN1N1/uP+jR6NnjZNnRV27nulcrCU1\naptI2ufIm5LZ5BwJo82bGdRgIIL+vOhspESoOzGVO4ldhvP/bDx/eh8JmOtEXu+j3Dba2mUe5nwb\nRxTt42dgt14vhCRRBHgJOdFg7w7sBvEE+KUznyMH8UFng6ihF3qR7+waC/RDQY+s0OvF/X9nPACS\nwdAXRYKqKfR6k2QwOKMwj9FAFooqPZvnM9dx5eZoXShOA6eQd8CMY+pepNIumH/6gZ/mzhWnpRCp\nUv4YRohgTgAnj/RSFzKdXFfzcM5Z6znVuTXkcTCIoDYTmTi1Bz3i0zG7QWMt5B2Hp86QogfPrCCK\nqabTtkAkdUQhcs3VmGlMI6lkGd5YAR/8w2pS4cAyyh2T0aXdBsa5aRjzS2GWzZnDh+hoCqNI7lEo\nmqAt4InC9NWAH6h9wM0MiY+wJ5XOBBDAB2CwmCO+/iP9JAnFPPALRXhPL6pgzwOgqwimFHAUqM63\nwiZKxsQHKLb834ADgBuIL2pEG3ssNjVJfl2SjLhSZGR6k3P3Bc5rginfTJKswydMGNagdOl7yaNG\n3TxuMukK1a9+seOsxuvRBoWZJZ3pHorUGwbE6PXiD8KPoduwUrQ9G87Lslf1C0Nbo8THHDSqcQ4L\noErDSMyOecwFNiP+y6z1+/MGqTeCFQXD2FR2Pu2iumH+MBiP5Ss39zh7dshpoPS/sx8lg6GAQ07O\nlkLJye3a3br1ediZM3alP3/uiOKTswVaSLL8BuVA9gBOC71eIEklY9zduxzQtx6givrJv8gXW5NU\nIOzHLs+WlUTRygqh8I6dCPH6W3+nS0lNSidy0dZMrG863gf92xwuEDW+1XprSe2ZGqfK0mZ0FMH/\ns3zyCCH+P72ULv/f7UNGrigj7+7f8+o+XclKgiZN1v/+m0xtWaaQjDxQRo6TkRv/kzbKy7YXHsk+\ne77KwQ0TZI+jX2Tp6lkZ2QuEBOI+iLZ/fDfhDmI+iCQQ60FsBPESRIl/c9ySjKyTkT0SqTU0g2IR\ntzhcR0auLyO7fHdfWRk5XkZuOLP8zLEd2nRKdJzmLgJGlMh2HOg4Dx3RQALOpDGckwSTTDCrCcYb\nhAP1Fh9jvK8F97dTQGj+nl7CB8QpHea7lzDclpHXyMhS/lxWlWUeyDKGHj0WtgLxEcRcEBHwdgCg\nzm/DQYNl4R7umPt53jlx4YL9UVkmSZb5RZYp+t9eQ7LshyzHI8sV/+I3FbLcElk+iyxPEkJAMMs1\n3bjn5IS5eQ+uOoZgQk8uGtIIZBLBxBBM5/z1WcnThesuzohmgYFRV7maICN758/7hSPcnAniAry9\nD5VGo5g1RthCUQEhAt5m4O7flchTRclMcSfBrMUkwJoF4hmIc6hzrqJLimFI1XSCySCYl75LOBSy\nX5VUooLmJ2AMiobwCAXV8RLFBnwE7lWG3EQIrosijIwHIhnPYoKJI5huf9pvZYAwVDyjJzeYrX5I\nzTWdmVj4F4KJJJjqAqYJ+E2A5ju61wcRB6IYCDWIwSBO5a973Xf3eYKYBeIziJsgepUsed9Oltkr\ny9yRZWIuXbLNnTxszKcThY7mjeHNYhDSX6x9Wxm5k4z8Ui618RdZJvL+FiYJeClgvQAtwRSvNoT4\n927cEhArYLIAh/y1qJVlVssy72SZ8kKxR9oKWP9+KEnXzhPu6Ji8BcTyf2udgVbAtcXdu58t+Ouv\n1gInTmQhy7OfFCv2MVOny5vVv3+6FBaWgSzfRZZfIsvrkOUqyPKmElvk9MOecsY+nwvXuk+b+7zg\nr79axo0YsUaASkA5AYsFxAkYJUDK0jAuWUfevAY8iLPH8kttfkuxxdTRc13GPt15y8haI3NVs1XZ\n/1Pe+R/J5L/b8BKrVr3F1dXK7t3D/2JhBcnIcdsD5GnI8kJkebPnYbl9qEZeJCMnyMhDZWRV/r02\nMvJ8GTlWRu4Eoi+Ii/kL3RVEMIhEEFtB+H+3CYblb5Ym/+13gOOpOC8FMSb/8JgPYgGIBR2IPnCO\n65lnuJ7eji+bpVJxyxvrp+XZdLG1Ml2Vy8RC95lFOq3IxIu5fNPaZDkAWfZkvM8e+uoTwPIIcmsD\nzVGk34b549aAGA8iUYtl7hXk0zLywW/z8fscyqgnThy0zNU11tq165Izsow9iOog4r8/3GSZhke6\nj3y90+GqxckxMcHDI7rXX232f4OmKmQ5DFme+m8/E4yWYK7VGs/JAgUw2+pYRSM+U55MbBE4kIEK\nGXiEivgOfcjbMp1wKzw3cGmurLx78/Nc+6LB8jWfDprv6KQSsE7A41mElAFxPJ+htynOu7JfKfR2\nNsH3QCSDWIVtSllqrXiFJjsXrBewj7mMQ0wsUm+BOlBI3iUSKM8OitAUsFeEiqNeIO2B0plwY+af\n9tQ6YB/B1CCYV8ziCMVoACwGKQGf+sdoNj4dr0dvwJoC4i2IRHxu/srEwonq2fQTcFHAoj+2K8aC\neAzCDkSf/EPcM/83JxBz8gWaLSAq/WldaGSZn/MP8+ogpJ58GnWaG+aKpISDaFWJZLWMXFtG3iAj\nJ8rIsozcQwjB5470/e0Elo99WfcnWlYlmPgpTekn4IiAuEx/FhoucUuWOSfLuOWPzwHEPBDlrCp6\nPp+F0bDT7olKlZcEwu+v1omM7CQjb5WR179jpCGOhneyKXLylbfvamTZIF29ai1w4oRFd/Hip2ZL\nl2a0WrToserq1anI8lRkOZMwOX1UJ/nkVUlOkpF7f2t3xNixA/0OHsxpvWhR4uk6dZrlr5liAl5Y\nISrClcQKw3m0vwLGdTXIIBhji+Zl8+IkD9GoewHzcv/l1oWBC8T/lHf+R5pr/tCfwdCAxYsvotVa\nmThxutDr1+b/3wboUOId44KDqfUxgMfhFXn043H6vCmFautgTkb7she4LPT637HdBslQB9hjRrrT\nkbotM9HuRVHDzgDzheDDP4xBQo9i818ArFNo/C/GLeFbhpczDDQc0pgR917Q9CY0/GZWEgAjeF/V\nnyzvKVQywHlfpN6d6riNtOmfW9s0ufS72LTXXXRkftyF1KgtRb3T2LjRjJ1dRSCDzIggHgw6zMNq\nKZzV6OFzNhg3QnJXcE+GA67QIlKDdcRlrk8EAoC2eqHP/dM4WwO7AgMfTtyypXobFKz7yKAgEQBi\n+J49pWb7+r6bABQmV7vE0urSz2srF7hx6m1pPRph5MfoXfSK+qa25gFhQq83/g09x6KEpjcUev1f\npxD+q+dmOHiRVvRJq5j2aU/vuLolE3LbWNLUjmzu8RF/cihIVa6360LVfoHEB7ykVJXxHLege32d\nC21NqIovo1RWGIW6CEHYdxOgRXFQevvxaWMUfiuAfcBsITBKIZL61AHqtH7H6R41a885+nV5YxLK\ntECXaiW16G+grgjEwuQdsHxa5779r4ddmdA5xZqbaQ2ariWqwVceDXIg27OAokHMKg8L7YCfRH5+\nE0mSiqI4Qu8j4YaKujgEqDCuTkCq54ZNtgaV+SJp/geBMCGIlSQCgAlI5j5UOCjsK62/lLL/bl0b\nK0MR4rwkoUNBNK1BqRHwBSWr5itgCEoqhqsajWnW5cu6FBTop9N3304oprhXer2Y/G26rkjXOuUh\nbTtDkZx6JBZ0wJJmRtpRgNwNeqH/lD+nw4E5HwYxP6ons1HiNO78PuUhUlPgQNOCdF6opbnJk7FF\nzqIO2MEpycpmDXnpFjQHUExepYB62UWkAq+mc/P+x0YZo5ZfvpwntL2+o6FNGmUbvGT2dgc+We34\nEmXGoXIyNb4INKXNOKVLmN8W4lL5j4WLrJw5saohyTu2SL+LF4Pf+vrmHNHr7xSKVRtGbGBVybdY\n3VJo2NSsf/X9+qu4Y4e9c1bW8Xc+Ps2nHjhwbfyxY2uBtUk6NJ+dcZvfCOvyS9i8KcAh/QftXFty\nty9i6v2pLOljRfP5NT/7lGOBx/+Ed/7HM3kA6ejRS/TpU59Vq5IoVWovCoqmL0oY+NZFUzHUvst+\nlJwjI4JkngKdUJwpFYCLKPbwB8BTOQgJWJaJuutCyty+TYHxQvDub8cgUQwlZ8ptYD4QK/7CGStJ\nlAcmA+0g+mAZStTNxFTmK2QJeGhVSoadEfkRksozUiMUFM8IZDm720GODNhBxuLKhRaGmQqP5KNU\nCvtuOeQ8SKCgSxDbtnXi/v3RrJieSB9zZa46L+ZhggvY9FNiovbZQbAVCqW3ZWRqT0q5DaPahTRs\n7FFS4TrnXy4o6QE6CMEdAINBagGst1ql5/PmHayfne2kXbiw3bBT6g7H1zJmQIuLLB6wA23wLG6/\neu7rxmHfsgRkJTMi4hWBmToUZ/V44KzQKzlvJIPBC+WQSUGxEdcmSP8BBQ8dhFKDNQElF/u370SU\nALYgQA/oURu12tJnnH1NxawlAh/ZDR83NPVpmtCVcmLpyuXoHtxg9Kjh0LEDNfR68QJJ8jKjft6P\nKxml8VAtoGz1HKFOyJ90BxRH+KhEPNKK8DU+D5sGqE39maWzQwks06M44lJ/voG5Vzj21YdyxaTh\nMxAs5ogUBZFlXADVx0LpxXBsftu2m+c/f153wqdP5VVos+IImv0KrydV2XvZGdePt8moshRz5g7A\n4ArVMsDDoqzl8sBIHCdWIMd5DD6XNbRIfoBrXGOxJPH36Ng/rTdPbNMmgRinLXI7e/aXC9pduWNe\nRlCiFBCO4qvoi1KPNh3FcfsWmCbL0lOUHDk9ULLFZuRf6fnfMcAUvV5kf9+nQTIMAKqdpvDdVZSs\nLZC6gbjVlCsXT9O+oh3G+kB7hHhvMEitUUxXQXq9eGUwSE5AxzgjExw1VLQKTjlpWaUP4qkJm95z\nmT1jE8MKDGLbgSVMnSAh+uTTov7bkZJjXEPVi00HF9rXOhk9fjTr3IEGeTjWfMIqlS3x7ysw44KE\nmAQ8Q/GFDE6kjlnCvCOeJq/jaGFBCT57p8J03V/1S4Moj9iSMaapusfF3hlXzPDTmYwPTUSsz0Hx\nqdwB5oo5yiFVZvfuJilOTkeqv3ljU/3mzuUh1d+Nrf4Fx9MH0HgYSUqvTVqyk6Nd+9dydvjn2ndt\nrJaWgMaKWq/G8uj/mPw/69NgKM+vv95i69a3nDsXi0r1Etgm9Po/pJ81SAbpWzKt7571QomOrYGC\nCCiDEnTyYOA28jofpfOKCQSHtiAGBZ0j5X9/FXq9/Ie2JJxQqsjoUfDK6fwRK14QqAysgYpP4NlG\n4Ewq2Guh8THw2QDWCGVAb0ZA2CbI3KFIVl2FEHL+mDu3Ocu+/jvRLJvM1ruv/Peyp2gnmD9cAQ05\nRWGbW5SSE3Jx0Dyj3rKqbAh/TU6BAJSiGZshQV2DKZMiudAmFfsPuYw7DGMe5Y85HSXwJB1IFoI/\nRLHm17QcnpLi+alTp7jJlE1/wfrHNVEYwTg5iCrAUmBcEPoTwDgUfPY+Vj++QcW0+SipYccKvf6t\nZDBsAXphVOVx0juUzcXT8mmSiBLzoEbZkAX+9J2IkvjKkH+VQJe8B222J5mFcgsV/HL6x18CrK5a\nGjYtyJcvX6gwbBg5WVlMBrEdGNmbPQtWMj4zB7tiPnwxA01QHM7tTNjcmc3c50uZ8iO6lJuMLPcJ\np9heKBDabSiHeryYI8z5GQvPf/XweO197FgBYJ/QK0gjydNzGhb3UVjDC2JRSWjEq4p+t4/NnNFj\nyOrVG17fvNm+MohUim55TnpAU7Js7bD+kGNrSVO3AMs4MM3RcumGSmqMybYgKj/wzvhI2tdMfClG\nJ4LFXPHLn/fFH9ame4QTAWHHJKNzw2EpoaYJMdd2lyBiAkJYJIlSwH0UKX6qEMj5AIVlKIFBzfR6\n8ZeHyD92JHmjJK8LBxyABqm46I/SOWgLQ7QRFDcDi1JwXy0EmfnrqQ+KYHQLJfr3OnCg0y18UvIY\nCNQjWDgAe0GIffRa3ZMDHVEO4S0OZNpm41ANx5hm3VYUady7kP3JDcvWqFxvam//KG4ccqfbQIHq\nWiOa3pewLkdJf9IHBXkXhbIvhyPEEQCDZLABagpE0Huvd2OLJRRyLm3ZmGdrd+niyualdi0cs3Yx\nluxQ7nRbiNXYCQX99AqN43zqnQH4Aau5M1iL2sbcEKEhW3PqvY/XxrQV5g9DyZUeuzqLElmScM8z\nCTUPnN6RVvgM9bzP4/J/TP7v+r1yZRc9erQgIWGyEGLfv37in7RjMNiiSPfVgGpdD1G162HKjF5L\n2BcfsgErijklCOgr9PrQv2xHQoXCjL7VGC0M5MKRE9B1CgrjHiyEOPvdQxrAeznoj0GvcKR67thq\nDmOMqgtzgMPk57+QDAafS81pZJPHcqC1XugfSRKusHIpZJaGCXdpn1oPnxx3+ClCrUtw3L3y6Hlv\nkRuGggyqClxIJrntj/xYAaX9+8B08R0q4F/MVTGi7dYxtnJzguKXMCpi5jfp3CAZKqKgOgzA2CD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PPTrCvnRHz4NXcWd2hLjdHRBFx8GT45Q+TPS/rG8MdwHwr/CkebQL1RYdSYfwJn5yvE2yv1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\n", 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BHbJvsM1NNE317Ss6Iw0aJC46ymTimvOIEcC8eUD//v0R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\n7RYU1hcSqOyBbdPOJHuHMK0OGgWIWfFwOu/GvcRxzBxiEuMYPE6Kd84uQ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wsO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\nY2Ha2xTOm50xMnwkDOUGnB90HgkfJbSoz2Y7f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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -922,7 +1205,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 163, "metadata": { "collapsed": false, "scrolled": true, @@ -933,9 +1216,9 @@ "outputs": [ { "data": { - "image/png": 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Au9Zj3twY1bC0GX/DayS3vY4KXcKXN0h2LnS74FAE16UrVAyc52OBZu6p9JGU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\ns+fwwjaD1IX0+m24+j9C8L9DM07v83FSUL1NmvW/ucbah3+fv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OnBAi/+yza9qQbS2QZRm3uxOb7X1stvex2y+QmJhHauqTpKY+RUrKYVQqw/qXZUqStBkh\n+OXhn58G/h/giCzLE3MeN7touw8R5byHsmiroPBYWczNv/LKItrtcsHPfiYim5YW0ZXy1Vdh585V\nvb/XO4LV+hOs1tfxePrJzPwqZvMpkpOrkaR1jkpkWQj7bP/40VFxpjt5Uiy+rnLNYa3x+azYbB9i\ns72HzfY+shwkNfVJ0tKewmh8gsTEhRVP612l8zpwDEgHLMB3gf8NUAOzYn9dluV/H378nwN/AvhR\nyjIVFB4L8938c8+JRdgFbl6WRQ5/7pxYdK2uFm7+2WdXtVnI759kbOxnWK21TE83kZFxMtxX/gRx\ncevcssvng4sXxQaoX/4S9Ppo6eT+/auKotaaYNDF1NQlbLb3mZx8D4+nD6PxaMTF63SFD/3mo2y8\nUlD4jDI+Hq20eaCbnx30XVMjetu8+qpofrOKQd/B4Azj47/Ean0du/1j0tI+h8n0MmlpXyA+fp0r\nW+x2scZw/rwYFlJUJDZAnTwpbn9CkOUgTmdjOKZ5D4ejjqSkClJTnyI19UmSkvYQF/fgzWmyLNPa\n2sqFCxe4ePEiP//5zxXBV1D4rLBsN+/3i4lJs4O+v/IV4eYXDfGXRyjkY3Ly3XDL4d+SknIg3Ff+\nJCpV8tp8wKUYGIgOCblxQ+zoPXlSRDZrsNlrLZBlGY+nh8nJ9yI5vFqdFXbwT2I0HkWlevD8XVmW\nuXfvHhcuXIiIvMFg4Pjx4xw/fpxXXnlFEXwFhU87c918fHx0F+wCN9/WJkT+Rz8S1SenT4sNUoZH\n27wky0Hs9otYLLWMj/8Cvb4Uk+llMjNfRK3OWP0HW/qNRf3/7KLrwIDY/HTyJHzuc4/8edYan28c\nuz2aw4dC3oiDT019gsTE7Ac+X5Zlurq6YgQ+ISEhIvDHjx9n05wum0qko6DwKWXWzZ85I8z6yZNC\n6A8cmGfSp6bEIJFz52BwUAz6/s53YPv2R3xfGaezDoullrGxN1Grs8PdKL+GRpO3Jp9tUfx+8YFn\nF13j46N5/MGDazrP9lEJBt1MTV2OVNO43V0YjUfm5PDFD83he3t7IwJ/4cIFZFmOEfgtW7Ys+RqK\n4CsofMoYH49W2qhUIpv/5jfnuflQSEQ1NTVi89CTTwo3/7nPPbIwTk+3RPrKS5IKs/llTKavo9MV\nrs0HWwyHA959V4j8b38L+flRkS8tfexDQuYjy0Gmp5siMY3TeQO9fkfExScn73toDj8wMBDj4D0e\nD8ePH+fYsWMcP36cgoKCZZepKoKvoPApQJaFfp89+xA3398vzgbf/z4kJUUHfWc8Wrzidvditb6B\n1fo6gYAdk+nr4b7yFetXKz88HM3jr14V7n02j1/FQvJa4Xb3RBy8zfYharUp4uBFDv/g9Yrh4WEu\nXrwYEXmHwxER9+PHj1NUVPTIf1tF8BUU/ohZlpufP+j71Ckh9BUVj+SAvd77jI39BKu1Fre7i8zM\nr2IynSIl5eD61MrLsqj1n83ju7tF++STJ8WwkOR1XvB9CH7/BDbbhUgOHwzOxNTDazS5D3z+/fv3\nYwR+YmKCo0ePRkS+tLSUuDVq16AIvoLCHxnLcvOzg75rakQ+X1UVHfT9CA29/H474+M/x2J5nenp\nBtLTv4TJdIrU1CcfGkk8EoGA6JM/K/LBYDSqOXx4TXrlPyrBoAeH40qkHt7t7iAl5XDExev1pQ90\n4GNjY3z00UcRgR8ZGeHIkSMRB79jx441E/j5KIKvoPBHwmJu/pVX5k3BGxuLDvqenhYi/+1vw8aN\nK36/YNDFxMQ7WCy12O0XSE19EpPpFOnpzxAfr127DzbL9HTs0O6NG6Miv3PnHyyPl+UQ09O359TD\nX0OvL5uTw+8nLk695PMnJydjBH5gYIBDhw5FHHxFRQXxj2mDlyL4CgqfYGbd/JkzYk3y5Ekh9NXV\nc/QvEBAbiGpqRPOy554TQv8Ig75DIR8223tYLK8zMfFrkpP3YTa/TEbG86hUazNPNob796NDuz/+\nWPTGnx3a/QgnqbXC7e6b05fmA1Sq9DkxzbEH/i3sdjuXLl2KCHxXVxcHDhyIOPjKykpUj7FiyGKx\ncO3aNa5evcrf/M3frF8/fAUFhUdjrptPSBAi/0//NM/Nzx30vXGjEPmamhUP+pblEHb7x1ittYyN\n/Qydrgiz+RT5+f8varVpbT+YLMcO7W5vFzn8N74hav+NxrV9v2Xi99uw26M5fCDgCAv859m27a/R\naJY++TidzhiBb29vZ//+/Rw/fpx//Md/pKqqCrV66W8Aa0kwGKS1tZWrV69GLhMTE1RXV1NdXb2q\n11YcvoLCGiLLoq3L2bMPcPNOZ3TQd1eXyHRefRVKSlb4XjJO581wGeWbJCRkhssov4ZGs+nhL7AS\ngkEx4WpW5N3u2KHdj0kM5xIKeZmauhqJaVyudlJSDs7J4cuWXICemZnh8uXLkYXWlpYWqqqqIg5+\n3759JK7jIPK52O12bty4ERH3uro6srKyOHDgQORSVFREXFwclikrG4xmJdJRUPhDMj4uqiTPnhXa\nN1tpE3HzsgyXLwuRf/ttEdWcPi36za9w8XJm5m5Y5MUYCrEh6hR6ffHafiiXS/SNP39ezD7Myorm\n8bt3P/Y8XpZDzMw0R+rhHY4r6HQlkRw+JaV6yXm3brebq1evRhz87du3qaioiAj8/v370WrXYU1j\nwWeQ6ezs5OrVq5GIpq+vj6qqqoi479+/n/T0dALBIO81vcu7F96k+fp1+u8MMjzgwetFEXwFhcfN\nfDf//POi0ibGzc8O+q6pEcL+J38izgRm84rey+Ppx2p9A4ulFr9/LFIrn5RUuba18mNj0Tz+wgVR\nGTSbx2/Zsnbvs0w8noE59fAfoFKlzKmHP0ZCQuoSz/Nw/fr1iINvaGigvLw8IvAHDhxAr9ev+/G7\nXC5u3rwZce/Xrl1Dr9dTXV0dEfgdO3aQkJBAr6WXn3zwfW5ceZ/ehrv0tNtRqySKctIpjSugNOUw\nh0pfYPc/7VUEX0HhcTE2Fs3mF3XzXm900Pf166KPzenTYnTeCsTZ57MyNvYWFsvruFz3yMx8Idxy\n+DCStIYVIZ2dQuDfflvUyj/1lBD5L35x1QNQVorfb8duvzgnh7eRmvpEpPnYUlGVz+ejrq4u4uDr\n6uooKSmJCPzBgwdJSnpw07K1YHBwMCZ7b2tro7y8PCLu1dXV5OTk4PF5OH/tZ3z48dvcvXGT/pZh\nrBY/2/L0lBjyKEmooGrjc+w6dIikfUkYdhmI18QTmAqQYExQBF9BYT1Zlpu/fVuI/OuvQ3m5EPmv\nfGVFQzcCgSnGxn6B1VqLw3GD9PRnMZtPkZr61APLBldEKAR1ddE83mYTDv755+H48cc6tDsU8uFw\nXIvUw7tcrSQnH4jENAbDjkVzeL/fz82bNyOtCq5du0ZBQUFE4A8fPkzKChe+V4rf76epqSlG4L1e\nLwcPHow4+MrKSrRaLbd7m/jZ+z+g8dpH9N3qpLfDiTFZRbE5k5K4InZknuBAxfOYDm4geW8yarOa\nkDfE9O1pHHUOnHVOnPVOPIMejs4cVQRfQWE9mOvmExOFyMe4+clJIfDnzokHv/qqaFq2gvgjGHQz\nMfErrNZabLYPMBqPYzafIj39S8THr9GEJo9HlHvODu1OS4vm8Xv2PLah3WK2bUvEwU9NXUanK5xT\nD39g0V76gUCAW7duRRz8lStX2LJlS0Tgjxw5Qmrq4vHOWjE2NhbJ3a9evUpjYyPbtm2LWVzdunUr\nDreTn370Yz6+9A4ddbfpa7PgdATZnpdMqXYzJbq97M//CkWHd5G0Lwl9sYiWXPdcQtzrnTjrnMy0\nzqAu0DJekEGzSsOVER9N3SqGR3IVwVdQWCtm3fyZM6I0ftbN798fdvOzg75rakTTry9+UQj9iRPL\nnq4UCvmx2d4P95V/h6SkqnBf+a+QkLBGZY0TE9Gh3e+/LzY+zYp8fv7avMcy8HiGIpU0NtsHxMcb\n5tTDHychYWFsFAwGuX37diSDv3TpEjk5ORGBP3r0KBmP2DtoOYRCIdra2mLcu9VqZf/+/ZFoZt++\nfRgMBq7cvcw7H/6Y29cu03+7j/4eFxsy1ZSkZ1ESX8qunM9TvecZUg+mkVSVRLwhHu+gF2e9M+re\nG5yoMhOwF6dzV2/gqtVHY28cg8MZwCiS1MjmzWNUVKv4yQ/+TBF8BYXVMjYmKm2+9z3h5mez+UhZ\neXd3dNC32Rwd9L1MZynLIaamLkdq5bXafEymU2RmfnXR2aWPRE9PNKq5dSt2aPc6CuRcAoEp7PaP\nIi7e5xuLyeG12oXffkKhEC0tLREH//HHH2MymWIE3rzChe6V4HA4Ykojb9y4gclkinHvxcXFWB1W\n3vrgB1y58i7d9a30to8TDEBRbiql6q2UphzkUNlX2XyogOT9ySRuTCRgC0TFPXwth8C9I42O1BSu\nTQao74O+oTRCIQeyXE9e3n127pEoeGYD6h159CUm0uB0Muj14jqqRDoKCo/EXDf/7rvw5S/Pc/Mz\nM2LQ97lz0NoaHfS9Y8cyX19mevpWpOWwSpUaLqP8+qLCt2JCoejQ7vPnwWqNHdr9GEoNQyE/Dsf1\niIufmWkmOXl/pJrGYNi1IIeXZZm2traIwH/00UekpqZGWhUcO3aM7OwHDw55VGRZpru7Oyae6e7u\nprKyMqY0MjUtlXdv/prfXXyL1us36GseZHTIy6YcDSXJORRpdrFn47NUHjyBsdqIYacBOSAzfWsa\nR71w7o46B36rH3+5kS5TKnXOEHWDMp0DRvz+ALJcT1bWEGW7gxR+0YS2MpdBnY7GmRkGPB7K9Xqq\nkpKoDF9KdDoS4uMVwVdQWAlWq8jmF3Xzs4O+a2rgpz8VHc1efVUI6TI3GLlc97BYRK28LAcwmU5h\nNp9Cry9d/cF7vaJkcnZISFJSNKrZt2/dh3bLsozL1Raph5+auoRWWxBx8CkpBxf06ZFlmY6Ojpie\n8DqdLuLgjx07Rl7e+gxWcbvdNDQ0xMQzGo0mpjRy586dDEwO8NZ7/0rdlffpbWynp92OVhNH8YYM\nShK2U5Z5jMO7XyD7UB5Je5NQGVW42lwxi6quey5ChUn05aZT74G64RDtgwbc7kRkuZ6MjD5KdgUo\nfDod/f4c7icncWtmhn6Ph7LFxH2RtRWll46CwjKQZaGTZ88u4ebv348O+g4GRWTzrW/BMp2mxzOI\n1fomVmstPt8omZkvYTafIilp7+pr5W020Vbz/HnRnKy0NCryhes4nCSM1zuMzfZBJKaJi9OEF1pF\nDj9/1OGsi54r8CqVKmbox+bNm9flWIeHh2Pq3pubmyktLY0pjcw0Z/CLK2/xwUdvc+9GA/1to4xZ\n/RTkGSjR51FsqKK64HnKj1STvD8ZbYEWb/+83P2WEzlby+CWDG6F4rlxP0TroBaHMwlowmjsomiH\nh+1PpWE8nIU1NYUml4u+sLjPCnvVA8R9PrIsExcXpwi+gsJSzLr5s2dFxWGMm/f7xcJmTQ1cuhQd\n9L1g6sji+HxjjI39FKu1lpmZVjIzvxKulT+6+lr5/v7okJC6OtHCYDaPX8c8GyAQcIbr4d8P5/D3\nSU09EYlptNqtC57T19cXM7YvGAzGjO3bunXrmg9U8fv93LlzJ8a9u1yuiLAfOHCAqqoq7o628fb7\nP+LW1Y/obeqmr8tJmlFFcYaJ4sRidmY/yZF9XyHjoImkyiSCM8EYcXfUOwgmxnN/ewa3VYncGAvR\nPKRmwpYK3MVguEtBiYuCp4ykHzUxaUrjtttN71xxNxioSkqiVK9flrj7gj5ara00jtRzc+hDmu7f\nonWiD+d/8SmCr6Awl1AoWjc/W2nz2msi8ZAkRB5/7pxw9IWFQuRffHFZg7EDAQfj429jtdYyNXWN\n9PQvYDKdIi3t80tu7V8WsgxNTdE8fmhIiPvJk2Iz1DruDA2F/DiddZF6+OnpJpKT90XKJZOSKhac\nwAYHB2McvMvlinHw27dvX3OBn5iYiMneGxoa2Lx5c8ziakZWBm999GMuffwruupu03t3jJmZIIW5\nKZRqN1OcVs2h0hcoPLKTpH0implumI5ZVPXaAoyXZNKm13FjMkjTsJr742lAL1ptM1sLnRScSMJ0\nIpOp7HQvtBAsAAAgAElEQVTueL30eDyU6vURYa8Mi7t6GeJu99hput/EzaHLNAx/zB1rC912Kzk6\nFdv0AYpTTewwl1GZfZiKwv9DEXwFBYh181qtEPlvfCPs5mcHfdfUCDFdwaDvYNDD5ORvsFprmZz8\nPUbj0XAZ5XPEx69CiP1+0Tt5No9PSBBnp5MnxbeMdcrjRQ7fHolo7PaP0Gq3zulLc2jBHoDR0dEY\nB2+322PG9hUXP3x490oIhUK0t7fHuPfR0VH27dsXcfBVVVU0j9zmnQ9qabl6hb7mPgZ63WSb1ZSk\nZlOkK6dy0xc4cPAZUg+mot2uxd3ujllUdfd4sBemc9dooM4hc+t+PIOWdGAUlaqJLQV2th0zYH4y\nnemN6bQFAnS73ZTodJFIpjIpibJliLssyww6Brk12kj94AUaR6/RbO1gwjPNNkM8+QYoy9jMrg0V\nVOScINO4F72+OMZIKBm+wmeaWTc/Wzf/5S/PcfNy+Jc1NaIB2FNPRQd9P0RMQ6EAdvsHWCy1TEz8\nEoNhV7iM8oVFa8eXjcMhtuuePy8WEwoKonl8Scm6NSXzekdjcnhJUpGa+lS4Hv4EanVmzOMtFkuk\nDv7ixYtYrVaOHj0acfFlZWVrOtXJ6XRSV1cXEffr16+Tnp4e496NWUZ+dvHHXP/4XbobWulpnwQZ\nirLTKNFso8x0mCO7X2LzkXwMVQYCE4GYRVXn7Wkcecl0mVOod0s03I+j15JKKOQgLq6RvC3jbDui\nJevJNNz56bSHQnS53RQvIu6JD/ns/qCf9vF2GkfqqB/6gFujjbRO9KGSQuTrQxSm6CnP3E5F9n5K\nNxwjJbkSjWbTQ0+aiuArfCaxWqN18wvcfH9/tGY+OVk0LXv55YfWostyCIfjGhZLLWNjb6HRbA6X\nUb5EYuIqygSHhqJ5/LVrcOhQdGj3OpUfBgLTTE19FMnhvd5hjMbjc3L4bTHiMj4+HjPVaXh4mMOH\nD8eM7VurqU6yLNPb2xsTz3R2dlJRURER96o9VTQMXOd3F37G3as36GsbYnTYx+YcLaXJuRQlV7C/\n4CS7jxzDWG0kThfH9M05rQhuOnEYEunJS6fBL9EwFkfn/RT8gQBwk+yNVrYeVJP1VCqB4jQ6gE63\nmyKdLlotYzBQbjA8VNwdXgd3LHfCkcxHNFma6bLfx6yJZ5s+SHGqiV3mMiqyD7HVdASDYeeSjd+W\n+IPByAi0tCA9/bQi+AqfDUKhaKXN738frbTZtw8kjzs66PvWLbEp6tVXHzroW9TK347UysfHG8J9\n5b+OVrvt0Q5UlqG5OZrH9/bGDu1eh0ZeoVAAp7M+Ug/vdDaSnLxnTg5fGZPD22y2GIHv6+vj0KFD\nEQdfUVGxZlOdPB4PjY2NMfFMfHx8jHvXZmp559Ib1F/6gL5b9+jumEKvi6fElEGRrpCd2Sc4Wv0i\nWQdz0WzT4Gp1xeTuDpdE/9YMbkkqGiYk2i0GXB410IA5e5jN1SqynkhB3pFKjyqejrC4z11QfZi4\ny7LMiHOEW6ON3By6QOPINW6P3cPqcrBVH0e+QaI0YxMVGyqozH0CU+o+dLrilfVBsttFE7uWFvFv\nqLlZ3FapoLwc6cMPFcFX+HSzpJtPkcXGo5oaePNN0c739GkhrA9pAuZydUX6yodC7kjLYb2+/NGy\n6EBAVPrM5vGyHI1qDh1a86HdsizjdndE6uHt9otoNJsj9fBG4+GY9YWpqamYqU6dnZ1UV1fHjO1L\nWKNjHB0djSmNvH37NsXFxRFxr6isoK7/ChcunqfzRiN97feZmAhQkGug1LCJorS9HCp9gfLjezHs\nNOC3+mNy96lBP4PbMrijTqTBLtFm1TE1oweaSM8cYONeiewnkomrNNKbmECn202hTkelwRCJZsr1\nejQP+MYSDAXpmOigYeQ69YMf0nS/gZbxPkKyn3y9HI5kCtidvY/y7CcwJleSmLhx+f92vF64e3eh\nsE9OirLb8nIoKxPX5eVY9dBqbeXE1hOK4Ct8+ljMzb/2WrjL8PiYGKdXUyMGdbz6qqiZf8gMVa93\nOFIr7/EMYjK9hMl0iuTk/Y8m8k6nWDg4f17UyW/ZEhX58vI1z+N9PktMDg9E6uFTU0+gVkfLNZ1O\nJ5cvX44I/N27d9m3b1/Ewe/du3dNxvYFAgGam5tj3LvD4YgpjYxLk/jd1Z/S9PHH9DX30Ns9TWZa\nAiXpZooMpeza/DmOHHyejOpMpHgJZ6MzkrvbW1wMb0yjRa/l5rREq1XDuDMVaCUltZuNVTIbjhlI\n2JfMoD6RDo+H7VptTOa+4yHiPuObodnaTP3QxzQMfcRtSzP3bCOkJ8axTR+kJNXEDnMplTmHyTcd\nxWDYtfyeR6GQaHkxK+iz1729sHXrAmEfNxloHb9L61grrdZWcT3WSjAUpNRUyuXTlxXBV/j0sJib\n/+Y3IUU/b9D3yZPCzR8+/MBuj37/BGNjP8NqrWV6+jYZGc+Ha+WPExf3CJHF6Gg0j798WfRInh0S\nkpv76B98EYLBGez2j+fk8AMYjcfm5PAFkROVy+XiypUrEYFvbm6msrIyZmyfZg1aH09OTnL9+vWI\nuNfX17Nx48aIey/ZWULj4DWufPwbuurv0HtvDLcrRFGukWLDFkpNBzm6+yUKjpSizlLjandFOkTa\nbzoZNibTnm6gwR3HnXE1o/Y0oBt9Uge5FQGyjulJPJDESIqGDo+HgkXEXfsAcbdMW0SVzNAHNIxc\npdl6j5EZO5t0ceQb4ijL2MSuDRVU5p4gK23/8iMZWQaLJVbYm5uhrU2sHYUFfVbcbRtNtDq6IqLe\nYm2hdawVb8BLqamU0szwJXx7g2EDkiQpi7YKf/zMd/Nf+YrI5vfuBanjXnTQ9+bNws1/7WtiMXYJ\nAoFpJibOY7HUMjV1ibS0pzGZTpGe/oWV18rLsvjqPZvH37snRhOePAlPP73igeMPIhQKMD3dEKmH\ndzpvkpRUOSeHr4qcpNxuN9euXYsIfFNTE7t27YoIfHV19arH9oVCITo6OmLc+9DQEHv37uXAgQPs\n3buXQLKXj+p/TeuVq/S19jPY7yF3QyIlxmyKjDvZk/8MB48/g6HIgG/MFxH3qToHw0ENHVkpNPri\nuT2pYsCWjiyPoNG1kl3uI+uoDu0hPZYMLR1eL/labcyC6k6DYUlxD8khuia7aBy5Qf3gBzTdb6B5\nrAdv0Ms2PRSm6NmRWUBF9j52Zj9BakoViYl5y/um53QunrOHQguEfWpbLq2+oRi33mptZdo3TUlm\nCaWZpZSZyiLCnp2UjSRJeL1ixn1bm/jnN3tpblYEX+GPlLluXqeLZvMpcU74yU+Em+/piQ76Ll56\nbmso5GVi4rfhWvl3SUk5FK6VP4lKtcJF0mAQrl6NirzXGx3affTomg3tFjl8Z8TB2+0XSEzMizj4\nlJTDqFRiM5jX6+XGjRsRgb958yZlZWUxU51WO7Zvenqa+vr6mPzdaDRG3Pvmoi20jN6g/vIH9Da2\n0d1hI06SKMlOo9hQQGnOUU7sfYnc/ZshBNNN01Fxt0h056ZxS46nya6idzKNQMhGgrqZrDI3Gw4l\nYjhiYCxLQ4fPR37Yuc8uqO4wGNAtIe5uv5sWaws3hz/m5tBFbluauTs5TEqCNCeSKaEq5zDbzcfD\nVTLLiGR8PnGCny/sVqv4tzhH2Ke3b6E1foLW8bYYcbe5bULY57n2vGRxcnE6YwV9VuAHB0XiU1ws\nLtuLQyRv9/DlvXpF8BX+eJh182fOiBnZETe/R0a6fEm4+bffFq0ETp8WLnqJxURZDmKzXcBqrWV8\n/G30+nLM5lNkZLywoL/LQ3G5xNeL2aHdOTnRPP4hlT4rweezYrN9GMnhZTk4pz/8E5FWyT6fj/r6\n+ojA37hxg+Li4ojAHzp0aFVj+2RZpr+/P6Y0sr29nV27dgn3vm8vDrWNm7c/4N61OnrvjnB/1MfW\nHB0lKbkUpVdxoPTLVB49QoIxAXe3O7KoOnovQHd2Kk0qNU2OeDonjXgDPuJVTZiKpsk6qEZ/RIdt\nk4ZOv59tWm3MgurOB4j7uGucptFG6gbfp3HkGnes7Qw4J8nTxZGfFEdZ+iZ2bdhFZe5xctMPotMV\nPTySCYVEKe98Ye/qgk2bYoTdVbiNtmQPLRN3Y4R93DVOUUZRjKiXmcrYmLKROCmOsbGFbv3uXbFG\nW1goRL2kBDYVB1Dnu5hJd9HlddHuEpcej4cNajV91dWK4Ct88rFahZZ/73uiS0DEzTuHRFxz7pxw\nzqdPP3DQtyzLOBzXw33l3yIxMTdcK/81EhNzVn5Qs0O7L14U059m8/g1au4VDLqYmroUiWk8nj6M\nxqMRF6/TFSJJEoFAgIaGhojAX7t2jW3btsWM7TMaH304itfr5datWzHxjCzLEfe+YauZe9ZG7lz9\nmN47nfR0OUjSx1OSmUlhShE7Nz/FiUMvkLo5A/+kn+mb0zjrnYw2uuhOM9Ki0dA4E889WzIzXhVS\nfCOZBVNsqFZhOKzFXqChJxhgi0YTk7nvNBjQLyLusizTa++lceQGdYPvcWv0Ji3jPTh9nnCVjIGy\nzHx2Z+1jV+6TpKfsITEx9+GRzNjYwgXU1lYREc4Rdk9RAXczZFqc3TFRzP3p+2xP3y5imDkZ+2bj\nZuKkeAYHF7r1u3fFl8aSEiHsRcUymSVe4ja7sBlc3HNHhd0eCFCo01E051IoJ5IzCKFuL+avmxXB\nV/hkEgrBhx+KbD7Gze/0Ir3zSxHZ3LgBL70khH7PniWd9PR0c6RWPi4uMSzyp9DpClZ2UPfuRaOa\n1lax63Z2aPcajMmT5SBOZ2OkHt7hqCMpqWJODr+HuLgEgsHggrF9mzZtihnbl7aKIeIWiyUmmmlq\namL79u2iLLJqN7Y4C62tV+isb6L3ngW7LUBhXhJFSZsp2bCPIxUvUlyxm1AwhKvFhbPOibVumk7J\nQGuKjiavijabnim3HuKaSNsyiXlfHMlHtDiL1fTIQTbPE/ddS4i7N+ClbayN+qGPaBi6SJPlNm0T\nw+hUMtv0oUgkU5l9iOKsEyQn7UKlesjaycyM+O8737V7PDE5u7dkO51mNXcCImdvGWuh1drKsHOY\ngrSCBVHM1tStEFLR07PQsbe3iy0Ws249vySIochNKMfFqNrFvbCo33O5SFapYkS9KFHLtrF4UnoD\nuDvduO+5cXW4cN1zEZgIoM3Xoi3UUv6z8vUTfEmS/gV4FrDIsrwjfN+LwF8AxcAeWZYb5zz+z4HT\nQAD4j7Is/36J11UE/1OMxRLN5mPcfG+TEPnaWjFE5PRpUW+5xKBvt7sHq7UWi6WWYNAZqZU3GHYu\nv4wyFILr16Mi73RG8/jjx0VD/FUgyzIeT8+cevgLqNVZc+rhj6JSJREKhRaM7cvOzo70ozl69CiZ\nmZkPf8NFCAaDtLS0xLh3m81GdXW1GOax0UjP+B3u3bhOb2svvT0zmNMTKEnfQFFqGbsLvsCR6ufQ\n6LW4+91M108zUeekYzKR9gwDjX4VrVM6xmdSkKVWUvIsmPdA0iEN7h0J9MbLbJor7gYDuwwGDIts\n3LK5bTTdb6R+8H0ahq9yx3qXXscE2VqJ/KR4ytI3ikgm5zibMg+HI5kH7A8IBMTq5nxhHxkRWUlY\n2P0lRXTlaLmtGqd1rC3i2gemBtiaujUi6rMLqPlp+fi9Kjo6Fjr27m6xQXrWreeW+knc7sJrcjFA\n1K2PeL1s1Wqjoq7VUuhWkzsAcT0+XPdcuDvc4rrHjdqsRleoQ1eoQ7tdG7nWbNQgxYl/7+tapSNJ\n0iFgGvjBHMEvBELAGeB/nRV8SZKKgdeBPUAu8D5QsJiyK4L/6WMxN//aa7Bn2yRS7etC6CcmxOLr\nt7+95KBvr3eUsbGfYLHU4vH0kJn5Vczml0lOrl4wOWlJ3G4xx3U2j8/MjObxlZWrHtrt841jt0dz\n+FDIG3HwqalPkJiYTSgUorW1NWZsX0ZGRszQj0cd22e32xeURmZnZ3PgwAHKd5UzyQhdHTfpaWyl\np3Mcr0emOCeFopRtlOUc5njVS+TkbiFgDzB9axrbDQf3euK4l5HMbRJodiQy6kwjRBeG7GEyK2VS\nDqrx7k6gTy2zUaOJCHtlUhIVi4i7LMsMTA3QOHKdusH3uTVaT8t4DzbPDFv1sD3FQHlmPruz9lKR\n+ySZxr0PjmRkWaxkzhf2jg6x3hIW9kBpMX15STTpp2mxtUeimF57L5uNmxeUOxakF+CeVtPevtCx\nDw9HF06LSkOklXqI2+JiJs1Ftz8q7ADFc926pGHbSBxp/UG8nZ6IU3d3uEEGbaEQc912nbi9XYe2\nQEu89uGtK9a9LFOSpE3AO7OCP+f+C8D/Mkfw/zMgy7L8V+Gffwv8hSzLNxZ5TUXwPyXMdfMGgxD5\nl78WJKX+fSHyv/udiEtOnxYzVhcRW7/fNqdWvpH09JOYzacwGp9Yfq38+Ljobf/22+LMU1ERzeO3\nPWKLhDDBoJupqcuRahq3uwuj8cicHF5UD7W3t8e0DE5JSYkZ25eTs8I1BoRwdnZ2xrj3/v5+9uzZ\nw/79+9GY1QxN3KX31i162wcZGvSwcYOGktRsCjMr2F/8HFXFx5FkCfddN1M3HHTcCdJhTKYpXk3z\ntJoBZzoheRhNZh+ZFQGSD6oJ7EmgXyfEfe6C6i6DgaR54u4P+rk7dpf6oYvcHLrAbcsdWieGUMcF\n2aaXRXtfUzFVOYcozX4yHMksXVbL5ORCYW9pERszwsIeLC1hcJOR26k+bk93RYS929ZNXnLegihm\ne1ohU5OJi+brU1NQVCSEfUtZAEOJCznXxWSSi05PeNHU7SYnMTEq6hothZMq8gYlErp9uDuFU3d1\nuPDd96Hdql3g1HWFOhIyElbVVfSTJPj/AFyTZfn18M//H/AbWZZ/vshrKoL/R8ysmz9zRhjpiJtP\n60b6/jnRo3jDBiHyX//6otl4MDjD+Pg7WK2vY7d/RGrqU5jNL5OW9kXi45e5Qai7OxrVNDXBE0+I\n9sLPPAPp6Y/8+WQ5yPR0UySmcTpvoNfviLj45OR9SJKKzs7OGIHXaDQxDn7jQ3b+LobL5VpQGmkw\nGDhw4ACFZYVMyiMMdTfT19xOd4edhASJkg1pbDcWsmPzcY6Xv4BRn4F32IuzfpquOg/31Mk0J6q5\n7U6kx5FKIGRDndZFxg4fyQcSCOxTMZwik5uYGDOJqWIRcXd4Hdy+f4sbA7+ncUREMt1TY5gTRSRT\nmp7HLvMuqnKPs8V0FJ2ucOlIxu0Wyjt3AbW5WcRu4cXTUGkJI5vSuWMKccs/GIliOic6yUrKWhDF\nFKQWMn5fu8Ctt7WJ5aHiYigukdmww4tmuwvfBheWRBcd4YVTx7xF02JvIluHJTL7Q/i7PBGn7u5y\no0pTLXDqukIdiZsSiVOtXSfRuSiCr/DYWNTNn5wh5b2fiiqbtjZRYfPqq8KJzSMU8jE5+Tus1lom\nJn5DSkp1uFb++Qc7vugLwM2bUZEfH48O7X7iiVUN7Xa7eyIO3mb7ELXaFHHwRuNR4uOT6OnpiYj7\nhQsXiIuLixn6sWWJmOpBDA4Oxrj3trY2duzYwb79+4hPk7BOdjDU2krvvREsVj/5OTqKjRsp2rCH\ng8XPU5K9h4AjwEzTDP3XXLTOaGlJ0nHHm0CHw4g36EOV3E5auYeU/SpC++IZNkXFfXZBtcJgIHmO\nuM82CmsYvkb90Hs0jtTTMt6N1T3NFh0UGQ2UZWyjImsPu3Ofwpy6n8TEnMXdazAoShznu/aBAdEe\nurycUGkp1q0mWkwSNxOstI630WJt4d74PcwG84IoJt9YzOiAboFbv3dPdEwtLoaC0iCpO9yotrpw\nZ7oYxEW720WHy0VqeNG0UKejOF5LoSWe3EHQ9fpFrt7hwn3PTcgbirjzGMdeoEVlWJvmcg8iGAoy\n5Biix9ZDj62Hf1P5bz4xgj8/0nkX+O5Skc53v/vdyM/Hjh3j2LFjj/IZFNaZUEh0Mjh7Vrj5F16A\nP/tTmT2Ba8LN//SnojnYq6+KCU3zNiXJchC7/aNwGeUv0OuLMZleJjPzxQU92BfF6xVfJ2abkqWk\nxA7tfsQ83u+fwGa7EMnhg8GZmHp4jSaX/v7+mKEffr8/Zmzftm3bVvT13Ofz0dTUFCPwfr+fAwcO\nsKVoM/bgKNb+dgbu9tDd7cSYrKIkI5PCjBIqtj7FoS1fIjGkw93pZuT6DHcG1bQZddz2q2mfTsLl\njwd9G2klMyTvi4f98Yxkh8jVxi6oViQlkTJH3IOhIO3j7dwMRzJN95tonRhClgPkG2SKUzPZaS6h\nMvsg5TlPkpJUsfgJek4b3xhhb28XZbbl5cilpYxvy6JtQzz1ejvN9nu0WltpH28nTZu2IIrZrC9h\nuNewwK339orovqhYZvMuP4YSF2x04TC66A0Kt37f52ObRhNZMC2dUrNlSCJzQCbU5YksmHqHvWg2\naoSQz3Hq2u1a1BvUaz65az5Tnil67b0RUe+x9dBt66bH1kNfUx+afg0pcSkkB5K5e+Huugv+ZoTg\nl8+7/wJi0bYh/HMJ8GNgH5ADvIeyaPtHi8USrZtPSgr3tHlilKS3w4O+ZVlENq+8sqCnuyzLOJ31\n4TLKN1GrN0Rq5TWaZcQcNpvI48+fFyvAZWVRkV/GhKrFCAY9OBxXIvXwbncHKSmHIy5ery9leHg4\nxsHPzMzEZPCFhYUr+p/farVy7dq1yOamxsZG8vPzqdpbhWQMMjney/2uDno7rTicQQqzkyhK20JJ\ndjVH8l8kV7cN36iPsfoZmlol7iYbuCOruTutx+bTIWnuklLkJGWPBNUq7m8KkKOLXVDdPU/cZ3wz\n4SqZ92gcvsId613u2S1kqKORTMWGnVTlHmeb6Vi4SmYRJ2u3i7LHucLe3Bxp4yuXlmIryOVelpr6\nlBmaXKKevW2sjeTE5Ggde1jYsxNKGO5JXuDYR0chPx8KS0OYK9xot7sJZLuYNLjoDG9MiiO6aFoS\n1FA0EkfuIOh7A3hmSxw7XcQb4hc4dd12HZqtGuIS1ieCAQiEAjEuff7F4/ewSb2JvGAe2dPZZI1l\nYRo0kdGRgWnchDHPiGaLBs0WDdv/fvu6Vum8DhwD0gEL8F3ABvwDkAHYgSZZlr8QfvyfA38C+FHK\nMv/oWMzN/9vTPirv/1q4+UuXxJ2nT4umYfPEb2amFYtF1MpLUjwm0ynM5lPodIUPf/O+vmhUc/Om\nKJmcHdptMq34s8hyiOnp23Pq4a+h15fNyeH3Y7FMRMT9woUL2Gy2yFSn48ePU1JSsmyBDwaDtLW1\nxbj38fFx9u7dS15BLs6gBdtQF4Ndg/T1ucjKUFOcvoFC0072bH6ayswnkZwStqYZmhpCtKsN3I5X\n0+bSYfUkIyV2YcifJLkK4vbHY80PkK1PjEQys5m7cc6uZMu0hYbha9QN/o5bo/U0j3UxMuNk82yV\nTMZWKrKqqMr7HFlpB1Crsxd+3vltfGevw2185dJSHNs30ZWj5Waah/rgQETYdQm6GFEvySglUy5j\nuCdlgWOfnhYLp/m7/KTudKHa6sJjcmFRi41JfR4PueFF0+IELWVjCWwZljD1h5DmlDgGHIEFmbp2\nu7itSlm/CMbusS8p6INTg2QkZLBR2kiuJ5csWxbmETOmLhOZvZmY08xot2jRbtGi2SqEXbtFi2aL\nhoTM2EVepXmawqq5fz+azc+6+VcqWjC8dU60IS4qEiL/wgsLBn273X1YrW9gtb5OIGAjM/NrmM0v\nYzBUPFgsZRkaG6MiPzoaO7R7idr8B+F2983pS/MBKlX6nJjmGJOTXi5evBgReYvFwpEjRyIOvry8\nfNlj+6amprhx40ZE3Ovq6jCbzVRUVUCSH+fkAGMDvfR02QgGZIqzjBSm51Oec5iDWc+TJm9gustN\ny/UgrV4tdxI1tHg0jLiNkDCAdouVlEqZuL1xjBcHyE6OXVDdPUfcQ3KIzolObg5dEL3bLU20Tgzi\nC/rI10NxWiY7MovZnXOAXTmfw5i8e2F/odk2vvPjmHAbX7m8nOntm+jJ0dOUEeCa6j4tE6KePSEu\nISaKKckoI8ldxv2+1AWLpyoVFJXI5FV6SCp1IW1yMZ3mYkgSbn0mFIpEMOUziRSOxJPTL5PUF8AX\nLnH09HtIzElc4NS1hVoSsxMjNetriT/oZ9AxuLioT/bgD/jZmLCRPH8e2Y5sNlg3kNmfSWZHJtlk\nY9wUdelzhT0xd2ULvIrgKzwS8938iy/Cvztlp+LeG0jnakQWOzvouyB2N6vPZ8Fq/QlWay1udyeZ\nmS9iMp0iJeXQg2vlfT7RwmA2j9dqo1FNdfWKh3b7/Tbs9mgOHwg4IhueUlOfYGZGHzPVaWhoKDLV\n6fjx4+zcuXNZY/tkWaarqyum70xPTw+VlZVs2GLC5R/DYelnqHeU4WEvmzdoKMrIpci0m/3Zz1Cs\n2Yf/foC7dX7uWNW06LQ0+zT0u4zIqjES84ZJ3h0ifm8ctjI/ZmMCVcnJEfe+22AgNSzubr+bO5Ym\n6gd/T8PwZW5b2rhnt5CikilIUlGanstO806qco9RuOGJcJWMau6HEXndfGFvaxOVTeXluAq30peX\nxJ1Mmav6CZpsoj+7hBQj7NuNZWgd5Vj602LcekcHpKVB4Y4g5ioX2kIXwWwX9mSRr3e43aQnJFCk\n01Eqayi3qNgyKJExEELV7Yssmsap4xY69UId2m1a4hLXNoKRZRmbx7akoA87hzGpTOTJeeTM5JA1\nmYV5yExGVwbZk9mYN5jRbdVFRF2zRYN2qxbNZg3xurUbRq8IvsKKmOvmk5PhtT8N8UruBfRv1ojc\n/HOfE27+qadiBNjvtzM+/gus1tdxOm+Snv4sJtPLpKY++eCdkFNTsUO7i4qiIl9UtKKmZKGQl6mp\nq5GYxuVqJyXlYCSH9/lyuHTpcsTB9/T0cPDgwYjAL3dsn9vt5ubNmzHxjFarZcfuHUgGPy7bMJMj\nQ1FHOgMAACAASURBVPT0ONCoJYrNGRRmFrLTfJzqlGdRO/X0Nvlp7IqnTa+jOZBItyuVQJyDhOwB\nknYGUO2TsO8MYk6Pj4q7wcDupCTSwuI+7hqnYfgq9YO/o3GknuaxTganp8jTwfZkA+WZ26jIqmJP\n3lPkpB9Crc6K/VbldC6es4fb+HqK8hncaKTZDFeTp2hwif7s/pA/tp2Avhy1vRxrXzp370oRce/r\ng9w8ma1VPtJ2uVDni92m4zrR+Mvi95Ov1VKs1rLLpmb7cDw5gzJJvQEC4RLHwGS4bcD2hZuREtLW\ndkqYL+hjYGpgSVEPBUNsjNtIri+X7KlszKNmTL0mTAMmNiVvImlTEpqt0bhlVtRVaao1XdgNTAXw\nDHrwDnjxDHjwDnojt3d/vFsRfIUHM+vmz5wR1y++CP/Ts33suPWvIps3GqODvufUrweDLiYmfoXV\nWovN9iGpqU+E+8o/Q3z8AyKXwcHokJDr18WQktmh3VlZyz5uWQ4xM9McqYd3OK6g05VEcnhJKuXK\nlbqIg+/o6GD//v0Rga+qqlrW2L6hoaEY997S0kJJaQmZG9PwB+xMjw0xPDjG2Jif7Tl6CtM3UWze\nQ3Xal9gUKmHoro/G5jja1DruyIl0uFPwEkBl6sOww0PCXgnHriAZGyT2zHXuSUmkJyQQkkP0TPZw\nc+hDbg59SNP9JlomBpj2e0Ukk5rJDlMRlTnV7Mr5PGkplbGRjM8nbPV8YQ+38fUVb2d4UxptWSqu\npzi5FuyjdbwNl99FSWYJZZmihj0vYSeqSSHs7e1SxLFbLLCtKMTGfW6Sy1xIm0U3x1GViw6Pi8S4\nOIq0WnZ4NZSNqNgyLJHRH0Ld48N9z42n10OCOWGhU5/XNmC1yLLMhHticUGf6GF0ZhRzvJm8YB45\n0zlsGNuAacBEZncmG6WNmLJN6LaEXfocYU/MSUSKX5tjDPlCeIe9MWLuGRDiPntbDspoNmpI3JiI\nJi98vVGDKjtE5udyFMFXWJz796OVNikp8O9fdfMN/S/Q1daIjUqnTgk3X1EReU4o5Mdmew+L5XUm\nJn5FcvLecBnll5duWCXLcOdONI/v748d2j0v938QHs/AnHr4D1CpUiIOXqWq4saN1kglTWtrK3v3\n7o0I/J49e0h8SG8cv9/P7du3Y9y72+2mdFcp8boA3qn72Kz36eubIS1FRbHJRGFmGRXpJ6hQPcn0\ncAI3b0KbrKM5LpF2TxLTIRXxGb3oylyo90g4KgJk5MjsM6ZEcvfKsLh7A15aLHe4MfAujSOXuGO9\ny93JUXTxcyOZHVTlHqMk6yl0uu3RSGZ+G9/Z63AbX39JIfe3mLiXlUCd0cXHCcM0T7Th8DoiwzZK\nMkrJYjdxE6WM92fGOHaXCwp2+zHvcaEvchHKdTGV4mJQctHv8bBRo6EsTkuFNYHtI/FkD4hsPdjp\nwX3PDayubcBy8Aa89E/1LyroPbYeJFliE5vI8YRjlxEzmd2ZZDuy2ZyxGcNmw4IcXbNJsybHJ8sy\n/jF/jIjPF3X/uB91lprEvMSoqG/UoMqWidtgQzbdJ6AZwucbxOsdwuMZYmZmEJ9vCFn2cOKERxF8\nhSgL3PwLMv/z4ZsUX6tB+smbYozU6dOi5UB45J0sh5iauoTFUsv4+M/4/9l70xi77vTM73fuOffc\nfd+XKrKKOylRFEWtvUhyt3u13R6PNzmGk+4gGAdIMMAkmAzyITEySebDfAycoNsDdXtguO2xM7G8\n9aZubS22SLYoibtYVaz93rr7evYtH85lkSVKanW3PeNFf+GgCiB0C6B0f/Xc5/++zxOJHJ6NUf7K\nnp7UPcey7pR2P/ecPw9/d2n3B7BO/JcZMhy+eJcPPyCT+QSZzCcJhT7CxYtbuwr+0qVLnD59ehfw\njz322I+s7et2u3tGI19//XXm982Tn8vgWiOUwQ6NrT6K4nKsluJwfpETucd4LPLzRAZ1Lr7hcnUc\n5bIc5roRZeBECaTXiRyfIJ8B5bRDZp/NY9n0ngvVvCwz0AZcbJzl3Po3eaN5nsudZdYmA6oR/Hjf\n/CIPlh/i4blPMV/4OLJcvmMNdDr3+uyzGF/7xHE6C0WWamFezxq8GG3xxvAGPa3HsfwxThRPcCx7\nH3nzDIHuCbobeW7cCHDtmj8SHwx5LD6uk39QRT6kYpZUenGVVVtFc12OhSM8NApzoimyf2um1ldM\n9JsaZutvLzYAfGh21M67Wi4rvRXaaptKoELdqu9ejhbXihS3iyzEFyjWi753frePvhAmmPnp7SFH\ncd7TajE2fYUeiAb2gDw0FyJY8xDKQyi2cdObmM4WhrGFqm6iqltY1iaua2KadcbjOr3eHI1GnfX1\nOVZX6zhOnVBojlQ6w3N/Jn4I/A/PvWr+nz/T5tftPyD89a/60u1LX/KLvufmAP+NNZ1epNX6Q9rt\nPyYYzFMqPUOh8GtEIvvf/YdMJr4P/9xzvi+/uHgH8vfd94H8eNc1GY9/sDsPr6pXSSafmHW0fozL\nl6e8+KJ/0frGG2/wwAMP7Knti77P9I7ruly/fn2Pet/Z2eHwiUMEoy7WtMuw22FrS6dWlDlaqHI0\nd5KH4p/igPYEV66JXG6EuBKKcNWK0LJSiIktQkcHyGc81NMO2UWTRwuZvXAPBlkfrnNh67v8cPN7\nvLFzkau9DYaGxoG4wNF0npPFo5yuPs7puc+QT53ZbbJCUXzf5J12jK7j3HeC/mKFW/UYF/MWL8W7\nvKbepK20/bKN4gmOpO8nozwM3eN01wvcuBHg+nXf4cnVbeYe10jdryIuqKh5P0Zg1dTIB4OcssI8\n2JI5vB2gOvPWvSUDbeVvNzZAt3XWhmv3QH2ls8LqaBXZk3cvR8u9MqWtEoXVAvsC+5gvzhPfH78H\n6nJF/qmsIdd2MZumD/D3gLqruoTmQnuslmANAuURFNt4hW0scQPD2EJRfKBb1haeZ2IYc4zHdbrd\nOtvbc6yt1dncrANzhMM1EpkMybJDuGwh5E2cvI6W0xgkFJquwbZh0LdtrKee+hD4/1iP6/oTNl/5\niq/mf/WXbP7H+77JwVeeRfje9/xcmS9+cU/Rt6LcmC1EfR3PcymV/Fz5WOz4u/+QRuOOH//qq/DE\nE3dCyT5AGJjneSjKlV0FPxp9n2j0yCx07OPcuCHx8stneeGFF7hw4QL33Xff7rLTRz7yEeLvYwdN\nJpM9o5Hnzp0jnU2Tr2XAnqCNOzS2R4DAsUqGI/lDnEx+lAf5LI3lIm+uBLkqR7jiRNgyUwRibYKH\neoQeclBPu2QOGzxaTPFwMrm7yJQWBa62L3F+45u8vv19LrWvcq3fJCg4HEoEZ5bM/ZypPcl9tU8R\nuz0lY9uwtHQv2BsN3MOHGB6sszaX4K2CyyvJAS85KzSmTQ7nDvtRArFTJKeP4HWO0tsocuO6D/a1\ndY/6SZPKoyqxWejXOK2yLar0bIujYphHeiFONCT2bUFu3SW0YmEs+bEB76bUf9rYAM/zaCmte4He\nW+FW7xZdvUtVqFI36lSGFcrNMoXVAjWlxkJmgeJc8R4fPbwv/BNP5niehz2w91otd0N9w8DcMQkW\ngnuslmAVxMoESl284hZ27DbMfWVu25t4noWuzzEa1el259jernPr1hytVh1RrBMK1wmnk8TLDnLR\ngoKOmdeZZlR6MZWmbbJtGAQFgVooRC0UoiIJpNwxYXuAZPZxzS663mGstvjaL37tQ+D/Yzt3q/l0\nGv6nL9zgFwdfJfTH/96PHf7Sl/xSkVnRt65vzGblv45ptikWf41i8RkSiTP3fgT3PN86uG3VLC/v\nLe1+n/Lw20fXt3YnaQaD7yKK8Vn5x9MsL6d45ZXXefHFF3nttdc4cuTIntq+5Hu8vud5rK6u7lHv\ny8vL7Ds4TzjqYat9Rv0BrbbFQjXij0WmT/NQ8NO4mw9y6abMNSHKZS/MqpVBCA+QFjvIp22M0w7J\nozqPVVM8fJfnHvZ0Lm6/yvmNb3GxeY7LnSVWRn1KYTiSjHN/YYFT5Yc4M/ezLBaf9usJ3yfG161V\nmRyan03GCJxNjXg+sM6assWBzAFOFE+wIJ8hMX4Ep32E3kaRt2eKvT1w2fe4Rv4hlfAhFavib5uu\neyoRQeCRaYQHd4IcagSobHjEb9mwYvixAfvC9y4jHYkgl37y2ADN0u5R6Sv9FW51brE6XiVMmDnb\n3xwtt8uUNkqUdkosxBeYK88RX4jfs2j0ky5GObqDsWXsufh8p38uiMIuyENzIeS6QKAyRSh28YpN\nnPQaprfJZLKFpm3iOH60hKb5MG+362xt+RbLYDCHKNYIRmqE0kmiJRuxaOLkDYycxjij0A5rNEyD\noW1TlmVqoRBlKUDGmxC1hwStHp7RwzK6TNUWHWWH7eE2zUmToTkkH8hTcAsUjAK5aY7cIEe6nebf\nfOvffAj8fwznnWr+N39hzP8w9x/Y98JXEW7d8u2aL37RH3UETLNDp/MntFp/iKreoFD4JYrF3yCd\n/hiC8I4LKtveW9ptWXesmo9//D07Ze/86yOGw5d2VbxpdshkPkEi8TRra2V+8IO3eeGFFzh79iyL\ni4t7Wp3eq7ZP13Vef/31PYBHgGI9S8BV0Kd9Gg2FeFTkaDnP0cxR7o98nHL3M1y5meS6HudyIMSS\nlcYNqkj7WwQfNDFPuySOqTw+n/SV+8yWsfUOF7ae5/zm87y58wZXu+t0dJXF2G1L5ginq4/z0Nxn\nKGUeQRRj7xnj60UiKEcW/MmYUoAfZKZ8R97ihr7JQnqB44UTzAuPExs+hNM5Qm+jwI3rItevgyZb\n7PuoSvqkb8PoBZV2WKXp6BxxQjzaCnGiKTK/KZDbcJBXLMxlDSkh3aPUf5rYAM/z2Jnu3KvS237G\nS9/sU/Nq1LQ7l6PF9SLz0jwL2QUK+wr3+Ohy6ce3XTzXw2yZ7wry29/bQ5tQLbSrzoN1ECsKQmmA\nV2ji5tYx5XXG4010fQvH2cTzHDRtjuGwTrs9x9aWr8xVtY4o1RDkGsFMnFDJJlA0sHM6alZlkFFp\nSRoNwyAiitRkmUpQJIdC3Bkim30we9hGB1XrMFB2aAwbNCYN+mafnJCj6BbJG3kf5P0c6Vaa9Haa\nvJ2nFqtRzBSJVCOEcgJyVEOWJ8jCgJDdIvZ//PaHwP+HfPao+ZTH73ziFT7TfBb5r/7Mz5e/XfQt\nSdj2mG73/6PV+jrj8Wvkcp+nWHyGbPZT9xY5K8qd0u6/+ivf278N+QceeF8/3nUtxuPXdlW8olwm\nmXyMZPJn2Nyc59y5Bi+++CLf//73mZ+f3wP43HvEFjcajT2jkZcuXaJULxKNCrjGiEF/xHDkcKQe\n50h+HycSD7M4/TkaNw9zbRjjshThhpPAFFykuR2kUzrWaYfYcZXHF2I8kk77BdmxCIPREuc3vsEP\nt17mrZklg2dzOBnkeLY2s2Q+zsnaZ0jEjyLopj/G8g47xptM0I4cYGd/jrcrMueyKt+JNrlobTCf\nmud47n6q9keIjh7C3jlEd6PIzbdFrt/0kOd1qo+qu6Ffk4xKQ1LxbIcn+hFOtYIc2g5Q3pyp9WUd\nZ+L8jcYGqJbK6uAdoV3dFVY6K6xP14kR82fSx1VKrRLFtSJ1vc5CaoG5yhzxxfgeqIfmQgTkH++X\niz1+H6tl08DYMpDS0i7M5bqAWFF9mBdbuPlNjOgtpuomur6J627heS6qOsdg4CvzjY05NjbqGEYd\nQaohhCuImTjBoo2X0zFyGtOcRj85pSnoTByHiixTlSXyqMTtASF7gGj2cYwumtZmrLRojZo0Jg16\nZo+0kKbo+CDPT/JkB1nSrTSZRoaiV6Qar1LMFomWwoSSFnJYRZZGhLwusrmDrGwi9rb9N/ztB/wx\n5nJ59xF+93c/BP4/tHNbzX/5y35Q5H/z2S3+efr3qX37q/5kze2i72IRx9Ho9/+aVusPGQyeJ51+\nilLpN8jlfs5XoXefVutOafdLL/lpk7f9+PfJbvc8D1W9tjsPPxq9QiRyiFTqEzSbi/zwh2Neeun7\nvPzyy1Qqld2ogqeeeupda/ts2+bSpUt71PtwPKRQzhAUNLTpmJ22RjEb5GixyNHUfRxyP4Gx9DPc\n6KS5KkW46sZRPAmpuoN4UsU+7RA7ofLowTCPZTOcSSQ4KkOjd5HzG9/gjcY5LnVusjTqk5M9jqbi\n3Jffz6nyQzw897McKD5NOFj0M/bfCfaNDczFedoLJZaqIX6YM/hurMXLrFNL1jmaeYCK/iSR0Wms\n1kG6GwXevi6ytGWTOalROKMSOaxiV/3S6gYqhydBHmnLHG9IzG9Dbs0luGJibxrvHRtQC/1YFozr\nuTQnzXt89JWWfzk6skbU3BpVpUqlW6G4VaTSqbA/tp8DhQNk92XvWTSSEh/8F4tr3Zk5fy+oe9ad\nmXO5FkCsav4laKGFW9jCSN5iYq5hGFt43tZsN8OH+c7OHJubdTY353DdGq5YxQ1XEdJRxKKFW9DR\nsxqjjEInobDjGiREkYocpCCopOwhEXuAZPVxzR6G1mGitOgMGzSnTbpmlxQpim6RnJ7zQd7Pkmll\nSLfSlAIlqokq5UyZaEYkFDd8NR4YEHI7yPo28mSDQKdxB+KjkZ8NdRfE3wn13edd7q8+3LT9B3R2\ndvwgyn/376CQNPjfH36Op1efRXrjwp2i7zNncD2bweC7s1z5Pycef4hS6Rny+V8iGHxH2ciNG3es\nmmvX/Ln426Xd72GnABjGNoPBd3dn4gOBEOn0J2m1jnLxoskrr1zgpZdeIpfL7Sn9KJfL97xWv9/f\nMxp54cJ5EpkE8VgAz5zSH0wwTY/j9TRHsoscCj5BePPnWNtc5GogyhViDJ0IUrGFeP8U+yGHyDGF\nR48FeSKf46F4nDpTNtovc2FmyVzprtFUFfbHBI5l8pwsHOZ09THOzH+WcvoRxPb4XrDfuIFdyNFd\nLHOrHuVi3uLFWJfnpQ1y6QpHEmcoah8n1H8Qs3WA7nqet98W2VANSmc0sqdUggd8G6YTUTE1k8e7\nIU41gxxsBChveMRXbbxlAzH0NxMbMDWne1T6ymCFlR3/cnRD2yDhJfzArtnlaHGryD5xH4vZReq1\nOrGF2B4fPVj8YKOVnudhda13XRy6DXWrbSGX5ZlnHkCqGgjlEUKpi1fYRo3fQhFXMM1NYAvXhen0\nNsz9scRWq4bj1nGDVexwGS8VRSiaWDkdJasyyCi0Ywqa51CRg5QEnZQzIOaMCFp9PKOLpXdQlDb9\n0Q7NaYOO0SEhJHxFrufJTXJkez7IM90MFblCOV6mkioSS0AooiEHx8j0CNktZHWL4GiNQLvpv2lb\nLR/OHwTiudxPVbH5IfD/nh/X9ROAv/IVX83/i6ff4J/JX6X43a/71sqs6NsLhxiNztJu/yGdzp8S\niRygWHyGQuFX/cvC28dx9pZ2K8qd0u6nnnrP0m7bnszm4Z+f+fA7pNNP0+/fzxtvBHj11cu8+OKL\nJBKJPaUf76ztc12Xt99+e49639jcIF9MEQwY6OqEbs9irhTiaKHK4dgpUt3P01t5ghtugktilLYV\nR8p3EI9PcE7bhI8rPHwiwEdLeR6Mx8iaW9xqPs8Pt1/mUusq1/oNTMfiUDLI8WyVU6X7eKj2JKfq\nnyHpVBGu7rVjvMuXcQMCg4N11ubivFVweTk54JuhDcKZAgdDj1NQP05ocApz5wDttTxvrwj0Iirl\nR1SS9/k2zDSr0hZUFnsij7SCHGtKzG9CZs1BvmXiDZyfOjbAcR0ak8Yelb7cXmalvcLqZJWJPaFu\n16lO/Fjd0kaJmlFjIbnAYmmR7P7sHh89NPfBxiod1bkD8HfzzzcNAuGAr8zrIlLVQKxModTFzTfQ\nE2tMw29juT7MPc+Hea83R7NZZ2OjTr9fx/JqOGIFM1LGSUfwZhefk6xGLz2lHVZJSyLlgE7aGRF3\nBoTsIYLR9X3ySZvhqEVbbdI220S96C7I8+M8mb6vxnODHJVwhUqsQjWWJRZzCYUVZHGI7HYJGU1k\nZYPgYA2hNQO5ZX0wiJdK7/m++ps+HwL/7+m5reZ/7/dgf6LHvz7+hzx+/VnE0WC36Nvbt4/p9M3Z\nGOUfIUmp2ULUrxOJLN55MU3zf2vcLu0ulfaWdr+LYnNdi8nk/O48/HT6JonEI4xGp3nrLZnXXrvF\niy++hCzLe0o/3lnbN51OOX/+/G4d36tnX0UKBojHJTxbYTBUCYpwrJbjcPIweeVnMdc+y8q0xlty\nlIaZREwPCBwb4py2CR1TOHM/fKyW5/6IRHh6leXmd3ijcY7LnZvcHPZIBT2OpOKcyO/ndPk0D899\nksOZjxFaHd4Ddq/fY3ygzuZ8istF+H5qxDfDW5j5LAfEJ8krH0PuP4C+s+BHCjRczLJK8YxK9IiK\nU1MZJhTsicEjrRAPtIMc3BIorXvE1mxYN5HfJTYgeiRKaO6DJTdOjMk9tstyc5lbg1ts6Vuk3NSd\ny9HtEpV+hf3R/SzmFpmbm7sTBzBT6WLs/bdGPcfDaBp7F4feYbU4U4fwXHgGcwuxMkUo93ByO6iJ\nNbT4TezgLXxlHmAymaPX85X52lqdybSO6dWwpApGuISVCmPPZstHGYVOaooZsagEDHLumIQ7JGwN\nEM0ujtFDG7cZj3foqDt0rDZhL0zRnlkrM5BndjLklByVUIVqpEglnCIZ9WaXnH1ku01I30aerCF1\n1xBaO/5Fey73/gC//SSTP1bW009yXNfGtnuYZgfL6mBZXSyrg2E0MIwtDKOBZXWw7R62PeLjHx9/\nCPy/L+duNf/S9xz+l8e/w29az5K98G2/h/VLX4Knn0bVl2m3v06r9XU8z5xB/hni8fvuvFin48P9\nuef8jwYPPXTHj19cvOdn+z78jd1JmuHwJSKRRabTh7l8OTG7aH0F4J7avtsf8T3PY319fVe5v/rq\nq9x4+zqZTJygZKFpKuORzcF6hCPZOcruRxG2f55m+xRvheKsW0mE+ATx8BDntEXw2JTTD7g8NZ/n\nsGQijF5jufkib+5c5Ep3jS1lylxU4Fgmx/0zS+bh+qepjSqI15b3gJ3VWyj1Etv7MlwriZzNTPh2\nuEEnn2Z/4JPklI8S7J9Ea+xnZyPDzZFF6JBK7pRKcBb6NRYVKk2PR9shjjVF5jYhs+4QXDEJIBB9\nZ2zAkSiRgz86NuCdNXUr/RnQu7dYna6iOqq/OTqqUtopUW6WmZfmWUwtslhZJLOQ2eOjv99Gq+d5\n2CN7D8jfabWYDZNgLog8JxGsWQTKKoHyYAbzDfTEEnb8JoK4ieuKs4UhX5lvbtaYqnVMahhSGTVc\nxEyFMPM6Ssa3WLqpKdmQSt4bk3JHRKyZT250McdtJpMWPXWHjtkmSJCiXSSv+Yo83U+T2clQMApU\nQiUqoTzVUJKU7CBLI2Svh2ztEFI2kccrSJ0NXz2FQh8M4vn8B94C/3GP53m4rnoPvE2zhWFsouvb\nWFZrBvABjjPBdQ0EQUYQJEAAHDzPwvM8JCmBKKYIBnMEg0VCoQrHjn31Q+D/XT/N5p1Jm5PRZf7X\nfV/j1FtfI1Cr7hZ96+Epnc4f02p9HdPcnuXKP0Mi8cidN/fS0h2r5tIlP9Hyth//LtMvhtGc+fA+\n5AVBQtMe5+rVLBcu9Hn55dfQdX2Pgj948ODuzzMMg4sXL+4C/uVXXkY3NFKJIJ6jMRgZpOIBjpWL\n1OUHkbufYbz5Ka7IWVbsFG5IRzw0wHnQIHhM4dRJi6cOZJn3Ojj9l1luneXN1hWu9bdRbZNDCZnj\n2QonS/dxpvoxToceI7WqI1y5dkexX7uKkYqzsz/PjUqQ81mV70SarOXTVIVPk5l8hGD/JGpznu2d\nBGuWSfI+ldR9KoH9KmpGITDUub8pcaoV5MC2QGnDI3bLQujY98QG3P7+R8UGjPTRPT76cmuZ1dEq\n2+Y2GSfjb452/c3ROWuO/fH9LBYWmZuf27VcIgsR5Jr8nraLa7gYW+8dvGVsGACE5iWkuotUVhHK\nQ+xcGy2+iZFaxk5fJxBew3EkxuM5ut06jUaNra05NLOG7tXQpRKTcAE9HULPaUwyCp30FC81oCSO\nSLsjYvaQoNUHvYs1bjOdtBjobTrmDpInUbALuyDP9DJkWhkKboFKsEg1mKUqxUkHbeTAANnpIBsN\nQtM15P4yYnvT/+RaKv1oiJdKP1F/wo86nudiWf13wLs9U95bmOYOptneVd+OMwWYTcQFAA/Ps/E8\ni0AgiiQlkaQMwWABWS4RCtUIheoEg0WCwTyilEV1Q0ztAANDpa/16Wk9umqP1rhHc9jn93/twymd\nv5PnbjV/7nsK//rUn/LL42dJbN3YLfo2D5fpdP6UdvvrKMoV8vl/Qqn0DOn0U/6svOvC+fN3ID8Y\n3PHjf+ZndrNwbh/bnjIavbwLeMPYxjAe49q1Eq+/PuWVVy4ymUx21fvTTz+9p7ZvZ2dn92L1le+/\nwhtvvEEiESYUdNA0DU1zOToXZyFxmMjkE1iNn+eme4S33QR2wEVc7OM8qBM8NuXESZOnDsepmEvo\n/VdYap/ncvsmN4ZdYqLHkVRsNiXzIA9nPsKJfh357cauancvXcKxTdqLRZaqYS7kdL4XbXMtkyYn\nfp7M9AnE3gkm2/NsjaK0ZJ3cKZXoLPTLERRyTZszLZnjTb+gOr3mEFy3CGYlYu9Q6tHD7x8bYLs2\nm6M7BRjLnWWWG8vc6t1iTVvDdE1qeo3qsEqpUaIyqrA/vJ/F7CIHqgdILaTuLBrtf/ewLs/1MNvm\nPRktd0Pd6lvIVYlg1UOq6gjlEXa2jZbYxkit4OSuIyaXcNwgo1GdTmeOre0aOzt1NKuOThk1WGYc\nyqOkZdSsyjCjMki2ySa7ZBmRcAbI1oCA1sMZd1CmLYZ6i57VBo9dRZ4b5cj0MmQ7WYpegXIwT1VM\nUxWiZEULmT6yvUNI20IeryJ3bxLotfxhgR8F8XIZMpm/UUvFcfQ98Patkya6voFpNjDNHSyrRLAH\nTAAAIABJREFUi20PcZwxrqsjCMHZ3oqA5/nqWxAkRDGBJN2tvquEQnVkuUowmMcJJJnYIiMLxqZN\nXx/QU3u0Jj22Bz12xj26Ux/oI7PHxO6heUMkL0bQyhEwsnhqDmeSwxzlQMsRD+QYfee//xD4f5fO\nrpr/isfT4R/wL/PPcuTq/4vw0Y/Cl76E/ZmP0x39Ne321xmNXiWX+9xsVv7TBAIh0HXfovmzP/NH\nKLPZO378ww/vueF3XZvJ5MLuPPxkchHTPMn16/NcvGjw6qtX6fV6e2r7Tpw4gSAI2LbNlStXdtX7\nCy+9QL/XI5kI4rkGw5FFJS9xKD9HynwCt/M5tpUnuS6k0VwRcX8f9wEN6diUY/frfPSwQNF4g2n/\nLEvtN7nSW2N9MqEaETiWznGyeIgHCw/zqHWCuc0ggas34coVnEtvQatFb3+RWzV/MuaFWJfLiRxB\n+XOkp08Q6J5g3K6xqclM0zrZUyqhQypuTiE8VDm4LfJgW+LAtkBxwyO6ahEwmUF9r1J/v9iAgTbY\na7tsLbPcXmZtukbTapK1s9QmNX9ztFVin7SPhcQCB8sHqc5X76j0xQhS5t6MdHti77kIvQfqWwZi\nIoBcB7FqECiNsbJdjOQ2RnoVJ3cDqXAN25MYjeb87c/tGt3uHKpdRaPKRCoyCOWZpiWmWZVesoOY\nbpCLDUg6A39dX+3hjttoSoeR3qZnt/A8l4JdoKAWyI1ypHtpsr0sZaFAOZCjEkhQI0oOA9ntIJtN\nQtN15OEtgp2bBEQ+GMSLxR+5yPdBjud52PZwD7xNs42ur2MY25hmE8tqY1l9HGeE4yh4njMD+N3q\n20UUYzPrJEMwmEeWy4RCdUKhOqJUQHFDjG2JseUxtlwG+pj2tMd2v0dz1KM97dFX+wyMHmO7h+L2\n8HAJ2jlEIwdaFneawxzn8KY5okKOpJQjFcxSDcXYL0nsl2FecikFx2SFARm3T8IeEDP6hLUB4riP\n2x0gXbzwIfD/c5/bav7LX4Zr323yfx7993yu9VVfgH/pSzj/xa/Ql/3L137/W6TTH5/lyv+CH6LV\n7+8t7T550s/B+YVf2NM25XkemnZzdx5+OHwRTavz9tuLvPmmxw9+sESzube27+TJkwQCAYbDIa+9\n9hpnz57lpZdf4vz5c4RkkZDsomoGnutxtJ6hID6EOPhZuqPP8zbzjK0Q0twA96RK4PiUw8cUHj86\nJKefZ9S/wM3uNa71thmaBocSMseyFU4VTvBw8D7O9OZILo0Qrl7FufQmLK8wLmdYq8d5q+DwUmLA\nm6ECZvRzJJWP4HWOMRyV2bZF3Krub5vuVwijkm4ZPNAIcrzle+updYfgjk1oX5j4O5T6e8UGWI61\nW4CxMlhhubHMcnOZ1cEqa8YatmtTV+u7m6Nz9pw/k547wEJ9gfTinZq6UHVvRrpruZgN873TFDcM\nXMMhWBeQqiaB8hQr08NINbEyq7j5txHLV7ElGA7r7LTm2N6qMRjVUewailBiLJboRXKMkgGG6R5K\ncpN0pk1a7hO1B8hKH8YddLXNxOjQs1vYnkXR8hV5dpgl282SG+UoCTnKQoaqF6fuhsi6GiG7jaxv\nE5qsIfeXCE4bCKXCj4b4e8yM/3jvIRPL6t0F787M997CNLcxzRa27atv257guiogIggSgsBMfdsE\nAmFEMY4kpXfVtyxXkeU6jphn6oQY2wHGNowtl56q0Bj0aQx99d1Vej64rT5Tp4fBmKCTQrJyCFoO\nV8lhjbM40xwRL0dczJGXUswHQuyTAsyLAnOSS1lUyQkDUu6AhNUnagwIqQOEQR+3P0Do9wmMBwDo\n0SxaOMM0mGUsZhgIWXpuhraTpWVkaOhZttU0RiTK88rTHwL/P9dpNv1Jm9//PZMviH/Jfxf7KvOb\n30f45V/G/eJvMTyi0u78Ed3uc8Tjp2ZjlP+UYDDrd4Xetmpef923aG6Xdt+1rGSarT0+/HjscvPm\nUd56K8hrr62zudnYU9t36tQpAoEAN2/e3L1Y/c73vkNzu0EiFsTzTKZTh7liiHriGCHlSSajn2fZ\neYi+GUOsjPDuVwgcm3Lg6JBHDt8ibf2QXu8iN7vLXB90CAUcDqfi3Jfbx4PxYzyqLHJiM4789ib2\nW2/CtavoUZnNudlkTGLM61KRcexzhNSP4PWO0jPyNAkQOqQSP6YRziokJypzDY+TOxIHGwGKGy7R\nNRsxIRI/EiX2Dm89vLA3NuCdNXXL7WWWNpe41b3FmrJG02lSMAtURhXKO2Wqkyr7Q/tZSPsqvbK/\nQuSAXyYd2hdCDIu7r2v37ffMODc2DcyWiVQAqWbPYD7ATDexMuu4+SXEylXsqMFg6M+WbzfqjCZ1\nFKfK2Cszkgq0wzl6KZNhcp1gukEm2SUh9AlP+wiTDqbWYWK0GThtTM+gYBUpqHkyI99WyU9zlMlR\nJkXVjVGzRPK2SshsIaubhEa3kPvLSAkQKh8A4j/hzLjneTjOdBfeltVF1xsYxsbM+27OLi77M+9b\nmVklvvr2PBfPsxGEAIFAfNf7luWCHyEtVdDJMnWjPrwtj6Hh0JoYNAYDWuMeHaVHX+sxNHtMnT4q\nPQRP8u0SPYen5rAnOexxlpCbIy5kqQpR9glB5gMSc6JHXXSpBAyywoiUMyBu9gnrA6RxH2How1sc\nDwjoKkYk7UNb9qE9FLL0vQxtO0vLzNDQM7TMBKREpJxLOGsSzqikkj0ykRbJSJtEpEMs3CccGRKK\nTJCiKoGYjhCzceMungBPfZoPgf+f8riun0jwla/AzvNX+N/2P8uTW39A8ORxvC/+V4w/PUd78me0\n239CODw/m7D5VUJy1Qf7bcjv7PgNUF/4Anzyk7uXTo6jMBy+vDsP3+2usbJyjEuXopw7t8Pq6hZP\nPPHELuBPnz6NYRhcuHCBs2fP8t0Xv8trZ3+AgEtI9lA1k7AssJCfI+E8jj75DBvmz9LWSoiFCe6J\nKYETU/YdanLm0EWS9pu0+pd5u7fO6mRMOQxHMzlOphY5Yy/yaLfK3IqFe/kK7uW38DSNxnyGq2WR\nswmN84EivdincY0nsceH6TkZeiGP+DGV2KJKFoVUT+fYtsTxHZG5bY/UmoOkeIQPRUgcje4J+Ioe\n2hsbcHdN3XJ3meXNZZZ3llkdr7JuruO5HrVpjUq3QrlTZl6cZyGxwIHCARbmFkgtpnYXjYJp31pw\nNGd3tvzdoa4jhDyCNQehqGJlh1iZFlZ2HS+/glS7ipVU6Y8rNHfqNJt1JmqNiVNlTJGeVKQZitJN\ndtGSmyTTbVLRLnG1hzjpYmldpmaHodNGRyVvFSgoebKjLJluhoKao+xlqbhJqnaEmiVQ0Kf+7Phk\nndBwBdlpI1XTPxriP8HMuOc5d6nvLobRmsF7E8PYnlkn71Tfwl3etw24BAJRRDFBMJhGkrIEg0VM\nIY9KBsVL7Hre7YnNzsigNfatk94M3BO7h+r1sNAI2hkCRg7UHM40hz3OEbSz5Lwkc26IOjJzQoB6\nQKAacKgKJjmmJG1fbQenfaTJAHHkQ1uaDrHl6LtD28nSNtPsGEkGQhghDcGMi5wxCGcUYqkRqWib\nZKRDPNIjEhkQjkwIRqeIMR0hZuLFHQiCNAFJAUkRkDQRyQgimTKSE0by4khiCknOIUULSKk6Um4e\nqXwQqXyQQLZCIBb7EPj/KU6j4Xvzf/zlIc8IX+e/DnyVvNlA+OJ/ifIbj9EKvUq7/UeIYpRi8Tco\nFn+dqDQPL7xwp7Q7Frvjxz/2GIgirmsznb6+Ow/fal1gaWmRy5dTXLjQY3l5i0cffXRPbd/Ozs7u\nxeo3v/0NNlY3iEUlPNdCM1zmCiny8mkc9RO0jM/TVE4gpDW84xM4NmH/kWs8sPAaEfcqzcENbvYb\n9AydxXiQ4+kyD4r7eWRc48xmgtj1JvalN5GaLdq1NDcqQX6QcDhPgVbsZxlbn8A0D9AXkowTDsnj\nKvmcQk5TKTcdTmxLHNwRKG64RJoOYlUmcSRKfKbSb3vrt2MD7q6pu+2jLzWWdi9H216bol6kMqhQ\nbpWp23X2R/azmFnkUO0QpYUSkcWIn5FelsEDc+e9rRZ9U8MZO0hll0BZw86NsDIt7OwWbn4FqXYd\nKz2hpxdoNmu023XGWo2xXWEolGhLSbYiNr14m2Bqh1S8Q9rpIKk9bK2DanYZuW00FLJmjoJ6x1op\nGDnKTpqKG6dqhqjrHkV1QkjbRh6vEpqsI+cFxEr2R4P8x5gZdxz1Lni3Mc3tmfe9Nbu4bGPbg7vU\ntzEbGwwA7swLlxHF2Ex9Z3EDWTQyqF4KxUsycWS6ikdrYtMaG3SmU3q3fW6rx9TtoQsDRDeCaPp2\nyW1wh/QMFTtKzQ1R94LUCVAToIpDKWCRdxXi1pCQ0kdWBgQnfYJT/yuehxrOoshZxlKGYcCHdsfJ\n0DZT9LwIEylIIC0gpW3klI6cVglnRiTiPRKRHrFIn3B0TCg6RYqpBGIGxG28kIc0xX9UAUkVkQwJ\nyZCRnAiSFyUYSCGFskiRIlKyipSdAbt6mEC2ihCNfqD/Tp7jYU9snLGDPb7zNf/Z/IfA/9s6t9X8\n733Zxf7OC/zPlWd5aOevkD73abQvfYbW4U3anT/GcRSKxV+nVHqGmDWPcLu0+1vfguPH95R2+z78\n0q6Cbza/x/XrGa5ezfP66xNu3Nji4Ycf3mPRXLt2jbNnz/KN73yD1149i2kYhGVQdZtENEgleYSg\n9SQ9/XM0lSfxosCxMRwfsv/wWY7v/wGSd53twS1uDjvgORxJxjgZqfKQXuORdo6jNx28S9eQb60x\nzMZYqsqci4mcCxRohp+iKXwaRTjAUIxiFi0KBxQqokJ2pLO4FeBoI8B8A1LrNoFQgMjhKKkjMWJH\n76q6m8UG3F1Tt7SzxNLGErfat1idrLLurCM64m6bUU2psS+4j4XkAgdKB1jcv0hiMeF76fNhXN19\nz+AtfV3FbFqIKRehbODkRliZLnZuG4oriJW3MdNTuk6CnVaNTrfO2Kgysit0hQzbwQDbYR0l3iUZ\nb5ER2oTtLo7eRbM6jNwOqjDxQa7kfJD3sxTMHBU7RdmOUjWC1FWb0nhMWNkiNFxGjqjI5TCBav69\nAV6p+DPj4o9YovJcbHuAZXUxzQ6muYOur87Ud3M2993bnft2HA3wdhNTPc8BBETRV98BMYVGCsVN\noZFk6iYYmRHaU2hPLNpTnZ6i0df7jKweU6eHRg8HC2lml7iKb5WkpklqZpSqI1N1g9QIUPU8yoJD\nyTPJ2DoxbUhIGxCa9pHVAfK0j2Sq6KEUipxlche0u06avhthIIRQQiGElIuUsgmmNeS0Qig9JZIc\nEI8NiESGhGMTglEFMW4gxE0Iu4gKBHeBHUDSJV9h22EkL4YkJJHkLFKkMAP23C6wxdwcQiz2/sGC\nprsH0D/uV2NkMB6PmWpT9KiOGTXRIhp6SEeXdX7n6u98CPy/6XNbzX/j/1njt9yv8Yz5NaL1LPZv\n/1PaT3u0p3+Orm9QLP4KxeIzJIc1hNslIefPw5NP3intLpUwzTaDwfcYDL7Dzs63uXRJ49q1Khcv\n6ly7ts2DD96p7Ttw4ABvvPEGL778In/113/B6vIaIdkfCbNtj3KmQlx8HMX8NDvKZ7GlPBwZw/Ed\nDhz7Bgu18+AtsTHcZHU8Jh+C4/EMp9wyZ4ZFHloPU3hrm9CNFYygwEo1yg9jMq8FCmyFPsZa5NMM\nQocYBsOINY19eYWypVFq2xzeEjnQEChtucgjF/FAiNSRGMmjsb3JjRlpt6ZuubvM0tqSfzk69C9H\nu16Xklai0qtQ6VWYD8zvXo4emDtA+UB5dx7dGTvvGrylrSsYmwae4xGoGLi5KXaui51rQGENqXoD\nIzOlQ4Rmt0J/WGOoVxk4eRqBEI2gQzOkIUV6ZMQWEaEDdhfd6jD2OkyFMRkzQ36aIz/0YV4wM1Ts\nJBUzQlUTqU1MKsMh4ckGIbOBXJQI1qIEKsX3hnipBJHIe/6/57rGXfBuYxjru5MnlrWDad72vse7\n6hsCu963b52ECARiWCRQSTB1E6hekrEVo6+F6SgiHcWmq+j0NZWhOfTtEnqYwgjRTu6OBUrDNNlh\nnLIVpWrJVByJqheg7HmUXZucY5E2NGLGhIjWJ6T6l5NhbYAlRVBCd6A98NL0vRhDwgzFMFo4SCDl\nIiYtghmVYEojmJkQyoyJxoZEomPkmIIU0xATBkQcApqANIWgwl3ADvrAdqNIgSRBKeMDO1HxFXbp\nAFL1EGJhP0I8cQ+wPc/D1X4yUNsjG3WkMhlPmEwmKI6CETUwIgZaWEOXdTRJQxM11ICKJmgoroLq\nqii2wtScohgKU33KRJlgOzbJRJJEMkEikSCZTJJIJHafr33twwKUv5HjOP6AzFf/b43Ud/8j/yL9\nLAfUS/Clf0LnV0u0Q68ynb5JPv+LFAu/Tno9Q+DPZ5M1m5v+ZesXvgCf+hROWGA0emWm4L/FxYu3\nuH69xhtvOFy50uTkyQf25MFfvHiR5/76Oc69+gOUyRRZBsNwScZiZCIPYDmfoqN+Ft25n8BhBemB\n6xw49heUS69juqusj5q0NZ3FWJD7AjlOa3ke2kly/IpC5soG4kRhtRbnYjzKebHAWugJllOfYjt2\nlEk4SL6isBBSKCoGcw2Pw1sicw2PVMOFokTkUITssTixu/x1r+KxPl5npb/C0voSS1u+7bKqrLLp\nbiI7sh/Y1SkzZ82xL7yPxfQiBysHWVhYILYYQ0pKuJq7xz/XN3T09ak/c95zCeQt3PwUO9fHye3g\nFdcIVm9ipBW6AZntUZ7BpErfKNF0YjREgaZookga8WCfqNhGoI3pdJl4XabCkJSZJqdkyQ+y5MZ5\nSmaakhWnooepqlAfGlT6PSKTDUIpG7kcJFiLv/tF5+1RxHT6XUHiOOPZ5mV3Vkq9Olvcue1992Zz\n37fVt4MgiPhvDwdBkHCJoHpRFC/O1IkzdWIMzSg9NUxPleiqDj1NZ6ArjK0xitdDF3rgBRCNHKKa\nIdFLkh7FqegRSlaIqiNRcgTKrkvedshaBilTJ65Pieo+sCNaH8Fzd6E9CmQYkmAkhBkGooykEGZU\nIpC0CaQspLSOlFYIZhSk7JRIYkwoPiEY05DiOkLMQjACiBOBoOIhazMf25SRrJAPbCGBFMwghfNI\n8QpSZg6puIBUPYxUWkRI3vl79lwPR3GwRz8eqK2RhTpSGY1GPqinEzRRQ4/qGGEDTdbQgz6o1YCK\nioqGxtSZojoqiuUDempMmapTBEG4A+d3AfWP+j4WSxAMpoA4th1GVQWmU971+Vf/6sMsnZ/qNBrw\n1Wc9zv/uBX7LeZbPq3+C8ORphv/sOO19ywzH3yeb/TTF3K+QvRpHfG5m1wSDu1aN9/ijTLRLDAbP\n025/i3PnznHtWpE33xS5fHmH48fv46mnnuaRRx5BkiReOfsKf/mXz3FraRUxwEyZBcgm9iMKn2Co\nfxbF+BjCIZf4mVeZP/RXpPNvorqbrI162K7NsVCEB+wcp/sp7l/2OPZGl3ijz3YpzlvxOOfFIrei\nj/F29me4mb4fJyawkB4z7xkUByYHNwMszNS6KAiIh8Kkj8bIHIv78+qHI0wqE9b0NZYaS75Kb925\nHB0IA0pKiUqnQlWpsk/ax/7Efg4WD3Jw/iC5ag4xJPpdoQ3zjme+PkHf0DC3XYg4uHkFJzfAzbdw\ni+sEq8sYKYVOIEjDSNJTyjTNJE1Ppil4DD0DAgohqU9AbGPRYUqHaWBIwkySn+bIDbPkJjnKZpqS\nEaOiBalOPeYGKpV2m2hgTKggIldDSPXUvSC/DfFCYc/MuOvas7nv7sw6uTVb2tmebV3eVt8TXHeK\n6xr46/K3vW8P3QszdaMoboSJE2NsRRkaYXpaiJ4q0dM8BrrGyFSYOGM0+tiBKaKRJjHOkB4kSQ1i\nlPQIZVOmZPvgLtguOcsmYxqkDJ2EPiFqjIjqfWRLQZNTTKUMIzHFJBBlJEQYSREmwRB2XEBIOggp\nCzGlI6VVpOwUMacipyfICRUprhOImQiWQGAaQFIEQqpHUAv4F4/2DNjEfYUdzvnATtWQiou+wq4c\nIpDM4LrgTJwfC9LmyGQ6nDIa3oH0VJ2iyZoP6dA71PTtf1wVxVFQLAXFVJjoExRdQQ7KJGIJEskE\nydQHA3U8niAcTiJJaQKBJBAHYpim/K5wVpR3h/Y7/0zT/A998bh/zRePv/sTi8G//bcfAv/HPo7j\ne/N//H+1qb/4B/y34WfJpDTUf/k4rYfH9LWXSaU+QjHxi+R/KCP92bf90u6DB33A/8IvoB+I0B88\nT7v9bc6de55LlyJcuhTmzTc7HD58hKef/hmOHDmCbdv8xTef48LZcwyHY4ISmJZHIpolEnoU1fo8\nE/WTCItRch/9a8oLzxNJX2HsNFmfjMmIcB8JHpymuH87yAOXFQ5f7TJIRLicTHFBKnIz+Shv5z/G\n1cL9xMICixGFqmFQb3oc2Aowt+0RH7i4+2Wih6Pkj8VJHI0hHhBpF9vccm5xc+0my9vL3OrfYk1f\nY4stQlaIyqBCtV9lXpj3L0ezvkqvp+uIsggOWG0LfUNHWx+jb6gYmzauBl5BxcmNcHItKG4iVm9h\npKb0JJEtK8q2naRhRWi6EgPbQfcMECcExB6W2EYNdJmIfeJmfAbyHHklS9FIUtZjVBWR6sih3p9Q\nHQyIpQRCJQm5FkGsZ++A/O6FoFIJ4vGZ+lZm8L5tnaxhGJuYZhPDaM1W5oc4zhTXVWfTJiLgYbku\nEzvI1I0wdcKM7QhDM8zQCDPQg/Q0kb4GQ8NgbE9R3DFGYEDYlMkN02QGSZLDGAUtTNmQKdoSBRPy\nlkfOtEibBilTJWEoxPUhEXOIJYaZSmmmUpyxGGUiRhkHw0xDMk5cgIRDIGUipnUCaRUxqyHmFcSC\nQjChI8YNcECYikhKgKAKIQ1kQ7yjsPFn2KVQDilWQkrXkfILSLXDBLKLuEIaR+EDgdocmYz7Y8Yj\n/5lMJkyUCVNzihE20EM6mqzdgbTgK2nVU301bflqeqJP0EyNSChCIj6zN5IJUunUe4I6FksSDKYJ\nBjMEAkkEIUEgkMB1o7huFE0TfyI4BwJ7Afx+cP6gfxaNfvDp1w/TMn+M02jAV3/P5tbvfoPftJ7l\ncfsFJr/9KL3PB+lKPyAWu4+i/FkKr4rI//G7fu3fRz4CX/gC5mc+yjB6jU7nW5w79w1ef33KpUsJ\n3nprwMLCAh/72NNUq1V2Ok2e/863uLW0iue5uK6HKMrEIkex3c8xVT+DdDBO8alvkK2/SCBxk77V\npqXpLAQk7jcTPNAJc3LJ5sxbY6KawNV0htflCjcyD3G98BgruZMkIwH2BzQqY4v9WwH2b0Gx6eFm\nRIKHfbWeOxrDmDdo5pss2UssbS+x0llhbbrGur3OSBhRnpQp98rUrTr75H0sJhY5kDrAvtj/z96b\nxlp2Zfd9vz2dc+74hvuGelVFFrs4NsmepW51JEuCkBixojgIIORLgCDjNwP5aBtw4HwQYihIkAQx\nYkRGJFiwhMiyYTsaHLXk7pbUVqtnik022WQ3WZyKNbzpTmfaUz7sc+97RRbZbDbZSSSdwsJae+19\n9rnvvlf//9prD+cKQzMk+og9tlTXptSvLKlfcvjbEDda/M4UP7kFe6+hL75As7ngtlS8iORazHjd\n5Rw3krm3OFHi9BSrD6nUIXNzxKAdMFlsMzndYbfcYr8es18VXJxLLp22XD6ccTFEhtua/EJGdm8f\ndXn3rodkxa0NrJ922+Vfpa6vUdcv0bav0TQ31gdWOTcjhGUXfUdChKWLzJxk7nLmPmdqC6ZtwWmT\ncVJrjhvJSR2Zti0Lv6AOU8ZNy2S2wfbJmPF0wO4yZ7fJ2GsVOy3stIGt1rHV1oybknG7YNhOkcGx\nMBss9ZC57jPXBfO8R1kY/BAYO8TYIjfrBNiTErFbIXYr1FaDIMJCpyi7lOQVFA0Y260UYYhRm2ni\ncbCP2riE3LgCow8gelcJTN4WsMuTkvnJnNnpjPl0znQ+ZbFIqYtlWFIXafKwMgmkK1mxZEkZu2ja\npmh60SxoXMOgGJxF0uMxo40RG1sb5/LSY3q9LYzZIsu2UWoDKUdIOSbGATEO8L6HtTllKd8xODfN\nncD6XoDzYABZ9r1Q5v29/hLwv8e1iuZ/9398hge/8Cv8J+pXqf/tPeb/2R63d54iKy6zH36a3c8L\nit/8fHrj0c/+LP7f/2tMf2zIYfMFvvzl3+aLX3yRp57a4IknZly6dJlPfvLH2dza4OlnnuTJrz/B\n4dEUpQTeR/rFAVL9JIvqP2D4qGbnr3yR4cGf4Hrf5VZzTG09j8aMD836fORlwSeernj8muXF0YSv\n9S7yzPbH+PbuJ7gxfozesM+9ruHSbc8HXpZcvh7JHMQH0luRNh/Kae5Z8OrwVb7tv83zp8/zwvQF\nXmpe4jXxGj3b4+DogEvLS9wr7+VqcZUHsge4N7uXiZmAjdjTBdXLS6prLe51iDLgd2b4yWEH5teo\nNxa8pC0vysg1JTiuMk5tpKSmVQsafUKlbzPLjunZgp35hMlsh51ykwvVkAvLgoMZXDqpuDxvuGQK\nRjsZ+aWc7MoYeWnvDhD3e5vYLUHLCXX9Yhd9r46LXeW+p92JgxUxWmov0vZ3B3NnmLmcaZsxtdka\nvE8awWkbmLUNol4yaOdM5n22piM2T/tMlgW7tWGnUew2sN0GttqWzSZF3ON2Ts+XlGrAUg9YmD6L\nrGCR59T9DrRHFjZb5EaDnFTInRp2K9ivYeCJixyxVKilJKsEeS3oWYnxRYqw5QbabCPzHWR2gMgv\nQ+8+YnY/Lk4IS3lHPro6qZgeT5mdnEXSs/mMRZmAulJVyktnNaUuqWSVJg/jktKXlL5kYdPkoQuO\nUX/EcDBkNDwD6fHmmOF4i15vhyzbJsu20XoTpcYoNQZGxDgghP4aoNvWsFzePR99HrQqn0QvAAAg\nAElEQVSz7J2B7/cDzr3e+36y8f8r118C/ltc16/DP/7fZhz9g3/Cf9z8MgdXn+f0bzzI0QdfRWR9\n9qtPs/cHkf6v/xGEQPzrf53qrz7KzYdu85UnfpsvfOEJnnxyyBNPlOzt7fH44x8nSMe3n36al669\nSts6QgSj+xjzYarwc2x/SLD16Wco9r9OaV7h9XrORoDH65wP3dJ89HnHp55pEX6Dbwzv4entD/Pq\n5kc5HD2IHm1zsfTc+xpceVWwfRRoLxmyhwuyqwJ3ccZr2Ss8K5/lGfsM16prvBxeZiEXXJhe4OD0\ngHvCPTwcHuZ+cT+X1WV21Q6qdbTHJeXLLe56JM4lYXuJ3zki7l1HHbzE0dYxL2YVrwjP9ag4cjAN\nLZVa0qgpS3PErDgmdzmT+YSd2YTd5SZ71YCDheFgGrl80nApaC7nfTb2crJ7+pj7NpGXLhAv7OP2\nh7Q7imajoRY3qJuXuzNPbnabdo7xfpbWiPuKuY3MXAfgVjFzhpk1nLQZ00Zz2ihOWyhLj1rU9KuS\njSqyMxuxcTpge5GzU2fs1IJJI9huPFtty0Zbs9UuGNklVmqWesAy67PMCpZFTt3XhFGAsYMNi9iq\nkZMasVPDXkPYtrQxg9IglypF2R1g97xGxwEqjpFiCyUnCLUP+hJR30sQV7FuF7+QzI/nzI5mTE+m\nzKdzZrOzlMeyXDJv59Q6pTxqXacVHjKlO5YhAfXSpUhaSsmwGDIajBgOhwyHIwajXfqDPYrhDkVv\nJ6Vp9AZKbSDECBh2AN3DuR7OJYBOqQ5xV3B27t2nLd4uan6fTiv+4V/WprOw6jol5lf2G8vvtq6u\nEV/96l8C/uryHj7ze5F/8/f+iMe+/Mv8u/v/nJv/6T3Mf2qJKxx7sx9h7/ccw3/8RcSV+7A/+5Mc\n/5UBf1p+mc//4b/hiScUTzxhGY83eODBh5jNj3n5hVc4PJ4jEEQEmbkXX3yazccGbP/oy+j955jF\n69yyDfc5yeOnho+8DD/6rOWekz4vmSt8e+sxbowe43R0FXoHXJgqrrwiuHQ94gcSf9XA1UAzOeV2\n8TLPZs/ylH6Ka+Ear6nXGDZDDo4OuLq8ysPNw1wNV7nMRSYMUaWgObS4GxFua8KgwU+OiXuvU1/6\nLq9v3+IV1XCdwE3hORKWBSWVmrLITpn2TjDesDOfsD3fZm+5wf6yz8HccOHUcbkSXJY9LvcHbO4P\nyO4boj4wxl0e0OxJ6s2aejinVse09uYdK0+cW7C0FVNrmVqYW5g5wdRqpq1mag2njWJWC8IsomeO\nrGwZVS3bi4LtWZ/NWY9JZZjUku0GJo1j2zZsNSVjW6FCYJn1WGY9qqxg2ctpB5IwijC2xE2L2GoQ\nkwZ2O8DWhpYMGo0uFaaW5I2gbxWFz1BhhAwbyDgBsU+MBzh/iVmzz+lswHzWpMnDaZo8nC/mzMs5\ny2rJwi2os5ra1JQqLcMrKdfR9NKlycNMZwyLIYPeFoP+Pr3+Lr3eLvlgQt7bxuRbmGwTIcZrgE75\n5wLnCto2p2kMda3XqY4VOJdlOkj1vQbnPP//QdTs3HsLst9PHaShRVEkOWfHPCfkOT7L8MbgjcFp\njdUapxStUrRS0kpJIyWNENRADVRAFSNVjPxXv/RLfwn416/DP/2fXqH9h/+In+/9Q9q/Zpn9hxnN\neM7uyYfY+92Gjd94ivjJH2HxM1f50qVb/P5Tf8rXvjbniSfAmIKLly9wenTCzRsnVI0DQOkN5PZD\nbDyyxeZHD9E7r3AcjlgEzwdLxYdvSj7+fODx65Lg7uHl/oe4Nfog8/59RH2ZnaMBV14R9GpY3iNp\nrzjKyQlHxUs8XzzDk4Mneb54nqVccvnwMo8cPsJD5UN8oL3CJSaM7QA1y4hHEg41eHCTY5YXX2J2\n+TleHx1zXbXclC23VM1ULlnoBbN8yknvBB1UisjnE3bKERcWPQ6mmoNZ5FLMuKz7XB6NGV/uIx6U\n2PstzY6lGle0xZxWz7H+GOdOqO2Uk7rktG2Y2cDckY5+tYqpVUwbRbNQcAJqHskWjl5l2S4l2/OC\nzXnBdpmxXUl2G8+ktWy1DeO2pW9bapOxzAuqPKfs5diBJIwDcexgs4XtFnYa2G5pMkMjMxwZ0Sl0\nrclqSdEq+laR2x7SbxDsBpXdYl5tMK+2OZlvcjLtMVs0a5BeVAsW9SItvdNn0XRJuY6ml25JZRt6\nZodevku/2KNX7FIUO+RFB87FFirbQKpxNzk4IMbeufRGRtMYqkpTVXKd6ogRRqMfHJjP1/f733O/\n1vt7ef/DAdy7tY1xDbSxk5Bla/HG4DqwdVpjlcIqRdOBbduBbQVrwC1jpIyRKgTKEJg7x9J7lt4z\ns5aFc8ytpWxb6rqmaRqaprnD9t6T5zl5nlMUxdp+Y/nt6vI85xd+4Rf+YgK+9/AHv13z5C/8Sz7+\nnV/i6k9+icOf36S5dMrk8CH2frtk83dv4H7yY3zjUcnv1M/zp3/2Ot/4hkRKzeZmn5PDiuNpSYwQ\npSK7eMDggQ22Hi/RW7d4XSzoe3jsWPGxl+ETL0W2ql1a92Fu9x+hLK5AvMLWjQn7h4L5tmB20bHc\nO+b24CVe7D3FNzee5Omtp7l8cplHrj/CQ/MHueoO2LPbDJYjslmBPMlR84zF7i0O7/kOJ/vXuNU/\n5UZWcluVnOgFp9mc03zGSf8UEClHPt9mbzlif55zMFMc1BmXRMHBoGD/IKN4sMV/YEm7V2IHDTZb\n0oo5p3bOST3ntK2Ztq47ORBmVrAsJWGqYCpQMzCLSLH0jMvATmnYXmRMSs1uJdhuIluNZdw4Rk2L\nl5JlkVEXGVU/ww0kfhSJGw62LGy3xElLk2salWFlhsMQo8Y0iqxWZI1CNgpf55R1n2U5ZLoccjIf\ncDztc3SaMa9aFnXKOZeqTDlqtYqkS8rgWTpBGzIKs0M/26PIdsiyLfJ8gsm2UGYDbTYReoRQQyKr\n9EaxBui61lSVoqokdQ39vnjPo+b3bRJwBbrvd1R7rhxXtvfQ6xGzjFAUBGNSZJtl+BXQngPbdgW4\nQqwj2xXgVjGyDCFp79eAu/CeRQe207Zlbi2ztmXRAWzTNMQY3xWwnrezrEDrPlr3UaqH1n2kLO4U\ncoTLUDFHeINwGuENWAVOEq0itgLfxCR1wDeR0AR8G4ltIHQ62k67CDaCCwh3pv/O6SN/sQD/+nX4\nnV/4Btk/+d/5yY/+Goc/V1A/Nmfr9j3s/V8Ltr4U+PbHL/M7gyM++/qrfPUbkBmJJOP4xFI3FvKI\nvjykuG/E/oMNfnvODW25XAk+fEPwiWuRq9MRo+VDtHyEytyLrD7A+OZFvFEc7ntOdmccbr7Ii4M/\n47nhU5S65IGbV3l0+QHurQ/YrrYZLMbkpwPcUnG0dZPDi9c43nmVw/4Jt7M5R9mc43zGcW/KSX+G\nl56d2Q6TxRZ7iyH785wLS8O+NOxtCPb2IjsXHb3LNW5vyrK/5CiWHNsUeU+dZdoGFi3YOTAVMJWo\nqUjAvYj0y8B+LdmvFDuVZKuUbFaRcR0Y1g7jI2VPU/UMTV/jhivQ9rDVErctbV9R64xWZlhpsGTY\nqAmtIlaCUAp8BeXSMF8UnMxzjmcZR6cZRyeKmW26nYctCxGpUJRBUnpBjANyPSE3E3KzjTFbaSOO\nTtGzUCOQA6IY4GPvjvRG0yiqSqF1AtUUOYv3BJx7vXdxcGQI73tUG6sKqop4rk40DThH7FII68hW\n6xTdKoXV+o40QisE9Uo4SyFUMVKGwKID24X3zDugXUW207Zl1rZMm4YK0EVB3oHm3YA0y3KybECW\nDddgasxgDapK9jD00RToWKBFjgoFKhiEN6iQIYNGeIXwSUsnwEmEk+AFOIgtYGMCURuS7QJiBaQ+\nIlbaR2QISB9RISBjQIWIJmJEwHRaEzEEVIxoArrDMScEQQq8EHgJXnZaQZDgVcSrSOi0V4GgAl4F\nnPZ45fHa4bXDqaStbnGdWN3gVMM/+K3/5s8/4HsPn/unR7z49/4RHx/8feS/c5P5j1k2bm2z91sL\nFs8O+b1dzf9d3eKr1wJEQV3lzBYVcRiQFzWb9+RsXvGUOw0zE/ngseDjr8CjhxkHs3vYnH+Y6B9E\nza9CeYXXdw23d0pubb7C7d6zHKuX2I4Z99b7XFxcYHMxQS/7LLzlJJtxuPcqJ1vXud0/4rg347CY\nctSfcjw8xUqf0iqLTXYWA/atYlcJdgewuxnY3GnJJyX1eM5pvuQkNkytpS4D4RSYgZyCnAmyuaC3\njGzVkUkNe5VkuxJslIJRGRmWkaINtLmi7imaocINxRlob1vagaQ2GbUy1CqjFhkVmipo6lbQLqEp\nA+XcM1tIjqaao2mfm4s+h9WAWciooqKOmjoohBiRq+204UZtodUmSm8g5BAhRyAGhNjHxx7W5bQ2\no20Nzkn6/cBgEBkOYTSSjEZiDdLvBpgHgze8XyOEtEbvPYxqY1URy5LQ6XiuXtQ1ommQbYtwbp2v\nPZ+zXUe251IIzRvAtjwHtiugXXjPsgPZeadnbUurVJey6BHzEbEYgRmS5SOMGZDpAZkaksshhR5i\nRI9M9DArIUWnmhwVDCoaVNSooJFeI4JCBYXwAukl0gtEEAlkQ0R6EI6kfYBzQHoeRFWMyBWIEtZA\nqklAqonoGFCAFQk8nTgDTyfpwDSeaRVwHYB65fHS45THaY9TDqcsTjucbLG6xaoEnq1qaFXV2SWN\nLmlUSa2WNGqBzxpiZhF5gByEEaAjQoPUAlREKhAalJRkKHRUmKjQQWKiJAsy2UGivcAEgfES40ll\nJ9Aesk4bl0S7tBLPONA2YlwS7SL/3mc/8+cX8F972fOHf+dfsfX8/8A9P/4nnPxUZHBYEP55zRPf\n7fMvfcWfzSPLpmC+tNhRQ3YBdi9rskuR412PifDR1wUfeV3ygdNdLp08xO7Rh8mmD/Dq6D5enRRM\n+ydU/ZfJ1CFbztCzPYIzlC4wlTVHW69ztHmTw8ERx/1TDgenHA1OaUzLZLHNTj1g12XsSME4D/RH\nlmzYoIYN0lTIuiZWHqYgZ5DNob+EcQmjEjYr2K5hqxSMShiU0F9GghI0A0k7kLgh+I0E2m4EVZZR\nmYxKZZTCMBeGZVRMveaoyZiVitlcc7owHC8GHC2HHC9GHC2GlG0fJTbQcgsjN7u8c8o9I4YEBoTY\nw/ke1mdYazAm0Ot5+v3IcBgZjQSjkWRjQzMey7cH50FklDWMdMXI1PRlzUDV5KFCNO8sqo1liS9L\nwnKZpAPaeK6taBpEXSPbFtm2qLZFen+WRugiWyvlOrqtO7Bt6CbHgLoD3KX3LEOk9DBzkaUTLJxg\n4cGJIVGPwIyT1iOEHoEaEVVKE0k5wMgCIwoMGZocg0HTAWrUqKBQUaGCRAWJiALpBTIIZAAVQIYE\npjLGVPYRFWOKQGNExqTPA+mqnMAULB14CsE6CF6DaMTLiBMxgacMOBVwK/BUDicdVjmsslhpE3Cq\nFisbGtXQ6opW1bS6JmhHMC2YlmAs0ViEsQjdgrEI0yJURMiIFAEhI0J4hAgo0s9mQgJI7SHzMoGf\nFxgfE2A6yPwZQBoXMT6SWVIbFzqgDGQuYpzHuEjmAsYHMus725N5j3GB3AUy78m8Q8WAwCNwCGJH\nSaoTTRSKKBQIRRSrsiZKnXxSg0x6bSsDUoHSRKWhk6g7+7zWptMasjN7+1/83fcP8IUQ/wfwc8DN\nGOOHO98W8BvAFeAa8B/FGKdd3d8G/nPAAf91jPEzb9HvWwK+9/DHv/I8J7/x33H/Q7/B7Gca5q9K\nnvtNx++8kvN02+O0DTTDOTv7keEB2APBrZ3Ipbng46/Dw0cjPnB4hQde+TDF8jFeHt/HYX9I0z/E\nFTewcsFS1pzqmuPhEUcbtzgaHnE4POF4eEKV1WxXYzZ9xkhB33iGytLHMnSOcW3ZXQa2SxguEkCP\nlgm8NyrYWIH2ApSDdiBoRwI3hLARcaNIVRjKzFDqnIUsmImCE3qchB637YAb7ZDDasB0UXC6GDBd\nbFItdqnLCzi3hxQjECNgQKCPDz1cKIhRUGSWonD0VtHzCEZDwWTk2B1adoaerV7LRlYx1DUjUzPU\nNUNV0ZcJkHtU5LFC2QXKLgjlEr9cpsi2LFNk+0awbRpkF90qa1HWoq1FOoc/l6+1UtJIRSMkNZIq\nSioMdcwoo6EJGVU0VD6nCYY6ZLTR4BgQVI8oBwTZJ3aC6BFEQZQFURRAThQ5kCMwKBQqSlSU6CCS\nHUBF0WnQsdOB9F86cgeIno9CDRFLGsY7IRJ4rmQFnDLiRcTJgJMBKz1Oeqx0nbZrAG2VpVUtTrV4\n5QjK4rUjqJaoHVFZorKgWqJqEaoF3SJUg5QtQtVo2aCEBTwShwoejUNEjw5+DZSZS9q4gAmxA8GY\nQHAFlD6B39rnPZkP5N6TBU/mIypItFcYr9BBoYPGBIWOGoReg+AK/M4A0KyBcA2Cqit3gLcGQ30O\nBI1Jft3Vd+WgNVGbJFITTFdWmqANvqsLyhC0JugMrzRBGVxX75UhaINTSazUtErTKkmrdCLGGLE+\n4Lxfa+cDLvg32F1d8IRwrj4EfOi09/jo8Z3Pd+1WvrCqix4fPCEmO8TAU7/6X76vgP8TwAL41XOA\n/4vAUYzxvxdC/E1gK8b4t4QQjwK/BvwocBn4A+DBuyH73QD/+nMLvvaL/zP78n/l6OO3+MOvwr/+\noz1esJ68P2U0ccgLMDsQnIwij96GD90qePjmBa5ef5BefZXXBxOubcK0d8Jp/4TjwQlH49scjg85\nGh5TZhUbbY9Nq9iIgY3YsuUte3XgYAEHc7hyAhenMF7CsEyReFaC64EdkSLtMVR9SVUYFlnGXPeY\nyT4nccxx2OC22+Cm3+ZGO+Go2mO5nFAu91gsLtAsJoRqSEZgJOeM1JyRWTDOSjazinFen0nWJCDW\nDQNZk8eSLJZkfknmS7QvMa7CuArlKpRtUlTbWKT1KEcnAhXBktPKnEbk1KKgJaMmp40ZDTktGW1X\ntjHDkuFFQRAFXuQEURBETiAjkhFFBmTEaAADaETUEDUShYgSGSUKsQbVFWCuc6GpJxzgEFhBiqsE\nOBGxaxANHZAGbAegd4CoSraXthvaO4JyBGnXOsqW2GmhLIgGIRuEaBGyQskaJZJoUaEp0dRoLCpa\ndGzRwaJDSxYimQ9kIYGi7obr2qs0hA+qsztAjCutMdGsbRUNGo0JGQhJUBlOSpxSeKVwnSTClHh5\n5nMrn5LYlV8KrFI4mWzXjWaclFghcErQyrOyFQonoZUCJyRWCqyQWJHSKhaR6hC0Iv1+LCIBELED\nogRG4bzNee0J3Tn6gdQu4omk+jOd7Ignik6fsxEeRACVRgNIj5AesRoCiYCQ59rJ87qzOx1F8sXO\nF4UHVv4IUSBQiKgQyDOd/poRyPQ33vlWthRndVIoZOeTKISQKKGQ4swnz/mUTPeoVb1MdVqe1f/+\n3/jl9zelI4S4AvzWOcB/FvipGONNIcQF4PMxxkeEEH8LiDHGX+za/Svgv40xfumtAN+7yJf/l/+T\n46/9TX5t2vLkDUkuThFbNdWFyI0LECV8+Ibk/qMhF2abGDfiKO/xzQs1r02OORwes8gbtlrNbiPY\nbSL7VeDCMnBpFrn3BO47giu3YNKAHyVpBlD3FVWhKTPDUhcsRa87kXDMst2kcmOWzZi67iOXkWwB\n2RLyOjCQloKWQjgKLJkI5DgMgSyGLkIMmJgiSR3Tn4bH0JJjRZ4mO8mx3Z2eDEeOJyOIjECW4sqY\nrSESdAJWJMRzf2jrP8PVaS/cAaIOkhZguyjUioCTESs9rUzRplM2AamyeN3iRMqDemXxqiHKFq9s\nij5FKgfVgGwRogZVIzogTb4WKVuksEjRJC0tUnggpt0Nke79sxIRU1oDodIvX0jiSnc/ZSCVA0ki\nkiBll/MVuG7yzElwQq6j7zvSGULgRFxH516AB7yIXT8pQj/zRYKIeBEIkLRI8BRkBxLCdyDUActa\n3wk2bwSguK6LENN3QOyAJaafmU6vgEeiYA06dwLQyieFXAPNmwBInNWfgc45uwOb8/YKdM4DkJIS\nLVM7Jbt6dc7f6bVPKfRKdz6z0lphlEKrs3bnfUbf+ezVZz1vrz+X+P7bnYGxfNP7kP+/cv0gO23f\n7f62vRjjTYAY4w0hxF7nvwR88Vy71zrfXa+f//GrlMVt5lsVhxPPyx+A/QXsHwu2agUNbB06jvuG\n04HidZYoPePSFB6z8NPXYPcF2I6KEQafGRpjaFRGIzMaUVDHgmrY41qvzzMHPdq2h60M1uY4r7EL\nhV/KFFEJQRACLzst0ux6lBAGnjCKeOE7CXjp8TJ0w/YUHfl1VCVwMiTgWOVCRWofZLo3SE+UniA6\nWwTiOR1kijaiCJ3vfDl2UUogyJUdiSRQSjbJJhIF6/ooIkGEtZ36iogoOrCVHZnILrKR0EU4INYR\nDh21rOz0Qo1OhEaIFQVphMiTLdJr72TXPrXpoqL1f7Q77aTvLEt5dk967+mZXwiJOlcvpUJIkSIn\nKcm7/9xCpj6l6iIq1ZWlQMhzIKdSv2rVT9e3VN1zOlATUq5/PnHus0F6Nl159do/KVff26pdp4Vg\nFYatArLYCed1jN+z3arN+bDu7foJ59u9zbPDuUDxjue9oc/2e/0Mb6h/q36IEFP08qb6u/edRhNv\n1eddP/u57+HtPuPb/h7e5nlv+j283c/9Nn3/INd7taH5XX2Wf/ahPvLWT2FuHtB/eoe9ow02rYdo\nkRb2I3xECjZUj7HsM1Y9hrKHlAIIICItgRsi8LqIpOFYPCcpAlMi0heQi0joZvedDOdm+unAvavv\ngH5VH7qJrdC1812kuC6vlmJ1ZOG7CDNIkchCQJSpfRTpeVEKoqBrIwgCglq16e4VEKQkqHM+eaa9\nEnfxS4Jc9as6wkrRb5TglezukUQlCaK7R51/Jmu9kjf6/V18/BADojc/6vv8E/wen/Wu1ZFuCPC9\nOl+Nrd7Ro97U6r38GsVdv5e3f8KbasXb3fHmmu/389+t/TvpQ7yDX6K4s/g9e75rrXhz3ff1fdz1\n+3vn39t7+ffwbgH/phBi/1xK51bnfw2451y7y53vrtfBZyb42BLdqxT9EWrngNvVkFfKbeqwS10d\n4P0IqW4DN4jxOsFfR5pbyPwE2ZthBgvMaElvs6IY5PR7OcNCM840m7lgy8CuDmwaxVBC5nwSH+g5\nj+kms2QMiJhWQchE6QjSCDvNwiegBJns1RnnYoV05ySm9ErskDEGSQyK6CUhKAiKEFKaIgZJiCq1\niSqlLrphRQwScQ5RxTmEXfnX6Y+OWURih67+7J61PndfYqKu7fo55/sS6zYE0fWx8nGuvrOBqGL3\nFSRNClzTKEJ1WnJHmzfec4eWZ/estYx3+M63X9t38a9HM+fuu0Pf5Z4gwlmZSFCr0RXre4IMd/bx\nhj7TSO2cn3P38Ib6c+2DeHObN/nO9RVIo0cEazuIJOu+V+XuZ1u3I93npV/7okh9rkeGHXmkqDOe\ni1Dj2vfG+ndyzzvp593c835+3vgef943XgKRRosI4ouReC0mgvsB0f+d5vDvI+XwP9SVfxE4jjH+\n4ltM2n6KlMr5fd7hpO2r01f5jc/+Cn/y679F+O4m2Y1HWYjHOd68QFsYBuNT8v4SoT1tLanmAxbT\nDY6PLzKdHVBXW+hsisxugz4kyNt4bhLCdbAvQ/VdEDdguIRRhhgOEIMeYthHjAbIYR85GsJoSByN\nYbyJ35zgtnYQgwHGt2RtjWkbsrYmayqKtqaoa4qmotdU9OqkB1VJvykZNCXDesmoO9N83JZs1A0b\ndc24bSlimuY0gBYRLSJGgNNgM4HVEqcFzshzovBa4o3Ea4UzEq8lwUi8UXgjCSu99kuCFoQMgoZg\nIkFD1JGokqAjyICUASEiUnikCCjhUSTRuGQLt/YpEfCxy6UHTQiKEDQxyo7UEpmlYUo3u7DKk8VE\nfjJ0qaHQ5atjl6cOyZZoRJDImJYviqg6O00My26SWESznjAWJFt0ft5oYyDoszZoCOZMr/tc1Sno\nPtO5YVsS/2ayJApiiOBJOtypo3+zj8Db+u/Wz1v1/27ueatnE+liG/Fmrd7CL0Eo8X3dI9Rb9/N9\n9f9u7nmvPtMP8HNEEde+VYC08p8PkiYfmbyvq3R+HfhpYALcBP4u8C+A3yRF8y+RlmWedu3/NvBf\nAJZ3uSwT4NrpNf7gO3/Ab331n3H0jS9wz7f32Pr2Y4SjRzm1j/NnfJgXxP1cOHiW/UeeYvPe6/RH\nFZmMSCcRZYarcqr5mJs3r3D79mWOT/ZZLjZQpkYVp0RzitdTnD4Bcwj6FsjrEF8Cdw3KWzCbda+j\n6SMGQ1S/jx4OyEd9eqOc3mZOPsjQgww5yBBDgx8NcOMhdjymLYa0MqdVOa3oYUUPJ3sEmSGCRfoK\nfI1wFcLV4GqEazBtTdY2ZE1L0TQUdUPeNvSqmrxp6NUlvbqiqCt6TUmvKRnUS3LbkkWPCQEdA7lf\n6UjhBIWV9K1IthPkK+0juaNbeRIxIZD5gIwxreaQadWHlQorNVZqnDK0UmONoc0NNjfYzODyjDbL\nsCtdGFqjaAtFkynaXK7FFoo2k9hCYHOBK8BmsiMnkUgpYS9RR4TsSEi4TjwqekxMBGRw6JjISeMw\nHUnpjrC0cGvi0rhz5OWQ+DXBSXw3HewR3bTw2QSxuEPOEifiDdHaKukguogtWbKL3iQCKVZyNg+y\nmgtJtj6n7yYGKQ1C3CnJl3c6Q4gMKc+0lEVn50iZd21zlMrf9AxQHQGuiLArh0S+yZ8IkZRZfc+I\n6/smxh8Cmb7ds38YBP+pZz/153fjFaRhz3eOv8NnX/wsn3n+M3z+u/+a+28EPvpNw/bTB+Qnj1GH\nx3iaD/FN8SFe5zLDwYvYgyep73meK1ducemBJbsTGAF5CWqpUKUilIblbIPXrmQvVbwAACAASURB\nVD/AzRtXODo6YDqdUC43EMIjzZyolzi1hGIKxSn05lDMoDiG7BZwHdwpNFOo54kkZnNYzEFJRH+A\nHg4pRkNG2yO2JyMm22N2t8fsbo3YGvUY9Ayqp6AwxL6mNpJpbDl1jnkMzCMshaIUmkpkVDKjkQWN\nKGjpY0WBkz0iAhVqpKuRvkb4GlxF9BXBVwRfEkISaRuUbdE2bVBSjUXXDlM7dGuRTYtuGvKq7tZe\nB7Lgu5VHMaXDYrhDdEy+bF0O5CHSayV9K9MRwlZSOEnPCYrz5OMhd5D7bk14SMSTJK0ljwic1N2S\nQ42TJh3toLJk6wyrc6zOaXVBawyNyagzQ51lNMakHcYmo8oMdZanvRAmo8xySmOYFzmlyViYjMbk\ntDqjNRqXK4JWhLwbNWUCpQJaOoxM5KOlQ0mHEQ7Z2Uo6pHAoYVHSI4VFdIQlzo2YdPRkeExHXEZ0\nulu/ZcQZiWlxpldEtiIuFd0ZgeESTcW0Nl92ZCY6n2C1uSjRWUpVpHAyaTr7jRnw2NFc7Oxwbr4g\n5fJWq67Wk9FrvZrQVtyd4O5GcuacXpGa7sjMdGRm3kRsQmQdieUduWXniNDwdkT6zuvUD3VFz1+4\n8/BjjHzr9rf43LXP8dkXP8vnXvwc/dDjI9c3ue9Lx1x4csHEPkqMj/E8j/MN8TGeFY8yZUyx8TzV\nwTexF78Bw2dg5wbDC1s8vHcv+xsDNnRkFEtGccqgbtBLEKXi9HiXV157iNdfu8rtW/dwcrrLfL5B\nXY2IUSDEghhnwBTMnJjVMAgwDOllGIMS+qegS2AGHIM/7ohiDuUcpjOYd0TR1Mh+HzMa0tscMdoc\ns7m9xe72Dhd397iyf5HLO7tc2N5kc7NgYyNjY8OQ9aGVlplbMC2XTKuSWd0wtw0L27L0jkUIzENg\nFiMzBAsUSzSlyKhETkMviejRih5O5sjQon2DcjXSNwjXnJGJ68gklPhQ4v0SHxcoazGtwzQWU3tM\nbTFNIKstqraoNqR0jo/41RrtGEiT70CMSa/y5QSkjGkTkYiY6LrNUAEtVoDZkU+nsxDpt4p+K+lb\n1RGOOiMfJyicpHCCnoPciTXpZP5stKPjinQCJjhUTM9zQuM74vEyw6lOZIbTOa0ytDoRTaPTKrIV\n+VSZocoNZW6oCpN2TucrMsqospxFnlOZjEbmNCqnFoZW5pQipxZ5OhJDZDQiw0udVhgJQZcZQEqB\nlKSyTCJUp2VESJAqaVRESo+SDq0sUjqkSoQlZIsQDiEdQnS2cCAsAgcd4XQLislw3TJlTyY8mXBk\nwmOEJz9PXKtRmXDo6NYjM3VOJyKzqLgirI7QVuWYiFPiEdF3BHbeDoiY1tjTEdNqZCZW825CvInS\n7oI8QOxeLr8iu9W6pjtJ7s0Ed57Y5B2klnx3I5asIzWzLgthePzxX/+LBfhvvEIMPHnzyQT+1z7H\nH7/0x+zmu1wJV5h8y7Lx+Rc4eOl1Hpc7GPcwr8ZH+So/ytPyY3ybq7QS9PZ3cPtP0l78GnHyTZDf\nRMpAPrqf4eVPcPHCg1weSvr6mIITBm7OZjxlIg8Z2QWyEhzevsy1lz7Ia68+wM3rH+Do+ALT6Sbl\nckjTDIjRIMQcJU8R8gQhpsS4TC90Dgr6CrEpEJsQR4rYs5A1YCpQJyBvQzyGcAJunohiMVsThVjM\niYsFQmuy8ZDexojh5gZbW9vs7exwcWefy7t7XNzZYW8yYXt7m+3tbba2ttje3mY8HgMR70u8X+D9\nHO8XWDdj4ZbM7JJZu2TW1syqinnTsHAtC5tIZBEiJbCMgqWQLNCJSCgo44A69qhFj5qCVhZEBDq0\nGNegXINyLbJLaeESmYRzIxMfSnxY4OICxwJlW0zjyBpPXnt6daSoI70mJ7c5mc3QjUFZhXIG4Vlv\n8kkUE9KS2E68SsttY3esgFUuLantDrZyq2W4Mp3XEkQiHENIEXhMRxqkHROewkG/VQxaTc9KBlYz\naBPp9J1aE1DfyW6kI7vRDuS2Ix3HmnTSKCqRjo4+AWRIEb0MaVVQkAavMoI0Z+SjMqzuRj/KpFFL\nR0B1ZqiNocpzqkxT5YZlR0KLnmFZaBb9REZNntHoAqtyrMpSqrLbxNeKnDp2mow6FvggCVYSHUQn\n8FYQXJrrUKIjISFQEtRKd2SkpEB1Z9UoxVoLHZNWiaikAnQgdtuko4pEGQgqTZAHGbqVeWmS2nf7\nKNI2P3tOJ0mkFSiEp5CRXKRyLhOR5SJ0BHZGaplIKcPsbiSGfRORKSwSj6ZFRt+Vu98jDnEXLbod\n0xLHz/zEa3+xAf+NlwuOr7/+dT734uf47LXP8sVXvsj9W/fzcPYwg1sDmi/fwH7hKzwwn/NjOme/\n3uTIPcSX+CTf4sd4VnyQ57iIyErM5Dv4C9+kufAV/MHXIXsWpiXGbTIeXGHv0ke4+MCnGV55kEJV\nuMV3wV6nL48YhRlb8ZRdDtnTNxmKKa+9/gAvXnuUV156hOuvPMCtm/dyOt1iMR9QVX2c6wEFUsxR\n+hhjTsj0KYiKEAKt1Vhb4L1Ayxky9zDWhFGPMCwIoz70I2Q1ZDdB305EwQmEGbQLaBYwmyFmM5jP\nEIs5LJeEpiEbDemNRww3N9na3mZvssPBzh4HOzscTCbsdESxIomVnb3NGb8htB2BnJGIc3Mqu2Du\nFsxtxdxViUjqNBKZO8fSeRYxsgxQIiiRlGgqYahin2UcUMY+FT0qCmqR08oMHRzGtxjboK1F2xbp\nGqRroCOTNCqpE5H4JT6kUYll3hHKHOkassaTNY6sFRTOkNucwmYUtqDXFvRsj37Tp98OKJo+eV2g\n2wwVQPiIl7bbP+Hw0oPyOBUIOmCNo8laatNSZy2NsjS6pVWWRlpa0dKKFissHn9uD4jvgKwTlQBP\nqkQ2hkBhYdDqNeEMWsXA6eSzKpGPVfScSrpLs63md3LbjXRcJLeroxfiudGT7+ZKfEohRYcKFhkt\nstNBaILK8CrDK4PXOV4bnM5wOpFQ25FPa86l2/KMyiTSKfN0/Mi8l+x5z7DMNVVmKDPNMl+l8TLc\niohWpCRyrMxpRYGNBuFU2nloJdFKYiuIrSQ0ktAKQiPxLfhG4K0kth0hCYHuSEhLgdYRLQVKgVYi\nnfygIlqf2etjcgxIExAmgokIE7oD2FaLJcJ64URUiZyiOiOq1Ua/1cY/LyLP/cQn/xLw3+5qfctX\nXvvKegTw5de+zON7j/PJ3U+yM99h+eySb/3JV6i//GU+aQyfznIenNeIapsv8ime4NM8x4/wHFe5\nJiYUvWOyve/iLzxFtf8V2gtfgtFzcNwiphmF2GFneB8PXv4Ijz7641y8/wHsZMJzdcX09nOExYvk\n8QYjdcyGOGU7nLIXb7OrbrKZnXA42+eFFx/j2guP8uq1h7l5/X6Oj3eYzYYsy4K2zSAWwAApl2h9\nSJEdkuenZKZGSghomqbPcplR1RGjphi5RElPiBLfy/HDAX44TCQxGsIoQnYdihtgboM4gXgKYQ52\nCdUSpjPkbIpYzBDdK5b8fInKDL2NMaPNTTa3ttnb2WV/MuHCZML+ZMLkLYhiNBp93/nP9GL4Cufm\nd5CI93OcW7DoSGRmS+auYeYaFq3tjvQNLH1gGSNllCyRpAMUcuo4pIoDKvrUsaCkoCKnkjleSHLX\nkjlLZlt0a9GtQ9kW6VqEaxGuJnZk4n1JCEtcXODjAhdWZJLEijnCNWReYZzGeENuDbnLyK2hZ3N6\nbY+eLei3fXptj34zoN8O6DUDes2QoulR+IwiGrKg0s5uKVFCIDW43FH2Gsqipiwa6rxhaRrqrKE0\nlkbVVLKmFjWNaKipaWlpYpPIhha7+hc70sGfLetUIe2zM92KLx0JOqXplJP0W83Qaoat6QgmEc7I\nnZX7VtF3iXzumOOxaX4ntzGNdhwYG7qD0EKX0kupPBPPT7xbRLTI2CQdWmSwEANBZUl0GgV5k+P1\n6oUoGc5k2CzHZoY2SwsOmsxQ53kimI58ysIwL7LuHCxDaTSlNiwzw9LoVDaaZaaps/Qcp1KKz6kC\nK7O0iiyItMDES4QT6VhnK8+RUiKk2EpiIwitxNeS079/318C/vdz1a7mi698cT0H8MSNJ/j4wcf5\n6Ss/zQP6AeyLlq9/+et8/U//FL71Lf7qzg6fNIYHZwsuHs94UjzGF8OP8wz/Fi/wEb4tDrjFgOHo\nJr2L1wgH36Lc+xrV5E9w28/ATMIth6yGjMwBF7cf4iP3fYJPPfoxPvjII5idHb5VVTy1XPLC/JT2\n9Ltk9csMuM2mOWFTHTHxx+z72+zqW/TVksPlPjcPL/Lydx/h5e8+yvWXHuLW7XuYzYYsFjltqwhe\nAQUwQsqaLLtNUdymX0wp8pLMWJTSRDmgbcbMporZXNHahp45JJNzJA6EwMUMywDbG+JGQ+JwCOMh\njCNsTKF/C5HfIpojkKcQ5+CW0JawXCKmp6j5FLmYIxZL4nyBny8I1tIbjxltbrC1PWF3e8JuRxS7\nb0ESK23uOAP5B7vSKGR5B3msRiLeL6jdfD0SmbumE8vCOpbWsQgxpbSioIzdSIQBdRxRxz4VfaqY\nRiIVOaXIqKXBhEDuLJl1ZK0jaxy6cWhrUa1bk0n0TZp4D0t8nBPCAhvnuDjr9Bwb5lgWWFHiVdqS\nqoNGe43xiswZMq8p2oyezSjagn43Uunb/4e9N3mS6zzX/H5nznmeaq7CWEQVBpIAQVGiJKpv2BF2\nhNveeX/Df4BX3vpP6AgvvGyHN/a27bA7um23dPsOEieQBEVMBFBzVc4nM8/JM5/zeZFZBRQB6lLS\nndQXT8SJzAwgE6iMyu+Xz/O+3/tlyPhZ0vPbjJ8jHWRJezlSXpZ0rJNONFKSTlYyyCop0rqOnJaI\njJg4nRAYMWE6xkn7TFMBU8PD0V0c2Z2dIDY/RcyTPBzh4OHhJd7sVnj4iU8gAvzYJ0gCAhEQiYiQ\nkIjozN2I+bdgSZWQNOkMNkIRM9jE80FusYYqVPREJxWp5EOdXKSRC+dXpM2AFGoz8IQK6VAhG8yg\nY4SQDma3RiDQQ4EeCNQwnsEnnkd5UoIqJShyiMxshIhEiMzssZQESGIGHikJkOOARFYRc/gkqk6i\nGsSa/uLSdSLdINT1edebRpjS8Q2DO//H//Zmwf9jNA2m/PXBX585gAe9B7y39B6/WP8FHyx+gNbV\n+PzTz/nkk0/49De/odDp8F+vrXFHUdgwRyx1u4zjLH+Z3OVr8ROeS++zI67wRCpiSwalSpvC2j5i\n5Vvc6ueMC3/JNPcNUiAjBkA3QUvKVDJrXGhe491L7/Kj7ev87Pp1pEKBPc/jieNwfzrlietyYg1Q\nJ8/IRMeUlQEl1aSsDmhEPRpxj5raQyWiO12kP2zRPVnk8PllDp9f4+ToCuaohG0bRFGCSGLiWEGI\nDFBEkhJSqS6ZTJ9sZkQ2bZM2PAw9QVE1IpHG87JY4yqmmWM0gZR2QkbtoskTZCkEIROhESZpApHF\nl/NEmQIiV4BiDgoOUmmCVLYhP0YYA4Q6BMmCxJ7l954LtoUymYFCsW0k20ZYUxJrSmDZ6KkU+Xn0\nVK1UqFUqtOZu4mVIfBca2Wz2772rQggxK2K/BI3XwWQaOUxCFyvysKMAKw6x42h+uIhgGgumYhZn\nuULHo4ArcnhzJ3IKEQcDl1n3ViRJpKOYdBhjhDGGF6H5Mbobo/oxSpigRBFyFELiQeIgxCzSil+C\nSMCYUEwIkjEBFpHkn036jKQYIYnZaOdkNjFTi1WMSMOINFKBRjrUZzA5jb6CNJkgSzbMkg6yZP0s\nKT83g4uXI+VmSUUp0mhkJIO0qqGlZOSUjJKRkNMKck5FyapIOZU4KxGmBaGREGgBgRowlaZMlSm2\nPDuycsoURzhnlytcnNjBjVzcyMWLvLMrSIIZdJKAUMxczcugmdVtEtBA0iQkVYLT/SzyS7AR6nxy\nqIYmNAxhoCc6OjpGYpBKDHKxPgPQHD7ZSCUfzMEzr/OkA5lUIJEKIOVLGAGofsKdo3/9ZsH/u9TY\nG/OX+395VgN4NnzGj1d/zEfrH/GLjV+wqq3y+WczAHz88cd88vHHXNZ1/pv1de4oCquDAY3DIyQv\n4GtW+ST6Mc+Vn3GQvMNTFngu5UhkqDXbVC6ekL60z7T8Jb3ULxkq3xAKFyYaoh3BWCWjNGkWL7K5\ncovbmzf4cHubD65dI5NO0w9D9jyPHc/jt9MpDx2HHddlYvfITJ9TpUdFHVJUh1SlPq2kQz3pUVP6\nBIlOd7pA31xg0F6ge7DI8d4VDne36PUWmU51hPBB+ERRQhzrCJFBkkqAhmF0yeX6FAoj8jmbfMYh\nk/JJ67PeBT9SsN0c42GLkVlnOKoQRTZF4xkZpYMmW8hSgJAkYqETiDReksGLsvhRnkgrQqEEhRSU\nRsilMXLFhrKDKDqI1IREGQNTiD0IPXA95PEIxRqh2tYMFNYU7CmxZRNOLOIwolCeRU+Vcpl6tUrj\nb4FEuVymXC6jqn9X00h+fyVJ+Eot5LtOJI4t/GiKHTlzFxJgxwFWHGNHMXacMI0FTiIxRcYjhyfy\nuORnEBHnIeKh4Uga7tyNpKOYdJCQ9mcFct1N0KYJmpughgI1jlFEgCzmpwsIh1hMSYRFxIRQTAjF\nGF+MCZIRnjQhkH18OcCXQyI5IjrdDcxsvpMiXkwd1aMZVFKhRjrQSAU6mcAgE6bIBGmyYWYGkzBD\nJsiRCXJk/fzMrbh5Ul4Ow82SClOkJA3VAMUAOSWhpGXkjIycUVCyKnJOQ8lryAUdJashZxWUjEJs\nxLOR1mowH20dzFwLDpawmIopdmLjxA5WZOEEDlNvihM4OKGDEzh4kYcbunixhx/5r4DmzNUwB408\ny/olTUL8T+LNgv/3qYEz4C/2/oJf7vySX+7+kiPriA9XP+QXG7/go/WP2G5ss7uz+wIAn3zCV199\nxXurq/zL1VXe13UWOh3Kz5+jTyz21CJfepd4LP2CA+knHMbXeCqnOaCArge0lno03+pT3+rilb9h\nX/p/OPS+YRL3kAIVMZQQxwGym6WQWmaltsn1i29zZ2uLn21tcePSJZT5CdajMGTP989cwm+nU566\nLoeeRzI9oR4c0pAHFNUBRXVIK+nQFB1q9CnLJnaUo+ss0B8uMOy06B8t0j1c42jvKkcHV3CcFIoy\nRWJKHAdEkSCKdITIMjs2IYuuD8jlBpRKIyoVm1LJpZRzyKUCZCkgiAIcX2FklRkNWoyGDcxRncm0\nTE47oag9I6u2MeQJkhyQIBFJGr5I4SZp3CiLG+bwwyJxUoRsGQogV0zkygipbCNVHCh5iJJPlHEQ\nsg34SKEPQYiwHZTJEM2aoNo2im0jWzbYDollE0wsfMsmnclQnIOgXqlQ+w4ovu9+JpP5B+3V/iGa\nuRD3tdB4XW3EiR0moTeHyPyIwySe1URiwTSRcNDxKOKRxyOHm5zWRNK4IoUjDFxJx0HDlVVCSZ5B\nJBKkfUHaE6RdSNkShiWh2wJ93gqrnkYk0uzEW4FDIk2JsYnEmECMCTDxxQhPsnFld16j8PFln0AK\niaSZO0le2iAni9mpVGqioMcqRjS7Zi5FI+PrZAKddJgmG6bJBhmyQY5MmCUbZMmEeTJ+jkxQIOVn\nyXh5dC+L7mSQEgVZFyiGQDYklJSEnJFRMjJyVkXOqig5DTmvz24zMkpGeXE7h5CSURCGIFRDlv5s\n6c2C/w+pjt3hV7u/OqsBDN0hP1//+ZkD2KxtEoYhX3/99RkAPv74Yw4PD/lwe5v/anWVu4bBYrtN\n6uFD0u02R6ki3/p5HkW3OdT/M9rRbY6SBk8UnU5SIp+1WVo3Wbk5ZnVrgr7wnIfRv+fR4Cu6zgFB\n4iHZOkkngXaIllSo5je4tHSDG1e3eP/aNX52/Torrda5hWcax+x7Hruex47r8sBxeOy67HkeHc+l\n4B2zIjqUpT4FdUhN6rGQtKmLLlW5T0G2MMMKHWeRwbCB2W7SP16id7RGe/8ih7ubWNMShjFCUyYI\n4RCGAWEoiCIDIXJIUpUkKaPrY3K5IaXSiHrdptl0qdV8SnkPQ54QRRZe4DEapxmMyzPnMGhijuoM\nx3UkIajobSraHjntCF2xkOUQIUv4sjrLzuMUTpjBCbI4fo4gKJIkFdAqUJiiVEyU6gi5YiNVp1D2\nSEoecSEkys4OIJFFhBSExG6EZE3QJiM0e4JmzUChTB0ka+4oLBt3PEHEMcVSicocEpXvAcN3H5dK\npTN4/ylo5kJerYW8CpWX3YiLFYVMY392Xm40q4mcxlkeOTyphE9+FmeRxU1eciJiFmc5aLiSiiMr\nqIkgHSVkQmYQ8STSU4mULZOaSKRsSEcCQ8xyeEUKUAiQZA8kBySXSLGJJIsIC58RHiNc2WYqT3Gl\nKY7k4koevuThS7MSdyTF84ZfwSlTJGZQ0ZJTl6LMgBKqpEONdKDOoOLrZMI0uSBDNsqQDbJkwxyZ\nMEcuyJMOZlD5b5/9928W/H9MHU4Oz779/4ed/4Af+/x8/ef8Yv0XfLTxERfLF5EkifF4zKeffnoG\ngI8/nh0T8NN33+W/WF7mfcNgsdNB+uILUru7dDM5vo2zPJvWOdH/jI70c4b+ZZ7LaZ7JGcw4T6U8\nYu3yhKt3Qi5ec8ku7fCMv+bTg8/YMb9lFHaRAwNhyiTHAdJAIqMt0Kpc4erGNrc23+KDrS0+3N6m\nkM+/8rP5ScKB57Hn++zOXcIjx+G563IcBEwDh/Vo5goKSp+CPGAh6dBK2tSkHhV5QEryGER1Os4C\nw2GDYbvB8GQGhc7BBY72rjIaNshkxqSMEbI8IY5tgiAgCCTCMIUQBWS5SpI0UFWHbHZIuTyhVnNY\nWPBZWQlYXHTIpMeIoE/gmUwmgu4ww3BcZDRszd1DHXNcw3KLlPQhNa1HVe9Q1I4wtDGKEhFqMp4y\ny8ztJIUdpLC8LLaTxfOyhGEJqAF5pOwIrWoi1yYoVWsGiYpDUvaICwFRPiLOCSRNRokFwkuI7QB1\nMkKzx2jWBN220aYOij0F20HMQeGMxjiTCdl8nkqlQvVviZy+ez+dTv+TcxW/r164kNfHV+dvX3Ij\nkXMWZ83OAo6wYzE7rlJo+FIJXyrgSYUX3VnzOMsRKVxh4Aid0yNwXFklkCTSsSAdCjI+pH2JtCOR\nmsqkbUjZkLIEmQAyiUw6AUNOUKUQSfFR5AChOAjZIZItYtnGl8d4qo2t2Exlm6lkMWVWf3AlBxcX\nb941FYqISMSE/2P8ZsH/p6Qdc4df7r4AgCIpfLTx0YsaQHEVmP0yHxwcnIuC7t27x/LyMj96913+\n85UV3tM0lrpdwt/8Bv3xY8aGwRMlyyMrw0DaxNT/SybRLbp+k8eqyh4FnCRNszVg45rD23c1trYT\njMWnnEhf8OuDe3x9/DXH013COESepoi7CeLARXazFDOrrLbe4q0r17h97Ro/3dri1pUr39sREyUJ\nR0HAnuex53k8c10eOQ7PPI8Dz6MfhhSEx2rUpkyXnNKnLrosJm0aokNV6lFWhsgIulGTrtvCHNQY\ndhoMjxbpH6/RO7rE8f4VzF4Tw3DJ5wZo2ggYE4ZTfD/E9yWCIIUQRWS5QZK0UJSYbHZIqWRTr3ss\nLISsrcWsrXm0Wn1UpU8cdpiYFv2eRG+Qpj/KMzYXXsDBrDGY1NHkkJrWpa4NaOgmlVSfrD4CIyAw\nwFMUbHTs2GDkG4ycNJadxnEy+H6eOC4DNZA0FM1Eq4xQa2PkuoVScxAVh6TsEBd9wkJImI2Rsjqy\nkYFQIXFiEstBt0bo1hjDtmegcBxU20GyHYQ9JRxbOOMxlmkiAaVymervAYlKpUKxWPyTchW/r5Ik\n+t5ayMvw+C5YgsjGjn3saFZYt6IYO0mYJuBLWXy5hC+VcCnMayMziDjJLM5yhY6T6LhoOKg4koIj\nz47azESCdDiHiCeTnkqkHUhNIWULUpMZZDKJzP/8r3/2ZsH/pyohBN8Ovz3rAPrlzi/JG/mzxf+j\n9Y9YyC+c/f0oinjw4MG5KOjZs2fcuHGDu7dv82fz7qDG0RH+r3+NfP8+niTxbSbHA8dg360R5X6K\nk3zI1F1nPynySFU5TsrEksLCypArt3x+dDfH2zd09MUnHEe/5X73Ab/Zv8e3/ceMgi5KkEKMVKKT\nEI48dFGlWrzIhfVrbF/d5O48ItpYXv6d3yITIegEAbtzIOz5Po8dhyeOw77v0w4CZKAuprSiY/JS\nl7zUZzlpsxC3qdGlIvcpKUN8DLpRg57XwuzXMDs1hseLDI5X6R5donu4iTWskMQSpeKIbHaAophE\n0YggsPG8CM+T8f00QpRQlEWSZAFQXgJDyOJiwuqqzMZGyPKySaVyhK6f4LkDOsc+3U5Cr6/RN/OM\nzBaTQQtz0MQ0awzHNaZ+jorep670aOhDmoZNLe1QzI+Rsw6+keCqChM0JrHOwEkxsAzGEx3LSuG6\nWcKwCNQQooos+6j6GC07QmtMUOo2Ss1G1BySsktUcgnzAX42JE7JqJkcil4kiXWiaYxkWRiWiWFP\nSNk2xtRBmzpoUxd56iIsG388wR2PsUcjppZFfu4qfhcYXvdn6XT67/0z9U9NMxfi/c5C+vcBJgwt\n3NibOZEowk6ieZSl4MvlmRORSzMnIuVxkwz/60/+hzcL/p+KhBB80/vmLAL61e6vaOaafLQ+cwA/\nX/859Wz93HNs2+bzz2ddQacQcByH9957j/fu3OFn6+u8K8sUnj4l+vRTks8+I/E8dopFvooUHg5T\nxJmbKMZPCf23GNnLPFY1nioG7bCGboQsXxix9W7Cj++UuPtOhtTCcw7cRzzsP+Tzk9/y5fF9DibP\niOME2ckQ9yE+cKETk0ktsdi4yqVLV7m5uckHW1v8ZHubSrn8g96PwbywfAqF567LY8dhx/M4CQJC\nIchKEvXEpByfkJZ6NOmyEp3QTNpU6FFWBuTVMdMkR0c06LktRoMK5kmNUOevIwAAIABJREFU4UmL\n4fEqvePLDE6u4I2LTK0UhbxLuWxiGD2gTxia+L6N40R4norrZoEasryIEAsIkSWdNikWber1gOVl\nhbU1nY0NwfKyRa3WplzeJ5U6YDCwaR/7dE9iun2V3iDD2GwwHi7O6g7DOuaoytCuYsgeNbVLQxnQ\n1Mc00y6tvE+x7GAUp0TZCFvTGKMxCDQ6lkF/pDI0FSYTg+k0je/niKISklRHiAKqaqEZE/T0GKNk\nobYs5PoUag5x1SEqugRFFy8dEOgJcjqHqpdBKxCTJnIiNHtEyhqTsi1S0ympqYMxddEcF9l2SSwb\nfzyegcI0GZsmsiz/IDB8936xWESW5b/t1+WfjZIkmm3ce018Va//yzcL/p+q4iQ+Nwfor/b/itXi\n6pkD+OnaTymnX104T05OzkVBn376KbVajbt37/Lee+/xk0uXuB5FGA8ekHz+OdEnnyD1+xxVq9xX\nVO4NBf1olWL5A3LJDaaTDdp+jW8MiV1y9IMq+eKUtasTbr6r8rP3qrx706Cy0mNn8phH/Uc87D/i\n06OveNh7iOl10aIcYqIRtWOSXQvFTlHIb7C6epWrV67w9uYmP7t+nXc2NzEM4we/R+MoOouMdj2P\nXdflqefNuo18HydJyCkKBgmVqEcuOSEv9ViLT1iK2tRFm5I0cwkZZcpIlGjTpO/VGfXLmCdVzOMW\nw5NVhieXGXUvEtpFbDODLAtqNZtCYYBhHBPHPXx/hOvaTKcxjqPgeQWEaKEoSwixSBwXSaUmlEoW\ntVrA0pLC+nqOixc1lpddarUe1eoRpdI+QhwzGEw4OQnonoT0ujLdQYqR2WAyXGTUb53BYTCu4oVp\nqlqfutyhqZk09SnNnE+rElGtxWSqLlLRx9IVxpLO8VTjeKLT7Uv0+zAaqViWgetm8f08s1pEDdDQ\ntDFGeoKRsTFyFlrdRmlOoWETV6ZEJQev4OKlXFwtROgZNKOGrBURWp5QyhAHMSnLJG1NyDgW6alD\neupiOC664yHbLtFkQvASKCajEbZtUywWf1Dk9N0NeKlU6u/s8/inoH920zL/U1aURHx+/PlZ/v/r\nw19ztXp15gA2PuLD1Q/JG68WV5Mk4fHjx+cg8PDhQ9566y3ee+897t69y/tXr3J5OkX+6iu4d4/o\n00+R9vbo1Wo8TKX4teXzaJgmX/sxK6ktFOcKI3OZHSnDN5rMkSgx9stUmyMubLncfjfFz+5UuXlD\nYXHFZ2f8lEf9RzwePObr7gPuHd9nb/QMkYDi5YgGEuGhB3tTdFGlVrvE2sXLbF+9Omsp3d7m0tra\n7/1N77TTaO+l4vJz1+XpvNtoFEXkVZWUJKGJkHx0Qkp0WBJd1qJjFuITKkmXotKnoJrocsAgqdKW\nmgyCGmavxOikgnncYHiyxrB9BddcJbGLTMc5prZKtRpQqUzI5zuo6iFx3MV1TabTKbYdYdsqrltH\niEVUdeYYoqiGYUwpFGZgWFyUWV/PcvlyntVVQb0+nLuGQxTlhCDoMJ0OaLc9Ou2Qbht6fZ2hWWMy\nXGLUX2A8rGOaVQajKsNphawypS53aMh9mvqEZsqlVQ5o1BIaLZlCM0athIQ5QTvW2LcMDgcS7U5M\nrycYDGQmEw3bTuF5OcKwiCQ1EKKKogTo+oRU2iads0nlpxhFC7llIzUc4ppFWHZwszZOysVRg9ms\nI6OKqpeRtAKxmiOUMxDHZKYjMrZFdjol6zikHY+U46M7HpJlE08m5xzFZDRiOByiquoPjpxevl8o\nFP4kXcWbBf8/YQVxwCdHn5w5gE+PPuV68/pZB9AHKx+Q0TKvfa7runz55Zfn6gGDwYDbt2+fOYG7\n16/T6nbh3j344guSzz5DPHzIpFrl23yeT/yAv2l7xOnrXK7eoRWv443W6U2aPDRkHikanaiGG+Vo\nrphcvRny/js5fnK7yPXrEgsLgs60zePBqSt4yFedhzzofMPQ62HERYRtEHZiol0bjn2yqWVaq1e4\nePkyN65e5UfziKhRr7/25/xb38Mk4eClyGhv3oL61HXZ9X26QUBWUcgqCqokIUdTMvExedHlYtxm\nNT6hHh1ToktBGVLQTAQS/aRKW2kxCKqM+gVGx+UZFNprjDuXiKxFhF3GneQxhwaplKDV8iiVhmQy\nJ6jqHr7fwXGGWJaNZSVYVhrHWUCIBVR1EVgkipqoakChMKFW81lYmIHh0qUc6+sGzaZHrdanWj3G\nME4Iww5B0MHzurNoqR3N6g49hcGwgjVcYTyvO4yGVQZmlaFVJYg0amqPJm2a6oimMaVZ8GhUIhoN\naC4qVJc1jIYgqQiOApXdscZ+O+LoKKDTien3wTQVLMtgOs3g+zmEqAJ1hMijaTaplE0qMyWTm5Iu\nOKRKNnrNnjmJmk1QsXAyE2x9iq2EuEJF1ouoRhVFLyHUPLGSJZAMNBGT86dkpzZZxyHnuGRdfwaK\nqQu2TTQen4FiOhoxNk2GwyGO41Cat8r+EGfxsqv4fRzq37XeLPj/jOSGLr8+/PXZLuCv2l/x7uK7\nZzWA95ffx1C//5ex3++fqwV88sknZLPZFwC4e5d3r18nu7d3BgFx7x7iq69wcjn2KhXuCcEvB2Oe\nW0U2l37GldQV8tMlJr1lDoIi91OC51KaXtAkkVSWLo3YvgE/vl3kvbczbG9DtTr7Wb4dfsuj/qOz\niOir9jc8Gz5BFjpqkCc2Ffwjn+T5GNlUKVYvsHzhIleuXOHtt97ip1tbvHvtGpnM66H3QxQlCcfz\nTqOXi8vPXHfWfur7GIpCXlHQZx82pGiEnrRZTTpcik9YjE6oxCcU6JJXTfL6CD8x6Isax8oCw6DE\neJjHPCoyOqljnqwx6V1CcppITpnAKmAO0ziOQqsV02hMKRR6pNNHyPIuvn+IZZlMJjaTScJ4XGY6\nXQQWUNVlYIEoaiJJEoXChGrVZ2FBYm0tw6VLOTY2DBYWoNHwqNV6ZLMvwHB6O5kMaLdDut2EXldm\nMCgyMVfm0VITc1DDNCsMxhVMp0RetWhIHZr0aGoWzZxDozRzD82WRGNZp7GWprBq4BVkOkJmZyDY\nPXA5PHQ5OYnodhMGA4nRSMO2DVx3tjdCkurMDtnTMAyLdNomk3PI5F0yRYdMySFVtpGaFkndxi2N\nmabGTLQxFhG2UIiUDLpRRTUqSFqRRMkRKmkiVDJSTC4JyTkOBcch7wVkHZ+MGyBb1hkogvEYdzJh\napqMTBNzDgvDMH7v+KlSqfxBwwK/qzcL/j9j2YHNX++/mAP0sP+Qu0t3zzqAbi/eRlO+f8iYEIJn\nz56dA8D9+/e5dOnSGQDee+89tjY3UZ4/hy++eOEGPv+cUFU5bjT4WtP4i8mEXx1PWKr/iHeq77Kc\nLCMPWwy6LR5rKl/rcCCKDNwWejpifdPi5k2VD2+XefumxrVrkMvN/k9H1tEsHurPnMFvew/5pvsA\n0x2QpoI0TeN3E4L9KeLpCC0pU1u+yOqlS1y7epX3trb46dYWVy5c+KPHIZx2Gr0cGe3NawnPPI99\nz0OWJIqqSkqWkYAgjiHqUxRtNuMO69ExzeiYYtImJ/XIaSOymsU0ztGlzonSwgxKjEYZzKMCo+Ma\n4/Yq9vACuttAcStEdpHRKEu/r5LPw+JiSK1mkc930PU9ZPk5nrfHZDLENKeYpsp4XGc6XZo7hiUk\naeYYhEiRy52CAdbW0ly8mGV9fQaGhQVoNn2KxS5R9AIKp5fvd+l2XbrdmG5Hoj/IYZmrjIdLjPot\nRsMq5qDKYFRhYJdJYoW62qOZtGnKJs2MTaPg0ahGNBvQWFBorKZoXshSXs9j51L0JYlD0+P5jsXB\ngcvhoUe7HdHrCUxTZTLRmE7TeF6OOK7M23ErKEpAKmWTybpkcy75oku25JGtuKTKNlptQly3cPJD\nJsaQiWYxjiMmQsIVGqpRRTcqyHoZoZ26iRSSJJGVYgqSoBCEFFyXoheQdwP0qYsyncJkQjgeE04m\nZ47CHA7PQOG67jmn8EPjp5dHkL9Z8N/oTGNvzH/c+49nNYCd0Q4/XnkxB+hW6xaK/Lt7rIMg4P79\n++eioKOjI955551zTmB5aQnp4GAGgJfcQOw49JaWeJzJ8Neuy/95eIQjL/HT1Z9zxbhCdVrD7zQ4\nGVX5OpvwQJZpJzXMaZNC1eHiNZd3b6b4yZ0iN67LXL0Kp+P27cDmyeDJGQhO6wVPh9+iS2m0sEQ0\nUvCOA8LnE9ibkskv0bxwkYuXL3N9c5MfbW3x460tFr6z6/gPlRCCYRSdi4zOOo7m94MkoaxpZOfj\ni0MhcKIAOeqwQZfNuM1KeEQ1OiaftMnKA7K6ia56TKIiXanBsbLAyC8yHhmYJ1lGx3XG3VX80Rpp\nr4bmVYmdEpNxnl5Px/clFhcFCws+tdqIbPYYVd1Fkp7iujuYZh/TdOn1coxGDWx75hg07RQMC8Rx\njlzOplLxaLVgdTXFhQuZMzAsLs7gUK+HCNEjCNpnUHgZEuPxmE4notuFQT/F2FxjYi4z6i8w6tfm\n0VKFwaTM2M1TVCc0RZum6NEwLJq56avuYT1D82KO7FoNr1BgIEn0RmP29obs7k7Z33c4Pp45ltOC\n9WRi4LoZgqAwdxE1kiSPrk9Jp6fkch75gk+u5JMve2SrLpmKQ6pmk1RMJpkuE2OAKVmYccwkFthC\nRqh5jFQdRS8jaSWEOnMTPjq6JMjLgpIiURJQ8gOKXkje9dGmDvJkgrAs4smEYDLBG42wRiNGc0gM\n58BIp9NUKhX29/ffLPhv9HqdzgE6dQDH1jE/XfvpWQ1gu7GNLP3thavRaPTKLmFZls8B4Pbt2xSL\nRWi3Z05g7gbEF18gOh1Ga2s8LxT4JIr4d90uv+6M+WD959yp3mFJLJEdFpkc1ngeZ/gyE/OtpNOL\nWtjTKrWlCZvbEXffznH3nSzb23DhwuwUJJidenYwPjiLhx4PHvOw/4hvug8Ye2Nych2cDEEf3AOH\n+FsTqZdQWt5g8eJFLs+7iD7c3ub2tWvkX7Pr+I/RZN5ptPud4vKu67LjeUyiiIqmzWIjWSYWAjdJ\nsEKHUtJlmw4Xw2MWgiNK0TFZumSUARljjCzHjOIynVMoeAXGYwOzYzA6qjLprSKsZbJ+Dd2vIdwK\n1qTAYGDQ68lUKrC0lNBquVQqQ9LpQxTlGUI8wXG+ZTjszWKeXhnTfAEGXV9BkhaJ4xZhWCaddqhU\nXFotWFkxuHAhfc4xnF6aFhGG/Vdcw+lj1+3R7Xp0u4J+X5/DYZXJYIFRv4E5qDAcVhiMSvTtCjIJ\nDblLM+rQUIc0s3P3UHnJPawYNC9kqVwsoyw0EPU6YyHoDwa02312dsbs7U3PXES3KxgOZcbj04J1\nliiauQioIoSKYdhksw6Fgk+xFJIvBRSqAbmaS7bqkqrbiFwfK9VhlOoxiG0GUYgZxViJhC8ZGKna\nrNNJL4FaIFZnbiKSVNJSQlGGsqpQUVUqYULZC/lf/sWfvVnw3+iHqW23Z3OA5jWAkTc6NwfoavXq\nD/rWK4Rgf3//XBT0xRdfsLKyci4KunHjxmyXrmnCl1+ei4TEzg7T1VUOajW+lCT+P9Pk3zzfYal6\nmZ+t/IxrmWtUphWUkyy9owoPMhJf6jF75Bn6i3h+nsWLE7a34YN38rxzy2B7G5aW4OUfYeJPeNx/\nfFY4Pq0XPB0+Ja0UMOIy0VjHbYd4Oxbi8QCVHLWNC6xcusRbV65w59o1frq9zealS7/zdK8/VM5p\np9F3isun+xJ6YUhFVSnMYyMkCT9JGEcRbmhxRerxluiw4R9SDw4pxCek6ZFSh6RTExJkhnGVttzk\nWFlg4uYYjQyGPYVJu8ykt4LuLJLza6T8Ovg1pnaR4TDFyYmCELP3dXExptVyKJd7GMY+ivKEKHqE\n4zyi1+tzfGzMwdDCtheYOYZlZHmJJGnh+zUMw6dScWk2YWVFZ2MjxdraCzCcuoZsFoSICcPBa13D\n6TUaWXQ6Cf2+wshcxRqtMRnM6w79KuawzMAsM5iUmPhZqopJQ3RpJF2aqTGNvEOjGNCszzqXGksa\nzfU0jYt50is1aDSg0SAABoMB/X6fw8MBu7sW+/suR0c+nXlXk2mq84L1bG+EJNWRpFnUJMsB6bRN\nNutRLIZUKhHFSkixFpGv+eTqHkZ9Qpzu4OjHmGqXXuTQCwLMKGI8dxPJn//vbxb8N/rDdDA+ODcG\nIoiDc7uAL5Qv/ODYI4oivvnmm3NR0PPnz7l58+Y5J7CxsTF7TceB+/fPAMC9e4iHDwmWljhZWOAb\nXecvbZt/s7dH1wn46PJH3KneYZVVsoMs0V6Gw0mBLwohX8uCE1HFdJYQQmftqs2tGwof3C5w87py\nVih+WXESszvaPQPBaUz0oP+IaTCloDaRvBzeQMI9cPG/NeH5iEytRf3CBS7MI6L35xHRyt+y6/iP\nUZAkHH4HBrungHBdjnyfgqpS1jQysowChEJgxTH9ICCHzXW5x9XwmFX/gEpwRDY+ISX30LURqZSN\nnxgMktoMCvIMCqapYfZlJr080/4yaa9FMaiTDuooYR1nWsI0M7TbKoMB1OunYIhoNm3K5Q6q+gxJ\nekIYPsBxHnB8bHF4mKHbLWOazTMw6PoysrxMkizg+3VUNaFcdmk0BCsrGuvrKdbX9VccQ7E4A7wQ\nCVFkfgcI7XOQcJw+vV5EtysxGq1gjdexBsuY/Sbjfg1zUGYwLDMYF+nZZXQppCl1aUTtuXuwaBQ8\nmpWIRl3QWFRprqVobGQpb5SQWzM4UC4jAMuy6Pf79Hp99vZM9vamHBw4HB+/KFiPx+pLO6xLKEoD\nqBHHOXTdIZOZks/7lEoR1argV7+68mbBf6M/XkIIdkY75wbBaYp21gH00cZHZ3OAfqgsyzrbJXwK\nAs/zZruE5wC4c+cO1dPVOAjgwYMXTuDePbh/n7hapb+6ypNcjt94Hv/XyQkf7+5yfeM6Hy5/yPXs\ndcp2Gb2tM32W4YmqcS8T8lBS6CctRpMljEzMpU2Pd2+luPt2luvXJba2ZoXi78p0zfMgGDziYe8R\nO6Md8nqVlKgQWwZOO8bZtYke9WHgUlxbP4uIbm1u8uPtbe5cu0b5B+w6/mMUC8HxfAz2KQjORUie\nR1pRqKoqeUVBm8dGTpJghiFmGHBJtdkSHS4Fhyx5+xSDI1JJB13uoxtj9JTDNMrRT2qcKC1O5BZj\nJ8doqDLsSUzMNJ7Zoug1KIYNcnELNazjOhXG4xzttsLRkYSuz6AwA0NIszmhUDhGVZ+SJA/x/W+w\nrAccHvocHmbodEqYZgvLWgCWMIxTx7BAEDQAlVLJpdGIWV7W2NgwWF09D4bFxRnwT3kshJiP3HjV\nNZw+9v0OpunQ7SaMx4tMRpeYmMuM+wuMerVZtNQvMRgV6VtFpkGamjKkkXRpiPbMPeRcGqWAZi2m\n0ZRoLms01jM0LuYxluszONTrMB9JEUXRmYtot/vs7k7Y33fOFaw//fS/e7Pgv9HfvYQQPBk8Ocv/\nf7X7KwpG4YUD2PiIVq71e7/u8fHxOQB89tlnNBqNc1HQrVu3XuygjGP49ttzcRD37iFSKSYXL7JT\nLvN5kvDv+33+3cOHZLM5PrzyIXfrd9lgg9QghfRcZnic4atCzJdGyDPSmNEy49ECxZrH5rWIu29n\nePdWiu1tuHoVXtdqHcYhO6OdMzdwWi940H9IGEeU9EVkP49vykyPfKZPR4iHHVTVoHLhAisXL7J5\n9Sq333qLD7e22Lp69R9kp2giBN0geC0IdueXDNR1naKqYsw3JHlxzDiO6QQBBjHb6pjN6JgN74CW\nt082PMagg6oM0FIWihZgRQX6os6RssCJ3GJiZzCHKsMeTEyd2KlR8WqUoyaFuIUWt/C9CuNxgW5X\n5ehIYjyeLdQzMAgWFgIajRGFwhGS9Jgo+i2+/1t6vV0OD6ccHmZot8uY5sIcDIsYxjKKsowQCwRB\nkyhKUSy6NBoJS0sq6+sGq6vauRhp1rr6ojYEs89BHE9eC4WXH0+nQ7rdGNOsY1lX5nWHRUa9OqN+\nhUGvxNAsztyDUyQjeTSkHo34hKbSpzHvXGrO9z00FhSaqwaNjSyljTJSc+YepEbjzYL/Rn//SkTC\nN91vziKgv9j9C1q51tm3/5+v/5xapvZ7v24cxzx+/PhcFPT48WOuXbt2Lgq6fPnyi52RQsD+/rkO\nIe7dQ/g+3ltvcVivc19R+NV4zL99+pR2r8eta7f4cGXmBupuHelQInosOPJS3CsFfKGEHFNh7C1j\nT+o0lh22twTvv5Pj1k31lULxd9V3+q+A4FH/EfvjfSrpFmmpRmyncboJ9p6N+6gP+33StRr1CxdY\nv3SJ7atXeX97mx9vbbG2uvoPNrVSCIH5uk6jOSB2XBc/SWgZBiVVJSPLyJJEmCRM4phBGDKMItY0\nwTX6XAmPWHP3qHkHpMMjNLrImomSsZHkhHFUpkedQ2WRttTEsjOYQ5leRzAZGahhllpQoxo3KSWL\naPECgV/Dtov0ehpHRxLHxzN39sItzDqSGo0hudw+kvSAIPgay3rAyckJR0cWh4epORgWmUxawCKp\n1AqKsoIQC4RhA98vkM971OsxS0vyOTC8fLVaL7rHXlYU2d/jGl50MM3cg89gUMKyNrHGG7P9Dr0m\no14Fs19iMCgyMAv07QJuqFNXhjREhy/iW28W/Df6h1ecxHzV+ercHKD10vq5OUClVOkPem3Xdbl3\n7945J2CaJnfu3DnnBJrN5vknfqdDiC++gG6XaGuL7tISD1Ip/tpx+Ld7e9x/9Gg2inrzR9yp3uGS\ncol0P034NCT8VuZJTuKzrMfXEvRFk8l0Fc8psHrB4eYNZR4LyWxvw/Ly+ULxywrigGfDZ+cgcHpJ\nkkI1vYwSFvFGKvaRj/VsTPi4gzSyyK+s0Lpw4VxEdPvaNWq12j/4rHvr5U6j1zgFMwxp6TpVTSOn\nqqiSRCIE0yRhGIacBAEpWeayFnEtaXPBP2DZ2aPi7qFHx8hyH0kfo2SnxEJhFFXoUOdQXaIjNbCs\nNMOBTO8kZjIxyAiDRlSknixQFksYYpEobDCdluj3dY6OJI6OZqWixcXvgsGh0RiQze4Qx98QBF/S\nbu/Qbrc5Ph5xcGDQ6ZQZDpeYTFpI0iKGsTLf5LZIGDbxvBKZTEi9HrG4KLO6qp8Dw8uu4fuGiMax\n8ztdw+l9yxphmjksa5M///P/+82C/0b/+ArjkM9PPj/rAPrN4W/YrG2e1QA+XPuQnP6awPwHqtvt\n8umnn54B4JNPPqFQKJwDwLvvvvvqrtvRaNYh9JITYGcHsbnJ6MIFvs3lZq2iJyd8/PXXJEnCrRu3\n+GDtA27kbrDgLpAcJPjf+Dg9ha+qIZ8ZHk8kjVG8zGS8hogNLl51efeWwe1baba3YXsbar/D8Agh\n6E6750Bwens0OaKVXyGrNImnaaZ9mOxNsZ4M4dkRsiRR3thg5dIlNq9c4Z15RLS9uUk2m/2D3+M/\nRm4cs/89kdGe79PxfRq6PouNFAVDlkmEwBeCURTRnXejLOs6VzSXq8ERG+4+C84uBW8PNT4BeYBI\nWygZBz9KYcYVOlKTfW2JnqhjWSkGPYluO8aa6JRQaElZmmKRKkukpWWiqInrlhkMUhwezqDQ6UCp\n9AIKy8uwuJjQak2p13uk08+I468YjR5wfLxPu93m6GjI0ZE+B8Mi4/EMDKnUCqq6AizN/60KmpbM\nwSCxuqq91jEsLEA+//1fHOLYIwy7pNNrbxb8N/qnJz/yz80B+uz4M240b5x1AH2w8gFp7Q+fny6E\n4OnTp+dcwNdff83ly5fPQeDatWuvRiOv6RDi4UPExgbe5iZ71Sr3hOD/HQz49cOH7O3tcfXqVW5v\n3eZO7Q5X5CvkzTzWA4v4YUxPhs9LHp+pLvsUscJVJoNVjBRsvhVy++0079zU2N6Ga9dmH+zfJS/y\neDp8+ko89Kj/iJSaoZFbQ41L+GOdyUnA+NkE50kH6fAYo1ymtrHB+qVLbG3Ozi748fY2FzY2/lEP\nYT/tNHpdZLTreRz5PhVVpanrlFWVtCwjzTepWXFMLwg49H3yqsqarrEpj7nk7bPq7NKY7pL19pGS\nNolmEmemKCkPN8wyTKqcSC0OtCX6SZXJJMWgB+3jCMfWqEoyS1qKBalJXVomo6ySJC1ct8pwmD5z\nCkdHEEUvgPDCMSS0WhNqtQ6p1Ld43pd0Ok85Otrn+LhNuz3g6Eih0ykzGCwzHjeRpEXS6TVUdQVJ\nOgVDFZCp10MWFiRWV1VWVrRXis/b22922r7Rn4Dc0OVvDv7mrAPofuc+txdvn9UA7i7d/Z1zgH6I\nfN9/ZZfwycnJq7uEl5dfffJ3O4S++AK++grqdaKbNzlZWOC3qspfWBZ/+eQJ9+/fp1KpcOvmLd7b\neI8buRssB8tEOxHW1xZiB55XfT7NuXxBSFdqYnvrTLpLVGoBW1uCO29nuDGPhTY3X18ofllCCE7s\nk3NtpI8Gs/udaYfl4gZFfZHEy2H3BaMDl/GTIeHzIxgOyS4usnBxtuv41luz4y1vX7tG6+9o1/Ef\no1gITk5bT1/jFPZ8n4wksWwYVDSNrKKgSRIRs4mpwzDkKAiwooiVVIoNXeZq0mPD2WN5ukN1ukPK\nPyARXcLUhCRjI+kR0yDPIKlzIjfZ15cYxFUmY4N+F06OQwJboibJrBgGy3qVlrxCVltHiEU8r4Zp\nZjg+fgGFfn/m7E6B8DIYGg2TRuMEXX+Maf6Wg4NnnJwccHzcptMZcnSU0G6XGQ5XGI2ayPIS6fQK\nmrY6B0ML01x9s+C/0Z+e7MDmr/b/6swBPOo/4v3l9892Ad9evI0q//HfSE3TfGWXsKZp51pDb9++\nTaFQePXJcQxPn75SHCaVQrz9Nub6Oo8yGX7tefxqZ4cvv/qKyWTCjRs3eHv7bW43bnNZuUxxWMT8\n2sT7rUdkC37bdPhEn/JbJCasYFsb2P06iys+N28ovHszxfXrEttoeqk8AAAf9ElEQVTbcPHi9xeK\nX5YTOufGTpyC4PHgMQWjyFLxIpqo4FsGo5MQc8di8qSDODhECgLK6+ssvRQR/WRri+tvvfX69+Uf\nQUIIumH4vZHRaafRimFQ13UKp8PuADdJGEUR7blLKKsqq6kUG5rgSnDEmrPLgvWM8nQH1T8kUAb4\nKQuyU4QssMMSPVHnWG6xry8xjCpMxjq9juD4MEA4gro8g8JGpsCivkpW2wCWCMMGo1H2HBQOD0FV\nX4XC6ea2ZnNAtXqEJD3k+Pgxh4fPOT4+4OSkzb/6V4/fLPhv9KevkTeazQGa1wB2R7v8ZPUnZzWA\nHzIH6IdICMHe3t4ru4TX1tbORUHXr19//Vm+L3cIvewGXBfefhv3rbd4XirxaRTxq6Mjvvz6ax4/\nfsyFCxe4efMmty/fZju7zUq4QvwsZvDFgPhxzCTnc6885WN1yg4Z7GSDqbmOOyqxcdnjnRsG79zS\nzuoDv6tQ/LISkXA0OXptPDR0h2yUL1HNriL8AtZQwjz0GD4ZMN2ZrUxaNkvtwgXWLl1i6+pV3rt2\njQ+2trj897Tr+A/VaafR90VGe56HlySsGAateWyUmsdGQZJgzdtP930f7/9v71xj40yv+/57OLxT\nEu/363AokuKQnCG10q6yq0i0XdjpFweJEbRp3MSO06atCzcIUCdoC8NAiyAtChRukQAuEtT9UCdB\nAzSG0/TirqTd1a64unBIiiLFy3DI4W14GXJu5Nyffpjhy3k1lDRLihyu9PwAQhzpnXmPHpDnvM85\n/3OeeJyWggJaCgsxGcJ0BBdp8s9R65vjgs+OjK6wm7dNuMiPOLdLLG7AE6lgk2qWc+pYyG9iO1KG\ndyefdZdk2blHXjBOTU4OTfn5tJ87T+u5Js7lG8nJaSYarcXrPcfKSg5LS2iBYWcHamv1tYUf/ECl\ndBSvIZu7m9xxHMwBWvOvJeYAJWsA5hpzRnOAMiESiaR1CTscjrQu4ba2tuenPlwufcNYUiFEfz8x\ni4WV2lpGhOCDzU0ePX6MzWajoKAAi8WC1WJlsG4wsRvYKsVtc+Mb8yFXJSt1Xj497+c+QVyiit1I\nO/71NuLhQrouRXjLWkh/spu4tzfRx5MpvpCP6a3ptMLxzNYMlcWVtJRfpMhQR8hfwM56jM15P+6Z\nNSILi7CxQXFtLXXJruP+FBVRY2PjmTxcZF9p9Lx+hO1olOaCAhoLCqhMGXYXlRJ/Un7qDIVYCYep\nysujtbCQlvx8TMKHMTBPo2+WSu8cJYF5wrE1AgUewsUBRMke4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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -963,7 +1246,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 164, "metadata": { "collapsed": false, "slideshow": { @@ -971,21 +1254,30 @@ } }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/matthiasgroncki/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:3: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", + " app.launch_new_instance()\n", + "/Users/matthiasgroncki/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:4: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n" + ] + }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 15, + "execution_count": 164, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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gAtIU249LmgRcAxxt+5qW/HnA6raPk7Q+cCswHXB5vRuwDPgvYH/bt7St37Y1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\nrkJggg==\n", 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1009,7 +1301,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 165, "metadata": { "collapsed": true, "slideshow": { @@ -1029,7 +1321,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 166, "metadata": { "collapsed": false, "slideshow": { @@ -1040,7 +1332,7 @@ { "data": { "text/html": [ - "
    \n", + "
    \n", "\n", " \n", " \n", @@ -1283,7 +1575,7 @@ "19 5.831976 4.371024 3.875515 3.999242 4.013709 4.016788 4.007910" ] }, - "execution_count": 17, + "execution_count": 166, "metadata": {}, "output_type": "execute_result" } @@ -1301,12 +1593,13 @@ } }, "source": [ - "###Varianz in AbhΓ€ngigkeit der Sitchprobe" + "\n", + "### Varianz in AbhΓ€ngigkeit der Sitchprobe" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 168, "metadata": { "collapsed": false, "slideshow": { @@ -1327,7 +1620,7 @@ "dtype: float64" ] }, - "execution_count": 18, + "execution_count": 168, "metadata": {}, "output_type": "execute_result" } @@ -1337,14 +1630,68 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { - "slide_type": "skip" + "slide_type": "slide" } }, + "source": [ + "## Verwendete Modelle in der IKB\n", + "\n", + "- Black Scholes / Bachelier fΓΌr Cap/Floors (Ibor und Inflation)\n", + "- Linear TSR (CMS Swaps, Caps/Floors)\n", + "- Hull White model (kΓΌndbare Swaps, Bonds)\n", + "- Sabr (Swaptions)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Beispiele fΓΌr Modell Short Comings\n", + "\n", + "- Black Scholes nur positive Zinsen (Wahrscheinlichkeit Zinsen < 0 = 0) (Black Model ungeeignet fΓΌr Cap/Floors)\n", + "\n", + "\n", + "- Black Scholes oder Bachelier kΓΆnnen nicht alle Marktpreise reproduzieren (konstante Vol vs. Smile)\n", + "\n", + "\n", + "- Interpolationen der Vols sind nicht immer Arbitrage-frei\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Modellrisiken\n", + "\n", + "- Welches Modell\n", + "\n", + "- Parametrisierung\n", + "\n", + "- Was sind Marktstandards\n", + "\n", + "- Wie gut sind meine Sensis\n", + "\n", + "- StabilitΓ€t" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [] } From 77600c366360b60a8b3984806181d58a1f0d3ef6 Mon Sep 17 00:00:00 2001 From: Matthias Groncki Date: Sun, 26 Jun 2016 12:27:22 +0200 Subject: [PATCH 16/25] Added Part III --- MonteCarloPricing-Swaption-Part_III.ipynb | 1121 +++++++++++++++++++++ 1 file changed, 1121 insertions(+) create mode 100644 MonteCarloPricing-Swaption-Part_III.ipynb diff --git a/MonteCarloPricing-Swaption-Part_III.ipynb b/MonteCarloPricing-Swaption-Part_III.ipynb new file mode 100644 index 0000000..839e629 --- /dev/null +++ b/MonteCarloPricing-Swaption-Part_III.ipynb @@ -0,0 +1,1121 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "# American Monte Carlo for Exposure Simulation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Copyright (c) 2016 Matthias Groncki*\n", + "\n", + "\n", + "Redistribution and use in source and binary forms, with or without\n", + "modification, are permitted provided that the following conditions are met:\n", + "\n", + " - Redistributions of source code must retain the above copyright notice,\n", + " this list of conditions and the following disclaimer.\n", + "\n", + "THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n", + "AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n", + "IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\n", + "DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE LIABLE\n", + "FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\n", + "DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\n", + "SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\n", + "CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\n", + "OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\n", + "OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n", + "\n", + "This disclaimer is taken from the QuantLib license" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# import the used libraries\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import statsmodels.api as sm\n", + "import QuantLib as ql\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Setting evaluation date\n", + "today = ql.Date(7,4,2015)\n", + "ql.Settings.instance().setEvaluationDate(today)" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Setup Marketdata\n", + "rate = ql.SimpleQuote(0.03)\n", + "rate_handle = ql.QuoteHandle(rate)\n", + "dc = ql.Actual365Fixed()\n", + "yts = ql.FlatForward(today, rate_handle, dc)\n", + "yts.enableExtrapolation()\n", + "hyts = ql.RelinkableYieldTermStructureHandle(yts)\n", + "t0_curve = ql.YieldTermStructureHandle(yts)\n", + "euribor6m = ql.Euribor6M(hyts)\n", + "cal = ql.TARGET()" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Setup a dummy portfolio with two Swaps\n", + "def makeSwap(start, maturity, nominal, fixedRate, index, typ=ql.VanillaSwap.Payer):\n", + " \"\"\"\n", + " creates a plain vanilla swap with fixedLegTenor 1Y\n", + " \n", + " parameter:\n", + " \n", + " start (ql.Date) : Start Date\n", + " \n", + " maturity (ql.Period) : SwapTenor\n", + " \n", + " nominal (float) : Nominal\n", + " \n", + " fixedRate (float) : rate paid on fixed leg\n", + " \n", + " index (ql.IborIndex) : Index\n", + " \n", + " return: tuple(ql.Swap, list) Swap and all fixing dates\n", + " \n", + " \n", + " \"\"\"\n", + " end = ql.TARGET().advance(start, maturity)\n", + " fixedLegTenor = ql.Period(\"1y\")\n", + " fixedLegBDC = ql.ModifiedFollowing\n", + " fixedLegDC = ql.Thirty360(ql.Thirty360.BondBasis)\n", + " spread = 0.0\n", + " fixedSchedule = ql.Schedule(start,\n", + " end, \n", + " fixedLegTenor, \n", + " index.fixingCalendar(), \n", + " fixedLegBDC,\n", + " fixedLegBDC, \n", + " ql.DateGeneration.Backward,\n", + " False)\n", + " floatSchedule = ql.Schedule(start,\n", + " end,\n", + " index.tenor(),\n", + " index.fixingCalendar(),\n", + " index.businessDayConvention(),\n", + " index.businessDayConvention(),\n", + " ql.DateGeneration.Backward,\n", + " False)\n", + " swap = ql.VanillaSwap(typ, \n", + " nominal,\n", + " fixedSchedule,\n", + " fixedRate,\n", + " fixedLegDC,\n", + " floatSchedule,\n", + " index,\n", + " spread,\n", + " index.dayCounter())\n", + " return swap, [index.fixingDate(x) for x in floatSchedule][:-1]\n", + "\n", + "\n", + "def makeSwaption(swap, callDates, settlement):\n", + " \"\"\"\n", + " Creates the swaption of the underlying swap.\n", + " \n", + " If there is only one callDate in the list of call dates it will be a European swaption\n", + " otherwise a Bermudan.\n", + " \"\"\"\n", + " if len(callDates) == 1:\n", + " exercise = ql.EuropeanExercise(callDates[0])\n", + " else:\n", + " exercise = ql.BermudanExercise(callDates)\n", + " return ql.Swaption(swap, exercise, settlement)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup the Bermudan Swaption" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "1) Create a ATM plain vanilla swap using the helper function above and \n", + "\n", + "2) create a bermudan swaption with yearly exercise dates.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "settlementDate = today + ql.Period(\"2D\")\n", + "\n", + "swaps = [makeSwap(settlementDate,\n", + " ql.Period(\"5Y\"),\n", + " 1e6,\n", + " 0.03,\n", + " euribor6m)\n", + " ]\n", + "\n", + "calldates = [ql.Date(7,4,2016), ql.Date(6,4,2017), ql.Date(5,4, 2018), ql.Date(5,4,2019)]\n", + " \n", + "swaptions = [makeSwaption(swap, \n", + " calldates, \n", + " ql.Settlement.Physical) \n", + " for swap, fd in swaps]\n", + "\n", + "calldates = [euribor6m.valueDate(d) for d in calldates]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pricing of the underlying at time 0 using the QuantLib pricing engine" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Swap NPV at time 0: 2233.4684\n" + ] + } + ], + "source": [ + "#%%timeit\n", + "# Setup pricing engine and calculate the npv of the underlying swap\n", + "engine = ql.DiscountingSwapEngine(hyts)\n", + "for swap, fixingDates in swaps:\n", + " swap.setPricingEngine(engine)\n", + " print(\"Swap NPV at time 0: %.4f\" % swap.NPV())\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setup the Gaussian Shortrate model (a.k.a Hull White model)\n", + "\n", + "Don't worry about calibration, assume we know the calbriated model parameter" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Stochastic Process " + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Assume the model is already calibrated either historical or market implied\n", + "volas = [ql.QuoteHandle(ql.SimpleQuote(0.0075)),\n", + " ql.QuoteHandle(ql.SimpleQuote(0.0075))]\n", + "meanRev = [ql.QuoteHandle(ql.SimpleQuote(0.002))]\n", + "model = ql.Gsr(t0_curve, [today+100], volas, meanRev, 16.)\n", + "process = model.stateProcess()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calculate the swaption price using an integral pricing engine" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "swaptionEngine = ql.Gaussian1dSwaptionEngine(model)" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Swaption NPV : 15693.67\n" + ] + } + ], + "source": [ + "for swaption in swaptions:\n", + " swaption.setPricingEngine(swaptionEngine)\n", + " print(\"Swaption NPV : %.2f\" % swaption.NPV())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### Pricing with an Monte Carlo method" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create a swap path pricer in Python \n", + "\n", + "Convert all Dates into times in years (using the same DayCounter as in the yieldTermStructure and store all fix cashflows in a numpy array." + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "mcDC = yts.dayCounter()\n", + "\n", + "def timeFromReferenceFactory(daycounter, ref):\n", + " \"\"\"\n", + " returns a function, that calculate the time in years\n", + " from a the reference date *ref* to date *dat* \n", + " with respect to the given DayCountConvention *daycounter*\n", + " \n", + " Parameter:\n", + " dayCounter (ql.DayCounter)\n", + " ref (ql.Date)\n", + " \n", + " Return:\n", + " \n", + " f(np.array(ql.Date)) -> np.array(float)\n", + " \"\"\"\n", + " def impl(dat):\n", + " return daycounter.yearFraction(ref, dat)\n", + " return np.vectorize(impl)\n", + "\n", + "timeFromReference = timeFromReferenceFactory(mcDC, today)" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def getFixedLeg(swap, t):\n", + " \"\"\"\n", + " returns all future payment times and amounts of the fixed leg of the underlying swap\n", + " \n", + " Parameter:\n", + " swap (ql.Swap)\n", + " t (float) \n", + " \n", + " Return:\n", + " (np.array, np.array) (times, amounts)\n", + "\n", + " \"\"\"\n", + " fixed_leg = swap.leg(0)\n", + " n = len(fixed_leg)\n", + " fixed_times=[]\n", + " fixed_amounts=[]\n", + " npv = 0\n", + " for i in range(n):\n", + " cf = fixed_leg[i]\n", + " t_i = timeFromReference(cf.date())\n", + " if t_i > t:\n", + " fixed_times.append(t_i)\n", + " fixed_amounts.append(cf.amount())\n", + " return np.array(fixed_times), np.array(fixed_amounts)\n", + "\n", + "\n", + "def getFloatingLeg(swap, t):\n", + " \"\"\"\n", + " returns all future payment, fixing and accrual start and end times and amounts and nominals for all non fixed \n", + " periods of the floatiing leg\n", + " \n", + " Parameter:\n", + " swap (ql.Swap)\n", + " t (float) \n", + " \n", + " Return:\n", + " (np.array, np.array, np.array, np.array, np.array) (payment_times, accrual_period, accrual_start_time, accrual_end_time, nominals)\n", + "\n", + " \"\"\"\n", + " float_leg = swap.leg(1)\n", + " n = len(float_leg)\n", + " float_times = []\n", + " float_dcf = []\n", + " accrual_start_time = []\n", + " accrual_end_time = []\n", + " nominals = []\n", + " for i in range(n):\n", + " # convert base classiborstart_idx Cashflow to\n", + " # FloatingRateCoupon\n", + " cf = ql.as_floating_rate_coupon(float_leg[i])\n", + " value_date = cf.referencePeriodStart()\n", + " t_fix_i = timeFromReference(value_date)\n", + " t_i = timeFromReference(cf.date()) \n", + " if t_fix_i >= t:\n", + " iborIndex = cf.index()\n", + " index_mat = cf.referencePeriodEnd()\n", + " # year fraction\n", + " float_dcf.append(cf.accrualPeriod())\n", + " # calculate the start and end time\n", + " accrual_start_time.append(t_fix_i)\n", + " accrual_end_time.append(timeFromReference(index_mat))\n", + " # payment time\n", + " float_times.append(t_i)\n", + " # nominals \n", + " nominals.append(cf.nominal())\n", + " return np.array(float_times), np.array(float_dcf), np.array(accrual_start_time), np.array(accrual_end_time), np.array(nominals)\n", + "\n", + "def getFixedFloatingPeriod(swap, t):\n", + " \"\"\"\n", + " \n", + " \"\"\"\n", + " float_leg = swap.leg(1)\n", + " n = len(float_leg)\n", + " for i in range(n):\n", + " cf = ql.as_floating_rate_coupon(float_leg[i])\n", + " value_date = cf.referencePeriodStart()\n", + " t_fix_i = timeFromReference(value_date)\n", + " t_i = timeFromReference(cf.date()) \n", + " if t_fix_i < t and t < t_i:\n", + " iborIndex = cf.index() \n", + " index_mat = cf.referencePeriodEnd()\n", + " # year fraction\n", + " float_dcf = cf.accrualPeriod()\n", + " # calculate the start and end time\n", + " accrual_start_time = t_fix_i\n", + " accrual_end_time = timeFromReference(index_mat)\n", + " # payment time\n", + " float_times = t_i\n", + " # nominals \n", + " nominals = cf.nominal()\n", + " return (float(float_times), float(float_dcf), float(accrual_start_time), float(accrual_end_time), float(nominals))\n", + " return (float(t), 1., float(t), float(t), 0.)\n", + "\n", + "def swapPathNPV(swap, t, timegrid):\n", + " \"\"\"\n", + " Generate a path pricer. \n", + " \n", + " The path pricer calculate the npv of the swap conditional \n", + " at time t on a given path of the short rate.\n", + " \"\"\"\n", + " fixed_times, fixed_amounts = getFixedLeg(swap, t)\n", + " float_times, float_dcf, accrual_start_time, accrual_end_time, nominals = getFloatingLeg(swap, t)\n", + " df_times = np.concatenate([fixed_times, \n", + " accrual_start_time, \n", + " accrual_end_time, \n", + " float_times])\n", + " df_times = np.unique(df_times)\n", + " # Store indices of fix leg payment times in \n", + " # the df_times array\n", + " fix_idx = np.in1d(df_times, fixed_times, True)\n", + " fix_idx = fix_idx.nonzero()\n", + " # Indices of the floating leg payment times \n", + " # in the df_times array\n", + " float_idx = np.in1d(df_times, float_times, True)\n", + " float_idx = float_idx.nonzero()\n", + " # Indices of the accrual start and end time\n", + " # in the df_times array\n", + " accrual_start_idx = np.in1d(df_times, accrual_start_time, True)\n", + " accrual_start_idx = accrual_start_idx.nonzero()\n", + " accrual_end_idx = np.in1d(df_times, accrual_end_time, True)\n", + " accrual_end_idx = accrual_end_idx.nonzero()\n", + " paytime_ffp, float_dcf_ffp, accrual_start_time_ffp, accrual_end_time_ffp, nominals_ffp = getFixedFloatingPeriod(swap, t)\n", + " # Calculate NPV\n", + " def calc(path):\n", + " \"\"\"\n", + " Calculate the npv conditional on the given path \n", + " of the short rate.\n", + " \"\"\"\n", + " if len(df_times)==0:\n", + " return 0\n", + " i = np.where(timegrid == t)[0][0]\n", + " x_t = path[i]\n", + " discount = np.vectorize(lambda T: model.zerobond(T, t, x_t))\n", + " dfs = discount(df_times)\n", + " # Calculate fixed leg npv\n", + " fix_leg_npv = np.sum(fixed_amounts * dfs[fix_idx])\n", + " # Estimate the index fixings\n", + " index_fixings = (dfs[accrual_start_idx] / dfs[accrual_end_idx] - 1) \n", + " index_fixings /= float_dcf\n", + " # Calculate the floating leg npv\n", + " float_leg_npv = np.sum(nominals * index_fixings * float_dcf * dfs[float_idx])\n", + " # Calculate the already fixed accrual period of the floating leg\n", + " t_f = accrual_start_time_ffp\n", + " i = np.where(timegrid == t_f)[0][0]\n", + " x_f = path[i]\n", + " df_e = model.zerobond(accrual_end_time_ffp, t_f, x_f)\n", + " npv_accrualperiod = (1. / df_e - 1) * nominals_ffp * model.zerobond(paytime_ffp, t, x_t)\n", + " # Calculate swap npv\n", + " npv = float_leg_npv + npv_accrualperiod - fix_leg_npv\n", + " return npv\n", + " return calc" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1.01369863, 2.0109589 , 3.00821918, 4.00821918])" + ] + }, + "execution_count": 122, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Convert call date to time\n", + "callTimes = timeFromReference(calldates)\n", + "callTimes" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "swap = swaps[0][0]\n", + "swaption = swaptions[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "####Test the t0 Swap Pricing" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Swap NPV at time 0: 2233.4684\n", + "Error : -0.00000000\n" + ] + } + ], + "source": [ + "npv = swapPathNPV(swap, 0., np.array([0.]))(np.array([0.]))\n", + "print(\"Swap NPV at time 0: %.4f\" % npv)\n", + "print(\"Error : %.8f\" % (npv - swap.NPV()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Monte Carlo Simulation\n", + "\n", + "## Generate time grid and paths" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def fixingdates(swap):\n", + " leg = swap.leg(1)\n", + " n = len(leg)\n", + " fixing_dates = []\n", + " for i in range(0, n):\n", + " cf = ql.as_floating_rate_coupon(leg[i])\n", + " value_date = cf.referencePeriodStart()\n", + " fixing_dates.append(value_date)\n", + " return fixing_dates\n", + "\n", + "# Define evaluation grid\n", + "fixing_dates = fixingdates(swap)\n", + "fixing_times = timeFromReference(fixing_dates )\n", + "\n", + "date_grid = [today + ql.Period(i, ql.Months) for i in range(0,66)] + calldates + fixing_dates\n", + "\n", + "date_grid = np.unique(np.sort(date_grid))\n", + "time_grid = np.vectorize(lambda x: ql.Actual365Fixed().yearFraction(today, x))(date_grid)\n", + "time_grid = np.unique(time_grid)\n", + "dt = time_grid[1:] - time_grid[:-1]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Random number generator\n", + "seed = 1\n", + "urng = ql.MersenneTwisterUniformRng(seed)\n", + "usrg = ql.MersenneTwisterUniformRsg(len(time_grid)-1,urng)\n", + "generator = ql.InvCumulativeMersenneTwisterGaussianRsg(usrg)" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%%timeit\n", + "# Generate N paths\n", + "M = 10000\n", + "m = len(time_grid)\n", + "x = np.zeros((M, m))\n", + "y = np.zeros((M, m))\n", + "numeraires = np.zeros((M, m))\n", + " \n", + "for n in range(0, M):\n", + " numeraires[n, 0] = model.numeraire(0, 0)\n", + " \n", + "for n in range(0,M):\n", + " dWs = generator.nextSequence().value()\n", + " j = 1\n", + " for i in range(1, len(time_grid)):\n", + " t0 = time_grid[i-1]\n", + " t1 = time_grid[i]\n", + " e = process.expectation(t0, \n", + " x[n,i-1], \n", + " dt[i-1])\n", + " std = process.stdDeviation(t0,\n", + " x[n,i-1],\n", + " dt[i-1])\n", + " x[n,i] = e + dWs[i-1] * std \n", + " e_0_0 = process.expectation(0,0,t1)\n", + " std_0_0 = process.stdDeviation(0,0,t1)\n", + " y[n,i] = (x[n,i] - e_0_0) / std_0_0\n", + " numeraires[n, i] = model.numeraire(t1, y[n,i])\n", + " #df_times_temp = df_times.copy()\n", + " #df_times_temp[df_times_temp <= t1] = t1\n", + " #dfs[n,i] = np.vectorize(lambda T: model.zerobond(T, t1, y[n,i]))(df_times_temp)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simulate Exposures" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5.42465753425\n", + "5.3397260274\n", + "5.25479452055\n", + "5.17260273973\n", + "5.08767123288\n", + "5.00547945205\n", + "4.92054794521\n", + "4.84109589041\n", + "4.75616438356\n", + "4.67123287671\n", + "4.58904109589\n", + "4.5095890411\n", + "4.50410958904\n", + "4.42191780822\n", + "4.33698630137\n", + "4.25205479452\n", + "4.1698630137\n", + "4.08493150685\n", + "4.00821917808\n", + "Update\n", + "4.00273972603\n", + "3.91780821918\n", + "3.84109589041\n", + "3.75616438356\n", + "3.67123287671\n", + "3.58904109589\n", + "3.5095890411\n", + "3.50410958904\n", + "3.42191780822\n", + "3.33698630137\n", + "3.25205479452\n", + "3.1698630137\n", + "3.08493150685\n", + "3.00821917808\n", + "Update\n", + "3.00273972603\n", + "2.91780821918\n", + "2.84109589041\n", + "2.75616438356\n", + "2.67123287671\n", + "2.58904109589\n", + "2.5095890411\n", + "2.50410958904\n", + "2.42191780822\n", + "2.33698630137\n", + "2.25205479452\n", + "2.1698630137\n", + "2.08493150685\n", + "2.01095890411\n", + "Update\n", + "2.00273972603\n", + "1.91780821918\n", + "1.84109589041\n", + "1.75616438356\n", + "1.67123287671\n", + "1.58904109589\n", + "1.51232876712\n", + "1.50410958904\n", + "1.42191780822\n", + "1.33698630137\n", + "1.25205479452\n", + "1.1698630137\n", + "1.08493150685\n", + "1.01369863014\n", + "Update\n", + "1.00273972603\n", + "0.917808219178\n", + "0.838356164384\n", + "0.753424657534\n", + "0.668493150685\n", + "0.586301369863\n", + "0.506849315068\n", + "0.501369863014\n", + "0.419178082192\n", + "0.334246575342\n", + "0.249315068493\n", + "0.167123287671\n", + "0.0821917808219\n", + "0.00547945205479\n" + ] + } + ], + "source": [ + "swap_npvs = np.zeros((M, m))\n", + "swaption_npvs = np.zeros((M, m))\n", + "cont_value = np.zeros(numeraires[:,i].shape)\n", + "for i in range(m-1, 0, -1):\n", + " t = time_grid[i]\n", + " print(t)\n", + " pricer = swapPathNPV(swap, t, time_grid)\n", + " swap_npvs[:, i] = np.apply_along_axis(pricer, 1, y) / numeraires[:, 0]\n", + " exercise_values = np.zeros(numeraires[:,i].shape)\n", + " if t in callTimes:\n", + " exercise_values = swap_npvs[:, i].copy()\n", + " exercise_values[exercise_values < 0] = 0\n", + " states = y[:, i]\n", + " Y = np.column_stack((states, states**2, states**3, states**4))\n", + " Y = sm.add_constant(Y)\n", + " ols = sm.OLS(cont_value, Y)\n", + " ols_result = ols.fit()\n", + " cont_value_hat = np.sum(ols_result.params * Y, axis=1)\n", + " swaption_npvs[:,i] = np.maximum(cont_value_hat, exercise_values)\n", + " if t in callTimes:\n", + " print(\"Update\")\n", + " cont_value = np.maximum(cont_value_hat, exercise_values)\n", + " swaption_npvs[cont_value_hat < exercise_values, i:] = swap_npvs[cont_value_hat < exercise_values, i:].copy()\n", + "swaption_npvs[:,0] = np.mean(cont_value)\n", + "swap_npvs[:, 0] = np.apply_along_axis(swapPathNPV(swap, 0, time_grid), 1, y) / numeraires[:, 0]" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "swaption_npvs *= numeraires[0,0]\n", + "swap_npvs *= numeraires[0,0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot Simulated Exposures" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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groDCuEIKkwux59hP5aPLsePINZ/n2vG60Is2t7WhxXUtsLiXP5PU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oRjMAQiAxxxdkabDRswTixGJ5Gofv+iooeorc2ChcWid9qtlMtxpqICFrNmoePXX0OxeTOarVuH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pO8NrPXJccBLE4p5pnTp3hD25FHhl2o4ax2uZiUnU2py8WXbdsS5eUdLKwrZNxXE9lXUULr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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,5))\n", + "for i in range(1, 100):\n", + " plt.plot(time_grid, swaption_npvs.T[:,i])\n", + " plt.title(\"Simulted Swaption Exposures\")\n", + " plt.xlabel(\"Time\")\n", + " plt.ylabel(\"NPV\")" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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yoRL/a/2JmHpiihUpJclPJ5M1L4uStSV0+77bKd2blJI1JSWsKC7GRafDQ6/H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8c8j6aAtH387mtSaWohPfcdLSD43SgCg6MBnUvEXCL6UepTofHt91o9ulD3C0ScHzVmPswfd5\n5VOPefP9sD33Ysw9HQevd+fi8QnoHyWxrdSL39Zt4djRjxHFS1it6+nTpy/Xr9d4/06YABfOS1Sl\nmcjX23FiMDPqtSYjOgPXDq40bt+YrpFdifCocVjOqsjiQtoFLqRf4OKFixj3GfH098SatZBqJqEJ\njURhPM/4Oho0i/fR4r0WdEroVHP/2x9ZuBDy82H7djhxAiZNYmH//txKLsRkOoGno5Tt0V/id2En\nV5rOpc/LTjiKGyGK94iMTGHDhiU0XhpIVt0WbN7dCqMYykSLhVHa7oTXX05qqjcffzyDadPSKdog\nI6l4AZlhT5gufUzF0iV0dLLTTz6Q2DdVLI1cwpXi0Ux3301k5zDWbncHYOVKiZVnKrD3ysdetxKX\nTxfTIS6LqVMdVOl24VdcgiHkez79dCdd69pZ1/EUYkpyzV9zr16B2UxG1CoMXlGkfX8OynbjrdSA\noxqf8I28rqziyY5vObTZzi+R+xidEkettk5cvPivl8nhMPPy5RDOPoxiyWcrmRNRxIDMPBSy0wjW\nFI5GjCOvvUiGi0hFYSKBtW6SuC4Jz4AovL0fEZtcQp9iGW2rrTwEbooiryQZpbTmB1UpUa5JyI0g\nmJTI+/eBTZtqojpw5dw5jg8ZwnKTCa2XCxmfduBF5Bk8PeUEBU3F2/whyrg4ZK4uHKyvIbWvmeat\n7ChFO2n6cEqzy9C/LcHi5otJG4AlvzbdUl4y8EYisduvkmVTMMDfkw9CvOnl6YlGJuN0aSkFFgsy\ns5nHN25w7uxZdE5O1OneHXlUbZIseqodYJegjqKUYKEUTbYMzfUw3GLvERF3lEZiEqBEkixUVEQz\nd+4JgoNvoaiaS97LcozBXnjH+WCPVpDjmocgl3g/vDa9g/JQYsNKTwqr2pFRFE1RuYWSCiMlFRZW\nPJlOUKmeg016o3RYUVvMuOgNuFdU4a6vBsHGGz9fCt290TlrCS4qJiL/LT4VxWgMFTgEBwpEfCpt\n5IR7kt0wjKKmdTA0b4Srbwi33tziQOIBPu/0OZOaRbW0KAAAIABJREFUTkIu/g0B+V/gbJ9GtD6b\nhffnsxEiQ7D7BUFRMhTehKrLNffj5bSFR3NB3Qoa6hAaluNUuxinbAF1lgyjiwpToAOzr4BDFBDe\nOuHI1iC8dUYoUyK62JC5WlBqq4kNfkhT/3iauN3BW1lEui0Ku0KoSSgRJRyCiB0RtQBamYhGrgAU\nmCURiyRhNjsw6WXYjRJ1J95B74hh5tk1/+XX//dMyv4jcUgSZoeERZKwSGAXZIi6Sj5eu5F9Lz8k\nt7gzgmD+p4xN4/SaKn3nf8pz/yO5fv06/4u9t46O6trjvj9nfCbubiQQSJAECxDcrXhxSnEKFChQ\naIsUh+It0OIUd1ooWtw1QYMlxIj7ZDKT8fP+kfrte59733X/edbb71pnzTlbZ+bsffb3/GxfvXr1\nt+sFCxb8QwL/T/hFHTxfFMXOv1z/v6qD6365mxVLArmkVNFWOprMqI7MaOWNbq8B2eY4Rs27wKJH\ne+nXqhZ3bv1EW8oZO3AlkyOyeLzwLN42Gw2ld2kekEBc3+t4CGWUfjuBcr93lJhkyOUVeOo8cFZZ\nsVukFBXWwF+p43TbM2xmAB5Xa7Nzp4Tl2Z25uesmkqTR2GyHcGppZI7wIb6igK25ji6FyciK8hgS\nV8GNunv4aMkXrO/wlG7OGtyVBlJDZxEqy2Tuvh/xuCVD31BEVmbBYAui2fjl7F31FfEPHiBVqrCa\njZg1UtIca9Ox6AqdZWcpmuzKxA1KTMMO8tUPk5ALjlT6h1NYCP0+vcQx6yiSGu+icFdXij4YSL/b\nJxm1fDIuLt8zuWULLGu3ktgsEUuhhSZpTZA4yGjVCvr1g5qRIg69mtFDWkHp0Q1sS53C8E0mCjev\n4lXBUH6ct4CQlzFsGfox6RXh1PihFitsLTAKJsQBZ+h5+givFP0Y/P1ICvYeYLo8mKZPI9Gt6IsQ\ntBZNyRauXq3FoEFbCA93+u3+3rkDvXvDkAlJbD+9EP1TKzN21GBIuyFEe0X/W3XW8+fPadu2LfO/\nm8+G9RvIvdaPcr7Ay+dHoh2qsfG4C58v/4zP8j8j7nLc3zdSXg6RkdCrF+zYwVWbjcE2G08aNsTD\nLmJ+/oYDDCKhyUTGfePHppLtXHh+l6zFqzCb05j84R6wS/kpdTm51114TC1ux7Xm0cVxJDj5k+XY\nEZPwjBljIGbreQzf9sIpYB/r7hzCf/oPLHB05oGxI4sNk7ktxCORCHirdbyV1UTduVUVSa1bl335\n+WzOyWGWOo2MxLHMGF9OpwGzUevnkpQkoaBApE2bqyQkeBMb68uePR7I5SCKdvISz/GmnYBkx2S8\no9vwqjiRQqtIn4YncdcEYDKZqFkzkmnTGvH8y9n0MerYIIlk8xtv/P2r/qZCs5nRL57Q9+wWgjZ3\nJacylH1OEXxpXois8h3Vyw9x0T2Q7+UdyYgeRvkjP7qWrKCfZQ/1EbgdHMiDaoE8qBvNg1g/FP4h\n9H6Rw9CbCahMAeQM+57Ti78k9GU2Q9lLllcYP3XugzVcZMyOzegcHJhjNJIqCBzV6TgeqCB63juk\nzl68yVESasjDWzQTsFqGukLNoTaNSHF7Rnx8GQUFM+nbdyqnTnkyf4FITEORkdIadL73lnt7VejK\n6/AosQWjRrXA2zueBVk6DhUU0NrVFa3NhtZqpcxqJb+iggK9DqNUDjIZzXIeMDewlNrBNSheEIj+\nlpKIPe64NvRFKnXEbjdisxm4dSuNIUOCqVlzO5WVxxk1qisdOoRhtb5Ap3uEXv8EicQJvdWC1arj\n0EsnHur0tHaX09jHgqfaQkaBHwX51TFp69Dl8hEiisqocPIh9Pk7bAo5drkcpb4Sm0qJ2cMVuU6P\nvEyHoVowaTF1ee3rxwuFhmcurtQuKKBH4kOiHz9BG+bHm/hIEhsF8tJLoMhYTKBTIHNazsFD41Gl\n5s3Kqtqm79mzqiM8vGpcqv/mperf4EzLWjR6mM/ClvPQutgQ3XVIg8pxrp6Dm3sB3mIJDlI9csGA\nI5VoqESGCUFQI5E4IggKLOZCysu9yMqKQK7xQhMEOkeRZ5Ve5JqciSSDCPE1QYrXZJWGcTuzDY8y\n4kkrrEtlhSOyHPA1FOOkKMfTI4cgv3Q8fbNx88jF1TUPN7eqAwTy8kJISOjIkyetKL/vyHjNAp4d\n+XOQvT+qgf/8rBL+Je3vVMZ/auv/wBL/1Ndf2vpr3T/mV5h1JL9LwPPmPfo/t9LhRTkpqnpctr3H\nTO14Or33GWdP7Pq3ff+D/w7/SAL/AwhVBiCvqXIMyQXuA4NEUXz5l3Li+sgxBGW/z+EOdg5di2Gz\n9lOOLmpFUkkGRedMVObMI65TdU6czOVwUBwzXh0kQO7CGM97eJTY2Wy6jSgEIgvrRKE1hfBqeRS+\naMkQ+TNe54bzsPpLGjRbxyllAUUHzuJUbS6HR/ThxfpUfliRh/yH3tzaHYlVuosmPb8n604G+SVf\nsnPXFgyaNzhV1qTktpLBO9NICh5ERn5bxm5TMGvdEi70e4i7QUIE0Zx5ZKVgyGr2dd/AucE76TnO\nm/r10xnw6gW+Ja/4rv+nfLtexvLXZmp7B9E0eAzfTxqA2ubFxIFyfNc+5dwIGbs6WPl83iWaTx7I\n8MXVqdfASHqXuqyuN5MWX37Gi7ng6tuR9KwjzJ/tyk/FDvh8uwk6d8aYaaQypRK3tlXG4cnJ0LRp\nlTbog2YpDFaNZuaGhXz/ajKeisHkenmh+fkGnq1v8cE9P3SRz9jU4hhH24fzoek5t8VrxBLLTs9t\nyCxJFDipeLR+HT9OEzDUfI5l4kKCNFKOHl9BZM2PmDDhD/PCYuHupYuMn7OeJwkbqBZwkknTUgnt\n0gZ3tftvh5vaDbVM/acHXUFBAXFxcSxcuJBhw4ZRXl5OqMtsSllPqKaA/U2zWTt6GYOXD6bZ7GY4\ndffmxAkIDa0Kmvon7djEibBtG6WPHtGgUydOursTnZSENjaW0++9x0n3YM5faUr55RCiv3jMG30f\nAu4f5X7IXopf3yf105d88slwGjaMZ3dzHbavv+b0pAUMn9SdjcJ51o1+S+fkVoREzuWN/0NW6+zI\ntsup5jWPr1Ny6WBfSn0ScKyt5El6FFZrlfpnZOxjJqVMJSrOCfPEUSx22Etb6V2iIrczaPE5bjx6\nyCfrP2JR7UGkJUvYtw927qyguBh8/StZsukHrOrd+M4bjG99H4Jm+/Dq1TA8PfuyyjqCdJOVn+rU\nYcPXX3Pu3GVyck7SoUMCvaMnof9sKe/cAxiZGMFVcznzjyXxyRYpAQYJsgn7kTfP5OKdA8Qv+wy5\nrpgh4jwsWitTld/Qy3gKbyGf44Kdm61ac+pxLzJLulC90Xz8hzZCI5pQ577Go+gdV1oMYHz6Blyj\nXzKzOA5XaSCO6Y1pcdXAe4+v4mTS8WLidBw/i6fMbOLwsmUknjzJp127UpC/k5judnyCRLZ/B6vP\nC2yNaUQ1Zwf63LnDs6bRhG4cTHLWGhYvPo/dHs3y5VU+C+f7+hOfmEvqz3Pwdopi//5U3D2u4x96\nm1LBi5pe7fBxa46zcxNUqgiKi0+SljYXmcwZb+85XL1exqULF3hx/gUTCidgDbAizBBo060NYWFh\niKLI3bt3WbToFOfOTaJly7MsWrQOH594qlffSJVfHBiNGaSlzaW4+AyOhT0on9ILWdxbxJ7HsOi1\nyC1ByPAEpzKsLm+xOqZhXORJk5wsXEyBVZM3Pr7KsKtOnd/t9QDy8+HwYThwoGqiv/8+lQMHciMq\ninNlZVzKz6fGgweMefCA+GvXUCkUSHv2hOrV4fnzKsL3/DmoVFVt+/rCo0eQmVk1gVauhBEj/mMR\n14WGNairfMuLMUEICjOixgAaA0htYNBgM6mxSUREmQWJwoxUbkYQRCwWJRabEhtSHJTlmC0azGZH\nrGYVdrsUmWBGo9YikVrJ14aQLgsi39kJV1slwQod7kIFKmsJSkkhEsGMTudHSZE3uXl+aEu8MJc4\nYi11QK/zIyOvLplFNSiqcENnkqOSWVFTidKWx3iHufRpXOW3KIr8pkIVhV+jE1Yl/Lqq/4kCiOIv\n5X6v93vdP0P823wR8df2/+bv/lOdX09+qetSVkl4YgYGpxoYreWUVFQnVZjKNBwoVh/i+O5OtOz2\n3r+5c//gv8U/JPA/xC8hYr7m9xAxy/+mjHhZcZLPHb7gc8tqhn+jx76gJt2y7nPuq0i0syLRDB6M\noaUzw+6dZflZO9d1Br6pNZm0xM9RSAxoPYopy3sNPVtCroluPsuQuFbj8r7p1I4awf1na3m/yTJe\nvJ5Isu0sdT+YR5qDwN61+5gT9RGP3xbRoXU1irO+Ij09EBcmUNN9Ks3dRBSuXzC/fibLDrfg/Yyr\nXPCYQrC1Iz236hi1YCO7Gr6kOLiE2j/Voah0GCUOg3FIFenyPty+5UqxQYLQvoBjYzWUHBzCWJeL\nBOvs1DIIpD4poVq1mzhKExn4sDPH09w4pHZn2MzFXPKNQjutCyHWLBrtPUVKRSLHlqeQsEpHSIM1\nKFM7s3Hel+y+8TVPlQJvrw8lqNpMHB1r/8t9WLcOjh2rEogV5s7l3MD2fPN6Ogt25GDOV7DVvhaH\n+av42f0Bq1Os1En5gGXnBlJmL2eM6kNijDF8KvmU+673OWo7RI2Bq0j1Lqdr+Eg6uAsY8zz5euxB\nLvkMQeLlgbGsjCOFhWwwmcgVYKiDnN4BjRmSvoH3xHO4f/wD56OVlFSWUGospdhQDIC72p1gl2Bi\nPGO4NP8Sbdq04dvV3yIVJFx9PIJOHxRjeX6QvUFX8HN0QJ+hx9nuzNvRdZl3zJVa0QIFBVWmgPHx\nVVsptQlJJfajJghuzhwSBJoWFFBasyY5MhkaQWDfpk00cXcn3sWFnK0pDJrmjzZuP8067ODKMSvc\nukVWxW5yco7yySdWAgIC2eXkhLqkhMme+zEdvcLgMgsGbzPWrQNZz+eY1ufz6I6Wz5iPv6OVB76f\ncfzJFsqV3pz9WUHTpjBtGmzfDkqFSJh7FrM/6UR1bTrP8gcwZMZ8Kr29qVGrBs6TOpPfZCjOMiUi\nIgarkeJKEfuyKMR8NU26vuST3UYOb7TSly9oHT8TP79+WO12+iQloamo4HLPXri7X6Z372iWLoXy\n8js8uz+MlKmLcNYHklzNTuOXUmouDsdnuA9IrFx4OpKx5X05POISrlnlaLxO4F1SQZnEGzdXGW2z\ns+gBTAHswFpGs5glSKUD6BeSyaetWxNepw53o6qTopiGu+coBFkN8iryyCws5vyuGJ6dboFdMGEz\nK3ENzGPcwvsMbleXlNspjBwxkrDgYGzFxTiF5LLoMzmxn6hxSdOS5CnlZ9chRL/LoJoxmy1x/Wnz\n+WYebJqHNi0CwWajR+1RNHheSOqJ0VSUnsQkiaLv0+W4u0i40wW0Bbcxme6i1V7BYilCKtXg7t4d\nX99hvHzZjAMHXHAr0uF/Pp2GU0SeBt7n8pXLXLlyBZVKhbOzM0VF0ZSXb2PDBjvDhztitZbz7Fk3\n1OoahIQs4tWrRWRkHMRJMZDSb+MpeWzAY4oHRicb6kp3uk+Mx2rNxmhMpbIyFaMxDYPhFU+nvKZW\nbiX1X2b85w/btDQ4eLCKEOp0VS8+o0aRrlJxvrSUs0VFFCYkMOz+fZqVlhLRuDGamJgq8mexVLmS\nnjoF8+ZVvUUtXw4//QQaTdWOLAMH8i42lhdGI82cnXH6GxvEq3UiiPRLxbTkGb6B4ZScKqHoZBGl\nNwuRepiQ+ppAlGLRyrEUyRCKJVTIVWRKJLy0S3hq0VCKG0rXCkIC3xIamImvXwauXu9QuuZjsMqx\nVLihL/WgqMSTXJ032WW+yM1uuCndcFd7oDA7ILPaECtFzJV2SnUSCvRSCgwy0q1qAjUVtHFKIK5y\nD47aEyQLOh6LIq8UP9ND8T3jYur9zuEE4BdbQOG3618h/qEMIIq/OIT80bbvD2V+zf+Tbd9fzoU/\ndvHnuvwLv/w931QsxZqcio9tP6fFodhV/akUNvGRbh2dO7Th+Om3/3bo/IP/Hv+QwP8hBEEQ27pl\nEN4jlLBDH1MaV4eVs9yp94WNp4/7Iun/ES0UW7jbeDwmW03cVs/kQIEbKnsBsZ4ip6a5M71UQu5G\nPQyfBhtG0rXhhwybfpPBp6/D+eoEe35DUcbH2CuTcJAMZLu4mSPeW2lT0IdXndcRMtqMh7YmsWuf\nEPukJ3KW0tj9HDnyOvS3fcdjv730keloaOtK1vORlPssZOKaaQxbto0yT5HvaxQjHlqAm74boVH7\neNQ9A+nmZSj8gqgYuRXZskysBe1BmgEDBiOY85GEKWi//0uadQjA9Zocc2o4qhU1WbUog5eS5rT8\n5iApD1xJOjsaed5jVO5eaMcFUtTNlXr1LvKkwxNOeZ3Ccn0RH7Vuj2lpM7KzN6BQ+GG1lhIQMAlf\n3xHI5e7YbNCqVZUw4buKz3Bo0YlFSTN4ZZhOjZ01KYu7TTtZGCWTB7A5KZhuGz+nlsmf3a5O1B4f\nSffuWfRt25d1QevQvNKgHFXJe73KGVCxHu2REPr02c/8L71pUKsV7kolu0+fxquaF5lR2Swcu5Ce\ntXrSfk97hkdMZ/2QMdyStabGitFVUoZfUGmppLiymLclb5k+fjoF5QWoBqoo0GfTK0DKD+8q0F1v\nhHDhOx5/IGdg/eFM+X49YpIGD6UFL42NwA+9CVsYRkm5hGvX4Mo5Ixf3ZJMhC6KpZjcLSxdxb+58\nqo/vTjMHDZ69e0NEBGzcCIKAKEL7liaMD+6SPqEt+9vtpFW3D7DbLTx8GIO7ex++/fYeCQ+LOGSQ\n8K7BQNrun4CrPgAcdTRu58nVt3Moeb6KTY2P0lgtpe6ZukRMmUTJ1jW0iy7g+POQ337z48e5nDv3\nBUlJNXn9eiTXv33Hga+WMezSJWQNG/JTTAwfHd5K720dkTjVJqzyJFYgU9kZR7eeJM02MOiEhBvN\nrOzrL0HvokKyqiYRVme8vZ9QUrqJ568PoHEex5ghX7F6dZXwIEmvZ13qQ2KKPsbhZBsauA3Ab4Qe\niywbszmHfH0GN4peEU4KHhN6IC8UKBqQTLdVZ+jRV86rlAGkpJ+kV9eu7Jo9G5NGw8mbN9m6LYP7\n9yfj5bWMysptNGnShDlzumOUfsOn8n3cqd+QHw5J+ewzaNmyimd4e8PilVpWLtVgsdqR1T2ApcUn\nOObZkFyXoC3UommrYdBgG228NDQc48F30v4ccWlFRW4Ys2S7+KhwLUn16mCc/5qXlxZw94UzHztM\nJeqVFs0zPUVW6PLkLj4VWdwf0IHmUi2di/S4YEbmWIFLdR0OsZmcMBk5mC2j1OUVLhHXcCiohSVl\nCAWPe1Cvtor45gLNmol4eSVz/bqZr7+O5uBBgbZtf3+e5eWl8+GHcVy4UIBKJcdB7oxcp8TJ3QmF\njyvaYg1eeVYKyKacMoIdOxLYYADNO3aiTh01NQKt3JlRl/pZ5dR9nfWfPUTtdnjypGrLiCtX4PHj\nKiKo01VJ96pXB19fbGo1uXI5F3x9md2yJUMDApj6ww/4r1oFo0fDF1+Ai8vv7RYXw4gRWG7e5Jve\nvVnaqxdRlZU89vIi1smJDp6edLHZiH30COnu3ZjPnCZngJ2Py4ZiMMqoqLBjMNgw6G0otFZcK6xU\nw43atnCibbUpR0sxRSgw44oSZ6k7FptIIQ6k4k0mHmTjSibOZOOAAzZEwCYIiJKqA0HAZgebKKCQ\niHg7WfFxtePjZsPVqQIHaQEqYzrSihR49wx9iR4jGtzxJFQRgp/KH2e7M1/YDLSTnmC+z71fVyaA\nKuncb9K6X5mYwJ9Z2R/yf8mrkur9sfyv5f7IKKvKi7/tg1K1hV2V0PEvff7h+wi/tilW1ZWXPCNV\nVs75vBmoA5rgHLaGFVcXkqzaSL+RFezccPw/G0f/4D/Gf0oC/zfWtv8/QJM4J3xqx7Bq7y623N7F\ny4uXOb28DSuX9mF3WAaJdrBlb8e5+moCJ23n4OXHdH15FGtxDin+JXzpo2RCi2bYT+4DsQm3HrTn\nvdL7xKf34KZTIpmyyWB3QSUfyjDpUOa2mc2S259jDilm9J2mFFXTMLRbNyJYxFAnE2N3QN+BXciy\nqyhe15p783ax2+bKBdkQzOt8GHXWB43dRihWzt7+COvNdnjW2YVTYhjmAgm2Jrv5rE5TjniL+Jtz\nsS84zN28iQTnuqHX+lJyLBFzky9403MDPht/pIe6CKcxt/GXPKBJ0Hf88Lo3SZ82x741ge4tGjLx\nJzNDUyxk1H1CzERftLKPMaf25KT2JMdwYkukiXkhswkImMzdu1XOFTpdIhkZi/H07ENAwER27KhP\nXFMrdAhAYbJiMzth3dcMjcdtuh/rwJqtO+mwaBmfLPkc+7TvMFidqdPAi/SfgrFYXNhzqj9fLR/E\niDHvI0T/QK2cGM4GLsEzNR6bzZVt2+Hu3UiS05OJ/qIuZQ5l3O1zF5VMRdvdbZkaN5Wh1d/nRadH\njErZx7ezm+Gdmornl18ilclQy9UEygPZvWE3FMGr66+woGP4oVjO5RbiblAgrTBRhoZJxkQy5p8h\nQP8Wz32RxPXTUPG8guSPksndmkvAxADqdqrgcvpm8n6Iwu+75bx55sGmji+5ukWD4nvo0gW6jP6R\nNova4rByJcycydGjkJ9jI8FhEH1fDaN74TYeVUQTMaABtWrtIzv7a/r1K8HR8Rk9z1tJ2p2ASlWP\nBhF96Tp2O6e/NFBimEysdyOScs6xeXsbllVq0ZWZcMZGfGYptspABKWVrKx1VFSspG/fUVgsH3Hw\noDPj1nsRu2YtQ/PzOPjmDV22bSMquwKHGWcYuccRv6hN2NX1uF9ezp3ycpo455IRZOFwZgRd6xai\ndpFwqPsKCk+cJu9ZEbExY+hW/SlXXaHL7BISdDKWZmZyW6tlSmAo71e7yzeXviAofhS2Yn+UygC0\ngiebizV09h1Ga+9IEtR7KQj0wDJ2FN8lbiVZ9i0paS8Imdgaz1buTEhfQ0P/hnTu1ZmRo0bx4EEl\n3bsvIiSkKTdvfsjt2xdJSFCQqTuFT0oYfhIlK1aVERT3jl1pVzG9MREaHkqzJme5fbsb1sdDUb4c\nTMNPVnNPsRhOginWwp7nroTGVFI2soDPvlnJ9x0uIQ1PpcH7e8jTt8E2uCfB40WE2bPId++Fo06H\n+YmSRculbNek4F5eDWFVA9YoUpGVmjGOTETXtIRTSSK3ihIoc76PRGohXFaPzumhROX25k3EA26E\nj0fScTB6vLiX1pEr80eSnNwUhVzKvlklxCJBn6RA5iXjx8s/MvWTqQwZMoTdaz8ib3olhkwTSV0j\n2XjRGVu+ie/Mjwmc40/tBYHc2PKEw2sPcvPGfJZcH0qULI6WljgCWtqQiTaGvUgiVK0hVKUiRKkk\nRKUiWKVCCVVq3CtXqojf9etVbLp1a8Qhg7EtnYfMJq9S6x45UiXRCwhA2ro1geHhjLh0iWHr13Oi\nQQMaTppE9/Pn+TQqiuoaDRabBZtoQyVT8UqlYulXX2E+dYpNa9cysbwcVXAwpgsXKCwoQGmx4KzT\nYREEStzcuNqwAW5DE7nd/QeUAsio+h1S0YZdtFMu0XBXIue86E8ZXqiIxFVZD5UiFrs9mAqjF1pU\nINhxkRfgIH2LnJ/BegsPy21MFCFHggIHVHZnnGxu+Ag+BAj+BNgDcba74FniiUeJO1JkWLBjRcCC\nErNQn0ppY0yeChRBDjj5O6AwWZDnGhBTtSiEYkyiiiXV0n4XAApVdA5+oWy/crrf8kDyqxevCJJf\nKv6B/lXRQeHXfPHP6b+d/+EQfu1b/FPf/E2bv9LCV6IvUclrMNYO4W6zO3Tb6kieVIWb2wF8vP7v\nd8r4vxn/SAL/AwiCICaNf8OKysOUv9hMm4ct8K1Tg1MxORx/vzNjt+xkTfhL2LUGIeY7hEn9EYov\no/jhEZx7jkUNgz524HCdr7EN/xiFLRqF02UOH6tOSoqeCRPGASuRTHuC0nqe0yXlmCri+CRgOt9s\n24BHAy/q36pP5LJlaFdt597SS3gMCaGar5VSvYQG4afooplB1/RJpHTyYkmbJdjPnid10BV+XL6O\nKa87Uuq9GW0vHTt2CGQv7M7ZmIXcSjWCKODw02Uqam6gToEKN3s2Mc82MO3Jp3SWz+f1pMUsOvY5\nvYK/Jnu4Dre7blQ/XkhT/UuMogPdLp5lY6WEzFUbKVvri1T0w2FDKxRbVpLiKDJL9Yorchk+Hxt5\nOikJdGd4mfcTl8xhOLt2QKkOR6u9Q5n2NhKZKz/ejiOtvAbesucMubWf9lenUtgvG2l+IPKHxXjr\nvJG3ykKrecrpuOO4Nq1H8atQVEUWOvcsw6bN4cmdG6RqmiOEVuOmOAnrPXcSZhXy4kVfXipnM/vK\nXEbEjGB+6/lkajNpt7sdM5vNZHyD8cTHh2EwFPH6dTJ+PlPQ5/xImSji7umJj48PHh4epKSkcO/e\nPXLtyfQ92AlPpYCvNZrjm8qo7ikh8/YlCHvBgX6eRN6TEHst9rexVJ5QTmL3p6z/0ZnrZbmMvHWL\nx4mJiGYzx44dw8nJCVGsWj/Pnq06Hj6w09R6k87vO7H6Qh2OWHrTbNc4jJ06ELAoDPvOHeztbCRo\nUhjrvv6aH378keiYevgEeZP/9hQNPbtx/P4ilg4dx9b19xmqGUuOgxuXgi7xLPkVYvvWaNPN+N/Z\nx2zHN7T4oAD9sIk4OEQTHr4KjaY6UKWuHzAAmrcQSZrykKsdotiZm4v25k2OjRlDy6go/Jo1w8/P\nD39/fwIMAWjma/A7F87EZXru3tiI2boHoVZNbO+9h6ppU0YFBjLM15drZWV8kZqKh1zOrOBgRvv5\noZFWxWy7cqWq39Gjodu0cvq+fsa6iAgG+vgAcKHRaPRWP3ZO2MD5BxWY9llRDgwBtQ8xdWUo5FKU\nUiUvil5gF+3U8qyFxhbAk40jadV6H+933M29nAopAAAgAElEQVTEWYfJy/fCFjYLWa3H2HwMiGUi\nLkUumEQT1kgrnb06M6vlLLbsvsyBnfWwamORKtfQtHELnj/rg4PDCeLiLjJz5mU8pjhwWpQwr8db\nKpJjkSX3RpXZhI1h0+n/5CqZA8EhBQxXQqnR+jaq1w5YhNc4+N/FqXYiBr/HFAuvcayMwpzalEZu\nDelfGEz0VRnPAv1ZnxOIew0lkZFVYfEcfQooUB/khekACfpH+CllNHC10dBJSW1DOJWv/Fhz6BUZ\nhTo+951OXYfWGLMsJIQGsiwtiPadJYzqa8bly0f4trES4nMRW24xJeb6FKQEUZwoxehn4nrRDW6Y\nb3DfdJNbGgXPm/gQLvVAaVAgqzCj0OnQ6HR4lJdj8PLC0qoVfh07Yo2PplT1hJKSc5SWXsRmM+Dk\n1ABPz554ePRAIwuBI0fI+v57zgYEYHJwYMT9+zio1djevuXnUaMY27o1YaSS9nQpenM5AeGDyfHs\nyoduegZqktGUpeP91QWc7uSiDXZG0FpwSS3jWbico1EqztVrRN3sGvTvuovwmcNw1gRgNjuQU+7K\nlZIwblQEkiD6o0WJo8KKTG7DaDVgMBUiFd4gER9j5zEyMpAiQSH4IhV8EfADeyAW/KnEBRMGlOSj\nFApQUIicIqQUg1iMiXLK0WKiqEpCJvHBWeGNh8qTAAdPAuTBuJjiMJXVIteookChJk9QkWdWEK/Z\nQ23bc7pKmvymBv41LMzv4WF+DdTya/qvEH+x2ftr/h9KCL/l/Gt5QQSxyu7vr6FgfmtR+GNvf+7L\nvdSbi23VnGuVy53lo6ivS8IevIK4uIP0bziYXjO3/tfr8j/49/hHHfw/hCAI4p3QO9ycIKBx7M+8\nCcUckW6ndOEyrtz6iINjvFHd3Ejhun0Ighyjqwzh26dEHs3CJymBu5kTMQ27gn/HIrzmfE6gg5pM\n7Rl69vOnfftGdOmSSmXlLpD0QbmwAP/GOuZ+Vkbx8BO0LZpC+dIyojKiqFGnNq6TFpA6bxyHj0qY\nvuRDdJmdqSjvzhkmUi404RPvJeTXq4vt9jYkThqm1uiC173HKF1n8PXAVRx+VZ0xPQUaR41nP9EM\nt97g8os9xJapKH73hpOzn5PTsSkj9bsJKzahibzAkWYHwLmYI40lRK2y8y5pNt9FTqfPu1eMGteN\nduII8lt1YLZhBJLRu3CK9sZeWMGB511p4+ND1wHdmdjejIfalRjVGWawlroqIwrjE3y8+lBlni5i\nMqay59kZLHcGEJC4iZIHx1jqtBJTxxiuKnfTuksMw937k9QjCac4J+6suMPU81MJd6tDfsIx9L1z\nEF0sRDy5w8s5X/D1wet8ObM+wreJfOVWiDp7EBfKIpnQfAtxgXG8KX5Du93tmNtyLmMbjGXmzJFc\nvryXTzct4vUFP44eqMn+75fgvzQNEzXInzWL/JISatasyfGs3Sy4Np84DyndAz6n+8qrJL7/PqPT\n31C2ZgE1pjRh4fGFtFrSCt+hvgDc0WqZkpJC/1kGgv10NLs7hQHu7oRVr86OHTtQKBR/O/7Ky+HS\n9nTOfnGd6upsPl3sAhMmALD54Wa2/byPV3PrYFTtwzZ0AIq2nUDlh6VMSvCLc2R/txqp+BxaHGDc\n9TcMsrYndnQSig2LePriBfEz+6O/7I935Y9Mf38/jS57Eb5XTVDnjv/yXebOrbLzzyqyUf2THLJb\nZ5LUuBFlJ0+SNH48OXPnkltYSF52Hu2Pt+ei8xX2FPTAbG5HZOQuHBw6cv16GEVSI2vevWNLTg42\nqkJI+CkUuMtk3KhfH+c/2HPl5e2lqCiTSZsncLNDEkvcIpnVwvO3/J8bjsJoDOPt3LlsWgAudQRy\nGkgo1Dsiszvi66kkW5eNyWbCS+1KW18HGruUUcNBT0KeB4fuNiDZJQGbvAwntR/6kmgCLqVgyq0g\nunU0Xr7VyE8v57ZwCnOEASFDgfvbIeifL8Nk9EEUq5xUlUoRvb6MoODXbJzZhSYTrdSRJVAt/AYj\neywkpNk7PG8LlN90w/9NOUHFFi54eTJ2kIZCVT4hoh+1wlvhYmyEJaMhr6/XwZYu0L/8LY2V5YTP\nCiTwI39kLjLMZrh3r8q2NDcXcnKqPnNzISfPQpZwB0vIOSRhP2PLfwoXbPjU86R7D0/aRhTipjFS\nXFADtXMdooKCcHtZSfrianhlPycs9BqZ7pPIfBiBk0cx3k4P8dT+hGBJxxgbQGloDZr9eIsZ/s60\nNhbyQm3Bp4YzfpEm9OGx3NZHcM5owBTcgHee0RQgpYVwhz7Oejr41MbTrQM25JSUXqSw+Bw3S7O5\nTxz3hTiKRGdaWsvBbuWG0oOBjvmMST1P0PrzCI8LWd1MRnpEJ+RoOOtylUJpPj5GCeGVgYQZvKmm\nVdP+2VsicnI51BjONBGxe0twV4sEKETs10cS3+E0EwY+Ro8rZhS/qC9FlIIZR4meCHkmtaVvqWlK\no6Yih4iIfJTRUOknJ98sUFr2Fn1RKuaifILtTgTZHPE0yVHpnDDr3TDLHSiUepGHB7miG7kWV/Js\nzhRanRGxUl1IprY5k1DLOwxiOVlSCblygRyNhdfqcp5X5lNmMtAivhkt2renZcuW1KtXnx6+Wwmz\npEHP+CqpnSgiiGKV1I8qnoa9Kl0CIIpIxF+kguIv0rk/XYPwy/ov+dV8T+S3NiWi+KcygihU5cFf\n+hV/k0z+VgcQ7L9LEi/HePM0pIxz6wZxTjuar+0D0Mp6M2pKNF9O3I/GL+y/WZL/wX+Af0jg/xC/\nksCax6K4nhvK2gG16WZzpbayE69HnSCtSM7unsPxvbKBhttGkmPszLXqSqwrn+PyRRhtCp7zdFcq\nXY/6UPxqOZliIv4+/ly4ls769bGYzZuZOnMIuuIv8fUdhPOgIoq6pdD/cDntP9iFV6ePORpylPzc\nfB4dncp3MZGMn7yElLA1NHf4ANvcpcyxvmarsBrZyKX8fLQ25VYdVDrTum97zp9/wYt6P3LEuS0m\nhYQZS6/S5Fw/wrzqoq3IIkar5m1qAmdnPCL3/DGeLDrMRGsiu9yecDo7hc0jBrLosYQmLe0ovaDO\nbBdKuq7lbqKKJN+t9BAmMGB+BQNLlMxu35c3Y9/g0tmFpjNjSdGWIjt+iuTmocQf6I2y3ld8Ht6A\nSf6+3LsXQXT0EZydGwFwPeM6Y/aOoXC5hJpt3/HgohGhWhht1kwhK3kTT8Y/RipIeTn4JYFTA7HW\nVzP07iF+rtQgLddi/7Ydq5fqWflqDvknkrG8kTJo6PdcizyKQebG4aDXBKqM1Kq1i1dFr2i/uz0L\n2yxkZOxIzpz5iQ8/7EP/FbV5YFXwriyHorVn6Tt8ByM77sAtHULvV0dYeJxe5yeQbNTh5VUPJ8/e\nvCmpQC6X08DPj3MpKdj6t+PwljJUo5/iesGVyBZxzEpN5eeSElaGh9PjRgaJ/XP5JHg2bfp1ZsWK\nFRhtRt6WvEUhVRDpGfm341C8egUx8SGSaZ/+lvbT6Z/o80EfQhu0IqvnFBRftKBHDxf69IYu5z7i\n2aVdJH+7nk/7PKPMMIcuGh314vcxJ3kn9rlz6BIdyLXL/Wn6ag1JuwezZMZRejZuR/qcTBo+bohU\nLf3TdzCZqmJb9xtkZcFOIzVCJFzZo6kK49K1K/ToAePHk7kqk5JzJeyuW4/ERIHp02Hs2CpnGK0W\nth41sSwvnaOFhYSoVGQYjdRQq3mm12O222nr5kZ3Dw/aK95w8vvtvMutib5XHkHaRWwd706/frB0\nKTg6wvlGI7BU1GR7szdcfpHAxe0tMJUcRKqIZsV3PWjQth792urQFh2mrORHUiqsvDGGopPG8lPy\nFaSiAx5OGkRRpLiymGKPttg8m8PZqfAiHLQW8C6AQAGFrTUq71wMjs+p6VaHbrXac/m790h54on7\nh+NIL3uNzeBEM8dGbCk5Ct5miuNF9CnVOJBp4ka1mnyU05APt22nTGXgyxgRk8scBoY50vHYDYwF\nCgwx3al0qEnFSxOCRED/XhALb/uQXSBh7lwYNOjv4y5Dlf9EQkKV9PT+/VwePBhPhT6FyFYTMAbk\nkK++TrEmEbfKUGqW16R9uRc+XumEhRQhhGWCkwF7VihieihaVw1+gWmonHIwyYtBFFHpHFCWyOk+\nz0S7NmGMUOo5oRa45CAjzaEUvVBGA79Q2jsYea0v426pgjLXPlSGdEOUqREAV4MOZ6OOElcnKqSO\nyDGjQY+aShSYAQEJNtSCQLngQU7OLRrc2MTndxR0eVaGVASTQonaZEIETFJIc5eS7O9DiqealBqN\nuRLfnEnWFKo3ak2AeygBzgG4qdyYOnwiB85NwCo6ERyTjH+DpzhEP6bQnk12RR5F+kIkggSlTIlG\noiH6XS3iEhtRN6EOOk8dac3ScGrgRHTjaBrVb4STyunvb8Rf564oUmYsI1ObyaviV1xOvcLtjPuk\nlSZjtVQCUqyCDYVVhkVqxi6IKM0gWMBiBqsFGp9ZSmSRnjtTg6va/GWXkCpJ3e8hWaquhd/ldcKf\n0/mFDP69V/Af0v6a/y/t8Lu08a/1fvVa/qWQXG/g00tfMDBRRy1zMhVt3sfpySMmrY3j82FX/6P/\n8B/8d/iHBP4PIQiC+GbyGxQ+Cn5w+JSMZ05s33WEc/bVnG6zDyeHbDJCe3GgcSx+V1fifWg8+ore\nuEZlINZP4/6d1pjXvWDTxxWsGvEpqV+lsnEx7D83jxYt8ujeWaRt+z0YZC2wFn2Hi4s7Xx8RWKa7\nRPybdHqe8OXc3SsMajyIx4f9WPTyFoU/fMysGtO5K1zhsxmfsU1RjXyzhvsKJTKlKwYfA6Q4Ez92\nAGe3J6H6sD0p727Rvlkymfu8eXt0K8ve7kKTnk3im6v8POEOmrBICoI9GBR+j4lWJUVZNlxW12TV\nGldy2tt5fbiSh5th+xFYf0DCRe8W+Bd+gufBp7z2e8YY0xQuhdVGW+spht0Gvp81hb2p2VSEteft\n/a00uX+NQZoSdsWPAeDdu9XodAlERe0HoPuy7lxbdQ33cg/cOmbwrFJAzHVDbNyFWuNG0tIjgLqO\njlRXqzlRVMT+ggLe8/Ag5+U3BEvNZJ78kKuJOdj7fED13OO8PvkVtPBjxie9OGILZXOEL4rnjTAo\nLzHmTheWtVvG8JjhZGVlERtbky8WhrLBYiBpwguUUiV7f8xn5lQN3dfNxKI/i5tjHbYr+2KSOREh\nLaWPb1Piln1Fg6Ag/JYsAUHAdctqtKsL8M9bzOqG15mcNgb71q18GBLCxz6uHLmyGnHFd5xTxGKL\nFLE3tvO29C0llSU4OgRSYSxlRN3BrGi/FEeF45/GYUrKJ5SVXad+/dsUFpYxdepU7t27R70p/ThT\nuyVJjVoR2qY9so/GQGoqecd2Mf3TWDaE7SFzcwa3T+i4F1DJ3ormeAiVDC/fyfGPntOvawDndsaR\nub8zO9Ym0GtqU5IGJqEMUBK+KoJbt+DoURg4sCq0yb170KmHDeWWRAJ+DiPriCdr1sDQiLswYACV\n556R0OIpDz9uzKYDCm7fBnf3Kt+W9RtECDKQWmlk4pZS5kaE4C6Xk1ZZybHCQvYXFPC4ooJYR0d8\nJToa317LsrkHwctMi7ArbNmkxcXlQ6ZNg8t3rMxbbyJg9kfYtXXpoV3Jvhs3GFijBjabkaKi47x6\ntRmj8QEyWQDh4R/g4tGX7c/OsPTGUqQSKWabmbiAOB7lPUIhVdA0sCmNA5qwxVyP1OK3yF6vRKPQ\nYw0cCOpAgovP82HNDjgrnNn9/CAPcx4Q5BOH5+UFZMpPYKvzA2WGHQTEvuFbxce42pRENNyFl1dP\nTIcPUjRtLPnOMioXzWNi5g5StKns3LwdZ70vucESInwtBBYkoEm7gfq9GBwbeyJYzYgmM1eTA/jy\nSivyKxyY2+g8A0PvYjdbeZgXyLXcGlzNi+ROcQ1C1Km4SlfyRHeMNuq69BDrIK80Izo4Ijo5Y3bR\nkBZUwqXIu6S5ZjD2xmQa0pOSLiH4f38Hu/cTyt3OUewTxN20OJIyo6kut9PCt5j2NbOpGWwgZN1G\nmse74nR1PtqIJNRtnxMQc51YxxwcRQ1qbTQo8rE4vaPQZOdWsYhG60ZgZR/uObYl0ySh+YubNJHJ\n8Zk4CklwcNUWbNnZmC/tJzvvMm8tRVieZtAz0YLK7sCurj04Ed8ez7POVE/Nxxbig92egWfqefq+\ne4OzMouHse6EDhjHwVotuSp34kydOgSpVFhKLRSeK+SrM4v5Zu8yfOr1wmDOx1BQgE2rRXBxQenl\nhaOPD0ofH/DxwebtjcnLC4O3N3ZHF+KTZLS8IVAtHbwzRSQVFooDK9D56tB7lvBOlUaxpBiZswyb\ng40KTQV5Yh45Yi75kjwkogSfSl8CLP742/0JIIAAaQDugjvWSiv5+nzKreW4yF1Q44jFLENaXoGq\nNBdTeSqHzXUJzVWxIL0fEkGCIAhVnwi/Xf+783+X9v/l87/BmtnD+Hj1XhYrP2OfNRZzyDyaRafR\npr0f48al/w9W6X/wV/xDAv+HEARBLLlYQursVNJmPOKN7geOjsmna60IOj0fhM23EFmBG0bBHYNS\nRKvKRsRGijYNZ9GFQE0k5zvaEc1F/PS6CSWKesi9NCwcp8UmyAiQycl4OYtVrEO2+nPKykZTvbqN\nL7Y0Z6p+Mc3e28k4cRxOMU6M/HAcOZFrkc5cRfnDCyR2TUR29SALo7rx07O2OGChUg0+65+S+1Es\nysX9+OL+M7rdz2TfnJ4crLjDSEMkHc6ncKpPbS6lXODSh5dxbdSC+xN68uJaIQnpe+kXVsrw4hje\nZEhJPnuCmB978VmgF/3fb8KrR2f5arOd2EwHHI1t6PL9WZ4/HsF1n4bcdvGm95ILnKw4y1KNhpB6\njZA8TqHHqtl42xMoTdlPh3aHsIgiVmsZ47TdWKHax4tDFync8g3y+Fa0SwkkNWQvUn0U2q5l5K/J\nZ83+E0jq1eOpXs8LvZ7Wrq5MDAjAT6lEZ9LRaGsjpjf+nGlfJVPhdRn2nMRn/Pvk734Mn8xAXkNE\ncIqgy+EUTu+bwqoTl5jSqRcWi4WWLRtTt14yt5s0pH6dz2nsW5sJ/v4IgkCrvibUA7NJ8M9FZTdR\nmDCLaz8+xxI1gvgHDxCaNIFvvvntTdj7m8WU/HwV5ZmeiJ7tUdf4iPZ1qxPU04WD1zdSbjVizBTo\nXK87Nc/UpNZXLbiodOS0XkJ/b19KDMVcT1yGSveMzd2+o0v1LgDk5+8jLe1L1IrqnDpr4euvnzJy\n5EgGzZhBx9evkbxcxNGOc4kvd4G4OLQesSwNjqV7Wn/Uvmp8P/DFe4AHipnjSMnNo3WLaYiXHMl7\nEoBC5gzuFuTJHvy4K5G2H8Tx7rmZl80ess41mjQHF3r2hJ07Yc4cGDrOQuCwQpqKHiROfsA+aSMm\nDVWybBm8v60TT95N4XGzhnx+xpubN6vi+lrtdnbm5fHJZAHHHCciHdQEekvZswckkj/Pt+05OXz2\n9g2exhxem4IIyHfDw6ChzG6gWFqGXz0zhYKCSosdW56SH9ZtQJbTnKMr3dg2sP+/zN+ffy5k6idu\nVMpz0bYYR5sWaua0mEOsXyzpZek8yXtCA/8GBDoHYhdFxl58x/bKTDQaOBZTi04e7hjtdta8e8ey\n9FQEfTIgYHOMoIUakl5tIfvdGaROTbD7TKYjFuYETuTCzz48epTOjAblhG8DXZGEucFqjr9XgV0D\nMar3+Lz+STQWCU7OLhi8J/N5aX2iXSNYo1Dgs3cvlJaCXA4KBSgUiDI5V95FMP9ic9JKXCirVBDh\no6N1VAFxNTJ5lXeATT+foGXt2swbOZLajRtXOWS4u4P0z1Jdm8HGjiE7WBm1En9/f8Z8M4z4gqeE\ntM9FWLUSQqq8xAsK4PJlOHMGTp+GioqqLX57dO9AeHB3ourux90zk3sXB/D2bg9SyyvRRN6mRtxd\n7H4VKJyMeMvzCFcX4a6w86AE7pTIKMoLpu8VCa2yshDCI9DYrciyc3ELi8KcnYlXcjYno2TsjpXQ\nq+96Hn7bkkMXw5DUL8ZWX4skV41YKEcsUmErUGMvU+CiKCJETKe6KR1ndQFmHyPaEDm5ke4kx/ox\n5M5PbPx+BduWfEuNpmG4e3nh7uWOocJAbm4+OTl55Ob+P+y9ZXiUZ9fu/7vHM9GJuxua4O7uLkXb\noqW4llIKRYsUryLFoVC8QHAPTtAASYi7y2QkI/f+kFB7n2e/+7/3s/9fds/juI778pkPM3Ods661\nzlVBenomaWlpv5fy8nLcvT2xc3OiuLSY0vwijBV6JGoZ2IlY7S0o1DaorXaIpSLGMiNGbSVKW0fs\nXHyw9wjGwS8UiX8wrp61aShzoDYqwq1KPM0yxEorlkoLYpWIIBcQZH8tSGHI9m345Pvwod8irDUW\nOFGgui6IWAWxpg4Wodof73379/4aXz+LpHqupcbHzyKpnvN+D4tErHbTEMAqqV5jFsAioSb6GayS\nP96DKFRHEVv5kzXxvTVSEFh12US5p4amyW8xDGyDTUwh8xYr8NB58+FXj/4zB/U/+Av+iQ7+D8Ox\nrSP6t3o6RHVA+nYhrX3nsundBvrPzMNvtw6TVw7loRW0/mQ9ZQ/vIJHs44MLI/jg1XCWf2pi6J5S\nXHUyMjvN5p2mHmnnnvLznAms+W47CrWZdbLt1LYLZ1pMbT5qfY+kJCcKHy3E9vG3ZDQvQP1QTUpW\nCqsuf8Xy63FEt/mcpIlJKAr0uDic5XTBKLz9tORmKOhkKSIvdQv5kp30fBxG2y51qHP6a8JdwgmR\n53NAmsq1zgLq5xe5+PE5nJq0oejdS0L2nOFk4Et6qvJZYNbQsEUcCkVj6sTG0v2xlLWuBcSvzGP0\nB2aUChv2h1XxXd8XJMT4U7hzL7UbxxPX52MOdHPAsLeSTqmpTAoN5fa6L9izfD0l2z7j87fbqW1J\no7ZnY2ylXiiEofS8/hkJZ8qJWtCIrK2vGNSoGZsqoHPrJB4mT0Y/+CBLx40m4WUCU/9FUnh7pT0H\nBh6gzc9t0NexwPZ72E8o4sySU5gGvaRbv37otm5FJfPmvLUx3bud5eSawbRuXsHcBXMwiIlkDf2E\nNEtTOjoEsyc3l9+KinCQSnk6vQTjOXc0XaR4667Q0DsIzddbcenVC1NFBdZWrVBlZ4OPDwAKgw5V\nI2dmvLLhO6OZsoe9OXJvHiFv1CjCjMhOq1D1lDNq1DoyX5QQv7GK0DV+JNT1wV2hwCqKzLHfyIlX\nZ5hwaBJR9vUYFdiZhOdfcPlneJP5DvcgONqlLvVtbGj+9CkrNRok9Qey8vxKfro8mjzjZipznIls\nUUqDHxpgV9eO3377jRX9V/AuIYE1FRX8+uwpY9euxk35GmmyH55HBF7TiZztOQw8ZObaPQUzm4Wx\n4O0bWj1qjMxWysSJ1ZldtsToGbiwkjtjvGn/NpjD0TfxnfstU5aupEH3Jby6Vsi8QjdOnwbPQAs/\n5+TzTUYGrnI5l34K4asRdgQGwps31a6N33//19ukj7280D39jGXj1xDk60RVs6cUuj4l74UWJ5MX\n6T/UpVXY93ioc9BW6BE1lUhzJHyyIYSKiArsG/xxRVeoK+Su4jvyx3xHaMYy9MdPIeTIsY0GvMDL\nRomTpxOO9j6k6w10uPiGzByRI3Ua4VrXwMjXr/mtXj1iy8q4XFqKVCrH1SWKHKMO5/wYMgpisFRp\niWy8iqT0y9TNG8bkWlZyNGv4YkBbqq5NR74jmecfupLb5iX2OQpaPfyOh0/0yIZcR18Cvx5rhLNr\nMkOGrGKLQkl5sSffWKOJ/KA3fq5TKDSLFJpMfykOE9/S/HUiLSIDGRXShF0/nmLGxo106NCBK7dv\nU6dOnf/p75nVaOXlwJe0cmlFL7co1h/YxYwBnzIqtBdLR+3ESVWdLtBiqVZzOXOmmgC2bw9dusD8\nebEolCbGddmLh91ACpzak9SsgszCCpyfOFJ27WOOxnyJp2CglVhMgKKIYAcdkoACWkdfpWXTS0ia\nZPHEwZnpBTLqvX3JsHholwZPxTh+a+rEzwNtiCrtif7gOBbtbM0Q4SRPxX6I90XKXtvhrCyjMrwB\nBSER/NoxipMt3BCLBYR4I8lpYQgFrljKjVjfKDG+1CA75MUNPxWgxH9RGI62j3GTnMfW+Ai9k4jo\nIcHiCgZXEaOLHEuoAkk9GQqpGYvBhN5QhtqqpL5HJJFBkdQNqkuoayjBmmB87H2osEKByYStRIKd\nVIrCaiUzLY2EhITfy/0r23mQlkZ527bcb96c7KgoChS2dKm0o1WJijB7Nf4tnQlxs8Wk0xETc4iz\nZ3/h8uV7ZGWNQqlU0+2d4S9ygL/X/5Ya+C/1f58S+E9yfv/VIPT3df9l/C+b/PvswXoHWJ79OQ7q\nY5Rq3mGHCrzq01Ta+X/+Av/g/zr+sQT+L0AQBFEURV4Nf4Wms4bbjh04/XouN1atwDFsJeWZtflQ\nM5bZKS8Z2MiP56vW0eveI34r3UPtglbcGTMO5d3ZNHrWgY/PNkUhUTJVOhWn4SItK5wYPLSAc689\n2VmeglOGE9PDh7L+6+mojFeJtj2OT+8pDD2jRKVSIxQLlFFOvH0G2aa3jPRJ4Wp+B5YZZ4J9LuZi\nDwIa57ErNZGe2pb07b6aI8mnwNcXGjUic84E6n9fn5w5OSiNZrC1BeBWlwiyinqjfDaI4+3KORob\ngI1Nexb36MTYo0dpqDCT2Ru6q0NZMT0EiQT2vrlLR1czjmNP0XCfP+ZNS+h//QK3du3guNlEu8WL\n8fn2WxRrvqLBrTcsDm/Ik596kHH/IlOKQnjr6czqI3tZuCCLFSl14KENknwJUzM7s0SxjPbNPenZ\nzcSV2wP54dZ+aotRPI2N/ctVhCiK/Br/K/MuzUMvSijQZnKnVwEvPXTszc/lZnQ0Gzdu5Lt9+0hr\ncgTzgFw+V//MgU9mURaZhfj8Iz77vrL5wC4AACAASURBVDkr06wsaLcMo8yBi8XFvKysxFUuJ9rW\nlqufedGntpqHAQ0oDppJiW0twsvKsHFyIiI2ltFXrlCvbl38Pv2UsMPfk6UqZE5qGN+77YfNt8B/\nKqXFvyJmC0jqeiALjsJiMNEAW+RXi1A0UaC36NHpdJSXl1NUVITeaAR7e9Q2ZnTycuzNEG7yRKt2\nol9vT3r1e8q68vUEp5Sx4bvvsGblcte6EjtJFVUrwlBuGE7Yko3YTPyE3bt3s3DhQnbs2MFzHx92\nVlZy8fu1lMc+o/UPP9E3NhaXzHJ+2Lmexf7rqWVpSOcbbdCEKIkfEY/CU0HohlAA7hZU0GlcJb4J\nHiyYJzD/Cy3FEyMZHtWJ2DvPWP7tj3wuhrJw1ANezA/mSEEBbRzsmOxhS3f3UARBoKwMWraslmA8\ncgTatbPy9dcmpFIlAG/ebKFfv44kZwYR3GAJpYn7GDt2LKJoz507KpKTnSkv78ewYTfo0sXI2hnT\n+HS4I70b3CF5YTIeIz0QZ4pserGJgy8PMqjWIOa3mk+4Szg6XbXhdv16Kz163GfIkI9wdzdxyTyY\nFeV9cL/px52p/vj7Vn/Glqemsikzk94uLgx0c6OrRoONVEp8ZSXz3r3jaXkRINDZxQO7nW/p2+1j\ntj6sQ89TDxjyWsKJMb1ZG/YWIybaiCNppYrB28NI48bfM+u3U1xbvAKl3JPCoiLMZhOOjhKcne2w\nc3RAalOGg6OA1rMdNp518XK1JcQxjUD1Y9xtE5A5NufQgcecO1pK/WbefLlgAq1bfIxC6kPq0lTy\n9uZh19AOhxYOOLZwxL6JPVK1FH2Knvhh8ch9pEiyH/DuoQ/+H1VS+lFHNt35mZtJjxkePh7b/I4c\nOijBxwfGjKl2B3CticextY3B3ekH1rtcwKPMSESRiI1ZQq6zhnJNEDmKRuQ5iJw12nGnKBptbicU\nZiVd7V7QQ/aAliUPMNT1pKx9Aqa2d5G8DMd8sRHSOANhxjscdfPhSvZc7ltbMEZ5imCbNM5YQlGP\nNrN73Sgqy6tY+0UaNyuTSBksEJ6exKJDh/F/bUVvDcbZ9hzyVjpyuoM+UMpVWXvItqI4D0sOXaT3\nyt74PA2kyZ0m2FXaEdvsDm9avkDhbsHHqsan0oR3cSXe+aX4pmvxSBGQ56uxGBwwBNlgivbA2rQO\n8noNsHEKxsbOH6lKARIQTSLWKitilUil3kxiuY535TpSynWIJRa8UktIj7vNvcTrPCx+iC++RKsb\n4O8WjI1ZSVZOOvdld0kyJxLi7UDzVhaadJMw/9PehJi7sC5mNO+PbfF9+Rftfzn2534ExJq0I38f\nA7CKf8o8Iv43e4siVuvvQcRgqRZoRwTRCuFHfel//ClCtwbIU+W08dIyeJ7AwJaPUDn81z/2/+D/\nHP9cB/8H8Z4E5h3MI/9wPkkf/cSxEhk+U9RskexALqvAq7YdB+MV1Kso4273TvSfN5appV9xIbUr\nJlUgvXI6EWMppyK0iKXPtBz+dh8SayXD5L44z81A51BG8XdTUJeqca50JsWiYC4tGCc5jtE+joAy\nV4plen4z3yFEEkn8kDqo9AZun57FNZ9hDM/ag8y2AH37Qyivj2er2wFmFXyI4/LrfFByn1Z9+9Jy\nzBg8nj+nw4EuTG86nQG1BgDw7PxunAavId68nX21pBx+qSKw70jySpI5+MaEfa86+M4fwIFftawu\n28CrHisptT2EoaqQVS/y+PzFCWyNfnx4YSJyi5KFgxtRnpRInrMzLUYMRJRM4NHlCWz+Zj3BPu58\nXpjLkzB7Nr3WMh3o/LWExDQLU3bImNdpMg1fBzFVNYfGDRsyMW4Bzjt2kJCVw9jprwnxnsvRXV9T\nrx68yHvB9JjpFOmKWN9tE31/m0NDB1c0CgUnhp2iyZM4vgwMZKCrKz16jOfq9c1ITzzDxt6KylBM\njkmDUl6JDZU4SwU6etfFV6mkga0tpWYznyQmIhcEDjnVY+iI2xhHzcApfD3bXrxgaqdO1K8h0HeL\nirAzmdCJImL6K4xV8fzk5EPj41foYWul6NQFsHZACMumqv0QZLmXUYl5TGk5mcA7gcgr5ER+GYmt\nrS329va4uLjw5s0bJpw6wch2MVTeTuGk2gn7El/G7x7POvt1DNxchxDHdEa1vINMIuXdnDekX37D\nvP6fUuXpwkqXofT5dDPrJk3i2/37iYmJ4bFGw2fJydyIiqLDua9Y+EsiQy9cZvDOnTyRqikb1Iva\nfp5k5OtQmBVENY2icVRjuhzqgv16exp91JjWT58y0dsbS4yG6WdnQ0oHvIMaMmytQMDohyyKa4dD\nVCnX0zvx04lb+Lw24Sgfj5vbIwoLxxAV9SW1a/uQklJNBNevz8Bg+ABPzwK6dIklI+MJI0ZYSUqq\njZ1TT3K9nTi3ezedQkJ+/z6mpsKqVdc5eTIEheILsrL2suojLxbuyubes3vEzYjD9akrWbOzGDZr\nGF72Xr+vtVh0ZGVt5cWLXRw9uo3TF9vhtiqJFFUpza+k89OcN4SFzUcqVf++Rvw9k8J/xbncEixW\nEceks5QUz6NN7kg0i/dxxKY7Xw/TURSUgtpmLMmlbbEJrMKk0dNdcpEPxR94nFyPdZ/e4FzENcIm\nh7Hh2QbOXz/HggUaLJZy7O1nkpZ2k4SEI5SWyikqMlBRoaGsTElRkZ6ysjKGDRtG308/4JHsMc76\nWzTOzkK+bC4KZRU+dc9Ahi/6zBC0Rb5UVrihUFZSXGXHNjcnLuQ2QCUxYa+RYecow9a2OtDGIisn\nURuHxC2Bz6b4MLN3N6SSv14lOzicROnwKxXqaZDmjtnkjEaspA6PiSSZSBLxIg9P8vAU8nEV88nE\nl18YwlmnbryuiMIqShBFAYVSR6dOh+nX73vU6gpOn57Mk5jhdFNU8FTtRppVRosWMTg4/URh2V0i\nXT0Qo13QB3gi0RYjFGiRG/VEVtrhW+SNxT8NVUgaqrL2BDUdh1tQT+KKXzHgbT7Ddx1i/c/fsTzm\nDE0JxC/XEck9CWU3ytAn6kEmIlEKCHIpWMFqsGI1WpG7ylF4KpA5WhCL8xALSxFLDIhmAb1ajkEh\nxyRVYhHlVMklGGUyDHIZVXIZosSAKOgQxEq0Kj2pvjLSAjXk+qgps4+nKucSimfPKIsrwGoS8amj\nxFvjSpipJT6p9QhOCUdhUTDDeAlXcy+OOX6EIFQzLuHvRWJFEKw1bSsSyR/zamQYECX8LidDzTVx\ntaDgH/Xfn3/qex8O/HsgyJ+kaf6cjk58HzbMH3stur+DW+ossj9Zjf0WJ5asKiAoUELf/vr/b4fx\nP/hfxj8k8D+I9yTQVGLiXsA9Ah6XcjR2LUWHF/I4fTPOoRo+uDYUc3gCLZI3kxfeihznSkZOHY+D\nSsuk6zls/mk7Ot0bOsw7Q3Z9N37Kfk23T6dz3X4yrxsYcVqwG3PpcQ6ljqPM2ZkLBclYLnxI1e11\nyIJf0rx/GVN/KePTiiS2ix1IdEjn7ZAnTNi2n962rQny2s7LbCWGRXVQnquH6clWvLRqFkas5FgP\nK9ahQ3lWUoKLQoG7shJjyRN+7bwAf4WcR6GRaPO3scpSh2vhc6HDrwQ4+9NVdOGMNg6Li5W+tUfT\nI7Q/41aOoa0BFs33Q2njz5lCfy5fO0nOamcCVbX4/PQ24h/MYMjXMRzq1YA+j+7hky1gbduR7Lvx\nnC/NZKGDgioknPjlBN3bt+fksbGgOMbJT6MZVbiYS+GT2VuVS9vW7Vnpth1Dlh77Nae5GbuGcRO1\nqO3O0WRkHs885rGk3RImNZ7Ely+vseH8SIpnpdL3cF9c1a6MbbeJae9SedW0KZPHmThxYg/jlxnY\nXqsWjt+sopVPS948HUDxyMG8nhaPWq4m12hk3Nu3ZOr1HCovRzx0CPtz5widFIrVuw253SfjGhBA\nYVUVH799S05VFYMcJCyNO4qYH4upJA7RtQcx288xup+VlV3XsWZ0GRm5QzD3XEbXaeOZ6FrIhDMT\ncFA68GLkC57WekqD2w1QR6i5d+8ey5Yt48Xz52z40Ip9bSsf+hxkt3M4J+et4GTrk7RO+ZIzHWw5\n47WOyLDRqJ+OI3FqIo2eNGJK7BT2PttL3KQ4dvefwLkXL7gQH88dpZIZiYlciY5mzetb/FJURuKi\nNfhrdehyc4lasoO02QNx83SkXYc+TG04ldgVsVQOq8T01kS92HocD74EIwewuHkgc2PnoLbx5NVP\nG8nIcSLig+ew35c3tR5SFfQrFy/n8ybcmcBlZzj5oiGn02FGWCD1Qo7z4MHHqNVzcXe/yNy53dmz\n5yJxcc8IDLzFoUMf8/BhSySSrmzcuAY6d2ZFWhqPGjXC7m8hsYsXz2Tt2mOE2uoY1FvGSa9hlHud\nZlbzWQypGELGtAzUEWrCtoah8JWSk7OTtLTlODq2JDBwOVJVOPXPPufdYwWKLREM6mWkX79l+Pn9\nQmjoJlxc+v5L8ieK1cExO3dWB8z07r2ZoX1X0vBrB3w0gbBpE2V+denUqZqwNm4Mq1dDgwbVhLLI\nZCJFm82O3cvYOf9nlnZYTjtzO4SnAumB6ewv2c+SXxpiMn2PKJpwdGyDKJrQap8TErIeD4+R73/g\n/3h/paU8n3aGksMaXDwOYpj+BkW0DKO0AIMkD6tJivDDJF5d784KSQSNAl8xp/MFNMMSKNPewsGh\nOS4ufXF17YdK5YcoisQkxbDi1goKKgv4rPVnjKo/CoW0WsbI2ekQyK9QuWssdfNkDH5oxf6MFHO+\nAh9VCfYWE4JZwGoWkCBFihSJYwGW+auw+KeRdqk2+ZE6HHzK8HQowVGtJS3Hm6JiN5wdCgn0zkWf\nHIhdmQPGoARUbmVUVmjIMYfz3DEQx3cOBGTmUx4Zh1eOFx5P6pMmk5LgWoV7/dfce+FI+K1o2sW3\nx1DbBW3bYNJCbSi+NZ4Nh7cSvuMuWd4q5FYLAVot/lX5eFQlEBQvx6yHUmkVpTIJJQoZZVIperMM\no9yK0d5Kmb2EcnsVerUdZhsNgtINRAGVtgK7Mi2a3CJccorxyC7DNb8StUmO0iJDYZFhb7DFu8Ab\n72JvKu1seVFHzou6Ml7UF0kOhqhXZkbeLKVFbj5Km0zkkmQkliSKjeWMe9yCBHEDVqVNzQcRRPE9\nY6PaSQ+qs3SI7x32JDWs7z3+RAr/pC0ovNeNeS8cjfX3PuG93fAvbf429qc1/LH/+7lWkwpVdBgV\nwSrsjupYv98HRXYxo2bk/u8dyv/gv8U/JPA/iPckEOBpx6d4TXchzqY2c5KP8e0UBbooI/sVVsZv\n+ASPcUq+7zyO1YFh7HpyhK+GDuXZplGIxc6kvXEk0KmK1R8MYNneH+mt1fJFSAjdCwq5friC0ri2\nyBrdRuW5G/ORayzJiCOuuB7uD9ZTEV3OoQtpzAmwYWf5btSGFgQ5L2Ka/3yO3vsRieo2Vdig3r0Z\n3YN5dM88z/PDrflRNpOE1ZGs+vpr9nXtip9UyqUVS1jwYC8OXl358eoRPJY35ecQgV0d5iMRS/h2\n4EYmuTdD6NKFpKPj+S52Awb/cSRYE7iTdgeDycAsSy1GTv4WJ4s/kc0jaS304XP9ZAr2PqTLiC+Z\noLDjcpUZXz8DVic5yxOMdKlS4qS1YlArWd7fmXtdg1kQuYDxK8expFcuTvumk4ArTfs8Y/imozQd\n68E3U69S0amCoBVBZDY6w6Fdk/hpuwVB/pCTR0Np38IJncWC28+DGONfh++7rqSkooQPz36IyWKC\n2kuJKvNlR383jjQawdBrx2js40Oshzs9+3fh1Z4+9Pc6xTL7V+h9ffnOYqFORQXd7t1DsLOjcuRI\n6jYKJvXRJNQHk7j/ixu1alu4n3WfvfGn2Pf6DDptOl4ezXHUtOZN3EEIisKz+Ca/DD3KtvnbSCtP\n4035HIyStqidJ9E9NIT4d7d5lfoUX8GH3hm98Sj3YIl1CTKZjFUzZjAi4zCpI4uo1+olh/VK5j5L\nZPIzW1KaxfLr3WXMuDWH86nr+OFbAckXq6m/YRiSBhIiv43ko6iP2HB9A2F3Q7maYyF2/Hi2urqy\nq6iIPd26siz9Bdu/Xc6YNmOxzpqK2d+F+cIXbM+ej8ewidiW56LRavl59s/kTsoldGMohe+0nP75\nHurCQmzsbfCX++NQ5YC5wsJVrYZV1MZBbkHla6CofwsCM5rz4M4u+n/hi8uL5lw/eZ1K10oaNa7F\nqJZ2hATfpqAgkOXLr5Od7UvfvhVERg7FYLDl+nVnDh1aire3NwDj37zhRGEhLnI59lIp9lIp0txc\n7owaxcT1/Xg85yqdPs7ixrOTnDjaDWenarJoNVpJX5tOxuZkpMvWY9vKTFDQKhwcGvM6wUq7q/GY\nDAIXWtcmJEhgxw7Ytg38/Ero23cJXbumUKvWJmxsqq2QeXmwbx/s2lUtxTJpUh6dmk5Hl3sW9xnO\nLKnYitC3L3PmCjRsWB3T8fp1tcXzX6H55EM83D6Cm3UbsD8vm3cOgYwNn4YmVkOxtphac/0IH1ML\nuwg3BEGgvPwBCQmTkcmc0HmtYVuBDe0yMhix4yAZZ0LQqupT+ysZT0Y0Y3F2NlmVOQxSxNIs4z6O\nXwzhsDGaY6V1WLnyCp07X6Go6DdUqkD8/T/DYqmgsPAURUVnUan8cXXth4fHKFSqYG6k3WDlrZUk\nFCUwv/kcPr5YSNDmQIzmJ4w3uXKvWx2eDvZEoTXTNv4dLaxlOPnaU6orJTs3m9jkWLwDUpjYV4vh\nanvEneNIlYiE6R2QCBZynXJ45xdPXqNHGOsk4eNcjkZlJVAhwV1houJGL+Q7x/Ao0h6dUzajX99C\nkHegKNMba6UVVZCK2gdqo2pgz+bNWs6cMSEIZRhs4yj22Eu9XAUDHw5BbdIQ12APK2+u49IlHyqr\n5BSZFeSLKnKsDuQJXpTKA5FLwEHQYocWO0GHvaDHTtCjxoCNaKLC4oTR6oWdohaRLt2IdG+Gi70j\nUgUIBbkITk7VJtW//YEoM5SRVZGFQqpAYRUg6RnW5xfhxX2U8e8gQ8qhhn3Z3K8vokUg6mYGdvHF\nFGCmRF6J7EEsZT6FDBxVXpP6Q6h+ChL+ZZo3STVBFAUBkWoyKEprnshqMsBJqscECaJVWmPRE7AK\n0up5ggSrKAMBrFYZIhKsCCBKsQoSrDX7WiUCgihgRYaIFKtYbVp8v8+jnSfJ6mCDcNtCe9GBpgs8\naZwup9vCy//x8/ofVOMfEvgfxJ9JYMamDHSvdCT1n8BXBUNZHDiQtw+9ePlKYO78kTzdnYTrbRkV\npnUMOB7Jyb6dcM/1xnnydS4/ac2py9M45jSRy7XbsL5bE3JXreKaxULlHC05jZTkXWiDz7GPMXsZ\nyazwZV/tH3keHIf97d1In9WiT7sDPHnUltNlvUhyHUAL/UrwT0JIigPfwXy23sy5/e1IqZ2Galku\nMz2Wsm9hDG2d2xC3LZZzT15SeP48cwu3keTxEd02V2DKfMTWZheQ32rEq4PfE3ZyM+KmTZiWbeBp\nozUUFY/ik09+5NmzZ0hUEmrNi8Ag5GLrYE9mlRbMLrTNqUdYtgczEg7xc7DApi4SuvuFoI0tIPqX\nCu4olTyuqCRS40lsfglSqYVmXhrelBYiaAR2NB5O+KAkWn5wj0f7JtBx+k6aznLimY2Utsq2jFg2\ngsXTFzOqbU/OrfiRAO9KOnYLp3l0H65bA/jy/CJeTXnLyf0nmT17NouXLCbd9zkh5x5w9dRmWqsT\nWfSFlJ+sVtb9uJl1G/RstFtHQpIK3azOnF63jdtPc3E8fBi9Tke3HTsIHDaMDs+e8ez5N3TTaLh/\nYz6S/HNURX0BSjdMTk0YFtSPaHN7VpiS0a8KR9twEAQGsLHXJxxe8xNvHsVSPjAbSfSPWOb3wN39\nJth/T3leHoaUV6Ctwjm8ATuTl/Fk4UMis/bR7nwK8T8qWWW7hVhLGNHFcoavrmLRWgkGrAwXX3Dp\n6jLG3htLU7MRz/mn8Gh0kknnZuOn9qNsfxlpDmnkRecxyGseayZ+iY1Kxdo5c9gcHkDgzdncFYdg\n3bSeV68GoUo1sWryYE7kfEjvqdu4NqwpLX75hZcXL3J001EqPqnkSn8Lx5r+QEn2TTRXNDhWOrJz\n507CwsKwWkRmThcZPlKCk/eP3HzyGVPuyZl5Jwq3kiwue3mxceNGHj+/x8v0+bSpW8bPO2WkpTkQ\nEWEgIeEQL192wGIRmTBhPSDn448X06xZ9fdPFEVyqqqosFioMJspMRj4tEdParX+CMeAEVxZcJ7R\nHt/Q/Mc07j3cwtdLR/z+3c3M3EraiRjEZXOJimmMsp49a9aJrDS+JbCRkcc96mGr+MNSYjLBiROw\nebOVtLQK+vbdSFRUOOfPD+X6dRkDBsDYsXrUNhupLFmNx28Wjh2Yy8Omi2jSWklSEsTEQGQkLFgA\n3bv/1+jn92j00Q+8PPQJ5bM/R/H4EZZbt0izteV4uZZE5/Z4lzSjo1NHZIIMx9aOOLZ2pCi8kpOS\njTRQHKLypT8ZQkNqvVBARBV+XZQopJWYTAVUVr6gqqoQS0p3CubN5ku7huQ5S5AteoPJ3UCkWo2/\nQkIt4xma6H6kVBrII9upFMnrEKE/Tj39fuwsmTxQfcg15ceUWaXIXl1EcWc1r5wNmPeux1KeTmDg\nMKTScFq2duBASiE2ozOR+Ojpa2egMu4Gshs59A95iX1YCg9OjObsgzc8ybqAt50aV49WGMo/xTsv\nko6SZMIsVdiLKgwYKHQWudRHxdN+qYyR7CRS/hLtlR44/zwaq2sVsvwSJBWOIHHA6m9AiHhO1dA7\nyHyeIJfnI5M5IpM5I5c7YxZlvCvJIj3NgOlRGEu3HyRwRXf8qY+DzhNpuRqlVoqrUxnuvpkICilG\n0YEqiT1mmS2iwgapSo5MLcfWwYiP8inO1leozZnIxAoArIIKk8wLkyIEq9wHg2hLankFyWXFJJXk\nklicSaGuFA+1BqPRgL6qiiqLiEkUsGDBKphAsIJVCnpncGwNYT1B7Yz8+T3kcc+pf7GUMo9CBIeM\nGiugWO2fZ63Jy2EF8U8OgNb3/TVGv9/n1/jpWWvW/t621ozV7GG11uzzfv1f9qq5Fn5f/9038F8X\njzpSsrs6YL/VlrlzBHzDJXRwmUNAz2n/R2fzP/j3+IcE/gfxZxKof6fnSasnaK5c49urabwJnEfW\nvHB27oQmTQq4ci2CVsMsrJm1j9Fp4UTsjOD7+l9jlTwheu1Zbo0fxVjLMZI0e1jxjSdJN4+j37eP\n41E6LBOLMR1uTEKEF5yfS9NQFy5VVPG6YBfH2m+n3um7vEwKY496DI5dGpJ9vA33ezux71ZdKgP7\nYtVPZFqvPjSs15szxQ+JnZnGhNA1HNUm0nSjD7fyTjJnazqlGRbOT2yDf85ArvQJIOebGBwy5nNs\n/Fs6n5qGWLsOT+6NosInC6ZvgAm72SzdjEFmYHn4ct7ZvOODFh+gSpFh5y1S6mRGbZSwJaZa4HTJ\nBwqCAhvzMP4ObTOk7G++FvXUWTzMSaTLkYNMWH2HpYXXUQgiOnsFz8vaUqUeiuLINKLLF/I48Txd\nV8TSdXYj7jolY2eRYq4ws/pBU0YrpaQ/ekS9kkLC5trSwtGfpgFpeLrpsOYpyYlXUC9gAGnzT9DT\nYORY7e5MSPiB4VfSmewj8tnlz4jPi+Vgj+kMi/mZpYOvsHqTCcfrJtz9B9Cp2ac0bNiQD6dNw3X3\nbjKtRrg/jGcTH/EoO4lhe8egKjpA3fJmVMbbkposUK8e2LtYuBNnwTCyH4KmLuGppRRcvYLsYwtz\nuy0lXd2MezHxPJ7bCh+fm3z4YSqdmtfimwvzsVXXw+jQnXshvhS4ylBLKpGXllDHtzaRDg7YXqjA\nU5CR2CyJCrOZq6IfM0lkw6k5zD47G/3Q1ShrmxAcviBmxQUCAwLZtH073S9s4F3cN8xMb8+tNr1J\nj4hAGzOI4899aPIih0dzRbKFLhgdd/DNghRSMgOI/moCfk08OZydRHBeGm+TXiIN9MZWp6WBNpo9\nH+/GK8qLrVu3smLFChYtWsT06dMp1Wdy4UFvhKp4MuSjSH7Umh+LFnLhVyWdXiejNTzh7dtx2NiE\nE1MSyTe3d7MgaAGFL5M5ckROauoiPvkEsrLsuHRJhyCING/uzLRp0K4dqFQQFwe3b8OPP74kNdUX\nT09HWrcWOHYolxHiTNqN16LuHodBMZvRfeaQlfUDmW9WEa1bQbmhK68mJ/OVJorUT7JxbVnBr8E+\n7P7hB9LT09m0aRP29n8V/n30CDZsqCQhIZ3o6N/w9m6LyZRM4wbzcU40471Xwwr1EZ5Z6jJpErx7\nV10SEyEpqfrwk0qrFVq8vcHBAZTK39VeeGTaQs6FGZRklCB3cqpW4o6NxXjuHOXHjnEkNZXVIlyO\nbIeNMZxEQxgGfSi2WjW2oWmYh5+lSm9F5udKsr0N7wxS6sgKcDe9wcGhEZLMMM6lZPPN4UWM/TCe\n5csjsbX1pMhk4q1OR3aN2LIoVqEq2Y9d/hrAiiixoUIzARvtJeSmVATBloAbXfH/5heSZo2ge6NI\nMqdXIMurxM9nNh1HD8cl2p/sl1B6z4CcEHLrNMQ1KIkPAtZxT2jGnspxqK7cofSXfWj8/HEKCiTj\n/j3MBiMenTtjrN+BQlldpGqwVxvQaax0eC7S0cnMMVkK+vKHTA+6hotNARnnOlCU7IK13lVCvPLw\njLBifheM9XFj0p42YcfbKGT2FrwClNjYVyFKSxFlZcjV+SitV9l9ZgvqRc3RCuU4iC54Kj0IsPfA\nx8YXi94PXZULZZVySssFyrRQoTWj0xsxGqswmaowm6xI5BakKjMqtYFQ7xwiPbMId8shSJOPp10l\nhUZIK7UjPdeX9Hf1SH3RkdS4nhh07kjkuQhCOpaqRKTSbBD12NhUEhWdRUTEW1TKZFQqOzSaOpR6\nteWKR0PeqtTUH7mBYts8EkrkY/VOvAAAIABJREFUNQeTWJ3qQ7BWGwAlABYQqv0Cq+tiTd2K8P4q\nWCLW6P3VGCtr/PuqdQdrNABrnr/XJZJ/3yeR/GUNEkn1ltIaPUKJQKGyPrY2F7GczufQb0Yk1io6\ndkhHaeP9/8sZ/v8i/iGB/0H8mQQCPKj7AL8tFq4UjOdD1U8EbmhA4nU1ggC74zdTf+l67qg60DD2\nEyI2hCH3lHO182Ve1xnL3HgdHwfN4MOPnqFcu5zH21xZfG4r5rMH2bu2FE9bEdm4xSjV/kQnf4RW\nEOngnEjnkF1k+Nyl7YU5fGnTDHWH31jSXs26Rf0JX6rDLusuh3ZsQ+X2K4f31Gf55wZy7yYz0m8b\n130+4/59N+oFN2OSayBjH+3jlnUPtx0esn5nFOJXnThXMJ2OtvcQtmwhfl8gBScKkK5eiaNLK+oM\nW0xZdhnNOjZj0cRF9GnSh9k75pFSlEP4ivHMfepJxqxJBOqTuOP2ORmRX7KnMUx4LuPBgJaU2stZ\n0XsfIxLS+MDdndaZCTh2eUKA13aGRLZiyZXhOE2J5Z3jN/hplBhSnOj+bQbjxtfiuxP5yJBwqX0A\nk70TCLcP5rthW9m2aTfFBXmccFRT7nWZQfe1TLf3xa+pkuyodwS/HcmovQ9JKDuKz6BL3HNZgKvK\ngVl1G5BX/pa9KcWMqT+WVV030OzUGVIW1ueDzj/xxbI+2No3ocmNG2RUVNCGJ9gbn7G5+2Za7mzJ\nvOADxJ/rSJPGAk2aQL16kJtb7fd15gw0PzoAvIJRffEdbde2JTggmKi8hnzq6krPy5eZ4xjOV6da\n8SgzFKexcWi76WiU/Zj6xndE7/4Q5dKVaO1yePi8KTeeSfhg4gxe7yvkXJsHCI7eVIgSvo+ox9Wd\n5UTcvsqWZptwvmfHqk+LOHXKBi+vD5n81VeMuHIFn7dvuRRdD1NuEh/+WkRuaiXxNvUIcRrBixeF\nFBVqCJVnE2F5TbmfCy/f1UH/VQAdwtqSqQgizmRDSE4ZeTv2s2TulwypGEL68nTcP3AncGkgaUVp\njBs3jkpdAaMnvUXpF0aPJqf4Zccpvv32G5qN6IdH8EFG+bthsTcQGroFN7fBCIJATOJFhi09geLm\nKuqFObF+ffUVKoBWm8yBA1PYsmUjWm0tioqq+0NCICAgnTt31nL58lc0aODCjRs3GNjJjq6WDRy4\n8SmbVu3BbfQ13JWeRP/8CPdYOVZnd16aa/FN+TaGyNNYubKQqGdXOHrwIIMGDcJisfD06VPOnj2L\nl9cfQSTZ2bBuHezfD1FRNxg3bhLOkgLqbrBwOmkq25y/ZPgYBRMnQk0a49+RmAhHj1YTV50O7typ\nJoLt2kGzZtVk8OtzX/LmqC9TFHUZ7PqcAId85Go9gkyPWaLHoC9mZtxVbpsq+bLWcDpWynAtLkPQ\nVWJRajBY3LBapIhOrpjsbNApKtDZOJEoDwKJM6dLwrin0zBpUxwNQjbjqD1DsU0HUuxGkiONwlOp\nZJSzhaqCXeTk7MDWth42NsEUFp7BwaEpTk7tyEnegpCcTsg2Kw9nuDDZfzOj7LPYMEqLOUNJO9dI\nnmR2xc9qZJTtW1rYX8TSLo+HzYsJCLnN8YubeHItj3dJa5E5RiCLmoFdHVd83V7zKrMh+uR0eH0U\nMes3BNtGSJ3GYpX0wpprj5tCQjNpCT1s8omcrmBZqzKcpY+ZKt2LjSUbq11HUiTN+O2kM85bQuiY\nV8lDwZkQqRaJ1YpgrdZAk0utKCQCUkFku/shLudNRHSIwlxqReICgrOA4AI4C1idRERRRCWXYKsE\nR1srDrYSHNQK7GzUyFBjMjpSUuxAcZGK8lIZWq0Eo07EVGUFox2SfFd85BIiQosIDy8gJKQAP79C\nNJoSDAY33mU14FZabUpVBrzs43AsekVFXhW5xR7oqzRoFZ6USmypEARKAZlCgaOrK4FnCiiWJZIQ\nUfffH1bvfQB/L8Ifdf7Uhr9GfyD8bY3w133+HCXyb9vCX9f/ec8uC1FdNdAxJ5LOGyFM9ozeA43/\n+4fyP/hv8Q8J/A/i7yQw+fNkQCS5Y1OG7b2IYbSeVbUCmebjwyutlqen2tBjRhrXD93Hd3IxzRKb\nEet2De+qgTytZ4d1jhWLrhyvc/tRJXmhWzOdz0+oSTh5l50T5Xhmu1N7by52Xp4Uh4TQ4rInoeE9\nOZgyhQeWA6xbtJ+kUzORaBtjV1RJvTFPiBuhpM3YY/yY9BluB+4ySzaHHwY/YrDzYZp3vkrR+Q4E\nVIaQI0mik/AZeVN0fHTWhqrOB7BxLmNe9lXUXy2m8TUZOeOvU6fNCcqdTpPxcDcR+3vg1M6JBw8e\n0KdPH/beusWckhKWTTLTeJYfRxfNYlTWcdoNkPHlsz00nx7INZcRrM4r4snE+wy9sJYrGffZOuBX\nJgdW65ft77OPTtc+57Z+BbGTnVm3tRdlunQeP4zg2lslW+ZWMHFIHTYuO18tbyMIFNwt4ItlX3Cs\n/TH2dN/DmE5jKW/XDHPSIOwGLGB881Gs6rSKrLTFyGSOXH44mMmHlyKrc5rggJbUkjxkZoQLeG6l\nx+HhRHs2wGgziVczZrNw5jbWrO/Dms3t+SV0LiXSABT5+by4OJCtg7ey7dU2JjacyJDEIZRcKiHs\n2zBkdjIsFujQoTpj2mefgTBrJAS5YrfkJzRdXLF7ZY+0RUtMo0fTKyGBXT4+NJFKCUwNJGa9J75u\nDxg2+zdmv/6eJZUzaRHwG0e3HuT88Eqq/O9T/zS0lSnos6U10Z7R1Ln2C/LcElZ+EsK0teV0N97l\nYMYONLFmti0ys09/huN3vXCNM+NU4EFJoSdlZWZMLnFIXV8xrXcnfDTbqFVLRseOy5HLpVhfvGDy\n4hTOnmlH8aQA5PUboit6iMYxFEf31qz2bM+c0bNw1jjTolELwtPDCXwaSNMVTTF1+YW1mxZw6LDA\nT9/OITn5GB4eOQQFWbG1bcT2y1UUViawd3YCthpXRBFOnarOQSy3MVLZZib1mxeyq+8u7JV/WOLK\ny+/z7Flvvv/+PKUljVjxpRWdpYABAxryxRcHCQtrz5MnCaxduw2JbjRtLNtYUXcgtTz3kvfsJmnL\ntZSYIjh04iyXHrsT4zqKPNsEZumdyX33iIlTJjJr4Sy8vb0RRZGVK1eyc+dOzp8/j1odyZo1cOgQ\nTJ6cRe/ec9FV3kTcHkjQ8Vx2th5JnfEGBgwYj61tOAClpdWCyhcvwqVL1cSva9dqsvfrr9V+gQ0b\nwtu3EBMj0qpVEblOy3l6+CtaOefxpDiIIKGMNtYs2koKcRZAbpVS6Czwc9UO7lecY81Hmxn5+SBs\ngm0QJNW/7WVZl8i+PBObBC1ehY1RJhRz87GacZWbCdUk83Hn42jr+5MfGYk2zAs/6zn8KvYjSuwo\nFLzQGO+RbtOX6MAZtHRvjCAIWCx6cnJ+QnLkNO6r75O41p+UMCOxlrp0lt4lIuRrIrq9piLei86+\nxwl0ExFctGjqlhAaVoRaG4l9eX0eXHZh57MDhLhE0q/udNwi9dhE7MfJ7y75mWF4+MRTnhaOpKoD\nbpE9eJ2WycGDu3n9+jWtW/ckOTmApCRfDHpfsPgQJtXQuJmcB/0LSLEx43LJg7Irbhh0EuoHvWRq\no70EN7sG7gVUJYdjSgzj6bMqticlIhrCaSQdSJ7XcZ4UfILMoTUe9t4EBATg7++Pt7c3UqmUwkID\nmZmZPHx4A7ASEhKMXC4lLS2d7Owc7OzUVFRUolLJcHFR4uIiQaMx4+iow9VVjp2dBpNJjk4nRauV\nUVYmUFEhoawMdDorvr7FhIcXExFhpXZtKChQkJPnTbquLq8stdGrnPCQgKfMgrusEmepFpVUiyCU\nc/ZoJqnvRILdev6ejg3+JutSIxotCiIWwYpVsNaIRouIghUr4p/aYo248/s+a43QdPXTSs2amoCQ\n6nERsaa/mvdZawSja8ZqXoeaOe/7GlbIuXX/KtMGByKp70Ej4Qn9Z6b9Xzqx/wH8h0igIAieoij+\nPx++83cSWHa3jISJCQg/r2XJkj6EdxjMo05vEUWRnRERDH56gWMzpnBm8Cc039CZRo8boe00CRu7\nfDrKiqgd3pXhA7egf6sjrM+XlJR9T5LZk/MrVNx9/pwt6/W8ezKYCScfkpmTgx8CtkYjpT4Krlcd\n5Ls23+HnXoSv2wl0/iKd5qez+KCOvU43aNGlHsHR5cQvDMe7V1PG846+wllsxZu4E4toJ8Nqp0Oh\nlZDm1Ipm5b9gOfaI1bLPyHkwmakLf8VVeEjGhNpo9BVojmeRJfbH/clahBBnus2Zw53YWLYcPYjl\nVAwVCxIYrd/K1U+G0WDpIj6f/TkLq/ojzlzCqPt6podUUe7YkpQKP2JTb3Jx1EUCnAIoSy7jdshO\nVPJi1j8cQoC0hPnqK2RmbuBJkpnln1UxcKA38S+cadOmF926daNVq1ZkLs3kt+zf2Bi1keKNJrSK\nfKbM/JlDa7rTbNmnJFXGsbX9GCryv2fYFT093MZTEXOfZtFGmnZ5ybwbcvKvQMfabbic8wRDQQ7T\nWk6ld89eXLjgxMlzVazcMJh9mo3M8vBm+N5B6NP09Ovaj31D9vGo7iOUPkqM2UbqnqzL+kNqbtyo\nPvjBiNv8YRQHeeL59Y+UFMCqLQP4qd50MirNfHT/Pp9PnIi7xobExCkUFz/j0qULbNvmgarxNTaM\nH0Vlxhpyf9BQN9GeZKWK+7XknJpTQv0IR/q4uZGVHMeBUgcavc7ndWMNOW+tkJoCiiW0SPqIuSN2\nc+boFNwcGtKwfQhNm4YQFKTiwrsYYrOfEKB8Sr6yMWX2g0jU60nQ63mn11Nru5qCw0FktHClR+RY\njn/7A1dTrzP57m5K827hqlAyxHsILmku3L1zl1vXbyLTCTR2DiJqSgodWuh4+1ZPWVkwY/psQPY6\nCu0DM+diLCyoP5VuVSWM63+H5VuUVFXBihXQqxcYLQamn5/O4ZeHaeDVgMZejWni04TG3o1xtLwk\nIeFTds84w4m42ugUvXF0akVY2FfY2CRiMu2ia9divt3SlzpFuzlVdYk8OnGOISyx78DCHwfj5qJE\nVjKTFd+tI+v+feZ6eNCr/6+YbgpEX49G5vhHxPHatbtZuvQzZLJjTJnSivHjfyUt7VMeHOn6P9g7\n7+ioqr39f6ZkSpLJpPeekEZCh0DovXfpIAqIgiJIF1BERUGUjoAC0qsiTXrvNSEkpEAS0nufTGYy\n7fz+GEC9771v9V2/ddd7H9ZZe5+9z8xszjoz+8m3PF/ePnyV+tiB+OxfhVQtoaBgHTk5q7h/fw1H\njozg6VM57duL6NXLKqYcHf17XkBdHRw4UM+WLfUUFIjp3n0Pbm5mLlc8J2nvak7NnoKkww3uPZnE\n+fOjSE4JwrFrDr6DTXw/wJvc2xoWLfqAFy/OMGhQF0aP7kyjRmUYDOnU16cQFPQVHh5j0WrFLFoE\nv/wisPHzKob63IekJHjyxNqmp0NAAEKTGJKaeWB0NRNW7UlqdglZhYW41dYS1dCAh0aDuKICVCp0\nO3fytr09frU/MJjjhDdaQ27uCsbP70BJSiMMtdOxk4GPlyt6pR3pLywI1eWABZWiO3aqafTtl0C/\nftto0DqTdXcQmqRIlHITQpMmSJyLUCrP4+19AbU6j6dPu5Kc3JT8/AYkkhoslgoqK0vJySmiujoP\nECPBB7FIjcJOg6OqEpVDFSKxgE7njKFejafBge4BbrTrbMQuLgPBrYDiWjUZj+q4eGwgCZmzaBA6\nIDaJkcvlCIJAQ0MDHh4eREZGUlBQQFZW1ssYOgGTyQSARCJBIpFgb2+Pi4sLarUaBwcHVCoVYrEY\ng8GC0ehESIgRe3sRcvkr978FmcyCXG7BwcGRxo37Eh7eE4sFrl07z4sX55HL7+Dj84y6OgfU6hpE\nIjAY3GhocEWvd0WncyX+4THuXAjmR6dZVvftqyA90UulP4GXQn0i69xr0T6sY69OhFdZwZbX8yKs\nsYGv3/MVd3idgQyCRfSn2D8QIbwuXfLy/OX1v8cOWuc/9N9M0r1Mdh5RkZftTjupL3EfHv+rtuh/\n4e/gryKBxUAycAD4RRCE6r9uif88+FsSKJgFbnvfxvf4M7J0l5g+fS/JTwU2FRbweXY2AQoFK6+v\nwXZnIsaoPTQNeY5q8yyed/8Z181hNGu2jEED7Bk5cgWCWUShLATt/t00yy1gs8dkfjsl4dCbdaz1\n/4bq69e5cP0G79V8g7GnjnfLn3LMtQ+ng9+nr0RJ11QPxKJhyFV5qOZf4NMFczl31QP38BuUpsfi\nG/2CY28s4GliC7ZeyyN4xRDeqT9Mizk6eokvomvcmqYbb+F3+gCfbNxMnnQEt/bMwNluIDvtf2Kf\nYzTG4fOQpVxj7Yy3uT5yBHnvT6dAnoNMBD/fqiRXGUnxV9Z6wEeTj7IktIGHRcFs9hHT4FTGxa5t\nqS3+ntMVwezJzOfs+Asojiq4M+0mXZpOo2RzB0oM9yi0HcSQoNF8f2gA21YBId8QZGpEUFAqxcX3\nuXPnCl3bd2FK/BTmdZ7Hc08zlp0ZXDt7jaeJHdm0CebtPMzyW/NY37iIJV8kc+9yGOXlF3n4sD8b\ndjfh3O1CBBtb3EKjqLh+DlWMAg+5B74yX6qrq0l6WoVFrGHpIguPQ5pRZ5KSlpWG3c92nP7yNKWL\nS2md1JqiH4o4trCcz4jm8VMxbm5VPHjQh2m/SUnyC2NyiT1Hyy0YhgykAQlXl8/Aa1UvVAHdycyc\nj1rdkUaN1iGR2LHldCk/LEniWX4wdqPmM29MLAP8BnJ3ylWM90PwqJZzy76Bi6NyGf7EjcfR9hzp\nC6KpoUgtzgR2yKVFkw85LblOu0Qx0yYbkUrD8PS0pb4+FY1tVz69P4fEA5H49Mil/UAHopwVhNna\nEqZUEqpUsvKtTPYdVlM5rRnGwxq+WvQVM2fMpNpopM2jR4xQVHA78VtKtWW8HzuFsPrvKKm1RS3K\n5vl1d3YfsaGNzWCOPt/LFPspjO0yFodYBxxiHfhgZSlHAkdjm9OJLeNXM3Kc5E/JEg0NDeSV5pFt\nzOZBwQMeFj3kQcED6gx1fKuNIyDqMUf2x3Ln7n1+OhBHnc0damvzkcubY3aK5P1xfamrvc8HQ0ZR\nX1pA20tuPNCXstrwCJXDeuxUAp0cxzJRNZnGORuxq37ME/tNmHQ2SIIlFNSV8cS9BdvTvena5yLn\nzo3n009jaBKRSc38CPoVP8Vm5zZrlsdLlJfD1Kl6kpMrmTZtAY0bH0OlckQu90ehCEChCEAuD8Bi\nqaei4hQazUPU6k6UlU3k6NG+7Dlgi2uTeby4too7HR5hTNJh283E005PSHRPoPJacy78NgCRSKBP\nn+P063eDQ4cSuH27mP79vamre4egoEaEhQ2kY0clGRkwZQp06gRr1lirxP0RZjPcvWnixK4qjp+V\nUasRYTLBgthrzO6VjODsRKJczmGTiTs2NvQKCSE2PJzZOTk0t7dnc1gYNWV7yXy+AMuBtZza3ITT\n4TkUFLWie8/FJJedIv1RDa3jHBnRS4OPzyy8vR8jEl3D1jYUk6kSQTDj5jYSd/dRqFSt/o38TkND\nMbm5FykpuUB9fRJGYx0WiwaptA65XIter6Cy0p6iIjkiUSWCEIyra1vc3NpiZxds/W0WBF68eMHx\nY8e5cukKEbII4kSxxE2OJLNPPr/MKOZZ/kQUdiNR2XpgNgvodHrq6zXo9faYzWFIJFWoVCV4etrj\n4qLGxsaGkpISsrOz0ev1SKVSJBIJIMJiicJo7Ar0QRBiEYsrsFjckUqzsbPLxsFBg1IpwmQSo9Wq\n0elEGI3JNDSUYbG4Ae2BKOTyYvz9U3F2fkZGhpbKyioEQYWdvQvuIbY4BjfQyvFb7l9TsKBmnrV+\nryBC9NK1KxKsNYRFlr8R6Hvdf9kiQiyIEb102Ypenb+85tWcVerv1bVYx17+e60x+O9umn8+7RHb\nh26POzHj13jKcgX6Rn6Hd/sp//57/Av/I/xVJFAC9ABGA/2Au1gJ4XFBEP7PqDz+LQkESJuchjzK\nQGHbHrz3Xj6bNsno1AkydToGJCVRUa8leeQwfn1/EpNW7cGydTcP56iIK4ojMzOP8HBb1m3silej\nAio3rKfJ80bYfG9DhbEX0993RzCnc1SQ4lehYOqCTzjj3xXTRTu+b7wIz10DkWyahaZOS22eB73m\nBJBlXMIWbyOPbXuRlroQpWQH9eZN2LQtYdGwW0T73WPp2yEs3P2MLesmMOKOFg9LNY7iZBRN7Zm4\n8BN2nr1Iw/A9yPyH4CXRs9SyEDPQwtYOr0FnaF+8Hkd9PnMNevabzawHmotdWS/qgvskT8RyMWKb\nfHp0OE3Ce1/zCV/D26X0fNqTAW270DQ8gwsNB9mcaWFuVk/8ulzAFxHV9xU88nyPLUmZuGm1NAmO\nJ/FyMVK/BUg6TMTziY70B46Ul7sSEZGJnU0WvRpEfN5vHDEF0aiyVVy8eInJk0Xo9bB7N2zb1pPQ\n0A+Iiwvl8eNuhIXtIiLyAyrlBTDFAmekHFl0iq69mhC3I44P23xIq4gJ9L2Ujs/XWlIfT8QSmYfH\ncEcSPkph7fK1eP3gRf9F/QmdG0pVFTSNsjBNn4J/13NIRy7m4SMJZ6TteRrigmfUDAJranA9dQp5\naCg7h7ag1HKWysoL+PnNwcNjDABlGjPfrZtEaJiZi8/6cWRlT5RDZuMWdJNNazZQdfAjTCUx3Jk3\nknZV/tSgZKltA7ULs2kRpeSAeBvFljNID4ZwQDOUky0OsyxyFtVV83F1deBZ0FbWaZ1gUnNGD0zn\nRWZr7t4VMWgQjB8P3bpZkxdm9ErkzHUXQld/jFafzePlj9m/cz8DBw4kvb6eDgkJCBYLjUu+JkJ3\nlv5eYpLMUSSIB/Nu+IeID1bxvbGUkBA9hxZMZu7cucyYYc38q66xcHj9LdaW9mDkC1+Wbr2OyMcH\nQRA4duwYc+bMoby8nDFjxvDxxx8TGBgIwPM9z0lfkM6xkbM5sus5K5fH4Hm1DV8+O8acBetQtVQz\n6fgkVDtXUlKVzpHt20lLe4c9By6RmZSBh2kIWs+xTPr8FO0991GV25YnFz+i3Y0TNK08yxnJDsz6\nZ5wU3aaPJJoYeWM8PirlofcM5i+sYqZOyfjJiwlYOd2a1fESJ4+YmfoevNE4mWFRW5AHVeI1sBky\ntRMikQRBMGM0ltPQkAuIcXHph5NTdyQSq7h4pk5H3J3HhH6/itu/HOZu9ENq33TmYEkpPa6J8M0S\ncB/mhstIZ9JlThz8WcqhQxAaKgD7KMpaRxtjKEM9ZlBS48SuEi8yLHa8EVBJr1gjYc2l+EVI0Mgk\n3M+w5extG05fEuPlJWLwYBg0CNzcIDERli8HT0/YuROcnKz/v5S6OuZkZnK+qgpHqZSRbm70s3Ui\nfKeWvEtHMH20gt8+2cDZ2iB2H9zHvbx07J1ycaor5tqVes6etVBTI/Duu9ClixQHh+a4u4/By2sy\nUunv9/EVdDrIzYWcHKuuYk6OtVTdzZsQHAzdu0OHDhZcXevJyNBw714dCQkuPHnijI8PtGkDHTtC\njx4QFPR7ieT6+nouXLjA0V1HOXX2FI4GR4zqntSahqG09UStboLJZHXdV780b3h7W6V9Xp3b2Fij\nUaKjrS79gIAKrlwp4sYNCZmZ3pjNeiyWB9jaFqBWaxGLXdFqg9FoAjAaPQA90ADYArKXQs7WhAmZ\nTCAyUkPnzhV06lSHj6+JtNI0rmdeJ6EkgYz6TGptaxBqgAIbpjqFcveygRfDM4A/cDz4nefxb/jX\n67E/GPd+D9l7Of8nucE/9F/XHf7DH20vPcG8khZ8pQ39Ok/lT4c1AcX0gzMzmnVFPkpHrN9ZuvfI\nRa74V1LI/yb+8phAkUgkA/piJYRdgUuCIIz7H63ynwR/jwSWHy8nf0M+ou8W8uTJYM6dm8bevda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WLatGnk1+YT81kM4r1iQn3OE+LxDcfvHub7LdC82Uck2PRiWnohewLVDI8azoGS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\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8uHfvHp6enoQ/jOT4hADyg9tRa/BAPCw9qGTugl7pFVKSFyOVGlOt2hEMDd0BKCrSyWd1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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,5))\n", + "for i in range(1, 100):\n", + " plt.plot(time_grid, swap_npvs.T[:,i])\n", + " plt.title(\"Simulted Swap Exposures\")\n", + " plt.xlabel(\"Time\")\n", + " plt.ylabel(\"NPV\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Expected Exposure" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "swap_npvs[swap_npvs<0] = 0\n", + "swaption_npvs[swaption_npvs<0] = 0\n", + "EE_swaption = np.mean(swaption_npvs, axis=0)\n", + "EE_swap = np.mean(swap_npvs, axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 133, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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ciEwDfg8cMMZ0dcueBQYBp4AE4HZjzDF33URgNJANjDPGLHTLewAzgNrAF8aY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\nMBEREZEyC75r1d0LZvZ3wGPEkzduU4gTERGRLAi+RU5EREQ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,5))\n", + "plt.title(\"Positive Expected Exposures\")\n", + "plt.plot(time_grid, EE_swaption)\n", + "plt.plot(time_grid, EE_swap)\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"NPV\")\n", + "plt.legend([\"Swaption\", \"Underlying Swap\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## CVA Calculation" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Setup Default Curve \n", + "pd_dates = [today + ql.Period(i, ql.Years) for i in range(11)]\n", + "hzrates = [0.02 * i for i in range(11)]\n", + "pd_curve = ql.HazardRateCurve(pd_dates,hzrates,ql.Actual365Fixed())\n", + "pd_curve.enableExtrapolation()" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 135, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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Vcb2CHAOSC0ceCccdB2eeGTsSkapLwRi2rOWwQs5fK1aEre8GDoSOHWNHI5Ie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\n2MYXZgZ4AAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot curve\n", + "# Calculate default probs on grid *times*\n", + "times = np.linspace(0,30,100)\n", + "dp = np.vectorize(pd_curve.defaultProbability)(times)\n", + "sp = np.vectorize(pd_curve.survivalProbability)(times)\n", + "dd = np.vectorize(pd_curve.defaultDensity)(times)\n", + "hr = np.vectorize(pd_curve.hazardRate)(times)\n", + "f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(10,10))\n", + "ax1.plot(times, dp)\n", + "ax2.plot(times, sp)\n", + "ax3.plot(times, dd)\n", + "ax4.plot(times, hr)\n", + "ax1.set_xlabel(\"Time in years\")\n", + "ax2.set_xlabel(\"Time in years\")\n", + "ax3.set_xlabel(\"Time in years\")\n", + "ax4.set_xlabel(\"Time in years\")\n", + "ax1.set_ylabel(\"Probability\")\n", + "ax2.set_ylabel(\"Probability\")\n", + "ax3.set_ylabel(\"Density\")\n", + "ax4.set_ylabel(\"HazardRate\")\n", + "ax1.set_title(\"Default Probability\")\n", + "ax2.set_title(\"Survival Probability\")\n", + "ax3.set_title(\"Default density\")\n", + "ax4.set_title(\"Harzard rate\")" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Calculation of the default probs\n", + "defaultProb_vec = np.vectorize(pd_curve.defaultProbability)\n", + "dPD = defaultProb_vec(time_grid[:-1], time_grid[1:])" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1137.1215765934307" + ] + }, + "execution_count": 137, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Calculation of the CVA\n", + "recovery = 0.4\n", + "CVA = (1-recovery) * np.sum(EE_swaption[1:] * dPD)\n", + "CVA" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "913.40656293382506" + ] + }, + "execution_count": 138, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Calculation of the CVA\n", + "recovery = 0.4\n", + "CVA = (1-recovery) * np.sum(EE_swap[1:] * dPD)\n", + "CVA" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} From 6e3601be21789430b4544986037b7c3ca0faa71e Mon Sep 17 00:00:00 2001 From: Matthias Groncki Date: Sun, 26 Jun 2016 12:28:21 +0200 Subject: [PATCH 17/25] Update --- ...ng-Swaption-Part_II-Copy1-checkpoint.ipynb | 834 -- CVA_calculation_I.ipynb | 10577 +--------------- ...eCarloPricing-Swaption-Part_II-Copy1.ipynb | 841 -- MonteCarloPricing-Swaption-Part_II.ipynb | 148 +- StochProcess..Basics.ipynb | 1198 -- 5 files changed, 120 insertions(+), 13478 deletions(-) delete mode 100644 .ipynb_checkpoints/MonteCarloPricing-Swaption-Part_II-Copy1-checkpoint.ipynb delete mode 100644 MonteCarloPricing-Swaption-Part_II-Copy1.ipynb delete mode 100644 StochProcess..Basics.ipynb diff --git a/.ipynb_checkpoints/MonteCarloPricing-Swaption-Part_II-Copy1-checkpoint.ipynb b/.ipynb_checkpoints/MonteCarloPricing-Swaption-Part_II-Copy1-checkpoint.ipynb deleted file mode 100644 index 53bd87c..0000000 --- a/.ipynb_checkpoints/MonteCarloPricing-Swaption-Part_II-Copy1-checkpoint.ipynb +++ /dev/null @@ -1,834 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "collapsed": false - }, - "source": [ - "# Swaption Pricing Part 2: American Monte Carlo for Bermudas" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Copyright (c) 2015 Matthias Groncki*\n", - "\n", - "\n", - "Redistribution and use in source and binary forms, with or without\n", - "modification, are permitted provided that the following conditions are met:\n", - "\n", - " - Redistributions of source code must retain the above copyright notice,\n", - " this list of conditions and the following disclaimer.\n", - "\n", - "THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n", - "AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n", - "IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\n", - "DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE LIABLE\n", - "FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\n", - "DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\n", - "SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\n", - "CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\n", - "OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\n", - "OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n", - "\n", - "This disclaimer is taken from the QuantLib license" - ] - }, - { - "cell_type": "code", - "execution_count": 455, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# import the used libraries\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import statsmodels.api as sm\n", - "import QuantLib as ql\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 456, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Setting evaluation date\n", - "today = ql.Date(7,4,2015)\n", - "ql.Settings.instance().setEvaluationDate(today)" - ] - }, - { - "cell_type": "code", - "execution_count": 457, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Setup Marketdata\n", - "rate = ql.SimpleQuote(0.03)\n", - "rate_handle = ql.QuoteHandle(rate)\n", - "dc = ql.Actual365Fixed()\n", - "yts = ql.FlatForward(today, rate_handle, dc)\n", - "yts.enableExtrapolation()\n", - "hyts = ql.RelinkableYieldTermStructureHandle(yts)\n", - "t0_curve = ql.YieldTermStructureHandle(yts)\n", - "euribor6m = ql.Euribor6M(hyts)\n", - "cal = ql.TARGET()" - ] - }, - { - "cell_type": "code", - "execution_count": 458, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Setup a dummy portfolio with two Swaps\n", - "def makeSwap(start, maturity, nominal, fixedRate, index, typ=ql.VanillaSwap.Payer):\n", - " \"\"\"\n", - " creates a plain vanilla swap with fixedLegTenor 1Y\n", - " \n", - " parameter:\n", - " \n", - " start (ql.Date) : Start Date\n", - " \n", - " maturity (ql.Period) : SwapTenor\n", - " \n", - " nominal (float) : Nominal\n", - " \n", - " fixedRate (float) : rate paid on fixed leg\n", - " \n", - " index (ql.IborIndex) : Index\n", - " \n", - " return: tuple(ql.Swap, list) Swap and all fixing dates\n", - " \n", - " \n", - " \"\"\"\n", - " end = ql.TARGET().advance(start, maturity)\n", - " fixedLegTenor = ql.Period(\"1y\")\n", - " fixedLegBDC = ql.ModifiedFollowing\n", - " fixedLegDC = ql.Thirty360(ql.Thirty360.BondBasis)\n", - " spread = 0.0\n", - " fixedSchedule = ql.Schedule(start,\n", - " end, \n", - " fixedLegTenor, \n", - " index.fixingCalendar(), \n", - " fixedLegBDC,\n", - " fixedLegBDC, \n", - " ql.DateGeneration.Backward,\n", - " False)\n", - " floatSchedule = ql.Schedule(start,\n", - " end,\n", - " index.tenor(),\n", - " index.fixingCalendar(),\n", - " index.businessDayConvention(),\n", - " index.businessDayConvention(),\n", - " ql.DateGeneration.Backward,\n", - " False)\n", - " swap = ql.VanillaSwap(typ, \n", - " nominal,\n", - " fixedSchedule,\n", - " fixedRate,\n", - " fixedLegDC,\n", - " floatSchedule,\n", - " index,\n", - " spread,\n", - " index.dayCounter())\n", - " return swap, [index.fixingDate(x) for x in floatSchedule][:-1]\n", - "\n", - "\n", - "def makeSwaption(swap, callDates, settlement):\n", - " if len(callDates) == 1:\n", - " exercise = ql.EuropeanExercise(callDates[0])\n", - " else:\n", - " exercise = ql.BermudanExercise(callDates)\n", - " return ql.Swaption(swap, exercise, settlement)\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Lets start with an european swaption" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create a forward starting plain vanilla swap using the helper function above and create a european swaption which allows us to to enter the swap in one year from today." - ] - }, - { - "cell_type": "code", - "execution_count": 459, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "settlementDate = today + ql.Period(\"1Y\")\n", - "\n", - "swaps = [makeSwap(settlementDate,\n", - " ql.Period(\"5Y\"),\n", - " 1e6,\n", - " 0.03047096,\n", - " euribor6m)\n", - " ]\n", - "\n", - "calldates = [euribor6m.fixingDate(settlementDate), euribor6m.valueDate(ql.Date(5,4,2017))]\n", - " \n", - "swaptions = [makeSwaption(swap, \n", - " calldates, \n", - " ql.Settlement.Physical) \n", - " for swap, fd in swaps]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Pricing of the underlying at time 0 using the QuantLib pricing engine" - ] - }, - { - "cell_type": "code", - "execution_count": 460, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Swap NPV at time 0: 0.0091\n" - ] - } - ], - "source": [ - "#%%timeit\n", - "# Setup pricing engine and calculate the npv of the underlying swap\n", - "engine = ql.DiscountingSwapEngine(hyts)\n", - "for swap, fixingDates in swaps:\n", - " swap.setPricingEngine(engine)\n", - " print(\"Swap NPV at time 0: %.4f\" % swap.NPV())\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Setup the Gaussian Shortrate model (a.k.a Hull White model)\n", - "\n", - "Don't worry about calibration, assume we know the calbriated model parameter" - ] - }, - { - "cell_type": "code", - "execution_count": 461, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Stochastic Process " - ] - }, - { - "cell_type": "code", - "execution_count": 462, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Assume the model is already calibrated either historical or market implied\n", - "volas = [ql.QuoteHandle(ql.SimpleQuote(0.0075)),\n", - " ql.QuoteHandle(ql.SimpleQuote(0.0075))]\n", - "meanRev = [ql.QuoteHandle(ql.SimpleQuote(0.002))]\n", - "model = ql.Gsr(t0_curve, [today+100], volas, meanRev, 16.)\n", - "process = model.stateProcess()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Calculate the swaption price using an integral pricing engine" - ] - }, - { - "cell_type": "code", - "execution_count": 463, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "swaptionEngine = ql.Gaussian1dSwaptionEngine(model)" - ] - }, - { - "cell_type": "code", - "execution_count": 464, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Swaption NPV : 17068.93\n" - ] - } - ], - "source": [ - "for swaption in swaptions:\n", - " swaption.setPricingEngine(swaptionEngine)\n", - " print(\"Swaption NPV : %.2f\" % swaption.NPV())" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "###Pricing with an Monte Carlo method" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "####Create a swap path pricer in Python \n", - "\n", - "Convert all Dates into times in years (using the same DayCounter as in the yieldTermStructure and store all fix cashflows in a numpy array." - ] - }, - { - "cell_type": "code", - "execution_count": 465, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "mcDC = yts.dayCounter()\n", - "\n", - "def timeFromReferenceFactory(daycounter, ref):\n", - " \"\"\"\n", - " returns a function, that calculate the time in years\n", - " from a the reference date *ref* to date *dat* \n", - " with respect to the given DayCountConvention *daycounter*\n", - " \n", - " Parameter:\n", - " dayCounter (ql.DayCounter)\n", - " ref (ql.Date)\n", - " \n", - " Return:\n", - " \n", - " f(np.array(ql.Date)) -> np.array(float)\n", - " \"\"\"\n", - " def impl(dat):\n", - " return daycounter.yearFraction(ref, dat)\n", - " return np.vectorize(impl)\n", - "\n", - "timeFromReference = timeFromReferenceFactory(mcDC, today)" - ] - }, - { - "cell_type": "code", - "execution_count": 466, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def getFixedLeg(swap, t):\n", - " \"\"\"\n", - " returns all future payment times and amount of the fixed leg of the underlying swap\n", - " \n", - " Parameter:\n", - " swap (ql.Swap)\n", - " t (float) \n", - " \n", - " Return:\n", - " (np.array, np.array) (times, amounts)\n", - "\n", - " \"\"\"\n", - " fixed_leg = swap.leg(0)\n", - " n = len(fixed_leg)\n", - " fixed_times=[]\n", - " fixed_amounts=[]\n", - " npv = 0\n", - " for i in range(n):\n", - " cf = fixed_leg[i]\n", - " t_i = timeFromReference(cf.date())\n", - " if t_i > t:\n", - " fixed_times.append(t_i)\n", - " fixed_amounts.append(cf.amount())\n", - " return np.array(fixed_times), np.array(fixed_amounts)\n", - "\n", - "\n", - "def getFloatingLeg(swap, t):\n", - " \n", - " float_leg = swap.leg(1)\n", - " n = len(float_leg)\n", - " float_times = []\n", - " float_dcf = []\n", - " accrual_start_time = []\n", - " accrual_end_time = []\n", - " nominals = []\n", - " for i in range(n):\n", - " # convert base classiborstart_idx Cashflow to\n", - " # FloatingRateCoupon\n", - " cf = ql.as_floating_rate_coupon(float_leg[i])\n", - " value_date = cf.referencePeriodStart()\n", - " t_fix_i = timeFromReference(value_date)\n", - " t_i = timeFromReference(cf.date()) \n", - " if t_fix_i >= t:\n", - " iborIndex = cf.index()\n", - " \n", - " index_mat = cf.referencePeriodEnd()\n", - " # year fraction\n", - " float_dcf.append(cf.accrualPeriod())\n", - " # calculate the start and end time\n", - " accrual_start_time.append(t_fix_i)\n", - " accrual_end_time.append(timeFromReference(index_mat))\n", - " # payment time\n", - " float_times.append(t_i)\n", - " # nominals \n", - " nominals.append(cf.nominal())\n", - " return np.array(float_times), np.array(float_dcf), np.array(accrual_start_time), np.array(accrual_end_time), np.array(nominals)\n", - "\n", - "\n", - "def swapPathNPV(swap, t):\n", - " fixed_times, fixed_amounts = getFixedLeg(swap, t)\n", - " float_times, float_dcf, accrual_start_time, accrual_end_time, nominals = getFloatingLeg(swap, t)\n", - " df_times = np.concatenate([fixed_times, \n", - " accrual_start_time, \n", - " accrual_end_time, \n", - " float_times])\n", - " df_times = np.unique(df_times)\n", - " # Store indices of fix leg payment times in \n", - " # the df_times array\n", - " fix_idx = np.in1d(df_times, fixed_times, True)\n", - " fix_idx = fix_idx.nonzero()\n", - " # Indices of the floating leg payment times \n", - " # in the df_times array\n", - " float_idx = np.in1d(df_times, float_times, True)\n", - " float_idx = float_idx.nonzero()\n", - " # Indices of the accrual start and end time\n", - " # in the df_times array\n", - " accrual_start_idx = np.in1d(df_times, accrual_start_time, True)\n", - " accrual_start_idx = accrual_start_idx.nonzero()\n", - " accrual_end_idx = np.in1d(df_times, accrual_end_time, True)\n", - " accrual_end_idx = accrual_end_idx.nonzero()\n", - " # Calculate NPV\n", - " def calc(x_t):\n", - " discount = np.vectorize(lambda T: model.zerobond(T, t, x_t))\n", - " dfs = discount(df_times)\n", - " # Calculate fixed leg npv\n", - " fix_leg_npv = np.sum(fixed_amounts * dfs[fix_idx])\n", - " # Estimate the index fixings\n", - " index_fixings = (dfs[accrual_start_idx] / dfs[accrual_end_idx] - 1) \n", - " index_fixings /= float_dcf\n", - " # Calculate the floating leg npv\n", - " float_leg_npv = np.sum(nominals * index_fixings * float_dcf * dfs[float_idx])\n", - " npv = float_leg_npv - fix_leg_npv\n", - " return npv\n", - " return calc" - ] - }, - { - "cell_type": "code", - "execution_count": 467, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0.99726027, 2.00273973])" - ] - }, - "execution_count": 467, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Convert call date to time\n", - "callTimes = timeFromReference(calldates)\n", - "callTimes" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "####Test the t0 Swap Pricing" - ] - }, - { - "cell_type": "code", - "execution_count": 468, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Swap NPV at time 0: 0.0091\n", - "Error : 0.00000000\n" - ] - } - ], - "source": [ - "npv = swapPathNPV(swap, 0.)(0)\n", - "print(\"Swap NPV at time 0: %.4f\" % npv)\n", - "print(\"Error : %.8f\" % (npv - swap.NPV()))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Monte Carlo Simulation\n", - "\n", - "#### Generate time grid and paths" - ] - }, - { - "cell_type": "code", - "execution_count": 569, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def fixingdates(swap):\n", - " leg = swap.leg(1)\n", - " n = len(leg)\n", - " fixing_dates = []\n", - " for i in range(0, n):\n", - " cf = ql.as_floating_rate_coupon(float_leg[i])\n", - " value_date = cf.referencePeriodStart()\n", - " fixing_dates.append(value_date)\n", - " return fixing_dates\n", - "\n", - "# Define evaluation grid\n", - "\n", - "date_grid = [today] + calldates \n", - "\n", - "date_grid = np.unique(np.sort(date_grid))\n", - "time_grid = np.vectorize(lambda x: ql.Actual365Fixed().yearFraction(today, x))(date_grid)\n", - "dt = time_grid[1:] - time_grid[:-1]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 570, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([Date(7,4,2015), Date(5,4,2016), Date(7,4,2017)], dtype=object)" - ] - }, - "execution_count": 570, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "date_grid" - ] - }, - { - "cell_type": "code", - "execution_count": 571, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Random number generator\n", - "seed = 1\n", - "urng = ql.MersenneTwisterUniformRng(seed)\n", - "usrg = ql.MersenneTwisterUniformRsg(len(time_grid)-1,urng)\n", - "generator = ql.InvCumulativeMersenneTwisterGaussianRsg(usrg)" - ] - }, - { - "cell_type": "code", - "execution_count": 572, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#%%timeit\n", - "# Generate N paths\n", - "M = 100000\n", - "m = len(time_grid)\n", - "x = np.zeros((M, m))\n", - "y = np.zeros((M, m))\n", - "numeraires = np.zeros((M, m))\n", - " \n", - "for n in range(0, M):\n", - " numeraires[n, 0] = model.numeraire(0, 0)\n", - " \n", - "for n in range(0,M):\n", - " dWs = generator.nextSequence().value()\n", - " j = 1\n", - " for i in range(1, len(time_grid)):\n", - " t0 = time_grid[i-1]\n", - " t1 = time_grid[i]\n", - " e = process.expectation(t0, \n", - " x[n,i-1], \n", - " dt[i-1])\n", - " std = process.stdDeviation(t0,\n", - " x[n,i-1],\n", - " dt[i-1])\n", - " x[n,i] = e + dWs[i-1] * std \n", - " e_0_0 = process.expectation(0,0,t1)\n", - " std_0_0 = process.stdDeviation(0,0,t1)\n", - " y[n,i] = (x[n,i] - e_0_0) / std_0_0\n", - " numeraires[n, i] = model.numeraire(t1, y[n,i])\n", - " #df_times_temp = df_times.copy()\n", - " #df_times_temp[df_times_temp <= t1] = t1\n", - " #dfs[n,i] = np.vectorize(lambda T: model.zerobond(T, t1, y[n,i]))(df_times_temp)" - ] - }, - { - "cell_type": "code", - "execution_count": 573, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "\n", - " \n", - "pricer = np.vectorize(swapPathNPV(swap, time_grid[-1]))\n", - "\n", - "cont_value = pricer(y[:,-1]) / numeraires[:,-1]\n", - "cont_value[cont_value < 0] = 0\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 574, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0. , 0.99726027, 2.00273973])" - ] - }, - "execution_count": 574, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "time_grid" - ] - }, - { - "cell_type": "code", - "execution_count": 575, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "\n", - "\n", - "pricer = np.vectorize(swapPathNPV(swap, time_grid[-2]))\n", - "\n", - "\n", - "exercise_values = pricer(y[:,-2]) / numeraires[:,-2]\n", - "exercise_values[exercise_values < 0] = 0" - ] - }, - { - "cell_type": "code", - "execution_count": 611, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "states = y[:, -2]\n", - "Y = np.column_stack((states, states**2, states**3))\n", - "Y = sm.add_constant(Y)\n", - "ols = sm.OLS(cont_value, Y)\n", - "ols_result = ols.fit()\n", - "cont_value_hat = np.sum(ols_result.params * Y, axis=1)\n", - "npv_amc = np.maximum(cont_value_hat, exercise_values)" - ] - }, - { - "cell_type": "code", - "execution_count": 612, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "npv_amc = np.mean(npv_amc) * numeraires[0,0]" - ] - }, - { - "cell_type": "code", - "execution_count": 613, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "17127.228006267218" - ] - }, - "execution_count": 613, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "npv_amc" - ] - }, - { - "cell_type": "code", - "execution_count": 614, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MC NPV: 17127.2280\n", - "Error : 58.2987 \n", - "Rel. Error in bps: 0.5830 \n" - ] - } - ], - "source": [ - "print(\"MC NPV: %.4f\" % npv_amc)\n", - "print(\"Error : %.4f \" % (npv_amc-swaption.NPV()))\n", - "print(\"Rel. Error in bps: %.4f \" % ((npv_amc-swaption.NPV()) / 100))" - ] - }, - { - "cell_type": "code", - "execution_count": 615, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 615, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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EREQ62I4dO7j44oupqqqisLCQKVOm8MMf/rCrmyUSQ93BKVB3sIjInrG3dgeL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XtLF4EEBhIYEBCwiMgrJ8fpNx5Ir1dHk7PN+rmMXBPnrF1utMnMnEOeqsyaOUSdk8kbD1L1FUY0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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(10,10))\n", - "plt.scatter(states, cont_value)\n", - "plt.scatter(states, cont_value_hat, marker='x', c='red')\n", - "plt.legend([\"continuation values at t+1\", \"regression\"])\n", - "plt.title(\"Regression Bermudan Swaption\")\n", - "plt.xlabel(\"State at t\")\n", - "plt.ylabel(\"NPV\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.1" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git 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SUvSnzK2gtGnPLi5wFTcYO8ZC/7wFlxoTU9Efu+9ZfMVx4SN9H/kys2ufDwIf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8ldiC8r56zZwZcFi1GNHIl23jOSCp5SqmwgT/kQuekpKlsFkjkY0clGrN2FfI8ATG3ByEe+gogy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A2PI5uup4jFdGYmhOR5TuhhgPQouK2VJdRaQ7qIYCL+ByYBzL3WbR8a0zhRkTGOxcSUn/bTy9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\nRyr+pKsSqRddwLEMGjzwCzlB44fz+STMwt8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5k0Ahg7EOP0lKLyV/v6jH5WEU3SrjcTMM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dyBF2HRREc/UVxVnwhfXq2iY6kFETtmScQuwHcOmUwBFr4CW4fIbCsD3/yuFHs/YFpqOwbc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\nZPxX18uBc3/MTtxSM8DamkhmjGRE78lIl90Hv5Yvcz7l0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jag+0VWOOXLETuXTpKN7Y2YBBxuTcSpvH4zl18MZ5OskO4zyoGvgurvLcqiTb\n6uDvPzgPkRg1dxw5Bm7EhUG0VOWQCM8Bm1SzVJY+12OtFMJVzZxmjOaoxrWI9AJ6Ay2zuzJjcB2j\ncBrsO4D6xhxdIl5EngaKqmqf4H+JIurdszn7kkd45Ko22uajo9r+LO8Zu3uN+umKzbcfPJCQcIaq\n/hn0eRXQ3RjtFDKOW4BLjNEO2Xx9XQ4eZGSXLsyJjSUauEA1eX354F50AQbi1DMeBz425kivrxVb\nHfgKKG/UHKWukcZ4CuImAY2CrSDQ6yiPvUh3XDx+EzRQ+xF5DDeRaI1qjq5MWbETgFVGTY7lN6SE\niFyAk3msr8e6uI7I6biVsArJKadAoMwCi4FH1JjJ1kpxDnvJw3AG+URjdFuOD9faG4N+m6sJeS+K\nhO+uyV/fv0heDadaZsO3PB6PJ5FcbZyLSEVcLOJpgAIvq+rzIlIcZ+RWxiXRXZn4wyIiD+BUPBKA\n21X102B/Y+B1IB9O53dAsD8y6OMsnGHcLTmDIqvGuRVbBBdb2cuoSTWe1Yoti1PWqGvUbHL9UxxX\nyv1CVZYG+7oC16vSMbPjyu1YK3mBj3DhGTfnhFcsiMUes2cP5Xv0oM3AgXzdvDlbce+h0O0QLu73\ndWMyVuLdWmkGTAIWAu2A9sbo0uSOFZFiuNhwo6rLIyRiQGlKPz6a0aM6aacU1Ups7bGPvLj97UE/\nFvty9ooVzvC2Vk7DecpLGuNUYAIv9iqgkzG6JKTfXsCGxM9MBq+v3o4d2OuuY21sLNuAHqq6Ix3n\n5QE6Aw/h4qafwK0oxFsrwkOPjeDrFgVTC/sIuc5GwJkcNsYr4j5HS4OtPfC7MXrvfyeK1MUZiO3Q\nkNfDSRBOxn3vXE06Q0UygxVbHxeDXsWoOeZhESIyBiigqj2PcceDgaKo3pHs09aG4TziP8UQPQF4\nAJdcPBNnlH9pjGogF9kEuAy38vhIWopHmRqutXlwUpRT1ZgXkl7LD/+j279nMc0YPWEVhTweT+4g\ntxvnZYAyqvpDUKlxCe4LuBewTVWfDsqfF1PVgSJSB/eD2hQoD8zFJdepiCwCblXVRSLyCfC8qs4W\nkX5APVXtJyLdgM6q2j2ZsWTVOB8LhBs1aapxBMePAhKMmrtc/4wG8qjS7/CYSDTiS6qSY8bD8SJY\nun4FKIMzJI/yMmdTP7cBXe+4g7+2baPKpEmMwv3IhyXZwnEGXnPgeeBFY1L3NgaG/4M4PfZbcKoj\nG4xJXZ5B5olGAAAgAElEQVRRRO4CDDAiP/nnPM/z31SnelujJsXX2YrN93eejZt657u2yDPPHrr+\n5ptdgpq18j1wuzG6IGRcAwBjjHYO+usQDq8kwD51oVN3hYbVpHGNxX/9lR/uvpt8+/YxAXhIU/CE\nptKG4O7tQ0BZYDjQiENyC50/uMzs7DQ9OC4SqI1LiG4Q8jcKZ4D/wGFjfEXoe8ZaKYULTehojC7G\nhXUsAh5HdeJRg3L67F8An5FMDkB2YK3kNUYPWrGfARONmmOeVBh8t/6Ae82PWpXJqU6BFcB1aPLh\nVWLtcKDxbNqPjSRuLDACmGCM/iMiEbjPx2U4ucSduMJeDYLTr1TN/uJAYu0ZwIKSW2ny3pU0B+KN\nmvcQqbi/ND8tfJsEhAbG6Ibs7tuTvVgrNYB/jEnbieDxHGtytXGeFBH5EBgdbK1UdUtgwFtVPSPw\nmh9S1eHB8bNxUlh/AF+oau1gf3ecV7JvcMwgVf1WRMKBTapaKpm+M22cBwVI3sZ5wtO1dGzFlsN5\n2mtHY8rgVCRqq3JEkR0RVgOXqvJLZsaWm7FWHsXFkxpj/pOBy+4+SgLLZ82i69NP8wHQVFVT1Y63\nVuoA9wZjmwCMNOZoQ9ZaqYrzlu8FepKQpwH7873InSM78HutArhCKduA7UbNEROPYEVnRV7ylnqE\nR3a0pGU9oybN5XIrdvADEXddX+PKpaV696aHMfqRtTIc2G+MDgoZW35cKFX76GhiBRbOg7x1YWIJ\nN64+wBBgbGqGtrUS/uGHLBk3jpr793OD6uHk1sxirRjgLRLylGRXoQj25h9D+U0lcYZ4tWDcP+Hi\npRP/rk/Pqoq10gMYmG8DTZpdw7vAJlT7pXiCq9K5EBicrAGfBYJVg2XAGKJj/gQeAxofjyJLItIS\ntxpZPzWVl2zssDEwBahBMj8wYm034dCTH3LZlMLs7gF0iY7mN5xqzWW4ip6/4pLjp6u6hGdxib3j\ngdOBS1SzrygZgBWb585nmZL3IBc+fT+LcTlDNxs1sxCZsXQkYTvPZKMxemN29uvJHgInwAU4Kd72\nwDBjdPBxHZTHkwwnjHEuIlVwccf1gD9VtViwX4DtqlpMRF4AFqrqW8Fzr+Ck6dYBT6lq22B/S+A+\nVe0oIj8B7RO9hCKyCjg76Zd6Zo3zIOlrGTDQqPkgg+e+oHCgNeYc4E1VXk56jAhvAF8l99yJTJA0\nOQI41xjdnIP9jAXioqOJBPao6t0ZOLcS7kv+epzu/AiiY/aAXkHDZa0pvaUtK85Yw5+VYhEtQ96D\nFYkPj+NQ2CZcCFUBoCRQDBd3vS10m8GMIvHEt+1M53OMmhTVJ6zYPIkedSu29FrW/tYnrPehj2Zo\nXFQUvXAKFkON0XOTjP/e2FjO6diRGvfBgeHwGy4evoG4EuhjcXHafVX1u6T9ikhYy5Z8/csvNIiL\n47zduw+HyGSWwDP+NtuLlafnaxV56ZailNs0DRf28SPOG34gC+0L8FHJ+UTWG0RhnCpL6u25FTkL\ndEV1fnr6kSFSH7fq8yEwUgcdHbce6PU/ARRlT/6z6TDzE5yhNy8j15RdiMizQLnkVg5zoLNngP2o\nPnzUU9Y2zM+ez9/i6mVF2Zlv0CAGzp9PX1yy7gLcPZ2hqpuSb1oEt/JyCe67/a+sDjdI5O8APHYg\ngv2XfUjZ/VEMiIlmB/AW0NAQ3SyuCIO+/pDKOIfC8qz268keglC+q4E7AAFG4r4XuySuHno8uYn0\n2Jzhx2owKREsu74PDFDV3e671xGErBz/2UPKPAr8mFHDPGD4QWRFSQ6s2kbkqykcswBoCSePcR4k\n8j2HS2TMScP8TKDLtGk0xi2xV8rI+cbon8Cd1srjwK0sOWsRBXdHUvvXnVRflZelZ43kz8oLgb8Z\n3f8mKq6PMh139zhqHGLDgKI4L9w5uPyHOh3pWBUX6z7Dik0sO74zyVYJuNqKjTBqDhk1WxCmVkqo\n2vzee9fMHD2a13FhYPWtlRKhRZt27mTsCy8w9IxCLHtyN6VxCZBDgAdVtX9QpOZaYEZQRv5hVZfs\nJiLFSpcmZscOarVvT4PJk/X3jNy75LBWChAf9hErT6/CnSMjOBhxI+U2tcCFL0w3RndltQ9jVLee\nL5NW3s3k5Q/Rts5j6TD0VZcj0gOYgkhLNPVrlSHSDBdi8RiurPwKGSIDgXd1kPN0WCv1gXFAP6AS\nBfa+Qp6EURwKuxPnhDgePAQsFZErVXVKjvXiYsS743IvQOQcYB2qW8TaEhVYP2MM/feu+WH3jrvv\n5tChQ0zFGVP9Et9/qREkct8nIluBr0Skvar+p24kQ6QcsEUHpS/0yoptjZtE5Qcejoxjxv4oWgKT\no2OoExPN28C43dTqVmjnb2MLrGLinho8hVtZ8xxHrJUyuM/YzcB3OGfK50Hdh1q47zuP54TkuHrO\ng2XKj3EFYZ4L9q3AhaVsFlc4JCYIaxkIoKpPBcfNxkmi/REckxjWchVwvqrekhj6oqoL0wpr4cgP\nslVVm9rYrdhGOI9fA6Mmw0amCIXvYeXGeuz84Ho9+9oUjqkNzFSlWkbbz61YK08CFYzRZK85m/oQ\nnBE0KTqav3ArKSZTbTmv2m2gD3PRJ5up8kdZJtywjQP5KgOx5N/zN42XVGdpo3HEFloOrMHpaVfH\nGeKNgr/FcLG/33M4bnoLzotdGCiSZEvc1xe4waiZEoyn/o/8GDOAAbEffMB1RYsyFSdDGQd0Swz/\nEJGbixdj6Oy+FGj6JJejOgeRUriJylmJVReDBOwncZ7Du4EfIyKYddFFlLrmGlp07Zp8YmuG7qGV\nouwq9CWLm1bhqYHvE5/3DqPm3yD0YzTQGLgwy/GhLsl80bKneXtHU84FWgTVU9Nz7k24cKbmpBD6\nIUPkAlzeS08dpJ8E+wxOgelAj4q8eVM1zgJ64sKHFuLu61dsL/YWl097BGhu1KzKwlVmGnGG8nSg\noaomW2shGzppBTyPakNEOuPCUMLjwsJ+vertARVb//tiif89sX/52nVUAP4HvKiaubA2Ebke50Xv\nxGAO4JwlF+NyDXrqoJRD2KzYZjgVoUrBee+G5nyIte8A38ZEMxanKDPCEF01IR9lvpzFRcB1xqRv\npeVkRKwVnNOhMk4++Gs1JscLXlkr8gt1TA1W3RRB3EUC7wCjjDk8QQuOC8M5OMplx8Tf48kKImJw\njqhEBuXasJZgefIN4B9VvTNk/9PBvuGBQV40SULo2RxOCK0ReNe/BW7HfSnP5MiE0PqBod4duCw7\nEkKt2HBcYt1oo+a1zF0/I6qwp9JrLG4LnG7UbE3mGAG2Ag1VOeGTkKyVmsA3OJnBZJets6mf7sB9\nQNPoaJ4GdqhmXKbRis2P84DWx1UnnEOgsmPFCpX+KM+AUV/wReu5zOywEagabBWAtRw2xL8H1qSW\n8HkUzmh+5k+uPLCGW1oH/cYH4/r0Cq6otI1tw2JiWI9beYrEhYbMeO89ZNw47ptVls/yvUSnhALU\nM0ZXBe0+BpRBj4ybFZHmuOqTNe68k32XXsrNxuj7KdyXs4OHS42aVJUz7KOtq1Bn+WIWN83H87df\nZQ62/fiI591EaiRuhahdqPc/PQTn90W5o3FfDhT6jUk2hmdw2tkfGaMj092Y++45B6fucoTXXYZI\nZ9x74XId5CqwBsvpbROUzrM20/XVteSrX4SD11VmWo2CDMRNxk4DzgC+pOt77/BPyUNGze0Zucbs\nRESewF3jjaqaYuXSLHQwDjdBfQdn1F66tzzf97+t+Q8rZy6u/ds38Yduj2fnLTClFEwD5qUZepRa\nd83ldqoygurEEs5juMlAX5yM54PA+MQVDfivQNQo3A/lEOCN5N7DYm1zXC5R7ZhoagFzy/N+x5qM\n/mTBdO6JL+xkF3NCYSozBLUiWgLn42LyBxmjX2elTbE2L06AoQpuElM55G9lnMLVH7gqr3WBN4HR\nao6efForYemeKB85hgJhxJ/Vgq86ncO3F53JD7UEzfMJF8dNp1Oe3RTeCvyddGv0PQWG17+oc968\n+28xRk/6WiGeE4tcHXMuIucB83EGReIgHsAZ2FNwXwLrOFJK8UGclGI8LgxmTrA/UUoxCieleHuw\nPxL3hdEIFwfcXVXXJTOWjBrn9wFtgXaZSfAKPOLzgXox2MHAv0bNAykc+xEwSZWcW4o+BgRG1Exc\nUZpncrCfArhksh7G6AIRWYaLq/4mQ+2IrYIzHn7FGeaDgPxGzW0hfd2PC224KFt/pEUuxMU0W4Vm\n3zJJ9lP+caNmQjC2Cy12zBCGxAJnxsRQFpcA9fyWLczq25dLbr2JHe0M5RKiiEOIxSmTDDbRbMLJ\nLzZD9Ygf0fHjpWTRonxUqhRfGHN0vHDQdxFc0mbiZGQRruDQl8BCo4dLr9tWQ67gpvFv8XO9X3nh\nNmP2XJKsZzx4bzyFSwhsa0z6igMFBsmrKCXLzuDQtvNpEJ+fOq3a6bpArWEhcI4xujo97QUhGe8p\nNBQXo78B+KtPBypOPJPOvZZy89iZzF3wEQnxhRiNUxJZhnuffLjrIE+9vIYGMzdTBnjp4xbcWCCc\npsbon9bK/Wws24Gr36oLUi29yePZTaCE8ijOgP0El69zRPx0kFj7V4Y9w67tjTjjf2ICTC9dgnmn\nVc738cZ1cSXzFcv/bMLq2Ee3uvdNx2Crg3O0PI3qonR3NUTOCq7jbNYzhYlcxUHuVNXJwfN1cTK6\nW4AbdZButGI74CZY7wMPhL5Xk7QdCbSl2dTeRJb4RY15OPjOv+R8LthJWMJH8+fSD3jCGJ2aoXuU\nDQSfl8o4QzxxK4n7DM4HtuNWw14AnsqMUdzMPlWoHj8vOovvK0VyYE9eDu7Py8F9EcTtiyBubz72\n78tL/EGcgc5+Igtvp3iVfUSVLkjsgaL8uyeCOMT9JkfhFLE24b4vFgfbd4mrZSJSiKioCyhTJpoa\nNUpQo0aZspXDa55Tck3ZCwovij+tyP6DmyMqz99A+dcW0uwDawYfEmsjgVK4CXBp4LRyGzijznK6\n/FWBmreWGLK17ml2sDE6NtM32+PJAXK1cZ6byIhxbsUmen/PNmpSVf5Ivi8Ep84yQ5XnrdhKOO9q\nreSqCIpwH1BelQEZ7Ss3Ya10xCWBNsjJMtjWyjCgujHaQ0RK4zTFS6qmX6rRir0Ap8QyHBcfXxxn\nrJ1p1KwP+kk0/poak00eSJd/8QxOreIGVD9HpNwOzlz4M8MKJVCwjFFzwIoVRX/pQIfIvey9VVVn\nAcyZI1fcfz9vrPmNV3bt43zNw//mfc5XuPfXBFzc+dkmmvuAWqheG6xmJBpKjXEez75JCwaF3JsH\ngTpGzTVWbDHgXJzHriVOh/wX4EvO+LUsgwdfyYbyE81d39+Q1qUHBsdQXPGiNmnlIwRJxS9G/cX7\nTXtRN088UQum817g0WxpjG62Vu7GJQ62SafaS0EOMRG4OGwvmyP/YfvEPyjy7kHKT1nIyhaLiRSl\n/I/DyR+1gV1VXmNq/g28C8y3MdyNk9I8P3oexYFRjYpy9rMNedQYfS2Q3fyKG8crq2u8b9SMSGs8\nOUlQQbQ/MACX2/KEqi6xVlrj3gMKNMvQe1ukQxwMnARr5kPLN+BgseJUOadblTy/ntOz9brrBx1t\n7DvFnO7APUBdVFMNjZAh0hg3UW6M+3yO10G6T0Tq4QQCnsU5dyIpREGuox/F6Nb+0/Z/tFzUsvR8\n5j//KZ/+DqxR1e+TtF0GF7/cF9iG5D1AyzmVEWkVE81KIKYo3/9+JnfXszE8DLwI1EmsMZAcQehW\nbaAQsBnYbEz6Cl8Fn4nSQE1chduawdYUiMCF7s0Ptl9CP7PWSnncdxjAtcakP3H2LVu+HTBtL/l3\nV2T9Dfk4sB8nPyvBFvo4cTsA7NtEmUPjuLnNaqr32E++sDASXqrK2tee5MGdOA98U+Ds/QlhzVet\nPNR4wbd5479ZHB62cW1cZGTtqhpeufzusrt/O1ho398F9+6KD9u8iV07d0J8PEWA/biE+n9w3vrE\n3Jxd+cm/vzWtz6lBjabb2T5jSetiYed1j724e81XJxmjqdZR8HiONd44TyfpNc6t2Dw4D+R0oyb9\ny+VH9MXlOAnIRqokhim8DGw1ao4qcCHCucALqjROMhYBiqTlgbNW2gAxKRlbx4Jg+f8X4BZjMl4E\nJwP9VMUlBjU0Rv8K8g+6qx6uoJnq+e6e3gPcBfQwamKC/cOA0kZNn6AfAT4DZhmj/8uWwTuVoddx\nP7R3EJocJ1J+GSNWRrDts9r6VOdgTDdNYcotYxm7KzGeXkSGVa5Mz8kPs7t5H9aLciGqaq08gZtg\nlAWWR/3FvOILeX/jpWzRCPLh8j5m4JKpjqyweeT9KYgLWTBGzVFqFVZsFNCUOr90ZPj9fYmLeNR0\n2Z6hz4m18ghOeaFNcnrS1koR4HkOcV6doSw+bR6tcZ+ncagmBBKdlwOtcHHfXwPjo+fxCi55disw\nTwcdGYdqrVTEFcRaCtwdf4jK9/3EA6tjuWBkQ+ZXK0gFXHhKFIf4O2I7i4v8TFSRZVQ7WIRyf3VB\n6gzlnhJLeAvVf+W9Ie3yrBj20awWCTHtWutFQR91WHH6V9w6ei8J4ZWTSmweD0QkP25l6J58+Vj9\n+OPUP+ssrsBpil+PU1RKtSiXiBQC2rWDEV9B2WoQtglGXfNAvoRqbasNuEOe75Vg2hwlwylDpBjQ\nDehZII5GEQkc2BHFemB3sMUmeVwHtwo6HHhFBx05LhGpDEwFyuGMxQMFKBBRvFzxipu6bJL4nfHb\n+ZAf2E0srp7Bu8BDDKYuLiyyA25iMhq3YraWMx5+i9JtzgNMTDSVQBc35ub4Qvx+kY1hOC50akzi\nGIL35zlB+82BZgSSqriaDmWCa9mM8yQnbptxBmdljjTG43ArXb+F/F0G/JbWhDOIu74/uLabjdHp\naRxf5gARI/dQoMu7dPtyJae3/8HckWGvO/wXk35+0LcBJvLrr2F89935rFhRk59+iqJQoX3FTj9t\nfdMmh/68stXmvVULbK2UBy2By4t4F/gm8XcrCIMtDJTArRIUBQqHE17sPM5rX45yF/7FX2uXsOSn\nPeyJyBMe2aRil+hKr/ed/a2J1uaZuQaPJ6fwxnk6yYBx3gdX/v3cjJYbd/1QGWd8Xad6WLUhCKFY\nAtQ0apJqnUfiPAVlVdkdHF8QJ4XXKTgn2eQua+UCnBH5lDGabNjMscBaeQhobIx2yeF+pgFLjNHH\nwclthhH201zm5sPJBu4JttijHofF7wOGkBBeBehi1PwJYMUWxVXc/G+lxFrpCdyGC5nImoElkg8Y\nhjNI+5JCoZifZUjbnTSc1YQbH4rUbcOt2KiDHPzjYi6Oiye+K+4Ha+zYolzV9EliDhZmRLMe+mAw\n3lK4FYQYXMXO30t8xfryH8KPI2iX3ombFXs30MyouSLFY5yn8AtgtjEueTujBOFCNwGtA9WcxP2t\nUN4otJLVDe+iTvg+ZgAPobo15JjEGPamOMWQasAXXb7mrR0HaY0Lc2iGmyx+DnzxZlMOVsjP2ziv\n67PR7pP5P6AN0F4H6ebAeJ+LM+A/xhlPVYFGKBfkiWO/hpE/ciuyM6HInvllzo6cvGxe3osrlYq7\notSGssY4+VZr5X76jBvIqhp9zaHW72bm/uQEd9whRQ8eZNnEieSPjWUFMPuqq+icPz/7Jkzgf6qE\nKgrtIkhAxumStwiHRc9Ai0thd1W4ysawcDcFtzzOQ18sNAP/UzaRIRKG06LuiVsh+hR4/f4F/N7n\nO75640xuG2r4E/d5LRRsiY+3AZOSk65MShB+9SzuNewdPTj6a5zCTnfgZiawhPJMpT5nUYod5OV/\nwAQdpDuC9/CdI1bS75PNMo1WX7QCXlJjJlixPSP456lzuGb6lzH7xwKzcbHtzXDGeFXcd/k3uInh\nwtAwraDtxIlyWZyxnvi4GC6G+z9jPPF9kxWslea4fK2ZwL1JJ1vBqk6/Q8ijM+gY/xq9pu2kaH81\nWdfkF5HCVKgwgH37bmXPnoKULLmEyMgZbN78tsbGZkkG04q9EPc53QLcZdT88F+/4eFX5G3SeMqc\nJ77bK3kOFTqezimPJyneOE8n6blRVmx5XIJXa6Pmp4y1j+AKvzwGPKHKUd5EK/YVYJNR80gy538J\nDFXls6Ac+Hu4L/59QN5Ej+4R7bkv3KW4apcDcQlCk5Iel9MEeuHfA02MOTrePxv7aYNLBqtjjO4P\nPC3rnuO5bxvSsDTOECuA+6EvELK5/0tsq0mzhQXoP+YbovbPxRmY3xIdcx9Q3ajpGfRzGq44zkXG\nHLksnmFcrsREnJfullAjMzm+lJnTKzDtvKpMGIrqKCt26BjGRE9lKsDpYXBZPDy2sy4Llo7mZpzO\n71fBuHvg1F8SgHuqvMq5VSa560D1hxQ7DQi84quBi4yaZSkeZ+VOAs91ZmJdk7RzOy4UZyPwmBzk\n+jOeYlvpL4gFbk0pRjkwgCbgQgI6jfqdaQu2YXbHU3X/I7pVhkg+nCHVulheuu1LoEZUGD/tOMgU\n3Ot+Iy4U4ZLAWKuBm+S+YIw+G9JPOVxo013G6FRrJd+w/YMeiAsLu/MhO35tTNWttSfvjMj7Wu3Y\nmy+4OOHl4Jxw5rRbzvibws22rrlChSmY0LwJ0KcPPX//ncuBhnnzUvycc+j2xx/8uX49OzhSVagg\nzjB9C5ijLnzoBeAlVB9+2dYav4myV5/OytFl2DIueh5hOIP8Opyn+HXgbR0UYnyKXIuTwzubDISh\nHXU9YtvhcjZmAfcaNf+tksgQaQW8FlzDMhaxmFlch/IW8EhMDIVxn8lCq2M5s99S9sSdO6s9Yflm\nAfViotkWxt6ZZZkZXYMXS9kYHsAZ5N8E27IjwlwOS0tuQ3Nu1TAtrJWiuHj72jgZ2+XB/hbAiweI\n2N2fMaetpsb7wINZNcxF5Exc1eQrcZPasTgVtEwZycGqdVHcxKYC7jetKm6l8+OkuV8iUi2sWMnf\nP3l7f2xEZGzj/xLiPZ5cgDfO00laNyoId/gAWGbUDErpuOTbpjLwKs7z00uVZItXWLHVcEkyNYya\nIxLnRHhS0LgvmLcel+hzl1HzZuDVXYlLTD3CYLJW+uI8W61xy8ExwKXG6MKMjD+rWCtTgOU5WanN\nWsmLm4g8Yox+ACAiNaKI+n4mM1cJ0spoyjJf1sqluFjvc3GxrK2B1uzJfwbd34lg0JAXaLJkCm6S\n8Qaw0Ri9N0uDFukJPI0zRiYnV0nxqHGKrQUJ37Sgy+687HrGEjN1H/t+vYRL4hUdqm4loB/QzMbQ\nHqe+cpYxRxr91sokYLuJZhVwAappajZbsbcCbY2aFEOErKuuOp+MJGGm1qeV/sB9JBBbaCVR9R+m\nYMQOHgQmkMaPfDA5nfrRRkqO/J1qrzch9u0C9382hwvvVWP2Bgb8UOC6jft45N31VFm7h+Yb9tOg\nSF7+fbIez5XOx2qc/vVLwBBj9OWQ9vPjYn4/NEYfD5bxB+NWQC5UY1Z9MDtPrQE/ycoWxRrufvum\npUUSX2M7o1A9+ry8jGprupqvHspMjYRsxVq5AxfC0iJpWFMwMfka6GRMKknVIstxdTPqPB9Tv1xV\n1q79nkbrtm/4cv9nW6i1dg/7DhziZYXXdZD+nEIbieFin6BHTIIq4HIiygBTjdFknSPBBHIkziN/\nk1GTrDEsQ6QgUEYHOYNNnMTo6Kgomj/1FFENGjAO91r2uOZbxmzYz6W0iukIFFdjelqxpcLZ9Wcl\n3nqhkr57X0q35JCEN9rNGa9to0XJSLYVLM3ch/LqzjEpHZ/TBJOwXriwoKdwKlQXrKXKEzcwYQDI\nJOCxRMNcRMrjQn3+wE3M16mmHF8vIlE4Y/wWXGjRy8CrKRWUOmJsbjX4QuA83Cpg8WArEfwtglvl\n/AcXIjQJGGvUJJu/JCKSJzLq4OjR1XbWrvHLTcbotLTG4PEcK7xxnk7SYZxfgZPdamTUpEv2K4m3\n/H/AM4kx5qn0MwFYn3QCUEn2du3HqheasX0bcGVoRUkrtj8uie6CRO9B4CVZCbQ3xnlFrZVEpYJm\nxuj69FxDVgmSyyYAtdOKW81iP7fjfrzbJcZhtpN2b8QR12Uwg2umpkMfxIj+DFxjjB5RIMYW/uhR\nCu9qw6Rrl+IM9sq4H4d6qcVmp4nTgn4PaElQmjy9WLHjI9gWdy5XXAKMsMQ0+Yd/1l1O13E45aN2\niZ5w6zTlzwIuDvViWyvFgB/z/stNLTozHlcd89tU+ozEhfZ0MWoWJ3uMmyAtBMaFGrGpXot73UqQ\nOPFNGkMrcsbqPgzOv56LysxmkiiPkIGS7SWeFHMggU8frM2MuSUeXhBDm2c7MOPPu3l2Hy7cJRwn\nvbYJtzSeuClOrvV0XIxzAi4Z7S8CBRdccttG4P/snXd0VVX3tZ+dHgKh99470ju4I0hHQKVJR4p0\nUJAiGIIFpUiVLopSRFCQYqFlU6QoIiC9SO8ttCSk7e+PdQLpCYrv+/7GlzXGGRdu7mn7nnvO3GvN\nNWcnPwLdEABfDmhqtX5MY3hzmeflzy5nznng8wfDCxy6N+HxuTd5+3uuZ6vOpGE5/ptyfMYoP0Qy\nsHpilS3n3jEbaX6O/1tSqgGSRS+DtUcmm0oHQ/EqFnp956r3j1KnfHrGTShHJ3cXFNBda3si0QNS\nqqhV7Do8lq4361IFMfvJh2TBryHg7zaSsV8WfS8zyhRHflOHgTe01ckaGsU4P3fgvY0b6TlhAioi\ngvnARG9vdPvZfLbmMptv1gjsDBwBOlutzQk1xP8mtUaFkSVbzH0ZZTy9udjcmwvvPqRw6Qh8bkfi\nvdSVkDKuhD6fmV0bb1Hj5Zr2lX/tfpiC8y2BPM/OjuTDr3ZTYw0wy2r9WEVLKZU2HRwoClEn4fp9\nod3kRq75UwhYj16uIc+gzkjPz2xENS2551125L7dEuGn70Ky7FeR7/g2T8B40NP2aHhlz3ehbadC\nWXtOYLYAACAASURBVLo12zpBa/tUSbXUSI1/M/5POIT+H4l3gQFPAczzI2XVDIC2lsMp3M8HwB4z\nvPFyGv90UWt7zyhT7nMYv4EcmbeRpfi7tkxcQ4VoJ8LmCB8WYAzSqPSYrqC1XWeMmgKsMUbV1tom\nKCP2rMJ54M0AhvzLwDwrcr7PR4Mco0zjYILb3uSmfwoMoj4GftB+PABVHSuVBdE49+3Lfd/6Wkum\nz6G0RPxDYF4QaXbq8LTA3IlxYWTZf4HWjfKyYmVJPvjyKO/0isSzjCuPFsahqIxBaBqjEF47AFrb\nO8aonuEZmBuSk0neVxiHSDEmFl2Aw4kBcydGIw/p+cmegVLZrQsvuK7hvcg0fI3iW0CZLWpV7u85\nXmQmhVQULYG0heexBng+JdSbWLsIUCWAr7N60rZ6ZjXqMkffvUuGgL1UGhKKZ5gXj74DuiV2bRqj\naiMSia8gv6sMSDk9t/N6EJjjR6APPJY59bNaxzLUaZI9bPKyi3cmvtW22nvfKvUL1tFcvpS7M6cL\n3+T3iiPRfJjUuRhl8iBqTlsSOddMCMBpiSivTLL+ydMHjFH5ES5yh6QoZ869owqw3BhVPw5tI6Oz\njZ1Ye+RN0/yN5zlV9o5NN7XbUWoAI/8YbL8yRi1A1GF2GqM+AqbEmTCmAeoRSHPXYDzc7vM1ltko\nBgE7o3s7jFHDECDXAThgjDrIpnonSDvoZR6keweY9zTytk4T+TLg1osvUuLDD1GICsu5kBC8011m\n3oMIurHVL4LnAwcAc5Qxz1mmvh+F59DbVP7KKNMDaAq2uSKikRv33Dy58XsUHrWDqLQ7+nj2q8kv\nhpLjm0g8bxq12R9cZycm5/hvhmPY01YZUwwBwx9ZrWdF/10p5ZIXftCQ6wuY5wKvATtuQNdsAs6L\nICZrhZFMd16k4lHN2sSNnwCMMkWQXqlWQBnEO2IJ0PFZy4tGerrtOXDR7WUiXZ57lttNjdT4T0Rq\n5pykZzFGmYJIRjBnciYyTra8JwKyPwEmJpctj7c/942Lab2iCT3nH6Pp+iWEpBkLDPZDj0BoMXsT\nOMZGCLe8DIF+BZAydGmtY7sAOmXNhQh3tPW/2STjlMqbINn7f+0iM0bNBUK1toMAjDJVooj6oQEN\nPCKJLG1t4hJiToPhkhqtWel5i3ZAOAJm3zYEtgfqaKtfeWYHK6oWO4H5WDv9727GKDMFcNX4TQUC\n/2B6lAc3Mxfh0+ye9mbcZq9cSINaR63t5jh/m0MkXro+dYGuhsDdQH5t9ckY+3JHqjCdtNUJmnk4\nwG0dUD5BcymlfBH1lHrOkud0Ly4GPUep8kMo4BJBmTsV6R5UnkY36uAeloUIt3v8HJaF+cuusG3u\nX4Rb/8TL6fF2JxbuvwBjrb9dlNssmfAJb/bOzvWvgvHu8D0tgo5TvLDRYxOWizTqRQQwdNDabkx0\nP8ZkQ5rsDgJvWB3fzMYYlXvLDXX6w/MFPS58cPtazjtBlbD2MoDJ8fVCau5sx8AZpeKCYyer+CrC\nVy7lvN1SW73dOceMCBhvDdRCQNb3yP0nGOhs/RN3ATVGeSNAfmlKFIccGtA64LjWjmmc0FBWIsZw\ng1YGZj5znny/uxD1+5CtBya7wNsb6nLCVZED0bl2QWhChZ1/n0c8K1wRgPc7sNbzKj/VaM8KwB+b\nuI646dDTl0ee31B5b22q/GZxjdqITBT2I9SzJCfRxqi2SAJhPOIyGVMZxA94wcOD4sU/pmVwBH1P\nDbXzlTGrgX1W63FhKtO4vSwYGEYm3LmzNz+LC2Rl611PbvXG2nj3aWfM0gVRZsNZuucLorwbqE+A\nWUnR7p51KGN8kWTOBGC01bGN9Koq9YWC176DRrmt3YJSPki/wJvIBHwS8D028Z4So4wXUmks5CxF\nkEbgrMh1uhrYktKEV8pPTnkizbl+tX18XPdXLjN67ah9l/wahOV5pvtJjdT4B5FKa0lhJAPOByL6\n1klqNStFBiSDlhHoGjdbrpRyRzLcLwONrU34wWFmlP2cER+1p9iJa1zNkZ47Gavr0EbHlGIOcMxa\npj7epjEqmh9olPkR2ECgnx+wQ2s7IcHtG+WJo1KhtX03qXP6u2GMyo5QReroOLbKz3g/PZDybBmt\n7R2jTGFg+wpWTJzFrF7W2pJJrOvt8ojjxSegsm9hN5LVCwH8I/Ho8gur3RW2Xh3b5PdncrDSGLYK\noVH0SgnHPNFjVyYb0kRaUePnFoH3+t0stxGkW6GtjvedGlHt+RJoqrX9I8b7aYEDeZexpsA8z0rb\n+fEeqHpAI231VmdfXYCu2mo/5zw8kYdsRiDsUWb49UsmZ9rNotLvsRGZ4IQhHFE/BIyXQRx1NwOb\nt69FRaZlbZWunPE5RzmkFL4aeeCfcnjOrayl1ahDVP7zLhGZPVh6PoT3rL89l9TYqADli/Dev7H+\n9kNlTBEXIn9dROfNebj8ciRK9WMWTVkf3Jx1dxAgG3MJQaTwHjfTJrgfY4rwpCFybFINdFsC1bZ2\nv6erk802PXno7a9vARprHxll8uHx6CjfvXwbn+ANnM1/iInDMnK0ZG2sSwUEDC9HFE2a3fW++3H7\nwe0nhHiGtEIA+WbknrPO+tsHzvm7IXzpbkAn62/jZdudSfoiBBR3TOnk2RiVCemLGa21XYZSfZB7\nWu7dS2m5KmejH6uz233J2UdZvz0XcmBcaS7UycIZpAEzEjGsiURoQy0QPfFFzt/PRxvSyACr2s65\nl4olKxp9LJKB/QahVfQg0E8hVY7WiJJOLuS7vIxQkS4Dl12Due57GHu/GDUi0lMJaY5M8DfuSESe\nbjieU2fcyXz8LVtcGZMX6W+pYf38HkbicRhcvnEltAXwDvB5cv0QjlnTF/coUXw/U89E4VkXcS2d\n+TR0nKcNZUwOpMm6F/J7XGC1jtXz0F6pd7eD/zx4uYmNLb14WTV3S8/B9oqoIRH4ZLlL2ZXn6bAz\nnAxpEepRoRhLVuACIr36F+KYHG1U9uySQkq5IdS9F5B7TXXk3hi8CS41zJm73cYvr0W4uEVk1drG\nrTqnRmr8VyIVnKcwkgHnm4BPtdVJNm4pxQDk5vBq3Gy5Uqoektm+jDygtlpr45Wync75FTRdt5Yw\nD8Wal0rjHfqL1vZtpegItLSWV5UxBRCaQl2gpNU62ChTCrfwnXzT5g4Zg0pobR+pAFUFyQq1iamK\n4NAzfgVGaG3j6Q//0zBGfQ7c/MdNk4lvXyFc/raIaspJo0xWJCs9yQ+/9EBea+2ABDeglNehAAJR\nlC/zLl2wNpb76l41931XQvpUYMhfiIpKwlmwpwmxTa+NNGD+YxMmR3s9d/Sk0QHsu4Dx2uoF8T5v\n1OvIROY8UrZfqbUNNUbVIdRjuW/zj9I/isj12yOyj0eUOxoiGeEjENlHUz8M6Ihkc48gD16PI6Op\nGO6L93NvcwRwR8xR3BGO9jYEQO7ECn3EGOWLNNaO1H78CoRgE3YEVQGqlUto+tml/xx2Lk/TsSW3\n3YrwTu/O4eBIAoLCWWP9Y2fuVIDyQFwvT3TJT7+OBVxrD2T6yprs9OnIkinAp8C1tnxd9QZZ1/Zh\n9outWRmKZHNjLoeTUndwgPk2BJQny683RvXedN31gw/P58u4a4H7pmrHT5zF2t4ARgUuJ/OttHiF\nFuBmlqJU3BdKgw3uVNvzO96hu4A9Ky5yctZp3vMM82yc91be06dyngpAAHmi2VYV8HhCtgAYZ/2f\n8H+NUYMQ8F7zaSlaxqjngE25VtO92DQWAvOCc1F5+ZL8lUPxTu/Jo6Gvbz3jWSQtXedX4iaiV5/g\n9W6MKoDQoDIAXaIVRJ6chJoLRGJt31jrKfMqcg2PQ+7NcXsVMkW5Ujg4P8+F5uC5cF+KRaSjYERa\ncoRlJE1oDkK8ruFWcCGzPO4whkQSJbIpNTJNNmozgMYvZif/6p72gjLmTaQq+KL185uFTDZGP00/\nhDNZnwzUP8qI/tdo+DrSEPk2sOjvOE8nuitjiiKqJq2RysJkq3U8c6nxSr3+CczvD738rV3gjPNo\n5PvJgIgaPAQb5EpImBdXMnhyM10EPsdBHYWoY5F47wsm336Lx6V/RcdfpGdLIaZn9ZBn4AWk4rkZ\n2Ia1QSj1fBh85OXuUXXJwhwPcuY53ySpyXZqpMZ/MlLBeQojsYFytHIvIpSWB/HXjLkNtgDTrOX7\nJ++pAsgNuCKiyvE9klXYA5S29knZ2Skz7wdGRneWG6MyI4Bzip+f/YkMYbv5bucyFJ2RcmxlYKPV\nepoxyo2Phl/jdOHdfh17NUOc7sYhWYtV1t9+HOvcjCqH3Myaap1y2+zkwhhVHbHHLplYpsIY1Q6R\nq+ulddIcxQTW9USoOYUQ9ZkbRhkf5Oa8QVs9Rim1AZhlrV0dbwNKVblbiq///JjcuVZTqdACG6vC\nYZTxAE4qwto+T8MSiKrBt4ie9t/jRCr1GkJ1qpqcXGJKw1HqOYlQb4457xVDQGM3bfWP8dYRBZNm\nSLazPPA5R0p+waShP7j4Xk9X68DoU65E1jAEvgJMT8OZz6Pw7FyNDhFKspBfIcoy55ztvYAAwHIp\n0WN2JlVLgHs6Gdc+FaDSAUf46scwTje6BDZ309YfLM3VbMxL5galroUS7OvOZxdD+MiO5VG4CwPy\nDaHGtXSE/1ibZZ6uvLmJenlnMIDBTC0RoANjXYvKmClARqtFIjOlocyTSaDVem5K1jFGZbaW861+\nz+j6IHvngxGdp/kA07B2nlGmHJJxXYVIwj1wGrqrANXCoqg+9AANCvoQ/rpXjr2+vRdWof/MsTT9\nYS1iRJMotUAFqBwusCSNG5k+LMPXZdNTAPneCwI1dCLun8oYV8BanXCGc9dy9QYwtfwg/F1D6Xl4\nhEvmsdUH3u3KF25dfwmqFBLJyfmVuJ/fhwoxNb4TGZuYVMAesYxyhM9+GHgFa3c5jcmTENnGNtrq\nvTE+mw3JCPdGaHunid+4eAq4hLVRKJUTuTfXQGQ51yc4Fk72vNgHRBXPQuCaXra9MsYNqSBMslov\nSer8kgyhzwwF+gONDIFeyL3tGtBbW51klSjZzRtTFQH7dZEmzZlW6wTvPxuVat4DVleF91dY62+U\neQlRW+mEjFsQcC+ev4dSxZExL4H0YuQBvJFqxQXk+Rn9ehWpHEYvdxOtIMrkpRCiKFPGeS2LNGL/\nhdDWtgCB2AToW0Klu+KeLffDAa/n8n2pwW9DtLazkxmy1EiN/0ikgvMURhLgvA0CdBonvT6ZkRtG\nDmsJcVz3hiM33SnAZGufNJ4ppT4BvK21fR7vy6iPgQJa27axjsGoIiF4bR9hP/r54P1KnYhQS8kU\nPsxqfVUZUxFpVisSiF83bmbuYFuvKD6w+8BfDuU7VBih0Pgg7o+F4vJ2jVEtkGxiNZ2AG+PThhFH\nut3AdK3tV0l87leEx9wYGaeFKSmtOyojq5AO/o5a2xCjjBtCibgBdPfDz9P5d14bE0wLHWNslCvd\nd63kYbgv4/QL9ot4+1DmdaCttrqBs14m4EOkDD8MWPJUlBSlqiK85HpYezDF66UgjDLDgUra6jYx\n3quJjEcjbXWiOuzGqKLcS9cf/4A+eITd5P13wotOj1S51/EukP48bYec4fX8+VjyQ0EWjQb2xzxv\nR+XmIOI8+FOKjteobghvtWpyTcIqQE3lQfa8TLpaHeEjN0Cu1b09eoyanKfB+Dd23qLd9hu4v3CK\nhx5hPLzkS/Y1vxAR1JAzB1/I/l4bn2XjQXWxWpt42zdGwD90SujvCR6TMWkQQLDJaj06JevEOPef\ntt500WPP5HF5KaRb1+/9A6YCLbBJyBPKOEwDin5bg36ZPCjLjP7d+bVqXb7oehvXqJwIeD0QY0kL\nPIeA8PJRlnwLznBnzWUyts7Dl10KCCc7oYmzMyZ9sfZNlLoKDIo3NkrlA37+YyoRET6EPjeMipVX\nTPtktNv7/TNxu0WH3ZE9amam2aCi1IzZkJ6C8amC/LbnAB88vh8o1Q54Zwff94jAdyYC8ro/lptV\nqiJC1WiB8N9nAgdT/BtV6kUkC38QGEQCPSpKqZFp69Imf0tKzKxAOq1thAN8vwdKWR1b+vZp42qm\nTN2X1K8/9YOOHc+5Rditb0+KSF95V/pmGfjzi3K8vcWFiLiGTPuAdQmdozLGHfmtDEXA7WTgM6sT\nbzy9qpR+BTaEwDf7rO1olKmPZNibJtMEnnAolZYnjdN5Y7xmR+gu2ZzFG7lXxwTsEUBpJDt+G/GU\niLkcx9qU8dSVOpopf8E75WrlqT62y+6FukFYj6c+l9RIjX8hUsF5CiMJcP4VsFNbneSMWym6AC1A\nvYKU/SchFIO3rX3icBhjf5mAY4C21h4xRlVFQHa5mJkmJ0PTzZ2wD6qxx/fm5Bq/HltX/zNrWRTj\nM+vSE7RlNa2G/3iVHlfmdZpT83hN+vTqU8z6iyKLClCBwDzrb5fFO0ejRjjHXPcfqZDItnohclp1\nEgPbxqjyzrkWRG7AixE+Yq+ksmxOCfwHhOc7TGsb6ejPz0Vu/C9pq8OVUn7AeGtt9ccrK1UZMT05\nsXMlh8IyU50EGlUdoH8MmZBtj3UASlVDsk+RCFVgebKZdNEJ3gP0wyZtnf13wqkYnASaxQTiRplW\nCEippa0+m8S6a3CNuMYPTTbgEf4WllK51hDpeZ1fb91sevze9j5NCElzA1TduEoKDnUpVOsnE8wk\nj9WokkhWX2sdu1oRN1SAqgSsZ9rJz7lTJI21DAJQCm8ky9wbGDf19ZcWtDJrzaKKlDbPk6ZPftbq\n8ezKspPGQ/r2rbCzTJm/9vTt2yBR2owxrZCJV3mrk25Mc36L3wF3gK5Pa9JijOpoLROa/5re7WG+\nXnfDO04e7BYVNRepplxOZBw6I4o7Vay/XGvONb8ZWEeg3wIkm1geAeTlEC3o/c5yADimtQ1XAao2\nArhWACOt/xOqSeuxY3P+lSvXB8fy5Wtb+88/gz6eOzftbyVKXOw/aFCGMHf3ncAwq/VZlCqDSBp+\nYgL5vvBMtlw9U3jvx5MbNezG5/vHHwoe9eddzOTn6NGjmY3VZJjCMcqFAPSzQHf8AkMgqnFaTn0Z\nQi7XSNKOAOZq/NyQZtiBSNPhp8ACrL31tPsEoqkSI5BkygfAjJgmSEqpdLhx2nsMmQLKMGDYy5KB\nVcbMBNyt1r3/1m6NSYvw7t/Kc/36mSmfflpyZ+nSHiuff94D6xrlF+hq6wc+CG1+6sNfMthjl4H7\niJdBM2cT7wGrVWBgBiTR0Ryho51AKqvLE2pSjnPutXrAxh9h/2WoHUhgTeQ7eDnePfBZh4x7NFiP\nfvVEOOOH/nal8sn2v2qaLZv33jL5Wy0ffPSIbn6/7D895NRIjWcRqeA8hZHQQDlA7SqibZ6kLrhS\nrIJdm6Hmy4iBwgBrY2tmJ7DPIUC9wEBeQTIh70Xzvx1DkxYIX/wqMDwQv/zBwenm9eu368czZ0q/\n5mzEQwUGVkjHvY0TH7be/cbesPJpHqV5f934df0VagCBfvuBLi13cvpuOMOBatY/DiB9QjW4rLUd\nmrIRix8OBecIMbTVE/ncp8B1rW2A839PHDMYBKCvTWCdykiW6mOtn6icGGXaIZzI6tG0I6XUBwDW\n2neQNyoicl2Dtm7gN+vOLkSvOV5J3yjTCXhdW60TPHilXJGHX1ckO/UjAvo3xVMuEEOObcAqEugv\neFbh6Nw31VY3ifP+AIS+UktbfTvO39IizYZnkfONBEf5xtIWiCTMIwPuYTCrn+J4cZg47BFej6Jp\nDq4IzaVwCuks3sgkZbrWNh4fPmY4DY17gOmMtUOBPtayI9ZnFCXBzi7A2ZIL6X7RD1PLBILWYuuu\njCnpGhm583DXruuLX7zYFKkczcDaWFlA53e2Bthttf4g0WOSz30KFEV0zJ+6Z8AYlQ64uuEap8ef\nzV2UKp93s/UaFEG4yzpuH4IKUJWR60tb/3jUqyJIhaqqtjrFtDAVoDIDn7tHkOPgbKa4eOepPeXV\nV1t+4+eX64V9+24M+vbbtbUPHVrvbLtFsKdnQJ/Bg09/1aBBsXJ//bVmx4ABTdKGhg7B2qUopaLg\naMHvv3owx7dncW9C9aD9bMzrzd51vW2Dpx2fGOPkRbD3fNY3fZ55vUKJcH+QlpOLKtJ3jAsRTRCe\ncV9kQj8dWP1P3ERjhVLFkCx6FqB3TO1/pdRI7y4MfPl5wnsUJL/W1ipj0iP3vGVI0mCH1To02d2I\nWkp/YBBiZPWB1U9M5JzrrZKKom2aYLpnCCJ9/nOYP8sy9L4vB6yfH4saNHjjdO7co36qUiXjH0WL\nukS4um5CqbXAeqt1gpO9BM635jT4eQzcug/lAgkshiRAOiZm4PR/KpQa9J1StdsUyP/KxrkXw5V7\nhPe/qVCWGqmR0kgF5ymMRMB5HWC6trpC0uuSBqKuQJo98OgwMCw58wVnn57AkcGD2VmtRfZMr7F0\nusWlApIFq4woRwwHforO0i1YUH6Ku/ujPk0WHHsu6w7GAi/smEajAWUn7T15Zvvdhxe+b2L97W7j\nvrEVaYI/5dtX3HGLvB1p+ab+Ntoj8mo74x6LEb3jfUB+rW2S3PrEwhg1G4jU2vZP4jNpEP5h+bhG\nSMaouohyw0bEDv2B834zhIfZS+snHHKHc30EeEVb/ZgaoJTaA4yw1gaiVPooXPftYkVUOBmuU3Nn\nIZTdzy+1v+IJB/W2ttoaZVwRikA/bXUsycEEQ6of7RGgnhPhY3+BtccdLmk0F7XDP1FmSS4cjvxR\nYIS2ekWcv01CZO4aaCugwSiTDqHZnEJcFGNqTbsClVnwek3+qNCHT958BUhDjwUfAZlZ0ONHPMPy\nIMoMkYhiy2vJZcKNUbMQl7/2ydGXVIAaBLTkvdC+RHpuAvJaS7wHaqRyfXsuvQf0Z6aHxeUbYLS1\n3HWAzWbge6v1NJTKDLyOALrLCH/5sdyjMiY/IuFXzWqdoKupWrtjNOfTdGJ0mUkEeRRFOLBFEID4\nR4zlVELHGmMcvrWWxvV3pb0QVahfhmq38+Xf3a/fMkTZZguS8TzRsi1B35dkKzDE+ifsbGiUeRt4\nEfluU3x9Rbiol7u86rVsQ8nMUQ+qfWrz3bz7c6b790fs6tcvvua+UlmAD85ly9b27Tfe8FlTs+ad\nUE/PwcAy6+dXeE7z5r8eHuL+qJVateHnqzbrZ2eom9ubzAcGp5B2EP+cMgN9wPYj74U79J+ZjYr7\nWur6ETtQahiSKV6KTLT+SHprfzPkt9seoYNMwYrqlVIqHYU4n7M7Pktr8YLWdgeAMqaE8/kXkSrG\nTuQethH4MyZvXxmTEQHk/ZCEwYdW6wQdo2Oso+pupUWaYKb9WpUcD9JyKcyTCMAHa9f1Xrv2yqTZ\nsxunDQ1Ni4zPiqQkDp1zdAX0RljZDAiDyoEEeiNynL211c+8yvdfCaVqP4Qpad09Kq39Os2jtJmC\nyuhn4FycGqnxTyMVnKcwEgHnE4DQhKTpYq9LS5j0AQwLQ0wYksyqOWCgMlDBa/3KV8JXry8ROWfe\nXVzdYz7k9wOH4zZkfZHxNa/M5U4/aLT31yjXR0x6pKjya2eqT2hYKeiHrB+Ho1yLBeJXActs+swu\nglUzmfvGLGD/e0f4bMsNilh/2zqh4zJGrQJ+0tqmqMktzroVkYxLyVhyaPE/1xV4VWvbLJG/+yKS\nYnWQRqQKSGa8ZdymVaOkpKztk5KyUioDAv6zWngErLxKA9djjCxAn1k/4xL1KrP7LCfKNVp3t4iz\nanTDUxok0/x0Pwop93dB1EzOONsrCdTF/nsGTNFhlKmAyO010lb/HuN9FySrBwIg0iLf01HkIRwP\nSDq0iV3AJ9rqb5z33JHKxQ2E8hPlVFy6Iw2z7wEzE8pKGaNeQfSUK2odXxIvZqgAlQe59msx1rYF\nMlnL4PgfVJ0QtZ5aChuCVJhaAN/R4dwdOp9tioetZHUMtQgBJB8gjZYNYgIYZczbSEa2EX46L/L7\nlEY0n4hahLnkwHKCCJc/EInQP5HJXSHkGo1eMiHc5ejfcUFgobWcccaiFTDny3N8/fmVbN2punis\nrddgDkIrKw4Ue+RKsfqdKV39Ig8nbmQvcCJSqROra9e+XfLcuZ9KnTt31flO3BDFpena6i+SHFdj\nPNJyv8YL137pne/RxTbBuW2kObIpKCTk1p25FSLfS+fOHeAucC/GogCf0v509bjJmxG+3P69dGG3\n2a2buXl4PgppcHrnX1Ny9NbTfAYER0VFTX59L6MeRvL6rZF2aVLHklAYZXIDIxFjoe/u4D7tZWpd\neued15rnzn3qk/37/T5bMO/DX/JyIeIcBX56Wu+IvxVK5UK+63LRPHTlqka5DsP/kxr8MrCFfSHe\nKsZkQORDX3QWXwTwbkTkHXsj/SAfWf3ESyAlYZRRUYruh0sz8W56fqi8l7caheprzrEqZ3/+iOPu\nVETSNBvC847meGePgmwbIPN6eDAPosKgdSCB5wEDDNVWx6M+/p8N4b5fc82aPXxs/yxp6tQ93C5a\nbCE1UuO/GangPIWRCDg/CnROriFGqUMroGojCKllk2n4U8aUQYw/trkRfnCondBhdrtt3L0RPtZG\nRcXkkadBbuzXHnNbldLAp3uomv3OxDNXzxW/cev3Hyk3ZRW+i98g/Yjnv/tpPCNDS3C8DDCc+hv3\nE+m2AShOoN/LkZb+DbeTP9JSyfrHdwN0lDemA2WfxjTIiDnJDuAzre1nyXx2J1F8pOvhg3TdT8fG\ndyqNBjEIv7dJXEUXo0wVhI5QOiZlQynVEuhjrW2IUgOBzlvZ+KvNHHSXla1fR2Td/oyxHYUAqsII\nUN+lbXyJsRSHaO42dJaPsf+8yTalYZR5GZnYVNP2SVnbMQPZgPCPKyHgsW9iWsNOM9hMZGwjY7zv\ng4CMXchDXPT1jSqK9A3cQRw3H2elnT6BX4FmKVEEUgHqO+Cg9bdjleIQ0NtaYsufKdUQUYjxPn43\nOQAAIABJREFUwz6R3lOKIvhEtCd38GhOp31EpMv3CEf8Z2sJdj7kiqO1jrXvOaZhJXGPep5qtz5k\nb6YIQl0tQqv5k7rXo+h4vg/pIl6w7aofIJlQikxI5SsarOcH7ljLS854eAE3rGX/Czu8fCg6qDA5\nGhW0OsY1HKCmYyl8dRL9N9R8sfZyP792e0qWrB3h6uod6uHhXujKlbDcN29eznj//p+ZL2U5WXdd\nmS4ZglTpx0ANeMe8mOYWmV/25FHLDARVzcmVXAXtGRX+0JvwUM8TXlnurXGNCnUbvJ+X3V1wnVSO\nI+4upEPuOemB9FiiXB/i4nEX70dZOBblwS2vy2T0+YviR/PlD7qTxytbIXsmyt0tbMX3l2k6+zTH\nH0XFps0phStC+8qHmA65IKD/8b89ifTORUixu7jfD8IjPAqVDuEd3wPueniEhBYufCB/ZKT71RMn\nKtwGl1xIVWqRtRxK7jv5R6HUTCAIa0fLf1U6GnK1YQNcRlSknNY2SYCtjCmIgOb6yMR2otUJ94Ck\nNIwyOZGJTEekojhR21gg/QVECSsUUX25Dlx/CDdaQLlt0Clcxt4A+wIJFPlBGKetfnz/VgFKIQpj\nryMUtgcI3/1hAv9+4JzfNWe5CtxKyqXW8SPI7ywFYrx6I/cYA/wWszfCiOt0A0RCN1sKhmuS9mOq\nb6FCUfVqZykzqPXhibrZg+EpWC81UuNfjVRwnsKIO1BGmaLIDSt3UoYJSu11g7eD4cRUay++nex+\njPkUuGG1HmuMeheoVq8e70dF8Q1Q3Fob7MiY7Ub4rcojLOyvugcPpq929Gim85nTffXVQ5dKXpm+\nLNfg5C33n1fjMQputHqTZVcauHfcwgsuZThUpKO+eMs5j/nAXQL9hgEb1l/BY9IJ9lp/+1bcY3My\noYeAflpbk9Kxc7LhfRBptqRK+mWIZFPdRux3iSAnkr2thVB3lsWlfjgc9vC4qhIxMoafaKsXxxpf\neZiet8LjXBtBmho7WLOjbPbX0wTXPrct77e89G9STP7bYZR5B2mUe15bHRzj/UzI9bwVGJDUNW2U\nMcBn2up4ajsxtrNYW/3R4/flofkO0tzWR2u7ynlvK/Cd1nZScseuAtRLwETgOcbaQsiEIl8smoh6\nzMNuiY2vWezII6bHT49BMukvI5nyzUiT20+N+LFqHbYvm0n/A1fIVRJpsttGhTuXGXiyO1nCitlm\nte8qY8ohWc/WVusk+0cSPSeFJw5Vylp+dsbqS6B1z9/pdCos03yqLfvCvtBgiDMGXUGNpsqX00iT\npzUiT/c1QvfaV/TCBZ+ahw83UdDwro9P5WsZMxY8katwulAPd1vk7slrbXyWPcqX/nSmzOpWuutk\nC71F5rOP8PwlV8i179u3PTvM/T6/YO3IGGPuifQeXAB6PAZTMomZgcgMNsbaqzFOKgMw9l5B+u+f\nxZ0HbtByJyrc0sj6x/YDUIrmiLb+m4i/Q/RigahWXCxan2tT/iLtzMkUX4Fk7+8CD60lpjJQBqQC\n5DNjxrQPv/tu4PNIZe26MzbLrCVJyca/FUqVQK7h/Finn6GkmurWgr4/NWRBPb/Y+uv/yXCqDcOR\nakM0SI83Bg59shMiqXgLqTKts9ZGOUB/GzBDW/24l8fxC5iFVJB6I99ZWkT5yyfGv6Nf0yENndmB\nHM6rL08Ae7SEYjQgL4BMwM4C55zX6H+HI88FjVQb9hTy4VT7vGSpm5U6Hi6cQiZn8XotgsJxOxhE\nxtMPyezpgl+dLDzq1JTg2gUK5L5SJJPfZz0v7NZtrtd+imFOjdT4VyIVnKcwEgDnbwIltdU9k16v\n4SdwqTccTp8cz9zpzD8PlA3ELxPCMa2gtb2olFoO/GmtfV8Z0x9ofahr1/puUVHDr2XMOHRVjcp7\nZz2fN32Yh8dzuBUMY+YXPpz9K7xSnypXHs5eltVD4Vm7oXpl1itb3gamW62/ds4jO1KCb0+g319R\nlt+7/YY6H0K+hAxMjFF9gPpa2xRZ1jsPzaOI3njiFQal3PZ/wpZ0R6lceD7jgMlYG45StZBsfQgi\nY5asG6dRZhACvOrFpZ8opY7WgD47JVs3ZBdf31Eq/Oe8zTuF5lrLSWW5A/TExq8c/C+GClA+QGnr\nnzIdeqcSsBhwA9rFHB+jjGs8jeLY67ogD/vuyLWf4PXsgILtCG99VEzbcWNUDWf/BgFZJREd/SSb\nsFSASouA2K7W3wYqhT+QwVqGPPmQijb+6UsC+vUxwHTpmFrOjsxpM6CVIqqBRZ0uxZGzw5hYcxc1\n/Oba3gdjbGMe4m76McIdHmq1Xp7UsScXSvESQv15zlrCjVENgGVRlnfqbXfvQtHBZcjZpBLnFjfi\nwrIJVJgRhk+hTQjo/DG55tPjpRtl+6tK3hPubRd737iW64GryeRVZteVRaVOXBr7WKVGqRkIxaZF\nXD6yc41tQFxaB9ux1EZoSpFAK2zCXgV3S6sif8xk9fBDnP/1Nresv+2UwLlvAL60lsVx/+b09HwL\nvKGtTpZq4PRDDEBobhO+/Xbg1Jkzp9VFGslfQq6NL4G11vLsLOGV+hnR9l8k/1Xp6MudYWUIaVKS\nglrbm89sX38jnN/jCASkf4YD0pVQOnoDb7rgcqgGNb4MIOC2K66FkEqhK0LlWqKtHh+9Padp+Fvk\n99sh2nn2acMB+Nl4AtazI9WQswgIvxlXnCDWeRlV8FooPY7ep8v+IHx23iL45iMyWEnMbEOuz9zO\nkst5zYhMAi57uJCxaQ6yfNeLMfO9vRsNyJ+ryY8Trgf5Nb+X6e+cT2qkxrOMVHCewkgAnAcCU7TV\na5JYpyCkPQxfz7W26ZDEPvf488b0BhoF4tcayYzPiVauUEoVAn7ltdf86Nlzy3uffTZg9OLF7wR5\ncr9xR67vzktd4AsOsJBVrjOgcZ0uc22HNsW2LFoa0eZcoSaLs4wPj3SNqFp1MePHa1xcykXz1Y0y\ndZGbbUsC/Sqfecjonr/zfsS7dlrcYzRi534eadiMJwGZwOenAd5a216Jn7h6LtKLz3auoFzeFTQo\nsChOVl6ydF0RDvF6YFRi0nfOg+gAwguP1cCmlPCVI2CXq8gLvnWEd45E5byR5+bieW/qenyOaP8O\nBd4F5iRrs/1fDhWg2iFGIEWtfwJGGwmEQ2MxwI/a6oAUrpMTATZeQAdtdZLfvZNBn4iU7Ptpqx8r\n7DiqJFMRt8NkTWgAVICaDGSx/rYLgFIcBnpaizQvK5UdMR2ZiI3fE+HIHBpgsdV6TsI7UdWiUL+4\nYNchQGYMIj3YLPo6UAsWZCVHjj/x8HiIh8csq/Xk5I492XMT6sxPwA/WImZhcBM46reVUbj5rqDy\ngvT8/kYUORt/QcEeo6zWyUoCOpWuZsBE7mR4wPCP83CyWHEt7rjDkR6DLxFd8B5ANUNgFMKlL4Zk\nMX2BdDfT3cj8ZteBbeofKeXaf3NLFULuqxGk+9Ti/kli/RfGqHYXg+nX6TdKAeWtf+wGb1HUIRDI\nHxcsG2UaO8f2mrZ6Y0rH0tlvIeQ3kRF4XWu7XynSIVWSLsh3OhWYYi2JanunOJRqimT/q0RX3dSr\n6seM6an5XScmaW3f+8f7eKrDURl5YvTzeClAgdy1qNUqPemrH+HIiT3sKVGa0ve70S2yFKUyIdfB\naSTj/BeSwb6qrX58/CpAFUcqKauBEXEdeP/NcK7n/AglsBNyjS5HJvu/am2tQ4Wp7SxRiNHRZef1\nEnA9+pjdxqmGpdKxbtMaXnYzjMvs6fnchtVR1t0rPGNi5nipkRr/qUgF5ymMmANllMmIzOxzxKQG\nxPm8C7AZxpWHMbWsJdmOe6RB7O1A/CoiGYsGMbndSqlJ1KjRomLXrvt29+n94uBGnJtdhRxWMR2Y\nw1h8EfC6GcJrfPNNgfuZsl75qT6byxa6fLnKZ926XfILC0tHyZIl6dhxmB016jGIMco0BL7CK6TJ\ng7VNFn55jpwrLpIjoZuvA7gfaO1IESYSRhxGNwGlEsweiZSgP9D9+BBWX2lOPu1nGyU+SCo9Api6\nIJnGGUB4LOMbZVYARxNq0lVKdXkO3twvKjfPW9Rbu9TXAeFT3r1pnzte4LGFuFIlkTJwKNAD+7/b\nva8C1AiELrLc+tsUG2gYZXIgvOlh0U2dSXy2KaLbLuYvT2G5bZTxQ4DSH8BAbfVj+oMxyk3rFKgW\nBagKCHgtY/3tDaUo7fw/v7VE3VIqXUYIdIH1WOsfb31RwPgGaQBuYXXs6oCSmW+VzrBuByVvZSNb\nzrT4ekeR5X4m0mTwJoP1Ib3yxdclAxlUetITRJDdyc5Tm9i07ChHVwKH7NPeKJUqjJhWTVbYaIpP\nKWu54SgbdQUK+G1Vy0GVgqg51t8m2XweHUa8AiYjKkFDgR/xC5wLRGqr+xhlVC7WVPXh9KRwMta6\nTaVL9ykZaXHLhigcHQXugL3vy6Hsmfj1+aC013xf7L3DPfetwiumfjFjKSIduRF4Ky4NyhhVEFjX\n9Tdungtmu/W38QyZlOJT4Ka1xPrOHGO3GUArbXU85agUnr9Cxu9jYD4iQxvq7Lcokvmvg4DqhXEb\nSI1RPkj22A3JILvFWKL/r4BN2o9IREWnM1aUrlQnVZLcHJ5TlFvFC5M3et//RjhgvA5C89AI3fE8\nUm2MXoKj/52OdC5lKZuvGc221KDGbgSQX0jud60CVD1ECWeU9U+6d+hZhDNJfQ4B2rWcxRWZ0C1F\nxAmS1mhPIlSAyuDhwo11Zejx4svMdsmcxU4bkcajbOXzWuv4lLjUSI3/ZKSC8xRGHHDeHsnoNE/i\n8/3Btyfc8gK3EjH5kY8/E6BckVJbAXK/XJ/8nfvl39vusNfpJjU8Sq1+/c/7UV/Fap6aPbsFI0as\n2hUaese/TdjVDUVYDEyx/jZUiVb3GmCitXbac89t/fzjj5q0rj0guHm6BYHb3cPDv37hjz+a+0+Z\nUrlmzpxDOXnyNR4+7IO1nxEYiNXaOsY0s3l1Rc8HvWd9N/4YfX/pb+fHPW5jVDGEtpA/sYeO83Dc\nCixL0BJZqXqIOdBeYJAJ5BtgWoo65cUOei5QDXGImwR8bsQcYxpQNloWMGbkV+qnoVBzgMjclb9H\n8fn7Mr7ny8q2A/ULUbHPU7L1g5HGqvcQabb/uSy6ClArcMysgMbWP+XycUaZ8gi4ahzL5vzJ3z0R\ncNMK0TX+W4YjRhlvZFLVA5lIfJYUpz1mqACVHdEgn2P97UIApRgL+FrLmwDtlPpjParUA+wvSNUk\n2mDnKIGBhZDfxVrg7WhgrkRpI1oxo35dyqXpTQ+3jBR5GELwMR/OZvLlr3x3ybEuH7ua3qb0+NvU\nCfTF9ypw8whHKnngMTI3ufVhDkeuY92jnez8IZzwDcBGG0OKMeETU3mR39AthHfbWGGnAF7W8oYx\nqhYy0R7pt5W9QDegf1INdEC0Sc/7iDZ6ADA/egLkSIseRhILpSEyJCN/ZIjE86dM/E5WtmpvLn7n\nQsSHCGBriFSQMiG/geVqLNEc5PGBYwNXIAo9l4Eu2opJk9OsPfdAELMGH6Af4jwciyKnFOkR1aIy\n1hKzObmnc9yNtdXJNtgmF8aonAjQLwP0iJY3dI6hCnJ95/T2vj9mzZpM19zcIuohDZMVnHEKQxwp\nI53X6CXy4UPfcvPnj1+0enXfkYgnRVWsbf94+4PVX4XPkH7BEN5OrhH+acJRnIoJxovxpEHSAHuT\nUwR76n0GqN6I10Rb65/yfqOY4QgDuCLNpq4JLO6I82ctBJBXQSYZv8RY/noaMYLkIsOH6trIEgQO\nf4WSaYoUSdtGpynQtd61Ubrd1Y+f1T5SIzX+TqSC8xRGHHC+FDDa6nmJfNYxANmyHPweWMtwp7P9\nRaANwu0siADzG8BZVfbj3AWjjnp6zayd/si2Lp5Um36bRm9dQjLE3/J8oKvvgwdnWg4cmHHdxTNH\nb3/aohAFe71k6zfZpqSs+jnQ21q7CuC7WQV+K/hbULkKX9yNAhqpwMCd5U+e/NMjIsLj15Ili3P8\n+CFGjXKlY3sXmjXJR+ilWrZ5zz1GmQ7AhCMTev8RVexEzTLpyZzQzdAY9ROwVGv75ePzNqYHUNZq\nPcgY9RqSsauidTzznakI4OuLteuNUSWQbEi+ZDMhojbQBaFMfOOM5/VIPHPt5mtXXw6/Vda+E29C\n8UCprEXg6ifQ6zWhDJk92SfuDql5vi4DZ2R9nDWPv79iYS58diAHaQY15qtdeVlr/f83MukqQBUE\nfkPk+toh6gzPJ8XTjBvOhGw6YtJ0Kcb70Y2GpxGt82SNhFKwr3JIBj4E6BWXdhQ3VIBqhaipHANK\nWX9rHQrIYeB1a9l1Qg0pvZPih3JR8M55LmxaxKKLO9iRAyiPi0sh8ud3xdV1D6dOrUTK9rWQ32Eu\nYPNLvHRqAK818UGVvsW9MR0Zuv0ud3daa6NQqjXS9DYH4S1XwMY2VHIUalqHEtoPKGYwVxazOMdl\nLl2qACdrw+Rp1sZ1ks2GANx5SF/J18Awhd3hnGvDwEB1EGmSO6u1rZrs2IpR1wjEEXM+MD4hWUqj\nTDEgbxa2nyjDu6uBr7F2onNcmXiisX0LAaHxdLFVgCqCTLy3e4R7nHp9y+sNi1wtYk/U/qFdu+6b\nByEulG39tlISaGb9bZu4x6EUg4Gq1vJajGMbihjvvKjt08kIJheOXOcMpOl3JKIiUj4qyqXe5s3t\n2yxdOqKCm1tYSJs2n6x+8cUlXwK/aB1fJUop3BHgXjtr1guDb97Mncdal+oWdQKZbJSOdnNVw1V/\ndZ5P5tXjfJEiFHsacxunSTMfT54VBZzX4ohq1G6egPHfnjUYf3wckkCahEz2mln/pNVn4DH3vzjS\nLBq9lEfoNZHJLCd4AsR3pcTA7J9E9o/U2la5qTCnMz9XKF68lC3oUWlqm/ubdbczjf/N/aZGaiQX\nqeA8hRE9UI6e8zWgTLQcnVN++waY7efHFuTBtRJsD1zCe/CuR1akSSk7wiWdidx8zlt/+2i8qaHf\nZ/TmRguP/bZq8eAM1ro2A7uewj9vpVPj0kCOIq717hV00yXmjxxTs0BaN8OYcWmoWuNPXnhhPta+\nC7S01u5BKY97JZh48GMGLOw4O3ju/QFt3YmYBZTfXrZs2vc7djy9o1y5H4K9vP7E2qEc3wP7jnnR\nsvkx0mQu5WTQe1nsOxcWtst5K+P194a0jM+ZNEY1BcYCVf0IdEHAclPANR/nRi2i6xREr3xXrBWV\nqu6MVWmsZNOMUZOBMK2fqEQk8iX4IkCpHNAOaw+hVFZg4UkGVL1P0bsVGZgOKcnPANZibQRKueyB\nbU2gzFkonE4ahsaaHEtn03b5dL+M309E6AXjEY5tOYR3K4ulWPpHLsHe7vky3vQKiYiosmAE7mlX\nW/0PJBWTC2nWeg1ROlgUFxQCIqkHwdbfjnAeor8DH1h/uyLuZ5MKo8xI4BWgLgKco7XJRwPznlrT\nPel9uSIAbAwyKZiQUJVDBahCCABZDHwaPSFSijKIDnuBQEw5y71ty1jh1YYuz7nh1gnoZOHm5nqc\nmd0tpNbtH5eMZskSEHCQH8kwblzJSjc33CZ44Fa6APO9A1h/ezkRPghom26tFaUZpaohgO4EonPf\nKjE1H6NMKRdC+gDdLCc8dvPDvYlszKSxQX1hyQtClfoLmYiux9oxKHUC4fC+BJRW2K7I964DA9VE\nZ6wKaB1DDSXufkWq8mtk8jFI62SamZ8YYEUBneKdj9DHKgDbEqsWqQCVF9HrLqasKpbjYeZ6Qelu\nZXJxsWGhUeyPtBxFlGSWWH87I/bmcUHGs5O17HKalN9HOOEvaqsvJnn8fzOMURl5AjQ9kcnPZmDL\nhQvFtnXufLwZMhnZB4y0lqMOV706T3jMVREQvqN69bVXK1QwPWfPnnwJqGlRM4Gbzv0YFaAyEsGV\nqtsI+TiAX5Aq1fb9+zkwZAi+QF6EGx79mp8nYDwr8n2ecZazzutp4I9/C4zHDIe/vRQB1a9a//j+\nFE6FtCixgXgFZGz3OsvvCK3tXqxEj9AaszhLZudVIfSzpJab0c+Ofxoe41Qfv2xMX/0ewydGZHtp\nUvZ0ddaMCL6sX7uc91lsPzVS4+9GKjhPYcQA5xqYpK2uHP03Y9Q7SDbXpXlz5j14QDPcL/akhNnF\nyx3Po7AIyBjuvHYGZmdwZ/yqmry+hNc+XLOny+nrIxrkAmpbywmlyI+UvUe/2bJAnRl1s/aICtp3\nO9IlahL32wXwxc9B1Kpj2bQpguDgutba0yhVFvjy8LtkDKrA2tqtbCOglUX1Rrri2+0vXHhJnenT\nmz/w9lZE3INTM/cxbYsXY+aXInehflbrRQBGmSEP0t1+V83v6eWT/XbBuODAyY6cOE6xHm8w9y2c\nGzhQcQDTv2vGujUNdFg8dQZH2eBbrJ3nbMcTkWmrkaQzm4CkpYhqxJsxjXu2qk2lFZG7q9HhkSe3\nRiGaugOQ7OgsIMNYaP0hbA+TB+Be06f1Pr5ut7jD4PblLtuwVUgVww1RIPjTWQ7i4nWYaktr4ZFx\nVK7r11Zl2NW3Q3jmCo9O1RwVYV1criN0iXWIvfs/b44Syk5fJAu+HZHxawasRKg1B+GxYsJJhId9\n2XlPIyo0Ja1/yo2NHHD0JU8yWyWB9trqJF09/0kYZfLhaOYDg7TV66L/pgKUF6KE8oX1t9NjrqcU\nAUDaQIwB+9kp3vcZy5afLlpRD6owxXhmvcG3NXdSq+42XFws20MJXdKXvofOcKZoTWo2b03rl3KT\nO+O3fHu1I6vu5SLsZiWReDyKXDN7gVdtdMZbqQIIxSQLMB5rp8Y7IWlIHQT0isRrw5+8vy+ISq9G\nEG4/ZdCFjRxt1gAi3wOP0pIdfws4ZuGCEoD4ENipsJNwJlmBgeok4g/wttZ2VoLjKBWqachEeVaK\nSv5KDUd+q8/EAMsY9QqW2VE/NP715ryu5WY2mjlye6ntXgjInBV9fT7ZPY8n9oEYd6Q5sxpikHUj\n7vafdRijygI3Y+rtxzg2b2RC9DYCMAsiYH2Hs+yyouiEMco9KsrlWqNGwefCwz0/sajfkUpIfqw4\noKp31Vp+RBe4yX4XFwqGhJD1zh08gAhvb25Zy7n79zkcGck5hEYTDcQvpcRFOqFQAcodcEvpPcCp\n6mYDSiG//VLOUh6Rpxxs/eNXNI1R7teu5Vvj7f2g7tWrBf66ezfz/pCQtIG+vrfXDR6y9R4C2ksg\nWfTiSHIqJhB3RZJVt2K8RiETp6SW9EileEIsCc+/ESpAPZfdk52bQxjtO47u+X18ymxaGR7p4hXm\n8TSVjtRIjWcdqeA8hREDnE8G7kWrXBijKiOZvIr37rFw9WpqfR7CGPJmGU5oBsh8qjtSht/hEsWQ\nyHGsUmPJ4enC9DSuNOpZSF2abLd6RXau5UWESxdr+eHJPinpw4M9hbpu9nzw4sOpZ052WIx37qU8\nulOGqcGXIP1D5i98FDhuXAV94MAw4K27pfjwj5mMQVHSz89+RJ6dx3O3qbX7/S1q0ccdu189We7l\ncr3WrnebXf5eGJEPvMhWP4rt/RUbirsw+ZNgjn/4Ard++c36W7vWe+0HUZlvjvSYPGxj47Y3G8Yd\nkxmm7Psf8E6/a+T4BuhvtQ43RpV8SJo/+jB74XndObbGr1K1EQv74jiZH2NUO4QLWj+RgXdBstpv\nAn2wsTnpjryfAb7W+G1FAPxJoBdC9+gP1EwLZ0aBHSU3/+bm1b5Ht98Le/huzQW5nPWzILzUltZf\nsv3KmBpI09td5/wOl+mnSpxLzx9753J5YvehYz5r2rQUUsbP5VwH6xB5u5TLi4kxUTOETlAOkTub\ng3XUcAT49UQA5GlghvsYSke4kjduE6gKUN8C+6y//SDF++exgss6BDgO01b/666lzn4bIiD9FDBY\nW31SBag5CM+5bRzDGgX2yFCO/9yUq62LMGNOa74bvBt6WGtXqV69itKs2XLu3VMMHGjS3gkv6odf\nxed5PntxinOVq0G5yOV+lKNfrWf9uC0ElkeoJeWwTyggDk1sDlDRWnvDeTM9MhGrhriHbnXeL4jQ\nt9ojQGYyVgyxnCpBP+DdYIKmbeKVDguJKlgYjs+SDRWrBCHvglsLMaFZD5RT2KLAlxkyXCu5alWO\nE8B1rW3FWOMmDYvTkWxuW63t/hQNuFJNENpLtWhXy78bzsR6InLtttXa/maU6Q58CLTQVu9J+BD4\nGVgSiPkduR9cBDppq5N0h/1PhmMWVRTYn5TsojFq2apV/c5Onz6zM1DCor5DKl2LAVSAeoVgRjKB\n2UgS4uLgwYS0aEF5hDdeBwHE+5Bq6h5gT0ITh0SPVaQuqzvbqotwtT2QifYtYoPf6OUhcn+MBuMg\njcDRy1HE7CtB8GuM8n74MN3ao0eq1T43rvGXBR9czJON60VzciVnQc6kyc0lrpLj4Q2yXr5PuhMW\nta8Cf/yWkaArMY7l4d/ylFAqJ5Lo6oQkIyZgU6ZUFW9TAcrNXfFwcQXmtnmJHipDBrcvPnJxy1/o\nXlH9Yvj/BH0xNf7/jFRwnsKIAc5PIPrQ+4xRaZCb6li/yXyf6xjHZk8n78BD/Hpu4bdpOf7SWw06\nuB/0jGTPm7v4S58lD5DrUSb+ON+BEt8VYMcIdHlmr8zD7Tuf2FtFhsXcITDm3SqD+r93bEImrGrI\n2l/2EH73GienLeV8oAseaI/yM+3ceWsju/7880WguwmkP+CptR1YoVvnRVXqLuu4MKjYYYoMzZ/l\n/sO06syixR+t2FvjjWbkCa3y6Vl8S+Tm/vFufNR3Oc2G36NglCcnJl4B1rpEuax797t3PqkVkreM\n25j3u+ku5x6bzihjqiuiVvVmbvoWfF+wkQ695ml+ztWNzzdl5+oP4xjbCWhktf4jxjltAb7C2s+j\n3zJGbQbmam3jK4bITfixfN9jsBojjDLdiDY4sjoSpbwQesoriHqCUUq5ecC9M3ApF1RWRud6AAAg\nAElEQVQ2gZT+f+yddXQVV/f+P+fGlSCBQHF390KY4F6cFopVcKdFWmhuoBRaoBQo1lLcrbjDHdyK\nu7tbEuJy9++PcwOBBkr7tm/f3/qy15p14WZm7pkzZ+Y8Z+9nP3vVtx9un1B6YUSCc0J7NFgx0ODc\nStHva5C6xFC01N9nwMJnVVgBFaIG535E4wsTyAr0Q2S2Ms1saFpPQzQntLIYxh8lBfqjkyQ7o5Pq\nJqL5vSmDAaVcgMYRLvTK0Zvys39lfLmLqfd7E9HZlfg6iEQ76CAHgKIveyz/V81Upis6+bb/L0G/\n7JoXOK+QKCklwS/KmaVWccXtqJ0r2H3FQlyDEtRanhYKJMBgcuYcwtdf+3LgwD3Gj9+C3X4WOOfY\nLtqwpUMXzFljiBGNVrg4DrRHZOvLbVJKfYteKNUTeVZ4xxm9AEsqcNMQPUZ+Asa9CiDsUqvyuHF/\nl+Dico7QOm3pHQj084DT0VA5Lzw5DV87aWWVdxBpqxSLgZM2m3JBL05zJAE2h+d3EdrD39Uw3lBn\nWi8k9gFNUirQ9GfMIVe4GO3t/dgwJPTZ37S6z0ygvSHG2hebQH4QczW7RnmTOBDN//7lldQpzYNv\nie7veHRS4ra/Auoc+Qq10Qv2y2j52F0vK7X8GTNN1RpoERQk4cBNQe0BhiA6T8ARBbqNpoqF8WJS\naQKQkN8H14YZKZLdi6JpXSnq7UwJNycinRT70fdrP3DYMCTKcc7U6EVZEhgvgk6A3onOZdjj+C0v\ntJc6yVOdNtnm4+iDJDD+4E1zVUxTpUpMdFqzd2/9wmUPnL+WuuGZfB53ueJ5lcOpj7A5/HDW395l\nt/MtMudBg/8C6ETPXOioUFIUYo8If3lBdknlyhKPy1fReDQ/SJlN3zJg22VyuaOjAOkcv7HojzTt\n/b5Rx3rnwdnaglj33LkDOtbGv0l+155Gt1O/FzJ4a2/tv2RvwfkbmlJKbNjyo0PQWQwxxDTVBCBt\n0Hb6sDP1Kc6EqsmTZHku3HMtbfRDufGq69Z4Z3vde97cy/WEyQkerPttGs39jtM522yOud1RxTKX\nOOGpQuNiMlUrZT/0jkwSxTdiJQoYGerlVT/dypV+ieMKjGJ1pi8YdnALeVY24+zwALES2qMOs2aV\ncG5tyfPZo97mnQCj/Sw/tNe42JM4ylpg8aO47Oo3rzLRk1XXHnHVq/ve8k5sk78HWecuI66PdVGh\n2+nTHnEm3j1uQ4tHTCELy36N4e6qvlwcnwtogJB36Obe3u8+yS1rW4+pU6LElXubPds3v0eGnjXZ\ntLgkR8oCEoFXaDcmlvclPO4khZ+CckNX+esObJGgoKJoEJwfR7jWNFVu9ESSxTCSAVKtRtAKzUue\nCnxNCiFeU5np0FGJ2oYYL6qUKFUbzfGd9QXcXgXfn4RCysrlml5eV0/dSJUp0i2qYKhXaGfARYKl\nm1rxgxPnxxzGv0pucnwyFbCKYfxO79ZRNfFE7QuMWz+PHugJseezCoGm+QVaJ7uKGMaLEpJKeaJV\nMJqhwfwyYCIih/94FD77/S7pIml5cZTvVQOzbRvmRPRl7K9ooCkqRI0AMiVpgv//YtWaVTP25d23\nYezMsU/y3snbC1iSBNpMZfqNJc+hGJzUIM4WNwgqshiWtIRzlC1bnOHDBYull1St+ruqpSmaUrOB\nMER6pPxn5YKOqKwRkRHJ/4AGQRWBO+hCQEuAoykCRr3/VMGSeydrltvxCAbGhhDyo4n5EfDUG0IW\nQmg9vXg4CzRVyB3g0CefDGrauvXIDWj+/0x0BKU/8D2aQuGZbIsHNhlGCnxcnWC4C10sZ+wb9dEr\nzDRVZcc1jwDGp5gwrszyaCWXuoYYzwqH+am4mVV4UKUPF24DbQ0xfu+d1H1fG03/qwFsRPezH5oO\ncwcNgHe8SXsdoLwhug890BKTmdAc9yyOdi4Htv3Z4kSmqdIAV3v23FH8xInKB1PzuPxj0m4EPkBk\nP4AKUd3RRdGcU9hckn26kkyXXIHdxUKiuwXl6YyzBWKjE4l+Eo8LGrQngfEDEiwpSvr+3WaaKr0I\nG3bsaOpzcFrzC4MmvF/bJYyuUdnwRi8UKqEVtHY42rcTuGQYIkrhix7jSfz9MuiIWRJY3ynCrRR+\nNikR10ALCVRBU2RSAY9diHucl/PeBTiTPgrPkwcps/EB6W+jI5rFccjAivCyzr4zkOg2VI1umYUe\nv3RjTqFcRcqkzx6fd1iQ+xqjz5Fmf2PXvbW39qfsLTh/Q3OA8/5ALkOMzqapagE/T7xI9aVr8+5n\nb5hf3l69Lh8bNjj0yM/2EjFTCz5c6376xtJCXLnmRwtbFbwF5tux+Dhh/8Aw5LZLhqfBCZ7qqzOP\ni/6UK+JKrXA3AlbnI7H8Tfbnf0hqv9Wr94V5e3uJYbRXpR735KrXD75NG+wI+3nzwgTo5AxRjVuq\n8SuK+s7Bp+CcbYX33lSKd4BLEQkMGhzW6uG1yDau01O1TZPK59GHHVfa13gkcq+o4lqIHf/7VXkY\nmdYpp5UQ7z320olMnBhD5hxhNG4cB9QBzrE9KKOyq/UzbJ/nzbq7prt4RRGXKoo4r8eJsR5hcbGe\nEbHx6R5470ldLTzCK/FJ7hN7+309tMkqtLdkFRCDSNYC168r/9BQ245ixcYCe8QwYk1TjQScDEM+\nd9BXDLSHqT56Qh6LyL6U7oeDzrIUuGaIkXKBJ62KMWMY1F4Gvx6z8rGfC+uzP81Q5svJ4xY17/P+\nUHQouSBVbNmBSUTfsnOgbV6wV5JgeSXnWoWoGsBPYzZQru8+xqPlzJonaaIr0/wGDcKrSlBQEm2l\nMVqm7QC6iMcCRP6wmMxLv+sEnCPRuR3D4j/B/WEdt0S2x8T75wd+RuRHFaJ80CCv8ZtWDv23zVEB\n9AAw2ma1XUQn9D5C5w5EC6xpRoWAGJzqR4rzHpRa8i5k3OPv/8Tr55/rzvvuu8Pv7dkzFJ1o+Xqu\nqFKNge+A4sjvFTme76ayoNVwmktyxRU9Vl3QVIKGaODlih7vqwATkTgHMP8WPa6rIfLUwbX/GZ3w\n95EhxlF3pbpUgR82an5uJcc1l1dIMJDXZlMl0codFnTC3C20ZzSK5/rVUehk5opo7/5cNFCPd7R5\nIroSY7O/RCVwmGmqD9ELg9aGIa8tDmQqcwpwyhBjgqlMdQOPT7pScupojn2bj4jBL1Sj1X1VHA3I\nW6FB2yxgMfLcK++IXnyIlni8BHyFvJR0/mxXLOgI2mA0nW0YsEIEe7J9cqCfyyZo7+5aNFDfgFXF\nvkmRHdNUO4CRQUFSDCgnqB1AKURa/9GxrzIHD/wZWM/uSapq6SlR1I9P8/vwyNVCy/82J9o0VVZg\n886djW4Gf7XI+7fxbjldwjlc5EuplWwfC5omk+TVr4zmlu/AQfszDP3OUwpXHMo3jv0qoTn309AU\nsQSgJvre1EM7npaj845uAY9FSD6GMqMVi1rdx3/hTNqfGkdP39tkro1O5L2Hrg7qho5SpQP6YVVX\niqZi5po9/Pzt4VzGEl97sUWdXK4Znc7l+ds78a29tTe0t+D8Dc0BzncC32IL2gccPx9Oj05T883k\naLj3cg/P8K+++OJxtJvbqVwz4jJ+2rdjwTaHEvbF2Kltq0KmRCxrd/Ou5zCGeCXgUpUgwx+PhFWM\nPbpCOpVu45ic8u3KQu/zaWljbdXwixtl+nwJFMrYtOmTyEGDTvhPi85171wbl/QEhl7lhpfAdoEO\nLLbW9Lz+08QlxW7j7sTWWDvZ2p4rnOVh/rEx5S7NaVxgT7alH374TbppVxOfXovCb5wFu3sY+9Nv\nI/WZwVRaRYON9+8cTjPvYuoHfH2+GKtWRePmptAT2h7CTt7j2qyaTgW+258qjBwtfjlnTXP7hr9d\n2XNZxJI1pkCmlkRkVhnPXnqQ8272TPf87h3IcS9Hozqb3VMDu2YPH97LPyzsm/ojRsxNdHKqDuRN\nz73Ki3h/S8A6WuYfRSBax/kpmm89749Aq6nMkWggUiNJX/lVN84b9kZmYyod+CzIH59BIfNjXe5n\n7BlkDeoE7KaKbTGa4tAHmMH2oM5okFDpdZOzClELgYtiZQgObjHQEZEVMa6u2WqNGjU71Nu72O7u\n3ZV3TMwWNCBfm5LyypuaClHNgD5YZRbO0T0ZkMaVnYPemb3jQqU2zN2ABl87VYhqj+bdv/tnpBX/\nDXMAkTnoybiDBIuYynRG030mAPE2/EcPpVBrILugstrhsLNSUe7Dh2cY+ttvsZ8tX94LTW9K7Thm\nRopl5TWV6DjQNKlgzGvbpjnaU0nOP09hJ/RitKFjK4heXD5GA5TA5OPZkYDbHg3cN1/kYshnfHpk\nJvzcSOdW7AZ+Ushi4Ezv3l1Wv/felC5oHnw/w/h9op/Ds5gzX76DucqVW18vVaoHNeLj3TJeulT0\ndMyxjIkZ7ocWXkXDPXG4eQGTRFjwR9ee3ByqHCFoYFzfMOS1RdUc19kTveAYDEyZS9bKy8l87LG4\nvlhoTKlO6OfHF01jm43IxdeeXHvX2zvOfQoIRuSg/hPOaCrMl+h3yjBgbUq1Jl48JRmBRkATqlgr\nU/qnaJ5mLCNTD722Laap+gPZg4KkL3AqNxf6XSDvDKDAf5qwmMJvuaMLcB1HK/P8V55th9ztxoMH\na2zs339TvV2fpd3gUuhxqzQHSJt70qu99o5xkx29QG2ALq53BB2tWGkYOj8DQCmc0Aulz9GgHfR1\nzkQvql6ZI+E4tgzQIAeXm/Tl++xB2OJzctkjGo+Yi+S6M4XO8b/SJCAO10gnEqc8xfcm0Byr6uhu\n4bKZiemu3dyqlfZ0z7dlqktMUPMHHn+1v97aW/tP7S04f0NzgPOnpH2YgaXN5zyN50HD7wq0VRci\nPLYk2G9UvXWrlrLZbpLIDnZeK/ptkfaRhVLxo5czaxNwWjGdjywLeX++YPmNW+6jaVdW8c0JKPuk\nmhjG8eS/5fShcxt78/EzXDcdPBQwaWaGu05OWfH3J6fcTbx6b8bWGJrlhxlVoWcroAdK9ei5ufa4\nmrLB/1iYPAg573M7LuvHeT1v/BzpnBiZfkYR373bL2QvW6bAcX57Qt4fPqQQWs5RAS3z2qbm/iqu\n94y2e6MjxcoEihcvyNixgeiQch5E3iXyckcszrvwzNZQjOfSd8o023gQNXoW7S61CHpQM0exH+s2\ncbo6zjhlpIl1if269eI0roGnDnXd9Pnn3RFZAOC6efPoqg92V/46MiRP6Y4Imj/7C3D4WfnrENUR\nneE/8GWlAFOZH6M9JOUNeXUZc6UrMNZB8R0DCS+SnrnjM/m3pOUi7/f6N2oZ7hk+Be01n48GVTeB\nIdxevYAL39uAxS/LwL1w/hCVCT15vCvBcs6hKLMI7cVMn2CxrC46fXrm85kzOyU6OdVN3m9/xRwg\ndj+7P1vM5lH96Z19CX7X6vI4pzfjL0wVnHaiVQzKKCt30FzVsRIs8/+T3/1HTHtAMyJyw3GvewDl\nXg7Pm8rsDZwLwqiILtDzOUqN2gT+tfz8Wjbt2NFtzujRDT0SE9c4QHJFtGpKdTTgn/AM6Om/LwUu\nIjLgzZuqRqK9unXlTQpRKRWA9vSVA0IQSTFUbyozI5q6lW0AVW+8g7RcLpIapcqgwUsBhdSxWBIG\nrlnj16dOnQibPj3O6MVAknRdKTTv+A66bsIT4HHq1HcTy+Tfmj9/jkOlXdJFPgmPTrNx//465vHj\ngcOAjiKsekW7KqGfx0aGGGccgHA6WrnkPcOQ+2/Sb6Yyq6MXNm6JsLgGVRoKqq0IzxdFSpWaUZzN\n59My+JEnP/206k8qlGi6zsfAF3bUoa8ZfHE9dZo/xedeap58O5VOKwvK6T9VRVKFqEDslsUcbf+I\nPOtzc6F2kKyc/sqFnGmqgjgq1gYFSUPgmyg8dnsQcwuRkD91PW9gpqn80J7oeYYhf6pYjmMB0gsN\ngkcAs/5o0WKaqhSw5uzZ0j906XKwX4vACd169O25MOtCOmVdINP+ZNs90AD9PaBhQoLz/e3bmx1c\nsGBA5KVLxfKDKoeWIt6MpjF9gJ6nfgFmi/Asr8Mhc1kDDfrrosf+ase2X4RER5QrGzqfqJAdVXgt\n9cr9wsc5dhCocnEx5jfK+rgNVXcnFOXcx40o5eTt5bZ4arzFPzZTaqPDledRm7f21v6L9hacv6E5\nwPlKbEHLE+wMqjc8X/bEGwluO8Ijdle8d68hojVgVYduM2kW2Lag85kRE91CesbiQgjWxL1U7CyG\nsVApPPGJP03ra/60vHleDCPJQ4BpKqfdu1lborRTrZ+iWtgPr13AZ0s8t3w2c26p+Ds/ruHBtgcS\nLJ8rxQj0C64aqALlyrHsm2/IvCS66uMpv217iLNvXhe3VKFDst2QEee4HJ3IQuI9ggd7Vo43ym3Z\n5uRkb24E8SNQLhLPa96+oRs/ntrlx6WXf4kIO00D5rGSJUuWky6duxhGWwAVomqjQ+XjgZESLHEO\nNZOVfjyp+StN1liti+dv3968B54PapRoVqtNmx1t2hW+lSN8XL9EvxPFE9pee//9ROC9o7ly1a81\neYSPbfP7UwqOsn+WXM5NhShnNCe0Jro6nB1oLsE66c1UZlV0yDPw5SI2SuvmGmhKTh3Am6zcpxLZ\nyEsLWxX68c2gBwlbq6oaX9UoCHxDFdsl9EQ3Ew2svwW8ubNuIudHDQdKSbBce+W4CFG90ZSVGhIs\n4khgyw8cQCRBmaYTWlPaC2gihhGvFCXRE+TPIux61blT+C2DiAzTGH3HhXLjx1Ond3+gJAlum1k+\nJwunm2cXVEf0xFdFWSnt6KsCEvxq+sZ/3ZTKiQbORZq0pOWvBZiNjlKkWJTIwRk+C7QR1Gngmm/l\nyoftTk5B6/btOxwYHf37Ij2aktINDdz2ouUGA9AJiKWT8gPerLnKBYc2+Qv887/BHMmwRx9w95uO\nfDBrMNTqJ7IFpX4Bnijkc/T4PIbO4SiFLml+A51c90xHWoQXueZKeQEHRPH99m0cR3u93z96NPDR\noEHrspYps6Hrjh1NZyc/xNGew+iFXR3KHHif7waMRD+LHVLy2r/m2tKhqQwDgzDc0R7s0snB4K6s\namO1NgTGuXARRQY0yF0HbExJV/tVdlNldl9Ey+2B7Mifj3P3fXnqjOYk+6K1scOSbaHoROHDju0s\noh0ADonSI0AnrLKBut2WU2B5Qw52+VC2f5VitMHhHb4INAkKkuPApqIcO3iM4h3Qsop/uya5aap3\n0BGWYMOQWX+0v1LkQXukm6HfR2vQSjuX0Au130WFHBSVQGDxvXtZer3//vVgpRJH75vuPNDvGNF5\nx0qRP9tuB9WoCFAV7FUtFruROvW9mPLl1zmXKrUlsVSpLQd8fR9fQY/vG4mJTtfHjJnqv3Vrq/px\ncR6N0Xkge9CL7wroZ3sNsEaEN687oZRzFybNOkrxlvuoUNRtqBreKw+1RrbmlFvO3LkGNInyqeZU\nqIPxzaa5f/Ya39pb+zvsLTh/Q1NKia3UqAH2UZ8P6Dg+u+/1c65OOx+FTit3/35XRBKUwp2PKzTA\n8/5szk6a6dFNtRnOF65xuMUPPPJTNUIKXUDzTOcAsSzek5knLulYmqUXmwOONmky3qtpk88XpE6T\nkOmJawYyWu5IdCKJ+yPThl30bXRy075f8j6Mo6YEy0kHWBkP1Ojfv8OiYlVm9pt+DrX1gfIkVUa7\nU55ukV7nQrxL+8V13XYfT+Br7JbW7t+FfTpzZqESGTJcH2UEMfkSOY+/z8Icpyh0LNbLuah//hWW\nx1nXj4v/dWYYPj55WLUqEGgjhrEdSCo8MhnITkCdweTrPwn4VAxjrWmqwdu3N+1itS5dgeYIVg5s\n61Ry9MryC+4n9ONMQU9Lq5Of7c4UfWrehR4crdlk+s5Q/Oo+MRpvetbHuujFQjSftzla7msSUBKo\nb7PaUqG9Ki0NMUzHfcnDczBeCT2xrqcO0ZSlN4q9QD9bFbID86m69dLQpl+fthW2lSRbu0pkb78V\nDXjKimGcU6ap0OD2Gy5N8uDepjvEh72SGuJYTBxCL1hSnLyVaboAy7jlbqdNuSeIqoPmHX+KjmCM\neIE7+aox+JXLWiacz06k/1q+9KkNjLJZbdsXV1g8dHKFFY0Yd3myJHp8gU7We4xIRxWiFgDnJFis\nr2i/QoeDm6I9UP0kWNb/UVv+kmnPdXs033vEdV/Sl/+UHne9+chulUWvPoyiaC53DkF1XRoY2KLF\n48cVR9+86eQaGpqju7x68eRIwG2NXgzlASoicuiV+7+66ZnRQPhF/vnfYI7aCbMmU+9BOFH29SJl\nHfKZJ4FKCnFCqwadcrThiAi/p+y81GA0RSQR6JAUkXIUTKu8YUO77pMnj240fPh71wsX3rMQXWjp\nIEG2/mhAVp+ix/pwLdt39P1+OoE7O/8nHGel2AAsEGFWsi/z1anN8Q1xuLKNnlhZhfaA1nO04Sia\nA74WOPU6epZSfIfmLVcXITLZHxR6YZwq2ZYGTT0qgX63ZAFO2eFIge5USBWZ9sbYGdM2x+OXEQgf\nUXl5kFluQdWgNUMXDDxb4GcgHA3ynyRVzjVNNQ4teTlcKQoC22+Q+Uxmbk1B/pnIlYNqYqIXTSk+\ns0pRBi07WAX9Lv3RZlOPgJoxMZ45t29v+n50tHfJ0qW3/JY584VYNA/b3/EZAyTExrp/ULt2dB/g\n2v7p6oYlga8LDiWP5w35HRh2zE2evKhnnha9MH4X7Tx5jE5m3gaYItx3XE9edEQoCzrHIvlngN2u\nHj55EhD94ME7TqlT33+YNu3tJ87OCYqUE22d0ONnDbAxuZpQkg1XX5YezWcHn+LdNdHq6l4rA0Pm\nzWRridjiJQvnCsvUP3/OJcbXW9q+2d14a2/t77W34PwNTSklG9a5Hpm5JVWxFVv9LeadB93L37//\nEzqs9iEqoSFu4Z7E+SgP9+h4ccIlBne8JdIeE+MVmZDgakfLVz0gT/gevj9Wly4lT+ObUNh4Z/3d\nvj06Z3ZySlTzFg1g4fwBUVWNBS37DPpk3I0oyWFRcimTW2IOL2fO4ahoB+waueSD1VufRJVzyrMR\nf8/YuTdj8lZwKjcy9zj6xWy4fHnAmgd8gZ5IsgIBWMU1Y8bLJ2bNKuAybtyPIzaubT/gU35KFcDd\n/cHz6q0uvmbTkBNrP3FJDPc/DB8WZEzgYEoW6gwUF8NI8iwp3DK0pciIaYQd38OFH+pJsEQMHNgu\nd6V3V154x5qqxs3DRXtk5XqtIhxPsCsSinZyu1jm0ZSCLddktXhGW0bxayPXMX4fV1hDg/NiGJ84\nzpsd/SLdCfRMorI4wOMXfhF+HRePXYxLoovVEGOG4558i+aGrwPWA1uwkga9cMkNdJNgLZNnmmoj\nV7Kvj+o8MaTeoHqRKBpTxZYaHS49L4YR9ML9Nk1nEiI6cKT7RALqHiNLi7ZiGGdSHBshqiKaLlFA\ngn9fMl0p3HFN/BwXGULZR2c5mSpQ7ruHOpJ356EBVBsRXil9qEJUETZ9t4e9fX9jiNtOLIklgIY2\nq62nIIM6d+jtd/FEJ7H/1jWLoGLRag7jlJUNaO9gCQmWG45zOaHpH03RC6kotGqMHSgiwdLoVe34\ny6ZUWrTkYB7gQ2XlosXOvLbHCJyxkm6ILHz1oXwNuApq4NUMGS7mmzQplaV58zTt7fZ1k0XqveHv\nKzSN5i/LSyql6jiuoZTIm1E73tRMZc6N47xLCzo1S4D0ESKPUKofOpG07l9o7DOqEJIyJ9hiSWzk\n5JQ47dtv6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6BgWqL88fIMi8mS+5zbxbRZndjnTULxGXY+LxnTYLsU6juWBuikvVA0SF+C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AV4TJUs605nF56CwxpgsDrOadbdIowACqvytzilueKOwoKujo462eYDZxdeoPAARrUw3nUO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QtKxrMH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\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3TQgavaoQv6qdc6YRSrTno97R573XYz2YX7FWrnpCO+MyoA/qHnGroxpjlgIjReR7jAkU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\naMNEfmZd5nI0C7hPVSuu+FIsRwC7MYwDgVvO5/y+Xeh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WBmYHnmpr34gYD/yGnKYGuff8Zy2950lJSWnTpHQrcAb5e7dMFdXxVVRvtnHI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h7LPN8ZE6MvY3Kc94vQVzXrStzfgBlYpWkgBO+Q+hL/wzaa9a1LO4w2dlqoczswLvffBqfuIy9+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\nv4qI9/ZB/J4d6mSSG34niPjVkG4nj8ldPi7z08cfH3ja9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ww4HeBiBxGGkgmOdFcFePLtsvWWoJVffLSVls40g8mRTg9waOA95TfcIhT3r0AEPXqjjQ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\nSGe4O7ft1po5amm+3759yLNhSXPbHi6MiIUDXlLX9XkiWCPxCGlhmY4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lxNrAHsA+bbctSAWN9tv/RPDuUnFa5zqpej2TNI6ytZbi3cAFwJhPlKTS5AQ6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\n84F3RMRD+TkzgQ/kOI6JiMv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ALcDrI5hWOh6zkkpMePvo4CQn6TsA3Uhy1pRPAie7SDObifNXF0TwEHAJ8MHS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\no1ZPXwU+m2WlIirXIDVqEbU1DIlaatRqyOZcikWq3151LBFDLn3UImp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"text/plain": [ "" ] @@ -11358,21 +805,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.5.1" } }, "nbformat": 4, diff --git a/MonteCarloPricing-Swaption-Part_II-Copy1.ipynb b/MonteCarloPricing-Swaption-Part_II-Copy1.ipynb deleted file mode 100644 index 8e2e0d9..0000000 --- a/MonteCarloPricing-Swaption-Part_II-Copy1.ipynb +++ /dev/null @@ -1,841 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "collapsed": false - }, - "source": [ - "# Swaption Pricing Part 2: American Monte Carlo for Bermudas" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Copyright (c) 2015 Matthias Groncki*\n", - "\n", - "\n", - "Redistribution and use in source and binary forms, with or without\n", - "modification, are permitted provided that the following conditions are met:\n", - "\n", - " - Redistributions of source code must retain the above copyright notice,\n", - " this list of conditions and the following disclaimer.\n", - "\n", - "THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n", - "AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n", - "IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\n", - "DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE LIABLE\n", - "FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\n", - "DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\n", - "SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\n", - "CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\n", - "OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\n", - "OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n", - "\n", - "This disclaimer is taken from the QuantLib license" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# import the used libraries\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import statsmodels.api as sm\n", - "import QuantLib as ql\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Setting evaluation date\n", - "today = ql.Date(7,4,2015)\n", - "ql.Settings.instance().setEvaluationDate(today)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Setup Marketdata\n", - "rate = ql.SimpleQuote(0.03)\n", - "rate_handle = ql.QuoteHandle(rate)\n", - "dc = ql.Actual365Fixed()\n", - "yts = ql.FlatForward(today, rate_handle, dc)\n", - "yts.enableExtrapolation()\n", - "hyts = ql.RelinkableYieldTermStructureHandle(yts)\n", - "t0_curve = ql.YieldTermStructureHandle(yts)\n", - "euribor6m = ql.Euribor6M(hyts)\n", - "cal = ql.TARGET()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Setup a dummy portfolio with two Swaps\n", - "def makeSwap(start, maturity, nominal, fixedRate, index, typ=ql.VanillaSwap.Payer):\n", - " \"\"\"\n", - " creates a plain vanilla swap with fixedLegTenor 1Y\n", - " \n", - " parameter:\n", - " \n", - " start (ql.Date) : Start Date\n", - " \n", - " maturity (ql.Period) : SwapTenor\n", - " \n", - " nominal (float) : Nominal\n", - " \n", - " fixedRate (float) : rate paid on fixed leg\n", - " \n", - " index (ql.IborIndex) : Index\n", - " \n", - " return: tuple(ql.Swap, list) Swap and all fixing dates\n", - " \n", - " \n", - " \"\"\"\n", - " end = ql.TARGET().advance(start, maturity)\n", - " fixedLegTenor = ql.Period(\"1y\")\n", - " fixedLegBDC = ql.ModifiedFollowing\n", - " fixedLegDC = ql.Thirty360(ql.Thirty360.BondBasis)\n", - " spread = 0.0\n", - " fixedSchedule = ql.Schedule(start,\n", - " end, \n", - " fixedLegTenor, \n", - " index.fixingCalendar(), \n", - " fixedLegBDC,\n", - " fixedLegBDC, \n", - " ql.DateGeneration.Backward,\n", - " False)\n", - " floatSchedule = ql.Schedule(start,\n", - " end,\n", - " index.tenor(),\n", - " index.fixingCalendar(),\n", - " index.businessDayConvention(),\n", - " index.businessDayConvention(),\n", - " ql.DateGeneration.Backward,\n", - " False)\n", - " swap = ql.VanillaSwap(typ, \n", - " nominal,\n", - " fixedSchedule,\n", - " fixedRate,\n", - " fixedLegDC,\n", - " floatSchedule,\n", - " index,\n", - " spread,\n", - " index.dayCounter())\n", - " return swap, [index.fixingDate(x) for x in floatSchedule][:-1]\n", - "\n", - "\n", - "def makeSwaption(swap, callDates, settlement):\n", - " if len(callDates) == 1:\n", - " exercise = ql.EuropeanExercise(callDates[0])\n", - " else:\n", - " exercise = ql.BermudanExercise(callDates)\n", - " return ql.Swaption(swap, exercise, settlement)\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Lets start with an european swaption" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create a forward starting plain vanilla swap using the helper function above and create a european swaption which allows us to to enter the swap in one year from today." - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "settlementDate = today + ql.Period(\"1Y\")\n", - "\n", - "swaps = [makeSwap(settlementDate,\n", - " ql.Period(\"5Y\"),\n", - " 1e6,\n", - " 0.03047096,\n", - " euribor6m)\n", - " ]\n", - "\n", - "calldates = [euribor6m.fixingDate(settlementDate), ql.Date(5,4,2017), ql.Date(5,4, 2018)]\n", - " \n", - "swaptions = [makeSwaption(swap, \n", - " calldates, \n", - " ql.Settlement.Physical) \n", - " for swap, fd in swaps]\n", - "\n", - "calldates = [euribor6m.valueDate(d) for d in calldates]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Pricing of the underlying at time 0 using the QuantLib pricing engine" - ] - }, - { - "cell_type": "code", - "execution_count": 110, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Swap NPV at time 0: 0.0091\n" - ] - } - ], - "source": [ - "#%%timeit\n", - "# Setup pricing engine and calculate the npv of the underlying swap\n", - "engine = ql.DiscountingSwapEngine(hyts)\n", - "for swap, fixingDates in swaps:\n", - " swap.setPricingEngine(engine)\n", - " print(\"Swap NPV at time 0: %.4f\" % swap.NPV())\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Setup the Gaussian Shortrate model (a.k.a Hull White model)\n", - "\n", - "Don't worry about calibration, assume we know the calbriated model parameter" - ] - }, - { - "cell_type": "code", - "execution_count": 111, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Stochastic Process " - ] - }, - { - "cell_type": "code", - "execution_count": 112, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Assume the model is already calibrated either historical or market implied\n", - "volas = [ql.QuoteHandle(ql.SimpleQuote(0.0075)),\n", - " ql.QuoteHandle(ql.SimpleQuote(0.0075))]\n", - "meanRev = [ql.QuoteHandle(ql.SimpleQuote(0.002))]\n", - "model = ql.Gsr(t0_curve, [today+100], volas, meanRev, 16.)\n", - "process = model.stateProcess()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Calculate the swaption price using an integral pricing engine" - ] - }, - { - "cell_type": "code", - "execution_count": 113, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "swaptionEngine = ql.Gaussian1dSwaptionEngine(model)" - ] - }, - { - "cell_type": "code", - "execution_count": 114, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Swaption NPV : 18662.14\n" - ] - } - ], - "source": [ - "for swaption in swaptions:\n", - " swaption.setPricingEngine(swaptionEngine)\n", - " print(\"Swaption NPV : %.2f\" % swaption.NPV())" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "###Pricing with an Monte Carlo method" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "####Create a swap path pricer in Python \n", - "\n", - "Convert all Dates into times in years (using the same DayCounter as in the yieldTermStructure and store all fix cashflows in a numpy array." - ] - }, - { - "cell_type": "code", - "execution_count": 115, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "mcDC = yts.dayCounter()\n", - "\n", - "def timeFromReferenceFactory(daycounter, ref):\n", - " \"\"\"\n", - " returns a function, that calculate the time in years\n", - " from a the reference date *ref* to date *dat* \n", - " with respect to the given DayCountConvention *daycounter*\n", - " \n", - " Parameter:\n", - " dayCounter (ql.DayCounter)\n", - " ref (ql.Date)\n", - " \n", - " Return:\n", - " \n", - " f(np.array(ql.Date)) -> np.array(float)\n", - " \"\"\"\n", - " def impl(dat):\n", - " return daycounter.yearFraction(ref, dat)\n", - " return np.vectorize(impl)\n", - "\n", - "timeFromReference = timeFromReferenceFactory(mcDC, today)" - ] - }, - { - "cell_type": "code", - "execution_count": 116, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def getFixedLeg(swap, t):\n", - " \"\"\"\n", - " returns all future payment times and amount of the fixed leg of the underlying swap\n", - " \n", - " Parameter:\n", - " swap (ql.Swap)\n", - " t (float) \n", - " \n", - " Return:\n", - " (np.array, np.array) (times, amounts)\n", - "\n", - " \"\"\"\n", - " fixed_leg = swap.leg(0)\n", - " n = len(fixed_leg)\n", - " fixed_times=[]\n", - " fixed_amounts=[]\n", - " npv = 0\n", - " for i in range(n):\n", - " cf = fixed_leg[i]\n", - " t_i = timeFromReference(cf.date())\n", - " if t_i > t:\n", - " fixed_times.append(t_i)\n", - " fixed_amounts.append(cf.amount())\n", - " return np.array(fixed_times), np.array(fixed_amounts)\n", - "\n", - "\n", - "def getFloatingLeg(swap, t):\n", - " \n", - " float_leg = swap.leg(1)\n", - " n = len(float_leg)\n", - " float_times = []\n", - " float_dcf = []\n", - " accrual_start_time = []\n", - " accrual_end_time = []\n", - " nominals = []\n", - " for i in range(n):\n", - " # convert base classiborstart_idx Cashflow to\n", - " # FloatingRateCoupon\n", - " cf = ql.as_floating_rate_coupon(float_leg[i])\n", - " value_date = cf.referencePeriodStart()\n", - " t_fix_i = timeFromReference(value_date)\n", - " t_i = timeFromReference(cf.date()) \n", - " if t_fix_i >= t:\n", - " iborIndex = cf.index()\n", - " \n", - " index_mat = cf.referencePeriodEnd()\n", - " # year fraction\n", - " float_dcf.append(cf.accrualPeriod())\n", - " # calculate the start and end time\n", - " accrual_start_time.append(t_fix_i)\n", - " accrual_end_time.append(timeFromReference(index_mat))\n", - " # payment time\n", - " float_times.append(t_i)\n", - " # nominals \n", - " nominals.append(cf.nominal())\n", - " return np.array(float_times), np.array(float_dcf), np.array(accrual_start_time), np.array(accrual_end_time), np.array(nominals)\n", - "\n", - "\n", - "def swapPathNPV(swap, t):\n", - " fixed_times, fixed_amounts = getFixedLeg(swap, t)\n", - " float_times, float_dcf, accrual_start_time, accrual_end_time, nominals = getFloatingLeg(swap, t)\n", - " df_times = np.concatenate([fixed_times, \n", - " accrual_start_time, \n", - " accrual_end_time, \n", - " float_times])\n", - " df_times = np.unique(df_times)\n", - " # Store indices of fix leg payment times in \n", - " # the df_times array\n", - " fix_idx = np.in1d(df_times, fixed_times, True)\n", - " fix_idx = fix_idx.nonzero()\n", - " # Indices of the floating leg payment times \n", - " # in the df_times array\n", - " float_idx = np.in1d(df_times, float_times, True)\n", - " float_idx = float_idx.nonzero()\n", - " # Indices of the accrual start and end time\n", - " # in the df_times array\n", - " accrual_start_idx = np.in1d(df_times, accrual_start_time, True)\n", - " accrual_start_idx = accrual_start_idx.nonzero()\n", - " accrual_end_idx = np.in1d(df_times, accrual_end_time, True)\n", - " accrual_end_idx = accrual_end_idx.nonzero()\n", - " # Calculate NPV\n", - " def calc(x_t):\n", - " discount = np.vectorize(lambda T: model.zerobond(T, t, x_t))\n", - " dfs = discount(df_times)\n", - " # Calculate fixed leg npv\n", - " fix_leg_npv = np.sum(fixed_amounts * dfs[fix_idx])\n", - " # Estimate the index fixings\n", - " index_fixings = (dfs[accrual_start_idx] / dfs[accrual_end_idx] - 1) \n", - " index_fixings /= float_dcf\n", - " # Calculate the floating leg npv\n", - " float_leg_npv = np.sum(nominals * index_fixings * float_dcf * dfs[float_idx])\n", - " npv = float_leg_npv - fix_leg_npv\n", - " return npv\n", - " return calc" - ] - }, - { - "cell_type": "code", - "execution_count": 117, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 1.00273973, 2.00273973, 3.00821918])" - ] - }, - "execution_count": 117, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Convert call date to time\n", - "callTimes = timeFromReference(calldates)\n", - "callTimes" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "####Test the t0 Swap Pricing" - ] - }, - { - "cell_type": "code", - "execution_count": 118, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Swap NPV at time 0: 0.0091\n", - "Error : 0.00000000\n" - ] - } - ], - "source": [ - "npv = swapPathNPV(swap, 0.)(0)\n", - "print(\"Swap NPV at time 0: %.4f\" % npv)\n", - "print(\"Error : %.8f\" % (npv - swap.NPV()))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Monte Carlo Simulation\n", - "\n", - "#### Generate time grid and paths" - ] - }, - { - "cell_type": "code", - "execution_count": 119, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def fixingdates(swap):\n", - " leg = swap.leg(1)\n", - " n = len(leg)\n", - " fixing_dates = []\n", - " for i in range(0, n):\n", - " cf = ql.as_floating_rate_coupon(leg[i])\n", - " value_date = cf.referencePeriodStart()\n", - " fixing_dates.append(value_date)\n", - " return fixing_dates\n", - "\n", - "# Define evaluation grid\n", - "\n", - "date_grid = [today] + calldates #+ fixingdates(swap)\n", - "\n", - "date_grid = np.unique(np.sort(date_grid))\n", - "time_grid = np.vectorize(lambda x: ql.Actual365Fixed().yearFraction(today, x))(date_grid)\n", - "dt = time_grid[1:] - time_grid[:-1]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 120, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([Date(7,4,2015), Date(7,4,2016), Date(7,4,2017), Date(9,4,2018)], dtype=object)" - ] - }, - "execution_count": 120, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "date_grid" - ] - }, - { - "cell_type": "code", - "execution_count": 121, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Random number generator\n", - "seed = 1\n", - "urng = ql.MersenneTwisterUniformRng(seed)\n", - "usrg = ql.MersenneTwisterUniformRsg(len(time_grid)-1,urng)\n", - "generator = ql.InvCumulativeMersenneTwisterGaussianRsg(usrg)" - ] - }, - { - "cell_type": "code", - "execution_count": 129, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#%%timeit\n", - "# Generate N paths\n", - "M = 100000\n", - "m = len(time_grid)\n", - "x = np.zeros((M, m))\n", - "y = np.zeros((M, m))\n", - "numeraires = np.zeros((M, m))\n", - " \n", - "for n in range(0, M):\n", - " numeraires[n, 0] = model.numeraire(0, 0)\n", - " \n", - "for n in range(0,M):\n", - " dWs = generator.nextSequence().value()\n", - " j = 1\n", - " for i in range(1, len(time_grid)):\n", - " t0 = time_grid[i-1]\n", - " t1 = time_grid[i]\n", - " e = process.expectation(t0, \n", - " x[n,i-1], \n", - " dt[i-1])\n", - " std = process.stdDeviation(t0,\n", - " x[n,i-1],\n", - " dt[i-1])\n", - " x[n,i] = e + dWs[i-1] * std \n", - " e_0_0 = process.expectation(0,0,t1)\n", - " std_0_0 = process.stdDeviation(0,0,t1)\n", - " y[n,i] = (x[n,i] - e_0_0) / std_0_0\n", - " numeraires[n, i] = model.numeraire(t1, y[n,i])\n", - " #df_times_temp = df_times.copy()\n", - " #df_times_temp[df_times_temp <= t1] = t1\n", - " #dfs[n,i] = np.vectorize(lambda T: model.zerobond(T, t1, y[n,i]))(df_times_temp)" - ] - }, - { - "cell_type": "code", - "execution_count": 130, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "last_call_time = callTimes[-1]\n", - "\n", - "call_idx = np.where(time_grid==last_call_time)[0][0]\n", - " \n", - "pricer = np.vectorize(swapPathNPV(swap, time_grid[call_idx]))\n", - "\n", - "cont_value = pricer(y[:,call_idx]) / numeraires[:,call_idx]\n", - "cont_value[cont_value < 0] = 0\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 131, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "3" - ] - }, - "execution_count": 131, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "call_idx" - ] - }, - { - "cell_type": "code", - "execution_count": 132, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2\n", - "call\n", - "1\n", - "call\n" - ] - } - ], - "source": [ - "for i in range(call_idx-1, 0,-1):\n", - " print(i)\n", - " t = time_grid[i]\n", - " if t in callTimes:\n", - " print(\"call\")\n", - " pricer = np.vectorize(swapPathNPV(swap, t))\n", - " exercise_values = pricer(y[:,i]) / numeraires[:,i]\n", - " exercise_values[exercise_values < 0] = 0\n", - " states = y[:, i]\n", - " Y = np.column_stack((states, states**2, states**3, states**4))\n", - " Y = sm.add_constant(Y)\n", - " ols = sm.OLS(cont_value, Y)\n", - " ols_result = ols.fit()\n", - " cont_value_hat = np.sum(ols_result.params * Y, axis=1)\n", - " cont_value = np.maximum(cont_value_hat, exercise_values)\n", - "npv_amc = cont_value" - ] - }, - { - "cell_type": "code", - "execution_count": 133, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "npv_amc = np.mean(npv_amc) * numeraires[0,0]" - ] - }, - { - "cell_type": "code", - "execution_count": 134, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "18789.378507421919" - ] - }, - "execution_count": 134, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "npv_amc" - ] - }, - { - "cell_type": "code", - "execution_count": 135, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MC NPV: 18789.3785\n", - "Error : 127.2350 \n", - "Rel. Error in bps: 1.2724 \n" - ] - } - ], - "source": [ - "print(\"MC NPV: %.4f\" % npv_amc)\n", - "print(\"Error : %.4f \" % (npv_amc-swaption.NPV()))\n", - "print(\"Rel. Error in bps: %.4f \" % ((npv_amc-swaption.NPV()) / 100))" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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RERGRGKSFISIiUmGkpqZqjzqRCIXfCnBvaGFIBLQwRERERCoLLQwRERERkV1S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\ny+dWfoDp6/py0sKlNCxfnk6wSijlU1L5q/S+P8iqf1OnGgA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gVpaIqQllFrf+Cul7tX2M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\nRK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(10,10))\n", - "plt.scatter(states, cont_value)\n", - "plt.scatter(states, cont_value_hat, marker='x', c='red')\n", - "plt.legend([\"continuation values at t+1\", \"regression\"])\n", - "plt.title(\"Regression Bermudan Swaption\")\n", - "plt.xlabel(\"State at t\")\n", - "plt.ylabel(\"NPV\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.1" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/MonteCarloPricing-Swaption-Part_II.ipynb b/MonteCarloPricing-Swaption-Part_II.ipynb index 4df7972..9d4a595 100644 --- a/MonteCarloPricing-Swaption-Part_II.ipynb +++ b/MonteCarloPricing-Swaption-Part_II.ipynb @@ -38,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 455, + "execution_count": 1, "metadata": { "collapsed": false }, @@ -54,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 456, + "execution_count": 2, "metadata": { "collapsed": false }, @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 457, + "execution_count": 3, "metadata": { "collapsed": false }, @@ -87,7 +87,7 @@ }, { "cell_type": "code", - "execution_count": 458, + "execution_count": 4, "metadata": { "collapsed": false }, @@ -173,7 +173,7 @@ }, { "cell_type": "code", - "execution_count": 459, + "execution_count": 5, "metadata": { "collapsed": false }, @@ -205,7 +205,7 @@ }, { "cell_type": "code", - "execution_count": 460, + "execution_count": 6, "metadata": { "collapsed": false }, @@ -238,7 +238,7 @@ }, { "cell_type": "code", - "execution_count": 461, + "execution_count": 7, "metadata": { "collapsed": true }, @@ -249,7 +249,7 @@ }, { "cell_type": "code", - "execution_count": 462, + "execution_count": 8, "metadata": { "collapsed": false }, @@ -272,7 +272,7 @@ }, { "cell_type": "code", - "execution_count": 463, + "execution_count": 9, "metadata": { "collapsed": false }, @@ -283,7 +283,7 @@ }, { "cell_type": "code", - "execution_count": 464, + "execution_count": 10, "metadata": { "collapsed": false }, @@ -322,7 +322,7 @@ }, { "cell_type": "code", - "execution_count": 465, + "execution_count": 11, "metadata": { "collapsed": false }, @@ -353,7 +353,7 @@ }, { "cell_type": "code", - "execution_count": 466, + "execution_count": 12, "metadata": { "collapsed": false }, @@ -457,7 +457,7 @@ }, { "cell_type": "code", - "execution_count": 467, + "execution_count": 13, "metadata": { "collapsed": false }, @@ -468,7 +468,7 @@ "array([ 0.99726027, 2.00273973])" ] }, - "execution_count": 467, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -499,7 +499,7 @@ }, { "cell_type": "code", - "execution_count": 468, + "execution_count": 14, "metadata": { "collapsed": false }, @@ -530,7 +530,7 @@ }, { "cell_type": "code", - "execution_count": 569, + "execution_count": 15, "metadata": { "collapsed": false }, @@ -557,7 +557,7 @@ }, { "cell_type": "code", - "execution_count": 570, + "execution_count": 16, "metadata": { "collapsed": false }, @@ -568,7 +568,7 @@ "array([Date(7,4,2015), Date(5,4,2016), Date(7,4,2017)], dtype=object)" ] }, - "execution_count": 570, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -579,7 +579,29 @@ }, { "cell_type": "code", - "execution_count": 643, + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0. , 0.99726027, 2.00273973])" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "time_grid" + ] + }, + { + "cell_type": "code", + "execution_count": 17, "metadata": { "collapsed": false }, @@ -594,7 +616,7 @@ }, { "cell_type": "code", - "execution_count": 644, + "execution_count": 18, "metadata": { "collapsed": false }, @@ -635,7 +657,7 @@ }, { "cell_type": "code", - "execution_count": 645, + "execution_count": 19, "metadata": { "collapsed": false }, @@ -652,7 +674,7 @@ }, { "cell_type": "code", - "execution_count": 646, + "execution_count": 20, "metadata": { "collapsed": false }, @@ -663,7 +685,7 @@ "array([ 0. , 0.99726027, 2.00273973])" ] }, - "execution_count": 646, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -674,7 +696,7 @@ }, { "cell_type": "code", - "execution_count": 647, + "execution_count": 21, "metadata": { "collapsed": true }, @@ -687,14 +709,14 @@ }, { "cell_type": "code", - "execution_count": 648, + "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [], "source": [ "states = y[:, -2]\n", - "Y = np.column_stack((states, states**2, states**3))\n", + "Y = np.column_stack((states, states**2, states**3, states**4))\n", "Y = sm.add_constant(Y)\n", "ols = sm.OLS(cont_value, Y)\n", "ols_result = ols.fit()\n", @@ -704,7 +726,30 @@ }, { "cell_type": "code", - "execution_count": 649, + "execution_count": 51, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 15476.68970108, 22203.99784323, 9906.4788144 , 589.72935045,\n", + " -282.14303658])" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ols_result.params" + ] + }, + { + "cell_type": "code", + "execution_count": 46, "metadata": { "collapsed": false }, @@ -715,7 +760,7 @@ }, { "cell_type": "code", - "execution_count": 650, + "execution_count": 47, "metadata": { "collapsed": false }, @@ -723,10 +768,10 @@ { "data": { "text/plain": [ - "17127.228006267218" + "17011.521601274337" ] }, - "execution_count": 650, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -737,7 +782,7 @@ }, { "cell_type": "code", - "execution_count": 651, + "execution_count": 48, "metadata": { "collapsed": false }, @@ -746,9 +791,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "MC NPV: 17127.2280\n", - "Error : 58.2987 \n", - "Rel. Error in bps: 0.5830 \n" + "MC NPV: 17011.5216\n", + "Error : -57.4077 \n", + "Rel. Error in bps: -0.5741 \n" ] } ], @@ -760,7 +805,7 @@ }, { "cell_type": "code", - "execution_count": 652, + "execution_count": 53, "metadata": { "collapsed": false }, @@ -768,18 +813,18 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 652, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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EREQ62I4dO7j44oupqqqisLCQKVOm8MMf/rCrmyUSQ93BKVB3sIjInrG3dgeL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XtLF4EEBhIYEBCwiMgrJ8fpNx5Ir1dHk7PN+rmMXBPnrF1utMnMnEOeqsyaOUSdk8kbD1L1FUY0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bAwcOxOjRo3HppZdGHGPSpEmYMGECTjvtNPTp0wderxePPvpozPMJ/0xHHhaX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\n+GiDFRX/Hux/KYDfd+v2uGP4PoDrASwH8A435hys8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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -787,7 +832,7 @@ } ], "source": [ - "plt.figure(figsize=(10,10))\n", + "plt.figure(figsize=(8.5,8.5))\n", "plt.scatter(states, cont_value)\n", "plt.scatter(states, cont_value_hat, marker='x', c='red')\n", "plt.legend([\"continuation values at t+1\", \"regression\"])\n", @@ -796,6 +841,29 @@ "plt.ylabel(\"NPV\")" ] }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.65652302, 0.60137971, 0.96419477, ..., 0.58020483,\n", + " 0.62947565, 0.62142583])" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "numeraires[:,1]" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/StochProcess..Basics.ipynb b/StochProcess..Basics.ipynb deleted file mode 100644 index ea5d527..0000000 --- a/StochProcess..Basics.ipynb +++ /dev/null @@ -1,1198 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true, - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt \n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## Stochastische Prozesse und eine kurze EinfΓΌhrung in Monte-Carlo Simulationen" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "##Was ist ist ein stochastischer Prozess?\n", - "\n", - "Wikipedia beschreibt einen stochastischen Prozess als\n", - "\n", - "die mathematische Beschreibung von zeitlich geordneten, zufΓ€lligen VorgΓ€ngen.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "\n", - "\n", - "**\"Defintion\":**\n", - "\n", - "Sei $(\\Omega, P, F)$ ein Wahrscheinlichkeitsraum, $T$ eine Indexmenge (ΓΌblicherweise T=N oder T=R). \n", - "Eine Folge von von Zufallsvariablen $X_t$ mit $t \\in T$ mit $X_t : \\Omega \\to R$ einen stochastischen Prozess.\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "**Beispiele**\n", - "\n", - "- Aktienkurse, Zinsen, Fx-Kurse \n", - "\n", - "- Warteschlange an der Supermarktkasse\n", - "\n", - "- Arbeitslosenzahlen\n", - "\n", - "- ...\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "Eine Realisierung / Beobachtung eines Stochastischen Prozess nennt man auch **Pfad**." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false, - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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Py7WJpqqoOMpCWM/4Dujo1H2Xb3kiVUtQFgVYAa5DsRrbx4ciRZEqRrw/G8sy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\ncBxBEARBVYTjCIIgCKri/wNy61LHNgKTWgAAAABJRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "dt = 1./100.\n", - "T = np.arange(0, 1, dt)\n", - "for i in range(0,5):\n", - " X = np.cumsum(np.sqrt(dt) * np.random.randn(T.shape[0]))\n", - " plt.plot(T,X)\n", - "plt.xlabel(\"T\")\n", - "plt.ylabel(\"X_t\")\n", - "plt.title(\"Pfade eines stochastischen Prozess\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "###Wiener Prozess\n", - "\n", - "Eine besondere Klasse ist der Wiener Prozess. Er hat folgende Eigenschaften:\n", - "\n", - "- $W(0) = 0$\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "\n", - "- $W(t_2) - W(t_1) \\sim N(0,t_2-t_1)$\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "\n", - "- Differenzen sind i.i.d" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "##Stochastische Differentialgleichungen\n", - "\n", - "In der Bewertung von Finanzinstrumente spielen stochastiche Prozesse eine wichtige Rolle. \n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "###Beispiel Call Option\n", - "\n", - "Wir haben eine Aktie, den Aktienkurs den wir beobachten folgt einem stochastischen Prozess $S_t$. \n", - "\n", - "Eine Call-Option gibt dem Optionshalter das Recht zum Zeitpunkt $T$ (OptionExpiry) die Aktie zu einem festgelegten Kurs $K$ (Strike) zu kaufen.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "Zum AusΓΌbungszeitpunkt ist der Wert der Option mit $V(T) = \\max(0.0, S_T-K)$ gegeben." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false, - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "K = 130\n", - "S_T = np.linspace(100, 160, 50)\n", - "plt.plot(S_T, np.fmax(S_T-K, 0) )" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "###Aber was ist der Wert zum heutigen Zeitpunkt t=0?\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "Unter gewissen Vorraussetzungen kann man zeigen, das der heutige Wert den erwarteten abdiskontierten Auszahlung entspricht\n", - "\n", - "$P(0,0) * V(0) = E[P(0,T) V(T, S_T) | F_0]$\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "**Vorraussetzungen:**\n", - "\n", - "- kein Arbitrage\n", - "- vollstΓ€ndiger Markt\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "** Grobskizze **\n", - "\n", - "- Replikationsportfolio aus handelbaren Assets (bei BS: die Aktie und Bankaccount)\n", - "- Da kein Arbitrage Preis des Replikationsportfolio muss Preis des Derivates entsprechen\n", - "- Unter einem bestimmen Wahrscheinlichkeitsmaß (risikoneutrale) sind alle diskontierte handelbare Assets Martingale " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "**Wie kΓΆnnen wir den Erwartungswert berechnen?**\n", - "\n", - "Wir mΓΌssen dazu die Verteilung von V(T, S_T) kennnen. Die Verteilung ist abhΓ€ngig vom Prozess." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Black Scholes SDGL" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true, - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "Mit einer stochastischen Differentialgleichung wird die *Dynamik* des Prozesses beschrieben. Üblicherweise hat eine SDGL die Form\n", - "\n", - "$ dS_t = \\mu(t, S_t) dt + \\sigma(t, S_t) dW_t$\n", - "\n", - "mit dem Wiener prozess $W_t$. \n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "Die Γ„nderung des Prozesses von $t$ nach $t+\\delta t$ fΓΌr sehr kleine (unendlich kleine) $\\delta t$ wird mit dieser Formel beschrieben. Es besteht aus einem deterministischen Teil $\\mu(t, S_t)dt$ und einem stochastishen Teil beschrieben $\\sigma(t, S_t) dW$. \n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "\n", - "Ein sehr bekanntes Modell ist das Black-Scholes Model mit der SDGL:\n", - "\n", - "$ dS_t = r S_t dt + \\sigma S_t dW$\n", - "\n", - "mir dem risikolosen Zins $r$. \n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "FΓΌr dieses Modell gibt es eine LΓΆsung fΓΌr die Gleichung\n", - "\n", - "$S(t) = S_0\\exp((r-\\frac{\\sigma^2}{2})t+\\sigma W_t)$\n", - "\n", - "In diesem Model sind die relativen VerΓ€nderung von S_0 nach S_t lognormal verteilt.\n", - "\n", - "$\\log(\\frac{S_t}{S_0}) = (r-\\frac{\\sigma^2}{2})t+\\sigma W_t$\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "In diesem Modell gibt es sogar geschlossene Formen fΓΌr die Preise von einer Calloption:\n", - "\n", - "$V(0) = S_0\\Phi(d1) - K \\exp(-rT) \\Phi (d2) $\n", - "\n", - "mit $d1=\\frac{\\log{\\frac{S_0}{K}}+(r + \\frac{\\sigma^2}{2})T}{\\sigma\\sqrt{T}}$, $d2=d1-\\sigma\\sqrt{T}$ und $\\Phi$ die Verteilungsfunktion der Standardnormalverteilung.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "**Was kann man machen wenn es keine geschlossene Formel gibt?**" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "###Monte Carlo Methoden" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "Wie lassen sich Erwartungswerte schΓ€tzen?\n", - "\n", - "**Mittelwert** \n", - "\n", - "Sei $X_i$ unabhΓ€ngig und identisch verteilte Zufallsvariablen mit $E[X_i] = \\mu$. Dann ist \n", - "\n", - "$\\hat{\\mu} = \\frac{1}{n} \\sum_i^n X_i $\n", - "\n", - "ein erwartungstreuer und konsistenter SchΓ€tzer.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true, - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "####Beispiel 1:Berechnung von $\\pi$ mit MC\n", - "\n", - "- Dartwurf auf ein Quadrat 2 x 2\n", - "\n", - "- In dem Quadrat ist der Einheits-Kreis gezeichnet\n", - "\n", - "- $X_i = 1$, fall der Dart innerhalb des Kreises landet und sonst Null.\n", - "\n", - "- Wir nehmen dass wir nicht \"zielen\" und somit jeder Punkte auf der FlΓ€che mit der gleichen Wahrscheinlichkeit getroffen werden. Die WΓΌrfe sind unabhΓ€ngig und die Wahrscheinlichkeit das ein Dart im Kreis landet liegt bei $\\frac{1}{4}\\pi$. \n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false, - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [], - "source": [ - "def MCSimulation1(N):\n", - " x = np.linspace(-1,1)\n", - " y = np.sqrt(1-x**2)\n", - " plt.plot(x,y, color='b')\n", - " plt.plot(x,-y, color='b')\n", - " dart = np.random.uniform(-1,1, size=(2,N))\n", - " getroffen = np.sum(dart**2, axis=0)<=1\n", - " plt.scatter(dart[0,~getroffen], dart[1,~getroffen], color='r')\n", - " plt.scatter(dart[0,getroffen], dart[1,getroffen], color='b')\n", - " plt.title(\"Dartwurf N=%i\"%N)\n", - " plt.show()\n", - " print(\"Pi = %f\" % (4.*float(np.sum(getroffen))/float(N)) )\n", - "\n", - "np.random.seed(1)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false, - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [ - { - "data": { - "image/png": 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c7dSEJVARXqse4OrdgTh/dY0WjtM201WY7xuBHg36xaCje3N5UPTDqesjqXsW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\ntK18rFQq5nU5Tq//ktxpjZB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VtJVwUSllxSqaXSWraLVeFMfQOG47Utp0LHCaZfuS4L5pMSDTwNAyJne/wCE7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\nAtcdBS5A68bFsv/L1FdAV9CYh70G3ISWLoewr5B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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pi = 3.147000\n" - ] - } - ], - "source": [ - "MCSimulation1(4000)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": false, - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "###Beispiel 2. Calloption mit Monte Carlo Simulation\n", - "\n", - "- r = 0.03 \n", - "- $\\sigma= 0.2$\n", - "- T = 1\n", - "- K = 103\n", - "- S_0 = 100" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": true, - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [], - "source": [ - "np.random.seed(1)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": true, - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [], - "source": [ - "import scipy.stats as ss\n", - "\n", - "#Black and Scholes\n", - "def d1(S0, K, r, sigma, T):\n", - " return (np.log(S0/K) + (r + sigma**2 / 2) * T)/(sigma * np.sqrt(T))\n", - " \n", - "def d2(S0, K, r, sigma, T):\n", - " return (np.log(S0 / K) + (r - sigma**2 / 2) * T) / (sigma * np.sqrt(T))\n", - " \n", - "def BlackScholesCall(S0, K, r, sigma, T):\n", - " return S0 * ss.norm.cdf(d1(S0, K, r, sigma, T)) - K * np.exp(-r * T) * ss.norm.cdf(d2(S0, K, r, sigma, T))" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": true, - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [], - "source": [ - "def payOff(S_T, K, T, r):\n", - " return np.fmax(S_T-K, 0.0)*np.exp(-r*T)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": true, - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [], - "source": [ - "def pathGeneration(N, S_0, sigma, r, T, dt):\n", - " t = np.arange(0, T+dt, dt)\n", - " dW = np.sqrt(dt) * np.random.randn(N, len(t)-1)\n", - " S = np.zeros((N, len(t)))\n", - " S[:, 0] = S_0\n", - " S[:,1:] = S_0*np.cumprod(np.exp((r-sigma**2/2)*dt+sigma*dW), axis=1)\n", - " return t, S" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false, - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "image/png": 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q+w/y1ey1uG6eoPY1FXYo5CaeOOLZEZg0+n/f1q9/dFdMzNigdtGb6PDhQSrs\nVkhT+FzhV4VOf44lnjCJZ0V2dedpbfL+Kvwk9WfyGY7dBNysqrf7s/g0VX3RP/Y9EK+q8zKco2pn\n8v9eRNoAI4H7SK9kI3I9JoT7SlQ3ZHKOAB9izIO3pWsUT7ycwMYQjl3ekBZFMf7WG4B7UFVPvI7A\nk8BljjpnXWZNPC8PxkwxH3go44xdPK8e5m2khjrOGbnneeJ1w/iVN3TU+UvkpSdezsQIpt03kqJb\nShAWfpx8VZeTdDgno9ZcwofAYnWcVPE8weSXiYs6SotAGtdEH9GooltSZFOZwJL9+UL2YpJ6hfj3\nLTIyKamiioQX3h4uCfmYdTgXSzCLdT0wgUlPvx0n1/Sl9KRdfJYMPYfAvt8gzxuEpsXzw6xLEaq8\n9QCvVF7FW0BTR33PjzO5n8alsB/oHd9ww5fXM7kTxkQy/UWemPEmD63dSOluYaTEAHkOUaHTDpo1\n30LrIzOZvXMIQ3LuY188ZrG3IuYt5ivgf6qaUkUO9txD+OCHWLhzGNfu+IMjGzHBSQMxnlcFMG8r\n179Ft3HdGdYBpLYiCZjP+A1UR08J98reP4L5N32RFtHmm4MsZIX2pcdhQasc0Jj9/ljKYzxsblSk\nCCbv/VjgGVQTMf7+NTGT3EuBOv7n0MbDDQGmri/Da3e/GdKbWxp8S/nD9Xnzt7XAjeo4aYj8AEyS\neL7GLHJ30njqbMuX7/7yH3+sieHhjdVxtvj/M/mB3+tzy9Aodj+GeZMqyIlZ/COFIui6szdVs6U7\nTzsTMrPypVkcmwT4T3OGYHJhpB97B/MAyHhOtp5GdrvIG4hCb38GXyeT4w8qLD9pJnfi2BMKCzVD\nClUXt7uL+0VQu1wKyxS6+8fFxf3KxX32bOXFdWNx3V9w3ddx3SxT2+K6A3Hdz3FdUaiiJhNif4WR\nfgKwuQobFPalIQlJ5E1LJfQPhfUKa/wxf+7fm6vnVaoUG/aD+83lA9z90wLu3g+Ku6NyfeXejuu+\n5b857PVn3Vv9bSzTp99FrVrPFqd4QlOa3nqKz2DW4jJl2vZt4r7Y6zp3MTPcx3HdIbhuS1XlMpre\nGIWXFsbneyGs2p9jRCuA/gZp45j442u47rLnr3AfcnE3u7iZJtfKcI8K8e2soRQ5OhX0Z9BCvjxl\nFaYopB0ndH8lViQO5771xyi4ZAM3bp/GZP2Ad3YUp3gCZg3jpM8fs6A8BWQKJAwA3ViZ/QNiSNKn\nWbjPxW06I6UeAAAgAElEQVTivzV+oybxWoLCgKd4rgToBtAbg+5NjWSids/h0yH9G844WPiT7/cn\nBqJ2TODJ6bk4nvo6iya5uAVOvr7eCLoZtIBCAYWJCusU1iocUZinMFzhXoW6CuEubnUXd7uLezuu\nOwbXjQeNhrSVPLx6Ba77nEIzhTV3tiIn8Swgnl5qErDtUCiB6z6K667FdYsGfRc+cpnxsEIff7wf\nKwjxFCKe3Z2637Mzu7rz9A2yUPKYoIiJQX9npuTbZHKeYl4J0zfnoiswu516gwiFMb6izlopwBD/\nSx8WtK+FmlznJYLb+maPtS5uwwx9lFWTjTDOb1fExd3l4p5VSldc9wNcd9ipFLzfLgLXXVbyk0/u\nVfhDYZRCvJqAnjYKVyqUTyEy30988v18Rr2iUNKX8xKFGgodFF5Jhdl3/O9/yQ1ff/3IgYiYiXup\nNUghf4brFcZ1W+K6FXBdAXJjFisXAmUzlREtEU7i/DfpcfR1Hsrl4oa6uMtc3DbmOHkDhI4I46PU\nWFZvh+WZZIHUKNC3QVfTb9kAXHdP8yfcr6cF3N9c3FwZ2oaAlgNtQb0978q12xNz5juUFl5vpzZ/\nbNLReTn77thMu70J1Ek+SuGde6k92sXt9SK//RZDUlpbNmyfyI96LevTYNAkIG+Ge5AT140013o7\nCmYsg98SoUsjoMXjTDwaRfLhnqxKcHGHurg5ffNHMV++D0GHZ/hf6jSLbw4sCnlhV8lxEw6Pd5xN\n02hyDej63Bzv7OK+4uLucHE7upz4nwAdADoNNMSfyFzpP+xDM95DF7emi/uHi9sB162K6+7EdXP7\n/VyKpO0OjP1xa0KuXBv2R3AL8YwnngnHQiju/281DboHvXHdFbjmwePiNnRxV7q4osa0ufBNGE5t\nfo2uWHFTRHjPtAuq5IE7MVnjIoP2PYmpNp7+9/eYSL2M/WVLULtdpM3840/0Z6s5TtM2VGGywgj/\nvMpq7PVXZGzr4t7o4i4I/sIF9XO1P+sp47e91cXd6OKWPhOZcd2iuO4+XDfvGba/LPekScdWlCw5\nKqs2Lm47F3e5ixtxin4Gy4wZP8+tXLmewt0K49UU9fheTe73PCe1h+oCa2rDR/vhdoVXFV5WuFZ9\nWzRoANStx7zplzH/D0jbAEt7tqLDY4/x2O5wwgcAfxRiwu7iHNibh6RMC06cuKZ2At1NRMpiSh8+\nGFb64PEi4YcPCmnz/Vn6ctBjSNpmyh3cHn7t1sQ2RVYeG8ejexfnu/q1Bq988lvur6YeevK6b2e4\nTPdc3O1fMXtLezbNysWxdTEcTr6OTdqWx1z4Og50N2gZXDcc122F607AdY/iumNB84BOB/0KinXF\n2OlXP8Zjb7zFL3OFtC1t2TzfX0No6OJKfhLvCSdl/Th+au3b8fu7uPNcZszfQpvnRra4PuWSZz47\nEMGx/wVIWyakvRz0GdZ1cRe7uJNd3FL+/Qj1ZXjhVPfNxa3vPyTa+p/157ju4xnu7ZNFIzatn1m5\nUjLP555CPJMfak4uNW+DfTL5f3kW1/0N143131pXuLiN/e9AycQAe5ym9Y7F5Nyjr9V5+1h2dedZ\n2+T9jHivAI1VdU9QuyoYu2A9zALONExuaM3Qn6q1yf97EOmCcWWrh+86d5r2uTETgM8wLnuDUH0v\nYzNPPA8Y6agzLot+HsZ4al2J6mFPvB6YgKnGjhpbZpYieN4LQIw6To/Tymuu1fzxbt0+GX9lm/Uf\ndAodH1AETtrA+JjfnFXKBfG8J4A7gEbqBLnSmbS+N2BC4Ztg8r58/Z3xb2/pQGKUcZP8GWNXDsM4\nN9QC5nZg7PGvuLJKZSrkWkOqHKFRbhiYBkXTSjP6SDG8lQd58VgCNZ067Kv4tRbddPrhUgwoTlia\nBLqvvTms+JFHav0cWLvg26pd046GHWbsz4VDCx17r/FMIh5//cCK2kcerRbPE4+O5P4jQCFabWvA\nnRtbsTLXfl6stJMDYRVg0dYajCr9P9qEdKZJaiqh34E2IFcK1Ngfww1/KNUOLCZn6ljgB7ZF/sJd\ndQ9yPGQS8LgqqSJSD3j4cR7vegM3bPyQku3epezIyhxcNoxFV+wgMu/91IkcwNINVTm4FpMwbOth\ntu3dgXfHlJCrS3+Wt1WeAvv3i6TI+PtIaNSQPUtD0dsddfYAeOKFYUxHDwMPO+qME6Eg5k2quypf\nB9+rO2WjxJL04npy9thL+DuztMBD/lrO50AFdU5EZrsSl+NJBiQsazTz4NGbJ0ZTrE05bdunL1AK\naE2Gkof+usxLmCCza9w47sDUtJ2dKqk3D7m6d/1fpn3OfXc+c7zhhJtbxx29YXJ2dOfpXCjTXd3y\nY8Ju+2Gi48IxEXIAc9UPORaRPpgvZgrwsAYXpz3Rp1Xy/xZMYMt8oAmqS8/ivFIYpTUe1b8kn/LE\nuxSz4FbWUSfz9LRmsfZdjDnjFlTTPPEeBR4AHIe47Rj3zYZpISydP5YtiUWofIzIS2fTML4Rs9ZE\nkrQd44r2peNkSMnrs1YeLFmG0Yt/DXs6pfXo+lFX/MSc7m+xGGNWVEC3ERn5NmVjZlHwXlVSMvYh\nntcVY768Sh1n2ynuSwzQeg70/Q1iK8NzTYyr6FYyfBGHixR+n2uHLmBsmxrUT3qaDYHRUPdbWAma\nAlwTRtrgAiRWTyKEdmy9ZriWO1Wq3iAxyIlJthUChBa4bmOFmGZbxu4qrMcOJkb+FBqbeE2tFwuH\nbPup2OZkjpbZT57kFMKWYwpk7AB2kv/YPvr+1JYyyXX49nstEFZ9b8yKxoXXLy92PO1oaBihafDy\n4oNsiT7ExOJ5WJPrEMdDYjFh+r8SntqBJrt28X2RW9Pl8H8mqLLUE+9FIBCH8yIwuQB7tihcVoZ1\nO2uw9OAuChbZSaH8OyicO4F8kcXYtm5t01xrtNWOQMqDdSftp84DS3kxADIJaA+0c9SZ79+AAot5\nsdY+6g3FLL4+FIdTDfM/2UCVdSJUjSD1jmhSu0WSGlaO1dPnU7lRWs2D9VNeX/4ufl4fjAtoYWB7\nxx8ml5tzdONVG0e9E83Tqz7vt/qt6v3GjIkQuAzV/Zl+FkbRDwVqvtCHu6+Yy0pg6ye1f8jzzsa7\nI7oX/2j3S5vjA/NiqNx4M/vPu5L/O7BK/l+CCVRyga9RfTkb58cAhzLOXgA88cYCSx11Bp+mjwhM\nHo/JqD6HSMhChg1JpEiHOty3T/Ltjlz2HBwrTiHSCETuYu+KnBX2zsp7ud4eNvYuQiiOcQdsinm7\nmAB85Ti6zxOvIvBwRV69O5IdW5YwuF2cyxFgNtBJHWfqCTF4BzMb/x24TZXNfx7zvJsxX9LG6jhr\nTn9bpAnGE6mW+sFCGY7nA3pAVHdYGQkrX1Ft3h+RHGTw5RYhbw6SVyhy7CihHVSZn7G/v/ZPNMbV\nOQRIxsS0pIaQKrHNt1Q6XuVQWs338h7etj/3yOe59+oqrFhWkyX3YdIdl4Pk/bArDdbngt1KoQJb\nIqOq5E3dFlMosvjhpYca75vArZvm0uaql8hzfCLbot/AxASMxlSqqgrkBd1JmSNF2BW5gSOh+3w5\nUjCRu9e7eHswCrjEz0wuPoJuq/6gMNVYvjkfCTvykbC5ILvWlWTzqsv4ZU5j99lDmAdIA42LW7eM\n/psDJC2twoAWv/Fat4NUHlSML5aVZWSsmAdcShKxL8/l0/IQaATcGodzOfAgaFJIQAvVLbg9Mrr+\nji2hl3she/PkLLNsZlzY8S15AoH4pckpoaFTMDVs1wM78ifsqBKxe95TOwpVSMg7+TKN/ih/wQmF\nWup9Tz22YnH58h3UcbLOz+N5AWBUqY00Hd6NXZujdnxxf66i8bnScv/w1ZhLL2/cXL9dmYea1XZT\nyyp5y/lDpBdGsTUhe0UaMsUTrxRmFlTOUSfT2XUGOYpg3iZ+xrzWbl/BU3t3F6xYTj+6K5kFddcz\n8r6VkZslpAAzSzcd26LpW6++mXLNooWBPVy1cg2PLEqLSQrluu9KctWPZSn/ezGWVk/gqRdCCh+f\n8d0lvNxMSKuCX3xDPK8RxtTURB1nmQiXYqoIVcEEMT0G3KfKl+J5HTEuhNelp7E99VBEgHl58hB1\n4AAHjOmV4O+C+Nf5AraEQfEwzEPlL19SP53AVExqg98wMSuTgd6q7M5aBgYDJVXpkPGYJ15VoB0w\nyCGuD+YtvqmgrWHrC1AxCQrkh1rT4JYFYbQ/0JCE1rXZ36QSB1t31bpu0HWq+p+ZYNJUHMK8ebUA\nVqiS5t+/B4EG6e6tIrTDJECr7eJ9vYycU4eQq08YyQnLqdZAlV2ZjsvzXgPC1XG6e+JFQ9r2y2mf\nGsmeNODoISouXsKgS0GXRJDQ4TLuLYBxDon6ldfHH6Bm7yVVmPBw8zx3hxTR0KjyB0LyHEna3GSZ\nl7vY7t2/T2zUKP7gmqJrEl+ruWr08buTWqdMuh/4EFWV/pKj8m5WFTzM0SljeT00TZ6ty4Idi4tX\nXJU6dmELjDluIaYQyYzMgu++yenVfrknc3+qz4/JDwRKkBweNvX9RlNCQ5N3Pz+a4QOnsanedkKt\nkrecH0RqYKr+1EV149mfTk6MYozGZEacAsx18dKrCb3tqPPKWXRYFaNwJqO60fOkBAdy/8ZnbaP5\nsNNCjHJLHdeB8p+2o8VnbRmUX+fFlmdYm1AO5VvP/e/voLmZfRfcGc7LPW+TAn9saXRdai2BJ1H9\n4qTLed7twPMkBi7nukafAmNUeccfWwPgY2rs38TgJeWISGuujrM8w/jLYHKY71LlixP75cYcOfi4\nQQOimzRh2Sef0G/JEhLgJCX+O2gNTM6YWqr85UEoQn7MmtcU4H+qqAgxGHPq7ZisjyMzmpZEuAzz\nuVTPqCx9W/U7wC05WOdWpV+taLbVRnWnyJFFcFc5mHADMMfFVUy+nMGYot0PO5oh2vOEnAdVOe77\n2H8KbFCll3+fA8AioL86TtB9YgKw+S7WF6zN3o7HGL+xBZMrVdXlmZr2xPPKAL8AVdRxdnri3QZ0\ndoi7F0jDT9rmiReFSRFwOdDRIW4x5sH93HfVWs4f2KpL89m11wTqTf4x+fr9lVMf/W6o7oiN7VVh\n69Zh/nVaMaPg65HPlZG9xB6OIvH34ZfRs2czPrp/AbVensovYsxOdwn6B7CYdlvm8MC6vJho6J5A\nEkbZT1DHmCo98QLAnF2RoR+1b1C6D5uSCrW6o13tRxr9OhOoFDeTZ6vsImTFW9xtlbzl3DEmkgXA\nq6i+f/anE4JJprUTE1zSDGgm6CWXsi+kAElTvqfII5qe0Oos8TwpjTKDiTfvYViP/JgZYn1HnUTx\nPFOg2nHe94URjNJ7BRgOvIDqcc+T6IidrCswi13lh2nNTMfhefF8U+T20FfLJh7V6HlhpLQGZqQG\nAt/kH/ndZfvfqdOFRXk3kxy4yR9rnD/Wa4BcmFJ31wIVVUkQU/xhR/ny5Hj9dZ7IkYO8mGyItzmO\nzgy6f/mBxcDtqrhkwF8knAZ8AzyVcZbvz6CHYnzQH1Bljr8/DKMIX1Llw5PuqXjRGFOWlGb0K6lE\nT9rKzSlK6DcvU3Hyt4SOhBIDVZNe8MS7DKOk8mOU1g/paRdOkqO/RGHW70ZoP92eYWwdVfH8+3wd\n5m2oujqOn7eeesCPuTm4bhLuJYWY2ewSfXVaZp+T38dHYcdZP+Va3sSs4YzFvOUsxDiBlM6wxWLM\nVa/91IB+n9+x+tV7J03q1KNH14grJrw+90joj5ULH0lL++OqId8srFz1RuB9f8xTgCeJc+6PIHHu\n3EJRRZ5pwn13/kZim5UEBJ7GBGSlAsj13Xvh9e9J1aRoOu98lGoH3wOa+/etJsamv6n2ImKKfh9T\n/dufKisl5uZlAPpu3rvfLsuG6LiZjMI8mCsRzz6r5C3njsiLmGjENhkXA8/sdN7A2F2vUyV9plJh\nP2EzxlFiygRKKiYn+QhggCpHz7Rvz5NyKNP5tN02hj+QgrH1jgNmx7l8h1F+pdXJEB1rqh2NxORD\nvwMISSzED/M+IoUQ7nYcnZzxWsVzbMqxM6xAQt22E2XW6DtHhKalDT0eEtKwY9++fdaUKFHm6yf7\nLBq4p/+2d+jaJJXQ9OISUzGKYKk/ux4OHFLlCREZERXFXWPHsjpfPmo5jqZ6njTDJL96GXglLk7B\neG2sVeWJTO5tIczDYyLQLzMzjt9OMGsRQwFHleUiPIXJlHl98Hl+iuRvgLU1ePzRWBb9CLzm4U4A\n7nuGyv1zsyKsM7tvK0jBGzEeQv2A0Rnz8/x5/f4SjikiXgizKNlG++l8X7brMA/chkAaYWmRfDHn\nE9bm/J5Ha0/BRLj2juDYd0X5o8MH/PDdkfDKG5v/wB/AZUAEpq5sJEpU7F5KoFpgbGdJjT7GAYxp\npFhyKL+srEyFAzEcDj/OzxXX8F3e/azCvHnsBHoqxB/MTeDj21g3qcW+HOXnv5bvlwGzE4+H8EOH\ntlRKE3Z32XL5K8PatO+0pWDBm0vt2LFuWs+ew3ZrgcabKNm+aujCQGKYHow9xj6BG4OdE6S/PA48\nSFJOj1nPdGBxlwhqjf2Uxr176fOJm/xasmUKrQy7NN/Qkq+s3FQwTW94CJodTQot02nN1TKj2sOp\nL9Zs8SNjgFHaT9/Lru60St5yApGGmNfKmqhmadfN+nR6YLxfrlAlPdlYFYzii3fUSTd5FMPMrusD\nD6ryzSn6DAPiSpde1qB9+5cejFxYa2vsNEc+oNStC4ndNIWZJcLQ2ddNZnJiFOvUcZ7NoiMBOmNc\n1o4DfTyXtRiPisaOoyuD2tZ4lFe/WSmVc8z9OmXTwZw5pwF9MTO6Yi3nzGn7dd++tYDr/6BwuyiO\nfZ2HAz0yrl3441wKLe+Hbz4eMYL9l1xCG8fRWeltPE9KYtYAtrRsmfDz4cOxt2IKVCRl6KsIRsGP\nUyXzMQbhiRd4nJrPryB3m5vY2usHCo8uRFKDFZp7bVCb4pi0D9/W4/a+0Wz7Fvgd1W7+NYvD4XVd\nGTilI9cUwSR5G+yo85cF4z/l7C8hGFfqCIx9vwXGDPS49tMP/H5fwmTOPAYco9Y+eHpFCTpevoDE\nkO3AU4r0bcisq5bE1v6l0MBfbmr9yyer7vt20tHoxMTIyOPHI8OS03KHpITlD087GghochqwU+CP\nI5TOcSRQOGTszSViQtLSFuc/cGDLmuLF6x3MkaNC0YSE1TXXrVvScMmStX/ExpbcE1mhU8VNu0Mj\n2SHhaYcCx0JD9uVISVoikJYqRK3JR9WoZFJLHWBXaiAQlRYIHAxPSVngleJQfK57ui7Z1nX53r31\n6mYYv2DMZe2BptpPt0h/CSNl5Ezeblab3CsDXN99BXk3jOG1jYtjDxT+MjIi4fD2+6+IZGOcx9RR\nCZU6fnf7xnZh4YmLxg4k8eNmQAPtp2lWyVvOHmOaqYSZeVfFKMFuqGapdLPuiusxX+YrVNkA4IlX\nExMU18tR58NMzrkGYyNdATycbsIRIRRj/rgFuClHjv07Gl71ReldSy87dGx7yRyryb0xDcmLyR0e\nyCHJqUfLJEXpluhZJAe2A3uA3Zisk54qe4IuWgzzJvEWqup50gWTh7yeE0cK0H8jpTpXZmVEGMnV\nD7mLDmG8QtKzrnYI9o32vYgmAocxeXlOejMR2fYuTOrUsmW3PY89xmzH0fYZ74PnScR7Q5+fNWXm\n7XXHFmj33jUr5ncNukeCMfsMAd5X5YUM9/BBjInitfS3It/GO1KhSS9qxBYhMWcPfk+MIC0CY//f\nh6ln2gsY4hD3Kia3UDQmg6RvNtk0Ar7tzH3d21KEfpg0JoO134mc7yfJYhTc2xi3wuu1nykMLv2l\nKsF5avppZm6onwPz1HEGI9IpDfrEDHPHHO57xYCeZcfsGLSwR3QIx+9VAnvX8rCznxoPBEh6pTID\nXs/B5mSgkBIoslz6fDH1hoTQoodWrr3F81aIyTeTcigqKmRxuXKl1xYvXnFPTEzBI5GRacX27Pkg\nz4ZLC/fuVfHGCt98uf6Jid1TBNkG3OSoc0j6SzQmlfMO4A7tp6nSXzoAb7K37B28ue4t4B5Vpvnj\nDGBMT42Ba7Wf7goaX36SZTn31J7E5uhbCTsayvGc4YHYNaTd3TCF2X2WMO/R2SEhyVtuaDUyfnKR\nuOjk0kkBelfaSEr0LGA+yFCr5C2nR+RGjDKvhgnSWI/Jhb4MmEcm6aFP3yU1MaaSG1WZCyYtLsaW\n2N1R57NTnBuBUTaPYJJEFQZuAl1fu/aMZQ8/3KNaqZKrCvDeXb/yYaeywNWOOnuDzo/ONfiXl6su\n5+7EMeXu+Y28qZhEVgUwAUVXYZKezfC32aocCBIgMH80nwRSuOLS+4kOpPJ1Tg7lPELOFao8A+AX\n4bgNGJBuN84wiHCMT395oCV+kKCIxELhX2B56TFj6h4uWXJ9dcfRTRnOrdiHF8aMpVO94Z1azCl8\n1bJ6dbrRUVJ1oghxGGUcC/RVZWKGe3cDxvQxF7gC6HcD28c8zpphGC+d6+JwemBmlte5eC7G1HEH\n5uH6qEPcEIxiugxohl/tybhaHtzLJVes4tbl+TEPwg6Y1Ll3az9dcJIsRsG/jDEJNdV+ejjD8Vjg\nE//P9trv5LgF8bzKwKwJ/fo1v2rZsh+qjR69bG/u3LlKP5Dnk1krbxyYQOe1+2naEON+Wgbo6OjJ\nC94jKnnX5NnPpNs+ZogGeCKrFNLieYUxBcS3o2m/hi97reyLr1y9utamWo0wD6lljjrP+XIHK/qV\nmNq212s/XSJCa0zytJrES6ovWyWghfY72S9eBKH95jeYXaAr2yNXBWDVTXX6NP38hhdjVXQbJtju\nW7cx7YEH4rZ0O06xm+P4ucCj9KlxFKgP0tUqeUvWmCIML2HqAzyDcbtbg2Y+KzvzbimGUTI9VZng\neSLsKnA1C+p+wdoKzzhfvv7aGfZTFng6JCR51fPPt0q+/PLvugCpLLx0HE8OqktKWCXASY9c/PM8\nz4sANgzozRcN5pHHUadjhn7DMBkEm/hbfWBpDRZP+4KbcpVlQ5u0EA4sGE1Ecm5mNmyt72NMVpVU\nOWWO8QwDEIxCbgdcJyZw6HtA4uIG1dm9+6r1S5deWSuofWXgqaF0b/U0z6U2YG7jJ9zrlwYSWX2g\nV8PCHZd9s/wQufNj8juNUyWjKSg9i2IrVeaKUB/0pSdZVbU+e3eGoPVv5KpYzOLjk5jPvKaLlwSs\nxsQD5GtE0+kBUjsDDdPdSAGk+Mej+SPHnTzW+sNWW3n8y0+Iim/M5v5x3Ip5KHwMPK39/syU+AzQ\nFnC0n+7170dTYHp6rIT0l1CMR05L4Cbtd3Jt19Dp099rtGRJm/mVKsmRqKhh5bdujV/bqdP7X9C6\n7vsMKfIIaw8LMhbomzErqXhe3j4vsDotwJpBvWl4JjUCxPNe4NDam1l433y3/4wUzAPsEWAWUCHd\nxddX9OuBgpgJ0VTAIzH3bAYd+JiQpJk8HZmef/+mvzzghMbAINBo+q4MdNm468cii6feMKTFkMKH\nIw431YDGAIPyh7P/o/qUWriP/n2W8QJX/bCKkPDqQD5mxgnxJFslb8kckQoYxbUe6JpZ9J1fQu1a\n4PusFvQyOacaZuHwM9eVjzBvCHewN28uVlY+zJU/JQN1HOe0BRkaREcfeKJ58zE3dur03LGcOfcv\nDJ129ZcMfqIpGqiNUSojM7MFi+d1Bjq5cdwErAFaOuoszOpai6Vm1TVU/Hw87Ut8y/WBnByevYcC\nb7z55pU/V60678dbb12fa//+gk8mJUWdSf3PzAZz/3F4phSs2AFJtWpx+dNP50lud/OesNd55PEH\nGZoPs+jY4AGG/TSC++soAUeV9SJUKVBgy4ekUb1vwuCE6iyrGqduwl8vQTTmwTpSlbfAFOdQGHKA\nsKadqCeHCduGcef7TpWBIgwB8v0fe28dJcX1tf9+Tsu4MjDMDAzu7i41EDS4S3AJkGCB4DAzgeAW\nCJCgQYIGd53C3d0dRhj37uk+94/TEEKS7/vetX73Xfe9l7NW/dNTXX3qzKldez/72c+OQL+NYnZ0\nMRN3L5jN2fKwqQxSvkII9xdeNN1e1DB8+uVb1SeKYbGDbIdSUMVD6aiCpu9EGH7APFSU9DVQHOWJ\n1pGhMtJh4GejOn4NQMqlf5l/uOiOysmMRyUVJcClokVn9Bs5su/NAgVaZdWvfxIhugFjAr4qtSTu\nD31BgVrjE+7X+nUYsE6GqheHrgtxmC+KzskYv3Z7a0oZbRRtaNFe8F8MoeuVkba9nOtgwhJXNiIs\n4i0q0hCopuOvNKlNdMz3vbRHPaAooDmO6kSVesGKM0VxSskk1f860vgWeIvy/KOB1o7vTFzHuV3O\nfhkr7wTdaDO/UZiYvHHyi5KvSkYCYYcXtTtetmDsdT2awCVPeAn8Qt2IZUAMGVFnOd/JnzDKfDby\nn8ffhxBdgJ9QjIgl/8aYEYIfUMnFYVKy4N8vRw6gM9ADpH/lygcvTp/e1NtgkGWBjZyrupex09aC\nqERESDiQqWmy/z9cxwi0cndP+L5t2wWFO3WaaTII++l5EzZnu365fpkfuB1bmJQpwGpNahl/+76u\nm1HQwGJgpNS0fbrQv0YlvOr/E63Pwbc/hPK4lwqkBwr37wkUdHZOvRYU9KTqihVl7wshu2qafPxv\n6/Dv6yNM+SGiIFTJIbjYegVVam8kYcWhcV4nqJN8kMYbgMu+xHkl4DsSqOcoo28NLBXCNmX/fo9e\nJZZlvAneigXFcrL/eX0EiiJoB3pIiXR00pqP4n83DEFLBfqg8hrdpMQqBG5G7FdHcj/ItfjmuiMf\nhQVgDV5zkVVGL+40qMCQelYDo87lxn1XRsvY36J/CDxFhQ5FsV1HtUz0cKzdGWCYowioSVBc0LLC\nbws7t7jUYkiFpxUeC7KSKzBwsDMxtQRyiJmkLUD591z1D/cRLoqjjOp9oL8MIwBHty2kfIEQBe1w\nvlqzQxsAACAASURBVH4PLuj5KcDve1bwTPuG7/3f4pTmCnwvQ+XhrbrfcF/i5yYl5Ury3lM7WnTZ\nUE/T5H+lbeQCXObx4jhebYmQoXISqDaFqI5NxVHUy0IhYSG+jnvWZKj8az2EYhFVIilXSZadO0ly\n7hwouDEA9WIMQEVSyyLQKwBrk12SL/Yd2L9D8fTeT8J+aVAMaAcylNyvstN9zeMG5qNbs4Sc4ZhD\nJoHN11N4WE3z441Trf2Wjfts5D+PP4cQbqi+oHVQ2i/X/v1UWjnO7YBKjrWXkhMf/d0JBfP0QHkw\ne2rV2h4xaNDwH6Ki8gUdOdL1zokTbecnJ2fbEIG+CHitSW2crgtPVHXraE2TWx3XCgTae3jEDe/a\ndZqxTZuF3iaT5aAh1X0aLXbPAfynUezUIXK2BTFKSlZ9mIeuZ0fRJpuhOOmPUUnPGVLT7LrQTajy\n+xGa1P5KixSiPIod8h1Srv+HNSgCdHFyStt28KB7COqFNxJYo2n//pDoujACdk2TUghhQhkJr3Uw\nf3tzdg3swsuQbjQtlnX/zUMKP6jaaeT02iVvdLt3vm3+/Plvd05Kyn5s9erQiUBvWvZeT/lVcRF1\nuYtkat36JAnJPqT8kGx1MJj6ohLcaQ4DPwcVHTTQpPaPGikA08X1reGiWPPMwcWyWkQ9M67aSQtb\nRo0B3txsluiaeviLHvbK1wP4hrBLcyHiopQjW3+ySD7AQTvGyyc4GAHGfhJZwWqyXnHKcjKD9DAT\nnx/wsuKbDsLFlZev/YlIeUGnpnVlw08NvQsw2y3DpdmtOdVs8dY2r5MoneDD1V55Xb47Ob0WATNr\nsR4YRZhMB7aBvEmY4TowPcDdPeHXClQ4dnxKREs90Vd8tS6Zwo9KoyCVjcAWTfs7S0zo+gQs8V9w\ntk1hoOjH8IpjPfsAC7NE1pkGkxp4IVglQ+Xif1vX/zQcRWYTUFj+oLFdxobaDHbvi71nWBGMkJq2\nM+Kg01DLkXrjEpb3dI93S3BKdU59bDPY8npmeJoD4wOT5n/v7ZX3mTCtWR3CZyP/eaihDPxp1GYf\nyD9opPx5KsVQ+OyXUnJBCBqhwvLKUvLacc4uVCJzWbFi57cvWVKtf0aG68Rly6al7tnTv5LF4loR\n6FWMpHozuWEcSrm2T/E4CMgxY3q0q1t3y/KBAy8cfPasVEUXl1Tfb78d9qZRo99ym0xZu4Cpmibv\n6UIfiqqi/EKTmt1R1LMF96zLbDx7CQ9bRxQD6CgqobtPatrbT+9HF3oLYCpQ9kPnJiGqoV5eg5By\n66ff+aeh66IMCne+HRrKlBMnaAysj4jgDSpp3RAFb9UCbFlZPB87Ft/4eLLmzePXbduo3LYtjQ4l\n0mPlU3I3zWnoZDs5ptKzO5qhZv3fbMUq7rmfxw23qVPX5YqKypvafVRr/WjWkzqX48nMsNH9aF2m\nuj1leZXehKIgtv1CUBPYZsZe/RAnTCj4oDnKY/ziP8lELMi7oFqBqAJ6x/I3Uwtdqe31dSktuutt\nm59nJn+cMc0vsL7y/ZJLGi0ZQtiCSOiwH4YESrnpL92ydKEXdeLdN6WYMDCdoOh7jB0lMW/9EGkJ\nEY5qOF1PRETEBb6h8Yav0lyzy4vL3lHdJHG6C2zHZN1OltmGemE3lcJSz2i+YzpcJOZgnTtajL/r\njC+j/Y+6N+lGm6zwP2sYHPmfa0A9Du+r+oPl2yXBJ3MZ3BcNSc6enN1LIF7jnHGKLuvjab47r80n\nofaJGOb8cJcdQAIQT60D7hicbnKp51PSXiyRoX9XSHXca60Mc8aJKW2mxE7YOqFMY2vjv+21/7B3\nnIGinKhdj+lj+pLu9hLovaLeiv77y++f2PB6w4IbxvfPg926qeur1lvaBaQOGnyN51HJ5rl9j/Yt\n/tbnbec4zziTs8V517gd4za+CaTO0/yMnHAmxPjZyH8eaggxH5Uk6vqfCpqEwAulCzNTSlZ+9PlY\nlMGti4oElgAlIyJEfuC3qKhgz+HDI3zevi1Y4z3tURe6sCDO7SPw6U8UKYmi9bkBqUOHfhNfvfoe\nt759r7XbtctvsBAyFzDkPRyiCz0IVQlZW5PaPQCh6znJMAzDJoZxx8tKlviO6nFr/1bo9MlweGLH\ngd80qa1EiDrA1izoaVYsmyaoRGwkqjDmwyGlTPrLtXTh+ugRP48fT3dXV+5ERVGsenWyOnYkvmhR\ndqPgi9ORkbgMH84yaSC473QuOztRgnTKRxrJdMKYUS+72f3Rg8rywIFuLw8d6OtF0Z3f0mD0Lyy5\nEePmnHl+9JK8F16TMOPLQO6aDfj3uoj5x1IMK+hBaK2mDDCls2UNM/tsoeuajry8kpv0oo4pHkUx\nhnZoUvvL3P/yfw4XtWavnn3ojd/D26FXludqbT/oc5mKQgafTk4qcuqlT74DOdavmertanX9MoTk\n5ZARKWV7zbGeuVGGuwOKPbQ6G+c3lGHMYpQmzSCktCPEJBRUFiIiIgJc01iZM4qSuV4zY/PEWVd9\nubj0OnOHZZC7NmOmdSdnlInpY3a6RNmvFmXm6FN+tacsqfm4d/moxIITz991Om+cr0tbnhOOPfT+\neDzYVNbrlr9zz3prp1nHpfzsRsd1L44WPv1OIGrtrLxz2+idoy/njs1dMsUlpZmh0KPszlMnsve3\nRu90zyfydvBtN2uJYe7YUi08XnwHqCxD/1mXSYSLesN3D9/V4EaDe65W19zAAE1qOz45xxUYVTUb\nY0p4YSrgjszrhsjpgjH2WaDVb+QCc1aHTbYXTf6IO3Azx/2zsaKWb4rvvvu57+8DfPN4uo1dUDrD\ndf0L+6zNrxDCLnrmj87/pN/RfidLvSxVxGK0NPfM8Iw028wHfuuB/+rVIc0+G/nPA4TQUJBBaaSM\n+/fTMKCqK99IyaBP/iZQMMg7oJrJlPnD4cMueYGxp0612Dpp0rYWUhrrScmHAiJd6B1QZeyVQtDs\nKK87UUpeOiCNIyhsVwPKaNqfBlUX+ibgoSa1CULXi6FEwNqjQu55hGh1UeyQKlLyX3pUutCrAluz\n0XHYfaJXTIATD6CM48/78eE2CTjzSbm70YilWDGm/PwzB4Fsjx9TcPdupleowNOatfDJspL45g1+\nmZnkzZ6dLF9fwICT1YLRYACjCYyOnW2zIy2Zrml79nxtP3Cg55UnT8pOlJKTQtAb+A5zSj5qzHlE\njTn1cE4+D0yRoXK1rovvk6yM/+4615ZVxCxS3ZeZms/qlUjxOlEYnhUgazoYjgGP/jHv8P5/GC4K\noiIaa9UHVZuG/jHB2sjSJtOA9ShQ0JmM7hbfV+0IvDqNJ/WFW4bn2zpE+h7Aw60tesi3+FREsWWK\noSiEMah+silAmiuvZHm+HW/D/WkMNeMkTo2eGDsdepHLo7ZbGnmyxWGzGXkdk4M8OaOIcral+UiM\nZnvVq4KhPxk5XldS7xjG0O8yTffyJLjx+o0vF5xzioMFn8gBp6NoWh1VzfwASAMy4n2omuxG1wVJ\nBVzCV1W2u08dnXH7at2O2bDOTgm4Oab/gP41gH4onXlz27Nte3xbM6ovRR60osfql3aLS/G7xaXJ\n/03qcqdM2z7vdO+jmvyo5uHPtfMDrlV6VGnErHWzlgC9UDmPE8DIkLCQWKAFMF/LwYsJxSmaZee3\nxCwePUjm8fN1rbM67Bn4+8PAh+vGD/zm6PD8xjbno8wDMuzSms0rc+fW18QWTs/uOSdbnuYXnnV2\nz7hmlOXu5nwXkBDgbcDwXn3z6JguY3zPFzn/FVCFuhGuhIR8ljX4//0QwhPlXX2LlHv/86lMROlo\nhEjJ32iUDi//vhC2lCNHzPcNBuk5derqZYcPd58LNJaSD6qLutBdUDKvPTWp6f/0e7ouSqG89cGa\n9ie+qQu9odXEL+23MCbRhx4ovvZiYLHUtA94qmO+X6LK9P+WiP3r3IXXImbdtyAC9vPH/cOcXyp9\n5BGGUBED36E80ooyVN55/50jR0QZu51dqakEOzvzymwm8sYNSri68jA9kJRbiVSumZ1phTy4ER9P\n/OzZVL3+iFGpSXgjuEJdvpT75TshhF8FweNNxjxWD1u65xz5fYdZ8vtdf84NE7AOt5hljPJfjGKt\nHJGhcuT7cw4dEwNTs5h/93KRU1XzpNQ93WOv7aIt4cQO6gcZsVtRbKON/0Z/dSQEzwOBBrvBfc3P\nK51qJv6WkRN9itGD725OZ+PlXGz4+goLgTPYDWW4WdetzvZRlUqQ19qRqFQUvPUHqn5iGdj8nYl5\nZCTDz0CWL9i8jaR5FWKRn00Y5B3vtpcWtm9YIDa7uFv2OiO/3McVTWo2oetDgE5Hv/uuce0HV6+f\n3Yq7Mdalb7Wu9vTIFpawZ72okW8lmbl2cxcFG+5DygMO2G0hUCEkggRUZLmYR+6/Ln43rsWruNzl\n9FkzU6/h62ZQ/Y/ypoaZMx3zzgn4AMtMgpWHavMDgoQWyRFFW+ziZr/lxKOgrnIoWPOA47gfEhYC\nysF5IkPlSF3os1Cc+rHADzZh67a8/vK3m2psMgqDHHK0LlOB+Zqm8jwOmYjjwAZNalMBhlcevuk3\n7bfmP9VJ/CaPpxxm3dghp2FN95wpQS8sT0yeN07Xz1WkXoToWuIupz7OqThqD7YDd2WoHPu54vXz\nACF+BYxI2fcvHysuuQdKOMuDfhVL8shzDgp3/0fPWAg8QT4eOHCEzxdfrD/Vo8ediSkp2bYD7T5O\nygLoQh8LVNKk1vbfpqbrYj0KwikOlNc0mVR38+KqRW6bDj+r/dqQ3/QwpizXH3mTOOYr7dXfKJCO\n6GITyij2/HfdFlEd+L07zt4DGb01VdQJifOIc18Vssr1aOmj5y1myxygIKq5TY2IsAgb84aNpdi9\ncSzt/+zu9uKnBzGoDaopzkbC2IkyAItQBVHVZKiMF+FiKJIBrGAwrzgtpUxHCJetcKQ+xkqjWWSd\nwJTQYF4NB75Ayvt/m2u42IaCj3LK0D8jm63ijGtau80XcnVeWyo2MVtGQpZHaL9+V2eiRM4aoSKd\nEigjuPTTiE2EizkoT/PCyfDfLzqTMONUk29eB9UkV85yGDOcOWCx03DGffaeiaUNs+lPexa6uLpE\nbF+2PczF6nJRk1qWLvSGgqx1/hx7U5SZ+Q3YnqOisefAs1eevK3ZxzCk6UNZ+ZuLQjjbyNxazH75\np2ociPR0FNjVjXgEHM4bGXlqx+vOzV2iKF1sFmmowqKSsVVZfPNHWmLkJMoB+PAC3+Oh/xzrR/1e\n33p7yyOBRk7kyKxQ6LT3zPDWnpN7n31wPLFIbDCpdaNwmWxxi6vIKP/COPj7KHbMaKC1u5HMVVVd\n/Fcavs48cOtUfjn0UhKALnQflLFv7Djkndx3no7uOto3xTWligyVmbrQcwD3ruS/UnNEjxG9ir4u\n2n/yxsnJfsl+zw1zR2yl/LXuQCVNk3Zd6B6oiPUUqspbrsi5ou2Yr8Zs8kz37LByycrtMV7RM0wm\n24h3EyceLlzucTNNk1lC12c49mT7T/n9Ilz4o5yjdoRx6rOR///gELpeFVVt6I0SX0r55JCAd8HX\nr4sXfPOmxrHy5a9mmUweKE9GGXXF/U0GUrAICxZDAVLMewnImCg17R9ZN0aj7ccKFY52/n7418nf\n9rhZLC3LxZ6FaJ8hjX+RPNCFHohitFTVpPbY0emmOkrDxhN415V1fu3ZUqM3KyfOYUSrHMRUysLk\n7SIzzalpOSxm94STnqRcc9zfIBTFcaGmfdIuTeCOeoDWSsncv/5NGB3r9E13WLLMQFeXiUQgRYdm\nl5ud//rI1z7ume65gZ+tRuvmnxv/vLPR7RBLibZHilHxspkfJt3nXvF8ySS7DmGIiCXW2tenb+d5\nQ+dNRxAqQ+VGES5+QsEXq4EZQE3CeCmllAhRJwtW6njmHcu56PK40IUXiaUYtzc7Z7sADZB/Rg4i\nXLzXpr8KvJShcpQudPcD5OxejoQ5T3HPfDpinN6y8d4WLkaiJ9yi+NnBH9U3KDno71DGfA8KUjko\nwnhftek6/mCpho1fDjjrPHjS49RKcQGBO3BdE80117dUzFWM5wXakW3+PPbv2E81ctOWvswD3jS4\nVW7w1D8GLMvEv2lxpiZn49I81Mvk3UfzzwbG/fhVK0DxCXudboTNmLXpfJMvHvNV3kSK7yzKm4VV\nyTbwErvb3nfJm1DOXvvJaKulSD/58LELKWY71kJxPPJNl70dLKyVKNisbUiWHsDFbKHG8z715++O\nNl8WPvG/5X181LnS0le7W50ckLq7ydO2gQf72X96ugqXhFl8XTGZlSc3UOKPOfL0qL8Iu4lwIXDy\nK1e40tgjcwxjvAdfycp8nsYZA6y0hcoPrSd1oYtDZQ6FmGymfZUfVz7tmeHZQJOaHWCvy9655wuf\n/2pyu8nHgGERYRExuKQPZHnf+WzusJldLfuimp7sReV3+qG0e3L/2ObHG3dz3T20buG61unm9J/e\neb3rP7nt5GkPfn0Q/mGO7ymdMFlq2kY+GSJctAZmEUbBz0b+/ytDCOf+I0bU2VOt2oREd/dSXY8e\nPRLt47NnZ61acSij/fFhzJ6QkPnjihWjTpcqNX9No0YnUUyCRJRhTwYsUtOkA4c/Sb6UPay6JFBS\nt89RHuG29/rW7u6Jee124/1lC6vdyv3tbN95maUfFyE5x5dEPkKVk3+ACXShrwBiQyIYD7QFhiPx\nq3aO48Ct+Or3xDyGh89k1G6dEKsTmTkqctnZ/4HXwW+/KTHKlGUo/3HPVl0XBVE6KslAT01TMrV/\nLg15UFBELyk5oD4TeR3fsQDdbbDiu0YU/ak6O4DpMlRGOeZaFlWg0yw936O7yVPG1PBJzvba6fs5\n7qR47jzFqXlhhK2qQx3P7/gu/8xqMw2mIJN99rY250sw2dcsom8srkzInRxkq/eIoWvvE3Ad58G/\nkM25DsnOd/nWvoaW1g2UT13JxXaeZPkAC4JZH5mPFQUGYw9fAvkpgkYHCmKiKpDomeZ5e9mvyw5I\nKTq7WVyNq2tuStpec/0LaZC+FX0InFYaw+43WBY+pqwMlfc+2SsOGQha2qHmsfw4DW7Ks9z+nJ4s\n8nfKLBvlnO122qGiMynrlIAfcC3RmdNk4jyhIl0bjce78DxeFz3J0bce3GjXzrPbMy/nMuE7O6R0\nfH54pCcPf/sUFhLhIjeIQ4aAxp49i/g++kqs3yvgJ02TVsec8gADkp3oua8w2SOK8qTe1y6F5tv7\nGx7pSy7f/8laPMuAKDQES5IL94AdJsHO2TXLjiiQ9bTPpcsNOH+xcfLVmzV/986/IGPGmYZDZrec\nfaNnkUyfYN/M3G2iHunpzhm+PK8ZxOatgQws84ZDc6Zx46uRQKlPK5WFrg8CWh+l3iYp5fATMRwL\ncKHPkGssyJKMe19YJcLFHy4Wlzv7p+4PAY5qUgsT4cLPJ9Xn2Pr564uY7KaSDbIaPHHs0wFkOnWm\n8cE3QH1UYtiE6rblBlhPFz1tnd5quiUoPij/4mWL57/I/qLtmK/GbIr2jv76fQHYR3OsjHpZl5Wa\nFsknQ4SL3wijx2cj/791CFHoZv78E5wtltZCSvuUbt289lavzoBdu16M2rDhild6eixKVOsIShP9\n9iffXwMkIeU/Nq7WdeEOhNy+Xf277NlfVff3f/lACBZdofzGEcxtAAwWdoo03UdWTA775ZRdxjoF\ng2+7mIPzcaeA26V7xXlizCK17Va0gEgs67vwXembvBszna/SXenx1TqWpHjSF3iS+yXLV/WinclG\nQZDZWDDEiFfSXq3H8z4f5qMYMPuBI5rU/tZaUNeFCYdXDgx6z7H/83aphUoa1wZRFiWpOwuYI6FE\nqpnTOUZxI91MHRkqpaPwKi9klYD+fcuXX/vl+PFZph3bBBs2gE1KK6q4yIDyggf3qdlnYuNrjcOu\npF98FGb/1RBJ02yPct4/dqrolSZFo52NJZ96GoIzpdGDFEMkVWJ+ocStZRypHYVuhEFnYPfvwGtP\nPOv1pGfH7phzFmGRvWl9y/XT1WVRNpEZ/DD459WsDrBj6202vjQ+y7NGBGQ8upQtwz+nk8XXz8v2\n6oKPfHjnWBt7j+zdcL8WT3SxACo1rPdnsY+Dhy1CwkJs2dKIaPICq099anfMYTDLzV0pv+PwW8+0\nyPMoplR5pHwuhPB1rF/81lX87JebLW6/lb+wMMat4tXccdmzpxov68Vu5UawBpgkQ/9s4C7CRTHg\nYOUidQ3DA++7BxB5W6gIzA/opmnqJSTCxXv5A31IIdqU8uJaf4+jbxGGezIkZDeQJcK4Cmh4FutG\n3h7tONrAnP04icMGDFiXq8Cl+v7OlHiTQbz7g+KnAjd3r8O4qd7sb1JLW7LpzEd7YS7IsiCWAZ0Q\n1oeEOv0EBAE5yV77FSV/OAA0iCDkJrAmOoPWix4jUrPIuJzAeZRzUtexj0pFhEV4Axdf+r2c2H1w\n9+HAwWNhx2IF4gcgEoMtmioXSvM0/x6iAm6gdIM8UL0L4oG0kLAQN1crjweddZ/15bHdlR8HPK45\nvMfwKymuKS3/ldGj61NQBIGW/wDbeBP2ucfr/64hhDtqc/WxQ/FpXbo4/dKy5bvX2bMXAPZLg6GL\n1LSPhbQ8UVDGd6gs/xSkvI4QrVAGrtz7HqAiXDgX9WDXxBJczuVKFaCqxeJ8fd268eU8PeN6t28/\nPxYYgqoY/Q1YFBEe0fdGGdk1j/vB7Gt/Ge7epumVjOtlXVdfqsw1lIfsbbCRt9Qt2qS54R/rhznR\nG6QgXRrYBsyPCCGb43obgAns+bI7FqdQOm4Cq9N1IFyT2gVd6O1RbJkK/9rIG9B1URXloZ8Chn7M\nyBGCvpAxHoI8IL6xlPKyEBhOmMpvX1Q0X6NNkdN+dY0tlBsolYkxnwvvMqC7uXCRe6apM+LNP05Z\na719ufaBxN4hOchxbQ8zmIt6HtJEuMgFXKl0vVL/OduDN5TgpmFzrsGhV/PenFbjSo2MHJk5XC4W\nu5jlYnWZUf1R9RBgTQghwbCuGbR6AB47UFh7AHCC8twvU7pM2+l7K7QLidsmTvs2DpNxDark5MmX\n2biKnzidmuCa6nHePX9cy5jHcwF7MoVzx1OxiR0nt3SPa2cjQ240y5Ub05tmWM2ZrLT6Mp6QCE+U\njorTxhobLz3tsrRQ30KyqC3Z/V7ub+YWzxF9b31R5vVGJQLXIOVyofbdSU88L//BHxedcPrySY1D\nDXwnznDZe8fn7al7Oabdz3W/Cyp5mYjyTr+SofKGCBdVc7uyZ0jp3OZCrikmD1L6mMna7PiXfI2C\n2X5oeII7VsnvwBcRdfGy2tnR/hwi0bloXyr+sgSFPZ8Suu6Jkjboy2vnRfSoMIRedV+S+3wp4FYu\nFwavq4oH0Jw01/Y8LrhGG3xz+Ic9EC7cSA7owdnvviWqTBBvy3uR5mcg+KyFoEuxPK/rTNdUH7wt\ndpabn5Lh87Bi+9E5hjbdX/nK6+zn7tjelT8aTZZN4uLY45uAX2WoPDc/3/yGBaIK7JvSdsryi4Uv\nDpShUjqaqwQwevpo0l1LsGDopru57tZ8nPNxrRX1VqxN8EhYIUMVLTj7aLG61R2XDt/u+dHwyNcc\n2XXQyBiL2VL3vdbPPw2h604oSvNhYOynYnifE6//G4bCjasD3YD2diHOzujc+eSEPn162w2GvKhG\nGltQMgRvgL5S06I+uYY76oH6HgVbVAHaI+Xp96c0+kXM+aYgwy/GEVknB4PMBo6GhMhFQIyUjHh/\nnq6L/MAgJL24UsFs94vVv5n6R+OmL03W5hkJL1Ee2rh2WzhZ8TJi7HS6oOQRsoCEi5VoOGoWGREh\nZKIe8C5AL01qhx066ZeB+oREPEAlOsegZIVLAZ00qZ36r5ZM14UHqhq3CKBp2p8ytUJseQCVzJA/\nAiglsJfMxUu3mOxxz0PeBdhKkeifi/TnCUREzSO8QjC5r4Vte1pw/7us+JU3CsxnybU82M2DKb7N\nA+8X7eWZEbsdcrGHAV2GscIONya4NHMq4tbW/YrpinSLdrMUoEDVAZMG5HbPdN+6cMXCEz7vfIp1\nYKK/lWsZYCz5PpktwkVpVPKvMbDU2eL8072pnWbmZnN3MJBOzgerTC/jfm1nqnj31WiLrD0nHefk\nku8lah0Rz5cpzimLHgc8DDaOHZNR9rjFOXsust5pQvJHG4ttV+fwE03WuJWv8CRc5HlpW3vT893A\nH1bJwiz0yMWu7Kjq4DCg/CEO5h7EoF1BBOUKJ9yQac48NLrraJebeW/WqOsvZ04qzhAh2GqX3ImI\nJufRaFq8zaBIghWTSbCpYzCdGgc5Ox00fPnAjbSqM7T9fxHi0nVROC2LPx6kUHzXG7pMKsFeVPHS\n2JDjxAObyN9/Jnk6D0SpN4YDR3i2eg5T267AI6o0LftuRiVP+6LE5CYCaz/2fkW4KIqqIO2OYses\nA55gdX3D1MSeSHNfwBuj3Uj7Vy7E3ggn9W4ZCh4K8SoSkbNhRs2k1iVeeEqv1wmJNqv1m2sfePiZ\nKJVOHcjd/1D/W53OdCotEFXeF5rpusgB3D0bS4NxtxiKqiOZCFTxS/brWvFxxaia92om+GW6Vivp\nY7clB996VfbZ+ryvguxfW0az+gOk9S9D6HoOFAXaXJob3Rcw9L2aarmQEAZ/NvL/bxxC+KEe8i+B\nRlnuvEk2um6pvvCXhPt58nRDaVx4oLA4paeu3uihKMP4tdS0Xf9wXVdUgkci5cL3H/dZLfI2yMmT\n3Vn196y/ebUAku2Mj9VRHnYJKUn59FJ63tV1qHTpj4OuXpkbNo72n2e7WcIX61Ogbaob857mJ4d3\nIqZ0V+J2tWDFoYYsOdSI4agQdzDqpfQK6KNJ7Z2ui8oobfmNmianffgdpQ3SE/DRpDbjv7uEui4M\nKIbLWU2Toer2RWkwHYa4BeD5Drh1JHvOwc9yxTbLc3P/cbPdnH6f+50HMGAgMJHKrG3Qm/bdAPfl\nAwAAIABJREFU8uDX5xJjrJKWQBlS/DdwaHZxHjbRSM+2ix71H5JfrwVotjA27SmCT8tOVOQc1zhE\n9wgiWqIiqlWrtFXD1tZZG2O+6JxouHCglnfwnaioloOuIz50RHqvKfNLRFiEPdOUORnofbTc78+2\nVt07/EmOuONIsZPrHeqwwyTp9fsJ8hJAHH1ZQCZQiGJ0NrgYWnaJ6xLbPr5lUMyqrvYnz4XhqwW1\nhLXnMeIrSUuKGacHqaxfFJGn6eIly7zcbWlJnp4zTlQffO7Ry7miz87gWmdJ6BV09N3RIsfl8ZS5\nzO3ZZ3SfhGTX5OUo7ZhBMlRG6rrIi8rT5EDBL9kzbOS2SYKcDZhPpgbFLvWcfTOSwMb/VJQmwkV+\no+DUzNIcr+DLF6g2kqmaJjs4/t4cWEaZOSfxrZCbN7tn8HBuA2KKd2XpBWe+GFdL7ltw+aPrVUdF\nqV6ol6WLY+3LoKSdl8rQv/YgFoJqqEj3D9af86RvpTqki46EmiqYBANWVsJ1yDX6Z23aMHhEp1Wl\n61Q75Lv2OdbfnrMKldtYiXJGMoGf9k7d6+NmcctX2fzlzSg/r5IH1vmaYuxO+cLimud2NxROrH0x\nt1O18waPGmeIM3nHuKY02puUWEMPDsz/SkRlkfAqy+ZWycxbFyvBFl8kgliUAxeF4v+nf3Jk2BH5\n4sjW0JPknHYMT11lxmn/Y3iWnELLz0b+/y1DQSvfokrOSwERwN6LIzzvRjez7b5BGZcwwk5acP4V\nJb06RGra3xp1CF2viRKjOopqX3dLan8X63Kc6yWwt5+a0nnhW4uz0wLLzN/xzAjm8iiNJQ9tdIw6\nSas3G1GFFnekpn1gruwSp1Yfw7/oWvJU8Car4xPpsV3oeiCwyJhF8ckT2Rz4lht9VuBkN9IEaIwk\nZsIP1pS6JwzlTXbjEOBXIkL8UAU4zVEPyn/Uffm/M3RdBKCYKJ00TR4XQvwBnJFSzgXYVVR413xu\niI1wWXo9e2LBt8MY1vs615fgSjl6Y3PKScKOGuR2MvBV/RB5DECEi0Ioj7EnFncnjk2O4kbXovg9\n6LXTtbZX2ShmlRrEmRRn+spQ+eTDXIT+B9D8dOHTtTPMGcdvRxV0OZjewNqt5IwEs5BXf23463ir\nyfoWiOoR0cNW5G2R0FIvSn1/ucBlU4Y545XFZMlxPe91EVEm4ibx+X35+W40Ns9vwbqVKuSgLj5c\n4h23iCYPwbzkJ+Iolr9H/hbTD49+Z108KCj6arFTv0TdeTTyBe2bX8XmHmkwJthrOBts2TYbSW/S\nrY3N5ctn9w21X1rMHdt4vX3u89wjy5TlhYGH/m68ibNQPEsyFNj8aRLwn4ZhS9hm+WhhC6zJvyCt\n38vQv3qkIlzkQMFqC2SoXKTrogJKs2Wgpsmoj87rhNLaOYbqGrWMH1PKY3U/IiWzPv1dB1e8JSpS\nTERRWbd+nCf4cK6gLSoaDsds/5Hfz8UwoPwAUqw7GVD+5ZpGTy4Fu3Ff0+RkIXAB+40+v/q4dC2c\nnFsI5jY7xYVUG6tRNR+nAWeTzdRq+4JlTgG2mx4uAX9kmMsku070HZHe8Gi5t/lfuAdmemVcjJ+0\n0N1e8l7ZXOanaY8SZeST6xRocYpeNWvhA5yXofISQjSyG1kbU4dedycRiYLD3FAc/E+PV8C1DmzK\nle1FxvKD33+fmCc6+o0Ban028v/DQ9eFBpg1TR7+8KEQlVCY9EWU93wCKTMa62Oa9mbl9kM0fNyC\nXW+8SE5vwr5XGbi6SE3r9W+/IXTdyyeeHxN8qIOgMKoC8ArK4F1BbYoeQPNekZNiq9hO5h/ajweW\ndPIDJwmcasS9YiUWZ47G7FkVhcO7E+u0lwWFo00ns5V2krKZn+cT28t6U+2UX/sldY8GA9NRScgp\nHA+ZAUTKUOWVC103ABW5OWa+S+TNGlk+2WYervzqGSr8Xg+EhoRQFbgiP6Ld/R9Y7ybAr+PG0e3s\nWTYAhaSjE9PyCsatBe6Pbno6zfVOOOF3bAZbK8qSSVOeYWbSsTqUAmrWO8EaVEL3NupBPoOSOOiL\nZL641G+F2+GwrrdFCY9l1ZPn/qjZv//YCOpCDwAuSWTU3Vx38zlFe7sMszYxfe1xPaZBRmaAxWQx\nvPN8lzqnxZwV2VKyGbqc6tLXPcPdkO6Uftc3xTf/6G6j0955vkvOMmYVzhKSrJ9v2wi61kTeaXtE\nKM2hnHyPwJ3DKK53cxkqDwsh3EzNTZFVkqo4Vczp97B+z32lPI82Hldv9p5pg2r0rdD4VsmLRSwH\n9xfLPOhuMVD7lRcyX4Ix3YpvbRfeSeAoOWg18xeqlffhBwkPzQY6atrf+fufDqHrtYHfuT+rNpH7\nFqPouR1lqFKWFOHCHWW0j8hQOf6/vF646IDqHvULYbIyyjCX+LTl4X9niHBRAClq82NqTrJchwAt\niNBfsLDQM076nOEbzxccmdrQdGFIzP69vgVMJmuR96Jlou1XE8h1IXTfl883uTpb6gDRj1Lo1+8y\nkcANApt16nM//7xJq3aWumifJ52yv8H7SX6DmZjrmTLfLL6feY6m+9cAMbMYmXwoKbiEy7mhJc4v\nY1PJLRG9UTm3Z1LTVIMVIZqhIpAmSHnlH2/ow40JIzDYZjBMCuvRI2WqzRZtX7Om4mcj/z84dF2Y\nUaGuJ/BT3t+Yln8136Gw8sFIuQlA6LpbMC9mT2Vc/5cEbx7HtK4RhJii8D/8mlxVD9Io/0Ft2t8o\nUx9+RxVj3AYWh0QwHSgNVEBJslbgzh031q59Vc5yofikSTL30DnceHmKb4BLUHoUxISROymaL9J2\ny1WyvxCE4JY1EauhBiWS0iv5vHXtdP+iOazXdwkpfgHTyHg+lZKTH+FTrrPUtOsO/PMUKrQcKUPl\nFgARLioDu+uXa3mkk2lPFy+j7ZG/C21DQniKomQ2B14AIVLKxL/f2Z9DhAsvx7oNRenTfy9D5d/K\nzR3rPuf6dToPH84Mu13+BOBVxVi165WqZ7fZHmbFEPtQ5pHH6EAnPOgP7Iioi79dcnfAFa4+TCEQ\nhVHnQbEiagJWJGdq3KtR90KRC/5LNwXZsz2oQhhLEydx56Iv1ki1nvyCQy+md9/eQT8s/6XfdyRa\nYmh4RErvL3Whe6c6p7Y1ZZlmmOymbFaT1Wo1WO96ZHrk3V1hd8T8ZvM1aZB3gAAkfnnXbhGxbqme\nqW16CWmQq1GQRCwK4qkNrEEyzivda+mG+Rtens5xetrUdlO9WEa56qEue0JLiGDnHyduenercNML\nhS9cmXllZkiSs9hQYCj1O91m8s/7uC1UDcMFJyfGHzxIGZRMdFtULucHVCXn8n+LuISuG1EOy0yp\naRsdOYtRjv9VD1SUugOlnd77vxMVfLi2qv69CkySku3/3e8B73vJDkEynrhCNqyunryt2FTuWKUL\nXd/A6zuZHNrWAY/Id5wYWzdfStkDefPezdD19mUd33cHbrN13R3fV/WfbdsW2AxV4dvNimlB8/sh\nVV1injR6+eOrzBR3t3qbvy7YuKTBY0TJtW8bBz6/v/1VGyY+Gsw4bIbfaXTwZbrBntFjZI/Fo4/H\nmzZ1/an1xRJFf0DVsORDQa7bHDfdGqUF1RApb/zLwhRBwUZ2oLdwcWmEk9N0kpI8Phv5/8Gh66I3\nKtHYXVjY6X2bPCVDeWZOpiNS4YRC1+v5EL98CQN9XUlf2lJLHO343NNI1s01dH8axNssoKWm/bU3\n6IffUbouKSgu7jhNah9kcoUQg4HRAQFsW/kbnac9xHwylnwyVCY4KkQPQ/Rh3HJ6YDeOw77oEZa+\n3mAcBQsPCc+hJbpkdTkU4XucN1mZFtLSXelXbRfeR6qi9LPviXDxOxHAUx7SjW8w0xD10rk0uihn\nGgXQck5mj8y9FzZ68yBzBuvojXopDOF9azRo7GCtOKFC+Iooulk6CgMeg6JUzkMZndJAFxkqr3+6\nHgEBotqPP3IiMJCRX/bmNTamOieYC38hq8h7ttf1H098lo6R3UBnGSqPinBhXFiOkw+SKb/wMVNR\nBuk3YLwMlQcccED+Lie7zE11Tm3+xQM/a8+nG5xXBw6aNPXV5NLxmGsO58HiBkS3T8eQeRY/Jge9\nTfaLLFY/xZ5dliLB0p7o/qNlmTWO/5cvqkBpNuD3yP/RxSF9hsh05/SKKC2gw0ArYopKlty0brJf\n2vwq39k2U9tM9U5ySzJVfVg1ue3ZtpR+Wfq5URpzRHlH+c9sMZN3Xu+sQXFB35wLPvcjf2ChGV/V\nK8KeccEertvW1MtaXHpXkAzDMOYLIhdU5WDaFNlSCGEGDgcGcnX9ekoCRhTcFePYwyVRkddjoJ+m\nyb83KNH1vihjXudjWp8IF3VRCcI4VBesVp9COP/VEIKvUS0F60mJPGg+2PlJzifj8kfnn+tidVn9\nvhjpb99TPWNXABmsPXiSx1/UZ1SOLbjFjSeo7RGykhoTfTiLFP+l/PTYB6t7h/btZztv3z40OSvL\naZCU7BDhYiqQjzA5GLgdFtZ2Vt26275ewtehlbn0a0HrE9OGs/HO+RKJaNqcAFQ+YLimyd13x4oR\ncVWZ5fac8YnDIuxA56UNfrY9C95VvqZlPGu/rCt6rCay0UFu/taTTWu7MxmYIjVtsePGOzr2YJpj\n/T4+MlDwVDiwSKi82/hf+GXuAAbM+2zk/4eGw4tXWi0heNrNrLgxndcJ5XFH0DqEiMfAIg+Sm/xO\n1wwvkrejNNUlgND1XwBTBCFfo2R9g4HmmvbXtmG60NugDGU5lN7KUaCVJrUzQjWQfujiQv39+5l+\nIJJiM+6zUIbK+QBC0BXlHVcC/HF7fQTjg8Ikd02Gt2+AAm6+bsbaybWdzslzQxJtiTpCFEbKhXzF\nPAoxGOjJE7ayhizgMiaqUxWowZqQfFSdWJx8QqCFpO8zs3vZWTy2u3KdmfKgHKPmIAyo6tBsjCQc\nD5ajKgJfoTx9gYKcxr1vBecwul+hONbTgPnvi1Uc19xTogTp06bRZngYMtWcMyOwsI/7rQL3M7OM\n3ERVpI5FYbflinuybnppCs+8T/XTsVxFMSfuouQBDgEjIsIiGgGbBdaRtfmy22nfkKzX9l7lfVN9\nnyzNKnlhG7maepBlycSY02BISnPhlts4u6ttAUVW2TH5tuR1k1yk/1GNuCATsiqg38hzo9asFrMM\nb33fevum+so+R/pcjfWKPbi8/vL+SEMn5rxeaKjzY+45dwquKfe8XAuJzHWhwIXYma1nujpnOV+f\nvWb2qKD4oBdAdEhYiAWlAjkZieAt7jzjOTUIbh5Izm8LkeFkYFHMBOhbm+FJLuQjjEhgcblylJg7\nlzxC8Acw9mN2kmMvOzvWuT3QQ9NUvsKxT30c+7yp1LS/wAsOj34NCle/hWp0/fTvT8s/DyHwRkXC\njVf7rXl0puiZXXsq7qn5zvNditFu9Cr7vGyy0W5cdKr4qXkyVMk2OJyEMahk/wRmxOwiPfsNoDZh\nIgaj21yEoTsGtygs0Y3eOwlTpzYfvX374KGXLjUwI+xudGo5m6J7BwFlZKh863hWRg/6Znj6ibcN\nK9YovXdN2NlFnW8PwfliBva8bvQOcmWtY+pDgNEFfuGX3JtMA8/zu3l8L73r+VzL9sw+W0oOmjBv\nvlMGPb7YnjT2ydLR/QpSsPDhNd1rWYL9d6GYcxOkpklU7wFvVP/e94cfCgrbj5SPhRADgLEzmDG+\nMlXm1iMkx2cj/z80dF30BLprIWxDha5fIeUJXRd9galr+WrbJjqW2kErkwnbJZQmx3sD3wDFPCkj\nNS3RodC4DCgMNNU0pf2uC90PFWq316R22vFZU5QHUz2EkD5AcEQE95KsdG17FvcsSXEZKjMdDZ5X\noehz3ihDu5iJ5nzEZblwlm2GRoZ5i39bHFjkbZGlITLkm/f35ogOBjGE9XgzigVIEmkvpTwohChG\nRbYUbEWp+eXJPHWSgTNmsB9YTUBACSZ2fkXMPKXBESqPA4hmwh3BFUqRFzP9MHIGJSLlAZiB2jJU\nPhFC+KPKwu3AFbLzgmZ0JIhYnOhGGHFAKEa+JzsZ7Xu52nuV9PL4dWtJEXbosr1SYOJRWxeqOK5f\nGAWjiY1VeZfDmUX1QuRiES76oRLiHqjIwd8101WbsnGKd4WnZbf78kVCANQqDK3Txhqrl39Zfs74\nbePPTE2r3TQSFxEvrj5NMVwqtIRS6Y9twcdWk/dyQ8PLjnF2jwK38TK9wTVTwukC2S84PWs0sYY1\n6FpKYHzg5uXLf6pxpchZw/Q2UwuFbQ6zPHvS6vF+Aoo3rjIudp222nXIviE969+qHwtsSnVO3dhh\neIdCaS5pPijv+L2BK4oyxP2RBJNCFm/ZShHqlvUmfE5ZqqRl0vPefU6EjmVBejpjWrQg+7BheAnB\nN5qm4MP/sKcbOvbMGmCCpkmb0PU5gJfUtH4fn+t4ES9EOR9NUd7mGFQB2/L/DmQjBLPA7luxW5X7\nkT6RU4QUSelO6X1jvWLPOVmdTDXv15yU4pLS7UbeG2YhxdUMp4xdqBfdC2CADJWvhGANru/eMTpH\nLDAcvxrx5OsdweW+D1HP5QQDvNlUjRWLHvNGv1a5EFs2mkkKNlFmzQSu9dmEcjZaAHWdPFMjG2ur\ngy7ta8HKou2OOy8633HPW/bX96eEq5EpqB6wtYDmhETE+LuufG7LdthUa6Cn+9BjT/H3albx2+3b\nb4hFi1qxePFmnj8/Wj61SNWnPI1NaB3SgSFDfkW9EL9+X13+7+sjBgJjRjFq0BemJr+P/5HUi6ND\ngv6PG3khxEoU9S9aSlna8Vl7FKZZDKgsP0ogCCHGomh/NmCIlPLQP1zzf7WRd1Rj3nN5w7fVurIW\nqPWx+NQaPU+IG2lHPEl+5YzlCCoMVmXTirFyCeghNe3IR9c0oHC6KkAfTZNXdKH/DkRrUhv+0c+j\nC31IDDEDO4oOOVeuZFrevIzoeoG3bzOYRZjcwZ/NunsCDVCh9ldSojuw72tIvIfuHRrX6lKrXEBB\nTWpvdV0IIFjT5AshxByc0GhEOQrxGm+Ky1CZKsKFIciFcwvKUW7lPt7sO4IHrrhzj58YPOYHGjW6\nzNPlG3jx+7eoh18Cv2HnOQvwJYGbhJITuI4gHMX3n0g0rVjMQlTybh8q51AeqICB4vghiCdZZBNu\nxvpG45B7QywBL3M+zpg7sXSasMgt13n12Iw3B4CrbAPCCcOwrgrVcrkyHigbcpwcKIXOiygPMt3Z\n4vxtYHyga8n4KOPs/elpKUk49ZGcuaFwVH9ceYd/pRy8OGz3MpU4kxT4tka7PO0edbnSpdT4zuNv\nJ7smF3/t99oAHBmxa8SF6ldbdetZ/fiZZOncWTwLscjo0pnYzQYKHHaibRczWzYjnmlWAbIuMd2O\nSf/NIlx8j5KyrRMRFuECfG8X9m5TW0+NOV3stHOmKXOYNMi+KIjrD5Qq4VhSSAeaYeIsLvgZ7ASX\nMeLa2590Py8MSUkcKlqUskLQXNPkzf/m3s6BknfOGMmsSZepdAAoKTUt+v05DgM/C6iPzTyGm51a\n8iwkHw+bWAg+VxurayYva1zG4mlFVRDHo/D6mA+HwSoR9k2e/cqmBdnwq/ik4tz1tddPReU82qF0\neMZEhEW8THJNGnEr+Nao47W3vXgc9HDxY2PSLzJUSmFOr0vp9dto0S8dIU9TfMJx/OsPA8pJTUsT\n4aIU8HPd7HgMLkyeDmfpYYfzhMl4FMWyJir/sQvYDTx0ck67eD5Huej9r9o+G8fUYiBaEiaeBLlw\nd3EFrnqbiQf6hhARVOrc8913vXfmd369S7S7bROL9jLYwyIXCSEKAPuoUOEVs2blHDOVpa9OrJu8\nzrDewNdf76d16+oo6Oc2sBnYKLW/Nnt5b+AbVhr/3aAHX6xbMpCEg43pRUjI/v8njHxtFB685iMj\nXwzlbf0KjHhv5IUQJVD4XmUUK+AIUETKT0Wm/tcb+e5Aby2E/UBZpOzy8d+Frv8QyJti6+l6Clik\naaqIw8F9P8buwHvMLTpJSt58cl2Betin8ajgRYbPK0GKZylNan/B6nWhi6Vlvn3Y/JsXuR9F1Y+d\nM3+hV6LfdSsPm54FUQtFM7uF8joiUQb+44e09JjtY75rdL1RFeA+ESE9US+Cb1CMh1YhIRzAjWRs\nZDKKHRgJBH7wMTN6XlmaHY0mad0LvJA8wsILnCkL/ESFXy/hWWQV5zqNJDNqIWqffAf8Thge5Oc8\nbSnIT7zASgsp5V0xSXRkM6t5y3GSaCw/2ZBlGpYpHG+OX/Mq36tqTtFOcr4+f0Zom9ACiJh2hhzY\n1wdjsxTE+XU6KYEGflu3jhz799MwPZ1Ne/fSyGhkoKbJgyJcvM9tNPVK8yq8c+bO0c6Gu9393Gfm\ndOKZeVQDxB8lSZJGthsFTt5G8rpIERy78mhu93IbDbaKS7GnudpWz/89a0eVHU+C4oOKuWe4L57Y\neeJox/qFOlucPTJNmW7NrjR7sqfSnsIyVNpFuGiAwq7b7Qs77mSAlVmI3e7YJmtSaZSIcDEN+AKo\nJ0Nlsi50r+1Vtv+wpeqWgZG+kU5ln5XdW/pl6a5rjq1JdJzfGejJPPZSibGUIh5vimNDYkJo/mSN\nKIIpIcpH+sz/vnWzi+N2/tNediQv/R17JReQ2yjwD8lBEzdntzIn0gtcSoh7dBGXnAGYPLOTfC8V\nmVUNSXZsTqm8K24kMU86eU7ZSPN7w6HZv5PjdlX8bzfkddWVXPjmJNLo6/iNHOqQ/xd7bxkexdZ1\ni45V7Z3uuAshBoQESIAEgnaQIMFh4xbcnY3tGO4WCO7uG4cgaSC4hAAhQkLcXTqt1ev+qGx5933P\n+e79ce4533Pf9Tz9o7u6uqq7V81ac8wxxrQhFpkdzFsfFy4sExUEpQX17xPe5w8B0ANwNZsZ4Fov\nXgCwNr50hB16P3oHgZ7ApGHt+JeSTwX5njcgLy6ASdkkdI9PA5f1jqSKvwR2hKDjo0eCaD7fcFmh\noMf+9roYXNG5M4BulIIFIYLbjl3rBMUVhkPGPKffUdsFXGYzFlHEE8A4dH20GpVvNpHC+4GC8m98\nQgQHXaoqtvyIQTCA04SDRn8HsB7x8QcAbANFz6PT4HrO401W/Kv1nmjZ8hU2b+aBYbzA+fP7AniA\nurozGDmyAhrNIADj7CL239p6ouXsZB/EbV2BYVSh0PwvU7wSQpoCuP1HkP/b6/H41yC/CoCRUrql\n8fkDAFGU0jf/2O+/bZBvXMV/F5ViYdAonADQ8+8+Mo0r9W8A2v4hbPrbthjkSFtgckAQQB5TiiH/\n9hgzxnrAsvILejxVQ6hfAODCH1CPUknsa2oQYzBg+PPNRwqPfJjsqLb7pkOpLwXls+Am5TNwJkn1\nAOIpxb/cZJVE2Q/AMYg0tZi7/wcG3ukMrhi4D1xQvrFwHX7/8gVh4DDsJ4iEi4DAf3MrtCDA21+/\nYoqBQuNY6fh2xe8r7mwctnFviXnJcgB9YNcnC25Tv+PNyCPgPLmLGld/rqjHB5QSSwjktcjU86FR\nL8IbNIcUfbAEDuBjEo2k95rOaypwK3FbVGJeMifXOtfVN9e3KKksidG00wgIH4VzHsAzvTmEFlq8\nuuIDe1MhTnrLMb6nLUp62UFLKTrV14OXk4P6efOoHYkmA8AxVtJsamxe3dy1b6w7jthbIUG8D2bs\napQkiqZhVZcAHDflw91ZAkoIcj8rJ5U9O7/Vy35Vk5MiobZv7PMFdkyBiwkKHXNR5BgBYP9bz7dT\nV45f2RfAOFBIZBqZsE1Om4pMu8wuxRbFruB0DsNoJE1o/P0twGHgo8AVqE/kW+bfmbBgQgw4mGkL\nOOijiUQr2TrizQjFzYCbI5ffXM62KGyhGFE74lVjR6IC6NEKG7AOk9B/bD5Mzz1B20VnoeODfLr7\nxVrW3aeM180aiE7i16TrDNUAdIRC36QGNM8UpkYG9uBW2vkACkB4hTDxkELm6dNCXNm6PRKZp6SH\nsZC1KgCgB08iw4tvRly8Woh6Rwm4gHwTUUQIYGnjY0/jfDoCrvYx9u8FWaeFTlEivWhlzLGYw++8\n3v269pe1y8Bh7DNpJP2z81Ij7z4cwNgxLtCMsZKcrouImL5rQoQ+S6e3b13Zo3Ri53fek4fWVREl\nl/VSxV9ZLyFwcnVNzti3rxOVyWot/25h3Li9UdnMPjEF/93vDHOqHsRxhNGrXgedAXAIBBLsAVwF\nTzsP6112AYwd0aurp8dNEAalth82uWzyk78+jwwBRzmeQin9p1NrMIDD4zG+WwEKTgMQIDb2Cpyd\n54Fl7XD7thZXrtjC0pKFnd134cwFpgfWO5mZ1eD68Iq/oLL/E4J8DIA3lNJzjc+PArhP6T/Npf5b\nB/nxAGZ074EbhKIzKB3x9+1EqTwEoJYqFMv/8fp4UEQipFsmDMx7AOMATKcUT/CPoSTKUwBqER98\nDlz6yuGNXKFwxaNHpHTHjlMlOu14v/UkUfZVptZerOtcAK5oM45SPGzMCloB+PrnDYKTyM+HV3oE\nwo6XYdcSL5wbtwECw6G/Oz1u20a22dlj6dJX2Fp+ENsBvAawbdNVRNiIUOshQyuFgrJKouyq5+lv\nZNhnmLmVun0T68WhwVHBJmCEkWCEYyCyvQjVz1QAgSD8zqA8ORjCh7xXCV4kCsGUWKCWAB/1QBg/\nHjJDGowY41nimVtmWtbSrsZO3zKv5T0LVekB1x6vFn/6gi4J7hB6l0Iw8BNUUYNRaqFB4uLXcFj+\nCmNJFLSN57ohvjtOVVTAb9IkXFKJsRTzsRfAdtMGwW8ft42xcKXX+dnoqh2DNFExtE92XipYYmuL\n4wAq9v7A93vFGKmttxqMreWnAKymFLeURNkDoHGYciwd48/ZgGD1+pgeTT54fFwD4EWNSU3LZTeX\nneuS2sXrRPAJ97g2cW5qoboOBENoJH2pJEqm8T9yAGANLuPyA2dx0dLAGC6MWjzKvUZHUSCFAAAg\nAElEQVRa06x/Yv+E+ffmWwuMAgWAbw/aPPh6vMfxiVWyKgnLsKWU0LzGzygE8EGuxbCfu1DmugYr\njrdDjORxH/unL1rkxfTbUzDNhBf0izeR28fafHV9WlQv0cOHAIJaEeImDcWYW+epmiiVrUQazCEU\nk+xKoMlzBjHycSzcGPm9O3m+OakaZ5deHWyO5N0TkeugQbVwNig5Tyn+xWiLRBNXcAypP2yQlwE4\nRSO5VXTv4b3Hv/d4f2pb8pjY+OEHz1zIw1ZwkN6EPzj3/7jemLQ63N+XgRbfasEXGASmfd6OUCX9\nXCzcsz30ucy8KvguQssuYRRdi4ihXsgwafyNrS5fXjw/ICDO4fXrAaZHjmweTCmU/3KuhDgDAQuA\ne0t2oG9DF3m6Knj//hcNE3tOArYkABdaAe1+Ae7ngKH3MeutKeM+fW7c8t1jeJT3RkEVjcrrZyLg\nxk5gzxAAgyilHxuvNwm44v93BVVsVxJlPDiPo9PgIxISLEcdPqNv32yEhXnA2toVDHOZr4f+9ER0\ncChGHoCxfzCMSDQZiShc+j8xyN+jlF7/x34UHD3oj6GklCr/3574/9ejsUCaLKjGos5DcQxAKCj9\n04udKJXe4LC+Zn/H2IhS6QfgERa12YIki0ngsNWB4GwL2lKKP9kOSqIMBVfQaq2givrGzOEPf/Xn\nBw6YHbt8OeYcMPYHiMHDXlAj2a9LqzYDe34IOl+th+CSt/ebnbGxQUHgXCv7KBQ0rrFz0wEA7XAn\ntAS/rTfHN987Cn3vv/8PIIQ0AYNPy/ZD2rc54nkEA4OD4S4xwddufcD7+A79y/MRH4/4SeDEUhP3\n9dkXbFFvETbm5Rg9A2aEgipekyvR01D1YRt0Vamw6e4O4srDlxXmYOzX0LUpXKYnlc6EVrsXcr4Q\noT5Ga2dZhUl9nlWhZYGx7Yu2a3PS3tJhwzA9JAQu336gPsAbstYzofbrD0GpA7RbHiNu0mcMttDg\nkcCIQADn5vTH5QOBuApgIo2kcYSQQRiIE9KW4ifTP5iGRLzQmELXrHYHvkw4AJPLwaR7zryRdg5k\nwhkxstx2wTd5lUJBKYkmI/Bu9nFxfFTJXfX37QxXK5kIrjA8NPVmcKCMjztVekgfxbe4+1xYNHxg\nUp+ZUz+M3IDRF+ei5xNt1cVhuzUl1nqH533rwVkc2AKoBQehVYBzSWwCLlDngcNpPcHh2Pcaj/VQ\nQRXljXODsITNLJOXicYtHBdu5BmHgIN4GnY8gEW2OapNJkHVvsHJVPvrpsqEDmEHdz5kI0plEH/u\nDGI6TUhkj5u/b30sdbBQq6/R83gPEz090TE2ViLUGpv5J+QKjaX3ykzqjZbBGeP40f1GZbI81s1Z\ngvr1zcWy2ePzhXrzss2GYyV9IKT3qIILcv9ukGjSr3Eel4KDI07bV9m71MqqBvrL+cU2liqbx6Uw\n1hsQBWDrv/jSKJVWAGqoQmFQKskWAB0BhAQ/Q0uBQVDmtyf3U+6oysqScaWCpshqWI2NlZ7I8CXc\nTbMCen5NVa6nPOFrsH8IitO3Xg4/8rTIfx6ANpSioVFwtgXAOAFweQ0WdjiJhV5l19KfqSxFZ6lC\ncYGjH288CuycDARsxP71bljVeoBPbUPyFGSZmsDgPwvLKDBoIzB2MWAjAJ6OorT35cb/yg4cuSAX\n3NxZbWAMeekO6RcXTFlwjeWxo0AhBcFsGknPNn7vlgyLpYt3YVzzNNQt3oXNqj0bavDkiQtcaHM0\nwQAkwOR/d5BfCQCU0s2Nzx8AiKSUvv3Hfv8tV/JKJRkLYE73HrhAKPqA0kF/306Uyt8BJFDFX9a5\nRKm0BPAemSYbMS1gHYBhlOINIRCBo/IpAbwmoPauaPDviIqxNRBsug+Hg5Si+m/HZoKDqSnwPgMw\nrQCviRR8rdNCy3tvhhQ5ugLIBKg2e+O6VxYtk9Z8/Nj7XffuV07xeMYRCI6fDM5WNgcR0cvQ4e0X\nhN6lAPFVUEXBn+fK2c8+QieYSPth/90u+AXAi4hk0LESrNm9D/FpibAVQ+wRgABDBSqilmFZgxvc\ndlWaVGq/NPlS3T2luwkBWa2giiON3W4I1q1jUPN0Avoh91EwZvAZrEpJQbfly2Exdy6OyuV4dW6z\n++4CVbk1cXevpf4Z0hGKemaEO/QVFXhxKAP1+w6hFx2DioemsL+TjoTNLxEs1YNu6IYL11uCnL2G\nyHFfMRfA5O/WeBg8Gb1KZegl1oglrQvZhItX7YlGUghN5dqyQfTA8hKU7FtpN1nV4/zR7wDkWLXp\nAd51mAKuGFbSAF7bMTx/X8OYgeyc5PbvQhNDW4Cjth0ftmzY+SpZ1VE+waN9/tA7ijFHQvn1fIbK\noZboIK9PA1CSzja3diBFPuR9u2jZyojTAEr+7sMP/GkT3BQcTNOs8TEcHPMnQkEVuY1QV3cAY0xV\npkEXd19s9cPhR+3CKQsvwIhhVnE49/oNppZdxVKdTBiBX66ImDpRu27oexHAKRKFswD22gsw5XgT\neyNbZv1zuPPwZJG4df8nixfxXzqxzPp2elYrVLP1FrIaInGzdMjM0y+4O+/FpmGbhlZvrVZZWJRM\ns7fP3nDgQMfcx+g5bwN+Ow9gI1UojuF/MEg0EYMTXi21qbapsZcbOrZrWq1voDT1WRlu/+aNMF8z\njFYo6F8YulIZBm71yxuK67WjcVE6HzFXS2GXAiAXBeJRMNMP7PreqCu3o4NTWiIuPhgicGSPjgAC\nKeA8G20NbVD9YTZ+lgOoCIbCEkAeQC4DONu+PbIWz8PRsZMxwAjiKmI0NYYhpf0xP8ORKhR/agUI\niZ0J59P7cTDKOHeexcyabLt9F+FSYoDaFGgwB8wZwKADJAKA1AAYexMJxaYw/A4OOl371Pdpj05p\nnW5GjIoo+2n308692P3Je6/3S8Axyx4A2E4j6d7GTO9gOs+kY/RebUxhS0M7AAoY9U6o+igQC01i\nNYMWLP7fEeSX/ZGe/K3wGoi/Cq+e/yyk/XcM8o2r+G+8eizpOhCHAAwHpe//2M57ouwK4Pz5sQiz\nK4XbbTiE3IGj/w9PWFAVX4ciiQicEEUNwASAEECVAEYrJzRke0BlbQUdsiF9+w5WAnC/4U9wmG0C\ngEyg/jJwyR4Y3QCR3qx/07Pvl6f5qgDcgGN+OFqmsPjayjK/y8cpE65tneUv+Gi+81YnJyzewyLV\neyeALdi8IgpXR0zFh4BiAAF/NIImhHgAuAMn8VdM03QAQbPfO8GcB2TyGUgFBGU8kFrkuFoXHO9T\nOD/h0rEqVCvEEIeawOTjUsHSDWIn8THvfG+pwCBuuAKX74fhbkpx8Bv4czoJfoMs0gcZna3h+vkz\nTixbhqmhobiweDEIyqwHU3ld0/IKWpuSqRO0C+QJlfUWzIUCpwomJbfg0eWqFvo63AoRwP92MJo2\nUUIQrYDuYHsIWM7i2ARAEo2kK0GIE4DVWh4m7g8gxK5OLB6cRvFbR0pMqheqTn87f6kYxQOOh4yW\nuiy7aITAsAPAZoWCGhrN08YAEIShfdsCSG0O25wutKyznJdrnVtvqbL0O9v17J1XzV95OlY5zk5x\nTrkG4GNzGTYfrFUosWHNe7B8g4GHkN6PMRDA2PZ4nxmOdb0aIA0brSg+9/9orhGlKYBfjcQ4573H\n+68bh210qZXWqsDRYJ9f2nGJZ1Fn8eIMzjy9anKpk4AYJUK1ka7fZ6xrvvNQHX40W6JAsBQcVbQj\niQIF11R9i4RBn/AWQp7UqhneXvGt9nvSVDaiLJbc6ehdsHD5eh4Yfi2ocYvs4WBzANuHvBty/8zT\nM4MIQQIh7PanT/kuACJS0GLrHBxYCmAyVSge/M++T2To5A3G8WdW+1tQnUxIR4T0oLcbr6kpAMYp\nFLQnABClchAoPdT38afr7aTvkxVB19buYJYsf4B+IgDuMMIdl1y64KX1b/HJia0ASBRUMUNJlAfB\nMf3OAnjfB13ddeCtA+AXD6UdgG8RsAp8gaaJQUHt6OLFP3JsrGEmqIKpWTIaJHlo49n6YxeE+15A\ntbAfpfhTJ6AkSvGoA+yn8hdPW1idf8Vaw/NKCkYOA4JFjVe+HiAFAH4A6N4NpcWL8cPlGpzjzsI1\nCpFMHxC6oE9inzdLby8NYigzi0d52wF4KahC2whvxfFqHW+M25nQ9QVj7ZcFqRhOpedprsMEEk36\nQmh9Bs2W7Zxr9T50f/C1zv8r2DUXwK0irMG5pkWCU2XF4C9cMZFS2q/x/avB3VUNABZSSh/+m8/8\n7xjkRwNY0L0HThCK4aC0LwAoiXIMBRbOiUX7Qbeg9npg8mMHmlsXQGIyxDJLT53UgrPfW0WAZX4D\nEAKgbAnSpKEoGsgAY+vBa/UNZqkdUTkHwNu/gi4E4PDargC6ADQI2GEJkFR0MlhZpfY3u1pZoQXD\nshh6wwoTzjC4OJri0sgMUJ4LDwXKVfALGTLyFK9T4MU03bKTGwBmGuS1nWCi0qPYoQDcTWRWMIId\nAVzF0qVP0K/3aCQtXBFmlXbSSoiE3rbwEjAgootdJ9Gjc8P1I5/8xIwjDiAgiOv9QLN5YUg/OuAB\ngGHwkYyzspv5kKeMkNgb6ySjcYVdz5/IdJg+o6qu+e+SbW2wePhwpNVVCq4Px/BvMzGzO7j5NA7u\nmQPYw9PMszeN2aBMLSo5x/+k5oUZp2x+yie6SqfyhIxk4yEjbJ63BN6HQbOnCj8oBz8tAKcOfA1u\nJVcIwD8kzXTohC/s4hK5imzpjBSD0cKb2cdUSvVSzcGwDhbSYffrIVP1Vyjov+sla8eH8ccxvK9o\nAnV2qbzUb/zC8ZV6vp4Pim9HDh55717qPmvU4lFZ5abl6QCsFtxb0My9xF2f4pQaX9J2xNT7/UmB\nVsJ0BlDWCS/P/YqtI6phPnWSIuf0/2yeNYp9+gKYZl1r3W3ZrWVF7X62s0t2Tj6wetzq3K2b1ya/\nppe2Pcb3Ds7wIFNNqgwbF+XRqRop9uwFf6lh1ePkYeGLT/6OZ4v6Yv/BAJiC44DzIfP6BoFZB2IZ\nZLZIUE16m93iZQqc1M8bemh2TztLy2SWp/2OHVvyh6q17aS23YvNi+MEOZ3Si+6dtrx+3XGwTFaT\nDW4Bd7EEtjlTcLx9A0x6U4Ui8c/vwHnEdxXoEDK27ubI0eIj9rcqVez5PGytXv2Xr83fBIVTghFv\nBMU1933ItczVtgsPH2uMjLyi/fy5hx6NPWUZhiVWVgWy8+c95vGL7N1xdvwp+H1OgMqkM0LvnoNE\n87W21jJt/Pj0Y3V1VnMpxUMAOCzadz8+ZFX7/iP5ckpt4WCXM6v7IF0gEVC/V9eRSYUIXIWN799c\n/oXFAU8FgDbxUMoBzP7mg1mro4hUMdl+421VwVKgmylA9YDgADhNgAZAMEDnABg6EnkGe2jD9sLL\nHVapkzGtkysOfYxDtZthNHJdhyPfcwuaq/NgUlEC8VeI2MvErnK6KF/e00NSitRJDypY8TYzHHjH\nx5BJs9D89joAg+O7IwVAWnAwbP8jhvovjw0CriP8ZwBbKEXef7ELjii9TO1QklausVs2pV/GOgAT\nQOlLJVE2AfBp00oceNyeP9T4S+eHoGQSLLWxOPvW1YQ1Ki4OZhrGGoOs6yCYEg/lXSOMM3QC3Y4f\n9j9+qMSqPft/LHiYD+kXAEHx8aQKBl5X/Lb+Md52FAKQ6EGkc9E2LBP354mxlPUZ7qb9cu+K7XZ1\nvtq3w6NYREUFQ6RtwLPuyxEd1QlAJEAfMdAN1UBT9Izf1KaXOEco0+eW692rdurW7/wNoy6fg0Ew\nB8DSh3j42y7sgm595Ana0W8sar6KfjH9/nQke6pviRqlLU345qKzvUv8H8R5iMqImqEGLQVSi0JR\nnzPAsqvBrl4tKmCvrNg3of+L9GVOTa1VdP6ACOLbLo4KrXgk8UtHLNp9HWtMXhgM5e+1+7FftAqr\njEEIUgF4A86Jr5+CKkqVRDkEwD6WsJpaabmzBS+ON2PMY0NHfjvDtrOPZQsGW7687J7DP+SPdgyD\nZdMG0D0kmhwG4AZO+OQNQACK5B7fetjb1dgl3ml7Zw5YZDTUNwhYc5bpyrOuam/qpj0X/iTfSk+/\nd0DF2zHITROCsuCYRfxI+Bw3g95sCdKngsNuI4Kjgh+BW+xcp5GUtpzW8jDLsFMCMwK1z3yebTp2\n4NivyyesPJAUOmyI3OjpGrNAKNw0aFV5mlPaF4D88PcL848wvRrwrs78t02JuTtoJNU1eqe0xt81\nAdyKNBGcWO4KAB/TBtMovVHXEy91AsFbI4KNAl0YLJ86myxBXZ/c3hUzYtmEQpHm1O8SWdXjKmaR\nM9EF8WnlqJF4BEaciWZLxbDtORKElILV7oah9gRK4ojAZUTpMHLj50wcMhIj2jjdBF//3eKYz5rr\nL+MR/A5AkK7SrH9Olf0kF5efhG/U5/FFMANBKTitQRMVJD4rsVnwDa3vAMhhWHQnFH5Ns1E77McX\ns649InlLjEuQ8fXoVKhztwHwopF/+RgplWSSCtL5A+gdF+Gepi+bJVUN2rOzp44RaXSsSD+qVy/D\newCuU6euCR08OHaBiUktn2GMRQBK8MPTEl9btUKbpEPw+JkNoEVZmWMPubzKWSxWlxiN+J6SArGt\nLTrTAge9vFn10P6h9ZExWODUFS/sghGfUQXLgPh4MqgW8tNFcNg9u8d3qS+t6xuKItvnsPnx2k3s\nSXOlQrDMT3Cw2gYAG//OWGskNMTMgf/kDMgEevBUALZiuXU71NuX4sC3x+B0Q+wmfJlcBUHX18TK\n4oWJBQsVX9JLlq/vI8/OWzmtN49ltDbgSQLx4sAHFApFGDG2P42kD5RKsgmAbXAwpvwnyP+Xx0ZH\ncIq+6+BUelcAbKYU2f/u/UolMcuDc0o6mkmK7voLlly5Wu1z8mSH+GAUArhVJyQfBk1zmoMj7gKw\n5AaOfMiHu2ouOHOh9ZbBQRu9UTt1PZKjKegklUjVpNqkmrzzfPcmpn9MCwCFuHKhSJTTS/bgQlMP\nVFi5AKA4Ok3VkNC9dhO85dkwCtTwIy7+9pkZpv2dm72cJ9px1i8GdqVhMCKiyyDc56vgDcC7Ac69\nDJD15qGO+YnZNBNmZe8mZMuHtYhJN88xNC8XBiDnzGLH0SGja3EF4Tzwpg0Yc459MdzW0eLDqoYI\nxdccKrRonfDY8utYsZknv0Jc13HPW0GRQpr/c02pmeUHOrP1KtRXol1oA5xn57RNzZiUc60NYfSs\nq024/mX6XZMVPrR6GmBGCC7vsEXIh6oRFh/eH6BShGAL5hg94JkCjpdtDq5xyGUlUToB+GQj3nr5\npff9kYO/NDf7yUz5JCMpHdwMN5mN/cJr9rfeYVZrY1E00vTbkdkemAKgRfAzGMB1mCoHx+aYHR8V\n3wkc3NVnTeCaL29937acf3x+rmDV0YqPqra+b/bf4w8RpNFCPk+fonblq4xipqWwSNOGX6i2g0a3\nTdXNzAf17juQtAdAlYIqZv3LHIomIwCcta6xzjq977RGopfY6Pl4EPIIQgCuAAY86mnoQyiJPdbz\nWPSFLhcIAC/fFoMHrbWNb7o9nW94VVxZCsACXAOVPxxFE8GJtbTgspMlAJxcPuIBc0c4tcjZGYKe\nue/uTDIMMYLMYylvYeWPQJPS2735Zbliema6fS3JTDSp3RMjMK5cY6jq3bMMBBJwArMdVKF4RZTK\nyTyqPTyzcKDGXMj7EGtzvk01LILiEUyNFaI1Ar5ukkYgBJ8YDGaZbLlpEiD8YGJnnaYlhWIDtWsA\nqtxBfwbJUdVGRsSutUQiU6HIaI/zxonU4XJ3OuyctFxsMJQYTk3wiC1S897KthbWODcrVn0MyQT0\nGTQKG8BlXTk28ZfFe+jClMu3In4m7R3rfmDpGC3PrqhEEL7eiNMTzWBdEQKOeNAXwFyFgt4C/uxL\n8BKcFcAmBVUcIQSWAFLF4vqeQUFyX6MRG93dGba4eO453f0185/BhoQjSjMSl+224uCK12g7PBXy\nH3j4PMpJmPf6DCZksYWOouh5T93fVHk9NbSpqcCYvF447O6Hn7KLAF5Tiqj/W4wgyqUARo1A0P4K\niDiDOVmhCqywNwziaOhlPyDX9wVDe0HNs2tqUst00Jazpl0+J/dR2qtydDKv5SFnDMbAWD34Wq5G\npDP1wf4fZhg87tvOoY9H+JvjdVWVrf+wYaU5/wny/8UwNa2IHTduk/2oUTvmBAdTA4BF4Ayyfgd3\nh878471KJbEoh9XnD2gvV+b39Xoxednb2YsXfzwWGtq770Fpktllu1aXpI4EDhqC3iURGJm/ABw2\ntzg+GEUZMAkvhWhBR1QaCaik1LS0Kq5NXG3Prz0XO1Y7bmYJeyUkPCTeRSSepY75MqLb4JVGUvr8\ngWuX0uqm8pa9NkTfMNMJq5IqqrpUQ2yQCSbJm7W5cd9y3aYBhea66irf1ciQZSEI3Ar0e+MjpRYt\nliUj0paFyfguGGQy2eZAzJhjS62kGpb6rmGrOoYYDOkWsEItdPAdU44mQ+1lr9Ywyz3yeFSgp2er\nTmPKj+yGToG7Je3CGFKm7VeYg0l5GHRTj8W7WwDYip6Pu1UaxY+Ho/V8YIutPzamngVVlwB+gwC+\n92CkjpyIZuGfIeq0Fdpv2iU8IVaIz+Ca1gLVnxrQNM8AeWseNA4iFH2rI66WtZZPSZB6TwtzDYxq\nHgqERrgKWRhPBoxtyNK3krXMczNOOyYjDaYSRTzpOQdAikJB/+p4H00G9UnsE7v85nLBTdy884Z5\nM+TTvE+Wg5SDri/YeTP5ZfzwyZs2nHKZgc+pjm4v794MvPk2wTuhHun9PZE8MgiF7f1Q6ekFRZR6\ncbX24KCPg0IAdFTQv/z7G20RDqKxc5Fpg2lgs0zvvUl9g/l69175YHjDqYITrz1hngzT8/SHxQbx\nBQDrFFRROkC5bOM0enTFK3WTiObGr8c9ZHAFJ0DzMFK4Xy9A0KU8NJXyUN/bDi+MZyC5/oyECIZN\nMwonBTCVH+fiYHto0yXddRcxurhI5XHoxjCsEulQQMAu7IZe62KJLGkFxYyJZvPzLQJCJvd+jDoA\nbgWOaLt2rW7lanGU3r6qmrw0zRIF2VOSVB9s9IsYUrtsVnNZj3c/jXMSzqkudHU16oS8Z2VHA5Mf\noF/AftPUjcou7P24PhDq5VWC0Pi0hh5vv1f75KWopdJyZ2P7IlFRX4Jqbx4KqjxqxRp1eolZbusj\nt32vbD27p2preM3U0LwbjBc9K/CwIrTGmzB6o5AeajIBglqGBPDekAf6gYaZFtvJ5PwBjMOL7M9b\nSgM9TBYckIJnPAFguULxtwyAKHeBy+CiADwE4B0MxW9AiQNgLwHQGmRWDAJ7r0S1pKzzT+/0Pfot\nzdvhoDQUL29mw2fONnzOnYwOVlX9K+4blv/QxG+MWwivH/kYfFPPCtm9k3BqTAGcVyFY4QLQcXeR\ncEQK1gVAjIJyxVklUQY1xo7AYChKARQAtAETQkqgt7yNT0f7Q6ZvhnZVAripnvNal5+5whtrYd6g\n2wWRVmDctqxqgce11ylWpW3ogSQx1BaxWG0qBE//CDkZm/FA7/Pb5kC9Jnlo9Z5dh2z0eonkP0H+\nfzCUSiI2GARz6urMtwkEDR+lUpVbdTWiJ0/Gqbq6yzJgyGyAPwcgLwDcmjQp6u2wyTvvPEUPm1jM\n8dUF9+mkhni+CVS7eQLjXLGeduV1KWNrxhWxaFr/DiqBEz5YbMJm788LkB7cm1e8Mp2RybwMKpWM\nGr6tHb6WKbAqaN/7S2/v/a/3ZymJ0hbAfVhUpuLKL50fbtpkPJMw0FXRp3VpdvEw+YekGJPgPr8Z\nfFoc4Z06BXhN4BWP13S3c+35kWl7uEZl/wAPCMUjAI9A/2pmAUJ6Aoh9jvuTjRDfAqCqAz/n48oD\nXT2cvqDqTLJha2cqsEkU6FO6mxoN8iaCGbcGGDt6JLMOfZ/pahfsePOit0Vut4lhk7tGVldYv6ab\nCTXuaiwG/kTY8WH45UoM/erbvH/4pXfQvQn8hvFvPEBbAtj7/hjevdFg42/h8NO3hh4/IQp0gPFm\nMqGzyWPt/bq2GTlo8tYUdUH1cBMlYo+nXHCXVdGWgkDDYuj5xsLwYNwLKMS4kExkfW7hN7X3TOd4\nx59KcmPPDEG2bWt29AkXQy9+3PiVdMsR7Fo8CrcHNQMQkIe8bo/x2P0u7rJSSPWmgeb8jA455XcH\n1x6J/33WlL379tqH4dkHilseQzG0kIA4goMd9gN4oKAKlhDwu/zSfc4Xt8+7Xctc5305/iWWRBMB\nAH9zjWhZtVg7EJzQJxfAUGihh7SzM9hUBoWVDUiiJ/AW0ZTSchJNtoFiRq8vvVIW3F/gJdfIdwHY\nFRN/bclknIwWQ6Piw5BKgMxPVajZkgaFygD0tceeX+TQxkQgIq+CcRkR3RLtmlVqZbx65rc0J+EP\ng0uZptWqgQDeUYWCKolSDmCiA+6scsY1qw84tCwMs5KKUHRvCqaQQAQWWImccl/1NHS2XBwpbf1W\nqI+LPFhubvml4OCM6W1CWweRPpafebUN9sYm+waWME97SKrkfDr9CDQV8zvwEVJyHmHZg4RaFNuV\n4CTfgHM/p3A3MiVRLgcwImrmiDmhFhUJQ0tA1YyI6NpqxfVNCJVn0e+sCM5qZ5gZVGLk1lkhUW1u\nsPg6U//Lz7WSF8M8tbZOmQIzWTmpNgjpe41j1RnJqsQ55GDXprovwuxz/fUdg9KVPJ/Uvn/TewwC\nVxP0D46HThGPZ6+sjL665QFCCP0pRil0sBtAQLYJYZoP8ERGfm2G0b+Q4auJ1Z1Bb6Z3u5wzzqRL\nrVjXF0UFU0l7D+GMzDXXD5XwALRBfPCmVDTf4ox8BVExn2Jjd7Ts9ln+/nzIpvbF5sX6X2/+ym+X\n1W4/OPbMYwDzFFRxm7v8cAxm2c1hldVOsoetdc/mk84vcfLwTKyOR3BnADs1Gh8Wn8UAACAASURB\nVCmzcfV10076KnmnLZNtNsc7sUv37TYUaxw/rkdLcQnEEhBjIQjtLJBoxAsWTsND0TP9Enfx5bAJ\n2eP+E+T/MRqLO2EAwktLHQvCww+5pacPFHl5gS5bBrlaDWzfDjY/HwxgygfG59rZTcjbsiUsKOF7\nKI5dizgiy6RHJ+Dsg6OYxtdB9DEMWWwfk/zC0deNXWVHbWz0V5qZNEFDrQ9q9UNQILOHRlzPZyAE\nZQ8uZLX3/O9+ReZecmX7+dfWddZtAfRWUIVOubNtAE6EvWBqRMXt8yKd2+BFXgY8cgHSxAaloxfA\nzmw9cPXICJu6JlMqHV9/6Mdqz3sujUzdvQ+UsoTAFBzkBAA7KQjAqV0Pg9KzjfS8JgD6pc87vqu2\n4x3+yrQaLD1JqHWHUdjdaxTxPPWm+q1FtbmTxpOav/rFqNWYMnMmrM7397jl3HCm8xVe5qwJf9D+\nlES5HoA1BDpJTFBmr6T8AMcH8m7lnknqE7/txvMCM6xPrkWzxGrAUIHkLsfgrZLgo7x/W9XWHPu+\nrZLuk+CmN+qzy5oll4X5R1G+dkH4lfDgbindBDLLWR+sVTKPdONqUx8Szpjq2MLvxq3bBux9Oqmm\n9nxzfnJ918B0W5etn7adqnRNthgb09V49PUp1kZYoL0c1fLDTdx0rEOdl4fA42tfi74+IguPwrWD\nZjSZKjZleSnTcPnqBmYuUg8pUH5ADfWHpVgacRiH94BTnc4FJ6A5COAqS9jHsX1in1/veH04OLKB\n/eCyruzdFnxTlMZTKzVytDycrv6C55CPvIEJE/Lw5Xh7fLsRAUfMRSUkKEIK2sEOagyHGHMIj/Tv\n/KFzab/4fpY/ND/OXAhDkXbCxMkwajLwdlwm9FXjARzomAtVr3twPFmMsFYiU37mb7sFulr7N7VB\nmpdVKlUIPUyyEDJnGNodnEj7jTnz50QnxJQCKbkYszELM4IB9DiK4sQLvD2dJWyaUYd6ibUjgcTM\nSp+T0reSRSseEJaP+R0XwDb/Ec93HXPEfO88N2RPRLWZu25xjG6ntw/z8JO3Czn1toSKMIoqFM/+\n5doiSgU4n5vARqrnCD9zHN7oC3O2jp8blzxR3wZfm362aMlcce+vKhHbsqCsGVK3EjSdoZpx+EvJ\nzN9vOS1aLljeo/37Pd1sAAJkgMD1NYJeRBvXtBG92651yi5wnOFO7/oHZqhRbpWLUZcmsOAN7fVI\n7wi2YRd5+bgQL7oE2H3T1Nt4xg/+GqA9Buv7brAfz8J90KNBr98H79qzXRQ2ZWqN6/tqk3T7b/SD\nW6LamNOdOp29IRwkzRPfb3DS78PnBinYtsHxKAKQallTMzNof9WJ4QN32Ni0+Mb/oHaouagLLktP\nuW3rUdKsZuPplfY2ddafdsFr6i04nSWg51t0/OGWksXOZrSOmNAkK3nyl+oDcMldhwOz38GkocWd\nWzNu3di1bXw365dPXgyf7+7X7qdkqovYbNmkpDvNSyWhQSi3bSA8njr4maHDvLVJrzN6N4/dtkei\nmtHkKQTqckThP0H+j9Fo+DUWQBSlyDpyBHEXLpyLAAoygV8HUUpzG2mRC8B1i9+enY0d+/Zh6uJl\nvH2PTEcaTx1Zsgu/txcE4+n0EthJU9Fi1BO8yKfA9d575dkoFQReWJ/E2sJWSMErYBm+dXZTkKfB\neN/uIyLbfGZt+mxdOJj1mt4XZr5lAj1vXlwfzANQgke9YsFn71ncM7+o2RO+KMu6edXU5bblDXea\nOCKgsovr5n7JNaj5PgIjrMdh3MNJfL9vuQbzTpSiPyFwBtewYQq4VLUFgKd68O/xwR4E0BKUs5ON\nDFaKPXM1SZn7f3ON+fBV5FYymP3+LZJqPnjxZSoNvJlkmLTPY01+OGm8GyDJcs8oepTf32maILE2\ngCfk2ZWCB46F8xWc//XFm9AI9qGNJKFdR9Wthbm8E4VgVQaY2IvxKV+NvQ0s7qbEYGOzCsxgCfBM\nsk6/tc/z8qCaR/JhGWKTkbKHxNy8HK1aPWH9robyvPqdg3Wb8zTNHDn9ohkbptzhc5Lm2N1z/dJ7\nH+kUHrzuZGTlycxY81KUMtuwLdOD5+6SH3Cbv3J1Z+NhZpwxfI0ht1PS7IKBGKgXy/U+z6YmiDPd\n95sHWDfUJ16JEd25MU3QDlGpD7HFbAImGHzgc24lVk4D4ENpo686UQbUi+qXC/Wi4Q/9H7A7B+58\nBo4zP8yuTmAssTJzhUUA66vxffJ18bYGCvS86OzMnJgzRxB95szSoJSUS6C0jEQTPhqwEhKsRRka\nEA8JaqACDw3oCDHcIXN9a6Gf9VonSG2jKzjfn2evF8oFfF2tukmlVhN1CCbz9BBFC8Sazi5NRfbF\n1Q326jK1gcezpjodfQPSMH2Cl0mNfyBb8ZnZhNOn91BKy0HIdgCWoHQKAAyU5gbd/+XKc9Z9DZ/o\nBfAy0etrUjrxSp500KH822Po7kqBwU3ge7IeEXtlSNvsBJ91cbBoO3o1NpxsZ/w0clHEC4O8wPwz\nf1G6X50cn4wMumZP5pwTlUTpCM5ob7KCKuIAgESTxX5m2BDZErxSgWe2gBg8YjD/fCLangXHeqoB\n4AaD+iEuvmSYY9H8bzwv3dp+Gv7YBUW1cgEawLViTFAoqIoolX0IZU/PLdpU18vqqcejQl59Kzkj\nbQBrjMwQoE6r40PkrEXLtSKwGjDqPKMx5wgDeWvAeXoSzG2nG3r2NKkXix+Ej9iY0+Vem2aJ/sDR\naYgp572OwNfVb3jZUYnmV5aNsGpg+c5oYFfyU/1CHqEPKLq3GtTCOrvn9o51AceptfeU7PmW7xKD\n8Lr3UXb8q+Q76m454gdSuXx4mWzfWgu+VlRUZE8dqy2u8ZA0lMrrzffVQeTx+xn5e1NLdqXx8qiG\nN4+CdRlur2wTfOL1+da56JzWuWrJ7SW3pQ9C60DgC2BA4fdmt99cntVR8SxA7tx982ftkntNe64v\nlLWq+JpQEfqLR05MTZP/BPnGoVSSdQAGJCRgZ3g4xgESV6DGCRC0+DfGYG7EgMOCKrQy8iG9LBiu\njXnaJBG7drVtjg7nynB3vr3PiWOpGbMGH9J+1y3tZ2doGJ/rfHf66xphg18WT5TLrxK7tNr8q7D0\ndScyMT4YWh1Pd3r7oO2SXOtci4irUeEzT9ln1MuxXaA3frq4+01785lbHL986ld0zbKLc1LTFmz4\nOn5mi1QY58cYTxU6MvO6DtnN5KqLzFg8upYFU19w+OMccM6OA8BxpvdQimyu4ESfTMNR64OYtZpH\n2TPtlDukOXD9WM1aeck1aqPu5wq+RMOvdb+63iz1a7BOrUjp7OjZ/cSZtDHN8GyA0FEbV1TsdvJ9\n/paGgcc/tda/3fVUUNdPpXUOzut8eqGeBddBKToVRu1ytHXeZDlUvT5MmVMhgWCwCMmdXRG2eAJn\niXu+FQkblYzjDzxwskuGd6gp0qwIjK3B1QyufZLZ0d4ttvE9vnXrkK+3lFYaBfKOwneG84bRxkSX\n1njYtV1yzKNJkRgz9SpsnC5g9WU3IQSpcsgTKlCxfTmWr1GI+u650i5WUL/aQTBDfapB+MWPGJtn\nCg32FSS9Xqb9mGWUpG67n5Nd2NrFbllS3edQjctvwU9WxyFu6ju8I+DgFj64rkZSWMITlvxOyOoE\nMJ2vYM2m2wDGMiz6GPkicY/KVrq7sYmFGqHY1UylMtYBhmIHB6Fcq9XYV1byARgJcLtAjvXOS3EI\nXGG/AhRDQeACwAUUEks1dHwj5OVSEArAvRg1XWrsfn7tMM39/NZj8pDSUkpCrSpzQjpZ49Weq5iR\nNRrFxZm4eZO/++LFzguBDnua2YfvCyltdfu6HX2o1hi/AOptarXY/8SJuFx7+1poiQ0KaxQomMiY\ntllTFZattDh2cTipb5qkg9uRjxCoW0GFAsTwm8LiIQO20xqsaFcLq6BYNBlTC8J7Nb/w0KsTMw6s\nP9p5V2J1ZtDMjTOePyvVXeWzRHc0MMX71/XnVtwHEKeginWNvvI7+AQhV7pIzfewM22Uas/v50zX\nn3FE0UoAsxQKztqEELRF7/SXWPJVjI/Hnw6+ZNpxb068NH6nZbGRYda6uSVPBtBRoaBUqSTuDZCc\nKzDa+m1L0hgrC1ipU52LuutwGyZE8LJh04su9960njMWF21U8JzFQJguhdCXHZmyRvOlo3kqkWTb\nPlu0yHnFpNWlbG2graQBuRaVMDs9GbxCJ6iQf/UZCq4PkXmcym+z18ktM8GN0buoUbUsS2tbUKor\nrtgsZ9yKyoweIyfQUesfNsaKttAJ7uPcOOkquWTaG7OnW1HHuIj8JtSb1avvlpZt6oNjr1nSYClu\n2+GIcPXCPcJFL0Yb6upf8tXiSgND2Atqk6ozAJSNjqNxEKjfCc/1VxAWHpIiCExWTH+2wTC31fiD\nv9iLripKL2hDDMffDnI8YXzMjtWH8v4T5AEolaQ7pbg0aRIu5+VhLICNQH0uYDLHC3UhdeDnBKHi\n1AJk/KZAMAUwnAI7c0aR/I9uLZr12Z1lItNoVjeB4KEF4j7pvJJI6oQVQiSPAz5uBPakYNzvddr+\nt2V6yhDdN18qLfN+krf1WEzCJdPNunjf+HG/B/5u0PP0CbtP7t7aJt9nGVp/CdJPOfNd45PhXwMz\n2WH9LKPFWz46PXKH/xtbtUivKyGotNXBzvQp8wWxxg2Yh4t1FrDMkIBN3oYWLbMhdQDIXgCHKMW/\nWJM+J90Gz8aBS2lovvfWXYutZ6RjX2WoWjtHhTmIjq0frn+UbaNr2PSI6dguWzpEck07+E0VKRFO\nFBHvr6x/4rFkoUmh79dNMBQLkTz4PnZKHDxWNdw70ZIyzjXGfGt3qpVXRpODq09izAZTj734NjzC\nYHwKPT4iE5x/SQ2A6UNHwvbcdbwokpEPudVPfwC0RXf0qCMc28UXHAvm/Q/MUxZgePdOGHbRAH0o\nIGx7H/N12wQzEyXt33R4UeCqYxvseSg3B8AnIvDKfVDLeOKW4Q7m2g7FEGYohpGsAAP7Pfo+L+RZ\nEuVlSSujAwa/rT+NvlXf5pPOZpmYumJW7oKOsz7IU51yDs7GJABdgxHMgmtKsgAE59ARzWDJ+OPR\n1gqIA1zQkClB91/TPFqWf62UYvjC5Jbs+Nc/GmaE6ovKQ6K/5+nkbdXpObYtejrV7eT/SiSFMPc4\ngFTrBLjViiBNt0J92yKM5VHcA6UUXHOXTfUChOWZgXy3QdorZ+SsVkr6jdZTdSo0i3KBaTMdHc2P\nWFl7InIzQeJYPXh1vUBXT4Gj43j8+qsnbt2qBtAd1Ngbd+bP99QPZMfUlZDRN29W7a6pkR3ZsycG\nPr4DUCZww6fF1NrRmNgxfmr7J3HjDOpZhQb0LDVFlfACLLNWoerddNy6tQKvsghctxgxoqsGHt9L\n8POgG4waDyz+MtDCvGjIit3Nusf+pLo8ok7xNq5skmYZb2FUZ/Ft0ENVapL5GnXf5TDUNQdPkjol\nKNTKj5fqtKh66mRj0uIYANdkfASuboGm/ibSXf3718kpv2ExTPOrsXGVHC4jpOZCHjIXjsM+74l5\nUSWbLS4taqtX18vnN2mSZg0g4ks1ri5OwhCjy5gakcPkpoai40IXh1bJnsZyx7nSExZbn080vNZd\n5cN6IEXi/O3waFXCGJk1S+/M0EZ9umuVKhui+T1gmPZtB6jjQmDPY2GwqIKg3JpSaLXlSAu375je\nwcg4jNAVUDFPEWUpvNTsG3QD5sDe4JJwfMCHfSIepOAEd1Ikt8zD2vDtODpdbZBoX/fvX9NJHzr7\nHPxO9QPgDAqheaaiXnX9QpNTp5pir/RXvPlmBuLk2nAo0lLrlcm0+lNlToiLBrbzvmDTErbvM512\n2Wk+rg27iwPzfdg5+1+X+KWONp2xn5jA+Hm70DMgzVaYm5lv5/r/+yCvVBILvR7J69fD8Pw5EsC5\n4pUCibeBm+lOkLeowZjeFFbGzUgsHIqNNY64w1zr1mXLL9HRMwBUVA8YsMZMpdoVi9ltt4sWmWeN\n3Ed4jwYbbSra8DTHHxpdU38yK/Z6o9RGlSk32KtmbK6c2uva9i5ElLzzpQul2tL2T5mnW+StbOv9\nRo/e9t3fP96ZaEQ1eNAXvLsKuWX9mWJbdQZ+1B4R81G/V4WzPU9h5oArGMy0w0fVZ/SRuymWvXEM\n7O4wMxb71tT7TxTB6LoO32JCadd/8QtRDlk0AnrBzHYPrrT+bByYN5xEtXC1fS1OOsQwgQvOqqpH\nPajPZS3t2cNKFVP/u+RZ9+uv6nJHdCkuawLTiLWqUtu6Fq6uqQzR47HVTTT1jAU/hULzGhCe87eq\nfyXbbIakXkbYjP5Oqhb72pnIEVuio/G8p49i1DHjKaVlhGs4PI8AUT/EMLXXixves3erAcYOHNtA\nFYRfzISosCRAyzQsbl+G7pd8EJVoim+tjRALfmL6kyIM8gXQbG3UjMfP5STQ6aTz49yvT/Wz8HBe\nJ5i9OYmmt5+D39+AMlsgVneU1+uztYlp4NsApOxcrvf0PSWkhZdbiS2gxcJxUZXvgw9rLUUo6GzD\nd0rfcsHa87317iHVil/BtcRTNLWHb44WG/lzyWPL4ynGWo+b3XRfxphaSgrV5a63Tcw7b8aNO0Ja\n4+NtMJ/xqf5GBeSHfooYjf9ZRiAWN+zhL5/sjdSX4Nw+b9zJhfhEMppl7UaFqQ4e4G582eDaH+rA\n0Wp3gnLdk6pJq8li/Dz+HQ1GAfC0NeRtYX4VsA26hjFmT8Dyd8L/nCNKym/Bs6UD8qSt4KZ6C4JH\niNtqj3z9LPRbFwUHzWRkZdXAzMwPCZ438f5+R9Me62BxJsGpaZN0Xd/5+16vsor2wQeLO3BRT4St\nlgFBNgyGT5gRNgS+/Do008rBClajRatJKI7zxJXTDAbMYXmSKjLblc+3Y3j129JJskBj0qFe1EC1\nQi0LoWU9mozTwLyLhSUYwSnhZBpxYMWORNcjNjDLGQHOumMNwwpXh1gJzX30XobdW87x2Uk9zzrp\nmBND25T1NzXFwqapUnbATtbodur6xyBxQqfVgg38n2qvhp3lfq/TC5+2gpnPGniHRwL4LKxLsTYQ\naQfU8eD86ll6le2J5gp01rOHd4UsL6xygW3pyDy3rz1snHaIiSXfaGT6QnJ76BsUOPcKjsdCwmLQ\nkRnULarHi4b8EG93eWkuoy+MhKaC3IBdvZRQocKszF2kf7gLGwrM2TZun8vg9aMIrb/kIPBdDqKi\npsKpoBLLdoz58cPvOMOwlh4eX1v2fSRiWIbNOHrwaKl1uVXhDAeXzh3nncftV9PIYOm6yttspTmm\nLvy/yHuvqKi2cFu3j1mzEkUocs4ZlKAiZkERc1rmnHNcZjFnl3G5zDnntEwoAhaKAQMmRLKAgORU\nReWac5wH9zl3t/t2T7vttnv2fhivc7bR5vi/Nubf+t87P/wOm1/uRsLSNsVGAkjmRLj65rB9kGnd\ngfawrfuE7Ws6o8h3OCI/76so95uTPeH+5ThUy9N9JQc31rZdAKWI+W8N+cmTCenWDZlfvyLowAFs\nAhANIBCAD+AuBqqfWyOziw2usDaAthRLJQfwrlIsl4iOzSZiRSwSjDOizuKnrDMLwxAKZg4n5CBy\necVNLTVVM7MSndK8W/H7V3k2PbBvVrU16L3mzJpOdKyOumgcaI/miF9L2Rdu39t6tDj6FQmNRnHO\n48dT+Lt357uXlQWta4HshxTGC7Xozudgvu4lLM2egrH7jEg9A/5QM0T/EMhOTgECThKSInj2jOJv\n//544FJzkP80uRWUzwEExdCYurQ0wtLvQX8bXUvmquqNJkchJ9x7A/yXd+4ope8p61OhErnEVhls\nQ4KYa/c4uWA7OWNBTRblsUJu2mlCBz+mezafKS5qCeAPHIixSkrSfz2wDza1IInWoNH878Eixykr\nlrOZH8zxPX0xZJY5mO2xfk7/T6st8NucLDKGxnwFAPul9mE3LzIfOtbWCjvAUT0dazWhCJ2F3/Ky\nrgTGKW0xc4IIzcxHHFLpJdIznXQTF7BoERggv5br2DStuPHu8U3+m1p/Hvo9NFzbkZ/4wYr5+tw6\najImr+XA/YhDnBog04CzW6SIncnAsV0sagym2Ab2xxtPtUovMncd8J14jt6r3Jpxw1T+kG8+EoWv\nHaN8oiOcjE7TmQHNQYVHX5w5hjALLeQiHawyROAUtv3IWZ9grvLHAKZz8Nc7zVF/mv+qs+h7/7IQ\nmzpvNrkufhmdu+kKycyEo9bR8izWD7WFYw8jPs1vh873iwCcFarzA0Rf5kbMDXVqPG51+Haf1A/M\nmV27xkiMRhFDqYr8lvqdBP1PdreEMDrYf2kGaeWLKVSN9BZMupSAyaXhKCjogYo9PmgZpEba6i/4\nIg8RmigDkCwjmIXYSL5Db16Hi08VuKinAKJQVM+iQmgj4MYRycUnJLxPfl7GcLc0CaOfYIBIagZt\nklLhl4U7rtOxJuc9nPQZmDv3HXJyjiHwRAo6nxsMj7dqMG3toCwTQ1a1FoQm9qlp7TcryHBJK1ep\nBbeHWtjVOSa9+3P9/DXfMJ2FYCnerpEsH3HGdO+X3vhNoxUia7wOr5YJEX1aheDLFmC0Iobh9S7m\nRmmCs0V58LJDBWi26owxVzg645Rh8RfMObgfxxMDULevGztjmvugTicaPBfwdl52sAszQUQEYGka\nOO4ampv/QtUHC3FKpVrvnWxlrt2ivRu9OZ+VN4TBTENBiVakNLVIy6hE4Y7iJhMJ9rOgulkf8VzH\nwwDrqC4+X/RS80ZIs6Kai1mbLoFG2gDUFhlws4iLsOQ4Te2yt/n5He8RDFm/Gnnre6HaNwc5vR7h\nUagaLbVDd705aG4b8OfChekWly/7XbWUNcacvN5e1lRjTTq98f27oO+hNVqyRnrr+yhK3/9jAHaL\ngSc8XA8wXgnboVTLqOavAN66XlN+9onvB7FYZ4b4Jxkwitdh3KVbmH76D1A80fV4XKUDjd+MMIcN\nyBItDpWSkuyY/77tGkKI7bhxSI6PR9CaNeheXo5L+C1xSgQq2gMugxKQ8/kEvNc9xR/6OLwUdxU8\nbHpgGyXrOPbTpxWHTGYGE+t5A+7ir7DKrhUYvNSd1Jb62DZNF64E6a2tWftx6xP0x6eY2Oehh4QD\nPswUJCYsoCL3SuJnwcPHHChSMXx9uWWV9aMg8z0ZFy+UNfvZA6AsDNbt8KGrN4qlHxD1wQY6aTH8\nWrnhp0kM/eo3MPsHCOiF3yBoeQoM6wWkTyWnP5+VjxuIox/fw1HfVxGLvwFooIi9YFRKb5UWCv1O\n7ddX3i/Xq4sWw6gbBA8OAsPxxtnmd2YvILxdFoQ/Y9iuFrtMi6raCL0F2byL42Gmulfjd2MI7jrf\nx5Cs5eLg3f/8Y0x6ZqudAkycCNsz+I8A4Vi/2HArL+dk9aBwG8d3vxhW+gvOfF1llTmeznn4V0xE\nSYTNm4A3PR2bHHu3aczcGKU7JahA/yUb4Dj4Gq5Fa6A5BGAdpVT3e5qVPxSAvXYyUYroZihHYko4\n9B0P/Q9riCgBIRwojAIiKO1M/R+WQ29fTEqLBeD/Y7jQHOYGH/j03I/9WgD3nyEr7SUwsgQTBB1Q\n/OHO2JjggAC5vqn7FkNZXXmD+NvaQP86yl4/P4WWnUymOp2RiV1dxxWHc4YiVxi9lcgoSO5hbcZX\nR3XT5uK2RQfdjpGvKuyM8Dl61bIxbuAxi7oH31lorxmBIi1AZVi69Bf6970KQ/1wlF52BCMshtfU\nAmSMDCaWga+Phyl9XWrq/NrOhfh9QHD5kK1bBd2+fhWe27FD5VFT406AvfgdtqOuguOFKjj16Y8H\nZloEmvUU8dyt06dKkSO7jSsN82AYJoa6mdhQ09XdyoY2HtCoH8I55DS8GSsYX1QsDi4gZbFTBTMn\nCPvkpqc8PD+ng7RzvFzyIx6tervcTW8dKQHQF4BhLTZt7Im0JQBmx8bSJvxWxUylFA8IIZcAUgrw\nEYi41gLfgkDIhtlgSk0KgJmIjeE8BM1HuvxxYnZk+HN1pH2+ROBX2EIJtfhY4o6Tdc20iW8RqFzG\ncRLnnoZm1jWfa5Iq8dmCMMqHrT0Zn9qmvUM8ozqtNb2JPyCa48mo+jIOWahy7gLfos0xw+o36ljS\njmPw1GWJoEVnF+1C5JGf9E7DmlBlHgNKCFw0HKQmEYqaOCQ/BOwzBKhYZdxl/rw5/FFfq5K5exYH\n9Hp7PiYWnfB72LE9KC0TbiZh56JwyMjDNO0DjvGNdoHSN9ym2dwUY2GgtDn+XXjpgs3vZqPo6Bvr\n1uvVnd9eKnu4IysHQDAwLRCIYoEFLYBRyBL2HWfJiWkLbQPX1lTSWWsycyk16+IFMj3AiK8fwZUU\nQTBoCKGzlJe1leNGMb8T47qYgPvuwNRmb/tMbZehZ1o/GK4nsnN2LX2kiT/GX+p6AnrJCvxWyl0t\n7HVEqMzyFLNV7uw6rMtQYmBxJLYOvoPZYm8kCf9bQp4Q0s3LC9eOHIFcrUbHESOwDCAGgN+A38kr\nj+2hvdwOTee9UGwaTR6U7LbXynOaku1amR7rk3gXrQ4C0xBU3JuNH6I0YtP/MvG2Xeh1s77C5ouF\nZ2tZ1uYxUW3XbWLg3nkPtTq+kHybvNtUHpgtsHgzuTa5pGtzZm6Iu16gkyDylFoYclNsoRFznvmt\n3/dO7Vbli+KejbCufIv2NQEo7lKBYWwthFWzUHL1C9KGbcXWBiOMLH5D/i6llO9Bnk39hlYn14o2\nTVmUNGIo1MWilcffrentoHxtHHYdlw6xkj8SRbn9ofMDhMJHbWaUnfbI+5o7PrD3ODbRFKBtFm3d\nfYSZ5/2GehZKSFDdPZ7Uf2OanZCkPAM/EPwERav8rfjY5Zl5j79w4NddTHAN97793tn/jsdL75dW\nlbaVMqiRg9YjLQSqKgtR7Qvz+CKQVjVg3ruwvHfJeCbmW29ysk8CHt8sNxUF6AAAIABJREFUpiIe\n29KgyASwdwu2xD3Ds/3ucLc5i7PNAgj8OcLN2bYjblmHFPRb/QL8l7XIUnYEf/8+1v86iMMnAGeJ\nmZnwmkyG3bWNlRNXC3SuJg/Pgt1Dyw7i4GMBBOOMMJqPQbxuOwqSnJHbtQh4txHoul8As8XxIGMX\n4erSbMGlwhb2oSkvgiGWX6l9pg4x76n2IGC2igCXKQwSgF2PLu/W+y9oJxg8VmCeNZRowv/F3Bcm\nmN5BvVtipgcjsIGmlRp8OzGwGIjcWIySh16wt/fG8eNOoFw6vi7Xo+mTEcBrAEO5rfDnzPC2YjCM\nCQOHD7z6zssHAQEsfHyWdMjNlV7cvr3e+1dlwCe0aRbC6NXH/9bF+vBt3dvcy/K6x3/XT+Hmc4+x\nVoK4Zg42WNmZTohWyZqG7T+/fyFDmeMALKsgmnLTRbA+ySi31syMAuWTYHnY18RFHWFFEZep+a2K\nX2VwWItzZ3cS8BcoBBMA/JqNo5tH4cYBANf792++p9FYXgdwErA+DTR9wcIdG9E+Zh8sTUJUSYbC\nv2UmjISiX9cqmJjwVmge9g2WKz1EjeMO9Nwt3CZoX2cmVrusmDdLY0Y0TQTYjt+DXaMAjECswqZJ\nSi6MdI2wErpo8u8sbd3ns7F+/8EiDA+xBD/BylJrWrHzzNTpczcA0Jk24tLBuDVd1s/rbm1fL6Q/\nvKEDuJ24cNUT6c4zYBgKbEgGHL0JdrqTEWbHm0e/GSTaPH1pfYHBzq2b+YeMh3vgJ6AYif9kXZ6W\nRmQAMgwGHO/dG+MlERFfrql7TvYv5EXXAke035zT8X27f8ipWthNqGh1TdXz9ZsfuHixIS0/P9QA\nTyGwyAB2yy7MaPwTZrBBsYceb6Q2pLYA/WR9aOtApkrS76FbfT0a+/WDnIhFUPGWdck3pthVV3ue\nfvVq/u66OrM3wMcXwKABMxFuaN29f+3yBEcHv5Nu+OdWnYb8tijveR/3y7uia4gAgnIppDIhhI/9\n8M9NGRTHXiHOaiBumf1vsZNS+v/p+v3K/9eetZBlUXn/PgoUCszB71zTbKDhBEDVAK0EeMMxvGsw\nh4E+xFruMvuSHsZb3hwRfAj2GrqixugI7esN2Chw7vTFW2Cp4eMGXSoZOmAgf6rP/gb7m8l0Sf8U\nGkfiKu6QOwVzo+cWhk0Oq08QHc8C1BT4Xg5MPIZ2ZvfwJ3jJJHx+4AdNyBzwG7vDSIGvFKjRwcbw\nGhf4fEzh56OPCghptIK8ejVW1zzCo+D/a0+0LUBrV2PbAj2DKrel7KGg/WLj2YeEXjnoyV/znk1b\nBMSYaw/90IGBdHkPKf/NjlCvP3uqzf59ollhvu5nhwXeXMqM0TTlrxD69m9CX14GTX0K/t498Dce\noP5uEkyDN0LNuqM6XAS/XCy9MSditolZas9LB3f+KfITarcINjcsbzfxjPfGdspO+3dUGBkSW22G\ngTm2mFktdMkwQEZ/IUanhDf9IB3D35bdLlVAoVdAkaqA4lQKUo49wiPdfMwvHC4abuW1C1c8dsF0\nZiZGUGCkQgGyYAG22MjBbbZCw9FWrYzf7OwoB+h4gLYQN13xaFb30Kf/JwtIDRPQ/cZpOHJ1AD0F\ncG1/30ZlklBBXOtJoPUy8AvthUasJBTtrfk2YT2VzF8r6OLeIl5PYKwl5N2KW/3UGefBRVh7H4LQ\nzYTVjJZZI9KIe/XjxUJQM8A0CFAyK1d+x6XHi0H4coxMWYyepxphpaOw8MsD8BMREek4dEiHzZs+\noxMeQo4NA4DlH4HaamD2mNmW51l3Zwo7W54RszpCwImlAoPE3JEPISF0t9SO7xIhNwR2szcIWGLy\nd5F97Wc5RCdGHZ3qHdckepxyVQHFlWQm+Zs4QZyNjZioUECiUGCaQoHsZ8/Il8iANxmYMZySrjuo\nmftbKt0gohfvE83xYzAKF82hnf5dzgcojtEtik7N0xVjy2WKB/q5iuEvFAoUJScjZ8qUVe0A+gpB\nzSkYPiof7dqpsOPLHEwsPgiFogZ3X/ZCSFMpQpuasDDPU6FA4LNn5MOwYX/rHRxKqZVNFb/7esdT\nmxRdY2cqRo9XKPBEoUCZQoGTiosu9U9FT5QuUN8TmmlSbVNvVI9XTNUqFNiz/Ao83f7CQ8kGQbV8\nmZzHBjRjI/TYJOJJ4hWKA+0N2CThBesEPDaCYj0osw4m8VpovWY5PsCQn5wwUWGYO+gu13dQ3yEA\n3SxFC5dna8Wt7AOTbAt2YiMs/jMnFAr4PX4A9W5PFHFA4VtRtE4JK2qL2hahUPt26Ii/WixWylR/\ntXFUipYsMQpv3zY5Xr1qnLFkiT6s69MGud+DBsc/mYyONmddAVq1DDkzTuN0vROc3gEoYVnc2rQJ\ntceSfV8JniVnRnd68HLJkhm5u097Km8+QdGCBThGCD7FY/FFAls6HI8bzrkrPjpfVNDOk96pU4li\neSAChwCo7oAObRRQCNLxwKoMQ+8YYMkXYMZbCQZ/+99l5/+xN3lCiAWAn7du4YatLZx69cJqkwnP\ngcv7gbGT8FvRsWgGipaaQCxTYIsmSGHvn6uqLPORRiKJfNfNFOzD8d370GuRJ1tYw4srXEoDbPno\nsNXGNqOKuLXCfTKN4qpRsv3Z+kviSxE57jn9klsn6zre2/N6Kx8eC+gagc+VQGAIsOiVXHxJro9B\nJwdvYMYTYNcf4Na9hnZ2hvf51zjVbycKXNOxLN8VxlZ9EFZ7HOnpj/H2oQT8dgCD/2M0+hWA+dhI\nvjurkBSugd3iwSDfH7eWjD5VxeRpT2DHiAU0OaiCUFAQCuXJ+xA5aIhkyAgxpBqx8ciFg4KKNv8y\n85+loJDI+RfCn0mLtWhn1w751kPQsSYF1c0WcJC1AmNkiTGgPJTUy7+Z7h0T8TKth8wTZVQKPeEh\ngQki7Q9Pa4mZTqeT6o3fBWoHe2q0tZWg+jtAWklQUbgyauT6dz4FV/yq/MpGvR61UWqUmpsEsFJB\n+fAPOng9MwIxPm1htygAY+YP/p9yOjKSAY6cJ2hJ9HF2/LtWLdk/wW79jsM//tHC8Q0PUYDRs7yl\nspJaehhFEFERKITXkzAp8wxqNt3HLfDEaCZoDQhVAjjpBUjS6XHDJQob8p/D2mDCbtsZfFvJY2bQ\nxI5Yc9iR76p6Q67MCee0bVOYf3KaGFEFePpvL6i1aYwIRp0BCNsxevSRvaNGda6zsroGg2Ao6kVm\nuLQ1E0+c2oImcBCHaxHkawah8CC2bp2LtWsPtP7wIcAVGJQONKgBBxABQ4R+JlvbWKNuzyDqcEtY\nW/4o0N7R6WW2U587Pwqjorq65D3L0dxJstao4RIXh+qXz++GiCwKhPV5q3C4txBOf3ImsCZNjgp8\nwjdYnm2HFrkILxsb7f5Zt+7f2cGDUjraR9+SX/uaL5YIzdFgN5pjXYdSAwvWyAoAzgTHlpa6Szee\nXmfFGtkHLxv3v9r36OSsrNTlNmitZZoa2uIdmWiSOPbEZQclroTpAMMsSukTcuntUFgZr2Ov/wvX\nVSm/7CQ1g/dimUUt7KtP1c6WKcatNTMzUn3L0a9/Iki1Gb+tNYqj8C5xVdO+Lqn3p8Rfv/0ncXIv\nbNixoz9RWpg9mITzXTiwx2lMzO7/WcNpJO0OgFSdGHe3JfA/NA557LR71379yM7+7FxV37+rCEwb\nDfQsxWcTBF+aGKsZp+2X1lvJO6nnH9CYcQ0ZLDN5BX1jiCwpRmSr5wna5BzXhzEv66AzUawHcJJu\nBPcAWBshwQZLH5iWVx14e7JhQb1OIG428qKfB1f1HPbzabR7qlrAFPTcKcPR/o2gF++he22l5aAc\nB6N70SCtzMfeOb/FYP2KVfomBr9foi6OAjAmhsakEEK6jB+PrRMmC9wHCx5Y6PaEH8ATm9FY5vwA\nIKuE0qbGnWH0l5MGnuVHCXn9atD3xyhsH4Ekzs+z6ciVTS2zISm/iNHD+wMYRylNxW+2HQMQ1ky8\nJnV0kB4taKltb1LX4b9Vu4YQMqNHD0xatw5e6enouH49EoEut4H0eQBiAFhFoT51Pb5LpqIN1ou/\nUN2g9Ht617xBYVoDFfV/wOzYKiNWZR7oKttWdbiwrZMBDC7KD9Pm+CpyzbYHra7N0LfcSj6Shaw5\nPHgpJRSUCnkeLAEEBFA2ATQXiMlmcHjABLwT9sMSxRjnxiGeXcHrHSGstCTocaUH/6HyM5pFWi1C\nNVfllVftP5asbPce0dxYXHnxGK9uaCE4Ow7RBh+03KJTuuu+u32faZ0vf3TFv36kRwNY7gaDCSPE\nCC2ZBZ8yG/524Cu0+uWXcq334RIR37XNkys/2qWY6VssNMvMebYWkyv2YmW0E4a/ONhkZbDzj+0S\nK0NHfEUiGGTDAOCQpSXsx3iPn3auz1XRo/McvrgAp739TfWP7rBuHRc1TGtO17Yqa+3Q4uDCXOnf\nTpvj4SvJ9nGlejFppgRZ8U8Rqpbhrn8B+NYfld2XDR9eSmGUo9XOQ7CN3gveJEHWar2o5Ju99LGk\nqnnq6m5YufIHgNUsMD8Z4OcAuktubr63Q7uU3K2f79TY7CwwaSzZNdoN3IDmq+wiTyPkrVZrnn4Y\nL6xvdmom0gJLr7gDjCToHltUVgP+vZjyMX/wxCZRMOnRWPrXhxRS5GanlzRncS9kLYLlfcOMes88\ncyL15W2rCxiVxAhpI+D0Evq8LJFYIhmDffvP5z1WdhEonEdLVa4hzkE/f+qi8vKeXoztvRzx/94H\nLjkAdf1gnnQOuuZgmEYZAXrY5to1rZ1e3//kpF1ug+FQ0YRPwRCsJPDvVoXcxCvA3TlAJxnabtNg\n/K+TiGg6jt+mePVQqVpjxAgRgGjodc6Qmq7KLryWj1qTyD3I/0t0SoT9lknYCoCd+A67+zohfJiT\nqEyvN+trNIpMBXIvXttkId5bWMR6/Kxr+X67ohZnPrcnYv6KbzGX7b17clyxtiZw/mSZOjyCQiMU\nsAcFk6RJgniBxKiCUSyDiWNgXvKt0uJB1L+Vj+rHAhNbgBedBYJWiR5j0011U1pC15Btda3wzfIg\nnSdIMvUox/CYBqmIe+k2M3t+QShPkGcxDrG1t1EtHgUjs5lITT72z2SqaYFbv8aH3Q4DMCcmhl4m\naWmu+O0Au4fGxBwBgDSS1ikvANe3r4aszANyz48f8p+uSQj4znDkdbQt/6b/IuR5tjPW2IpzcdDf\nITatxCaxeTCjhSoz1ULMHUpY0bnZ2h7rN21Sj69sa9TghFwi6fHLt0uuXVkIdP2KgWOP0VhkglMq\nsL1x1bhePr5Z4ffvz3G7/2BOK06MtM97AJ9lwcs3GuKkhyYc3ja6cEJ9/9ezawpgzt2Jvu2aE33O\nhrn8/Ic+WOpq3qtMgnAltW7GpTIPrMHzWNUYd+wY447pS1rmaQvVQhMyb1nCqoIHoSYkHqDQWwrQ\n50+2U/EA7Yw+CnOB3spw4nx0w+dvdmYtLVtLYd2oxo6HbcHpMxASEktjY0P/44y88FuIW0U5OIhX\nUh/ov2rA+Vv8t4F8WhpxefoUn3rEwExIMCQmHqMAqRxoaQcwqwFYOkK78yze27wl5vwd6snsZr9k\nijatbZOU0WT69LyVXty7mxntWUierzpETg4SoXViILI6dOR9vtcypzpMhFktx+UpzpRlmF66zZPP\nI0pHYcONvHn6KggKABMPh8ZvOJApxocPrpbnzgnY+obeGrgbdPhDOMk+WXklMtucVAtLSD4ToHcw\nAL6tTej47RGEvBycIBQ6uU3/svpG7+xo5lBNwlshrDvzblnmfP/5hBIKUCBCBmwPBiyWAHP7MFyp\njBe42M9Xb9v6h6xFwPGzZw4q0L6lxZh5rs/Yv981hSi1cq/yNhjCzcPaSAP+7i6ilnX+zTdO7n3b\nZ12fZhDUYyP243fgetGOwB0TdgzYcTbhsooOo2DqZPjV0wc22sI4E35dsTDv17fU3jPTo9KcJXFZ\nMeRgagGUmm7KNc6rc1ICG8Ry1woPBxdqxTrYIyuMlBqF1BmlF4WofMDCb0E2Ss7ATah29Q8ZX6xY\n/8YROr09OnXSMPfuiT2WLWNL3NzJkl3nBP1aqtBL+dxER5UrQ7VKiYu+Rm1lcVxqczTR7G8QfpHT\nVvZ05XywPf+CrOtWtJcxNLpaXl9XvN/qwgAImezFWPB4tLbdF+etQqzvFQ8aM2w4aFIgIfZqSn1b\n5CSiUo9uFXLwXysxixJY2jl8LG1+Eg7fS5RdHawSu9oL3fRFMqFEwB/SLH7mc452GPccj9NrhW2B\nYhewTvVsSL2DMNtMa8kd5uuxWi5kRUR39iroty8UV/MKET8jFyMEcohoPGJjjgONbkAnT8DbHccT\nmhFgMgewKAnxiiHT7VZqnX2HYP3WXIyLbo1ZPzQhTjUjDs9H4XzEjmkC9lzyxhacgZhSzMyoZ0ha\nsaNF3vOpLxctfKDezG3uxxooMyTlyo51Z14M3aLZIju1rOsR9K+aDCAMsbEcgJkAliMuToslS+zx\n4QPFsWN3zS4eV2y6lTXaodtfgzbmDKIlbYYKvBrKuJ8L7xKBoTs/YYLRNHbsjuunmWnfH2Dgtj6q\ng29uFpSF4sEEA8TdHLC4igZmt6iC9/gY/q1vK4bFr8do8I8Phqp4qH+O5a7D2jSTECMcUZV5AjPD\nLKFaERNDz5G0NG/87j+vBfAQwBbLZsy0aDTRiqpXFJu3MOjMaRGNnRBiR5xBIRl3HrfPNfm0maTe\nYjMG1wVKqHgGIHpY6JfL57Xc215spgmaqQRhohAbPkwEZtkJqd+LPkzdiCoG7BobIA3IYH9q7sqg\nn3f1pJdW9ksgsj0c6WjBFckaGvrWl/HDBVMxFa6erhlvJ77tMD11+iYja4y80fHGyANn/in2qvX2\nKYZMdwfOsjRnCdFMLa9B11qH/vpDZJbsEZ9uavt5D5PgSs+fMcIz2REi7VoAd8S/woWBhZ12Fblk\nD9C7fRRIv0dW9TOztRo9Kl1SWelJ6utruKamSKIxNHIY6lzmWNWsm3nhqdvnehzr1x/tkIUQ/Cs0\nB/fFCAT3B8jr//KQT0sjdsSIBJ7HLHIfoo6XwaQ049oAIAqoLwZssgGcELLGl9dEr611EjXZ0dCR\ntHMv49jRX/nB8bOZ4TkbNfrAMBE+f67HnTvy8HwTY9acKTnc19WQ1NaD39B2juTPi6l4fSsd1WIz\nDPTqB3lTlHG/pIepYXjtE7C8Nc57dSWn3zawLOwIpUSs56ER8rwg/YkSqZetDLm1BC2gbnJXmAdb\nZOc6eoci+DFFKa+BHd7BDCKoXaMgqxZCaGKQvgrIHwhMiAEajbBtBvU2B13QD2h/CmSqC0hjELJ+\n1ePltDSnCaHaP81VNILeVy37+C66Q7h368GGfsdL8to02EQOVM2mBYuU/MJDdtUvIjZw6LPU3a3B\nRd0ibVI1WTT50A1UCwA9Ynq4ZYZklnZ/0aK/kc23ZLlBH1QLt7L2eBiajkFC3Nlgg6h1O7GeNEs7\nY1PcjmZDULHV7lSGdPs0FOV0Ms1CfrMTcbF6ShNx1fsNuI3rTLRF9QtfTn6ES1ZfqXUrjgldJ9IL\nLHab6pVHyIjhexmCETuWielpxTCtMX8M3vLjzUeEHNuXtsW5ecxNOqvvY4gnH9U183NnePmMaFsa\nVyCRb3iUav21gw4XinZB4dcRg+MP1BnVn/nz7l4OkvLncEqZoHbM/Nh0nHnpXGULzZQxIM1mkHw6\nIWzO8WtlHpZbWDBrSmTIW1kkbTxynpElJFCrgvbk16twYPMzJQoLRIiIuI/8fFfrywdNd3YWHD+8\nDVve5cB37Xp7Y3P6eEb8Nlbw3eeTKV/vV/fi9TA7zmyniWpuSWA9xoRhneswgNihVmzEM4e5OO/N\n4veMRjusWy+E2Dob7q4u+OLxa8zAMzkjm0+2nTQRVmsPOvxa/ybFXvfBRSgfmvWqeUMXGTWxzu7u\nuT+mj+vj17ZjqQOfb/vp5Itt2x88mLFXELsx3a7XlsEmk6sZ9ZqJeC2TOWtWFzcCk4xhK80Hn/fg\nur6nm/78G/tiaIyWpKW5gOcPQqPphI0bLZCZacRvK49rCihM8C24hqNzhie/k37cUTzCk0ZFW2PZ\ncgHZvCEXkZEmO9T5tfBSgZbIROCJHtVSFk46JZSfljqn7VaePnL67Kwu36rK9HK3cPtEsunpZLJy\n/Mp/8lzzHiJkcx7su24QQ9f1OkaZW0F5BcC6WCgCAChAKcs2lb73vfk2vmBwDwH/15QWRKsPwQln\nFBsVVQB6a8H8sQ8ew9ZgpsgaeXSThfbQz4Day939ZjX43+U+TzZdVA6fpH+d2GN2OLGNFV1cPflF\nr2xmaC7jQ3MsjYdE+tkzrFQeNuaMJcALIDBTQWBZa6R7EgR41dnokCp4GZh/z8ccpu068NMBdMJG\njMJv0zoJgH50A01LI2mCX2EZYUe6HeypaWnanlU3Uj0zlDKdOyksNzAbaJFJDdu8lSBVfrpGAwky\nHv7yEwDSSNpZACFu7N7s4zEPI870QKsgM7D0Gd629ZzztLAgeJ1ItBoyWXcEBVXVDtJk2X9tY88z\nFpX89uv0dM55wUjgNQXaT6QUif+77Pz/PeTtiV4S4vJt7paNXcPhph3lmAqqvYiGhzV444O+H3rj\n8fZ47Lr6EcvdZBaafqaOjUU9ggscHBp40u6qOdajNdwTLmkHtTvUUNzAOly2XdICVqAiJm1D9+8/\n5W0Kyj1v3rpNqh3WwrDLHniWqxX9c4pMIo3ilp6+pF60wxjByYUd34B/0Ie8O5vYyWuYRdEnt6hf\nsVfGQrTs6m3tztGxrPHXNZGx4SkJqx8ModVoTUYQMbN8KDc03r79L/CnD4LdfTEUApyCBnUAuoY2\nI6omgPkZoTe/c0ES22qCKdFBSccp36k7NcEytzc03SIg9T8J5tAslB0thiWA60HbiqN2G7+3NkmN\n7P7VhHyOZE3jVul39/5BVhvsNpZGdvhSa/cI7Y5o2F5rYFyG3otao8M/LjHfYrQbbm/oE0NjXhAb\n4icaLPoWUC7mPqeo1Gcj8TPuB0JqumJ6xAtc/LoLQx1mtx9+GqPHHxLNoqq/U8uNNhoj6t4Xy6pO\nBAdXq5z3J4pr3LQ9bSo9PCt7hc905Po2iuOvlED8tIL/KCnSlJk0ImJ6ItTbW/EYPpxn4nqwTud3\nQpVezOhqb8LKwZq/KBvJZLf2Kz4ev6jcuhGdDGKYGB6LPy6LDYJE5IE1bYzYdXuEz6SbeauLrrBx\nrz+a2aiEzs+E0eRJjxr6BJ2VdV9Of9vcwEdO4BnJ4ngxueoQnkrdM1ogEH9G1yc5cpVqOYB2ql+l\nVdzKFU4YOMmA/oOFLpNCyHIyy7Bd/8Sm9ndQui+A0TbAj2AgOwdWHQleSte5n6OByh6E9c9FYW2k\nbukB8lTz1qED/vFzgFdPDbzkLAZtUOCLVQeYm8rQuT4YGbYCXPX4BQfdfezM6gbQDxgyjycq4/he\n/caR9/zT0sZyVT0ZdikYW1qZsU5KznVbEgly+CKYQs6qbdh6yffv0cXndvFmpOKdiy1uIBlDlHMH\nrW1eknfEInDdTLmg9DQJy/ExuNIhex5O7pzT5vvP3T9tHZ38C8W1BglsqpygrnSGwMjy+zFggB+0\nWnP87vMuw2+bjMEKKL6jfUY6diS01xvo6T7jbH5ALl+Bnz+tnDt4NHj4cPQtoxcgZNouhMZvg0Hw\nA3KjCoA9gDMr/oKDXQ2denwG93jPSmbgmf5PTj4I3N0CoBuAEMIz32jwCmeBVbDNHm4hbzQ069Zn\nI1fHhkhRqw7C/TpzrD1IRl7hlHOS/frjt0fSeQA9msG+XQ0n/3MY6NbIFtSwR52sBO61UgAmHSci\nFJRzT1Nz/sdhltURdEunDaSFaqny7S7keHgTiVkL4krrEPuDUNsmS+JobOFdtCaTswbgXCH6shdU\nvgZz/b5j7lgK94dCxD19Cvq2AcPWZWEeYZBl4OEJYHE7a7Ra4Id1Og7K+lNIjBmNQc0OMDvx1enD\nv6UyNypRujCJ+0knuVjdKvRtXlOT3dR510KsJKi45YfD8Zbst9Q3N5DabIHg4a9g0lYiAl9gEP3c\nLvaKUFGleh83UGRikz3kyhJfQmwlrCxaWst8Shqmrizutw1DJ++mG6jpvyzkE1rt+9R7y4oIWYYU\nXy+NVL75NezGJUwddgmH7yQgYXwC7OEGM1FSxIY3x2eT9hoHk8AcHHGr0KHkWjhcdOlGfe+pd9Z1\ntxo2U78PIsP3t475GdVz39EeZS6eZmUOjkxe/U9T3oUHErZve7DX3sLf16fRo12W8M5Nzijk0aHe\n0rIDlMFxG3FkZAY8xH8LrvIxD/2YKQ+u5BeoL2WkeaO/SBAlsvWeZq6XOaeL65RtWxru6lTeM62Q\nxrE4bdUe+i5t0A6b0A8UBCJQWOD8fQ6l3YRSjFJlQqF2B+uSjVbvCka9m2E7Ae/DFkC0xRsVx5/D\nAhQF4Ns3rsa+OC3+/dAdnQmxtPTPibqvDXvZ1elO9PH6Nd7JQYV/oKZqAkonKNHQz3Hb5MfDL31C\nbSCH4HvsFMVk1bP0Z/N+Dvl52lvkyubeqPp6LtLE/pGDwEYp+vrfVqTnzR77SqkNbte6LJOMmX1M\nffdLXKF1qV4+etHmzKSfQ8U/GgM8rUu+B6lr9CxpCaGcyoOYNFKA8gBtVjEokwhQx1rBCS1oMVHE\nt0RHmb4PmMK0TljCW5j2XCRwNadjMxRk+a1bmHdoOmxL5FRZ53vvbVvhbR3R7cKLZCuwB97gyd9h\nqAtuwqQ4EQBzAFnuxgBt91cT43unZwn6MP9SEfRI8YUhIQ6H8lJvPUKj9yFM7umPzpeyITNr3HDq\ngkfjJ1b7T9HTULS15TEpnOCvrWUOxYxbCdwE2+FUvRVpfVgEnJgHSHmIg0/DQcBaJvIeE8o/7bjE\nhd8fZFANGzv+bs2io2Pr81sL1qD1HQ3YJKBhPcy8xPBwdYa5+W0ALvn9AAAgAElEQVR8qNkF8/xT\nWJlfgo71naEVWOGBy2Oc8Bu0eDHmpKay+7OyqFAokWHAyMko/PoHcfYqxOKF86EnYuMvuOjfo53o\nC8I/zsfh3MZMz7b7VrGtg7lUdI+Wt3i/M0k3Ld9LK7r4CvYuV+tWrK0obnFwDoKmvByWIQ4z7zwU\nZGi7vKqOsz4x6D4845PoilUNM+4VocgXQG9Kf//FEUIWABgCIE4BhQX6PfqCpXs9wdANMTF0S5cu\n5N2PEoRXe0HHf0Am9LIoiC2E0FcNoJSmkLS0cADzQelU15Jc0jHvDt/22epTq3cxgwBcPDobx53K\nmjaW2JeMrRUaf56bHMRXB8hsVlftNriJf8kXLx4o1OmOMpZTNnMto0LeJfchFxmK3gBqAIg3wW9r\nCX49T8Rgpxph9TP+MTWjwId/4gdmFPIh83OxJFqIMCbSwVyz48+fAq8MjvwYLCIDXG7+mnaxlh19\nWeZ8CWz1Tky23SBeYqy3uGQgPkUvv0UgosocwjozlO3zQusOHyGm5wATAem6AhAx4A08+MleyBnr\nDpc39RD/XQhZgDkw2AX321kjKlcFx6RiMImNUBsJXgA4ja2asd6mSvU0wUmdu3nxMG9VuW04/YI/\ntm3mJFRLduRuVzVMVD+nLMbFPoca5TgHF0wEJQ2iqnDZsKZvotpGO31KRYsIqtgseL738hwcLPOQ\nvWQ/NAuVWoNGCOAbNiLqv5yE8oJ409TXx4U0Y7bro31403YEfl4JQ5XaDEbqj9N6e3hrE/DRVM76\n0/Ejz/Di289p+J+v6RHL1/Qs3lAJq6Uup+4aOl7ax1k/uUVnzx9Zt70naL0YpmP+Dm9trl1rMhCi\nKSFIsmgt4M3EIk2bboEZTottDVflZ0y54jhTnYQ1jR1M9G7zHLVkkRcVr5bqmfUsxSYhJXNCGzHy\nj2fOvv2XZkNi6r9hw3U8S6nG/vBq/N0mDfu7crhzmOLUUQPOLV+P5Uu+4fBOir/X6zFkRAskZVpA\nWQNATAHfalhfdERl2cmT4c8/b8ePG44wAjACeA6QagnaG+0h56cj4IVCOK0qlb2tUTA36aTuY/ks\nexz7OBcPdm0DJwICKHA0xxaV8hk+bwCuHgOn5WI9qGCigHOe46ytYgMV6R6oTPJBRa6r0yUoFIdd\nLioaL7ldNnAQ0Cc926kTFRKHJ0+kHXr1Oq+1syurb9MmWd2//wn11GkJyuV9plf26RZtYMa5aV3N\nRPUOIJwD/tAAvQYCIPeQHuopqs/rGnFJO6CrJcfAlzJYTPuz5xLD3NoWVVpZ0T0dd5UO3HdeeeeZ\n/I1CgZyEQ5iCXqwSm0QmLAihkNZxdqtci4SboHXagU/YH/4QyQ81WLK+Eh035hO2gvp2/OcHzmz0\n+1/nawO5jl1TyrDlq34iOVuVgUAj4FUPj1HvcP1mFRYFtMBOzWPn5zebR0xQKRkB7YpJag88NwkZ\nSx6CVhSCvUarngtH3ZErsub/8ayl3M6ubIH/llP37tjyW4WZ2ovI+K6AQgJX9XmE1ejRa/cTRI3R\ngLhqAEcD0PU1hHuvw/zSaWBhHcvaNNnbg/Pzw78L11k1njpN6LQ5MzMkElXDoePtewxVLPCHQtEe\nCsU4KBS1UCjOIzmtGbMK1XBbu411c9OFRkTw9hERecLEVNo1IYky/z6kmDmLh42FFgdX6HFmrskp\nSvpjAWxMYjATAHTciZ1vF2FRNQCr/1sNCgHkAYinlEIBhYtiyvhGRSo4hQJP7j5Bg9AfOrkL7o0d\nuz0SMNUR0ns8gFoAXaFQCJGY+Dy0n0fZksReJtvUG9Tl2PrUvssUfv7HFXleZxSm+aHvbwjAfWHA\na52Iutph8WeT8OYLPvCPKN7dA9SvVTcOd55R+BXrhDj8JBEpLalIrfLEpd4hSNX9hBV3UoAXJ0+i\nct061AHQAGwOUFYHnN9xYLNliOKJqEyxsvcjheDxY8Xe1qYrt4Op9b2ndHPXpNeN8MvfhYRKIbhx\n+C3EKEEoLLERJzofRGFiCr6kJIPusYeJB3iVLc76LYAdNsIJGzEdG1Gy7BKK0g9CPXMWam1Wg/ps\nB3XZBDr1D7zPtcVjCjDXMWJXAXwNPNBMgacU2JQT1PPy4OObjFaXHutcViVT3ysXjVleXtkU6PC/\nvsFkiCkgV8I8bztZob182avR0fFuLdCmETiiA+jZBEXcH/cUFurBydPVuLrq3P8Tdv4fIaHUEDe/\n4jGq/B/Rct2oxdkmE6SEAYQC8GITOEoRRlZiFp6174/SGaUmZ0MpO/mAFqH5gaBgsQlh2hewqyHz\nF+wTDu1/gDMC0a/v02XXLhlfeurZA/3aML3K7SCUBOF5aBiU9k5gjTxE1Zno8oIzFEqumn7aFEn9\nGnlSaMvjyC0rCEs61bwbZp9+0WLuQMGV0GbSLLJjwBeyqPKrgws4wjTBqdQSbpYURjYXUQ3++KOA\nQCgSgdcBZY+AvG9q6M0lyN7SgiZnGbZ8ZqHJ1QkbMkXmdXnc8cwyk/XoJmnEZD1iWbz61oRRW4Fd\nMQhq5c2aQm3pT+aHrRP5EuSHcm1nvlzA8QdmVWsTXkIf3fGG7Znrqjf/PqWdxevI8JMPcHpMFr47\n8z9L6y3rh2FWe8DMyB682b56SNl7+fbB7SpXJP90bn/wXN20f2QVnV6hLaE36vvgiFEVyVd+2QdU\nw8F5Kfb+qIBbgwTaxHOYPMnaUCO8cAHiyuto/cwEYhEDw91JyHLIF4R/04Vr7fp8O1AnsiFSlXHm\nlInZ0DR9yjdhYudO6JHxFV+DDzlYWTb5scaFyiYKS8sreP06zz0evSqi0JMKgA52pPbb0feWqm6s\n2C7ubH2C6MQ1K0un9kuxN9L7Y2Pqp6UT7QCREhbxH2F7ewoo5BhfWoe2yZn4vrYHoq/97LLaOCHx\nw4j73jCV1gMfgabViOCvo0AYh8hTRdgcdlzeop4y4PXrkO0nTqD3rJWqnO+tf6BSfhofu5vvEO+d\ny4e1tl/v6bXR/Jp8jANqQo/81SW/yVn6PGfio8meYlXR1H/1dtycqC8okQngqwqBhdEBn7/fA87b\nmSM1lMIg12I0FQgHCgYM+Jg5df7a0EfMgIZzo1KP6WpergJ8ZlEquvy/aiMtzQHAWlSKp6OSYRHc\nSCARsxZqtSno5082MzAQPMNAzBtb9CpZGh47OqNcEIh5JQQnxm9GwK8FDtViV6tEtrygTl0dDg/h\nPpxyYyD0iKG/gz7+Ux0Ox+8w6naUUj6NpIUiYdsHbWyKcPEXmBa4Im/7EgRZWs5EZKQvM2vWSv2W\nLVj27Bk2Y+XKF7apN3qcXlssFokwIe2HX9c7PgsXW34NNKzeJbo+b7q8rOZs0Cp0qk/BvMJGCGkf\n6PSZQsXLMFOwv+2gW6d404IOTJnOtbhoXONZk3rC7LEgrskwUTcUkH8xgu5mtFniVbDJyADz7Bl4\nASAELGTW2Fed12rm+e9b6Z+OKcj/lgJ+X3urqPCC4bR7t9zqn/Fapy36zdy7qYv1VVWHKniID8fQ\nmMOEkGtCIQqSkkB0RmbhldfO1EZfYcHZg7rcBZn+ESixFvDDl00gElhreig11L1EKRPq+Lomc9mH\nhoGlbYUV+ZkrHpQ52X2Ci7QSjhRoKYObJBnxu6fhzHpQyqWRNKt3UfixditPx+18xyS9HctW9oNJ\nMjlPcmPTBkOP91/uV83U/FXZD9OjpmEAZfBaVmsodnAqH75jRx/vGTO+/jKZxBX4bcly5/Dh6Jv+\nwR+vftJ2lK/sny75r9OuIcS5xt2q6PNBo3TmP6koncXDjGphp9NAb+Bpk9hYxdTVOVg7+ENjYSSD\nj2fxx5+sYZWIoN/oTvLK5vPOdQ1TFiGoUizcU8LMaN6CE0WFDY7OcwRNLu3NtRIzIjGamAHpbynX\naDI+8HogMOjyNSBSIWPfVcI7xgIyTxqQ94afmPzCZJdlXr+8W51L7J0pNF01hrRjPmAGf5KPxgeu\nHmOE6zGIe+hM0jF0cnvLN9Ozo6oDSJafT3jNx0A9OFINp1MO6BdiwXQ0cE83DMmzrhT7ZOtiRHL3\np/+mmvWRa1ycgrgwRniud+eao8KVoc53RMhUdcCxfkOUvd5/+LX35FHvhBGzRV/8fUlxVTgnL7TI\nLCqun4eYzsdhjUh8diztEDPNJjsy2jLsQ+6HV/JXO1B5/7jUiAHV2zA3T4LhsTNlZi1f/mxoI91h\nfPKCcxw1QopLt7WYNcBGpTJGmNHaECpUmRKTPx3rRIB4CiRvvtBbuNd9rkwF888E1DOUZpvncX5i\nY3G5CZ8+1yIpyfxxxY9fQ63gRR0tlGvmB1x1+S6ZXW1rTbd1nk1MhG00PHCvxZVprd0DvWvKRvV8\nOWAthhxs2sy0tj1hamkYTcE33UEPyNAFfcGxJogdL+Jr56FI3iVCwtL+aD//TwB+AJVFNXzOWKA+\nNur169Lic+f0nE6HVsXwFExlTk/6wLZdox493oX6+jHtrf3U206dtNj06xf/1cKqgZHfe9b4OaQH\nKLmLI2mV0M5eLxR5Nj87Vl4dVvTDKc/JyVxpMKD3iRN5nEzmSHhqbaYG0ZiEPK0Ta0L0uc3xORlW\nf6Zcxs/dzeZWs3w/5VVvizST8lXxmhnzuyMp6zXbMR13XlcSc+MtvkePSgBHNS4or5PIvfa3n6y5\n83qG7FejN2fihXugVnHAj/lA+02UcvsAgKSliQC8QqXIhMQfUai8QKIa6u7vNRjaLFq4iPns5+cq\nU2rI+XWrUG5mlr9/1qyrJUbjUPAIw52vPJ4New0bpxE+nWdfbApMj5uZiaqNaZCLOEZMIdAyMFYB\naPiPVWcCTgiB3fDEWUxBHYDOMp1ZH2uDuf/KG+v+blXealUs3o4Adl0E7GIVivx2AKJi9w9qwKNH\nczes4xq6dcFBJk6RC9CdJUeGO+4UzW7J84hXYXZbCThyEOfeewPIRErKE2zbdgyAB6YvckXPkeaQ\ncdRlpS3qirxYOafjnLlKRo583MJUMg9RqjTbFzKe40hYc6AmnjqIzRGgXIHeVmJmhCkmniMJwUjL\nSUXG7P7CZZPTpnK3ou++rJHXdAy1AELqh1tldBjcsmOZ1QjXfItDAAIvTImNjI4UZviJPOoFCX8J\n7za8EDy1vao+crNWIklHt1Zbha8it2w282AEgm6l71HkIEWSwaWoNij0W6hrk5+A8k5ZaM1PfX83\nYee/B4/avIf+IxdpbIK1oSeeiQkwDJQqznqnHVl0gE6J2lpn+PG+naECrsuNEK1AVIM1WZNtN/R8\nJjny7zZBvcjGYB5QVf3jb6XRxLM2U6Z8Y7Rac0lo6BvhkiX/g7q3jM4iifp9/9Xdj1ueuBsJhBjB\nfUhwl0EG98FtcAYYZHAYdHAbfHCZ4BYIEIJFSIAE4q5Pkselu+4H5r3nPe8698td95y73g+9VndX\nrerVtap+VWvvf+09dZ7Npmx77NimAcmfu6obT3rmKOnAiEp7j/7vrZNPSCChAIKa/QIndRpz8s4u\nL/nl1J+tZ/t1l/x8kibnpWjDk8Qa2RzpduxRJOEn90msqU++sGz3VbZxXjW+Mb/wYXQvm6BYI/SJ\niKOwebJkewpmlp1CVnEinpqLqJMFVpNHT4ZvMkd8b8nltIi6C1EDBjhsb70gAkEhAGWTCjw/dBP9\niN1N9DouTlg1YfoXh0MciUwlhSGd6Vb/hZ45eRSZpvB6hjSoO9FPTDL5w/bbyDV0QbLJ1jfXIa2H\n2lztoZTka5Tb+xRG9wMGRcLSR7Tc9Q86QPQPWV6x0xIm7MBA1LwNQZ2bK6rCNGgQdHIxtYTwrOaz\nFXObx9pKiueIL1RMw+TWO6xVuo6OyVn12U1gzJ82bdqIry7lDyAydMHb6a9V/mcjOA+DJqBeYZdJ\nRvFJ3fpLIFjfwMRUMJkH+xy8lci1qnR1pAU2Ew1IfYX5UybwfV7dYY1V9fS2ZY4tOZD9Ymu984OC\n0Y9pXu64Hz/t6a87j+9NbFxcpMxdY8r+jIgg5zd3358/Zmme1+BswNJl1VxVVdjOzAx26v0HWDJj\nBvb37gno9YBK63CvsnFUyaOuzpXaixKJZN9eWPs9oKGv0viXufPZUetXfXt8Y4IF74gbesfJ0C5N\nDZfuAlz7sJA0BkZ1d8AqfQngDQgtwOpPQXCz2jC3RU+RKPMuw0TO27EDtgiCnHe/ollpA8xv3BT8\nK2+HqzijIy/h6xkxsgxmicYGUbJS5mMTWeYWQeZTWy+3WF7L9KXFuoKtkxh5oNEQtTIz/edp7RoE\ngblhtiI54EHF2FKLx5yfWb3zjcNf9jply8pdXIQ1Eydmljs7955cfcbWvDrTw2OLe6m4ZLSHJyrr\ntTgs34qFJ0a0OJI+ZfHifZmTJhHB1/pPyj4Mohys1mc+DTF7DS7T9MctN+GXB3hGAgMdQBoHIAcg\nBVi6LhieQVos3KIEV5k6ctpwVZjB0Ohqx95Fud5eAXIjNRK1Tjb0r5/WBVxyLJ0KqA55ePD7IyN/\nrXD2+LHbtX9aT+ad6BCU2Z46BYk3Df5mSgygBROfjT42NWHY9PYYMZCBwxmA9k4oercrwvQjzcGv\n7gS5Q4a7YJAI4OXO4/vLmheFH+AB537oLLNi4UNg79BevdB1ynTm/ejFPorBbUorHtwWXLfq938M\nQ1MWwILPB+LM3t5s0tjz702GyfWARJhEY2OvEEJcANwGUA/I2gKZBoxlKEA/4uzJt6OjH88bXzjH\n6VbUesuqxK/SmTIZyqO8+HbtlOTAweew22U8QChARF5eOekHDoSoFi+GPkcHBTuDdV7+z3JR94/d\nhwF4HLc2TjL/Hf785I3xT7qtYNRiie3wL+7f3Lok38ewKzPsl34U3T/rmptL8wfewI2Xu7E7L3rh\nQ9evYc9L/tCt7OAiRf3a6JVWERw7e/fGaaXVZXbjGNPspaut2uTJg63eTTVk3fJBknl3D2RHtH/E\njZ+4LcgHB04OQK5+BzD6q2zwtCHb5l3m39mo/VRIcS2cY3RU20AIFACeydsUJGm3JP7sU1FdsGDC\nq9zxtnOtHBDNGzNmU9r167PvWa1yr507uwhNwj4Id9Hnypm8+W10Pmxj+tqFck+dih2vfPz+20I+\nIYGoiB3Zqk+QNd4LTc44FmnBjeh043HSls2yy10r7RqnfDsypJrba2dAhBzs2rsEFi8ePqQEhuQ4\nMDd+gDiFwwr0RFZzAdzKYiHAfq9go9O5oLG6S+iQm/7RRGPsH/0zos0FW+pvnSx1TgwF9reFYBJw\nH0AcgK29vuH6vbO4QoHQduiU/QZ3Q3y7ZaC4L2dB4DcCsVEm5gIRnZOLuPR3GJT4HO0/f4GZOIOw\nujS5gz4dMXu21y2pdJhz69ZsuVZDqUiErgmp1nPbNknbeCXSoiEsINHVYHc1HWS2OsrgUNUEvJcd\nWbYPTXZy4AvkKQUeNf5tSnl3RuBQJXOhDc4y8sGlm2VOkzEPdX7ze0L5RdItdUfliKQYR6T1qnvi\nob3cumeevNmjnFXWtoSl53o4JAIY3sJ76GyCb12dSGq1411wUzQtLaJ/b/iNxBw9CpNUArHRLmiq\nRQxXy6FCuscmSJX4yWGxH595WegbE15dac/yKy/mBbsd84xGHPMC9twkGBESCWnqdBVtv8QkreBV\ntS07GTQ1z0QMlq1l2StT2V7ZT03J3ChFjURMlYpT+qTKNeq/f+xZ+im6Pb1ORs3Dh3Fn8HCbHOOf\nANrnVX6pxcuLCk71h9beH610f2JP40oAgQAC8D1krzMwheHAcnPFGY4Lotei0BZAduHAgsrm6X6R\nNQ7HhERzyXbMe1+B2Lbf7ZuKxRTksVksbpsQEzMpMzCwT76np8dnL83tJ3R/JFj55/sXXeMjcgt3\ni+rsMj0bh7WTW/IT7h7dFlNcvLwImh2tUW3iwbXD5NwHknHZG11pteS3/edeWa42Ca3Cz64/oAw+\neEg0wf80VPYoMpe1lLmTUD1T4fCw3M4ewV9p2ufD4zFjNnqXVV2sgkhzAJM2/o0dR4DRfwBpPdBm\nnxhLxWKcSPrCjA4XiV1VQUFl5dQ734lEfdCYPSoxae+If35jrl8LKXmRb0Pbtjn+KlXo1kePZAMA\noxzQf/H1dTlt19QfqSjyrMNPujTcEA70rbpytCWG907tKV37RHKmpfEfvZlDSLoHBqotyAyqg6qA\nh+sMYMdTSn8n03O2INQwG6H6vxvt8Imypzi3WuGV8vusb91ElGN6O3lJmv88Qaju00wuujdmDLvX\ndkTgwLXTU30WIQibOXPeBw+PO1gbsSUOrq4XUVt7ByNGxILnnwGSUcBNO9BrPmDaCqjL5GAVA9G3\n0WvEix+IHDgd1wnH584lXW1vLLHTui39WhN0disN0wHAxo2DwoKCPmY0NLgcmFuUup7YyRf2Ges8\n8s1I61iMbWDBqjjoc9pidEiW+4QrGUfO9P6Z26YRU4NoB04g6EC/c2l3fZYuwII3+O6T+HMt1m6B\np+3lrdNurUkDrUycvMQtY7b/FOPmE0oAw6EwRCnWjZK4/UFK3Mr0/jcgMu5q0k2VsmUmHek4Txwn\n3uuZu990fwPF8wARO31em/PBAxn58oDUPBrSkVKY/4NxPXqcC3v/vlt6v1EHH5wdHssDJAT1XDUy\nNS1hZsVwsX0CJ+hQKI8StaxwFqwi4vlUIoQ/0P7xsDR4JYDZANn13xbyibfJIe0TdrLXTo2x2qux\n7svBr0GLXh+npnZ2Mpc7YAiXv/+SUyajCfXNm95f8lrZi9zCK0NTtCYPBMnkBjiPTGJ8nlLs3bcb\nUzTb4PRDHr3v/Lamc+s8F1W2hm5OSbAZ2zFScaPbsOVtRWB5CN4eS4X/bAEWSimjh8AXos4rC7V3\n9AgK1oPs1Ywo2d5w0G/GsgWkQ+xNnBDG0xLGn3cvvsS9y/qMOS/X4V2MFA+6uaJFbqkw9kp2Q55B\nJd3R4eljXCnqg+pqdKM8jolh1bt5hIcXVyYNdXW1SENC7j9ftKhnmbNzoFivx57Dq6il2+eCwCBF\nUPVlVvjxot10VYi/3Yzs6lsbnKMcsWEm6fjZjIHPPqBr1mM4N1TgTijB64jB/JO4SSUGNe+04vZM\nhyGiXrqxlhG3Mm7Aiz5BnCRjA3acMSKuyoC7Gnd6btIi/JD3nLoUWsjfQzqTsopAoXpvtEM2ao/I\n0lpGiLYNBJZQ1gbCs9VAYjXm7mpw9MFdbgB7+DXsfl95XsGFY0fjK1je4r26n7CY21dVqfPlFWyD\n1wHHHKYXd8s2cmhT8vRuNkhD8KVgZIwoAi+aJRtk7WSjkrTWXsLGpUMYsDoIVTcpyuOJonaV3bRn\nINdN+cCs16yQpxSNob2bVpy6tW9udzDMPgDbZYc0E8xFzrtBty9jk/Zv6ATIskGE8YsUmV072Rsv\nrZqt/Vp8mMzeMv/+DtvvRgrm3xDTyAXQh1IU/d/jT8p7s82rdvEhlk/onvQRxTPWuel9DQcOr2jl\nZyzistt/oH3e3CJOFhteiFwtf9lXs08x6J9ChbcOSsd4sDR/5oGptec1I0KUVfbC1ZdPhAZ1TrHx\nTYxqWBjOKUXAkcZTkKSKoU0VH/iXQmfWXs/Vkr/vOcLvfEufZwx37YXzzQl46KHWv9fa2QnHD4qH\nvUh8dq5Hj7Z79+6r6fqqwlhSv86z3FVxcMwp+0Rm/ZICW25xh+Ag4XjO8uOu0GqdAQxBXFxAW+Cy\nCKh4ER6+Fvv3H0BC/nGsuz2Tw0O/edBb1DKqf9jbX/E5SCftlWh4pLQJTIkXcOsezrFAZzswhAW0\nZU7akj9bjvd+/mqI9Y28JeOol7NM58oaUYzOhZeWwLFzBKNR2ewnDxnF2h2bzuN1+zlxiBsGYBUw\ncD3w0zax+O8HvXv/M+zVKxRWU5dy/PprG2g0VViyhoOuoxIo2gDEzQ3BA+eheGe2g9O0gId1GEpq\nSz09FcmRofVfo7xkJ1v8qFi92uN9UD6iATxGr3tvsHTbrLTHQ9Mydqxq+XTSMG3rnFZs/0f9M6dh\nmsUEE+sK1435kA6kEIW+w4nnCM9s3bBpRbdR1T0ZEjietLN/KEiaeeCaKb9qEgAjgOZQKk+xO7d3\n7BxYKVpp2ilpNcrtk87cKagQo681xq5ideDdyeIaqIz6qIvZWJRvQoAcgFO6t2jM2p0WZQ/tDZv/\n0FZ0t2Es69kp3t4wWyxLnLHQzNWLgsNpevl/jLmEBKICcD8pqW/Br7/Gd4dY6If7iTKkODXBHa+N\nUDjqwVIFelT8RTJU3Ru9kAVOLf/k1PrEkCxGbi4EMCk2Du4ESP9vCfmEx6QVU09eaca5cmnKKFvN\nElbyQd8cZ5sPwLD7lmtXordlwaP7KEjCfcXHT7Bxt/Vkt82G7mhkK9PMEQvmaEBihUwvQdCg25je\nZQumrX2N9UuBjm1ZLK35kw64/p4/9WIIVz5lNqQ1Lc17XxdZqNZVO2PxdKDgLJWU3MeSt87/zL2b\n3/GUGJo7EXP41PxfJSOWr0OWxgv5wa72XewK0Tz7Llz4eRZm9rMJHz3AKCwKTH46DU7aVrbLoxwi\nnUNCK678xYiKv9LpP+XorlwC7/IFrk8JDOk+eLgzDKLzDzGgQg7byJEytn93JdtCW494nZct/kyt\neHydBcnVasGl2sRsNDMYNmGNMbpSZogPXO2ubXCrr3YuVoVVsnTdM6ut/zc7D0CS4+0tcmPKSK2V\nETaM/0V4FRnNXV+12hpSUsglQc68/GEjts8PJ72Snlq3HT+4VaZjogrFPu5/zBsU/qR1a8WqdXLL\nc59jFy92vDIY9U0eHxZI06q3a5r8tiCP55RNpd/GThX6m4Nep5vrVE0ZkWE9/dQxv0uU/e+fZj54\nL2meD4lQ16vw+caTz5c9I0WSCFoql/8d4tAdfqOvyQUa2SE/+Ct+nuLR5ot0/vLpwOFtQE4JsG4T\nnO7l0XbnPfVZRFDmO3YyFH0QRKphpNcETkxp1bBhDntsV6SSYsYAACAASURBVAb1+0TQZwIf/Cme\nlRFpr74Nzr2G1Lh8Y/zK5W6MRbyYGaFJr72/KXNbcXGTEfiu474NYBQAK4BplOImIXAO2ngzV9eW\nqkQ6WIMTPCTDHtXx8S0WiualNNBuhfVEarciN8DDfGWQUoob2SSmiEEXuxQfSEs8iG3m6PDLCaPB\nIFc6uenYPD7AoRRZuLvoI7gXNwjNL9SSc4N6Gz+4R6hlBgkMSmDwdYGGfLKTW0MZR2akmGn2UEJj\nHteaw78StqWjoGbRFi8fba2OvGzvKRzadLGoxRuNvASDngPML3GIg9hVlSA0Cg3uvSTyWbzLJCmA\nbwCm0NhY67/zSYzv+X5/hUKRi23botAk4kd0154BjjsBDYwEhJsVGCnZM2A9hAoNcKc5YKisBTou\nxtN+p4aMHZv3Z2WV/we7mh5DnbkZWBlBS0rR/PlljLNngY3zdJVx7bu0Q3y8FCLRuSqDoWcqgAoZ\n5npJ8OyHOpjTgW+vo6Lg2b49fjx2DAkCmOaYMl2Kbl2l2H4gRfQ+1X8pfJ2X4iMphLRGjc4KXzzJ\nrlEr/KYsWWJ/1KqVjFDKnd24kXnTtGlm32ODe/BTz6/CoJuz6e751Pb0h7rpY9Y5Odkkul0Xf8sh\nIF3jEOcAMDIUWP8GCBgJTLoPnKOU0oQE4tztGfqqLAF/QDLa2diiFRdpfX80deQWb8hknWXH99k6\nuOdIlpMtpzmeDldMWaGIKdgjBYjZrFGwReOtkuojp2qoVZUBIANAuQBwN+AzXeNkkJ06ka1VG0xC\nhx3ujqNrnMSq1SH01af51B9pSMMf2Tzk+VDqC3D0526wibMx9dis7vbuLXgw+/D9NPJRT5j/uIDk\nz0cQPPEmvAePQ4FxGIqfDUN7i1nMN9y/L6+S52FayxlgORuc/9tBPiGBcEwNl6/4hfXpEuCAuYJB\nAM8hv81P8BU1EYoGu1IoXPgmaQ9MRUp3J7pgB/bb19Dpqt3Uff56YvLzpdz2AEZVR2iw2EQeBXsB\nn52wyLALv/JbUR/MgIQ1oNjbFU98muGVezvMCfoDKsEEnUkhZPJ2y/ZscOG+bbh953KZrJZejhuf\nRrMJrwbziqVvueJKHhImVVicnWSvGyaTBHuWYOHnYMHrberb8OrwNs+aJRS5sZx7q9ddJcaGl0TU\nIhJPuk6HIGN4pvxvB1P1TLwqvp60KgDWdkD91duQ/9oG5n5dIHVvDnFROtfwODdQ7VpaBaXZgHRf\nLzwK10JVk4EyOQNSzYCtZKmv2Y+UxBRDYATBKrLB26yoWpQkcZr4rl58cbC3IO4hZdfZ1zlcaqWM\n5fcF5jJzpcMS3Etp3f0zYFWzg8+8zV1wM9IlAOfeBOBUU4DUpWBP2vWukb03ryAMz2EDnvf8x1ti\nf2AT4Bn8udXBBbcWj5+weqd2TKZHybVTt/0iu3a1RD9/Lr3k6Y7aikoBao0BXl6FiIsTYeDA95e2\n/Tb9fXDTqsKWciz8ba/dUQbVIcBil81Ahw6l0vm/TAD7cXO1RzEpqpGVNOt5eYrjzqYWYuHMEZD7\njwS7EMhDfsvkO6JcUtsSUpPxM9i/H4DPigfRqqikhsCmkRBhsBVgYkDIaCCwGaXKTB4Fyzm/K/EY\n1PeUIT7+53H5+VH/UAqeEFzBd+11H4nE9LDNnkMjcxp5KnYf+XzE7UXTEYTTOxFbvCmy8oXyQncR\nVk3bS0Pz6+nRLX9YfVFc8XY9RxcePhdUWVBHBxtm4zdeTt7DF1u0afzy4w7WaCSYFT9AMHXqzkiD\nguH1LZ3Ka99QSUHlmwh585ZJzTsw2X4ujF9NsXGz+Hcmragd2e8SWWn0jvYAq5YCPEAIBSUQ1zTw\nKqIubpRDricvi11ICOnNcuS8YuIwTehPLZLfv+/bHizdj3iveTTBXfhfzCsJgKkQSdfDu60axZtK\nwHfww/fgYfVq2DlOXiaqHTGWgf9LKcojKCorAIsZsFohbRCT2WaJpYNN5zjNNJKkmEscFeUmmacS\nCAuAENqaCLGxzNPTF1W1jx/rRwjC8E2/mCPZhdg5VY0GOQfHmaXAnR1AW6kUy61WMJQq8gBLELp0\nL+MWzPBcfuoWxt94ZiHjSn8vvn5pepiwLbnUq+yHtocOOTk47huACQAC5RbL5tezZga4cxUVOevN\nkkV7kPH6NWIxGESl9dAdO3VJmym4hK2jEV//4/8FQi4mAOgGRAAwA1gPIB4EAzEFl9t/aE+bz2u+\n5lFA9Fq9oDBYrURoJ0tVLcDuCT1i7ZcStrQ7KIht4xWLmr/Ue6lL2VN/DiKfwkfGzk+7/z/3M8YB\nWA1Q50ZexQ5hZYZHUagMYWcM9NPlPV+p1TfkAq5ZmsBcPYUNef3Toor2bTwCTFiyvVQi1DR1RaIz\nRRIUKBM/wmpDAMLsAoRUHXTfDsHZ6Qna9REgUQBGI1ApDcPdsufsMs+3P3qg3+W8/37x5F+cEW2U\nX3f82r2ewBithMLVCGn9bJSlVAJfnoKp1SHA6oV8GgqlRwFiy8JgiI6k71fFEf/sG8jeuQTiqBPC\n/k+tmR2LzaiuWkqVlWqUdlhD5ry8j8w7PRAifEFrzztoL3wQPOvsjFgH1IcC3zpLUNwBsDrZ4Euo\nUGfVMBv3nEB6bSuY1xcABRagYQjQYARyPcHOPMSfI6OKbvEjfW9sLuM6VKXrfZeWSu4VgC/Phgxy\nT4ysF4RGJar6fI+ZTs96OtPyYA+0yMklblkv6dTrrxiFc2GFdA1x9bxLGZ+LIGYHcNhXWfYgT+ql\nnXQQt4c4Obb8eZSdGX/DUOhs060a5sxSkaf3G2MWqXdQuIVose7lVNxWVdJ7YYlCHSlk5zb3xUnN\nLvhnGPGl4ZOeb9KGhUYth0QAeews0PvvPuPdJPN1JM1wgv0UQD8BRALABcDwuKeQArjojwLZXsxz\nXpPaMCutHtPEvLrrkORBzBX7OV3XREH7N8dYh2kF+dQd6DlmDBLBIAIa9ICRTEXz1ir8tiZVxHGd\nP86ZaJ3qVOYc9g5o6tLDZu7iKTk4fADOr12pb5Hz9euhVmgxsAhvFVGs36vMQa4j7a+5wOAIuPTt\nSj9FRxJJRlGt6ZHDySXnM2nocZU6l7qS+tTxpE7pgiaB3ghgC3l5+8N4aK2iRoYlUEgYuPW0udn7\nsMy+RiaRjmH71zw4vs+yQHMXffIG4p+5P/20bTgGlzx84BTH/jZL+WdMHtMrHL/fVpNX4+JJf6fM\nvVayI3waZTKOVpvs2U7wGSWZ+qIU6/86jWPNefzemcIskdZ6FIoWr33da1XU6kvB6nTy5XGLGP+F\nZI9sydZbiElLrXg7L0V6X91dnxs50I+pqqBhtXp7btNmbJ1MDWIHO1K3JXOk2/2wtSvwIkW+pRDL\n24+GxGhB3nEeJ1MFBAS9wbRfW2DFiqvilORhPlvnaJjIZndzekytBit0Ac8QAFoAyf9er/F9EVMC\n6AhgEGAOhXZjA6yH/GBqZAWqvwK5ZaJmokC2A9vEwlvsKAKPxpDieTCgdOchzmA13mqERpcLFh0H\nm8FGKpWg9SIwnF0EiZ2lIquFCDeAUb2Q1SwEgevXQDKNAe07E8XZHqBNHsI78hW4RAb0jA8wcgeI\nycTg7EFNSs8EXVQbb1+u19qjcK8tpL8fc5hC7OmOpx2r1OsmjSexbMLXGTh0RAZLUjVcPozApSc/\nmc5olv51Pszvio0spzAeH4d7rJbtpD6o9nDYg3k9JrwCuvQGojvMxd6uO7FwAge+MfkO+CEAfvu3\nX1h0xVp5G/n+kwdOHpHfnLz2BgbfqjJ5NqrYELsqOam/CwehtRenH7r1dAy3bdtxw7hxG6VpaV2Y\n06d/qwVQLhJZasLDX3uyrN01NzfaRa93hsKpvm40zVR3M5qY55M/0tZnAquphHO5GxxfUUQ+eezJ\n+UAaVHZ+gQ7W4fUomQ1YnAC/CnBJ19HIWgLPJguQGHoI1LIGI+QCFvOAF920qYfJ4RDbDh36zSAu\nDXB7iqHqLVjiOMpMtgmC/P9VPPn/3yD/8iYJIpnI+ek+SFW0EutDG+NvqxkpFk9gwwt4y+wNPQwR\nylN8IQPYQWDFXOe1OL7qI2h9PBxXzsKmKEHHdjqMudsOrwLvWO88PLtXjtrxh/rI1ZJBEmnWPFXJ\nnO6NfZEyB6KqjujT8xRmpZ+mqYIzGpkKSFdDOXKJP+LJYNy0D6cuP2TAc0oy+fBgADQtv8KlQcc7\nPXtGiFLFnHd1RZt354VVGygzV9iO/PUnLD6VqbaqKqjtI3tC1n8Q/vkFaG+eQz+G94YjeTgi2F/o\n/u1tX36qjmn2uFkrNaOyIepNKp119TXJHaQpa3P4YYWtxBGTyWzHls0slj5/YF83eYrIapRhlusu\n2oVLIuW5bXGrsLjsoTnLK/iRAtPW+JoqtT6SgpooocQQLHoXEIDgLKPha4CTgjgAR5mSdFIf5Vnj\n9fLAzJGuF0+ttzQ0uL3jeVHyUyT8ju+ZnwiAVbE01paQQFgLJLv2Y/aYO7SvIBD2FYBcWMqnebw6\nXVq5/0GIVzfeapFCQj1wTaeEDd81vKEAzLChGjdJY1h8GGb5dEeXA2u5xHReaPtDgBDWaKToZvcW\nWJx6Pm3yi5suJwl8wljYm4s48qelP78n/aWE/jiI8GNGCgPK7tt959ykq+u/SReEuenj+1fI2BrU\n1p3EvWAgoAfIl0EX1J+fenZYfwkj7AXU34ryuw9QcIYJc131qdzPd3WDWKXwrqqmlkQ/6njgU7sy\ney+f1sbH5LQmKeAMGccP2mu6Mroia2TL7L21CqOpsBfu+/EHUqrSmwYHTsLJzcON14eI6tGgTcHB\nmMSiHtq4rKG7kzbIOn54Tzb8+AP18n4otOtsExcdaV/ge1/qOvPYVPXGSwcd3V+mmEJ7vFDWDzIx\n0DgsoLj4VNZlI+WxtPR664mr2y9gS1y0DptEIlJ8Tnol27qxTc3WY6A37rAw+F5HyNbR0GEmL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CIUna3lHuqhLcb14119bxSlwckWsb7S5g8Psm2F1qUgU/v20OeDRMcIhZgdoEZBAS9oyS53VA\nh85yWmC04mwKJU4SF2Gw+qpD6gh6sXNHL4mv79emRqv465NkR+vMV0PNL1/e5sc5xFW7HYKXBA11\n6apQ70dNqvgZ74SffsQc+ggdj/ti4akjqGrfHrqY7eoIbntDBG9HLQNSzBCUEi+mHyqmfgCs7UAL\n1kLQFdjAfCpD+kxnLyoUKng+bDGaXGqCgz02rCRDH7cWLca4Nv3IjBuHpxlHjuzTU+BKBc9+eq1f\nciu8G2OEYksR/JxfWjudON/7QA/g80JKo24EBhJR1cxVpVbXKtcFr6sdVzvMGZw3Kfb2vwxRAVgD\niKcCW6xA2wCgoxX/oTL6HmUzH8B2ANUAWoBBBLp17IZ2r+V4xAsog4Bh4CQn3BzW2vtWV9dXBevX\nz7EHBsKnuhor/aSuV2wZ0buEQt8xNY+6CNpyH1G5+kul/+E1Rs61vlNsLC39H0yD5t9v9QYw7elT\nUgTgCATihCnHWVS6v4w19ptCCNwArMB3B/Mp+BnvYXeqjnG2NDqNcVsrshpZxm5ICckKMdOeSYLN\nwSPL4YAFaDonMPDSzPz8qCn/pyCvo5Rq/70nAGoppVpCyD4Aryml5/4tOwbgLqXf0739Z8gzi1h0\n9ByFdzondLQkQ2qUIq8kEhXu9yEV5+HaxSCMHFuF8IROGPbxR/ixLDSuU7BUFw8p8cQUeR00xgK8\nth3AE7xHOoB6iCGgFdrGVKKpVyEUgRr7gOpqVt5XYKwXFLabJQNFkWUCGVF/AyVKFkmGYXhJ2+CV\n0ApZCAUFD0AMkchC7Y5q48wZRwu7dLnmsXLl7DyzRd+8snKx0KLxOu5O3m4yRgK70AqOhQtRLpXi\nGIA+Sy/h3Klr2F8pBSPW+/BuPcvYb4NYunWxkk8u0XEuUi9eZ+l+h+JAbwvKREPdEumk3QuJ5VlL\nm+3sWP1809zsUTA2nyTWcoHQcZkqN75e94OwZVdAeVZEaz9jrRTmeXPh6OZN0aQQ+GYw475nESrW\n/wKMTsT0XmYMmfIO1upQfFw+ldgbXKa/g3rZS/QMMtfwxAAAIABJREFUrEPjTIRf+gHPxjXufUGy\nYv4S5eMCtSVR35Azt4nVrzgV6u2fkatvQuqHNOvUZLpTsmz4hmZljMquUXdIF0REkLpLkF9sYmWl\nXnO9BJeWLF7vBWMtgpuHmtZ/bGOnZWWMbMZsbt7582lnb93qUevt/dqgVDo5wqNliAmWwVcEGFMg\nrv4AG18CWAWKMpaiciRgyK6K1ckb+pb3dfrgYTfc+r11EN26Be2jOtMLF/aRQmkc1ZnHk3qzzZwj\n5JiynMrVt/+IFTV+ZxWGHLHbpTyns0g4a0YHGfN1TLlLepCrXEX11FXHwy4CdAY/Uudsg51w0JiN\n1jonOXOqbFp9SGGZIkHeybb8+QnCKczGIYHnPIa2O8qIRBa9hqtjGNBDBFgdG0vNCQmkqcOB5I22\nlars4pYVs33/cI+WpRJUG+2z8kVsLd/uo3FFwgsPtnziSbfR0s6GJFrV28E25BPiVyiyqmst4lIH\nIIaL0QGpXAlD1clJsTpjsOC3Zs1VWUcu0dCu+b3C82mzXaMsmc4fEWEpO/iZQj9TEoB8yYYIENQF\nUbtdguvXJvF77m6zJ2gM3EIOjkY85bbXCksjeE1Xi5Zt/u5PxvvF+/51pw9sK6lyWBrDoRKr4eC3\nYjHbH39jKVhiQgCcUYMwrhQtHEALEFgAKgWlL8HWn2ilEJUzoVzym7Em6sd/RhevFhBafQ7JktBu\nX8qiT0wZw/V6F2xv+jHWul2/QqzCPuqEVVw4DGuSoF7jq4n89OnCKl9sb5GEZ7U+EBY2AhbeHzQo\nO2TshN+aM0TQOzvVvQWwoSful9j/avwAH5yU2JvaHHFxlQAuqAYGxZpm7XM+ws2rzGcDxFJYAn+N\nfVX/n1gSDUQmAKUUqE3B95ANNQAogC747juikMGA4WopmCiJNrXmizWuMNTriNeMVhH99l5sc0cy\n3h71ZvSQ61Efb/tn55+MFbd2aRTuXxvDmEQ8zeSdCx/ModL8QKz4NjX2ZEICWQFgLIAusbG0+n9m\nG7oDOAbgSWzs38vXrBn1FXolT2cd0NR2Sao5oWshf/2637naWq81lKI8IYEQfD/ktwzAUAAFdRbJ\nrXEX+k4wnLulBM8bAERSSstJ3FqChHXC/3HI//tcSyl1/n+A/B1K6bX/0h6VKKZCbHQDAwoZOkDO\ntgVYB8ptLuiBcvQTfcI3nyfYP2w/pv79KzoVx+EJ3HCH8UK3wR8g/mbhV6ePJe9QzUSCwBkqfIQn\n0mJMyJSI8CmHx0dbIeRKoK0W6F8mQjsQXGwm4EKYAweuKZEZ6U+5WpCqb0V4bTLhOaVwUCqERFLB\n3Qcko0xKF2/QGl1kYuml+kjx+/cfCPtoH232Weq4aBmJOGoWlk/mRR2/okGbC4miEDIrJ6ZvJ9to\nfVcwdglQ9Q5C58eMdU3nXhy93Ju9XTiWoWQyNFIBcsN+WKGGDLvMxdhuorC7aCJU5rqBOtny8vaG\nH83lUp+SXFb7ASTPW4XkvoRuF3Ekw1oLiarXZ0uLn2sxbFhjAM74HqlSAkIs+P13Gb5+rcepU98A\nbAFwlQJRABbVyyWjzSoJV6JQO/p1nVA/MeKhNsL1LbN6oxdt2qX7Z3H4Zy6JloUK/svsdeOXiNCs\nWZOad8nVF2PYpFKR0je9aoQoh+1U4ze2QPWibZSyW8OfwuP3TxlH0XAoBgzFL7t+p5IhapOuvrIu\nN/eb8607C/T6sTfdEL2sDApp4fQjuWFmjVPN6VlBz8BwI5C5X4Zk2zd8fLyOzWAl67F+vBzyusdD\nnfLjp3ouwOE9tL323pEXJ9E724V5Nie27ZBR7xZ/3bDWqWXoO3NtadJdbebA/QRXcAUZcPj7w3//\nftIWrJSdf+waDHUay3KdQdLM9zfin5vbsCpqnqVpzxeSC36DNbkI5jnWajhJpnAZ9TEFTYUvIXVV\nHg6pWm/nJUT9h/gXYw6CG7o2vPg8VHrREaL53P6euZ/ujHxMwCHHdKqvdK/b6L3wbp690aCZFX9I\nr5c9pHIOVzcFa+MWbzyW6JMq6xnHPFZ+s0fTbDaE+crsBVFowGB1yqigE417WB7Lu+a8wbX+7fAt\n1INYBSk0mhp7Tk5M2YkTa2TjvBa+eDXy4IDC0E7cFvc6iE53tgXO2s/9mc0xvyXY4VMkRfe1qw25\nkmYs5W2cZ9p6drM2paLPOagVyc5Vl83XXb1gl02XTWZ2iQtnD6nH8iyEfTmFU119kMxWw0I+w93y\nPDzVVt46Wc2dSnB0wbMiDg5wPUt8J49ZC9m08LxwVZpbxwN/Os07eLBy4dOneRzQUI323E3trC7z\nps4hp9/Ww+V5Z8s44bStGNPFSgTqNokHWed1+MMXrz7ysC2UAsMAeLwHVAnoscTMtt89k9P7aq0v\nFw6kyXPvEAItQLOw+eN5tCj/GatWEQ+dlRiG5Ukc7U9/cagVtt/wu18Yvqjuos+RvzBpOY2NNXzn\nSUMokJUIZHwA1l4FCv9DOeYCIAQsojABDKqnSHFv17KntvenV45aWS+tamyqy5+uaNltCfXLaMl0\nSu2uI5Q4od1rVu9T+nnVwzkB6eUv8rGhIQJmcxU2btwE4BKltCohgWzC911719jY7/7I/8E3qABs\nGTRo/6jRozcXsSzvdn3PFtW41z4SumpDivSHZ2EAEvDdddECADUaIbp2DY9OnIABQF/IZSXYvSbc\nY+/RCk0FG+QteAquejfZFdvV/6PmmlhKaTkhxAvA03/NNcsBgFK65d969wCsoZQm/1fIs2PFmHJx\nKsZrvRHkuAmxwYEGuQ+MdQTbnafjen1rmIRURPc6DLFTJdokTsOZ0hHAplTwb1ygSaaoq3TBem48\n9lqXgHUz4dSMEQhINsA1keCbUoNsFyNcc+w4rOVwj0ihq7PBy4VxTDRY+I9hQNI3TiIIDodHczeH\nb3QTqaeoKRZfvoF0aRWuBwKvUjjoDRwiWghwbSrgy6eFKHm3XLDxatNE+1+KvZhPGqBAfi8O0Wnl\nqG0nIG8uBErAGKzA5SLgYTUgZgHZ13607MFSyBsWkdDIWoSHDKFV9zsRv0pj9jV/RSP32HPswO7/\nQC2zQlzlAsUnP2SKPlIfX+1XT/fqDTteYtpXJdqtSyRcnAV0xReKl2qpRd+4hV74kL4ZBsMMfLc9\n/um6e3drvb7d9PA1tY7PwlW9BSdfju3W5uAkR+2lJpk1qu3RMbaolFRxN7sef46QYP81O7WP2fBF\nbW0pN9b4+dsml/Dk8IrC1g2vgnp11t6euVHU7ip+VCfDv2QZtmlDoHdK91fTS4OjK/7ov1Iq/vb1\n/6LuPaOiyr5232ftvSsHipwzCEoQEREVFcw5tjm0qdXGnHMObc62OdvmnDOoqCiKGECQjORQxMpV\ne90P/M/7nvOecz/cM+65d/xrjP2lRo2q2nus9VtrzTmfZ/I+AoH1bxdXkJ3elabA8rA6f2k/NjQ0\nVfqC+UJeeIz7BccO9S73ZjicPHjYmhLq1cvUW026lZ7A5OzhkGUUIW2FPQwVlwH8BZk3C//5SbCY\nxeB8pLh6dTc9f34XgP0ZLvadIg/ssA9/p87yumTn2bdOyJn9vll+mmldwPeex7kj0/8QMCbrZ28H\nfwuJeOotWbyElThckvYtfUA+TmuL+tgMJHPBfEvB59o47qB4KK5/+g03fCmFs9Eo1Oh0CtHDh0Nq\nCwtVloiIRPu27VNIrcUKUomRKbE4YzHdhd+LLxj7Od4UPn5sP23/kJPHUJ++rOPDTS1aKUuG3bGD\n0EMK+qoKn2ESp+Jnv+e4evVWs2bEPi+P+XHsmDXj4VEtMhtYs+mzstb3k97W55GOFA8C4h3a6u//\nHJ01YPjfrqnI/7Ev1xQ83ZtRQjkSzy19ye6KFZRnWeg0Rr7jujq2zdGDDeEO77/NqjgS9FbaNnen\nbG7YuGdJfNA/AUy3ikvUoCykO+r92WW8DiL2B79atE3dqdbbphoisgEhsIap4jSSC0oYVjB+9tgQ\nvrTVBFy+pY6IeOy2bVuvTYSgV+zydmWYN+8dnjyRMMePyxyBD8nATldA8h2jw++6d1iyedxCPL6k\nM68sfJqRYrZ9qUePmSbRLfCewRr8PAHgFQvc1oNBCmLBIhLe/b2wr/JXi5HvjeUtqbG+J/bEjwHy\nHIBxV0DIeA/O19V1qJP39yD9i4bo7S8BrAGQ0B5vWsXhb5cc+NJD+DO3DM7XANzEX4G5eOK0Gk2r\nyTwAl7GWYKALDt15hLH0jkgA89yfgDgUWOs40enOo2sTxrQ8cmo/HMW06n5gvJWu9QddbIjm8NYd\nJwo/feqxFkAc4hOcwfO/Y8iQzairG/IvsB+TSLDjwQOsRlOuuUdsLG2Lpo2UFwAvljV5njsXEPbX\nX6chFBrK/vqrHxGMuDgTNTZ70ftBD92s7edzcuDx/j34r19hm5UFxssbjTIforbXtVO0KeuialkY\nRhiWR4ZNKrIk34zd/8j4MGpuVvT/V5DfBqCaUrr1X2BX/ZfEayT+M/HqR//LDxBCKGa4gkk2oGV1\nJ2zLnwiLTQbM7R5A4NYA4ZEADLU8RzfVTjykbdAwrh9ESTMxpKYzwlu/R8hTA86a1DRdOIj/UteM\nDeu7CmmGHfCMISgyAQ61Hhj5Q853tipnrvfrD/I2rKJnj00Oe88somHuG03fvtSVubqirmVLZ9sN\nmz59bDy8KxTE2kleE9QAa5H9qoOXMT7xAWY272m6HXNT4JKthEtjBTIzzGAYICiQoI0VRVSBgm+Z\nJ2OkEi0MXTX4PlqG9B/NeMnZX2T8zwpyoC3F2k4xvCx+PDjP26TO5w6xNjEY6i5HUW0j6q0tsJcA\ng10BKRXiyZNh1FNpoBGeqab9t7INT54SCfw4Ad/XRFsqYa65BK4fDSOTNdbUtj6eGOqA27bAWU5F\n01z9bim+fo2XGlsuqcQGV4ssBHadc4vtnsocu4ofYoVlO3ezgw/d7WlZmXH8y9zerRXpo/mGzl2+\nAAMYG90nGTFDW6+AVAqIVTqUl0sC2X6mp5Y3gl2IpRcFqVVdTemKl/CvdPDWugzWlhgHlFNTg6en\n9HyPHpzDuXPGDWb9e/oHEuCIyTIDvk17Yy86OK5tJ4H3BE3j+ymGk3t3pXtXeCfEILb0LMYtn4DT\noGF17bD7yxRQfhaKb35E7qFoNF8thsQlG+XPwpHhPhIdOhyHWj0EPj4pjmr1t6m3rsmiEq+yq0KY\nen2O0ZCZAXt3NwaRQQ60Z4yFeu7ao6NlriKLWM+ZvbItEVUr+DWzRsHZ5wOJtk3jCAhqqpxy81xs\nnLcziyV/a+N4IW9iZLIGVFbKLUqlkaSnh5qLisIELVvGE0CnvXbdOelB7MZY1pMllBUiJufyzzif\nw55nymnUnXI8BXD1sDtaOxoRPrsO8FPg3erFd8dCI48FcGmyT+xpfz/ErJwBi+QBZ6/paxaaJQTZ\nP4OgfhuIaS9vw+Jmgv6XEiMCI0zpnV8J1oeYESwRoLHOiv6p2E7MAjECsg04vmk5Hk3yhzCiBC2q\nchAxl0eZfRtkiqagutEb54br8Eycgf1HN+CPika8l/XDV6sZcNQIca2P1nLRNz8LZ9tYUHz+otSi\nyx2DpSekrQ9ZzsWczr3eTx1FKeJ//kT69D+JCMuXD4O9fSnmzx+P6Xw5/sYGAHYABlFK60aQX5M9\nmp85emzgJnireUaVshTiRiN9VHRKj8ZzLNBdCJg7Yb7AHnk4hiQ0MOWwc3GBWK+HVuMMha4XwDy1\nonyq/j1gyItEpHCI95BOK8auMFlYiz9dQ7UkIWEmgH40JqbX7IQhVoHI+NsTBb+dwe/0Oh1s4dX1\nPPT63ZjR4iXqm+9B+Fmj9eA4VyuhxWYIF6rbt/gCzOYuVWL4Khdjk1W0fRV7btIammWfy/RsGFB9\n7dFIJi1qgQ0S1tbiy+8ZAOYhPoFHUx/aKBoTk/svdrmjKeY/nGFw6Pp1+Ny7J29/8mQrJ0q1pYCg\nAiDFbdrkSyIjK3wPHnRaBCyc1a3bsejy8sJ89TcroYHRetaQGsZiAQVwIW4JOryRsZnun3p0GPdy\nvJDIdI3CVl9eWIWm3Z3ftY9fqjlwEO3TvwoWyw8Acf8nqmsuoim2ZQegHE0Cg9sArgDwwP9cQrkc\nTckPM4A5lNLH/4vvpPL2fo2NbbVy4S0pWAc3bDQMQ8QXd5jEZizi50PMx1Ib2xkkR2aGvs3fyPe/\nBSfFOBxd0x3zZk1GmaIaRh4w8wSkIhhWRje0aVuDr5WLUO1oj9E+x9He+AHfNy0wDU+0E+QMTeTt\nJx6mi+c9Myyty7CUchLyqrR1ynsbc4caiQtrLpKbYZ3DshKrGnIoxTrm9UZy8uQbVHrZomGuCYnf\nKYJbwqg8rrFk5vLie+4g7wuAhmIg1I5DHj0Fi7EjOna6i8TXLWGoOWBwW+BoyTy7TCq2vQ6rgg1o\n31cLr95AiUWDSBuKFkrgVx5obW54abE+0uFDwhD2y4/2ZPCw3ZbYAbtPT3mqDhbZkcjFzYUmxwaD\n2c6Bka7aZ5u2OHFT8zcO1xNZ21ctepbr7XqxFMJqBlnUBZk0DNWoKfwm+64t4I2B66VSQGKNqXV/\n0+RGrp5jZwnCwvia9esL7d8naYw1G4FdPJEvjVxMd/SwNmPnoQboo1RK+pHJQgWWOXryL60ykVti\nYqiJM/l7eaFPr4KyrCxTxwcPUBkCfO4O6c9d0IYBCKCAQ50IK46FY8yaIZ1FbOBsZgc7B7d2zc7/\n81k7l+lo8SoaTzs+Rk/mLMbfGIg7owD4od+r2YJJedNZuZIz11UZzN/nloNop9M19CHp0eM65s7t\nI+K4uukn78oPJ9bxC2uuKubr9bjv4s3PKXuS36rPLE7JPvJIfA2IxRzcXL1LLLGDizYlPQr1EeYY\nCpZpDbyZ7p82lVSO8O27o9ucBHlmWgidz88z6Otrc7Fl0aoHD+qGSySQADgLYCnPM6Jduw5r7t+f\n6Kb4Pd1W06tezE8cb4T7zkqM9LeeHr1a6mv5ZFryQ7r7eXhGAoBjoYvwZ0MpTneYAfnq62v0zb5H\naRrFVbajdZNFjyNQr0gyksM8pHPkiod1Oxt6agT4kpsJS7AjIkUPQWRfgM09gH4DAIVEDA0jtCwk\n23hZNltZ4O1od3n9hrygVKlnyrnCouzP0U7dd8nFv6TdLIpGA1fv8fFd4cbH6Xfs+k5JNrchio2L\n+S2ZRQtaqOcsrEBvtztwMu7t/Oocula6ICAgAuWVRixqp4ThpiWYuSUvm1PKLXKSWwLDq9i6Uo1h\njynux2ePQRz9PHU8tAWrAToQVuH7MP8zB0qjAfSOj0fFsQXX7vVMs+5Z5nHV9CDsh/CV32eoahvg\nVkPr2/n2OXVM8MCWAu1RqNwsfCYdTququlMXV605v5h1tG2zp23/0IVJPkfZaItD5YzD2wt5E+vW\n94+JdUaBcTVdQ68A/2HDnA7gCYAXAN6OOBzb0mDA3++SiJu6UcCaGAGBtwFobwYsAPOGAf/DSs+S\nDCyIWyv0eBHW+CLtmeSl+AG7Yr2J4T2DShZ9yxLQjL4bce3SdSyxdoCk9gmAXugcnwsgBcBCGhNz\n/X/BsFA0xd5b+/pKSfcewvw3ibVbv30DzzBw7N0b8z99wueyMlAAWoUiRDJmTHbXqsrArN4ZMf41\nmQ4ZizouT7QOYcZEF3cVT3g4vd5skpfvN4XVvIZ9awBHKcVMkpBAAJxATY0NhgzxARDybyOG8vFB\nj9zWAU9g7gTX3Fco4yUIHLUY7J1jKNblQPD7MpS5hAD2eohMgOuX5yis34spL/qir9gb/NJtxncV\nyoJt7Cxf3i6KYYwaWBsyUJ0sRYy5ALM7noFB1widKRAuSWKIDi1EvrOQ7hH4kJ/5gfBAPsJRCV9R\nI1x9i+DZ+gNKQhNxXpiLLwYfqg/eQ9ZuNGDM25OwQTIKyUiLteiMdkczd/5wiJ3S7P6BBNd48gEB\n2Xz8MyNX/pyBnak5lN4jUKmegsZGCUwmOSD455uSmRbUdxBlWgUHmjxDv1JGCGHOM2h2n4dBXR6o\n74s1DvHoRf3JT6ZA5IZGr5fE2H8u01IuapwT8Eu7c7PQ/mu+oL7PLDd2SutMadX+UYfd4qe3HYww\nzwBBhLjB+g95o3YUJnfcaon0vGnUf9aIJF/q4E4Z0xmFRXhdDLItRmLZ9T3O/OXbYlFw8G3q4XGf\nv//AnfDREyp/K/5oeyh7CTcHS+kFzKfOCCErUEYue1zWvh/kgt39Rkkfq3ug5Gu+qUXNLe7iLeSY\nSuEAwBCCEKMOOpdwOJ26jESiEwoHLv1tRvrfXQa2NTvV8y0T1mrvffwiyvpTJfg47erdutpEv1z4\nVD5Gz4TBuLXkEjO0oeXYZYr+/Y5Lg1SM+Qk/IL2jMdFn5d5lbFboVhOK233F/WUf4DtvNufQR8DH\n7ya+fnoMHCCo+VbcomB+YrJ9dCFRar2pnCgMpIKCl1SQ3J9E6KqpMUhCzMAwT7zLLMVJDw+8PnwY\nMyjFoPS/+95qcW3BqHftyLQVm3EUQId4xP5Ck4KURVN44EZsLEWXQ9uKPvv5Oxq3hCzRPP81E+gi\nBbZvZHuX/7Vn5nnpT0k7fT9yTyeEaVBMDH0FQlrnKZwep5mP2J5ePdYoOq5iq3NcyCKsZvJQaDYj\nSMNBrLBVlJpt9swUfv/QQndcXyaoUJVz+wt4dE+thLyQotzGylLUi9Oz4dXShkDkOT4h3zxPsf0/\n+i0rM6d0dIJtFaO2K7Ko3Y1boyLXZ7ifpAfbVaYY1bby3UEr5k75GDTB68+sh+j5xFjGVLpONr7u\nfn4JQulnWK9EfMIJpCs/QmTxgLXR95qsZ8tDFZ7331QLxCKvXZbo2q/HEpwUE1vnz8SHKj3PufYv\nb7SOVSNtTUvr8C35PfIuNQTXvfVvF2kxMQx+QStxNo24oZJqtNwiUa4mKepXPvz2BcAdnJ08WF91\nT25ESrpSOaCT0T42uCDn/k9nhIdLsX6j0c+4lp0nC645MGa+ta1XCQ12bRS+roZuXxh2MAQXY2Lo\nDwAgCQl+qKiYDK12AOzsAppU/xVZfWzeKj1EiQ4P8opYLeSNNd7zuTZWhKRmts3QnwoI7Z6agVk8\nbxC2+66tmfqPKq/0O7t1O4OoXlaNfoNE0iO5BqO59emZECivkpex3QDsoyHbs2ET8YXGxMz5L+zy\nAjAXwHjIZA/Q/4AbiSRtuWA78W7zbL0Xn5sikyEJTerjdjEx/wnX48flqW5u5paTl+0s3lqgsst0\nzaiPLAu005jY+r113cuSYEsA7EDT6SEZwBhK8ZIkJIjAmxKQvITF0s//W+3//n9TvNr6cWXqYUpH\nyYd18LacQHpWFUT1eoxePhgvX42H9lsiKl1vw3XOUINW7qCv2rNcRtpUcQev7sDH/s+KTnbt54q0\nHNP8nVbUe/pt0f0Ke+Toium20d/J4iRbZBk1QE0nIOk36prVSrdYWypyZHTsbtVKlMozUFbGwyEA\nYFqI+Ho3MzULKLpkONHh3+TcrTBf88NegWx4cjWvleXo3UqSTPFeAlVAiS/f7WtP2vUHKXUz3lW5\nk3z529kKyxIbM02/buDwFfDyM2PYqCicvbK4uLq4hSvLStC69RMIhY9QW/CMWnKqabLEHa30B/N+\nGNr52mK/JQn7kgZNrY37bm27VSLSdfdNm8Xmxc8B4d7y1dxsguoyAjk1Ri+k/Gjb5tz71Ye51DJb\n5IqdoVBdoULxJrJxQ5HOwQXVWshUHCyyWq2AqNgGnDkDevsmABNIs4HrKhw0tqL4Z35KqtQAoU4E\n768bOxnzUx7jXqs08OgHsygXyBCAFZkI43nXyWKYy/OS7pBgrtnWbBBUaopkBunnUheapY1kGpn3\n6okt1thfHd9QdDesuZOv6Rs3jTuEFlwhadBKUXh8qLmlVyVX5/LDsvnACT6lrH2pQlFjM3jgXvTo\nc0peq5Oa37/v/ZIXqzGk260ulVWuNfZWauXWrafIm6+RBAF3+KjSS9pa+8+iaVM1IjtHq61uNjUr\nY39/5cz2mJO6R1IujfQvZdVp9PLhegzu6dOW+W5oY1xx8p8Nh9Q1XzcCfqGhGDB/ProVFcG4ezfu\nVFfjZRd0ceyBHvMOrbJ7WtDWyRlDhw7v09VgWLQINTExTdqO3glLzibTNqPM88NT6lJ9wgB0AIgH\nCP7GTCJyeB5lPLY91f4mN0J3esa+3shWJFMK3Qdy+pqBaxxyuMNzcjn5DkTDRLRdfmcMTZlq9Guw\ntjDA+Wf2VY75ze/00bgUClpo3X72etXxIGaf2621KBiuQI7mN2shNtVCwpdBYqkApSzS5PPra7rW\nNYpDnjjro3IJlZpRUuKjB0i1jaDKWWpdR0xAZW2WvZ3I6G95FOBtPC0eKehMXvFDEsvrfbYOU+xv\n2VjzMj3mY02tY6Tg5ou77a1ej8pAoMSaLzPlvF1iCtAvzc6zz2ylq7mhUSlVD0eGtOrRX/BSKILh\nyd4sRNYYYTckKLT8mTnG/v1ro6WyxDzpd6uY5Q5VbMh3j3JTrmswl+XFEf75qwoc2GDNMYTvNVxh\nGTegPrFRbeImT0agPewXd2q/PPb6WPFk6byVvI1hqHnNubtvdlaUds5oADPeE8ZR7kgXs3AFUJqZ\nifvz5yNCq0VrADfBMBdw82KlUv92hRnC4VrraEs73bXUDbLzYR8+9mKP3NpgrkttTrabv9fApd76\nJL/GIvRPEi1dwqOceNS+vVXU9+ghfqlCiQj3JXCQOzdr/Oq9WxqOzyZDeaIkJectT82aQ6CW5XQN\nbSCE+ABYA0L6onfvp5g+nUCm6IXPKinklqWQpEmITDq366mxwoWzDDKGgY4Q7OM4HI2NRUXXrrg+\nfBp6nCmD6WOFzOxw9Ri21EjryPgzjmN2LASgmQd0/5vS/9CR9QewB0Ao1hJbEMF1WIUEY27Kv1dn\nKHt7MkzTyv6yztOf9NZGopBmIT+8HQYcjkWj5jD5AAAgAElEQVSakMdZ1QC0T/8dWs+jIM0ZXqAJ\nI8YR0UT0Yz/mJezG4Y6M7u7Wr1UL7Ke6rTnRgZB5W3XswT8lm7Yq+Ff640BRBAOny1Te/GCdxqVc\nxVbw6P+zBTqXjsPjnqfoR7efpNZggaIO0HKAUQigFiBmcaNLg6tc5CCH2sMRY+KTef+KBiboxzTK\nNAwhtkj6GIIVBwGkTcKJKuOY58aBgy/kz3lpbSrT1guCr7ZifupSmN+G82gTqrL8c+18SUpKj71e\nluyJpXANMsmyYbJ4goQf461C99xpyClvqfCEl86N1fV15ySuLuGWs8ccDF45w2Te3mo8vDuJV1pS\nShwgcW6ENaMRyEl015too6/jxXGr6LJ0UzZ/rlczccVj4tme6tNKUY0iSEk9FMJRg84bogf6YeGc\naMZsolSvIw6eDMqrBYCJUJhhgPmOAej+OwX53B9I+GWDXP8uUOspBn1MB2PIBruuBbC+CPAbN5xX\n1L0jdvlqjPq8F2UL95jPtRsmSCVhGFr7FIO5K5DLKlBjZOjPeqYyws5sw+rFXMmDCLNfm0ROlEss\nVR04lifUGJ/ngLwHkaWuCaENqnUPXHycf6lOHlhT6NflqsvQDq+pgDUbyrL8z18/Pfz3ngv3yLRm\nQuOf/j7q9vEDl1nW3HVaxF/XJwzfIDV7mAS/vkIzkoMWdp1s4TeLuGTONomNpcV5GryL84VXH2e0\nul2MJ8eeIIumwxUlcEYjqJXWSmXPOIfknFhO6bkzGjx/LkZTLimuQ/yG+RkInN0dT7tfij2SDKAV\npXhF1hEG8fiJZMhhVHaU9Wn3VdNupBFVbhQ7O4na4GfJdhT7WGDQbcTBnHjkNYd1vR59nf+BX6q/\nV7lX5Kg3oxpEZpHKtdotw7ve4sIGVzoUaXxMTooigc4sV8uLFa+FZbJMd1z5jYEhrzjY54Q05twS\nMVPhLilCI1ePhIzqHXYWr/wO2j8PWD3IdtB1scX6NhcaZlQiyLnQzq7ctm/SLVjX9i2C69lJOGlg\nwE+ccIJx6fRRw9TsXM0+NvbOfKNq62+PSpMn8kgMXmYt/0Y+QP3hD6UoVm2nmG7FsXb1PwMxLR6x\njwEMLdKheNwHHAIwE53jvwTezz3RqcCzl1Ujg4dRhRVt2j/H4DWVEjZlpu6t3lt5EMumy5Fy/dxO\n+531eTaT/zz22dLHMKR6DMYoSyH+tiTC4v1r5k97yfI/iVllJKrhNE0uxRMbEWak18PU1wl66wxw\nl45CNnAgTMOHY9OAFGwDMAbACncJpMPc8HJX3ucDTHDdPdE7ucr5JsP/1upmaY9XzV1LO2VT8R/7\nSLHRmlz7Kwtp3/F9716cdnfHYp7HmYcPMfDYVfhrx4HsCyMlD23nXHuDDgEbjDMjFn0of6qpRidc\nwFfUi9tg3rzP6NYtAAyjBnAOYyM7okT4TbBWcFfGkW61YWf6OpMqul0733XDCqj79EFAjx5gflWA\nPDdCdLsUxihbvFkcANc/fs+YtGtXt3vfv0dv3rDhUiOAiQA6UErN/8lJnEPbPVboPa8tgB2IfhiP\nbr2T/60gTwhhlCqUNUz1tqdJs3B0+ElaLnEkq+bex7qIbY2tJm2Ub7lvg7dOApDyFqDNPwIk0Ey8\nIjnrL2exuLgfVl48gfG/r0VPv/0moY+Aij52tJzSveauPrbjMCksG7vkfqTmCulu0wZWUdl4G1CM\nEqUZUr0MnmpvGhKaVeFibcjav+SDwVwa9vgl7xBwBm6TrqOINnCSLLv56/xDrbTM8bXXLDK2iNnW\ntd/R2Cd9Bnz1/Jq4bOzS10NdESFJRZitEDbt2xO3OfX9iiuLnztsPtTcfLZULy5FNRmA4Kcbxc8+\ngMO8juNE5tyizkX49T4QfeuM4CAARaX1PTjuGgPyTGZV+s/JOgW+wgR7dtj03nOW+fh97frX8gu0\nh/mZdgpOiJ+iW5n9xleW1i3eeWzdOUj3+FNGDTGmSUF5lYDTwEmMTKbBa24f9Ik+iuOzGUSxAitD\nwfkbX512nXezen3xFwOTCXBmzCg1VYNreQ3BcaPhvqkOjwq9mGl4N/w1fF+mwbozD8H4ljBt9MX+\nQUUYvTMbTtyuLVh+rrjRavID+ULrVejHX0fbujdwlhhAH3Xnn2XLGvZHXhxjWWe5n2B3tQtWbbiH\n0G+S6s/SclsfvWPlNqf6ST1K0hsJps9zRmSUK46WVaN8wlw8VtZg/Ds3bBgxC3H7QlChEqE5KCzJ\nn3rsXLr89qOlS6a8F1fy9ztF3+wshh6sFiZZHl4Fboen/c1bU6uVimtI+VPgbPoZt0KKde7ucDdJ\n0bg3G/+8rkI1ABmaTKts0FRD3SwiO0IzJGOd9Zp1MJk+/L4CO6qcSZe+cdI/J0h/5/4ZdbDHrWto\ncjQcCWCTaCWURg6/YQOTAle3YRg+nAOlclhZCd1OnzYcy+0jJOhN5sDjYiaihgC6BoBvTSktAgCy\njlgDaAfgFV1DG8VPnnS/fWDebeGcH5LHr0Zrtq4//wXAAwAPs+Gb5Yvc2wDKAPwJ4CeAGgDNgya3\nsJ/9YHaWse/dBkunBBvvX80lKgtvtrJ85urDaEKjUI76epsWjo6FdoTAQoHKND64Qr5wnd83H6n0\n3cxPTC88Mu3CPIsFArEeYouZsu/RmPUIigAr+3LM8M0Fs3o9+vbWxTz7b/PXO857cKv8VkcnJ8wo\nNQnZ4Gwf3uL1pbbAjrexhlj3QDnjz4gWRyo8L2oe/7SiTHMla+DNNsXCmeo1Dc29xKYFM4UVFVQ0\nfMSS57fbLuxUlyZPD29kmwHbaoxQm34B5e2fP8ehf15DfTIRfWALJ1stqqKUkDm2guSOEUYW5JdT\nQf9HIZo2Uy5fXphqNguCOve9yH35w6qIULH+5AxdC0XXm19ueQpr3JR5HcNaJwiW8RvUKUM230Z9\nfbtly7CqRw+0ARC/di2+vbTCXUaB0DXNoO/UCatLSxF05gy6P47nrBE3gEPv8SwY7ik42Sqv5Nhi\nSWnbzVpGN75a9RU2QrAhVqDftMqM4hZHgvisfWZa/U4NA0pktVAK3eDjJUP2LD8kesvgC6A9mkKC\nBMBdoxFxPXviDIAn/yEkXUeEMCj2QK+cirQRs+jjnYf+e3b+P2bu/58ulHI5mWYXITxQ0N6KY+8d\nMXlmdmLMDiYUht9iZK3mkUE6CyyHltFLxlnAikkA84qwySvQvMSC8k47YJ8xHxtnrIG+ztaSo2rG\nOM5a3eDa+k5d3Ptz0gKjyVbQXKlz8uMkjVYVsCqJtFSrY2hiynZTtvMwVvVlgolhzLXYuVCgda7I\nGzw2r5mb0VR0HG+aB2JpxgaUfItnRSNzty0lihIrBGWaaHvbJHI7MMogFrkKqj10pMqaJZ7l7+mw\nwm0IayVCndqFXHsdxD9RPWOYhDV8K/3VH1lZmUEmzgTandc3NNdxIOBhRAGS4IkIWNBiYXoXlV4y\nSX+gxdSzQlg/8tCv4QO+9MTXBjcUKe7OCGxV4GMnXLrsKm8wKgzhES8/2dpeb5Ec/5ah0mKrkFCf\n7Kqq38sJCW8b7fyQefvxHCupdeGnYeXbM7gc9RoXuU5MB2QwmWi0aPUtqVNZNOptc0il5AEBZwQg\nsiJ12nqJgpDV78dxa4MeG/XKTa4wHu0C04c3yEUumrPWSBpqh6joCTLOyd8ZkxRrjRvYbQL32gpy\n/X2b6lvyu0pJvY3+yJEjjTKD7I7EJIkD8NIG715JB62LK5hpVhkNogarBi3behKST26B2DkQkZWJ\nuPPHevRDkwfNbgqcPXwE5x08sERahXKJKxQg0OnUincSgbavKstChMVMiTqaX2q2xvWYGKqtlcsT\nN44bF7pzxIgheBnbJkiIzsd80cU+HhaH56jJnwSlOhIvweAMgDsxMVQPAGQdYQB4TnsybURG12Eb\nshy//irhnjmIvCZKBYsXF5q+5hUnASSsSfW6TSPA5mwbtDysUK4/7BU5BfHxrrAoamEry5DYK9qy\nag0rrNCgBjWUghI0edkHUkrz/6d5kJDgCGAngOjNjSvvxDS8mWqyhvn6w7jN+/YddEBTuZ5Kgfpn\n3xEc4oYiTwb0DoBW711xNuoPjHerdnt1dv/ZAQAkt6OvFF6JPRZyU23h3S7b1nnVlNSbLcKKiRNX\nuY8atU0tEBhvmsD2ofXK1uy0QwR97pstYy8VccTkwgCHYhF/FxWiK7jv3Ai1kMdr25eKvz4PjP5p\nsOL3+OeXQ8JORW5VIBr8U31+cDd7FEhbqHslj7yCqTE0JjWBJNgAmAPQGUr7BKGd+xndBs3BZ/0X\nzxo8abG1qbrajQOuC8TKCq243QGFNuwYE+xcZRjhjpSV9MIdk5FZh0nbGOhT9ADEADgAVWCQAUdI\nYU/8kUVk7sGzaPmXv6iHxw+Eh78g/v6fv7Rrd9dGJmvYbugZX3vsDxy73xP6qXmnRV29bnMKRa2A\nEAyMRXwLAKNx9GgybGxGICYmAXZ2TwG8R/b+H8i7kcHcBOtjAltawdgE/NmVFHWfCCemTGdTdKz+\nY+EPBxcxTL90EDUTSXlPVvEjyrP8cDtb3PjXf32VipaqeXS3HoXne8VqToaFWGG7jMO5zSPofABI\nSCCD0OT7xaGpiCUOgKPRiPSTJ+HD8xh4NQg5//pMOY5+uIqSNqsBtKQgOtLkKPDvBXlCiEgiQbl4\nYaC85ns7Fp/HUHboGAsr1HFC++a4EJCCZ0/tcLDFUGqx7UZ6rSmmH/psgdSs+tAnvXX4nYirgu3N\npbA22ljq1k/UyRqy5ddD9PRe1ANiNhHa61ss6ZUlp987ccbXYb7sz889TfZRP9llz08wA29W6dIs\nf6GUlbywW7G2f7WVjmhXbsEnnfP76TjqWYtOzuh+V5fY8k7ZJts4d2rhWW+dtWlERopQXeoFZXl3\nqGu0mqPzqMBopRJY7XYip/ZGYMn+1dlZhrqH9l239A1Rar3c3oH++OGqzshosPcmbvougujZHt0M\nx+eFXPmAz6qWTHCp4J92oIe/2JOivCoENYjUI64PTHuDju5fEWqbx+SmTli2qL2/v4ldvz6S5ubm\nkyZ32QFGEX4TWrjmfNfOIdoWAVTiqWnJOoptG25+fSm/+85CdDDA3gF8bT1IO1mnLfM08yoz3DMm\nuVW7tVDoJaYKq7dlZ4xn3N5UV7McADEHODoBDW1Flop0A4t8Anj5U9h0rItVnbCaM5Ulz17a4Ubn\nzXSpYidcqmrI6SODtbw0/2tsWuzeX3a/mCS/pLETX0zsLjVJy6w1Ki4jdBjZlPXWKPfJKj+8ZZSv\njIXKYgJEWhBlGupENZCYFCgqVjlYfQv1qAst+2lrbaiXFT7Hi+E+CF/5BSv6hUvWybQGJ0uSXalM\nXt/T957u23+Mp4QE95bZ2R/ezZghlRiN3ufuw8vRjI+uN5ETdBotAIykBEvMCijzx6GhZCCcqAB3\n0NR8owRAMYCST9v/CV83zfk0x+qoV8pfucqsRGX3fyDfzIMYgLUWexxFOJLtE1lrRsvbRlm51T6v\nvfS1Ef6RAjDiOJyi0ZJwolZpzXNMI5YM76NtFhWFgj//pH/9D+M/IcEOTc1MVgE4tRDb9/bFg492\nLxEnumF38u0UZ5FNrr48+JiuRKHROsugcSGgAgrCLsNfDxvCrp+J7/zmAq9qdbmg885ax3Iyxsyh\nsvrtnVNI3T2XaS8ytNr1wj4o+D07YeuC9CNHNntER99ROrgVZB1X/S5PpaF3Qw6fe7T85uxVrH+2\nPbYsdYaikVy9Ojf+76M7wtGsMRuZivuwMFVw1epVW5PX/7WOOvpnQfcTcuP7UY021wdVG7Tpo4w8\nMUXTNfTbf39/CSRBxZLGhYRalpPwZLKc7kt89bkBmILxcGGnAXSqwmiXvyaiStHaln8HYFJMDDWT\nhw+3oLh6PmYvMkJXlr17NyrDwvAoJobuJAQ9AJwH9r/juIXdly4Vqrt2bZSgaSF0goXh0O15Cwuh\ncdc75VSWjcwNfB7QVjRNt6uiUWy/4Wr9WFsAfauVynCx0WiOvHev5n1hoY15zJj7ZkdHXwB+MNX9\nQuVrR2SKHyO6c7hYYG7chBU+4fh8AcDorEZ8K9HB2h8OqqULXub8+hUYQykoAPxLuZoEQLYL89i3\naK+/guGuDOj8mBh6PiGBSADsR5OdwR0AvwC4ATiMplPdQrUaNV91cNuVD9rFAe9n++EmQ5C/bdvx\nmUplmWVm2MrAjsvg+m8HeQAQCsmy8I5k9fvO1mLAQGHdDfAeayZSa0EMnuN3/jwOf5sGn/O1uJO6\nBttFCfzxEZsJYQzayOxw6dUeJ+HzK/BKjqImslFc79mypCsz+kUPOIvF1KmQUC5ia/n61QOzCn/4\ntK3llaRMHUyG0at5cY67/D3XBiG/ahX5pKq72yruUDjq7V3ZY+NhTT4gY9YJFJspFSR1h0Nha7xT\nPiU365/Rac7i5BC+ecvAnFomrfYvYhEouL9Wi1HrWoN9bzYZxR2+ky9HDebASiKQdSOGhiGoHXeX\nb2ASkOeUZhWbThvEXsQTXR1GQOFghKrnK+ocnEe2T7n07G/n4Svm9a1JdGnA/Yfn6WBCescCCTeB\nP78HB//tUlRk8I7saFf28tVjlcHjKTPBkFP76udKB2vhfVO9dGOGmtU2q6+vEzk6Uuh1QO8+oIIO\nqD65AIdhxjjMBQsJdKBwDyj307s+d5F8yv4iWi5dggJTAVJdXvCpuvwGrd6kULri8LE1eCyXSzrp\n9fzQhjqzp+HvtWTFfCe6gllNgioVMK/cTfUWy2vrRkU9mkwJFQDkFmJRAbAzswb6UmqwXOQ8q/On\nd7jO1TRzW96yoUd7n19SicFi4HRUQClg5jiikYoIxxnNFobliu62sYz9+/1pyhv7VErhdI8fZKxp\nDJqxkm488T+MpYSETgCOADhBY2N9K6Nhlb4G3X12QB7bDOk5toigayhFk/1GTwCLKIPAqg74UBGL\nuup2YHkxnAG4AHD5WNRfYXO1W5bfHX8XMYqliYFv1TtbPVaX3C5phA5h8JU3YvR8izBp9AvuiVuf\nOPxNVmLj9TK49J6Jx9wfKLJa01NJZYqxqPz4rb6uhpfHdsE/t/588hcEgv4ABgAIBRAPYBViY7IX\nLZp06ePHHu7x8SN5FuYAO6ZSb5ZDYaICoUZjZbLwnACATgRD3XHmd9uNS7RcSYuReolUKtKJhDsa\nZLL9MJvDnGvSbsSIksozt46ykrBG5e6hIywWO/6r3hU+Gy/vL4jruSF03c8BP7/qjtsCqBKYOeWp\n9zNtnF/2rtrqoKr6ztuELl8+zhgUlLQLwOaYGNqYIHja0Qz2/KuOsFfb8TVPu7KmApXAZJjRRoLQ\njUXosI0DZ4yka+j/1MREQzxGPSCjT6k5L7JkxtI7daq6bgDOD3PDtThfHECT6nPOf0tyk4QEggZ6\nDnm6QVjUBUH+n9K2b+/ZYujQkgM6nXIigKGU4g0hpKNCgedz5qCha1fYY8cCEZ8Z+NSslba2bFkm\nqtJLi92+5b7beFvV8kVZbTOxlxf6tY0y9yqtKgpO/ZU5auuKEEkZLf1txUzRYVQ36wpEX4yP/wGg\nNX5d+RtWwQaiDFz1FN3Bgj8JQAggOiaGponFWuehQ/dmTZy4Wstx5pExMfQFACQkkC4ADgFoaQb7\nai72RAip4eTnLgvlDg5gLl1COCFwBrClBx6fFsA0V4XaccH4bohGIteaJttcKTCw90rA/umMiq7e\nUPM8PCiFtKQEyMwkyMmmuHQZ/2cUr/9vv/4r5AkhVgIBikdssmk8/zn4W6fuPldGeb9rRsD3W92w\nI2CReDM6JKhhV1FAsvzt9GoXTmTvVEjOFgJuh+Yj0T+Jfxv4AfD+Qwvnvnuurd/6Ivh1m9sV6CR2\nlf9hDpld+VMrxFSsvfAD8MkHFFqghSMrNNRGrzxC1j68JWXeLZBqoQXhCuEpOGG55RZaYFs+xM1D\nayUUebyvQ+Svp/T2YO96Vh123HjaOJJJupjWq/v4uU+ec2tHLzW6ZEQcSYpi2yW3Ia22121g665Z\n00dvq81J0nJLQ1222MaWh9ov0tAloIRXqUSWu8cLZWYDT6gRtFM04FcxGO2z+1Nro8rwTHbx3j+D\nLg5RvkR6XhH8wcAokkA0dCgEda2h7+eNBk8xIz5yeGf2y3uTPXxdkhI/VYR1w3w3SizC7+LDKe26\n/N7hc5R9ZVhoOHgFC7bSCGzcC5TkwTLEbyg/XObAX7U6prl8zSw91wmZ7kWe3hedWgtb1nsIHVqU\nsYhI1jOehcL6XDtj2Sc3Af0SalQU9KvasUPrutJqBfPrjQmXb1Cs2GMyblbMy7VD1aURuJzphmIl\nmoDpzNzs1bboqX/g+x8zNL1RzuZDyqZB19gOx+9Gi5/2XN9rqsupKbFqVmTKOrV167vRz54NJIBv\nVQTM6SsZYd7XlqaL9+ZfeVIn7UlHDGfAWHrSNfQjAJCEhFYANnMmGrDwUlFSj1NuN4Vj9wXzQ+6u\nctvnUiiPd/kndE7BgCpl1UbTetPl/zIAWwOYgqYdlD+AL2jahSVpPJApK8QKCsb3K7bsUCOio5Ez\nji63+yVtaPeOzXZubrl0Y2RNQ52y2s0j84+MzMhcCjL7reRcW5OKtDBYNdhOto9UF6cG22FQsc7B\n5uJH7sblzvWSZubGNnGv0eifBZk5FByNwBeVhcmQcc18U1Co9T6lLbQ/A558oBRGAEhIIN0ocLza\nYpu6qGrf3fxktyE417qPU0AOldtVvV784VhUUEiSujqu9h4htb14Hk67T5+tTPkR48Csv3V7z+aF\nfcZ8MQp6LR1Q+rzzeLeBH09afvPKYJbuWze96P3Yo6uukAd7nnaMbnfumHCOoRy8Tc1SxfVBgwC0\nhUEoxInJPJ535TBnrz63U1HjXOyxbYXPOYNwK2p+7GcDiHkVJneYj4qg67hzciylMAMAWUcIgAD8\nwlTWTGYvSp3DtM1rrpcptg0XnM+pN5u5axcuLKs6dWqtHGAK0KS1KQBQAKn5F668mwkhz2Bo+2M9\n2/9zLDFxkEqjUWnRtCiUiMWNgs2bnUevX68TRQR2KJ2RPdda6PpL8sK63DAx+QK9r8mv/Auw8wPz\nQ4V9ymTU+tRhNwFG1QEbc2EtqcPhTxF44qAZeHKKLIW+lMZB0X0prYsn64gPA3ya5oOi39wgYAgW\nAFjA1eF19CDI76Fv/xXY9GRvfNgNAP+gqQLmAZoAfxJABIDOmWiWPEe/bbxy89Ja28oMZWBzWBxm\njthylJneHEA3AOfQtKNvC97cB9QSRTRZmk5MRnZI1sNWWR9r8SNHhJJqEVWKxFQidNdUyDsS0+vV\nin9LyAMAx5EdvXtjyoIFqEJTPO4GgGv9cadCAt37CfS0JCKBJUHJaQyyzfxC27Vljyabpa5qnc3+\nTUEYvmqZyaz5aAIwmq7FZx7cSAukaznU64oVKF/VBc43muNK/cOYXfgy6h3g/EaA7h2nYD06oLmV\nUADe3mwHKfu1XmuOhFZC0p53Jt8PU+9W+kTHNhhdAM+YT40Tn2RKQp84cupSEa6QC1XyxW1enN6z\nu/fuYcNOrBo9uqMgIyeAenvK22+4jIhf1hYPgx/x1nozElZAiXUNKeUa+Jfen4wvAp7pdK/zdDIz\nHD28/Jn4dxXloHWOcou8mjfydmJOgDpSh8guwNQxgJsLqJ5HoZRDCQCqtcBVzMC9sNyVrF78jKh1\nIoMu+Ew6YteJOaMyoGV5X/Jb74s3HpYhK6ka0yYKUN5ZCr9DVziuhtqg0ScEpfHf4LU9DoyzPYwQ\n8kYqtFAjBCq1ES6FUuqSaUttS1m9SxlDbWoZ4Zm1OYIFDsuQctGJ/6avMzy8XS1qMVKLxeNElnrG\nGpWMK1dr5gwNmro6m1e25g5HFroKOEDjl2QRlH0wmhQBQlNdZ4u43pa71w+m8/1EyQ27gv2Ran0Y\nwPrXFyS2wvcBpwXispNCU1VxSaT47TNVJ+zLX35av+FCCIS1YWgXehiToxxAaMf5t3PS+l2RdSY2\n1QShXyow6pKdaP1UHfexhVwDn1wTY3Z81+wdbrS9YZN6OtXyfzMY5WiamFH/utqgqU55DmhT3D7h\nH1f7hiLf7KPnt8gS0luTseIM02+GCiHrXEbhVmSEhdWiRqWE2rYO9pUzYw8HXkGOLBl7/UXIkbuJ\nGF2jTP7TWS1VAK4KikZRKjKVr1j/2ozTG1svfyTv/usfjPUAIALwCk2uif4A/CXQOs3AQV0Hmig9\nVdsKARr1s8QdW6LKa9xtli0bXxtSkylJ+d6ROfjlgzY9/dwZSvv1jeh/a7R9h8wrH5v5u065c828\n4Po9cZmVeFf10cozlVqnZx8/drcrKAi6x/NM58tX5uvRbckrH9+bsXtO7zHJ9bLbJPrNc3xutRPB\n31WYvysJDpXTYmJo3h8JY5RjcGErgO4AhsfE0BTSa94AhJ26hlOJBjh/rEXAXQaeL1VgLBZkagWo\nDnm4vb5kQpcvy+4bVFyU4eBScm7vCv3JD4tXAriLJkGl5/9wcXxL7EupxXkvVpQi0Z87G2B76NCO\nyPj4kZEAbBcunNI2PPx5xNWZi5KTatdN6NvSjXZpcLp3LvvB/UfAbNoUy1wA0DhCeAmlzDiAOKDJ\nJbIzgDl4/DgVAuE5NHKOQVO32JnKHqnaCpceGX17V3WWQRe3IR21lUbUWOmxLPW2qkvR5tpF5vUh\naTkSL9+uvnepOgq59YEQgYEfmqzY76NJMCoC8Ob9e4zZ8KaLm2L2BAwxXdYeKh/CUZWNBirVWgBn\naExMPQCQdSQUwA0IrJ8i4uwzfMtcCmfnVrBSGlnCNHqo1VYWgUBd6OCQjZ8Kf0xv4/hvC3lCiCvD\nIH35coxlWdxfu/Y/j38kISEAwDaBkbruWGxpxXHv0D/lCJPh6pke1y/0xsQ7kcuM6qc1P6Mu2j5t\nA3L1EniRBmloxLppRiSvB/rZMxhLGeYCueQAACAASURBVLS+4wP+o5FN8M6P7dkJ00kKgugd2L7L\nV6jcQsz17sHmev75b0ZDxaSyanA0CUCSzWVJmflIwN8DW50+/3S2dUHg05ULfmM8HYQXJhHGytpU\n6amDT2oFx1I3KKDE/W619NQca2b2s6/VFZ8zBZ7lno/8IGOt3MoH3Zfm0ov+LzgTZ6S2UtPW6Fz9\nogf3wbaLo42tHPG9vgKRHgqOblwpYqaSybRxTCpj4/mqYM925DdwqMIclAIYASDHSYxrJ0KErUVC\nvtfRY5uUN2/Oohcu+J790Wjus/adhxPn8rnGCL50kheON2MlI7F5WWSzkGz9qp8XxLp6M7ZsEvIS\ngy8M1c4mU5k7q/kSxOWY6jR1YXa3iyMMdQYbja2qmmkjgNmD2KjpCOFZy5u9K/SxLwOsHtQnGs5A\n+7cZZ+YuCZToOaHIImItorzmzbk0326Yeiia7BpnRG1AFt1yihBhuQ12xIk/vmhj/aJDIioW7kCU\nqg7RZRDtmoDwwSvbnXHqUEncSbmjkVIiqQ7Mq8/Pr6eyhfeEvLJSumZ9LapreMDqOprpAs0rBanU\ngdXygthnW5hpR0vA0IPCKtxtNwwdSJNZ27ibshvv9QJ91Wfvz6e3pWyL+98Zq0cTmnES6DIPnNrh\n8uHSbz9gZAdTioIEkiAGa/bnPQo7PHP5/KdtjeOD1unRPQG0jqExlCQktEATsIcBmMDBNKh3/WX2\n6dRr3/XldYI5/nOyegRQobRdsjfWr+4Jg7gyNh6eaIKQFZqqaLIAFEie9LEKU9FvcwL4Kkcr4zOL\nhUm6NWLrqH+qJ/X3R9aO0Wg/cweGCBpwTHAPfXOi8dYdgPFNUBDzsG1bLDt/vlxmNIoAZJR3xYyv\nS4Svd+w4okpJ6Z5aVeU6ID6eVBzPQ39NrWzPyHMr7B3SwlliEkzG2fHX4Fa8AMACALsA7IiJocaE\nBDISwAEAJwB8WZOGKW+qEC02WX3XFIUnIXl6AdIPDAIYKyCBY1nTngP72/dxv967N3kdpu9inKQW\noy4LwEJQmvLfP+8EkkDWoUVQArF/Av8GByzOePnUt72ag+V9TAzdkZBAGFCkY/6up0htNbx2wiJm\nXvxHu5JS5FAKpn9/XJ4xA7pfvwL7MAzfys3tJwhBPIANMTH0DSEkFsDfAHLAsqtx4UJLODhscN+0\nRCTNTrabNyHIvP3c+UM5OUHG6LCpQwo6nfN2qrKiAWyPsvHjrtkbDNIf1qa6d873ECbLhV+jD879\nGoswEHREkwOmKTcX96ZNw2Cexyjn5+dm1MHKvjG38iCmTFkNSk9SSrcDAFlHRgPYC2Au1uIumnb3\nNgCG0qZ4/V0Af4HSA02MhD1AKv5tIf+v97ehqUxMjKYH9t9sQ9Vo6vJy4JHz1Vpi4vKuRmj4kPi3\n8jGC8zDrfSyZWM6FWsZu69fV6PB/sffW0VEl2x7/57QkHTcCgSQkAYK7Ox3cGZjB3d1lcBJcBnd3\nh8EtQE4Dg3twCZAQiLt1J91dvz9OmMfMnTv3vrfW77fW777Xa53VWelzqurUrtq15bv3vlmLHqRg\nxoqOnSRiIgcw2oKlnlS+cGmxzv2u5MsTdUEsLtGQ5gtNp6dRY1MOKSXtOblFRXYBMy3HyZQ8Hw0Y\nefVDRd43s+PtSAsZpIaG8jYyUtXowPAmxT0srg46ddKTcdKL6rM6xydcKO8+QlN7/x4Xa6JDpspd\nYyvlvq7Hb34Po97lZH296jarlEeq59jZrkt7TJVcPNNomJGbtXUVDk268bmQinHbtrFp2DCMP3jX\nSMxcMKVYuPdzl+h+q40579Js8jLK3Gj229jQgK8lA1Akz2KUe24R0xea32UUdtuxM9harVpYo/MX\n+07yqbOj2cwBa2Pjv7oZzWtGlfIevD89vUSaeXvqgHc/d3I+hI9fC7y/BBH4LsfsE+kSXyLc6u6d\nqbHk2ZCb7pKbl+Zuk5enzcoW6uT3MaULX7jQX9Wr18Kv1X2ennjVc2adIhSpfICjV25xLbArE/dG\n4xMVjbeqL9atUZJNzqIFulGiduI7h4eDi8x7H7it0uGhLjGFIuW97y/1vWS5VOUyl5M0WssBYVX7\n5hWKt+6VisYe/1K7aSGb1ENtdZ/KNcg0arK1n4ntNt9SoWdE9oVngZbqH+1cvb00pPp8tdq6pWNr\nmyOlp3vEXb3aLWTO0zULNJq8iAI3WSYJpgPVuzft1r/7ze6bzCpz2U6Znd7+t9apwaCaz4yHya+L\nBq4YccCIkCoLQfQf7gmRBgE/b9i6wS/LNmvGpIhJy757fhYwAVgJrJEJ6my18vPChY0yht4fXtml\n1iNSn5ZLKBDvo1GhkoA3+VcmUBjwEgivPHWeH2C2sdhkAw9RJNJQFbM3Dmd9nzfEaTXUzl3Gz8/6\nsrfmQbo/cSZ9SHcOvUeRjjegaCs3gAqferHqQ39pHHBMpRKVUXwEiVbB01dyqfKe/l+Ln47Iit9/\nz7qTRDbLKwFYA5RBgW+WQEGHqPP35xGzFa1GRVPA88IFXqxdS/nRo6l34MDrcikphY6NHj0m5+zp\noZ3XvsxrA9bqtei9V0vcnN+oP7kDl1925nOT8qQ1DiCrmj0W12RsUuM0Nje2jbC2jA7MiD5arpmj\nWrIWI96zLavGred2neiCbcZdSesTPi4qnuDXSQTXrUukVsuLmBj/2N2753QLDHw84Mcf11xGEYqm\nAR+BeT17cvPrVyahOEH9dHZYfHwl3Zd0Z0mTlkszU/HIH6xJNkFkWp7YlZvTedrdAhqVWFDXA+Pg\nAIwFbHEAhMpEsn0kztpkyPHhjtGHRYsWcSc0lDDgvIAlwECUmrxhvhAXrfhilhFMUj4d2xFMDnBK\nBdffwfpiija5GBjGX2Tw/f81k//udxuUE83ju+/qwDDgcht1y4OTdCMPra2Qk/n2bUBiwuYol/lT\n3R1/q8/5Xbv03aUQqR2KlHELRX1qJwfLmlS0i5ZTclg4rjEZaGYKatvBvWDsz8+ksLSGHu1BnbcH\nIVXhSd/qXF6ipfjlKFpMOI/IkYivNgzfa2a04qMkcWeAPzV8bbgTHM5HdIwb+oCTG8/SbmVt5k3s\n4DUDtQ2dqra4X1f9sO3ezzbmp9HvLVRe+VljV8ivbtzb7DHT3VwOlD6ed6bqmfQyWXl74g4xPj0N\ny8DBGOMq8ORoNGWr63QxAw39Ar2vtdQ6WNRYXFPRlnuJvdMr4Zb9UhT8FGFNKuP2Psq2s1+s0VVt\n02+HzatHjcT7r6UWnNs/acKUsgdOVah/qau2+RXSwzp97Nukd8DWgZrcggncA/ZtJyD0DPfn5lK1\nlweeUmteo9LYS8IrwuoQ/cn4hLLqazSyVSFQq/NE9TqXePCg2WujUTu6Hk2XT2ayTwwx9h54vLfB\ny84KJZwxW1VICZLisOqlF/rzUogkzXIpvLb+mf4jUm+U56b5Tl4zqYnWWuGpWWunPvfwTruaoXjZ\n3cLDzaJLy6Dp5EzXpN25NV5W8pkY0FbYOqoQte6qn6YlWm+kPY3Iamw6fy+ZnqlXZiwQ8nxfF1J/\nsKAuVqL8w8wiRd89mHJ+r1st7q21Czbt7Cf3e9vuQTuVe5Z7ab3Q5wFIBoNG6PXmv1h+5P9u04u9\n15qbLlfq1zbis9WsnScE+/5wT4jkD9yfcXzG+Lpv6i5rN7WdsKqsQWKOeAMgzbPzxNbDW0yJfpJ/\nv1TfVXVz6KJttXUdDkdcc7rksc6eI6jpXCS5yO7gI8GhgbGB3oAdEAPEjhw4ckCkZ6Rfli6rhRws\nq1Gw+pMAYvi6xcTgxb8xP6MSH16OZp3XQ6r2qc7DHij5zqcCuwQSKPnKF5hR3c7EqcylRlWcb5as\n+KJFh10THBzSH+n1InWHtKNZMMEXqhXxih2w7nHhBZvNWXev4YggFw3virjyvktn9nbowHUU5m4L\nrEDQXGWiW8OW4sHYsVKlLVsw/PwzTxs0UFcxGu2dtmxZ8u706WG5INWQMZiAtV/R/bgdf8+GxKiq\nkSFSsMn8gEg08o6SGOybkrInglHmPEn9Q449ZSIqp1DY6fE1r+eBNZHEg7LZi0d9XPHqscMnZpSf\nLZYYDJJarxcWScIBuAcsF4Id32hlMEhalACq6SjmnHlAqtXKjpgYIpctY/1TY6mSUkZGiGQ0Orql\npcUkWSxukkRUzZo4ezm7JL/P8dFF1Hnva6/VbE62yZot5ojUfIf+DygM+5kXJMdBwRxYrVPs9bEo\nVakaAz5P4UE9N2rmDEe94ioh4i4VgqHLTEiapPC6L/n3L0GIW3/BG/8zmPzfPOcMDAXGu+MWOdNm\napVtPs3y3piLpzdp+DK70z2j/8j11DU31d+XQqTmKImm8mq8qxE+fP/aUtOo5JmO5lwOmgFCYMxv\n82d86E8/m8LsP2flY9MfhOCaFCJp+dioNdfmLCQxsBT6cfC8SyZ53rfwuXsO16jSGofEik5OqfVc\n/R98ahcYN2HLBz60D2fr+nPUnNam0KptIw78MFVaEvvx41X/y7EWp3WVSSlsR//WnHtSw/jwU3h2\nPafGYarEGpfuquZ2DtHYanOels+kzhN3ruRYOHmwFt6etgwDRk7YWbncJ8/IESmGlEde0SQc3s1p\nq4YmqAjSfaVQ6WVcefb03JAxTuVvDu4dXDKw2QmVKrSZUOkNUsarknlO+/ou6BkS2Nii5sm0bq4T\njpPRLAe72a8oVdPMOfUYjt6dYXdr2dPik/0/NHh645Ha9cDevTPc0tIKugKHvQu+n791TbULV6aP\nd930YbJ9DrpcwafnNlQI7E3/PU95Maw0S1T9SRMq1E1AHYFSyHI6UEUv9AKgfEVpkt/XWosH16kj\nrY75YDY8uW6D6AgFu5or6bJyBn51t79fZ0fK3td7bxKHf5CGDgcb8uzVcGd7LLr7hpz4q3MnM1WY\nGcs4woGDHtnsTlhKt7vUmtap8H5dxYo3Vjy51VRjzNA456DbbW47OXTZizLbKkZWXNPU0nSaZDC0\nREmy9wDF93PiW5ZBAMlgcKjBvcuzmVttYM/nB+O/+jsBP32Dy4GCr1fDlQEBxPRYtqYlvp8vN661\n7KJQSp42BIqjqN82KFlZPwHzu9/9qe+gSH0B1bpRWe3acyMzkxcEswWYCXREgdmtFXNEshQi9UGB\nWdYUc0TKt74NkkHKIKPNZz7vK0pRqTTnP3lx6a0NSf1sRGqWsq6pgoI6ykXBY8v76Dl7NWNnupDG\nPnq+E6j8x7My/BDdgy5w0W0xi99HEPHbBjb85FTl3SsWT3W8n57XfEooDUinN18pwS0s7hbGJynw\n0zpAbYsNepUZF7OWnA4mzIUgbSO2D/arugf5+b+8GfThwckSvO9tRRUdif8YDeYWw/mwtgOfw725\nHBfAjlY64m1RpNxjKNrMAhSk1piFdeTE8HKWG7Xv5Xpk2X41bzDWbnOwUdOdkfUcTdvjRo7q8NvN\nTq4ZGZ6/Vaw4dMeypYtRgoz6fU+v3+fOIGmALih0KoRiOTjwLceMJElaata8TXh4RclkPN+3ZcGy\nzs/quX+MNjs8415WipTpGFArkBdNX5jy1HnzkVgn5ogcJEk3E3buhy734V0B5QCcAJzkG4OVpEIZ\nNnSqPoDlMU+w+/EOlpOQOxWW/6ysw9cIkfM3DNBJgvT/aCb/3fM6oJ899nMDVEXdildubL30eql2\np/WxdfnPlviHehEo9Pqsxj819jJpTeceF31R1XRngtl6f+R4cjzW/2GzzpICyOM5V3jNw6w5YL8B\n+ADGX3Cyd6GUmE9FVy+8cm0x60ASEmoTSFaBygIqs6TKKGKt55tsruuVo/HQ8qDTIUKLHWPIpwGE\nzKnP4gOfibcIGsmNqAxsMpl0H6OjS9ZZf2v73PigzJBUF2KWjEtKW9tkhvPbIm9NOo142LEIFcs4\nY/70tWj/7WMjISCzLs2bROB1bxO3ccRAT2ES5/InxBG4CeySEKESQj4euN1k3+asz/scD6tD7ds5\nYUmtUo9UbW5Xe0P8D0nHKu/PxNG7ue2pV28rLfFs1TH2ac3aaK1WVc3U1AJmSRJOTk4pUmamK3Z2\nmXG2tsZwQAWIGv1onRzpM24LQ+YsY7KNic9Af5UPWWItMyQX3H8IEo3PAthI1uLHuRV6iiKXt1Ms\nDT6UhBpt4HSaSlXb1mpVX4KXV6FONaT0H/Bic/3q9V9JZ6TVUURRjWqVR8u/FityirN+u3h9+zi/\noKLvy7c0nTQRzfCh9HmfzK2OjzB4ZuPQtif15EZ8IbxC20cXCm1bEhruIAkfkSYuGx387mTu+aKx\n+xRo03vkBjaghJFLKIz1BxTJ+VfHDK46Osas2iwNLXt016R5+3bPHgtUFIKEb2vGYJBsz8aws6Yb\nHT3v13svLZ5WkF87RWOb+7nXPe5/yWEC+X4BoByKVH0xEIcum1fuC5CmLbpArXvu798TNngww4AW\nQojHUohUHIUhdwQiUJyvfYET30MU86uxbQdcLnFpoQ0244EgFKb4AngOvMhF9XIEVQMjcByLoh04\nTWbJo0VMF2qsSXmoW6Xi5nCPWrmXcDMe57x1B8k1WsAHA3JN2p65zNjVSWgs1fRB+H10ZdID+GlW\nKjYlIWkLHPaCa8Cdu3sw77vAqTNhVDpdVZq57cKqLsX4kDWO1TeAInEU9KvKowY/szhvHrPtz6tb\nxlYVj5xTbXn8oAhPysXTJE2HtVF/QpPsMXlkMz9xKW2sSMt+YdKXaU4hATYb7roai2BLjgqbXDMu\n2Tm5hTMyNOpcB6NZZWPzvphWUyBWJUq+kXKcwx2k7Kce6sgYd+0oIqzlSP+OyQnQ5GlwyDJT9LMV\nW9NdIorvI8X9NvA6SEZb6WZmRK0NR52uxd6y/2z9mt4Y8/1V5FZ4DZZ+aJ2K2lbA1MGU/ar0K2FV\nWWeXXcKTyBwMBrBUV5BCTmcpu34uDvVccU1ZQZHD5djTcVJz0fe6H3GVtjEuTPDzVQgMgHEIsfdf\nMD0VcFxSUjz/5zP579rRdKDDplvc6uqicte2FktsvYU/r91Vlh668Eeqr4Vdl1M693yB5EIMaOiE\nfUoqSoTZCTFHCClE8gRuYmYN82kHvMdhxGS8xT5KJran3CU1aa6RuCVOxTb7hJgjTJIkruNzO5QK\nh8qQXqQFX6vZo86zpciDPG2tjZ+EY2xAgAPZLbNw0N5He6gCGa180Pcqy2O9Xog1axp4xMX53bt1\nq/1tWe7S66SLYdfCGXS+Xx267RNv64Y9L367Srj93SJxedEuUZpc2wgNGjtwCgTzGCvv3abg0KIH\nSZQjkUritGIaQJL8gNv7KjBzbNyDOc1SbX3PdhtxPmuvXF3f5PCq56PtZhS9k6V7v76lpkfr5bk/\n9vvlnQVzqehoKTkjo8GtU6dGlnz4sKlLRob7FqCyWp1beOHCdiE1a4buQbHpfgBC9XoRm9+feyyF\n5q9ibN91DNOt4Uj2fZ7ab+JhCoR8BL0X2KrbEBPbkyjfnlRfKSjTEXSv4dkM4Os3yF0+LSugSLEV\nVagenuBElLPGTs+mYc5qrw+9qrdldRoc8QJTgj8VO1ajU8JjNFvnQsGnJDyoRYzNhwrlnM61lrbf\nfCWFEWYd19st58Y9k33yCy9qsFq8UfmpRqi+iBGjnQ8lrKjY8/e+DQZ19fu0ah7KjNp3RE06/Zpl\n6nBsZecfY3oCE4XgFIDBIDkCQ8xWfn6cipspV9OvfvfLk4GFyEGnUnNZZ4E+C1+R+CiVdJT6sCnA\n88ou+K64PUwtPan8gs3DaqHYyu+PGcOqZ8/4CagphFBMSSFSAIrJ4TZK6gVn4BIK5O4EwYzlv3Kd\nZH57D4NkcEM5VMoD5QWUsyBV3Ytf7j783luRXG0xZkfiZ1eAxMNqrHOzsC+3jSF3Q9hqfxbVm7pk\neKCUznv+hvEOCaOeVbAvdUVUHk+SysyO927c0ndnUsJ16ti/xLrPyuNGPpLfB6vWXPtrrtfoAcxr\n0NFl+oMHzUyurgkVxo0zxH8bX4ECX9olJRU+2eKH+ZFD28zxT7FHCjNhlON5lZLB63P7qRCYhFtQ\nP+6996Aoe0JnqD40DvHmS9F99HKorPq0KUnUb5ioLl77hKWdOCMCpY84WoqTdV2N5bGX+vHYV4Fe\n5k8VnCWpRkqaKJuuAyQMnuke1eIaHusuojjZoS722UfItRlH23NhNLjekQKJQ0h2L0d4xUxS3OyB\nmJwCmV57e6Mua3lUtNqa4CXHoP0uQILkACgSD2SisvF09ySlQ7LJ6pBn3/285tWoD6sXn+Bz++vI\nrZ/xwq4pZbIjiNdkgU2TIvrcwz1PWlfvXL3IL9EvFIjTE+QAnMyn7RS+y13zJ2Y3y4RNax25tf9X\nMfnv2lMD3WxtXZYczNvr/bOHxZJmrm3xdovmTWzg8+xslzYEKwXHUWyJMSjohbYoNrBH5GHDW9rg\niTN25JLOQX47cpVXnfujOJx2oRRCaQZ4ATdA3KV4aAwZhY3EVxwCIlCyTbVxq3LE6lj5iI1G9864\n77hZXTBLZbOhcnfLQ0vNtEePGtvn5dkKF5ekZLNZ4+BgVNutzXpnd9AvmrcTM3kf4INRsrMW+qzO\nDPxota38zGLj+SXG9KKAHL2/4dEAUXyOmjh9OPcamgnIKo0drZSx0NArg7kWSdVg+obTqVOyW2jz\n0PgCpWkee4WuUSrfQQWlFNxUQS0PLcnJW+ZuMAzpbDZPEqB6gaIm56Lgx6OBPkKQYzBI1VHSDbTX\n68XtP899uFSx/H62XvBHU3g0aSYL3bQw6x7sKQoPzSqkk6c53f4c505sZGNroKoQwvRP6CihmDae\nyDL2nPjhpdg01Gl/7rGTe9neUgPO9eHZcDhxT0/y4ihWls7yvLqhSn8rN+tVemx6YzNHNc8tp0hO\nmrmiuaU8lmizmdfTpmJNf+idsFS1qmSK1VWKw92yGP8HJnQhp7j/VouYAHRHbT7B9IUlWD6x1rjs\nuiee4mYSgn4ABoNUDbhgFRjGPqHU83S2y8FyGoq6P7nruK4F413j13f35eLgAFqGxvF+6Rs0VrjY\nsQjD+9oV8nAYuCtPk6sL1At9dH6b/YRgQtOmfLVauSGEWCCFSLb56/S8mCPmAUghUjGgFTCQdPLY\njD9Z1BRCRCrzhjtKPpRif7oCfMhOWMWTNA9yjUZUA1vRsERRIkPuUqvsdBbeSKOCyzXaVvFhWNJB\njrjHUDjlOg2ezmSBSo219mPVCpG7aLnaLT5aVXI5SRK4WCFyQXOMD2pQrmkhSXXm9Hbx5GmQuoXt\ngKgd0Tc8jtt3UBVcfUlWFc+sgsRsoFC2WWq/fPGe6q8jyksa+5S8qQubvijjYp2HorlUANKwsqJh\nCzRfzUVG9rBfLd2zNHBXuUaE5MTVnf+OEfXiaH7JUfvcNrvt02zTk1ZBzT72SzejWoWS1lcrYb24\nn56aHzjpaYexpSTL6WRoSiJ7/karWFsv7den01hUriLPuuqDuAsUQoh3+bTwAyaQo+vNh2JfKPUm\nuZ35ZOrVcZOK13zzJgXoIEE24F8R3JbBqltguxB1wRJ4e5YuVlZcKnZNI91WmTVZmlRvvLf78nXZ\nRVK7WcDiXgB1ei5rPaweb+dnzn9YmtKtUGzwiWoyd1ehd9UwUp17w910xX7/BkW4uiKgDbDRj0/r\no/Bf+L+SyX/Xrnpc4Ibn2s8xgcs8Tgs/p66/bV0zp7xWmzdQrxenpRCpNYqauxgYDKQCu1EkF0EK\nTkSRSSV2/FE9pgrQA8UO+QDl1G0lBOEABoNUwWJRD1y6dMfQyMgy2Xr9kdW7dgWnm0wOJdWYc5Yx\nufgQtjTdq+5pmucwNaNtt00PPTxishy16V/bXHlQ2OFu647pqmLW+QtypJ8u/vbS3mgK6LhgQQ7w\ni1syO3/9EVegy8OAh1Mm95nlKLRTHSnb2MqvSzLwPwsKdl4DrHowf2odzL6dG9Hn1ywcP+GSm8GW\nBwvt5vuJ8896rGvGlc1mjBcAX5AegmMSSiFhC8phcRnY9S3laf77tQJ2Ai30evH0u3nxrE9C2BTe\nlBxB1efR2D8CKQ/FDj0YBbXRoTnNB3Sko98IRpwXiB0oiZgy/hkdDQZJGxtL2MaN2MZdL1VyIQvV\nJkwnznBmqQnTdA88OlSlarar5OoyghGa/qL/1VOciosgoikqxjIbb2Aa2cw915grWhXPho3WxH5y\nq1BwKBWMne/0dYgi15qIm6YURjKI+trIbvGv6aNeBmX58yVs7P33LmYGLaBM4UjhkGwwSDb5dF/S\n4oqNb8XIip2X7FtyXkKaYsVKslNyTrpdut3heoe7h/4a+qvBIHkLwb6P2ZR8m4FD04IkZnff++V0\nKUP17U22VxdzlPzo+aHwx9++JXboUDqjohGzmYwiuXf+cxSp5C6Vowb3qEk2GnqKOSJUktACd4E0\nlKIaESha1wfgoxBkGCSDlE+PBcCqT9gvTeTIiJ6Ezm+D1i4KR0MS19Y6kNlpFvM6ted07gF65MRR\nqE0PSibgkPmYI52zdOnGXRYd9/Nc6QG0S83l0aTVk6pHhPa3q1R+WULkpyWFtKpU0zxtz5O1xIO6\naYUbOeSJ1o5Peqwybr3f3jEudLzlbLHaL4ebzlbp8vlMzqysJc+BdfsWVHwYkVdyxefPJRs/e1ZP\n9Ty8vrWJx4U3c9XD7YvFpvinqH2fRuesKlVUtc8adexEui6OudWG8wRF2CoUTvlqn3S+3u2NF3rl\nz8MaFIRKS4RIkSRaOxRM2NbzwBjHfdbe2V7xKWLurl1OXWQ5U2uxxKAENR1EiC8Gg+QBtA9cxRX3\nCzaHL9WoUW3WgAHVnw0Y8Cx/0RdA8UXUA/oZwXMBiGVIUkO1ZIlobk2IrkpKrpZuYo54Dr/nRwrF\nyB3Xxa5SHnlT6lP/fRnKDLRgWfia1/XvclftiXNeO4TRRMzwjVjtNTDWATxngOsPak24xlKkWjGi\npP/VTB7gkJehoHO6+NK3y/moTvzB3gAAIABJREFUxGMbvZfOyWpbowa7UWBsy4OuMSz/78tARzHn\nn6hHf/ORJGYBvrIsLUcxMZQFdqamFtjdsWPCQhQm11kI8vLvtylK5InNDK3YnFCzCjEGJXBiBhCd\nhd+S++zatmUwS863z+z2uUuXEmqrtZ+90Xj++34NkmHee6/3bQcPHeZOxkAXKvRzIeOFGfPFn7FN\nXCWPDHYFXpVn+vk40sqU53lV3ZAXifV8L3sUmFX46GG61IQtMdCyHnjGgsMFFMZ+A/j0V46q3/s2\nSF3z33UrMD8oSBQtS9qVXwh312Bt3ZxGT4BHEBsMhecDg4QQlyVJklSojh3ikP4sZw/tYU8JlE3y\nFEWLivn+UqnIqlGDHeHhFDca2SIEy2XkNBQnehBwL5fc+0c5qvuVX5snOyUXIwMt8AInXmGDPxZ8\nMeGBEQ0+5E2frdla19c6fMAIu9h4bZa5dYnWhyefm9xdYNXcx1W7Vyok4rRa9/6Om1IGJu8z1+Ku\nbicvP7mStwvYwdhVq/niXSvlQpNErdmmfqYuMzndPt3WpDHppveYLmfZZl098csJrUu2y3Cgq17o\nbxgMkloIpguopBq0bRMfiu/qPKHz/ETnxNEojtSs/Hn1BJ5OmMDBxx78RD0SkWj47Xdl/Ui2KOkQ\nFgJLCeYDinBykHnGDCy2dVGEjr/dyAbJUBTYAhQEJu5gw/kXXLa2JdU9JF+7kiRGouDitwOjgK4y\nBme8ozexp48Glfjyre+gIFEJrPsZWvUunk9bNrUrqo49uk31+Wsh81TrS4u3MEmRBZ/lOX1oKk0z\nNWCXpm9IkePH+7+7WSt04NJbPSvw7JYVVYMIirt4khCTrbI716L7Vk3HjuuDPDxifBFYrV8Lapiw\nUrKpd9zsVPtXUKGuOJnPQGyuO+asYjhnlqCoTTIOHrchPoiHkT3ZXH0odW1SqQI0D7us6jNx0LUl\nXWLOREw2/1JgeZcu10L69fPJsbGpZJebm+oTH68q9fmzm73JFJ/q4HA/zdHxUeikSd2dc3IuaS9f\njjRrNO2ApiIoSIE1/pcp7RVKdai4HGhoByorjN5cneFTm6Iplcim+z5MQEE2tTq9+PRYJ6PTiQgi\nfh3EIHeU/EXXtGjPLWaxpipVh4K1mJpslSPvclVkq57yQb1HfY9blghVGXVt8yOLrPlfz+QBzjgZ\ndp1rw4+bM39R8+jRBflAzDiUwIIHLJw2cZ8xfmSkZ+SGy8cvpwIYDJIz0AEFnqZFkQTOf8up8edP\nrVoX/J88afTq6FHfbGfn5HnAOr1eOSwkCRsUG1sSitNMg1IqEaCLQGqU334MEIIQ1wAMkmEQioMu\nSE9QUxRmWoHvJF6DZFABB2JcY2x7ju3p45FQz7NUwhK/8I45ZDiKtJrP1F/q35Y+PNU/7Hpl0qRD\nF7waefXcPLH6lF9Ojpx2Y0QVKDMILHnwqQtUOP2vmMKfPwaDVBhYde9ei3obpx9y2mgJt+qw9tcL\n/cn8d68GXIC+P8PeYIg7DYvsYGWdc5xbYI/9WKB2EEHOKAWQC6NIY4WBwpJEYXd3yur1iORk6oSF\n/TFzo0EyqPRC/0cHZAOPqSQmL8BJCBxUSdha36DjMQ7cohCPuM4jIiX7OUt0ueU8ckyDBpKa7ocH\nejzl5bKRLoe7kKfd9v5cF/bmljL+hodbdy6Lbnaxzx1yilYUksVMmdfq+9r4dxcDb5R6VvTZG5ds\nl+Obt2weneyYXKNLWpe3342vBcphNEcv9Jt2StKgcKTGtTlY7xKX3u5kZzruNCAHB3JQo0jevzVr\nRk7J9gxYH4kTB1kkokVI/vuVRcFZ90Zxpm4UQhwFkEKkAnyuc4T9ZxtQf3ETcXnp9X+LhopU3+8m\nN9cuYpH1GVkPAuAuQkz7r3llBYqJcjUKQ58tYyiHylIEq3qAXujTJYkSKM7+rkJgkCTW1SSpwsLB\no4sczSgbsPPw1GyzUA+xw3JBizWqL58+/XC+0k7sjB2BoKAg0QhooNNl3rpcrwplst4PFipaZwby\nKseLZyXWUjympmelD8/XOCS3OyE21jhirePOvUYF2K5W0whoh2JW/BU4Abz22007z2sE2yZR8mNf\nzLaJkFYBu4Iy1jdhDWLbWM47j2dliXliVhIoKCoUhE1hl8xMX/2TJ008U1MbxHh4lLhVvnxKipNT\nR5R0F/cGnT17d+vy5a2AXgjxW/5EHUXRmBJQkDR7UPb+y6sBTJrWlClGDXnv3LFu3LZyqn9c5bnA\naL3QH8qnr0qIP/IYg2Qol+bwZbfKYl9e67hHY9HkJIQ5NT/17F3j3hn8fOMOoS3+j8kDBslQIk/D\ngw770lU5Q3rbVc0s1XS544xA/D/N5W3JguTaRALRbB66kxIRbVHs7AbgEAqKZDwKUmEVsEev/13q\nklAkqtUTJ4aKz59L74mP953zj++HPYod+w0KE7OgbIbcvxmzGkW6na4X+tNI0k4gAyHGfHePU37/\nq3K0OY7Te0w3xjumZ404tNorxdGZ+DpCfaCeTZ6lRC7qBO3zYtmRRb9EFkvIXlTeH04IMEzFdWYQ\n+uDSVNy3XISYt/1351aS6OOlyVi7yzHMwbb9yXv039VBrxcJ3/0+ChgIXbwgLQ8eeMHxTzY0GHSJ\nGxuBCXqh/0PdX4NBclGeYSwKMqGnXi8+/8uxKPUvT5AdlY6Nezoaxx4oBT/WADeFXi+kg1NGsGf1\nOtWNXNPKFdi+gKzts1BZHLlzaSO/2NiwGxhOkHwyrFzY4Axhu7JisjGnavoT+6MlysbVsxb2XtVt\nfFboCex4SxYZnNnABj8PPCK60nUgiGooOUtUkPuyDIeZg2PLZ9zR7GSFbW8amqszILsbfedasETj\nQiJ92ICWjazgPlAfB9pgpZG9FmN2OhJKDdNygCuKKeY3FKHAiMLYomDQV9hyjco73/HDwOYoUNVt\nYs7fb2ZJklxQMOLdJbXUyWoRr1FqmQ5BiAv5NNQCV/PHsR84ZYf5xll+K6aCupmozUOppgsiIaYW\nSfERODoVIce3OimpKgjhctNij8Ibdp406bKHEGpHCfE4VHcpRxMUVpPxK6qjtZQC+qDEvhRCcU5/\nlfKId3yHk9mZsup4h3uZE8/4gbQNOWj/62RGpW5hkrcDGQF9WGx24Yhe/4/pm/NfskZKJda/mkEV\nXSwJGaWxR6KOvpmYCzwUgsV/v7AkSZLlDig28UMDzp17caFmzS2R3bu/1losviimWzuUGgNVEMKI\nJFUBfkJBarkBp7K0nOn+o9Rl6JWBjd0SW/iUZPmFAtxZCcgI8ZdpNsqPlPSDH3Ku9vPuX/KyuvqD\nqnUD2v0EZArBpP94nPx/52OQDMe/Fsbti01CULkoR2EndKclteUk+3pVxTPhRzYN8+BaIwtj1syn\n/s1Nev13OGSFmdcHJmCV6rN+5EPOtSlDr32p9Nxvg8TIoCCRjbIBSgrBPxBMkvBDiU7MA8oLQdK/\nMebmKBoHYM22IdnZjGO0FV1ifjtlUQK8rgKjcjW5s1vMbFEN1N1/uP+jR6NnjZNnRV27nulcrCU1\naptI2ufIm5LZ5BwJo82bGdRgIIL+vOhspESoOzGVO4ldhvP/bDx/eh8JmOtEXu+j3Dba2mUe5nwb\nRxTt42dgt14vhCRRBHgJOdFg7w7sBvEE+KUznyMH8UFng6ihF3qR7+waC/RDQY+s0OvF/X9nPACS\nwdAXRYKqKfR6k2QwOKMwj9FAFooqPZvnM9dx5eZoXShOA6eQd8CMY+pepNIumH/6gZ/mzhWnpRCp\nUv4YRohgTgAnj/RSFzKdXFfzcM5Z6znVuTXkcTCIoDYTmTi1Bz3i0zG7QWMt5B2Hp86QogfPrCCK\nqabTtkAkdUQhcs3VmGlMI6lkGd5YAR/8w2pS4cAyyh2T0aXdBsa5aRjzS2GWzZnDh+hoCqNI7lEo\nmqAt4InC9NWAH6h9wM0MiY+wJ5XOBBDAB2CwmCO+/iP9JAnFPPALRXhPL6pgzwOgqwimFHAUqM63\nwiZKxsQHKLb834ADgBuIL2pEG3ssNjVJfl2SjLhSZGR6k3P3Bc5rginfTJKswydMGNagdOl7yaNG\n3TxuMukK1a9+seOsxuvRBoWZJZ3pHorUGwbE6PXiD8KPoduwUrQ9G87Lslf1C0Nbo8THHDSqcQ4L\noErDSMyOecwFNiP+y6z1+/MGqTeCFQXD2FR2Pu2iumH+MBiP5Ss39zh7dshpoPS/sx8lg6GAQ07O\nlkLJye3a3br1ediZM3alP3/uiOKTswVaSLL8BuVA9gBOC71eIEklY9zduxzQtx6givrJv8gXW5NU\nIOzHLs+WlUTRygqh8I6dCPH6W3+nS0lNSidy0dZMrG863gf92xwuEDW+1XprSe2ZGqfK0mZ0FMH/\ns3zyCCH+P72ULv/f7UNGrigj7+7f8+o+XclKgiZN1v/+m0xtWaaQjDxQRo6TkRv/kzbKy7YXHsk+\ne77KwQ0TZI+jX2Tp6lkZ2QuEBOI+iLZ/fDfhDmI+iCQQ60FsBPESRIl/c9ySjKyTkT0SqTU0g2IR\ntzhcR0auLyO7fHdfWRk5XkZuOLP8zLEd2nRKdJzmLgJGlMh2HOg4Dx3RQALOpDGckwSTTDCrCcYb\nhAP1Fh9jvK8F97dTQGj+nl7CB8QpHea7lzDclpHXyMhS/lxWlWUeyDKGHj0WtgLxEcRcEBHwdgCg\nzm/DQYNl4R7umPt53jlx4YL9UVkmSZb5RZYp+t9eQ7LshyzHI8sV/+I3FbLcElk+iyxPEkJAMMs1\n3bjn5IS5eQ+uOoZgQk8uGtIIZBLBxBBM5/z1WcnThesuzohmgYFRV7maICN758/7hSPcnAniAry9\nD5VGo5g1RthCUQEhAt5m4O7flchTRclMcSfBrMUkwJoF4hmIc6hzrqJLimFI1XSCySCYl75LOBSy\nX5VUooLmJ2AMiobwCAXV8RLFBnwE7lWG3EQIrosijIwHIhnPYoKJI5huf9pvZYAwVDyjJzeYrX5I\nzTWdmVj4F4KJJJjqAqYJ+E2A5ju61wcRB6IYCDWIwSBO5a973Xf3eYKYBeIziJsgepUsed9Oltkr\ny9yRZWIuXbLNnTxszKcThY7mjeHNYhDSX6x9Wxm5k4z8Ui618RdZJvL+FiYJeClgvQAtwRSvNoT4\n927cEhArYLIAh/y1qJVlVssy72SZ8kKxR9oKWP9+KEnXzhPu6Ji8BcTyf2udgVbAtcXdu58t+Ouv\n1gInTmQhy7OfFCv2MVOny5vVv3+6FBaWgSzfRZZfIsvrkOUqyPKmElvk9MOecsY+nwvXuk+b+7zg\nr79axo0YsUaASkA5AYsFxAkYJUDK0jAuWUfevAY8iLPH8kttfkuxxdTRc13GPt15y8haI3NVs1XZ\n/1Pe+R/J5L/b8BKrVr3F1dXK7t3D/2JhBcnIcdsD5GnI8kJkebPnYbl9qEZeJCMnyMhDZWRV/r02\nMvJ8GTlWRu4Eoi+Ii/kL3RVEMIhEEFtB+H+3CYblb5Ym/+13gOOpOC8FMSb/8JgPYgGIBR2IPnCO\n65lnuJ7eji+bpVJxyxvrp+XZdLG1Ml2Vy8RC95lFOq3IxIu5fNPaZDkAWfZkvM8e+uoTwPIIcmsD\nzVGk34b549aAGA8iUYtl7hXk0zLywW/z8fscyqgnThy0zNU11tq165Izsow9iOog4r8/3GSZhke6\nj3y90+GqxckxMcHDI7rXX232f4OmKmQ5DFme+m8/E4yWYK7VGs/JAgUw2+pYRSM+U55MbBE4kIEK\nGXiEivgOfcjbMp1wKzw3cGmurLx78/Nc+6LB8jWfDprv6KQSsE7A41mElAFxPJ+htynOu7JfKfR2\nNsH3QCSDWIVtSllqrXiFJjsXrBewj7mMQ0wsUm+BOlBI3iUSKM8OitAUsFeEiqNeIO2B0plwY+af\n9tQ6YB/B1CCYV8ziCMVoACwGKQGf+sdoNj4dr0dvwJoC4i2IRHxu/srEwonq2fQTcFHAoj+2K8aC\neAzCDkSf/EPcM/83JxBz8gWaLSAq/WldaGSZn/MP8+ogpJ58GnWaG+aKpISDaFWJZLWMXFtG3iAj\nJ8rIsozcQwjB5470/e0Elo99WfcnWlYlmPgpTekn4IiAuEx/FhoucUuWOSfLuOWPzwHEPBDlrCp6\nPp+F0bDT7olKlZcEwu+v1omM7CQjb5WR179jpCGOhneyKXLylbfvamTZIF29ai1w4oRFd/Hip2ZL\nl2a0WrToserq1anI8lRkOZMwOX1UJ/nkVUlOkpF7f2t3xNixA/0OHsxpvWhR4uk6dZrlr5liAl5Y\nISrClcQKw3m0vwLGdTXIIBhji+Zl8+IkD9GoewHzcv/l1oWBC8T/lHf+R5pr/tCfwdCAxYsvotVa\nmThxutDr1+b/3wboUOId44KDqfUxgMfhFXn043H6vCmFautgTkb7she4LPT637HdBslQB9hjRrrT\nkbotM9HuRVHDzgDzheDDP4xBQo9i818ArFNo/C/GLeFbhpczDDQc0pgR917Q9CY0/GZWEgAjeF/V\nnyzvKVQywHlfpN6d6riNtOmfW9s0ufS72LTXXXRkftyF1KgtRb3T2LjRjJ1dRSCDzIggHgw6zMNq\nKZzV6OFzNhg3QnJXcE+GA67QIlKDdcRlrk8EAoC2eqHP/dM4WwO7AgMfTtyypXobFKz7yKAgEQBi\n+J49pWb7+r6bABQmV7vE0urSz2srF7hx6m1pPRph5MfoXfSK+qa25gFhQq83/g09x6KEpjcUev1f\npxD+q+dmOHiRVvRJq5j2aU/vuLolE3LbWNLUjmzu8RF/cihIVa6360LVfoHEB7ykVJXxHLege32d\nC21NqIovo1RWGIW6CEHYdxOgRXFQevvxaWMUfiuAfcBsITBKIZL61AHqtH7H6R41a885+nV5YxLK\ntECXaiW16G+grgjEwuQdsHxa5779r4ddmdA5xZqbaQ2ariWqwVceDXIg27OAokHMKg8L7YCfRH5+\nE0mSiqI4Qu8j4YaKujgEqDCuTkCq54ZNtgaV+SJp/geBMCGIlSQCgAlI5j5UOCjsK62/lLL/bl0b\nK0MR4rwkoUNBNK1BqRHwBSWr5itgCEoqhqsajWnW5cu6FBTop9N3304oprhXer2Y/G26rkjXOuUh\nbTtDkZx6JBZ0wJJmRtpRgNwNeqH/lD+nw4E5HwYxP6ons1HiNO78PuUhUlPgQNOCdF6opbnJk7FF\nzqIO2MEpycpmDXnpFjQHUExepYB62UWkAq+mc/P+x0YZo5ZfvpwntL2+o6FNGmUbvGT2dgc+We34\nEmXGoXIyNb4INKXNOKVLmN8W4lL5j4WLrJw5saohyTu2SL+LF4Pf+vrmHNHr7xSKVRtGbGBVybdY\n3VJo2NSsf/X9+qu4Y4e9c1bW8Xc+Ps2nHjhwbfyxY2uBtUk6NJ+dcZvfCOvyS9i8KcAh/QftXFty\nty9i6v2pLOljRfP5NT/7lGOBx/+Ed/7HM3kA6ejRS/TpU59Vq5IoVWovCoqmL0oY+NZFUzHUvst+\nlJwjI4JkngKdUJwpFYCLKPbwB8BTOQgJWJaJuutCyty+TYHxQvDub8cgUQwlZ8ptYD4QK/7CGStJ\nlAcmA+0g+mAZStTNxFTmK2QJeGhVSoadEfkRksozUiMUFM8IZDm720GODNhBxuLKhRaGmQqP5KNU\nCvtuOeQ8SKCgSxDbtnXi/v3RrJieSB9zZa46L+ZhggvY9FNiovbZQbAVCqW3ZWRqT0q5DaPahTRs\n7FFS4TrnXy4o6QE6CMEdAINBagGst1ql5/PmHayfne2kXbiw3bBT6g7H1zJmQIuLLB6wA23wLG6/\neu7rxmHfsgRkJTMi4hWBmToUZ/V44KzQKzlvJIPBC+WQSUGxEdcmSP8BBQ8dhFKDNQElF/u370SU\nALYgQA/oURu12tJnnH1NxawlAh/ZDR83NPVpmtCVcmLpyuXoHtxg9Kjh0LEDNfR68QJJ8jKjft6P\nKxml8VAtoGz1HKFOyJ90BxRH+KhEPNKK8DU+D5sGqE39maWzQwks06M44lJ/voG5Vzj21YdyxaTh\nMxAs5ogUBZFlXADVx0LpxXBsftu2m+c/f153wqdP5VVos+IImv0KrydV2XvZGdePt8moshRz5g7A\n4ArVMsDDoqzl8sBIHCdWIMd5DD6XNbRIfoBrXGOxJPH36Ng/rTdPbNMmgRinLXI7e/aXC9pduWNe\nRlCiFBCO4qvoi1KPNh3FcfsWmCbL0lOUHDk9ULLFZuRf6fnfMcAUvV5kf9+nQTIMAKqdpvDdVZSs\nLZC6gbjVlCsXT9O+oh3G+kB7hHhvMEitUUxXQXq9eGUwSE5AxzgjExw1VLQKTjlpWaUP4qkJm95z\nmT1jE8MKDGLbgSVMnSAh+uTTov7bkZJjXEPVi00HF9rXOhk9fjTr3IEGeTjWfMIqlS3x7ysw44KE\nmAQ8Q/GFDE6kjlnCvCOeJq/jaGFBCT57p8J03V/1S4Moj9iSMaapusfF3hlXzPDTmYwPTUSsz0Hx\nqdwB5oo5yiFVZvfuJilOTkeqv3ljU/3mzuUh1d+Nrf4Fx9MH0HgYSUqvTVqyk6Nd+9dydvjn2ndt\nrJaWgMaKWq/G8uj/mPw/69NgKM+vv95i69a3nDsXi0r1Etgm9Po/pJ81SAbpWzKt7571QomOrYGC\nCCiDEnTyYOA28jofpfOKCQSHtiAGBZ0j5X9/FXq9/Ie2JJxQqsjoUfDK6fwRK14QqAysgYpP4NlG\n4Ewq2Guh8THw2QDWCGVAb0ZA2CbI3KFIVl2FEHL+mDu3Ocu+/jvRLJvM1ruv/Peyp2gnmD9cAQ05\nRWGbW5SSE3Jx0Dyj3rKqbAh/TU6BAJSiGZshQV2DKZMiudAmFfsPuYw7DGMe5Y85HSXwJB1IFoI/\nRLHm17QcnpLi+alTp7jJlE1/wfrHNVEYwTg5iCrAUmBcEPoTwDgUfPY+Vj++QcW0+SipYccKvf6t\nZDBsAXphVOVx0juUzcXT8mmSiBLzoEbZkAX+9J2IkvjKkH+VQJe8B222J5mFcgsV/HL6x18CrK5a\nGjYtyJcvX6gwbBg5WVlMBrEdGNmbPQtWMj4zB7tiPnwxA01QHM7tTNjcmc3c50uZ8iO6lJuMLPcJ\np9heKBDabSiHeryYI8z5GQvPf/XweO197FgBYJ/QK0gjydNzGhb3UVjDC2JRSWjEq4p+t4/NnNFj\nyOrVG17fvNm+MohUim55TnpAU7Js7bD+kGNrSVO3AMs4MM3RcumGSmqMybYgKj/wzvhI2tdMfClG\nJ4LFXPHLn/fFH9ame4QTAWHHJKNzw2EpoaYJMdd2lyBiAkJYJIlSwH0UKX6qEMj5AIVlKIFBzfR6\n8ZeHyD92JHmjJK8LBxyABqm46I/SOWgLQ7QRFDcDi1JwXy0EmfnrqQ+KYHQLJfr3OnCg0y18UvIY\nCNQjWDgAe0GIffRa3ZMDHVEO4S0OZNpm41ANx5hm3VYUady7kP3JDcvWqFxvam//KG4ccqfbQIHq\nWiOa3pewLkdJf9IHBXkXhbIvhyPEEQCDZLABagpE0Huvd2OLJRRyLm3ZmGdrd+niyualdi0cs3Yx\nluxQ7nRbiNXYCQX99AqN43zqnQH4Aau5M1iL2sbcEKEhW3PqvY/XxrQV5g9DyZUeuzqLElmScM8z\nCTUPnN6RVvgM9bzP4/J/TP7v+r1yZRc9erQgIWGyEGLfv37in7RjMNiiSPfVgGpdD1G162HKjF5L\n2BcfsgErijklCOgr9PrQv2xHQoXCjL7VGC0M5MKRE9B1CgrjHiyEOPvdQxrAeznoj0GvcKR67thq\nDmOMqgtzgMPk57+QDAafS81pZJPHcqC1XugfSRKusHIpZJaGCXdpn1oPnxx3+ClCrUtw3L3y6Hlv\nkRuGggyqClxIJrntj/xYAaX9+8B08R0q4F/MVTGi7dYxtnJzguKXMCpi5jfp3CAZKqKgOgzA2CD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PPTrCvnRHz4NXcWd2hLjdHRBFx8GT45Q+TPS/rG8MdwHwr/CkebQL1RYdSYfwJn5yvE2yv1\nXF4t+MSfQ4OPUFSX5u+Wy3Tz3++5L01VMzaeuj+epcnpCMqGx+FW1pvx7b1Zn209X4TWwMeelfKG\noS8oRUegl9Y8rWbNysS8hYvZernBjMweF+YE7M20wa+0Gx+vd3uURv55oInWuof197uIwXoFqKe1\nvqjEL2dqrUsmOfaxGnkAZZo2YmJ6MHbsVE6cOE+bNpX1xIn3LLUT768NEceyZuAfAdWxVgJKqRqI\nHO++DnR4tyc9FyM39XLg0nMLY5vdPLR1KCNGbCIurhligKYD63U6LoCpzNqIIFMTQxvJBvcAmg4z\n3Z5dwStl95LXleu1bUTXiiGrTeO0F2ynEXbPZu4wHBK2iN3lGDBwKuWAFmjdgmtbsnJsZiRR5/3I\nXv+icayj36DPcngtfY5jc7qyKt6JLcB3BARo5AHtAxT1xnv2XOZe8MGnFxKIngzMD8Dw6suRAW05\n005BuaS8+0RjnhlhNpTWhpFibMIaFw+E8jbI0MYyZZq+yIvXBaFiHkltbC3phjFANDBZa65Z/VDM\nmBHAhQvD6datEvnzhyBuqL/uaaNIkcLcvLkCf/+8jBx5hhw5LiOU0B7aMBLE6LxCCe3fiU6DSxPV\n/C95EfyEtoTJTLMIGzf+yujRuVq5uzdbHBo6DAlYdkTrY8o0+yP881qJ8zcSMNUsX20sby2qHLvd\np/+UqXHPrff0yfLSC/bwds1D67Dhk7CgiTv++F3PA04DbbWlqW4NQkXgma053W6ty/TmpNyhJW/O\nn3D1rXVF83/2DL85rQr1vhS1/6Pcw0OqH2v+W68inpxckZ3f2+RmWV7gt3N+fntjnGhU5VXsoa7h\nc4iPGoislqohK6D2wDlgAdFsZhKTyYK5zt3FbW3dti8Ed+vmQeTpMzh7F8Dm4uF3ZuvVHW9Mcduc\n3/WbF1pfH6yDiENWYYvQetxdYy/3y/cqPp7IJk3C3Ox2O9BxYSmaNjrKovqBPWiyqk10gw3hR+vG\nvZTLRux3kc7M9hpOded4Jq2eh7t7LCuuzeHNWM3adZfx+mh+n+OxKz/KR6Uf53PN6E24kzPVVirc\nj8KK17BV/I5A14/ot+k8B3K4X7/kdvPAkSwc3JaHXfmew7VxboaFXCYsUxwu2SLJUiyWa97xuAPe\nKpZrQIXK/fVZi4SxB3idzz73Z+bMGVy+HMEzdVu55p3fu/pZ2sxZRLvCN/jpURr5kgg9rCbiC1uD\nzFg6a62zWPso4GrC34mOfexGPgFq7txcTJy4CQ+P7IwY0REvryWIq6k8orOesBVCAkZbeestf3bv\nLkStWoZes+Z24NEqSxgIvFKNau+PZWwjO/bCdux53XDzjlYxRLjFRflGue9xwWUzsAIwDW2kXNcx\nEUxllkKYCF0MfTdjxxLrKopkrTZCjLgTYly+RanMdjLPDKdg1WP0mn2TMr8aBGy2vldl4CkNlW96\nepbcV7DgvM9athw9d8yYCKvt4kAzQjfl5NiMEkScqOcZ5bn57SVv5at1oNYZG7a+COuhVMIWQ0x5\nG7YCe9l7LT/5O2cl68qk1FRTmcuBEEMbyVL7lGm+CjTRhtEmuf8nMz71ET51GUMbaaPzpROWcmFP\nJLawH3kpmIgbojfQj9jYvbzzTiQHDjSnXr2NbNw4mtBQDbj1pGfVVrTqFUlkpmMcOzJUD62AUjmQ\nld54tJ5hqU+ajZ577vz8kJDS0S4u0/KEhg5F61hlmpWA1UDN+724RMdHr3Yj2rfUmpDc+z7+LHOV\n5xpEjG29+K/4LLrQuXNk9/FhY6ZMfAcsNgLIjdy71WPxWLaH0S210j4l1XD3635R2tXH5dRv+WtG\n7s1feMnUXPO/u+Fm/6nR/ryr3/9u5AuuXNMlmHgrhmy/2RWt+jzro2N8nz3n7lXXr9Fq5hY+zpeG\nNg4AqGDlhBj7EUABruHKF06KMSMo65qZlss+ZnSVE5HAQQ+3ktu2TQt9dmvp8tGvvDU8e5Zbty7H\nK/VHydOnj/w2ePDAHcWK9as1ffp32jC0FWdZDmyNbtDgjGtcXHOgHgk1U5VqfZge87bnbOYxbegZ\nW4V1IydMr3TtR2Cm0hRe+B03mxxhl2cszdE6zjSVT7xmwcUoivT5rpX/jcULfOi/ew37J7uyemI5\nwnL7gt5Lns0/0aZbVEGPq83Kng+vUeWcLfrpo2E38+fH5/xg3FwmE517B2FZI8ly3ovjRa+xAGHt\nbMKKY0n3aAO8zWczFjMqOBhn50XYTnWhG9OAYscm82Gh60xRUPBR++RfQVwO4Yj/KRphhGRJtM9V\nrXXWJMf9a4w8gFLKFT+/3ylRogyjRimcnG4hfuHE2zGgOO+8M5KDB+vz2WcnyJatOGL49yIRfSnY\nMGxYJrZsmYDMIrMBC5xwmtXppRWhZiPXPxv+ykcdv8EJcatU0vDHudxsmtuF06sb3xakckF06V0A\n10LHyDJ5EJ2XNWfdzFfZxR2apoheCZ/XFTEAqxERs4KIi6CcNozLFk+7CzDR6nNF5NptB/4K6to1\n95+lS7cInDvX2+/GDc/3O3c+Mb9BA1PbbNutfbZow4hVwSoz0AlNX99w3xztN7bPVP1w9eOFLhfa\nBRxAjN/+v/n7SF/6fon4rtsmTt4CMJVZHIlXlDP0vTN1ZZrbgHe1YaTsq00CU5mzgWuGFjdGcrAm\nKD2AEfp+hZPvAyvWkiDOFon4mH9G4hZ52bx5PoGBmujoTECoL76HhjK0cClK+X3O55u3sOXIQhY2\ntGEbaKmOFgXWmTD8GQgMhGMjoUjvwYOXzWjZsi3irtmGuAYDtWEsSEMffZAM1QV5Oneuef769VUN\nmjSJXr5oUav1a+M3Ao18/6JH3h9p4LMPfbUqP50uVCUobM4Hn1LkqD9TBvrfjLJ/O3oF1SqeJXLK\nL/zqFsdzQO4rnixv2456W3O6rwkZO71wFHnrunDV7VSWW9rFnnWXX5jfkYPFyXa4GLUa/kq0Wwx7\njuQ4snRwt8HlwzzCngU+Jn/H3ynQdQDLV9ZlxoyIGXZ71R7R0RtO+vDFr4XxbnaYHh6x+CqNy1VP\np7hVT5W/cS17cZfJL7T/ucb+/WW+mDChfM3p08OP5c69H0l6mx77zDOHnLSWVXYiZtoWNeudeNyD\nKtE3ouDX72++4ONag52DIC5c/fgtq1ofoIiCuiQup2gqpxhcxt7U3v2Gbxp5+fB7HfLiEhlLWM7v\niHcZhCREdQKaAOvJHHWegA3Nq5Wa4/12wA/uH2xsdG7PqkuembJHq7DyO+bGuDIoOfqqrCD1Ntpv\nOMGGl5oTHT2dvpeHICv98kATHajDUGqukkn042HXKKU+QGRQByLUrQtKqVxASHLuGiRglwBTa20+\ncCceAEopd5Rahs3mR1zcEmRlsiVJVmk/hDb2tNb6rMWXL4/MXgsj/nnZYmIys3XrcfLl66q7dNl2\nuw3TrIdwqacDJb1vUPapvyhR80/iq23B2TmW+HgbcUouiVaWr9rFjuv6Ovz+3kjWIXSqxElXoUhC\n0P6kDB5lmuOBAtow2if6skWQItE70PqitV8F6zvX0AEBxy75+LTwjI6edN3LSw3q12/vj/XqFUWu\nb6sEyQEVrBQSSOyK3OjhiBvqF8DUgTpcScr/EiQJqmtSxpCpzHFADkMb3ZL0+yngR6BIeqQLTGVm\nQ15gLQxt3KNoqZTqhLja9iP++tZa62TdRWmBRXVsgExyznLixLvMmfM2GzbcwG7vAaxvTevFnej0\ndAQRS/KS92VDG7esvtZBAvUVAwiIKgXjL0KP1yD0Pbmer6H1dWWaDRCZhDPATm0YPTLU13z5Gqnr\n11c0b9o0esnChV/btPYHqse58+HG2ZzcdlZ18Zkc2DRO2VWxL8fOxDnufcPQZ1SwckfuWQ9g8uRf\nsPfdwtMKOpz3Ivc3T/mdy+xZOcePpdFrazUdRZZKp0motgb5naPjhtU2r7s2WRGdudxh//idFdXx\nJa2dzmyrQgUUU7l6dQJt284FTmphgH2LrEIzA/2ueLAh+zCyAnUH/sm4EetwOpqF2tXO8Yzdyal7\nUMPFX5XdmTmgxIV9e4vzYR8PzjZ00lG3XWlHVJ/xF2k4pIDTmHHhvtuqhXpQuO7Y13PEZ31KfTh9\n+ozByze2BWom5r9b19YVWNSO7/x681nRNRsaTR89ZdJhfans/Lv263gyF2Vuvo9/VKXqWVcXG5bp\nA68R4/zYt11rcr0I4Ysht/8xatWsrD/88OY918U5/ll8Ls4jc00Prl2bSeWbgzD4CKjBVEZxlWoA\nWby8/K6FhfV71DN5f631JYtlsQqogVQ7CtVaj1NKvQX4/tsCrylBKeWKUBLrIw9uESRJYy1iUAch\n8YbjKTaS0JZpeiGSCBOQpenniVL0uwElEUNzADigDeOGFaDMZZ0rKWINbVxM93cyTQ+Eo/y2NoxF\nKezjicwQR2vDuFM1XthDnYD34pXaXnnGDNuuokXdEEN/1wzYMvjlEWPfBMkM3gxcIg53TvI0Htwi\nJ4cQvfJQ4Kc2m9qE9F/ZfzPQxtDG5kR9SlPANTmYyuwEvAFUNbRhVedSnogGS13u6K/8iDChOuoU\n9L3TCvXtt26sWrWGRYtq4OQ0i2vXXg8hxBVhGpWfxaw5c5mboCwZlPBiWaFWjL/EpVYv83IW4OeZ\nsLIHkJBZebt90yyI0A3f0oaRLpLAXe3kzduKmzd/eLpdu8OLFiz4qrKLy/pTYWHNiYt7vgMd/FvS\nwv6yrevNyPiYEVrrb28fF6ycEZpubaA4kENpjleKKB9xrNa7FbNeORp3zN/nCMoWB9qGxgbaRnyM\nOzFXcuBbOQwXn/iiR2zfv/IVuSvupHasM79nDuNFQxvxSsrq7QI6a5HiHQvUIEmeiApWWYN/Y1mP\n7VSbWN3n1e5rB7xxjaoFbURP8mXX4FBqhsXh6QJscOXyruxO3ze8ENep2qyGo3bNqr29iIed7fum\nk8+m+XVyx9cOvztv3vgOI0asWV21andtGHeSnCSA+y2SuPVCCAHlkVjYOmQiszaAkAuI62k8+/bt\neWbTaP/+bc9VGj+e9X8WeHEdPXv2ItLpbdqVakLets1RV+w827S2/vDD3bfPM3uLCyPynSO6vhe3\njs8mKuo1ghiNuFzr60BhJlovnF8JCHj6URv5dYiOsh0YrLUOsSiU3yPL1xP8yyiU6YF14yUY/VJA\nr7Rmet5uwzRLIMyBQ0BPbRg3UjnkH4cyzdpIZmk5bRj36Ggr0/wM8NKG0emegwGENjhIw+D+AwYc\nmN66dSTJGPq7DglW3sgs3wewcwMXfmUUmdlAYxHzQoK0ATmv5Tzeaksr7wV1FlS/Pv76pUQB1zIJ\nHPv0wHpZrgJ+NbQxwXLP/KBQez7l049KUMIAqsYSe2sZy56JIupsO9pNtmG7gPDrjyWuQJUalGHU\n4MKFlYSHa2y25vrkyQ0WPXUNQqcbbFFOcyDsm3yI//4ZZ5wHz2KWLZbY6d10txS54v8kVO7cnQkP\n/xIfHxeio8EwdLk8TXXQrAK2vp8oLoTuPc/rr3tit5fRWp9Nto1g5UH5Sb3xKRvEqQU/c3L2RcQO\nxFo/E34PA76jXshVJJHofWBASABLgTXO3NpRh5aLgPJfQtN3od4+CMsKAWi9O7lzAyzNV2d+9rM9\nOhzNduP085eDb3pwzQ/4VAXxQfvNNZ9/aWe2oCIXC5U46Vlcz6qzfOOPNX75BFipA/U15LneDGS5\n4u3dJ/vSpWUQt/MEhBwQi+jdZ0Xu82i4XaqxHVA/DluDS/g77YovH37o2+27rh+5UnXoUFx276bT\nsGF6MYAyzdLAIuIIY1gpN6LfLsKVX9xo27abnjZtnjJND1a7/8aE16vBX7OIjelJEAOQe6OODtS3\nn1Vlmp8CeQgIaOFIhvoXQpmmO+KrbQK8qI173QgP4Zy+yIwjMzJrzoP47xNKE/6iDWOXMs02yM1d\nKYHal0J7Bdf361e01r59MxbVrWvvPHz4qUh395b3M/T3tCFGbj0wRWs9DUAFKy9bvK1Z1SNVp/9V\n+C/PWOfYzeRudYTCvbPr+o1bpaFNhQTG45Ekm+vArRBCCgFb3uCN8R54vPMSL+0oRalCQqBhORLo\nzBxBRL6NbOyRj3w3ilP8skIVRBK/XkotIK6GD6/Db799zN9/V6BevTXcutVMm6bdesl8gRiJtveU\nLBQG2njk+owKIUQjsYmnDW38ndbxfBAopWoh+QlbpD4C24AehjZ+Uab5DJ999gM7d9qZMqWKbtw4\nqRtDIavTHsBz2jB2pPm8plnJ78aNhbPHjo1stOnv7Dv4OFt21h0vzJe/ALuLQOMzEBetdfvkjjeV\n6YJ4C17zcVn8UwHb1A4vt+J6l91s69iG8Fd20iIoBLcjfhx8q0P9H/9o9FY/bM57gTfueu6UKg5U\nIKGcomkWQQx8Ke4Ua2mSdNWkTDMb8L6NuFb1z8z5tvD6eS0qVMC3eHEinZxoZxiiCppofzegKvHU\nZGzJnlxYUITTo2wMHnycatUu0/qDikSFb4MddQniecRO1NGB+mSiNnojOTE1CAi44TDy/2Io02yL\n0OXGAJPvlwH7D5xrLhIQ/BhZbeVC3BXLECXHLkhCz1TkQd2UQlMo06yD8LLjyhw/PnR39+5tjuXK\nVa/NqFF/7ylSpFE6DX1BxAUWqLX+KuFzU5lVo52jl3Yc0HFoaB7/Twk/GkN8zIfADB14r/yEZSwb\nImnn/siM0RfI4oqrR13qRjahiVsZyrjEEbfdC68FCJtp/z0CdEr5IQyZBSGETESosAURv/5dmizK\nNL24cqUzP/zwNr/8koeSJbdTr15fPWHClkTf5XVkfOukleVjKrMnMpusYeh7aZEPC5bRXAusNbRx\nO06mhg71Ys6cQ7z4ojdt2gwHpmvDiLPcf18hrKzW2jDOK6V6W3+/fd8sbblmneKVmrCobt2wt3v2\nDH11as5uVbc6r0LKXn5tudV2AW9orZcm6WsZxGd/BehuaOMsSr0Y5cSnE2pz6LWt5PW0c8ol3tbf\nZe3a+oh7tZPVt0O9RlsAAB76SURBVGDE1fKONox7pcCFQTYECXBHISSGTMik4WqirQrwDZ07/8GZ\nM9OQEoGj0+LuswKs75JjRT+iO2XDyzeGCxXiie3gR1D7msiKv4EOvLOCUab5NLIKr6MN47Cj/N9/\nAMo0CyH8+Qigd1IVzH/oHK2RmWJFbRjhiT4PQJah5RDtj1XA9ym6aeSYZohuSEfEkM5R8fGbzr7w\nwj7PqKigYb16Hfy8ZcsaCYbeikXksdp/HqlsvzDxg2VRBBcjAdL+Cfo3pjK/PJsb507zqcf6Z1sT\nF9kfSWr6AZiiA/W+RFnCo5CZchDwQwghIFWSOmt0Wzv23cc5vjwPeXJ44dUQCEhqsO/6nkIS+AOY\nHELIJ4j+TzOgSUAIFwADu709y5e/yKxZNjJl2kpUVA999WpSFdNmiEumhqGNUymdLyms2f9i4FBC\nEt2jgKnMD5H4UPOkLiqlVClsto3MmHGIIkVsyOx9FHAY6E5AQCwy+30GMYCHEWmHew2euMw+Rfj+\nvVVIyF9IQlmVSa8zsPIOliDU4FVKKQMJNJfRWt8wlZkfoXe2tPrweeIXdV+lxh2E7mOhf+OlS5dd\n9faehbjE2ib42S3q6+sIMWQu8L42jKvKNGsgmjR1EIM9PUFnypKj8EXuM9kuXjzBiy92QupHdNRa\n35MAlRqUYhBu+9+g6hRv3nwhnrCVozk08Q2gvQ7Ut1VrlWkWQGSKb4scOoz8fwRWUKc/suz8CJig\njeQTgjLQtj8yE2qrDWNjMv//FKFlHkM0V/IAQ7RhzE9m304I3bJVwkzfeljGAK2HffPNhEELF37w\nW6VK8RNefPHKzmLFsiMzoFiEBnoOeUhisFxEyGx6HQEBCgnk9cLNbSRLl/4UsMGtxJBJrPizJusa\nrOUFQxs3VLDyR3yUfYjBzg2yEIUTXhzElyNeUV5xpc6WKlTibIlivhG+kVEuUav+zvv3DxtLbtwN\nXAoJColFDPYzQANDGylqFSmlCrngskmjL8Q66ahOrt0LPBfTNPuwkl/FH3M7e5MjR+KIijpETMxg\nre+tRWsqsyxSt/S5pEJwaYGpzOxIkLynoY001d59EJjKbIeMzVMpvQCVUoOAdqxaNRNX12BEq2gM\nAQE+yMwTxE8diyWrjLCo7FYDHsh93gt5QXySIEZmuX3GA42n9uftcnuZBTQztLFVKfV5TnK6L2BB\nKND1fE5+eO0TCl3PQjzibltOQMAJxED3B9bg6lqDefMU/v7rgP7JFZ5RppkDeWE8z9WrJzl7Njfr\n16/k55+PExmZl+yUIhsFiSOSKC4TyQVucZYoziNaSN0RHZ4uWt8pYJ9eKEV34E1mBr3LpY/nU6Dz\n3+Rp1SZhMmQ9Z+uBudowPkp0PRxG/r8E6009HXENvJqcUU5newpxqxzUhvFWCvt4I5l1HohUsjcy\no/9AG8anifYbgDBUmmjjXj+xpbD5VY6rV0N+HDnSu8yJE42O5snjMr9+/UuL69YdfyJXrq+0YUQo\n0+wDjATeRmZXTREBp0NAVo4cycHEiZ64uNh5/fXjJaMKXZw0hMuekRjhhH/1Bm/sOcCBajjTED88\nyM9XqqFaX39//adKnCvR2i3Wrdxx/+PbtxbdevhM1jMKRU64vWUHvupqdu3dzex2O0M5gcKYGL+6\nmIWj3JkRGX2jxt6s5xgz1K5iw6/v7rQ087VOO8rU/lYvmDCb2b+TQoayZaA3AyMMfe8LM60wlfk0\nYjznW21liMefhvOUQtwXje+XOW3Jd6xFJBzGWp8VQdx+axDCRaz1uYeCH6uAswk/eEqiZCPEWL1O\nMoql1j07EujwaW/GljzI6OMcb+ePf9M44oaFOt1YFvxFvhMnC9IBCdqeBZoRE9OU0aPd2b8/mr59\n+1O7tp25c+exaFE4YWHVtdYn7vOdcpLZay4+EXXJG3+JvNjJiRu++KGJII5DaFxR+GHDBycyAxBD\nDBGcIgvjsLFIB+oUY1hpgRqeOStuYetRzl/w9K/OyAtrIpBQWSwS6JbYresw8v9BWDf5C8iydwlC\ndcwQA0eZZhcs6mBy6e6J9qsCeGjD+MP6uzDywM5AZnbBSPp5I20YJ+/Tjg9yQ3ZytdsXzh4zZseL\nISG1lDCSliCaOBtUSEiCAmYfbRgLlWn6ITQ8qeI0aNAtdu3qj8zspwHOBSnY7HmeLxNAgNqW+dC+\nGX1sV67XLnPsh/ZOWz2ieA1Rx/wYmJsg7XxP/4KVl/W9zJCgkLeR5XhZRBIiXJmmf/ZLPNv9SwbV\n/JNyi9pw6/t2LJ3Zk6LZL3PS1U5HQxvayqRdgMxEtyByAJcMLfx9U5lu1nnWGdp45z6XKE2wXhjT\nEbdaV0MbW1I5JD1teyC+/zeBYYY2Zqd2jFKqABKYbYCwpb5HJG8/sXbIjuRK1NFQ+xJk2g5Xn4Zx\nmeA3tN6X6jk+/vh9tm17rdXX4dH94vvk3MrWc18WNv86ErGrKTNnLsLLa1CCtIXFeluEr28cs2Zt\nwNe3IfJC70BAQFVEBbWx1vqu8nuWq68zMJHW7KU8hVGsQ1a+O4FdycV/AFSw8kRiW7UQ12U9JHA+\nH2HtpGslroJVFiR57k8dKKURLVfudCQD/QSidXXXasRh5P/DUKaZBTGwTREVwpBUDkl6fH6E695Q\nG8bODJw/D5IpG44kojRNT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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "t, S1 = pathGeneration(100, 100, 0.1, 0.03, 1, 1/52.)\n", - "\n", - "for i in range(0, 100):\n", - " plt.plot(t, S1[i,:])" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false, - "scrolled": true, - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [ - { - "data": { - "image/png": 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Au9Zj3twY1bC0GX/DayS3vY4KXcKXN0h2LnS74FAE16UrVAyc52OBZu6p9JGU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\ns+fwwjaD1IX0+m24+j9C8L9DM07v83FSUL1NmvW/ucbah3+fv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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "N = 10000\n", - "t, S = pathGeneration(N, 100, 0.1, 0.03, 1, 1)\n", - "\n", - "for i in range(0, 100):\n", - " plt.plot(t, S[i,:])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "###GΓΌte der Simulation in AbhΓ€ngigkeit der Sitchprobe" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false, - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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4.013603 4.010228\n", - "13 1.725964 2.843935 4.052156 4.079586 4.001027 4.003518 4.006891\n", - "14 3.384860 3.360019 4.088791 4.065781 4.050036 4.005878 4.009252\n", - "15 3.635062 3.658024 4.142389 4.014601 4.035410 4.001456 4.007769\n", - "16 2.704679 3.800666 4.037882 3.967375 4.000176 4.005432 4.006744\n", - "17 4.208557 2.992062 4.055670 3.890020 4.035907 4.014299 4.006577\n", - "18 3.912931 4.234620 4.081956 3.962941 3.987279 4.006018 4.007529\n", - "19 5.831976 4.371024 3.875515 3.999242 4.013709 4.016788 4.007910" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "pd.DataFrame(V_MC, index=N).T" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "###Varianz in AbhΓ€ngigkeit der Sitchprobe" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false, - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "10 4.730080\n", - "100 0.328129\n", - "1000 0.025969\n", - "10000 0.003326\n", - "100000 0.000432\n", - "1000000 0.000035\n", - "10000000 0.000003\n", - "dtype: float64" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pd.DataFrame(V_MC, index=N).T.var()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true, - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Slideshow", - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.4.3" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} From e4c98431524a87e9417181c889eb785e050a9563 Mon Sep 17 00:00:00 2001 From: Matthias Groncki Date: Tue, 8 May 2018 19:12:27 +0700 Subject: [PATCH 18/25] New Blog entry about Logistic Regression First part of the series from Logistic Regression in Sckit-Learn to Deep Learning with TensorFlow --- Blog_DS_1.ipynb | 421 +++++++++++++++++++++++++++++++++ LogisticRegression_Part1.ipynb | 326 +++++++++++++++++++++++++ 2 files changed, 747 insertions(+) create mode 100644 Blog_DS_1.ipynb create mode 100644 LogisticRegression_Part1.ipynb diff --git a/Blog_DS_1.ipynb b/Blog_DS_1.ipynb new file mode 100644 index 0000000..153b4cc --- /dev/null +++ b/Blog_DS_1.ipynb @@ -0,0 +1,421 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Logistic Regression with Sklearn and TensorFlow Part I" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import tensorflow as tf\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.metrics import roc_curve, roc_auc_score, classification_report, accuracy_score\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load data and visualize the data" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "credit_card = pd.read_csv('creditcard.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f, ax = plt.subplots(figsize=(7, 5))\n", + "sns.countplot(x='Class', data=credit_card)\n", + "plt.title('# Fraud vs NonFraud')\n", + "plt.xlabel('Class (1==Fraud)')\n", + "plt.savefig('inbalance_class.png', dpi=600)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see we have mostly non-fraudulent transactions. Such a problem is also called inbalanced class problem.\n", + "\n", + "99.8% of all transactions are non-fraudulent. The easiest classifier would always predict no fraud and would be in almost all cases correct. Such classifier would have a very high accuracy but is quite useless." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.99827251436937992" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "base_line_accuracy = 1-np.sum(credit_card.Class)/credit_card.shape[0]\n", + "base_line_accuracy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For such an inbalanced class problem we could use over or undersampling methods to try to balance the classes (see inbalance-learn for example: https://github.com/scikit-learn-contrib/imbalanced-learn), but this out of the scope of todays post. We will come back to this in a later post.\n", + "\n", + "As accuracy is not very informative in this case the AUC (Aera under the curve) a better metric to assess the model quality. The AUC in a two class classification class is equal to the probability that our classifier will detect a fraudulent transaction given one fraudulent and genuiune transaction to choice from. Guessing would have a probability of 50%." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "X = credit_card.drop(columns='Class', axis=1)\n", + "y = credit_card.Class.values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Due to the construction of the dataset (PCA transformed features, which minimizes the correlation between factors), we dont have any highly correlated features. Multicolinearity could cause problems in a logisitc regression.\n", + "\n", + "To test for multicolinearity one could look into the correlation matrix (works only for non categorical features) or run partial regressions and compare the standard errors or use pseudo-R^2 values and calculate Variance-Inflation-Factors.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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TXAeq6jPjiVKSJE20004b7JpAbYq6fzp2GphN8hTgacA59M6LfW6Si5drnGRnkvkk8zMzMyMJWJIkTY6cdtpA1yRqxeKJxn7gqiTbgS1VdSjJlcBHq+oYQJI/AH4A+MjSxlU1AyxmdDXMPnaSJEmTqDXpaJO8zQG76Y3eAXwB+MEkD0vycHoLJ5yKlSRJg3MqduRmgfOAa5vPe4G7gU8CnwA+UVX/ZUyxSZKkSbYBFk+0aSqWqtoHpO/zA8CrxxeRJEnqjAkdhRtEqxI7SZKk9ZINcPJE91NXSZKkDaKzI3aPm3rkUO2npqaGjmHYZ4y7fRtiWIs+SJIETOx7c4PobGInSZL0IOn+RGVnE7uFhYVVt52amhq6fRtiGGcf2vIdDBODo4WS1C2+YydJkqSJ0dkRO0mSpAfxHTtJkqSO2ADv2LWih0nmkly6pGxXkquTvDnJp5rr8nHFKEmSJtxpGeyaQK1I7OgdJTa9pGwa+DKwHTgf+PvAlUnOHHFskiSpA5IMdE2itiR2e4HLkmwGSLIN2Ap8DfiTqvpmVf1veufF7hhXkJIkSW3WisSuqo4CB/lW0jYN7KGXyP1Qkkcm+Xbg/wKetNwzkuxMMp9kfmZmZhRhS5KkSbIBpmLbtHhicTr2/c3PV1TVoSTfD/xX4H8CtwLfXK5xVc0AixldDbP/mSRJ6qDTWjGeta7a1MP9wCVJtgNbquoQQFW9sarOr6rnAwHuHGeQkiRpQuW0wa4J1JoRu6o6lmQO2E1v9I4kpwOPqqqjSZ4FPAv40PiilCRJk2pSF0QMojWJXWMWeB/fWiH7cODm5r+IrwL/T1UtOxUrSZK00bUqsauqffSmWxc/fx14+vgikiRJnTGhCyIG0arETpIkad2cdvq4I1h3k/lmoCRJkh4iVTXuGNZDJzslSVJHjGVOdOFDfzRQfjD1j567YpxJdgC/BpwO/E5VvWnJ/YuBX6W3AHS6qvb23XsA+GTz8QtV9c8GiW85nZ2K/du//PKq2z78CY9nmH3wpqamAIZ+xrjbw+r70JbvYJgY1qoPkqSWWONVsc3uHb8FPB84DHwsyfVV9em+al8ArgB+bplH/E1Vnb+WMXU2sZMkSXqQtd+g+ELgrqr6PECSa4EXAP8nsauqv2juHV/rP74c37GTJEkbQpKBrlPwROCLfZ8PN2Wn6hHNcagfTfLCQfpyIo7YSZIkLSPJTmBnX9FMc4Tp/6myTLNB3uN7clUdSfKdwB8l+WRV3b2aWBeNNLFrTpb45ao60Fe2C/hu4DuBHwBuqarL+u5/B3At8BjgEPDSqvrGKOOWJEkdMOBU7JJz6JdzGHhS3+dzgCMDPP9I8/PzTY70vcBQid2op2Jn+dapEoumm/K3AC9dps2bgbdW1bnAV4BXrmuEkiSpm5LBrpV9DDg3yXck2UQvp7n+1ELJo5Nsbn7/duDZ9L2bt1qjTuz2Apf1dWQbsJXeKN1NwIOWH6Y3wf3cph3AO4E1mYOWJEkbzGkZ7FpBc8zpa4EDwGeA36+qO5L8YpJ/BpDk+5McBl4EvC3JHU3zpwHzST4B/DHwpiWraVdlpFOxVXU0yUFgB/B+epntnjrxZnqPBe7rOx920JcSJUmSAEjWfjyrqm4AblhS9gt9v3+M3hTt0nb/FXjmWsczjlWx/dOxi9OwJ3LKLyUm2dmsLJmfmTnZdLgkSVI3jWNV7H7gqiTbgS1Vdegkdf8X8KgkD2tG7U74UuKSFxxrmA2KJUlSB63xBsVtNPIRu6o6BswBuzn5aB3NFO0fAz/cFL2M3hSuJEnSYNb4Hbs2GtcGxbPAefS2MQEgyc3AdcAlSQ4nubS59TrgZ5PcRe+du98ddbCSJKkDctpg1wQaywbFVbWPJe/PVdVFJ6j7eXpHdkiSJOkkPHlCkiRtCJnQ6dVBmNhJkqSNYQMsnsiJt5CbaJ3slCRJHTGWDOtr8382UH7wyAu+d+IyQUfsJEnShpABz4qdRJ1N7N75kflVt33ZxRewsLCwcsUTmJqaAhj6GeNuD6vvQ1u+g2FiaFMfJEk6FZ1N7CRJkh7k9NPHHcG6M7GTJEkbwwZYFTvSyeYkc30bDy+W7UpydZIbk9yX5ANL7r82yV1JKsm3jzJeSZLUHclpA12TaNRRzwLTS8qmm/K3AC9dps2fAs8D/sf6hiZJkjTZRj0Vuxf4pSSbq+r+JNuArcAtVVVJnrO0QVX9GUA2wN4zkiRpHW2AXGKkI3ZVdRQ4COxoiqaBPdXRzfQkSVKLnJbBrgk0jgnk/unYxWnYoSXZmWQ+yfzMzMxaPFKSJHVJMtg1gcaxKnY/cFWS7cCWqjq0Fg+tqhlgMaOrYfaxkyRJmkQjT+yq6liSOWA3azRaJ0mStJJJXek6iHH1cBY4D7h2sSDJzcB1wCVJDi9ui5Lkp5IcBs4Bbk/yO+MIWJIkTbgN8I7dWDYorqp9LDkAuKouOkHdXwd+fRRxSZKkDvOsWEmSpG7YCFundT91lSRJ2iDS0S3kOtkpSZI6YixDZ9/4iy8MlB9s2vbkiRvicypWkiRtDBtgKrazid3//ujq97H7Oz9wAQsLC6tuPzU1BTD0M8bdHlbfh7Z8B8PE0JY+rMV3IEliQyR2vmMnSZLUEZ0dsZMkSeqXCd2bbhAmdpIkaWPw5Im1lWRu8USJvrJdSa5OcmOS+5J8YMn99yT5bJJPJdmd5OGjjFmSJHVEMtg1gUadus4C00vKppvytwAvXabNe4CnAs8EtgCvWs8AJUlSR22AI8VGndjtBS5LshkgyTZgK3BLVd0EPGT5X1XdUA3gIL0zYyVJkrTESBO7qjpKLznb0RRNA3vqFHZJbqZgXwrceIL7O5PMJ5mfmZlZq5AlSVJH5PTTB7om0TgWTyxOx76/+fmKU2x3NfCRqrp5uZtVNQMsZnQ1zD52kiSpe/7mEZsHqj+JO4GOY3nIfuCSJNuBLVV1aKUGSf4tcBbws+sdnCRJ0qQaeWJXVceAOWA3vdG7k0ryKuBS4Eeq6vj6RidJknTqkuxodu+4K8nrl7m/Ocme5v5/a9YXLN77+ab8s0t3DVmtcW3oMgucB1y7WJDkZuA6eqN5h/s6+J+BxwO3JrktyS+MPFpJkqQlkpwO/BbwQ8DTgR9J8vQl1V4JfKWqngK8FXhz0/bp9F5Jewa9tQdXN88bylg2KK6qfUCWlF10grpuoixJktroQuCuqvo8QJJrgRcAn+6r8wLg3zW/7wV+M0ma8mur6n7gz5Pc1Tzv1mEC6v4WzJIkSevjicAX+z4fbsqWrVNV3wT+GnjsKbYdmImdJEnSMvq3UmuunUurLNNs6RZuJ6pzKm0H1tlpzr/zAxcM1X5qavhFzsM+Y9zt2xCDfVib70CSNLglW6kt5zDwpL7P5wBHTlDncJKHAd8G/NUpth1YZxO7vzh636rbbnvso1hYeMghGKds8X+Ih33GuNvD6vvQlu9gmBja0oc2/FuUJC3rY8C5Sb4D+BK9xRA/uqTO9cDL6L0798PAH1VVJbke+L0kV9E7hetceoc4DKWziZ0kSdJ6qqpvJnktcAA4HdhdVXck+UVgvqquB34XeHezOOKv6CV/NPV+n95Ci28CP1lVDwwbk4mdJEnSKlXVDcANS8p+oe/3rwMvOkHbNwJvXMt4XDwhSZLUESNN7JLMLd1ZOcmuJFcnuTHJfUk+sOT+7yb5RJLbk+xNcsYoY5YkSZoUo56KnaU3t3ygr2wauBLYBDwSePWSNj9TVV8FaF4wfC3wpvUPVZIkdcnfnv7wcYew7kY9FbsXuCzJZoDmvLStwC1VdRPwkKV7fUldgC2swR4vkiRp46ka7JpEI03squoovaW8O5qiaWBP1cm/viRvB/4SeCrwG+sapCRJ6qTjVQNdk2gciycWp2Npfs6u1KCqXk5vZO8zwOXL1enfHXpm5mR7CUqSJHXTOLY72Q9clWQ7sKWqDp1Ko6p6IMkeeu/jvX2Z+/27Q9cwGxRLkqTuWWGCsBNGPmJXVceAOWA3K4zWpecpi78D/xT47+sdoyRJ6p6qGuiaROPaoHgWeB/fmpIlyc303qE7I8lh4JXAh4F3JjmT3mG5nwB+fPThSpKkSTep780NYiyJXVXto5eo9ZdddILqz17/iCRJUtdtgLzOI8UkSdLGMKnTq4MwsZMkSRvC8Q2wFW46mr12slOSJHVEVq6y9r7wV389UH7w5Md821jiHEZnR+zu+NK9q277jCc+joWFhxyCccqmpqYAhn7GuNvD6vvQlu9gmBja0ocu/FuUJI1GZxM7SZKkfq6KlSRJ6ojjx03sJEmSOmEDDNiN9uSJJHNJLl1StivJ1UluTHJfkg+coO1vJDk2mkglSVLXbISTJ0Z9pNgsfadNNKab8rcAL12uUZILgEetb2iSJEmTbdSJ3V7gsiSbAZJsA7YCt1TVTcBDlt4lOZ1e0vevRhemJEnqmuPUQNckGmliV1VHgYPAjqZoGthTJx/vfC1wfVXds97xSZKk7nIqdn30T8cuTsMuK8lW4EXAb6z00CQ7k8wnmZ+ZmVmTQCVJUndshMRuHKti9wNXJdkObKmqQyep+73AU4C7kgA8MsldVfWUpRWragZYzOhqmA2KJUmSJtHIE7uqOpZkDtjNSUbrmrofBJ6w+DnJseWSOkmSpJVsgG3sxjIVC72E7jzg2sWCJDcD1wGXJDm8dFsUSZKkYTgVu06qah9LDgCuqotOod0Z6xaUJEnqtElN1gbhyROSJGlD2AhnxY5rKlaSJElrLB0dluxkpyRJ6oisXGXt3faFewbKD85/8tljiXMYTsVKkqQNoaODWQ/S2cTuxts/u+q2O57191hYeMjpZqdsamoKYOhnjLs9rL4PbfkOhomhLX3w3+LUqttKUr+N8I5dZxM7SZKkfhsgrzOxkyRJG8NGmIod6arYJHNLNx5OsivJ1UluTHJfkg8suf+OJH+e5LbmOn+UMUuSpG44XjXQNYlGPWI3C0wDB/rKpoErgU3AI4FXL9Puyqrau/7hSZKkrnLEbu3tBS5LshkgyTZgK3BLVd0ErP4Na0mSpBZJ8pgkH05yZ/Pz0Seot2azliNN7KrqKHAQ2NEUTQN7auUU+o1Jbk/y1sWkUJIkaRBVg11r4PXATVV1LnBT83k5bwFeeoJ7V1bV+c1120p/cBwnTyxOx9L8nF2h/s8DTwW+H3gM8LrlKiXZmWQ+yfzMzMxaxSpJkjpiDO/YvQB4Z/P7O4EXLldpLWctx5HY7QcuSbId2FJVh05WuaruqZ77gbcDF56g3kxVXVBVF+zcuXPto5YkSROtqga61sDjq+qe5m/fAzxuFc8YaNZy5IldVR0D5oDdrDxaR5Kzm5+hl+l+aj3jkyRJ3TToiF3/bGBzPWTkKMkfJvnUMtcL1iDkU5q17DeufexmgffxrSlZktxML/gzkhwGXllVB4D3JDmL3rlytwGvGUO8kiRpg6mqGeCk73dV1fNOdC/Jl5OcXVX3NANV9w749+9pfr0/yduBn1upzVgSu6rax5IDgKvqohPUfe5IgpIkSZ02hr3prgdeBryp+fn+QRr3JYWnPGs5jnfsJEmSRm4M79i9CXh+kjuB5zefSXJBkt9ZrNTMWl5Hbw3C4b7DHN6T5JPAJ4FvB35ppT/okWKSJGlDGPUGxc02b5csUz4PvKrv85rNWprYSZKkDeF49w+eIB09XqOTnZIkqSOycpW194efumug/OB53/OUscQ5jM6O2C0srH6fv6mpqaHbtyGGcfahLd/BMDG0pQ/+W1ybPkhSRwezHqSziZ0kSVI/EztJkqSOOL4B3tRyuxNJkqSOGGlil2Sub2+WxbJdSa5OcmOS+5J8YMn9JHljks8l+UySnxplzJIkqRvGsI/dyI16KnaW3jFiB/rKpoErgU3AI4FXL2lzBfAk4KlVdTzJag7QlSRJG9xG2O5k1IndXuCXkmyuqvuTbAO2ArdUVSV5zjJtfhz40ao6DlBVA52zJkmSBPDAA8fHHcK6G+lUbLMD80FgR1M0Deypk493fhdweZL5JH+Q5Nz1jlOSJGkSjWPxxOJ0LM3P2RXqbwa+XlUXAL8N7F6uUpKdTfI3PzMzs2bBSpKkbvAdu/WxH7gqyXYm8xFXAAAgAElEQVRgS1UdWqH+YeC9ze/7gLcvV6mqZoDFjK6G2cxUkiR1j9udrIOqOgbM0Rt5W2m0DnqJ4OIhuD8IfG59IpMkSV3miN36mQXex7emZElyM/BU4Iwkh4FXVtUB4E3Ae5L8DHAMeNUY4pUkSRNuQnO1gYwlsauqfSw5ALiqLjpB3fuAfzKKuCRJkiaZR4pJkqQN4fgGGLIzsZMkSRvCpL43N4h0tJOd7JQkSR2RlausvT233jZQfnD5Pzh/LHEOo7Mjdnff+5VVt/2uxz2aYbZLmZqaAhj6GeNuD6vvQ1u+g2FiaEsf/LfYjj5I0iTobGInSZLUz3fsJEmSOsLETpIkqSM6uq7gQUZ68kSSuSSXLinbleTqJDcmuS/JB5bcvznJbc11JMn+UcYsSZK64XgNdk2iUY/YzdI7beJAX9k0cCWwCXgk8Or+Bv0bFyd5L/D+9Q9TkiRp8oz6rNi9wGVJNgMk2QZsBW6pqpuAEy5bSzJF78xYR+wkSdLANsJZsSNN7KrqKHAQ2NEUTQN76tS+vX8O3FRVX12v+CRJUneZ2K2PxelYmp+zp9juR05WN8nOJPNJ5mdmZoYMUZIkdc3xqoGuSTSOVbH7gauSbAe2VNWhlRokeSxwIb1Ru2VV1QywmNHVMBsUS5Kk7pnQXG0gIx+xq6pjwBywm1MfrXsR8IGq+vp6xSVJkjTpxjEVC72E7jzg2sWCJDcD1wGXJDm8ZFuUQaZsJUmSHmIjvGM3lg2Kq2ofSw4A7t/WZJn6z1nvmCRJUrc9cPz4uENYd+MasZMkSdIa80gxSZK0IUzqStdBZFLnkFfQyU5JktQRWbnK2vutD/3pQPnBT/6jZ48lzmE4YidJkjaEjg5mPUhnE7uvf+ozq277iO95GgsLJzzdbEVTU1MAQz9j3O1h9X1oy3cwTAxt6YP/FrvTB0njtQHyOhdPSJIkrYckj0ny4SR3Nj8fvUydv5vk40luS3JHktf03fu+JJ9McleSX0+y4tSwiZ0kSdoQxnCk2OvpnXN/LnBT83mpe4B/WFXnA38feH2Src29/wTsBM5trh0r/cGRJnZJ5pZsPEySXUmuTnJjkvuSfGDJ/UuSHGoy2VuSPGWUMUuSpG4YwwbFLwDe2fz+TuCFy8T0jaq6v/m4mSY3S3I2cGZV3Vq9YN61XPulRj1iN0vvFIl+i6dKvAV46TJt/hPwkiaT/T3gDesaoSRJ6qRBE7skO5PM9107B/yTj6+qe5q/fQ/wuOUqJXlSktuBLwJvrqojwBOBw33VDjdlJzXqxRN7gV9Ksrmq7k+yDdgK3FJVleQ5y7Qp4Mzm928DjowiUEmS1C2DTq9W1Qwwc7I6Sf4QeMIyt/71AH/ni8CzminY/Un2svyWMCt2YKSJXVUdTXKQ3hzx++mN1u2pk493vgq4IcnfAF8FfmD9I5UkSVpZVT3vRPeSfDnJ2VV1TzO1eu8KzzqS5A7gIuBPgXP6bp/DKQxujWPxRP907OI07Mn8DPCPq+oc4O3AVctV6h8unZk5aXItSZI2oBrwWgPXAy9rfn8ZvUGtB0lyTpItze+PBp4NfLaZul1I8gPNatgfW679UuPYx24/cFWS7cCWqjp0oopJzgLOq6r/1hTtAW5cru6S4dIaZh87SZLUPWM4UuxNwO8neSXwBeBFAEkuAF5TVa8Cngb8SpKiN/36H6vqk037HwfeAWwB/qC5TmrkiV1VHUsyB+xm5dG6rwDfluS7q+pzwPMBMzZJkjSwUZ88UVVHgUuWKZ+n96oZVfVh4FknaD8PfM8gf3NcJ0/MAu+jb4VskpuBpwJnJDkMvLKqDiT5l8B7kxynl+i9YhwBS5Iktd1YEruq2seS1R5VddFJ6u4bRVySJKm7jh/v/plinT0rVpIkqd+op2LHwcROkiRtCGNYPDFyJnaSJGlD6H5aB+nosGQnOyVJUkcsd6rCuvv37/vwQPnBv/m/nz+WOIfR2RG7v/3LL6+67cOf8HgWFhZW3X5qagpg6GeMuz2svg9t+Q6GiaEtffDfon1YbC9pOA8cPz7uENZdZxM7SZKkfh2dpXwQEztJkrQhbITFE+M4K1aSJEnrYKSJXZK5JJcuKduV5OokNya5L8kHltx/bpJDST6V5J1JHGWUJEkDqxrsmkSjHrGbpe8YscZ0U/4W4KX9N5KcBrwTmK6q7wH+B/CyEcQpSZI6pqoGuibRqBO7vcBlSTYDJNkGbAVuqaqbgKVLxh4L3F9Vn2s+fxj4F6MJVZIkdcnxqoGuSTTSxK6qjgIHgR1N0TSwp06cFv8v4OFJLmg+/zDwpOUqJtmZZD7J/MzMzFqGLUmSNBHG8b7a4nTs+5ufrzhRxaqqJNPAW5tRvg8B3zxB3RlgMaOrYfaxkyRJ3TOpo3CDGEditx+4Ksl2YEtVHTpZ5aq6FbgIIMk/Ar57/UOUJEldM6nvzQ1i5NudVNUxYA7YTW/07qSSPK75uRl4HfCf1zM+SZLUTS6eWD+zwHnAtYsFSW4GrgMuSXK4b1uUK5N8Brgd+C9V9Ucjj1aSJE284zXYNYnGsidcVe1jyQHAVXXRCepeCVw5irgkSZImmZv9SpKkDWFSp1cHYWInSZI2hI2Q2KWjnexkpyRJ6oisXGXt/fQ79g2UH/zaFf98LHEOo7Mjdl/6ytJDLE7dEx89xcLC6ttPTU0BDP2McbeH1fehLd/BMDG0pQ/+W7QPa/UdSBtdRwezHmRcq2IlSZK0xjo7YidJktRvUrcwGYSJnSRJ2hCO1/Fxh7DuRjoVm2Sub+PhxbJdSW5IcmuSO5LcnuTyvvvfkeS/JbkzyZ4km0YZsyRJ6oaqwa5JNOp37GaB6SVl08CbgR+rqmcAO4BfTfKo5v6bgbdW1bnAV4BXjipYSZKkSTLqxG4vcFlz7itJtgFbgY9U1Z0AVXUEuBc4K0mA5zbtAN4JvHDEMUuSpA544Pjxga5JNNLErqqOAgfpjcpBb7RuT/WtP05yIbAJuBt4LHBfVX2zuX0YeOLoIpYkSV1RVQNdk2gc2530T8dON58BSHI28G7g5VV1nOU3MFz2m06yM8l8kvmZmZk1DlmSJE26jZDYjWNV7H7gqiTbgS1VdQggyZnAB4E3VNVHm7r/C3hUkoc1o3bnAEeWe2hVzQCLGV0Ns0GxJEnSJBr5iF1VHQPmgN00o3XNStd9wLuq6rq+ugX8MfDDTdHLgPePMl5JktQNx2uwaxKN6+SJWeA84Nrm84uBi4ErktzWXOc3914H/GySu+i9c/e7I49WkiRNPKdi10lV7aPv/bmquga45gR1Pw9cOKLQJElSRx1f/jX9TvGsWEmStCGMesQuyWOSfLg5ZOHDSR59krpnJvlSkt/sK5tL8tm+2czHrfQ3TewkSZLWx+uBm5pDFm5qPp/Ivwf+ZJnyl1TV+c1170p/sLNnxT7x0VNDtZ+aGq79Wjxj3O3bEIN98DtoSwxd6IO00R0f/YqIFwDPaX5/J73Fo69bWinJ9wGPB24ELhjmDzpiJ0mSNoRBp2L798htrp0D/snHV9U9zd++B3jIVGqS04BfAa48wTPe3kzD/pvmRK6T6uyI3Te+cHjVbTc9+RwWFla/D97i/6se9hnjbg+r70NbvoNhYmhLH/y3aB/a8h1Ik27QAbsle+QuK8kfAk9Y5ta/PsU/8xPADVX1xWXytpdU1ZeSTAHvBV4KvOtkD+tsYidJkrTequp5J7qX5MtJzq6qe5rTtZZ7R+4fABcl+QngDGBTkmNV9fqq+lLzNxaS/B69XUJOmtg5FStJkjaEMexjdz29wxXgBIcsVNVLqurJVbUN+Dl6hzW8PsnDknw7QJKHA5cBn1rpD440sWuW7V66pGxXkhuS3JrkjiS3J7m87/5rk9yVpBY7KEmSNKga8D9r4E3A85PcCTy/+UySC5L8zgptNwMHktwO3AZ8Cfjtlf7gqKdiZ4Fp4EBf2TS9FSJHqurOJFuBjyc5UFX3AX8KfIDeShJJkqRVOT7i0ySq6ihwyTLl88Crlil/B/CO5vf/DXzfoH9z1IndXuCXkmyuqvuTbAO2Ah9pzoWlqo4kuRc4C7ivqv4M4BQWgkiSJJ3QpB4TNoiRTsU2metBYEdTNA3sqb5vOsmFwCbg7lHGJkmSNOnGsXhicTqW5ufs4o1mxci7gZdX1fFBHtq/18zMzElXJkuSpA3oeA12TaJxbHeyH7gqyXZgS1Udgt4ZacAHgTdU1UcHfeiSvWZqmH3sJElS92yEqdiRJ3ZVdSzJHLCbZrQuySZgH70lvteNOiZJktR9GyGxG9c+drPAecC1zecXAxcDVzTHZtyW5HyAJD+V5DBwDnD7KSwPliRJeojjVQNdk2gsJ09U1T4gfZ+vAa45Qd1fB359RKFJkqSOmtRkbRAeKSZJkjaEjTAVa2InSZI2hA2Q15GOZq+d7JQkSR0xllMH/vEvzwyUH9zw8zsn7nQER+wkSdKG4Dt2E+zYn/zpqtue8YPPZmFhYdXtp6amAIZ+xrjbw+r70JbvYJgY2tIH/y3ah7Z8B8PEsNheGqeOzlI+SGcTO0mSpH6O2EmSJHXERhixG9cGxZIkSVpjI03skswluXRJ2a4kNyS5NckdSW5Pcnnf/fck+WySTyXZneTho4xZkiR1Q9Vg1yQa9YjdLDC9pGwaeDPwY1X1DGAH8KtJHtXcfw/wVOCZwBbgVSOKVZIkdchGOFJs1IndXuCyJJsBkmwDtgIfqao7AarqCHAvcFbz+YZqAAfpnRkrSZI0kKoa6JpEI108UVVHkxykNyr3fnqjdXuq79tLciGwCbi7v20zBftS4KeXe3aSncBOgLe97W386N97xrr0QZIkTaa5f/faidtweFDjWBW7OB27mNi9YvFGkrOBdwMvq6rjS9pdTW9k7+blHlpVM8DM4sdh9rGTJEmaRONYFbsfuCTJdmBLVR0CSHIm8EHgDVX10f4GSf4tvanZnx11sJIkSZNi5CN2VXUsyRywm97oHUk2AfuAd1XVdf31k7wKuBS4ZJlRPEmSJDXGtY/dLHAecG3z+cXAxcAVSW5rrvObe/8ZeDxwa1P+C6MPV5Ikqf3GcvJEVe0D0vf5GuCaE9T1dAxJkqRT4MkTkiRJHWFiJ0mS1BGZ1A34VtDJTkmS1BGd309uXDr7/to3/scXV9120999EgsLC6tuPzU1BTD0M8bdHlbfh7Z8B8PE0JY++G/RPrTlOxgmhrXqg6STcypWkiSpI0zsJEmSOsLETpIkqSNGmtglmUty6ZKyXUluSHJrkjuS3J7k8r77v5vkE0353iRnjDJmSZKkSTHqEbtZYHpJ2TTwZuDHquoZwA7gV5M8qrn/M1V1XlU9C/gC8NqRRStJkjRBRp3Y7QUuS7IZIMk2YCvwkaq6E6CqjgD3Amc1n7/a1A2wBbcykSRJWtZIE7uqOgocpDcqB73Ruj3Vt5lekguBTcDdfWVvB/4SeCrwGyMLWJIkaYKMY/FE/3TsdPMZgCRnA+8GXl5VxxfLq+rl9Eb2PgNczjKS7Ewyn2R+ZmZmvWKXJElqrXFsULwfuCrJdmBLVR0CSHIm8EHgDVX10aWNquqBJHuAK4G3L3N/BljM6GqYDYolSZIm0chH7KrqGDAH7KYZrUuyCdgHvKuqrlusm56nLP4O/FPgv486ZkmSpEkwriPFZoH38a0p2RcDFwOPTXJFU3YFcDvwzmY0L8AngB8faaSSJEkTYiyJXVXto+8A4Kq6BrjmBNWfPZKgJEmSJpwnT0iSJHWEiZ0kSVJHpG8LuS7pZKckSeqIrFxFq+GInSRJUkeMa1XsultYWFh126mpqaHbtyGGcfahLd/BMDG0pQ/+W7QPbfkOhomhTX2QuswRO0mSpI4wsZMkSeqIkSZ2SeaSXLqkbFeSG5LcmuSOJLcnech5sEl+I8mx0UUrSZI0WUb9jt0svdMmDvSVTQOvA45U1Z1JtgIfT3Kgqu4DSHIB8KgRxypJkjRRRj0Vuxe4LMlmgCTbgK3AR6rqToCqOgLcC5zV1DkdeAvwr0YcqyRJ0kQZaWJXVUeBg8COpmga2FN9m+kluRDYBNzdFL0WuL6q7hllrJIkSZNmHIsnFqdjaX7OLt5IcjbwbuDlVXW8mZZ9EfAbKz00yc4k80nmZ2Zm1iFsSZKkdhvHPnb7gauSbAe2VNUhgCRnAh8E3lBVH23qfi/wFOCuJACPTHJXVT1l6UOragZYzOhqmL2OJEmSJtHIE7uqOpZkDthNM1qXZBOwD3hXVV3XV/eDwBMWPyc5tlxSJ0mSpPHtYzcLnAdc23x+MXAxcEWS25rr/DHFJkmSNJHGcqRYVe2j7wDgqroGuOYU2p2xnnFJkiRNMk+ekCRJ6ggTO0mSpI5I3xZyXdLJTkmS1BFZuYpWwxE7SZKkjhjL4olReM+fHlp125c8ezvD7IM3NTUFMPQzxt0eVt+HtnwHw8TQlj74b9E+tOU7GCaGtvRhLb4Dqc0csZMkSeoIEztJkqSOMLGTJEnqiJEmdknmkly6pGxXkhuS3JrkjiS3J7m87/47kvy5J1JIkiSd3KgXT8wC08CBvrJp4HXAkaq6M8lW4ONJDlTVfU2dK6tq74hjlSRJmiijnordC1yWZDNAkm3AVuAjVXUnQFUdAe4FzhpxbJIkSRNtpIldVR0FDgI7mqJpYE/17ZKc5EJgE3B3X9M3NlO0b11MCpdKsjPJfJL5mZmZdeqBJElSe41j8cTidCzNz9nFG0nOBt4NvLyqjjfFPw88Ffh+4DH0pm0foqpmquqCqrpg586d6xW7JElSa40jsdsPXJJkO7Clqg4BJDkT+CDwhqr66GLlqrqneu4H3g5cOIaYJUmSWm/kiV1VHQPmgN00o3VJNgH7gHdV1XX99ZtRPJIEeCHwqVHGK0mSNCnGdaTYLPA+vjUl+2LgYuCxSa5oyq6oqtuA9yQ5i96BwbcBrxlxrJIkSRNhLIldVe2jl6gtfr4GuOYEdZ87qrgkSZImmSdPSJIkdYSJnSRJUkekbwu5LulkpyRJ6oisXEWrMa7FE+vunr8+tuq2Z3/bGSwsLKy6/dTUFMDQzxh3e1h9H9ryHQwTQ1v64L9F+9CW72CYGNrShzb8W5TWk1OxkiRJHWFiJ0mS1BEmdpIkSR0x0sQuyVySS5eU7UpyQ5Jbk9yR5PYkl/fdT5I3Jvlcks8k+alRxixJkjQpRr14YpbeaRMH+sqmgdcBR6rqziRbgY8nOVBV9wFXAE8CnlpVx5M8bsQxS5IkTYRRT8XuBS5LshkgyTZgK/CRqroToKqOAPcCZzVtfhz4xao63ty/d8QxS5IkTYSRJnZVdRQ4COxoiqaBPdW3mV6SC4FNwN1N0XcBlyeZT/IHSc4dZcySJEmTYhyLJxanY2l+zi7eSHI28G7g5YsjdMBm4OtVdQHw28Du5R6aZGeT/M3PzMysW/CSJEltNY4NivcDVyXZDmypqkMASc4EPgi8oao+2lf/MPDe5vd9wNuXe2hVzQCLGV0Ns0GxJEnSJBr5iF1VHQPm6I28zQIk2UQvaXtXVV23pMl+4LnN7z8IfG40kUqSJE2Wce1jNwucB1zbfH4xcDFwRZLbmuv85t6bgH+R5JPALwOvGnm0kiRJE2AsZ8VW1T76DgCuqmuAa05Q9z7gn4woNEmSpInlyROSJEkdYWInSZLUEenbQq5LOtkpSZI6IitX0WqM5R27UVhYWFh126mpqaHbtyGGcfahLd/BMDG0pQ/+W7QPbfkOhomhLX3owr9F6WScipUkSeoIEztJkqSOMLGTJEnqiJEmdknmkly6pGxXkhuS3JrkjiS3J7m87/7NfZsWH0myf5QxS5IkTYpRL56YBaaBA31l08DrgCNVdWeSrcDHkxyoqvuq6qLFikneC7x/pBFLkiRNiFFPxe4FLkuyGSDJNmAr8JGquhOgqo4A9wJn9TdMMkXvzFhH7CRJkpYx0sSuqo4CB4EdTdE0sKf6NtNLciGwCbh7SfN/DtxUVV8dRaySJEmTZhyLJxanY2l+zi7eSHI28G7g5VV1fEm7H+mvu1SSnUnmk8zPzMyscciSJEntN44NivcDVyXZDmypqkMASc4EPgi8oao+2t8gyWOBC+mN2i2rqmaAxYyuhtkAUpIkaRKNfMSuqo4Bc8BumhG4JJuAfcC7quq6ZZq9CPhAVX19VHFKkiRNmnHtYzcLnAdc23x+MXAxcEXf1ibn99V/0JStJEmSHmosZ8VW1T76DgCuqmuAa05S/zkjCEuSJGmiefKEJElSR5jYSZIkdUT6tpDrkk52SpKkjsjKVbQajthJkiR1xFgWT4zCMPvYTU1NDd2+DTGMsw9t+Q6GiaEtffDfon1oy3cwTAxt6YP/FqdW3Vb/f3v3Hi5XVd5x/PsDEgKGcAlIiCjBKPVRSbgoyEUhwVSKF0AUiKDiDbVyba2tllqsfXyUWtSEqk9QQIJchBC0BhFLEyiFECAk4SpWUKEQLBhNsDYiefvH2odsDznnrDN7zux9Zn6f51nP2bNn3rXftWafmTX7Ojp4i52ZmZlZl/DAzszMzKxLdHRgJ2mJpDf1m3eGpGsl3SrpXkmrJB1Xev4wScuLixbfLOllnczZzMzMbLTo9Ba7y0h3kSg7HvgC8J6IeBVwOPBlSdsVz38NOCEi9gIuBc7qVLJmZmZmo0mnB3ZXAW+RtCWApCnAZOCmiPgJQEQ8BvwS2KmICWBCMb0t8FgH8zUzMzMbNTp6VmxEPCVpGWmr3HdJW+uuiNLF9CTtB4wFflrM+iBwraTfAWuB13UyZzMzM7PRoo6TJ8q7Y48vHgMgaRdgPvC+iNhQzD4TOCIidgUuBM7dVKWSTpZ0h6Q75s2bN2LJm5mZmTVVHdexuwY4V9I+wFYRsRxA0gRgEXBWRCwt5u0ETI+I24rYK4DrNlVpRMwD+kZ0UeU6P2ZmZmajUce32EXE08AS4AKKrXWSxgILgYsj4srSy9cA20rao3g8C7i/c9mamZmZjR513XniMuBqNu6SPRZ4AzBR0knFvJMiYoWkDwELJG0gDfTe3+lkzczMzEaDWgZ2EbGQ0g2AI+IS4JJBXruwQ6mZmZmZjVq+84SZmZlZl/DAzszMzKxLeGBnZmZm1iVUujZwN+nKRpmZmXUJDf0Sa0VdZ8WOuCrXsdtmm2244tYVLccfd8BeABx69nkt17Hk7FNYsOzuluOP2W9Pfrei9fit9toTgDO/dU1L8V9671EArLlsQcs5bD/7GH55zpyW41/4idMAWPXI6pbip714EgCP/+bplnPYZdvx3PPoEy3Hv3rXnSuvy1D9/6Fq/DOPtfYeAIyZnN6HutvQjveh1X7o64NH1qxtOYcXbz+hLf9Pv1u+sqX4rfaZDsDPnvp1yzlMmbgdd/289btK7r3b5La8j4/9uvXPhMnbja/+/7S69c+UMZN2Bqr/P1lzeVesmZmZWZfwwM7MzMysS3hgZ2ZmZtYlsgZ2ko6WFJJeMdIJDZLDGZK2rmv5ZmZmZk2Xu8VuNnAzG28BVoczAA/szMzMzAYw5MBO0njgIOADFAM7SYdKulHSdyQ9KOnzkk6QtEzS3ZKmFq/bTdINklYVf19SzL9I0jtKy3i6VO8SSVdJekDSt5WcBkwGFkta3PZeMDMzM+sCOVvsjgKui4gHgV9J2qeYPx04HdgTeDewR0TsB3wDOLV4zXnAxRExDfg2kHOu/d6krXOvBF4KHBQRc4DHgBkRMSOrZWZmZmY9JmdgNxu4vJi+vHgMcHtEPB4R64GfAtcX8+8GphTTBwCXFtPzgYMzlrcsIh6NiA3AilJdg5J0sqQ7JN0xb968nBAzMzOzrjLoBYolTQRmAq+WFMDmpLs6XAusL710Q+nxhkHq7bsjxB8oBpWSBIwtvaZc77ND5fhcxRHzgL4RXVS5+KKZmZnZaDTUFrt3kHal7hYRUyLixcDD5G15A7iFjSdcnEA6AQPgZ8C+xfSRwJiMutYBvty1mZmZ2QCGGtjNBhb2m7cAeFdm/acB75O0inQc3unF/POBQyQtA/YHfptR1zzgBz55wszMzGzTBt3NGRGHbmLeHPqdBFF+XUQsAZYU0z8j7crtX8cTwOtKsz7ZP7Z4fEppei4wd7B8zczMzHqZ7zxhZmZm1iU8sDMzMzPrEoqIoV81+nRlo8zMzLqE6k6gW3XrFjsNViR9eKjXjHQddcc3IQe3oRk51B3fhBzchmbk4Db0VB/YCOnWgd1QTm5AHXXHNyEHt6EZOdQd34Qc3IZm5OA2uA+sol4d2JmZmZl1HQ/szMzMzLpErw7s2nEz2ap11B3fhBzchmbkUHd8E3JwG5qRg9vgPrCKuvWsWDMzM7Oe06tb7MzMzMy6jgd2ZmZmZl2ipwZ2kl5Qdw5mZmZmI6UnBnaSDpR0H3B/8Xi6pK+2od5Zma+bIGnqJuZPy4yfJGlSMb2TpLdLetXwsn1enZ+rELt7kcMrMl//EknjimlJep+kuZI+KmmLzDre1ldHhbzfIOlPiumDJX1c0puHET9e0jsknSnpVEmHS8r+H5K0haQPS7pO0ipJKyX9QNJHJI1ppU2dJmlrSZ+Q9FeSxkk6SdL3JJ0jaXyLdT7Y7jybTNJLJV0g6R+Ldep8SfdIulLSlA7l4HVx03V6XWxhXZR0Q84864yeGNgBXwLeBDwFEBErgTe0od5vDvUCSccCDwALJN0r6bWlpy/KiP8wcCuwVNJHge8DbwGulvSBnCQlzelX5gJ/3vc4I/6a0vSRwL8DbwW+K+mkjBSuZeO69nngzcBtwGvJP3vqCuBRSfMlHSFp88y4vry/XCx7vqTPAucAWwFnSvqnjPhjgcXA4cApwH7Au4EVkvbMTGM+sBdwNnAEqR8+A0wHLhlOezaR35D9KGnz4sv8s5IO6vfcWZmLugjYGdgdWAS8Bvgi6UryX8vIYZ2ktUVZJ3zhuqEAAAtkSURBVGkdMLVvfkb8tNL0GElnFV/mn5O0dUb8KZJ2LKZfJukmSb+WdFvu+yjpakkntjp4IPXh7cDTwFLS58OfAdcBF2QsfzNJ75e0qBiQ3SnpckmHDiMHr4s1r4tFXKX1sQHr4jhJOwA7Stpe0g5FmQJMbjEnqyoiur4AtxV/7yrNW5kZ+70Byr8Cv82IXwHsUkzvR/rHeXv/fAaJvxvYGphI+uebVMzfHliR2YZHSR/W7wHeW5T/6ZvOiC/32y3A7sX0jjn9CNxXmr4T2KyF9+Guos0fAm4AngC+DhySGX8v6QN/a2ANsHUxfwxwT0b8qlLMjsAPi+lpwC2ZOfx4kOcezIjfYYAyEXg0I/4bwKXAGcX7cG7pueWZbVhR/BWwmo1n1gtYlRE/F7gY2Lk07+GcZffPE/hn0hfTIaQfbxfnrAel6UXA0cX0ocB/Zubw38BVwK+A7wBHA2OH0Yby/9MvBnpukPgLSQOyg4EvA/8AzAL+DTjV6+LoWBfbsT42YF08HXgYWA88VEw/DKwETsnNw6W9pfYEOtLItOIfCCwHxgIfBy7PjF1D+jV7SL9yKPBERvw9/R7vUnyQnZbzAcYgg9Gcf7zidROKL4BLgRcV8x4aRv+VP8CWDTcH4IfAzGJ6AbBbMT2xf5tycigeTyr68Fbgkdz3ARhXvKdbFY83pzTwHCT+7tIXx1b93pchB4bF65YC7+SPB7abAcdR/PgYIv7Zfh+eD5ce/z4jflVpegvS1tKrgS2HsS6tKE1f0O+53PdyX9JW39OK9g9nXSz3+wpgTDGd+2X+49L07QP1T04OwDakrbbXkn4oXQj8aUb8ncAepC3WTwKvKea/LLMNq/o9Xlr83RK43+vi6FgX27E+1r0ulurJ+kHh0pmSdXxTF/gI8BXgRaStV9cDH8uMXQr8b0Tc2P8JST/OiF8raWpE/BQgIh4vdplcA+QcJ/espDER8QxpgNm37HFk7kqPiLXAGZL2BS6RtCg3tjCt2DUhYJykSRGxWtJY0sBoKB8ELpZ0NvAb0u7Lvi1wfzGMPJ4TEauBOcAcSbtlhCySdDPpi+MbwHckLSUN0m/KiQeuk3QjaVfFlQDFbojcG1ofD3wB+KqkNcW87Ui7eI/PiH8IOCwiftH/CUmPZMSP7ZuIiD8AJ0v6NOmLLXdXzh2SxkfE0xHx/tLypwLrciqIiDslvZG0S/tG0mA717aSjiatv1sW/xdEREiKjPirJF1E2sq1UNIZpAHFYcDz+nWgJhTLXEfapTm/WA+OBf6G9PkymE+QtvhvAI4CPilpOukH2Icylv9M32eKpH2A3xf5rM/sA/C62LfsOtdFqL4+1r0uUix/rqQDgSmwcVwRERfn1mFtVPfIsukF+BfgoArxi4DXb2L+GOCEjPgLgIM3Mf9FwBszczgPOLCYFmlQe0nVPiB9ERyQufyDgFcCRwLHAPtT2lqQUcd9fW2o8D6+Hti/eDyVtOX22Jw8ivi/A/6y3O9s/FAfbj4TgR2HGfMxYPoAzw35i5m0O/7wTcz/IPBMq31bqkctxOwCHDGM11/Yr+xczJ8E3JBZx0mkYzyfJA0A7gM+B2ybGX9T1b7aRJ07AptnvnYm6Uv/QdIWsr51eifgHK+Lo2ddrLo+1r0ulmLmkw7T+SppF/dcYE67c3PJKz1x5wlJuwOn8vxfE2/LiD2d9At2F9IB/JdFxIphLLvW+Cbk0GVtmAxcPtz4jPpnRcSP2lVfHaq2oRv6oKrcPpAkYGJEPFlhWROAnaLYm1CaPy0iVrVabydVbUM39EFV7egDSfcDr4xeGFCMBnWPLDtRSAdyngbMoHSc3DDr2A34a9JB/PcDnwb2qBj/8k4tfwTb0LH4JuTQjjYMUO8vKsbPqjO+TW3ohj6omkNH+oC0pfox0rFh9wKvLT2Xe/LCBGDqJuZP61B8pTY0oQ/q7sd29EHx2ispThJ0qb/UnkBHGplxMPAw69u7+GJ/djTGNyGHXmwDFc+wHqLujgyqqrahG/qgah1N6AOqn63fhEFV1TbU2gdN6MeqfVCqZzHppLQfltfpKuuyS+ulV06e+IqkvycdSLq+b2ZELM+tQOminYeTdscdRjrQ9jOjJb4JObgNvB44kXTZmj+qlvShOtSyvzfQU6TjpEY0vlCpDVXjm9AHbaij1j4obBERjwNExDJJM4DvS9qV4oD8IXwK2DfSyWD7kQ7a/1REXE3eyURV49vRhrr7oB11VI2v2gd9zh7Ga22E9crAbk/SqeAzSWf/QFppZw4VqHR3idmkM1KXkY6vOjkifpuz4Lrjm5CD2/CcqmdY1zqoKlRtQzf0QdU66u4DqH62ft2Dqna0oe4+aEcdVeOr9gFF3PPWZatR3ZsMO1FIm5ezL9rYL3Yx6bTvHUZjfBNycBueq6PqGdY/AGYM8NyQZ8dVjW9TG7qhD6rmUGsfFK+rerb+LfQ7rot0LbUbgPUjHd+mNtTaB03ox6p9UHr9OmBtUf6PdJ3DtbnxLu0tvbLFbiXp0hy/HG5gRMyosuC645uQg9vwnAeBL0pq9ezghyiuWbaJ/HJukVc1Hqq3oRv6oGoddfcBpMNSzumfQ6RrsX07I34N6Qzx586kjIh1kg4nHfc10vHtaEPdfdCOOqrGV+2DvmVuU34s6Sjytx5bm/XK5U6WkG79dDt/fIzdkJc7MWs3pQsqH1+UccBlpDuhDHoD8rov+dKONlSNb0IftKsf6+qDjBwui4ifjGQOTWhD1fgmrEsjvC5m9+EAdS6NiNe1Gm+t65WB3SGbmh8+LsBqJmlv0kWop0VEzl08ahtUDVLfsNtQNb4JfdDOfqyjD0YwhxEfVA1SX1PWxWG1oUn92GIfvL30cDPgNaRLih0w3OVbG9S9L9jFpdcK6fiVt5J2dawm/dI+qsW6arnkS9U2dEMfVK2jCX3QhBzqbkOT+qCufmxDH15YKucDfwu8sNU+cKlWak9gRBsHNxd/ywd2ru17XHd+Lr1VgFmkX8JPkK5ZdgLwghbqqe2LrGobuqEPqtbRhD5oQg51t6EpfVBnP7arD1yaVWpPYEQbN4wLLLq4jHSh+pm5tX+RtaEN3dAHVXOotQ+akEND2tCE96HWfqzaB6V6dgUWkk5QfAJYAOxapU6X1ktXH2MnaXlE7FN3HmbtIGkxcCmwICJ+1en4JmhCH9Tdj3Uvvx05NKENVTVhXWpKP0r6UZHH/GLWiaTLpcyqK6de1u0Du0eBcwd6PiIGfM7MzMyGJmlFROw11DzrjG6/jt3mwHjyb+9iZmZmw/OkpBNJZ+NCukvPUzXm09O6fYudd8WamZmNIEkvAc4DDiDdyuwW4PSI+HmtifWobh/Y3RURe9edh5mZmVkndPvAbofRemCumZnZaCBpd+BUYAqlQ7zCd3eqRVcP7MzMzGxkSVoJfBO4G9jQNz98d6daeGBnZmZmLZN0W0TsX3celnhgZ2ZmZi2T9C7g5cD1wPq++RGxvLakeli3X+7EzMzMRtaewLuBmWzcFRvFY+swb7EzMzOzlkl6AJgWEb+vOxeDzepOwMzMzEa1lcB2dSdhiXfFmpmZWRU7Aw9Iup2Nx9hFRBxZY049y7tizczMrGWSDik/BA4GZkfEq2pKqad5V6yZmZm1rLhe3W+ANwMXAYcBX68zp17mXbFmZmY2bJL2AI4HZgNPAVeQ9gTOqDWxHuddsWZmZjZskjYA/wF8ICL+q5j3UES8tN7Mept3xZqZmVkrjgFWA4slnS/pMNIxdlYjb7EzMzOzlkl6AXAUaZfsTOBbwMKIuL7WxHqUB3ZmZmbWFpJ2AN4JHBcRvvNEDTywMzMzM+sSPsbOzMzMrEt4YGdmZmbWJTywMzMzM+sSHtiZmZmZdQkP7MzMzMy6xP8DVo0yY9b4vbkAAAAASUVORK5CYII=\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "corr = X.corr()\n", + "\n", + "mask = np.zeros_like(corr, dtype=np.bool)\n", + "mask[np.triu_indices_from(mask)] = True\n", + "\n", + "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n", + "\n", + "# Draw the heatmap with the mask and correct aspect ratio\n", + "f, ax = plt.subplots(figsize=(11, 9))\n", + "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,\n", + " square=True, linewidths=.5, cbar_kws={\"shrink\": .5})\n", + "plt.savefig('heat_map.png', dpi=600)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Logisitc Regression with Sklearn\n", + "\n", + "Short reminder of Logistic Regression:\n", + "\n", + "In Logisitic Regression the logits (logs of the odds) are assumed to be a linear function of the features\n", + "\n", + "$$L=\\log(\\frac{P(Y=1)}{1-P(Y=1)}) = \\beta_0 + \\sum_{i=1}^n \\beta_i X_i. $$\n", + "\n", + "Solving this equatation for $p=P(Y=1)$ yields to\n", + "\n", + "$$ p = \\frac{\\exp(L)}{1-\\exp(L)}.$$\n", + "\n", + "The parameters $\\beta_i$ can be derived by Maximum Likelihood Estimation (MLE). The likelihood for a given $m$ observation $Y_j$ is\n", + "\n", + "$$ lkl = \\prod_{j=1}^m p^{Y_j}(1-p)^{1-Y_j}.$$\n", + "\n", + "To find the maximum of the likelihood is equivalent to the minimize the negative logarithm of the likelihood (loglikelihood).\n", + "\n", + "$$ -llkh = -\\sum_{j=1}^m Y_j \\log(p) + (1-Y_j) \\log(1-p),$$\n", + "\n", + "which is numerical more stable. The log-likelihood function has the same form as the cross-entropy error function for a discrete case.\n", + "\n", + "So finding the maximum likelihood estimator is the same problem as minimizing the average cross entropy error function.\n", + "\n", + "In SciKit-Learn uses by default a coordinate descent algorithm to find the minimum of L2 regularized version of the loss function (see. http://scikit-learn.org/stable/modules/linear_model.html#logistic-regression).\n", + "\n", + "The main difference between L1 (Lasso) and L2 (Ridge) regulaziation is, that the L1 prefer a sparse solution (the higher the regulazation parameter the more parameter will be zero) while L2 enforce small parameter values." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Train the model\n", + "\n", + "### Training and test set\n", + "\n", + "First we split our data set into a train and a validation set by using the function train_test_split. The model performace " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(42)\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Model definition\n", + "\n", + "As preperation we standardize our features to have zero mean and a unit standard deviation. The convergence of gradient descent algorithm are better. We use the class `StandardScaler`. The class *StandardScaler* has the method `fit_transform()` which learn the mean $\\mu_i$ and standard deviation $\\sigma_i$ of each feature $i$ and return a standardized version $\\frac{x_i - \\mu_i}{\\sigma}$. We learn the mean and sd on the training data. We can apply the same standardization on the test set with the function *transform()*.\n", + "\n", + "\n", + "The logistic regression is implemented in the class `LogisticRegression`, we will use for now the default parameterization. The model can be fit using the function `fit()`. After fitting the model can be used to make predicitons `predict()` or return the estimated the class probabilities `predict_proba()`.\n", + "\n", + "We combine both steps into a Pipeline. The pipline performs both steps automatically. When we call the method `fit()` of the pipeline, it will invoke the method `fit_and_transform()` for all but the last step and the method `fit()` of the last step, which is equivalent to:\n", + "\n", + "```python\n", + "lr.fit(scaler.fit_transform(X_train), y_train)\n", + "```\n", + "\n", + "or visualized as a dataflow:\n", + "\n", + "```X_train => scaler.fit_transform(.) => lr.fit(., y_train)```\n", + "\n", + "If we invoke the method `predict()` of the pipeline its equvivalent to\n", + "\n", + "\n", + "```python\n", + "lr.predict(scaler.transform(X_train))\n", + "```\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "scaler = StandardScaler()\n", + "lr = LogisticRegression()\n", + "model1 = Pipeline([('standardize', scaler),\n", + " ('log_reg', lr)])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the next step we fit our model to the training data" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Pipeline(memory=None,\n", + " steps=[('standardize', StandardScaler(copy=True, with_mean=True, with_std=True)), ('log_reg', LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", + " intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n", + " penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n", + " verbose=0, warm_start=False))])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model1.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "### Training score and Test score" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training accuracy: 99.9237 %\n", + "Training AUC: 98.0664 %\n" + ] + } + ], + "source": [ + "y_train_hat = model1.predict(X_train)\n", + "y_train_hat_probs = model1.predict_proba(X_train)[:,1]\n", + "train_accuracy = accuracy_score(y_train, y_train_hat)*100\n", + "train_auc_roc = roc_auc_score(y_train, y_train_hat_probs)*100\n", + "print('Training accuracy: %.4f %%' % train_accuracy)\n", + "print('Training AUC: %.4f %%' % train_auc_roc)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training accuracy: 99.9199 %\n", + "Training AUC: 97.4810 %\n" + ] + } + ], + "source": [ + "y_test_hat = model1.predict(X_test)\n", + "y_test_hat_probs = model1.predict_proba(X_test)[:,1]\n", + "test_accuracy = accuracy_score(y_test, y_test_hat)*100\n", + "test_auc_roc = roc_auc_score(y_test, y_test_hat_probs)*100\n", + "print('Training accuracy: %.4f %%' % test_accuracy)\n", + "print('Training AUC: %.4f %%' % test_auc_roc)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.9994 0.9998 0.9996 71089\n", + " 1 0.8500 0.6018 0.7047 113\n", + "\n", + "avg / total 0.9991 0.9992 0.9991 71202\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, y_test_hat, digits=4))" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fpr, tpr, thresholds = roc_curve(y_test, y_test_hat_probs, drop_intermediate=True)\n", + "\n", + "f, ax = plt.subplots(figsize=(9, 6))\n", + "_ = plt.plot(fpr, tpr, [0,1], [0, 1])\n", + "_ = plt.title('AUC ROC')\n", + "_ = plt.xlabel('False positive rate')\n", + "_ = plt.ylabel('True positive rate')\n", + "plt.style.use('seaborn')\n", + "\n", + "plt.savefig('auc_roc.png', dpi=600)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/LogisticRegression_Part1.ipynb b/LogisticRegression_Part1.ipynb new file mode 100644 index 0000000..40900af --- /dev/null +++ b/LogisticRegression_Part1.ipynb @@ -0,0 +1,326 @@ +{ + "cells": [ + { + "metadata": { + "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5", + "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", + "trusted": false, + "collapsed": true + }, + "cell_type": "code", + "source": "# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np\nimport pandas as pd\nimport tensorflow as tf\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.metrics import roc_curve, roc_auc_score, classification_report, accuracy_score, confusion_matrix \nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nimport os\nprint(os.listdir(\"../input\"))\n\n# Any results you write to the current directory are saved as output.", + "execution_count": null, + "outputs": [] + }, + { + "metadata": { + "_uuid": "6d3b41843580ae037846b7079f2fd79d7add5147", + "_cell_guid": "41650b3a-dff8-4d14-9595-6b3fb4ccdea7" + }, + "cell_type": "markdown", + "source": "## Load data and visualize the data" + }, + { + "metadata": { + "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a", + "collapsed": true, + "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0", + "trusted": false + }, + "cell_type": "code", + "source": "credit_card = pd.read_csv('../input/creditcard.csv')", + "execution_count": null, + "outputs": [] + }, + { + "metadata": { + "_uuid": "c4e55ed51d60613c4f19457af0131b4fb973fad9", + "_cell_guid": "fd1b18c4-d0bf-4d81-b784-3fcec2578c12", + "trusted": false, + "collapsed": true + }, + "cell_type": "code", + "source": "f, ax = plt.subplots(figsize=(7, 5))\nsns.countplot(x='Class', data=credit_card)\n_ = plt.title('# Fraud vs NonFraud')\n_ = plt.xlabel('Class (1==Fraud)')", + "execution_count": null, + "outputs": [] + }, + { + "metadata": { + "_uuid": "5b132aff2b6d28b35e2c0ff256a3f97ff74f3cb3", + "_cell_guid": "19175b74-8157-418c-b6b0-6c2a1b719814" + }, + "cell_type": "markdown", + "source": "As we can see we have mostly non-fraudulent transactions. Such a problem is also called inbalanced class problem.\n\n99.8% of all transactions are non-fraudulent. The easiest classifier would always predict no fraud and would be in almost all cases correct. Such classifier would have a very high accuracy but is quite useless." + }, + { + "metadata": { + "_uuid": "5ee8b7fcd59def3b33733b5bbfddf39709dfd077", + "_cell_guid": "0c90460f-5a34-46a0-bd6f-88e699b27d2f", + "trusted": false, + "collapsed": true + }, + "cell_type": "code", + "source": "base_line_accuracy = 1-np.sum(credit_card.Class)/credit_card.shape[0]\nbase_line_accuracy", + "execution_count": null, + "outputs": [] + }, + { + "metadata": { + "_uuid": "8ecd2b9433bc7759f08a3c5c08627a57f7a2a2e3", + "_cell_guid": "0f4090aa-ccfc-442f-ada5-03466fccbb59" + }, + "cell_type": "markdown", + "source": "For such an inbalanced class problem we could use over or undersampling methods to try to balance the classes (see inbalance-learn for example: https://github.com/scikit-learn-contrib/imbalanced-learn), but this out of the scope of todays post. We will come back to this in a later post.\n\nAs accuracy is not very informative in this case the AUC (Aera under the curve) a better metric to assess the model quality. The AUC in a two class classification class is equal to the probability that our classifier will detect a fraudulent transaction given one fraudulent and genuiune transaction to choice from. Guessing would have a probability of 50%." + }, + { + "metadata": { + "_uuid": "16728c540fc206821df972fcd071646f48b385e5", + "collapsed": true, + "_cell_guid": "3de8033f-5ddb-4a12-9230-c1cd01ab68e2", + "trusted": false + }, + "cell_type": "code", + "source": "X = credit_card.drop(columns='Class', axis=1)\ny = credit_card.Class.values", + "execution_count": null, + "outputs": [] + }, + { + "metadata": { + "_uuid": "7fd243cb53c6126ef7001ac3a8c92649b6effd94", + "_cell_guid": "cacba1af-23cd-4a35-9603-c904387956b4" + }, + "cell_type": "markdown", + "source": "Due to the construction of the dataset (PCA transformed features, which minimizes the correlation between factors), we dont have any highly correlated features. Multicolinearity could cause problems in a logisitc regression.\n\nTo test for multicolinearity one could look into the correlation matrix (works only for non categorical features) or run partial regressions and compare the standard errors or use pseudo-R^2 values and calculate Variance-Inflation-Factors.\n\n" + }, + { + "metadata": { + "_uuid": "5c3a98163d12601711b33425f479edfb37a92fca", + "_cell_guid": "ff310572-91fe-40d7-8370-89088320d9f0", + "trusted": false, + "collapsed": true + }, + "cell_type": "code", + "source": "corr = X.corr()\n\nmask = np.zeros_like(corr, dtype=np.bool)\nmask[np.triu_indices_from(mask)] = True\n\ncmap = sns.diverging_palette(220, 10, as_cmap=True)\n\n# Draw the heatmap with the mask and correct aspect ratio\nf, ax = plt.subplots(figsize=(11, 9))\nsns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,\n square=True, linewidths=.5, cbar_kws={\"shrink\": .5})", + "execution_count": null, + "outputs": [] + }, + { + "metadata": { + "_uuid": "be916ae070cd9df31f67306364b5faa00de99b79", + "_cell_guid": "65c81b41-418d-47fa-a484-98e643aaf208" + }, + "cell_type": "markdown", + "source": "## Logisitc Regression with Sklearn\n\nShort reminder of Logistic Regression:\n\nIn Logisitic Regression the logits (logs of the odds) are assumed to be a linear function of the features\n\n$$L=\\log(\\frac{P(Y=1)}{1-P(Y=1)}) = \\beta_0 + \\sum_{i=1}^n \\beta_i X_i. $$\n\nSolving this equatation for $p=P(Y=1)$ yields to\n\n$$ p = \\frac{\\exp(L)}{1-\\exp(L)}.$$\n\nThe parameters $\\beta_i$ can be derived by Maximum Likelihood Estimation (MLE). The likelihood for a given $m$ observation $Y_j$ is\n\n$$ lkl = \\prod_{j=1}^m p^{Y_j}(1-p)^{1-Y_j}.$$\n\nTo find the maximum of the likelihood is equivalent to the minimize the negative logarithm of the likelihood (loglikelihood).\n\n$$ -llkh = -\\sum_{j=1}^m Y_j \\log(p) + (1-Y_j) \\log(1-p),$$\n\nwhich is numerical more stable. The log-likelihood function has the same form as the cross-entropy error function for a discrete case.\n\nSo finding the maximum likelihood estimator is the same problem as minimizing the average cross entropy error function.\n\nIn SciKit-Learn uses by default a coordinate descent algorithm to find the minimum of L2 regularized version of the loss function (see. http://scikit-learn.org/stable/modules/linear_model.html#logistic-regression).\n\nThe main difference between L1 (Lasso) and L2 (Ridge) regulaziation is, that the L1 prefer a sparse solution (the higher the regulazation parameter the more parameter will be zero) while L2 enforce small parameter values." + }, + { + "metadata": { + "_uuid": "67c42133bf74c0f3aaa84e49456fe04a2f5e9d96", + "_cell_guid": "763a278e-91af-4fe6-a255-ddaea0bbd790" + }, + "cell_type": "markdown", + "source": "## Train the model\n\n### Training and test set\n\nFirst we split our data set into a train and a validation set by using the function train_test_split. The model performace " + }, + { + "metadata": { + "_uuid": "d19b826169ff464f82eba4e0b349bacc4361c8a0", + "collapsed": true, + "_cell_guid": "d54b91c2-fc78-4b24-a7c8-5b1a974cdf36", + "trusted": false + }, + "cell_type": "code", + "source": "np.random.seed(42)\nX_train, X_test, y_train, y_test = train_test_split(X, y)", + "execution_count": null, + "outputs": [] + }, + { + "metadata": { + "_uuid": "d1ea1658ffca81f4f9c83e9759b90f1311ce7a71", + "_cell_guid": "88a09d8f-4238-4560-a180-17d048ae5dd7" + }, + "cell_type": "markdown", + "source": "### Model definition\n\nAs preperation we standardize our features to have zero mean and a unit standard deviation. The convergence of gradient descent algorithm are better. We use the class `StandardScaler`. The class *StandardScaler* has the method `fit_transform()` which learn the mean $\\mu_i$ and standard deviation $\\sigma_i$ of each feature $i$ and return a standardized version $\\frac{x_i - \\mu_i}{\\sigma}$. We learn the mean and sd on the training data. We can apply the same standardization on the test set with the function *transform()*.\n\n\nThe logistic regression is implemented in the class `LogisticRegression`, we will use for now the default parameterization. The model can be fit using the function `fit()`. After fitting the model can be used to make predicitons `predict()` or return the estimated the class probabilities `predict_proba()`.\n\nWe combine both steps into a Pipeline. The pipline performs both steps automatically. When we call the method `fit()` of the pipeline, it will invoke the method `fit_and_transform()` for all but the last step and the method `fit()` of the last step, which is equivalent to:\n\n```python\nlr.fit(scaler.fit_transform(X_train), y_train)\n```\n\nor visualized as a dataflow:\n\n```X_train => scaler.fit_transform(.) => lr.fit(., y_train)```\n\nIf we invoke the method `predict()` of the pipeline its equvivalent to\n\n\n```python\nlr.predict(scaler.transform(X_train))\n```\n\n\n\n" + }, + { + "metadata": { + "_uuid": "86ecda4d1700405da9ed0d8bc6f7d8eabc0f1ddb", + "collapsed": true, + "_cell_guid": "fb45b18e-9357-4a98-9d98-078f96c3a591", + "trusted": false + }, + "cell_type": "code", + "source": "scaler = StandardScaler()\nlr = LogisticRegression()\nmodel1 = Pipeline([('standardize', scaler),\n ('log_reg', lr)])", + "execution_count": null, + "outputs": [] + }, + { + "metadata": { + "_uuid": "7c6abdd6d0db2ffede02f01d2d3f1ff916de0eee", + "_cell_guid": "804537d0-b635-4e90-a88c-56fac8749e2e" + }, + "cell_type": "markdown", + "source": "In the next step we fit our model to the training data" + }, + { + "metadata": { + "_uuid": "bc769713962566efac8b2310f45d7ebe79dcb173", + "_cell_guid": "b8f46ca5-f69a-4755-abbf-9cc76723c133", + "trusted": false, + "collapsed": true + }, + "cell_type": "code", + "source": "model1.fit(X_train, y_train)", + "execution_count": null, + "outputs": [] + }, + { + "metadata": { + "_uuid": "dc612edede19c594a8f28e3c396f0797a98888ba", + "_cell_guid": "4c3a4e42-36ee-4814-b8b2-0513ed7404cb" + }, + "cell_type": "markdown", + "source": "### Training score and Test score\n\n`confusion_matrix()` returns the confusion matrix, C where $C_{0,0}$ are the true negatives (TN) and $C_{0,1}$ the false positives (FP) and vice-versa for the positives in the 2nd row. We use the function `accurary_score()` to calculate the accuracy our models on the train and test data. We see that the accuracy is quite high (99,9%) which is expected in such an unbalanced class problem. With the method `roc_auc_score()`can we get the area under the receiver-operator-curve (AUC) for our simple model." + }, + { + "metadata": { + "_uuid": "e4f22259d9018f233874d9e4afdb42ec882bd967", + "_cell_guid": "f708ecc2-9114-4b5e-86fe-bd8423aa622c", + "trusted": false, + "collapsed": true + }, + "cell_type": "code", + "source": "y_train_hat = model1.predict(X_train)\ny_train_hat_probs = model1.predict_proba(X_train)[:,1]\ntrain_accuracy = accuracy_score(y_train, y_train_hat)*100\ntrain_auc_roc = roc_auc_score(y_train, y_train_hat_probs)*100\nprint('Confusion matrix:\\n', confusion_matrix(y_train, y_train_hat))\nprint('Training accuracy: %.4f %%' % train_accuracy)\nprint('Training AUC: %.4f %%' % train_auc_roc)", + "execution_count": null, + "outputs": [] + }, + { + "metadata": { + "_uuid": "6c97c5b3180d8cc2cd7019137aae19d45f53906d", + "_cell_guid": "94f52985-d9ca-4fa3-ba67-84b1ab4f27c6" + }, + "cell_type": "markdown", + "source": "![](http://)Our model is able to detect 68 fraudulent transactions out of 113 (recall of 60%) and produce 12 false alarms (<0.02%) on the test data." + }, + { + "metadata": { + "_uuid": "a83a2229893813ad2986910d20f562b369b76eb4", + "_cell_guid": "ca356e72-46ac-4766-8640-11d644c4534f", + "trusted": false, + "collapsed": true + }, + "cell_type": "code", + "source": "y_test_hat = model1.predict(X_test)\ny_test_hat_probs = model1.predict_proba(X_test)[:,1]\ntest_accuracy = accuracy_score(y_test, y_test_hat)*100\ntest_auc_roc = roc_auc_score(y_test, y_test_hat_probs)*100\nprint('Confusion matrix:\\n', confusion_matrix(y_test, y_test_hat))\nprint('Training accuracy: %.4f %%' % test_accuracy)\nprint('Training AUC: %.4f %%' % test_auc_roc)", + "execution_count": null, + "outputs": [] + }, + { + "metadata": { + "_uuid": "c22dc7db8f1a4c9d133c74ec502cfe9614667be8", + "_cell_guid": "fddb2dfa-7826-40ce-b49d-6177fc659aa2" + }, + "cell_type": "markdown", + "source": "With the function `classification_report()` we print get the precision, recall per each class." + }, + { + "metadata": { + "_uuid": "258dda7a672ec39dd33f095dbc59cc42832f50b0", + "_cell_guid": "26394d30-80ec-4db0-929c-0a81585d4d66", + "trusted": false, + "collapsed": true + }, + "cell_type": "code", + "source": "print(classification_report(y_test, y_test_hat, digits=6))", + "execution_count": null, + "outputs": [] + }, + { + "metadata": { + "_uuid": "e0fed511c8db4728079d5210a3a6a97a5eadfb72", + "_cell_guid": "c715932b-cac8-4582-a866-dcc913a5b6d0" + }, + "cell_type": "markdown", + "source": "To visualize the Receiver-Operator-Curve we use the function `roc_curve`. The method returns the true positive rate (recall) and the false positive rate (probability for a false alarm) for a bunch of different thresholds. This curve shows the trade-off between recall (detect fraud) and false alarm probability.\n\nIf we classifiy all transaction as fraud, we would have a recall of 100% but also the highest false alarm rate possible (100%). The naive way to minimize the false alarm probability is to classify all transaction as legitime. **" + }, + { + "metadata": { + "_uuid": "9317259c2c241aa6cb00346b5b91bd485d7ec448", + "_cell_guid": "d5db8aad-8866-4639-9a1c-cd32e33e11a1", + "trusted": false, + "collapsed": true + }, + "cell_type": "code", + "source": "fpr, tpr, thresholds = roc_curve(y_test, y_test_hat_probs, drop_intermediate=True)\n\nf, ax = plt.subplots(figsize=(9, 6))\n_ = plt.plot(fpr, tpr, [0,1], [0, 1])\n_ = plt.title('AUC ROC')\n_ = plt.xlabel('False positive rate')\n_ = plt.ylabel('True positive rate')\nplt.style.use('seaborn')\n\nplt.savefig('auc_roc.png', dpi=600)", + "execution_count": null, + "outputs": [] + }, + { + "metadata": { + "_uuid": "9e1692cf8dd385592b20e30f229ff192a30ce5c7", + "collapsed": true, + "_cell_guid": "9d834f9c-5817-4df8-b4a4-e31103236daf" + }, + "cell_type": "markdown", + "source": "Our model classify all transaction with a fraud probability => 50% as fraud. If we choose the threshold higher, we could reach a lower false positive rate but we would also miss more fraudulent transactions. If we choose the thredhold lower we can catch more fraud but need to investigate more false positives.\n\nDepending on the costs for each error, it make sense to select another threshold.\n\nIf we set the threshold to 90% the recall decrease from 60% to 45%. while the false positve rate is the same. We can see that our model assign some non-fraudulent a very high probability to be fraud" + }, + { + "metadata": { + "_uuid": "dc371656eeeced96d8343a37312d92bf116eede0", + "_cell_guid": "a5a4d2a5-c86d-4e27-8245-08325644794d", + "trusted": false, + "collapsed": true + }, + "cell_type": "code", + "source": "y_hat_90 = (y_test_hat_probs > 0.90 )*1\nprint('Confusion matrix:\\n', confusion_matrix(y_test, y_hat_90))\nprint(classification_report(y_test, y_hat_90, digits=6))\n", + "execution_count": null, + "outputs": [] + }, + { + "metadata": { + "_uuid": "9f177e8213acc10e0dc7ab4f49c33a06989dce9d", + "_cell_guid": "d8009ffc-1caf-4cf5-808b-3347ad39eb49" + }, + "cell_type": "markdown", + "source": "If we set the threshold down to 10%, we can detect around 75% of all fraud case but almost double our false positive rate (now 25 false alarms)" + }, + { + "metadata": { + "_uuid": "69fbb96b4062e67e8df8beddab7cd48598b6e377", + "_cell_guid": "df14952e-6d10-4964-b977-fd732a878347", + "trusted": false, + "collapsed": true + }, + "cell_type": "code", + "source": "y_hat_10 = (y_test_hat_probs > 0.10)*1\nprint('Confusion matrix:\\n', confusion_matrix(y_test, y_hat_10))\nprint(classification_report(y_test, y_hat_10, digits=4))", + "execution_count": null, + "outputs": [] + }, + { + "metadata": { + "_uuid": "500e582bb9055575cf61d0e7fccd465afbab1c5b", + "_cell_guid": "9e9a216b-0533-46d8-bec0-a82b7bccc667" + }, + "cell_type": "markdown", + "source": "Where to go from here?\n-------------------\n\nWe just scratched the surface of sklearn and logistic regression. For example we could spent much more time with the \n\n- feature selection / engineering (which is a bit hard without any background information about the features),\n- we could try techniques to counter the data inbalance and \n- we could use cross-validation to fine tune the hyperparameters (regulaziation constant C) or\n- try a different regulization (Lasso/Elastic Net) or \n- optimizer (stochastic gradient desent instead of coordinate descnet)\n- adjust class weights to move the decision boundary (make missed frauds more expansive in the loss function)\n!- and finally we could try different classifer models in sklearn like decision trees, random forrests, knn, naive bayes or support vector machines. \n\nBut for now we will stop here and will implement a logisitc regression model with stochastic gradient descent in TensorFlow and then extend it to a neural net.\n\n" + } + ], + "metadata": { + "language_info": { + "name": "python", + "version": "3.6.5", + "mimetype": "text/x-python", + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "pygments_lexer": "ipython3", + "nbconvert_exporter": "python", + "file_extension": ".py" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} \ No newline at end of file From 2d6eb2afd8140d08422a1aea05ae98ea011ce51e Mon Sep 17 00:00:00 2001 From: Matthias Groncki Date: Tue, 8 May 2018 19:14:45 +0700 Subject: [PATCH 19/25] Updated Version of LR_part1 --- LogisticRegression_Part1.ipynb | 209 +++++++++++++++++++++++++-------- 1 file changed, 157 insertions(+), 52 deletions(-) diff --git a/LogisticRegression_Part1.ipynb b/LogisticRegression_Part1.ipynb index 40900af..228a62f 100644 --- a/LogisticRegression_Part1.ipynb +++ b/LogisticRegression_Part1.ipynb @@ -4,13 +4,23 @@ "metadata": { "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5", "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", - "trusted": false, - "collapsed": true + "trusted": true }, "cell_type": "code", "source": "# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np\nimport pandas as pd\nimport tensorflow as tf\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.metrics import roc_curve, roc_auc_score, classification_report, accuracy_score, confusion_matrix \nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nimport os\nprint(os.listdir(\"../input\"))\n\n# Any results you write to the current directory are saved as output.", - "execution_count": null, - "outputs": [] + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "text": "/opt/conda/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n from ._conv import register_converters as _register_converters\n", + "name": "stderr" + }, + { + "output_type": "stream", + "text": "['creditcard.csv']\n", + "name": "stdout" + } + ] }, { "metadata": { @@ -25,24 +35,32 @@ "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a", "collapsed": true, "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0", - "trusted": false + "trusted": true }, "cell_type": "code", "source": "credit_card = pd.read_csv('../input/creditcard.csv')", - "execution_count": null, + "execution_count": 2, "outputs": [] }, { "metadata": { "_uuid": "c4e55ed51d60613c4f19457af0131b4fb973fad9", "_cell_guid": "fd1b18c4-d0bf-4d81-b784-3fcec2578c12", - "trusted": false, - "collapsed": true + "trusted": true }, "cell_type": "code", "source": "f, ax = plt.subplots(figsize=(7, 5))\nsns.countplot(x='Class', data=credit_card)\n_ = plt.title('# Fraud vs NonFraud')\n_ = plt.xlabel('Class (1==Fraud)')", - "execution_count": null, - "outputs": [] + "execution_count": 3, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "", + "image/png": 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8HwM+kCROUUuSxmW6q3m/MIPnfG2SU4CVwBuraj2wH3D1UJs1rQZw26T6EcCTgHurasOI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\nAABJRU5ErkJggg==\n" + }, + "metadata": {} + } + ] }, { "metadata": { @@ -56,13 +74,21 @@ "metadata": { "_uuid": "5ee8b7fcd59def3b33733b5bbfddf39709dfd077", "_cell_guid": "0c90460f-5a34-46a0-bd6f-88e699b27d2f", - "trusted": false, - "collapsed": true + "trusted": true }, "cell_type": "code", "source": "base_line_accuracy = 1-np.sum(credit_card.Class)/credit_card.shape[0]\nbase_line_accuracy", - "execution_count": null, - "outputs": [] + "execution_count": 4, + "outputs": [ + { + "output_type": "execute_result", + "execution_count": 4, + "data": { + "text/plain": "0.9982725143693799" + }, + "metadata": {} + } + ] }, { "metadata": { @@ -77,11 +103,11 @@ "_uuid": "16728c540fc206821df972fcd071646f48b385e5", "collapsed": true, "_cell_guid": "3de8033f-5ddb-4a12-9230-c1cd01ab68e2", - "trusted": false + "trusted": true }, "cell_type": "code", "source": "X = credit_card.drop(columns='Class', axis=1)\ny = credit_card.Class.values", - "execution_count": null, + "execution_count": 5, "outputs": [] }, { @@ -96,13 +122,29 @@ "metadata": { "_uuid": "5c3a98163d12601711b33425f479edfb37a92fca", "_cell_guid": "ff310572-91fe-40d7-8370-89088320d9f0", - "trusted": false, - "collapsed": true + "trusted": true }, "cell_type": "code", "source": "corr = X.corr()\n\nmask = np.zeros_like(corr, dtype=np.bool)\nmask[np.triu_indices_from(mask)] = True\n\ncmap = sns.diverging_palette(220, 10, as_cmap=True)\n\n# Draw the heatmap with the mask and correct aspect ratio\nf, ax = plt.subplots(figsize=(11, 9))\nsns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,\n square=True, linewidths=.5, cbar_kws={\"shrink\": .5})", - "execution_count": null, - "outputs": [] + "execution_count": 6, + "outputs": [ + { + "output_type": "execute_result", + "execution_count": 6, + "data": { + "text/plain": "" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": "", + "image/png": 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SJGlD2AhnxY5rKlaSJElrLB0dluxkpyRJ6oisXGXt3faFewbKD85/8tljiXMY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\n9crAbk/SqeAzSWf/QFppZw4VqHR3idmkM1KXkY6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+ }, + "metadata": {} + } + ] }, { "metadata": { @@ -125,11 +167,11 @@ "_uuid": "d19b826169ff464f82eba4e0b349bacc4361c8a0", "collapsed": true, "_cell_guid": "d54b91c2-fc78-4b24-a7c8-5b1a974cdf36", - "trusted": false + "trusted": true }, "cell_type": "code", "source": "np.random.seed(42)\nX_train, X_test, y_train, y_test = train_test_split(X, y)", - "execution_count": null, + "execution_count": 7, "outputs": [] }, { @@ -145,11 +187,11 @@ "_uuid": "86ecda4d1700405da9ed0d8bc6f7d8eabc0f1ddb", "collapsed": true, "_cell_guid": "fb45b18e-9357-4a98-9d98-078f96c3a591", - "trusted": false + "trusted": true }, "cell_type": "code", "source": "scaler = StandardScaler()\nlr = LogisticRegression()\nmodel1 = Pipeline([('standardize', scaler),\n ('log_reg', lr)])", - "execution_count": null, + "execution_count": 8, "outputs": [] }, { @@ -164,13 +206,21 @@ "metadata": { "_uuid": "bc769713962566efac8b2310f45d7ebe79dcb173", "_cell_guid": "b8f46ca5-f69a-4755-abbf-9cc76723c133", - "trusted": false, - "collapsed": true + "trusted": true }, "cell_type": "code", "source": "model1.fit(X_train, y_train)", - "execution_count": null, - "outputs": [] + "execution_count": 9, + "outputs": [ + { + "output_type": "execute_result", + "execution_count": 9, + "data": { + "text/plain": "Pipeline(memory=None,\n steps=[('standardize', StandardScaler(copy=True, with_mean=True, with_std=True)), ('log_reg', LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n verbose=0, warm_start=False))])" + }, + "metadata": {} + } + ] }, { "metadata": { @@ -184,13 +234,18 @@ "metadata": { "_uuid": "e4f22259d9018f233874d9e4afdb42ec882bd967", "_cell_guid": "f708ecc2-9114-4b5e-86fe-bd8423aa622c", - "trusted": false, - "collapsed": true + "trusted": true }, "cell_type": "code", "source": "y_train_hat = model1.predict(X_train)\ny_train_hat_probs = model1.predict_proba(X_train)[:,1]\ntrain_accuracy = accuracy_score(y_train, y_train_hat)*100\ntrain_auc_roc = roc_auc_score(y_train, y_train_hat_probs)*100\nprint('Confusion matrix:\\n', confusion_matrix(y_train, y_train_hat))\nprint('Training accuracy: %.4f %%' % train_accuracy)\nprint('Training AUC: %.4f %%' % train_auc_roc)", - "execution_count": null, - "outputs": [] + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "text": "Confusion matrix:\n [[213200 26]\n [ 137 242]]\nTraining accuracy: 99.9237 %\nTraining AUC: 98.0664 %\n", + "name": "stdout" + } + ] }, { "metadata": { @@ -204,13 +259,18 @@ "metadata": { "_uuid": "a83a2229893813ad2986910d20f562b369b76eb4", "_cell_guid": "ca356e72-46ac-4766-8640-11d644c4534f", - "trusted": false, - "collapsed": true + "trusted": true }, "cell_type": "code", "source": "y_test_hat = model1.predict(X_test)\ny_test_hat_probs = model1.predict_proba(X_test)[:,1]\ntest_accuracy = accuracy_score(y_test, y_test_hat)*100\ntest_auc_roc = roc_auc_score(y_test, y_test_hat_probs)*100\nprint('Confusion matrix:\\n', confusion_matrix(y_test, y_test_hat))\nprint('Training accuracy: %.4f %%' % test_accuracy)\nprint('Training AUC: %.4f %%' % test_auc_roc)", - "execution_count": null, - "outputs": [] + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "text": "Confusion matrix:\n [[71077 12]\n [ 45 68]]\nTraining accuracy: 99.9199 %\nTraining AUC: 97.4810 %\n", + "name": "stdout" + } + ] }, { "metadata": { @@ -224,13 +284,18 @@ "metadata": { "_uuid": "258dda7a672ec39dd33f095dbc59cc42832f50b0", "_cell_guid": "26394d30-80ec-4db0-929c-0a81585d4d66", - "trusted": false, - "collapsed": true + "trusted": true }, "cell_type": "code", "source": "print(classification_report(y_test, y_test_hat, digits=6))", - "execution_count": null, - "outputs": [] + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "text": " precision recall f1-score support\n\n 0 0.999367 0.999831 0.999599 71089\n 1 0.850000 0.601770 0.704663 113\n\navg / total 0.999130 0.999199 0.999131 71202\n\n", + "name": "stdout" + } + ] }, { "metadata": { @@ -244,13 +309,21 @@ "metadata": { "_uuid": "9317259c2c241aa6cb00346b5b91bd485d7ec448", "_cell_guid": "d5db8aad-8866-4639-9a1c-cd32e33e11a1", - "trusted": false, - "collapsed": true + "trusted": true }, "cell_type": "code", "source": "fpr, tpr, thresholds = roc_curve(y_test, y_test_hat_probs, drop_intermediate=True)\n\nf, ax = plt.subplots(figsize=(9, 6))\n_ = plt.plot(fpr, tpr, [0,1], [0, 1])\n_ = plt.title('AUC ROC')\n_ = plt.xlabel('False positive rate')\n_ = plt.ylabel('True positive rate')\nplt.style.use('seaborn')\n\nplt.savefig('auc_roc.png', dpi=600)", - "execution_count": null, - "outputs": [] + "execution_count": 13, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "", + "image/png": 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4e+98T03Ps+1l4OVEfr6IiKSR3Bz4/GX4+D6wotDpKTi2qxYeFM1gKyIiKeCX76Op8hdN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\no++Dz1+C8rXhkg+gwamhU4m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+ }, + "metadata": {} + } + ] }, { "metadata": { @@ -265,13 +338,18 @@ "metadata": { "_uuid": "dc371656eeeced96d8343a37312d92bf116eede0", "_cell_guid": "a5a4d2a5-c86d-4e27-8245-08325644794d", - "trusted": false, - "collapsed": true + "trusted": true }, "cell_type": "code", "source": "y_hat_90 = (y_test_hat_probs > 0.90 )*1\nprint('Confusion matrix:\\n', confusion_matrix(y_test, y_hat_90))\nprint(classification_report(y_test, y_hat_90, digits=6))\n", - "execution_count": null, - "outputs": [] + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "text": "Confusion matrix:\n [[71077 12]\n [ 62 51]]\n precision recall f1-score support\n\n 0 0.999128 0.999831 0.999480 71089\n 1 0.809524 0.451327 0.579545 113\n\navg / total 0.998828 0.998961 0.998813 71202\n\n", + "name": "stdout" + } + ] }, { "metadata": { @@ -285,13 +363,18 @@ "metadata": { "_uuid": "69fbb96b4062e67e8df8beddab7cd48598b6e377", "_cell_guid": "df14952e-6d10-4964-b977-fd732a878347", - "trusted": false, - "collapsed": true + "trusted": true }, "cell_type": "code", "source": "y_hat_10 = (y_test_hat_probs > 0.10)*1\nprint('Confusion matrix:\\n', confusion_matrix(y_test, y_hat_10))\nprint(classification_report(y_test, y_hat_10, digits=4))", - "execution_count": null, - "outputs": [] + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "text": "Confusion matrix:\n [[71064 25]\n [ 25 88]]\n precision recall f1-score support\n\n 0 0.9996 0.9996 0.9996 71089\n 1 0.7788 0.7788 0.7788 113\n\navg / total 0.9993 0.9993 0.9993 71202\n\n", + "name": "stdout" + } + ] }, { "metadata": { @@ -300,6 +383,28 @@ }, "cell_type": "markdown", "source": "Where to go from here?\n-------------------\n\nWe just scratched the surface of sklearn and logistic regression. For example we could spent much more time with the \n\n- feature selection / engineering (which is a bit hard without any background information about the features),\n- we could try techniques to counter the data inbalance and \n- we could use cross-validation to fine tune the hyperparameters (regulaziation constant C) or\n- try a different regulization (Lasso/Elastic Net) or \n- optimizer (stochastic gradient desent instead of coordinate descnet)\n- adjust class weights to move the decision boundary (make missed frauds more expansive in the loss function)\n!- and finally we could try different classifer models in sklearn like decision trees, random forrests, knn, naive bayes or support vector machines. \n\nBut for now we will stop here and will implement a logisitc regression model with stochastic gradient descent in TensorFlow and then extend it to a neural net.\n\n" + }, + { + "metadata": { + "trusted": true, + "collapsed": true, + "_uuid": "27797e77d2e868b65f511b2fb0319d07df471155" + }, + "cell_type": "code", + "source": "", + "execution_count": null, + "outputs": [] + }, + { + "metadata": { + "trusted": true, + "collapsed": true, + "_uuid": "eeebb0cae9ddebb8fafab51d3c0a45e13736c6a8" + }, + "cell_type": "code", + "source": "", + "execution_count": null, + "outputs": [] } ], "metadata": { From 0e2bd635379b0480c16817bc072374ba8b6b1407 Mon Sep 17 00:00:00 2001 From: Matthias Groncki Date: Sun, 20 May 2018 21:36:52 +0700 Subject: [PATCH 20/25] Pricing Exotics with TensorFlow Added an Example how to use TensorFlow to price single Barriers (Down-Out) and Bermudan Calls. Use the automatic differentiation to get analytical path derivates. Performance is not so good, but quite easy to implement. Maybe useful for validation tasks. --- BarriersTensorFlow.ipynb | 517 +++++++++++++++++++++++++++++++++++++++ BermudanTensorFlow.ipynb | 224 +++++++++++++++++ 2 files changed, 741 insertions(+) create mode 100644 BarriersTensorFlow.ipynb create mode 100644 BermudanTensorFlow.ipynb diff --git a/BarriersTensorFlow.ipynb b/BarriersTensorFlow.ipynb new file mode 100644 index 0000000..38a3f2d --- /dev/null +++ b/BarriersTensorFlow.ipynb @@ -0,0 +1,517 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Pricing Exotic Options with TensorFlow\n", + "\n", + "When i was writing my post about gradient descent and automatic differentiation in TensorFLow I had the idea to use TensorFlow for Monte-Carlo pricing of some path dependent derivates.\n", + "\n", + "TensorFlow supports GPU computing (unfortunatly I can not try this on my laptop) which can speed and with TensorFlows automatic differentiation we can get analytical 'path' derivates. Calculating path derivates with a bump and revaluation is usally very computational costly and can be numerical unstable, depending on the bump size.\n", + "\n", + "In this post we want to focus on the implementation in TensorFlow therefore we will use a Black-Scholes model simplicity and try to price a Plain-Vanilla, a Down-And-Out Barrier and a Bermudan Call Option.\n", + "\n", + "Lets start with the plain vanilla one." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/anaconda/lib/python3.6/site-packages/h5py/__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", + " from ._conv import register_converters as _register_converters\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import tensorflow as tf\n", + "import scipy.stats as stats\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.metrics import mean_squared_error\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [], + "source": [ + "## Plain Vanilla Call in TensorFlow\n", + "\n", + "def blackScholes_py(S_0, strike, time_to_expiry, implied_vol, riskfree_rate):\n", + " S = S_0\n", + " K = strike\n", + " dt = time_to_expiry\n", + " sigma = implied_vol\n", + " r = riskfree_rate\n", + " Phi = stats.norm.cdf\n", + " d_1 = (np.log(S_0 / K) + (r+sigma**2/2)*dt) / (sigma*np.sqrt(dt))\n", + " d_2 = d_1 - sigma*np.sqrt(dt)\n", + " return S*Phi(d_1) - K*np.exp(-r*dt)*Phi(d_2)\n", + "\n", + "\n", + "def blackScholes_tf_pricer():\n", + " # Build the static computational graph\n", + " S = tf.placeholder(tf.float32)\n", + " K = tf.placeholder(tf.float32)\n", + " dt = tf.placeholder(tf.float32)\n", + " sigma = tf.placeholder(tf.float32)\n", + " r = tf.placeholder(tf.float32)\n", + " Phi = tf.distributions.Normal(0.,1.).cdf\n", + " d_1 = (tf.log(S / K) + (r+sigma**2/2)*dt) / (sigma*tf.sqrt(dt))\n", + " d_2 = d_1 - sigma*tf.sqrt(dt)\n", + " npv = S*Phi(d_1) - K*tf.exp(-r*dt)*Phi(d_2)\n", + " greeks = tf.gradients(npv, [S, sigma, r, K, dt])\n", + " # Calculate mixed 2nd order greeks for S (esp. gamma, vanna) and sigma (esp. volga)\n", + " dS_2ndOrder = tf.gradients(greeks[0], [S, sigma, r, K, dt]) \n", + " dsigma_2ndOrder = tf.gradients(greeks[1], [S, sigma, r, K, dt]) \n", + " # Function to feed in the input and calculate the computational graph\n", + " def execute_graph(S_0, strike, time_to_expiry, implied_vol, riskfree_rate):\n", + " with tf.Session() as sess:\n", + " res = sess.run([npv, greeks, dS_2ndOrder, dsigma_2ndOrder], \n", + " {\n", + " S: S_0,\n", + " K : strike,\n", + " r : riskfree_rate,\n", + " sigma: implied_vol,\n", + " dt: time_to_expiry})\n", + " return res\n", + " return execute_graph\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [], + "source": [ + "tf_pricer = blackScholes_tf_pricer()" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6.934486895210796" + ] + }, + "execution_count": 139, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "blackScholes_py(100., 110., 2., 0.2, 0.03) - (100/90)**(1-0.03/0.2**2)*blackScholes_py(90**2/100., 110., 2., 0.2, 0.03)" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.15 s Β± 21.5 ms per loop (mean Β± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit\n", + "npv = tf_pricer(100., 110., 2., 0.2, 0.03)\n", + "npv" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Monte Carlo with TensorFlow\n", + "\n", + "Lets try a path dependent exotic option, a down and out call. A down-and-out Call behaves as a normal Call but if the price of the underlying touch or fall below a certain level (the barrier B) at any time until the expiry the option, then it becomes worthless, even if its at expiry in the money.\n", + "\n", + "A down and out call is cheaper than the plain vanilla case, since there is a risk that the option get knocked out before reaching the expiry. It can be used to reduce the hedging costs.\n", + "\n", + "In the Black-Scholes model there is again a closed formula to calculate the price. See ... .\n", + "\n", + "We want to price this kind of option in TensorFlow with a Monte-Carlo Simulation and let TensorFLow calculate the path derivates with automatic differentitation.\n", + "\n", + "First implement the analytical solutions in 'pure' Python (actually we rely heavly on numpy) and TensorFLow." + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "metadata": {}, + "outputs": [], + "source": [ + "def analytical_downOut_py(S_0, strike, time_to_expiry, implied_vol, riskfree_rate, barrier):\n", + " S = S_0\n", + " K = strike\n", + " dt = time_to_expiry\n", + " sigma = implied_vol\n", + " r = riskfree_rate\n", + " alpha = 0.5 - r/sigma**2\n", + " B = barrier\n", + " Phi = stats.norm.cdf\n", + " d_1 = (np.log(S_0 / K) + (r+sigma**2/2)*dt) / (sigma*np.sqrt(dt))\n", + " d_2 = d_1 - sigma*np.sqrt(dt)\n", + " bs = S*Phi(d_1) - K*np.exp(-r*dt)*Phi(d_2)\n", + " d_1a = (np.log(B**2 / (S*K)) + (r+sigma**2/2)*dt) / (sigma*np.sqrt(dt))\n", + " d_2a = d_1a - sigma*np.sqrt(dt)\n", + " reflection = (S/B)**(1-r/sigma**2) * ((B**2/S)*Phi(d_1a) - K*np.exp(-r*dt)*Phi(d_2a))\n", + " return bs - reflection\n", + "\n", + "\n", + "def analytical_downOut_tf_pricer(enable_greeks = True):\n", + " S = tf.placeholder(tf.float32)\n", + " K = tf.placeholder(tf.float32)\n", + " dt = tf.placeholder(tf.float32)\n", + " sigma = tf.placeholder(tf.float32)\n", + " r = tf.placeholder(tf.float32)\n", + " B = tf.placeholder(tf.float32)\n", + " Phi = tf.distributions.Normal(0.,1.).cdf\n", + " d_1 = (tf.log(S / K) + (r+sigma**2/2)*dt) / (sigma*tf.sqrt(dt))\n", + " d_2 = d_1 - sigma*tf.sqrt(dt)\n", + " bs_npv = S*Phi(d_1) - K*tf.exp(-r*dt)*Phi(d_2)\n", + " d_1a = (tf.log(B**2 / (S*K)) + (r+sigma**2/2)*dt) / (sigma*tf.sqrt(dt))\n", + " d_2a = d_1a - sigma*tf.sqrt(dt)\n", + " reflection = (S/B)**(1-r/sigma**2) * ((B**2/S)*Phi(d_1a) - K*tf.exp(-r*dt)*Phi(d_2a))\n", + " npv = bs_npv - reflection\n", + " target_calc = [npv]\n", + " if enable_greeks:\n", + " greeks = tf.gradients(npv, [S, sigma, r, K, dt, B])\n", + " # Calculate mixed 2nd order greeks for S (esp. gamma, vanna) and sigma (esp. volga)\n", + " dS_2ndOrder = tf.gradients(greeks[0], [S, sigma, r, K, dt, B]) \n", + " dsigma_2ndOrder = tf.gradients(greeks[1], [S, sigma, r, K, dt, B]) \n", + " # Function to feed in the input and calculate the computational graph\n", + " target_calc += [greeks, dS_2ndOrder, dsigma_2ndOrder]\n", + " def price(S_0, strike, time_to_expiry, implied_vol, riskfree_rate, barrier):\n", + " with tf.Session() as sess:\n", + " \n", + " res = sess.run(target_calc, \n", + " {\n", + " S: S_0,\n", + " K : strike,\n", + " r : riskfree_rate,\n", + " sigma: implied_vol,\n", + " dt: time_to_expiry,\n", + " B : barrier})\n", + " return res\n", + " return price" + ] + }, + { + "cell_type": "code", + "execution_count": 501, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6.934486895210796" + ] + }, + "execution_count": 501, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "analytical_downOut_py(100., 110., 2., 0.2, 0.03, 90)" + ] + }, + { + "cell_type": "code", + "execution_count": 502, + "metadata": {}, + "outputs": [], + "source": [ + "down_out_pricer = analytical_downOut_tf_pricer(True)" + ] + }, + { + "cell_type": "code", + "execution_count": 503, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[6.9344816,\n", + " [0.69351405, 18.208786, 56.060673, -0.22123335, 1.7513491, -0.423125],\n", + " [0.002250306, 1.6649915, 4.7459536, -0.020622324, 0.15443888, 0.02270472],\n", + " [1.6649914, -159.15932, -191.44804, 0.9917286, -6.277489, -2.859783]]" + ] + }, + "execution_count": 503, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "down_out_pricer(100., 110., 2., 0.2, 0.03, 90.)" + ] + }, + { + "cell_type": "code", + "execution_count": 321, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.23 s Β± 95.9 ms per loop (mean Β± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "down_out_pricer = analytical_downOut_tf_pricer(False)\n", + "%timeit down_out_pricer(100., 110., 2., 0.2, 0.03, 95.)" + ] + }, + { + "cell_type": "code", + "execution_count": 173, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.85 s Β± 54.6 ms per loop (mean Β± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "down_out_pricer = analytical_downOut_tf_pricer(True)\n", + "%timeit down_out_pricer(100., 110., 2., 0.2, 0.03, 90.)" + ] + }, + { + "cell_type": "code", + "execution_count": 174, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "265 Β΅s Β± 11.2 Β΅s per loop (mean Β± std. dev. of 7 runs, 1000 loops each)\n" + ] + } + ], + "source": [ + "%timeit analytical_downOut_py(100., 110., 2., 0.2, 0.03, 95.)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now lets implement the Monte Carlo Pricing. We will pass the random varibales to the pricing function. \n", + "The pricing function we assume equvidistant timegrid." + ] + }, + { + "cell_type": "code", + "execution_count": 178, + "metadata": {}, + "outputs": [], + "source": [ + "def generate_random_variables_for_down_out(steps, samples, seed=42):\n", + " np.random.seed(seed)\n", + " return np.random.randn(samples, steps)" + ] + }, + { + "cell_type": "code", + "execution_count": 398, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(10000, 100)" + ] + }, + "execution_count": 398, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "N = generate_random_variables_for_down_out(100, 10000)\n", + "N.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 399, + "metadata": {}, + "outputs": [], + "source": [ + "def monte_carlo_down_out_py(S_0, strike, time_to_expiry, implied_vol, riskfree_rate, barrier, stdnorm_random_variates):\n", + " S = S_0\n", + " K = strike\n", + " dt = time_to_expiry / stdnorm_random_variates.shape[1]\n", + " sigma = implied_vol\n", + " r = riskfree_rate\n", + " B = barrier\n", + " # See Advanced Monte Carlo methods for barrier and related exotic options by Emmanuel Gobet\n", + " B_shift = B*np.exp(0.5826*sigma*np.sqrt(dt))\n", + " S_T = S * np.cumprod(np.exp((r-sigma**2/2)*dt+sigma*np.sqrt(dt)*stdnorm_random_variates), axis=1)\n", + " non_touch = (np.min(S_T, axis=1) > B_shift)*1\n", + " call_payout = np.maximum(S_T[:,-1] - K, 0)\n", + " npv = np.mean(non_touch * call_payout)\n", + " return np.exp(-time_to_expiry*r)*npv" + ] + }, + { + "cell_type": "code", + "execution_count": 526, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "11.5 ms Β± 1.55 ms per loop (mean Β± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "%%timeit\n", + "monte_carlo_down_out_py(100., 110., 2., 0.2, 0.03, 90., N)" + ] + }, + { + "cell_type": "code", + "execution_count": 521, + "metadata": {}, + "outputs": [], + "source": [ + "def monte_carlo_down_out_tf(enable_greeks = True):\n", + " S = tf.placeholder(tf.float32)\n", + " K = tf.placeholder(tf.float32)\n", + " dt = tf.placeholder(tf.float32)\n", + " T = tf.placeholder(tf.float32)\n", + " sigma = tf.placeholder(tf.float32)\n", + " r = tf.placeholder(tf.float32)\n", + " B = tf.placeholder(tf.float32)\n", + " dw = tf.placeholder(tf.float32)\n", + " # See Advanced Monte Carlo methods for barrier and related exotic options by Emmanuel Gobet\n", + " B_shift = B * tf.exp(0.5826*sigma*tf.sqrt(dt))\n", + " S_T = S * tf.cumprod(tf.exp((r-sigma**2/2)*dt+sigma*tf.sqrt(dt)*dw), axis=1)\n", + " non_touch = tf.cast(tf.reduce_all(S_T > B_shift, axis=1), tf.float32)\n", + " call_payout = tf.maximum(S_T[:,-1] - K, 0)\n", + " npv = tf.exp(-r*T) * tf.reduce_mean(non_touch * call_payout)\n", + " target_calc = [npv]\n", + " if enable_greeks:\n", + " greeks = tf.gradients(npv, [S, sigma, r, K, T])\n", + " target_calc += [greeks]\n", + " def pricer(S_0, strike, time_to_expiry, implied_vol, riskfree_rate, barrier, stdnorm_random_variates):\n", + " with tf.Session() as sess:\n", + " timedelta = time_to_expiry / stdnorm_random_variates.shape[1]\n", + " res = sess.run(target_calc, \n", + " {\n", + " S: S_0,\n", + " K : strike,\n", + " r : riskfree_rate,\n", + " sigma: implied_vol,\n", + " dt : timedelta,\n", + " T: time_to_expiry,\n", + " B : barrier,\n", + " dw : stdnorm_random_variates\n", + " })\n", + " return res\n", + " return pricer\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 522, + "metadata": {}, + "outputs": [], + "source": [ + "tf_mc_pricer = monte_carlo_down_out_tf()" + ] + }, + { + "cell_type": "code", + "execution_count": 525, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[7.035021, [0.31648976, 41.60718, 49.227932, -0.22376387, -0.21105061]]" + ] + }, + "execution_count": 525, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tf_mc_pricer(100., 110., 2., 0.2, 0.03, 90., N)" + ] + }, + { + "cell_type": "code", + "execution_count": 527, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.08 s Β± 275 ms per loop (mean Β± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit\n", + "tf_mc_pricer(100., 110., 2., 0.2, 0.03, 90., N)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/BermudanTensorFlow.ipynb b/BermudanTensorFlow.ipynb new file mode 100644 index 0000000..8f31431 --- /dev/null +++ b/BermudanTensorFlow.ipynb @@ -0,0 +1,224 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Pricing Exotic Options with TensorFlow\n", + "\n", + "Bermudan Options " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/anaconda/lib/python3.6/site-packages/h5py/__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", + " from ._conv import register_converters as _register_converters\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import tensorflow as tf\n", + "import scipy.stats as stats\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.metrics import mean_squared_error\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def get_continuation_function():\n", + " X = tf.placeholder(tf.float32, (None, 3))\n", + " y = tf.placeholder(tf.float32, (None, 1))\n", + " w = tf.Variable(initial_value=tf.random_normal((3,1))*0.1)\n", + " b = tf.Variable(initial_value=tf.ones(1)*1)\n", + " y_hat = tf.add(tf.matmul(X,w), b)\n", + " pre_error = tf.pow(y-y_hat,2)\n", + " error = tf.reduce_mean(pre_error)\n", + " train = tf.train.AdamOptimizer(0.1).minimize(error)\n", + " return(X, y, train, w, b, y_hat)\n", + "\n", + "def feature_matrix_from_current_state(state):\n", + " feature_0 = tf.pow(state,0)\n", + "\n", + " feature_1 = tf.pow(state,1)\n", + " feature_1_mean = tf.reduce_mean(feature_1)\n", + " feature_1_std = tf.sqrt(tf.reduce_sum(tf.square(feature_1 - feature_1_mean))/(N_samples_pricing+1))\n", + " feature_1_norm = (feature_1 - feature_1_mean) / feature_1_std\n", + "\n", + "\n", + " feature_2 = 2*tf.pow(state,2)-1\n", + " feature_2_mean = tf.reduce_mean(feature_2)\n", + " feature_2_std = tf.sqrt(tf.reduce_sum(tf.square(feature_2 - feature_2_mean))/(N_samples_pricing+1))\n", + " feature_2_norm = (feature_2 - feature_2_mean) / feature_2_std\n", + "\n", + " feature_3 = 4*tf.pow(state,3)-3*feature_1\n", + " feature_3_mean = tf.reduce_mean(feature_3)\n", + " feature_3_std = tf.sqrt(tf.reduce_sum(tf.square(feature_3 - feature_3_mean))/(N_samples_pricing+1))\n", + " feature_3_norm = (feature_3 - feature_3_mean) / feature_3_std\n", + "\n", + " features = tf.concat([feature_1_norm, feature_2_norm, feature_3_norm], axis=0)\n", + " features = tf.reshape(features, shape=(3, N_samples_pricing))\n", + " features = tf.transpose(features)\n", + " return features\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def pricing_function(number_call_dates):\n", + " S = tf.placeholder(tf.float32)\n", + " # First excerise date\n", + " dts = tf.placeholder(tf.float32)\n", + " # 2nd exersice date\n", + " K = tf.placeholder(tf.float32)\n", + " r = tf.placeholder(tf.float32)\n", + " sigma = tf.placeholder(tf.float32)\n", + " dW = tf.placeholder(tf.float32)\n", + " \n", + " S_t = S * tf.cumprod(tf.exp((r-sigma**2/2)*dts + sigma*tf.sqrt(dts)*dW), axis=1)\n", + " \n", + " E_t = tf.exp(-r*tf.cumsum(dts))*tf.maximum(S_t-K, 0)\n", + "\n", + " \n", + " continuationValues = []\n", + " training_functions = []\n", + " \n", + " previous_exersies = 0\n", + " npv = 0\n", + " for i in range(number_call_dates-1):\n", + " (input_x, input_y, train, w, b, y_hat) = get_continuation_function()\n", + " training_functions.append((input_x, input_y, train, w, b, y_hat))\n", + " X = feature_matrix_from_current_state(S_t[:, i])\n", + " contValue = tf.add(tf.matmul(X, w),b)\n", + " continuationValues.append(contValue)\n", + " inMoney = tf.cast(tf.greater(E_t[:,i], 0.), tf.float32)\n", + " exercise = tf.cast(tf.greater(E_t[:,i], contValue[:,0]), tf.float32) * inMoney * (1-previous_exersies)\n", + " previous_exersies += exercise\n", + " npv += exercise*E_t[:,i]\n", + " \n", + " # Last exercise date\n", + " inMoney = tf.cast(tf.greater(E_t[:,-1], 0.), tf.float32)\n", + " exercise = inMoney * (1-previous_exersies)\n", + " npv += exercise*E_t[:,-1]\n", + " npv = tf.reduce_mean(npv)\n", + " greeks = tf.gradients(npv, [S, r, sigma])\n", + " return([S, dts, K, r, sigma, dW, S_t, E_t, npv, greeks, training_functions])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def bermudanMC_tensorFlow(S_0, strike, exTimes, impliedvol, riskfree_r, random_train, random_pricing):\n", + " n_excerises = len(exTimes)\n", + " with tf.Session() as sess:\n", + " \n", + " S, dts, K, r, sigma, dW, S_t, E_t, npv, greeks, training_functions= pricing_function(n_excerises)\n", + " sess.run(tf.global_variables_initializer())\n", + " paths, exercise_values = sess.run([S_t, E_t], {\n", + " S: S_0,\n", + " dts : exTimes,\n", + " K : strike,\n", + " r : riskfree_r,\n", + " sigma: impliedvol,\n", + " dW : random_train\n", + " }) \n", + " \n", + " for i in range(n_excerises-1)[::-1]:\n", + " (input_x, input_y, train, w, b, y_hat) = training_functions[i]\n", + " y = exercise_values[:, i+1:i+2]\n", + " X = paths[:, i]\n", + " X = np.c_[X**1, 2*X**2-1, 4*X**3 - 3 * X]\n", + " X = (X - np.mean(X, axis=0)) / np.std(X, axis=0)\n", + " for epoch in range(80):\n", + " _ = sess.run(train, {input_x:X[exercise_values[:,i]>0], \n", + " input_y:y[exercise_values[:,i]>0]})\n", + " cont_value = sess.run(y_hat, {input_x:X, \n", + " input_y:y})\n", + " exercise_values[:, i+1:i+2] = np.maximum(exercise_values[:, i+1:i+2], cont_value)\n", + " \n", + " npv, greeks = sess.run([npv, greeks], { S: S_0,\n", + " dts : exTimes,\n", + " K : strike,\n", + " r : riskfree_r,\n", + " sigma: impliedvol,\n", + " dW : N_pricing\n", + " })\n", + " return(npv, greeks)\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "N_samples_learn = 10000\n", + "N_samples_pricing = 100000\n", + "calldates = 2\n", + "np.random.seed(42)\n", + "N = np.random.randn(N_samples_learn,calldates)\n", + "N_pricing = np.random.randn(N_samples_pricing,calldates)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(9.751283, [0.5057875, 81.654884, 56.51146])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bermudanMC_tensorFlow(100., 110., [1., 1.], 0.2, 0.03, N, N_pricing)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 9b8d724d669bc1bb369d34d611b4e6607e104c69 Mon Sep 17 00:00:00 2001 From: Matthias Groncki Date: Sun, 18 Nov 2018 17:43:05 +0700 Subject: [PATCH 21/25] Monte carlo pricing n adjoint greeks on gpus --- .DS_Store | Bin 0 -> 8196 bytes .gitignore | 4 +- BarriersTensorFlow.ipynb | 2 +- LICENSE | 29 + PricingPyTorch/.DS_Store | Bin 0 -> 6148 bytes PricingPyTorch/BarrierOptionGPU.ipynb | 185 +++++++ .../PlainVanillaAndBarriersCPU.ipynb | 510 ++++++++++++++++++ 7 files changed, 728 insertions(+), 2 deletions(-) create mode 100644 .DS_Store create mode 100644 LICENSE create mode 100644 PricingPyTorch/.DS_Store create mode 100644 PricingPyTorch/BarrierOptionGPU.ipynb create mode 100644 PricingPyTorch/PlainVanillaAndBarriersCPU.ipynb diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..8591f3d9657cc4f73e6984a12b0d9cb4eaaf1e9f GIT binary patch literal 8196 zcmeHM-D(p-6h4zy-B5pu3avM?UI-Q1khJ0tBE&RN0}8>EdZCgg*^n-5cEj$*w8lto zd&3Xi`7bf`xbkX`vb;OKat6L@FgpA8fbh;?|A zp*^$#?M?b2mJ)gwpvS(qsYXSrqRma;g?OLPUZPFfrU%e!N~=xm#z8NHFCM(9QF#X1 z_XOGkJZRxxmmZ_F;1lntJ-LHaJ)vb%3485AqY1mLr+$0beGd$`uq#{g9QSPMNu|Cf z@Gs$XZQukzuK{fj`|H4-gZ@+$dEOsl=Y0)KxIpm1iv_>f*8`3!7_~7XcMBVjfXtD> zQ8C>DM?l=dReB=N(UzVv-ZS8|fz-lC5VOW{9L_vvj*>X`3O$wQ$^34@FJH#Yq~B}c zxsSQ-cxP)=k!zW^^2|u!xQg4m@mK8qF81OU%|Eg@^>k#E#_`XCi})#5@RZmBr#{YU zHxc0+fk)PD^n-5nocYq|I(86dv)@E&ZvNEiGlpR-80U;v)m^V&4eMbqDAfHI{HszA zcN$cnSrLt|cu;k@)+qJxY!RxtJAaSZ{BAref>DuCz z!Qjru?NxJqV|}=44szL>xmEL4_U>?)HkPjE?w5Bu`)>wshwnaOhbSWYLxnD=-{bZZ zdNUb&UG-hZbK4ApnntXb@Ej`Q6ic`>9kM1l<@+d%*nKn+ts(da`|@PCr|wAx`uwct z8`DIaL3Pl#Y6r9x*0h?68a!ad#was~nh?Rn9t!FF_j$DXZ*xgU`AL-1#RdRY@A&AI( zusd(x*X|A6egFvN)AA6Q02s3ggOmZ0dC;|!gM}g0sBw=C&auYhK%u{A(%Ppeam#gY zu)Y2oEmkd@!K>A+iaJjGbZHI@sy@ zEdNL-p{{GOFlH<=c82s&f~68IHRLBouyopEjw>>DhL( B_shift)*1\n", + " call_payout = np.maximum(S_T[:,-1] - K, 0)\n", + " npv = np.mean(non_touch * call_payout)\n", + " return np.exp(-time_to_expiry*r)*npv" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8.24 s Β± 22.5 ms per loop (mean Β± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit\n", + "monte_carlo_down_out_py(100., 110., 2., 0.2, 0.03, 90., 1000, 100000)" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [], + "source": [ + "def monte_carlo_down_out_torch_cuda(S_0, strike, time_to_expiry, implied_vol, riskfree_rate, barrier, steps, samples):\n", + " stdnorm_random_variates = torch.cuda.FloatTensor(steps, samples).normal_()\n", + " S = S_0\n", + " K = strike\n", + " dt = time_to_expiry / stdnorm_random_variates.shape[1]\n", + " sigma = implied_vol\n", + " r = riskfree_rate\n", + " B = barrier\n", + " # See Advanced Monte Carlo methods for barrier and related exotic options by Emmanuel Gobet\n", + " B_shift = B*torch.exp(0.5826*sigma*torch.sqrt(dt))\n", + " S_T = S * torch.cumprod(torch.exp((r-sigma**2/2)*dt+sigma*torch.sqrt(dt)*stdnorm_random_variates), dim=1)\n", + " non_touch = torch.min(S_T, dim=1)[0] > B_shift\n", + " non_touch = non_touch.type(torch.cuda.FloatTensor)\n", + " call_payout = S_T[:,-1] - K\n", + " call_payout[call_payout<0]=0\n", + " npv = torch.mean(non_touch * call_payout)\n", + " return torch.exp(-time_to_expiry*r)*npv" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "56.3 ms Β± 89.1 Β΅s per loop (mean Β± std. dev. of 7 runs, 10 loops each)\n" + ] + } + ], + "source": [ + "%%timeit\n", + "S = torch.tensor([100.],requires_grad=True, device='cuda')\n", + "K = torch.tensor([110.],requires_grad=True, device='cuda')\n", + "T = torch.tensor([2.],requires_grad=True, device='cuda')\n", + "sigma = torch.tensor([0.2],requires_grad=True, device='cuda')\n", + "r = torch.tensor([0.03],requires_grad=True, device='cuda')\n", + "B = torch.tensor([90.],requires_grad=True, device='cuda')\n", + "monte_carlo_down_out_torch_cuda(S, K, T, sigma, r, B, 1000, 100000)" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "160 ms Β± 94 Β΅s per loop (mean Β± std. dev. of 7 runs, 10 loops each)\n" + ] + } + ], + "source": [ + "%%timeit\n", + "S = torch.tensor([100.],requires_grad=True, device='cuda')\n", + "K = torch.tensor([110.],requires_grad=True, device='cuda')\n", + "T = torch.tensor([2.],requires_grad=True, device='cuda')\n", + "sigma = torch.tensor([0.2],requires_grad=True, device='cuda')\n", + "r = torch.tensor([0.03],requires_grad=True, device='cuda')\n", + "B = torch.tensor([90.],requires_grad=True, device='cuda')\n", + "npv_torch_mc = monte_carlo_down_out_torch_cuda(S, K, T, sigma, r, B, 1000, 100000)\n", + "npv_torch_mc.backward()" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/PricingPyTorch/PlainVanillaAndBarriersCPU.ipynb b/PricingPyTorch/PlainVanillaAndBarriersCPU.ipynb new file mode 100644 index 0000000..6eca960 --- /dev/null +++ b/PricingPyTorch/PlainVanillaAndBarriersCPU.ipynb @@ -0,0 +1,510 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Option Pricing with with PyTorch \n", + "\n", + "Copyright Matthias Groncki, 2018\n", + "\n", + "This is a port of one of my previous blog posts about using TensorFlow to price options.\n", + "\n", + "After using PyTorch for another project, i was impressed how straight forward it is, so I've decided to revisit my previous examples and use PyTorch this time" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/anaconda/lib/python3.6/site-packages/h5py/__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", + " from ._conv import register_converters as _register_converters\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import torch\n", + "import tensorflow as tf\n", + "import scipy.stats as stats" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plain Vanillas\n", + "\n", + "Lets start with plain vanillas in a Black Scholes World. \n", + "\n", + "### Numpy Implementation\n", + "\n", + "I am using the same implementation as in the TensorFlow notebook:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "## Plain Vanilla Call in TensorFlow\n", + "\n", + "def blackScholes_py(S_0, strike, time_to_expiry, implied_vol, riskfree_rate):\n", + " S = S_0\n", + " K = strike\n", + " dt = time_to_expiry\n", + " sigma = implied_vol\n", + " r = riskfree_rate\n", + " Phi = stats.norm.cdf\n", + " d_1 = (np.log(S_0 / K) + (r+sigma**2/2)*dt) / (sigma*np.sqrt(dt))\n", + " d_2 = d_1 - sigma*np.sqrt(dt)\n", + " return S*Phi(d_1) - K*np.exp(-r*dt)*Phi(d_2)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "281 Β΅s Β± 25.4 Β΅s per loop (mean Β± std. dev. of 7 runs, 1000 loops each)\n" + ] + } + ], + "source": [ + "%%timeit\n", + "S_0 = 100\n", + "K = 101\n", + "T = 1\n", + "sigma = 0.3\n", + "r = 0.01\n", + "\n", + "npv_numpy = blackScholes_py(S_0, K, T, sigma, r)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And as expected its super fast. No suprises here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### PyTorch Implementation\n", + "\n", + "There are only minimal code changes compared to the numpy version required. In the actual pricing function we just need to replace ```np``` with ```torch``` and exchange the cdf function to use the PyTorch one and we have to convert our input into ```torch.tensor```." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def blackScholes_pyTorch(S_0, strike, time_to_expiry, implied_vol, riskfree_rate):\n", + " S = S_0\n", + " K = strike\n", + " dt = time_to_expiry\n", + " sigma = implied_vol\n", + " r = riskfree_rate\n", + " Phi = torch.distributions.Normal(0,1).cdf\n", + " d_1 = (torch.log(S_0 / K) + (r+sigma**2/2)*dt) / (sigma*torch.sqrt(dt))\n", + " d_2 = d_1 - sigma*torch.sqrt(dt)\n", + " return S*Phi(d_1) - K*torch.exp(-r*dt)*Phi(d_2)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "283 Β΅s Β± 23.7 Β΅s per loop (mean Β± std. dev. of 7 runs, 1000 loops each)\n" + ] + } + ], + "source": [ + "%%timeit\n", + "S_0 = torch.tensor([100.],requires_grad=True)\n", + "K = torch.tensor([101.],requires_grad=True)\n", + "T = torch.tensor([1.],requires_grad=True)\n", + "sigma = torch.tensor([0.3],requires_grad=True)\n", + "r = torch.tensor([0.01],requires_grad=True)\n", + "npv_pytorch = blackScholes_pyTorch(S_0, K, T, sigma, r)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Seems the PyTorch version is even faster as the pure numpy version\n", + "\n", + "#### Greeks in PyTorch\n", + "\n", + "We just need to call the ```.backward()``` operator of the tensor which stores the prices and we can access the greeks with the ```.grad``` properity." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "639 Β΅s Β± 9.85 Β΅s per loop (mean Β± std. dev. of 7 runs, 1000 loops each)\n" + ] + } + ], + "source": [ + "%%timeit\n", + "S_0 = torch.tensor([100.],requires_grad=True)\n", + "K = torch.tensor([101.],requires_grad=True)\n", + "T = torch.tensor([1.],requires_grad=True)\n", + "sigma = torch.tensor([0.3],requires_grad=True)\n", + "r = torch.tensor([0.01],requires_grad=True)\n", + "npv_pytorch = blackScholes_pyTorch(S_0, K, T, sigma, r)\n", + "npv_pytorch.backward()\n", + "delta_pytorch = S_0.grad\n", + "vega_pytorch = sigma.grad\n", + "rho_pytorch = r.grad\n", + "theta_pytorch = T.grad\n", + "digital_pytoch = -K.grad" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Its almost 2.5 times slower but gives us five greeks. A naive finite-difference approximation would costs us at least 5 calculations and would be only an numerical approximation. Here we have 'exact' derivates." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### TensorFlow implementation\n", + "\n", + "Using the same code as in the original notebook (but I removed the calculation of the 2nd order greeks. There is a bit of overhead for constructing the computational graph." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def blackScholes_tf_pricer():\n", + " # Build the static computational graph\n", + " S = tf.placeholder(tf.float32)\n", + " K = tf.placeholder(tf.float32)\n", + " dt = tf.placeholder(tf.float32)\n", + " sigma = tf.placeholder(tf.float32)\n", + " r = tf.placeholder(tf.float32)\n", + " Phi = tf.distributions.Normal(0.,1.).cdf\n", + " d_1 = (tf.log(S / K) + (r+sigma**2/2)*dt) / (sigma*tf.sqrt(dt))\n", + " d_2 = d_1 - sigma*tf.sqrt(dt)\n", + " npv = S*Phi(d_1) - K*tf.exp(-r*dt)*Phi(d_2)\n", + " greeks = tf.gradients(npv, [S, sigma, r, K, dt])\n", + " def execute_graph(S_0, strike, time_to_expiry, implied_vol, riskfree_rate):\n", + " with tf.Session() as sess:\n", + " res = sess.run([npv, greeks], \n", + " {\n", + " S: S_0,\n", + " K : strike,\n", + " r : riskfree_rate,\n", + " sigma: implied_vol,\n", + " dt: time_to_expiry})\n", + " return res\n", + " return execute_graph " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "820 ms Β± 99.9 ms per loop (mean Β± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit\n", + "S_0 = 100\n", + "K = 101\n", + "T = 1\n", + "sigma = 0.3\n", + "r = 0.01\n", + "tf_pricer = blackScholes_tf_pricer()\n", + "npv_numpy_tf = tf_pricer(S_0, K, T, sigma, r)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Its much slower than the numpy and the PyTorch implementaion. Maybe my implementation is just bad." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Second order greeks in Pytorch\n", + "\n", + "We using the same example as before but now we want to calculate the 2nd order greeks. Thats need\n", + "to create a computational graph of the gradient. We use the function ```.grad()``` from the autograd module." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "S_0 = torch.tensor([100.],requires_grad=True)\n", + "K = torch.tensor([101.],requires_grad=True)\n", + "T = torch.tensor([1.],requires_grad=True)\n", + "sigma = torch.tensor([0.3],requires_grad=True)\n", + "r = torch.tensor([0.01],requires_grad=True)\n", + "npv_pytorch = blackScholes_pyTorch(S_0, K, T, sigma, r)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Gamma" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "gradient = torch.autograd.grad(npv_pytorch, S_0, create_graph=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([ 0.5597])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "delta, = gradient\n", + "delta" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Delta: tensor([ 0.5597])\n", + "Gamma tensor(1.00000e-02 *\n", + " [ 2.6298])\n" + ] + } + ], + "source": [ + "delta.backward(retain_graph=True)\n", + "print('Delta: ', delta)\n", + "print('Gamma', S_0.grad)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Monte Carlo Pricing for Single Barrier Option\n", + "\n", + "### Numpy Implementation" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def monte_carlo_down_out_py(S_0, strike, time_to_expiry, implied_vol, riskfree_rate, barrier, steps, samples):\n", + " stdnorm_random_variates = np.random.randn(samples, steps)\n", + " S = S_0\n", + " K = strike\n", + " dt = time_to_expiry / stdnorm_random_variates.shape[1]\n", + " sigma = implied_vol\n", + " r = riskfree_rate\n", + " B = barrier\n", + " # See Advanced Monte Carlo methods for barrier and related exotic options by Emmanuel Gobet\n", + " B_shift = B*np.exp(0.5826*sigma*np.sqrt(dt))\n", + " S_T = S * np.cumprod(np.exp((r-sigma**2/2)*dt+sigma*np.sqrt(dt)*stdnorm_random_variates), axis=1)\n", + " non_touch = (np.min(S_T, axis=1) > B_shift)*1\n", + " call_payout = np.maximum(S_T[:,-1] - K, 0)\n", + " npv = np.mean(non_touch * call_payout)\n", + " return np.exp(-time_to_expiry*r)*npv" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4.78 s Β± 104 ms per loop (mean Β± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit\n", + "monte_carlo_down_out_py(100., 110., 2., 0.2, 0.03, 90., 1000, 100000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### PyTorch Implementation" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "def monte_carlo_down_out_torch(S_0, strike, time_to_expiry, implied_vol, riskfree_rate, barrier, steps, samples):\n", + " stdnorm_random_variates = torch.distributions.Normal(0,1).sample((samples, steps))\n", + " S = S_0\n", + " K = strike\n", + " dt = time_to_expiry / stdnorm_random_variates.shape[1]\n", + " sigma = implied_vol\n", + " r = riskfree_rate\n", + " B = barrier\n", + " # See Advanced Monte Carlo methods for barrier and related exotic options by Emmanuel Gobet\n", + " B_shift = B*torch.exp(0.5826*sigma*torch.sqrt(dt))\n", + " S_T = S * torch.cumprod(torch.exp((r-sigma**2/2)*dt+sigma*torch.sqrt(dt)*stdnorm_random_variates), dim=1)\n", + " non_touch = torch.min(S_T, dim=1)[0] > B_shift\n", + " call_payout = S_T[:,-1] - K\n", + " call_payout[call_payout<0]=0\n", + " npv = torch.mean(non_touch.type(torch.FloatTensor) * call_payout)\n", + " return torch.exp(-time_to_expiry*r)*npv" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.09 s Β± 106 ms per loop (mean Β± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit\n", + "S = torch.tensor([100.],requires_grad=True)\n", + "K = torch.tensor([110.],requires_grad=True)\n", + "T = torch.tensor([2.],requires_grad=True)\n", + "sigma = torch.tensor([0.2],requires_grad=True)\n", + "r = torch.tensor([0.03],requires_grad=True)\n", + "B = torch.tensor([90.],requires_grad=True)\n", + "monte_carlo_down_out_torch(S, K, T, sigma, r, B, 1000, 100000)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7.34 s Β± 229 ms per loop (mean Β± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit\n", + "S = torch.tensor([100.],requires_grad=True)\n", + "K = torch.tensor([110.],requires_grad=True)\n", + "T = torch.tensor([2.],requires_grad=True)\n", + "sigma = torch.tensor([0.2],requires_grad=True)\n", + "r = torch.tensor([0.03],requires_grad=True)\n", + "B = torch.tensor([90.],requires_grad=True)\n", + "npv_torch_mc = monte_carlo_down_out_torch(S, K, T, sigma, r, B, 1000, 100000)\n", + "npv_torch_mc.backward()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 598d34f3128409cb5e3e62bfc314c23db584c65e Mon Sep 17 00:00:00 2001 From: Matthias Groncki Date: Sat, 28 Feb 2026 16:08:22 +0100 Subject: [PATCH 22/25] Initial version of my first ai agent --- .gitignore | 5 + EconomicStressAgentORE/.env.example | 5 + EconomicStressAgentORE/.gitignore | 2 + EconomicStressAgentORE/README.md | 165 +++ EconomicStressAgentORE/agent.py | 195 ++++ EconomicStressAgentORE/config.py | 45 + EconomicStressAgentORE/data/scenarios.json | 899 +++++++++++++++ .../data/sector_mapping.csv | 4 + .../historical_scenarios.py | 96 ++ EconomicStressAgentORE/impact_summarizer.py | 220 ++++ EconomicStressAgentORE/ore_runner.py | 90 ++ .../oredata/Input/conventions.xml | 126 +++ .../oredata/Input/curveconfig.xml | 582 ++++++++++ .../oredata/Input/fixings.csv | 416 +++++++ .../oredata/Input/marketdata.csv | 859 ++++++++++++++ .../oredata/Input/parstresstest.xml | 791 +++++++++++++ .../oredata/Input/portfolio.xml | 379 +++++++ .../oredata/Input/pricingengine.xml | 1006 +++++++++++++++++ .../oredata/Input/sensitivity.xml | 158 +++ .../oredata/Input/simulation.xml | 79 ++ .../oredata/Input/todaysmarket.xml | 80 ++ EconomicStressAgentORE/oredata/ore.xml | 64 ++ EconomicStressAgentORE/requirements.txt | 5 + EconomicStressAgentORE/scenario_analyzer.py | 166 +++ EconomicStressAgentORE/stresstest_builder.py | 323 ++++++ EconomicStressAgentORE/test_integration.py | 98 ++ .../todaysmarket_analyzer.py | 436 +++++++ 27 files changed, 7294 insertions(+) create mode 100644 EconomicStressAgentORE/.env.example create mode 100644 EconomicStressAgentORE/.gitignore create mode 100644 EconomicStressAgentORE/README.md create mode 100644 EconomicStressAgentORE/agent.py create mode 100644 EconomicStressAgentORE/config.py create mode 100644 EconomicStressAgentORE/data/scenarios.json create mode 100644 EconomicStressAgentORE/data/sector_mapping.csv create mode 100644 EconomicStressAgentORE/historical_scenarios.py create mode 100644 EconomicStressAgentORE/impact_summarizer.py create mode 100644 EconomicStressAgentORE/ore_runner.py create mode 100644 EconomicStressAgentORE/oredata/Input/conventions.xml create mode 100644 EconomicStressAgentORE/oredata/Input/curveconfig.xml create mode 100644 EconomicStressAgentORE/oredata/Input/fixings.csv create mode 100644 EconomicStressAgentORE/oredata/Input/marketdata.csv create mode 100644 EconomicStressAgentORE/oredata/Input/parstresstest.xml create mode 100644 EconomicStressAgentORE/oredata/Input/portfolio.xml create mode 100644 EconomicStressAgentORE/oredata/Input/pricingengine.xml create mode 100644 EconomicStressAgentORE/oredata/Input/sensitivity.xml create mode 100644 EconomicStressAgentORE/oredata/Input/simulation.xml create mode 100644 EconomicStressAgentORE/oredata/Input/todaysmarket.xml create mode 100644 EconomicStressAgentORE/oredata/ore.xml create mode 100644 EconomicStressAgentORE/requirements.txt create mode 100644 EconomicStressAgentORE/scenario_analyzer.py create mode 100644 EconomicStressAgentORE/stresstest_builder.py create mode 100644 EconomicStressAgentORE/test_integration.py create mode 100644 EconomicStressAgentORE/todaysmarket_analyzer.py diff --git a/.gitignore b/.gitignore index 0e59d72..a2da2c1 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,8 @@ .ipynb_checkpoints ipynb_checkpoints *.png +*/.env +.env +EconomicStressAgentORE/oredata/Output +EconomicStressAgentORE/oredata/ore_agent.xml +EconomicStressAgentORE/oredata/Input/agent_stress.xml diff --git a/EconomicStressAgentORE/.env.example b/EconomicStressAgentORE/.env.example new file mode 100644 index 0000000..c552cc2 --- /dev/null +++ b/EconomicStressAgentORE/.env.example @@ -0,0 +1,5 @@ +# LLM API key (OpenAI) +OPENAI_API_KEY=sk-... + +# OpenAI model to use +OPENAI_MODEL=gpt-4 diff --git a/EconomicStressAgentORE/.gitignore b/EconomicStressAgentORE/.gitignore new file mode 100644 index 0000000..2b08d06 --- /dev/null +++ b/EconomicStressAgentORE/.gitignore @@ -0,0 +1,2 @@ +OREDir/Output/* +__pycache__/ \ No newline at end of file diff --git a/EconomicStressAgentORE/README.md b/EconomicStressAgentORE/README.md new file mode 100644 index 0000000..538648c --- /dev/null +++ b/EconomicStressAgentORE/README.md @@ -0,0 +1,165 @@ +# Economic Scenario Stress Test Agent + +An AI agent that maps a free-text economic scenario to similar historical scenarios, +derives market shifts, runs an ORE stress test, and summarises the P&L impact. + +This is just educational purpose, I developed this agent to learn more about AI Agents +and how to integrate Open Source Risk Engine into an Agent. + +The scenarios are mock and AI generated, as is most of the code. + +## Workflow + +``` +User describes scenario + β”‚ + β–Ό +β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” +β”‚ 1. Scenario Analyzer β”‚ GPT-4 + 20-scenario knowledge base +β”‚ (historical lookup) β”‚ +β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ + β”‚ structured JSON of market shifts + β–Ό +β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” +β”‚ 2. Stress Test Builder β”‚ generates agent_stress.xml for ORE +β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ + β”‚ ore_agent.xml + Input/agent_stress.xml + β–Ό +β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” +β”‚ 3. ORE Runner β”‚ runs ORE (Python API) +β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ + β”‚ Output/stresstest.csv + β–Ό +β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” +β”‚ 4. Impact Summarizer β”‚ Markdown report + LLM narrative +β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ +``` + +## Setup + +### 1. Install dependencies + +```bash +pip install -r requirements.txt +``` + +The `open-source-risk-engine` package provides the ORE Python bindings (`from ORE import *`). + +### 2. Configure environment + +```bash +cp .env.example .env +# then edit .env +``` + +`.env` keys: + +| Key | Description | Required | +| ---------------- | ----------------------------- | -------- | +| `OPENAI_API_KEY` | Your OpenAI API key | Yes | +| `OPENAI_MODEL` | Model name (default: `gpt-4`) | No | + +### 3. ORE workspace + +The agent expects the ORE workspace at `./OREDir/` containing: + +``` +OREDir/ +β”œβ”€β”€ ore.xml ← main ORE config +β”œβ”€β”€ Input/ +β”‚ β”œβ”€β”€ portfolio.xml +β”‚ β”œβ”€β”€ marketdata.csv +β”‚ β”œβ”€β”€ conventions.xml +β”‚ β”œβ”€β”€ curveconfig.xml +β”‚ β”œβ”€β”€ todaysmarket.xml +β”‚ β”œβ”€β”€ simulation.xml +β”‚ β”œβ”€β”€ sensitivity.xml +β”‚ └── pricingengine.xml +└── Output/ ← auto-created by ORE +``` + +The agent injects two files at runtime: + +- `OREDir/Input/agent_stress.xml` β€” generated stress scenario +- `OREDir/ore_agent.xml` β€” stripped-down ore.xml (stress analytic only) + +## Usage + +```bash +# Basic usage +python agent.py --scenario "A sudden European banking crisis with contagion fears" + +# With all options +python agent.py \ + --scenario "A sudden European banking crisis with contagion fears" \ + --ore-workspace ./OREDir \ + --scenario-id my_scenario \ + --output report.md \ + --verbose +``` + +## Architecture + +| File | Purpose | +| ------------------------- | --------------------------------------------- | +| `agent.py` | CLI orchestrator β€” ties all steps together | +| `scenario_analyzer.py` | LLM call β€” maps text to market shifts | +| `stresstest_builder.py` | XML generator β€” writes ORE stress test config | +| `ore_runner.py` | ORE execution (Python API or subprocess) | +| `impact_summarizer.py` | CSV parser + LLM narrative report | +| `historical_scenarios.py` | Knowledge base of 20 historical episodes | +| `config.py` | Paths, model settings, curve grids | + +## Market entities handled + +| Asset class | Scenario key | ORE name | +| ------------- | ------------ | ------------------------------ | +| FX | `EURUSD` | `EURUSD` (absolute shift) | +| Euro equities | `SX5E` | `RIC:.STOXX50` | +| US equities | `SPX` | `RIC:.SPX` | +| EUR rates | `rates_eur` | EUR-ESTER + EUR discount curve | +| USD rates | `rates_usd` | USD-SOFR + USD discount curve | + +Rate shifts are specified at **1Y, 2Y, 5Y, 10Y, 30Y** and interpolated onto +the full ORE tenor grid using piecewise-linear interpolation. + +## Example output + +``` +╔══════════════════════════════════════════════════════════╗ +β•‘ Economic Scenario Stress Test Agent β•‘ +β•šβ•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β• + +Analyzing scenario: "A sudden European banking crisis with contagion fears" + + β–Ά Step 1/4 Analyzing scenario with LLM … + βœ“ Scenario analysis complete + +Matched scenarios: + β€’ 2010-2012 European Sovereign Debt Crisis + β€’ 2023 US Regional Banking Crisis + +EURUSD : -0.0700 (absolute) +SX5E : -19.0% +SPX : -8.5% +EUR rates: 1Y -8bp 2Y -10bp 5Y -9bp 10Y -7bp 30Y -4bp +USD rates: 1Y -8bp 2Y -12bp 5Y -14bp 10Y -12bp 30Y -8bp + + β–Ά Step 2/4 Generating ORE stress test XML … + β–Ά Step 3/4 Running ORE … + β–Ά Step 4/4 Generating impact report … + +════════════════════════════════════════════════════════════ + Portfolio Stress Test Impact Report +════════════════════════════════════════════════════════════ + +**TOTAL P&L: +92,000 EUR [GAIN]** + +| Trade | Base NPV | Stressed NPV | P&L Impact | +|-------------|------------|--------------|-------------| +| CDS | -64,059 | -63,293 | +766 | +| XccySwap | 268,878 | 230,964 | -37,914 | +| EUR6MSwap | 5,924,804 | 5,867,079 | -57,725 | +| ... | | | | +| **TOTAL** | ... | ... | **+92,000** | +``` diff --git a/EconomicStressAgentORE/agent.py b/EconomicStressAgentORE/agent.py new file mode 100644 index 0000000..35c33af --- /dev/null +++ b/EconomicStressAgentORE/agent.py @@ -0,0 +1,195 @@ +""" +agent.py β€” Economic Scenario Stress Test Agent (CLI orchestrator). + +Usage: + python agent.py --scenario "A sudden European banking crisis" [options] +""" + +from __future__ import annotations + +import sys +from pathlib import Path + +import click + +import config +from historical_scenarios import ScenarioKnowledgeBase +import impact_summarizer +import ore_runner +import scenario_analyzer +import stresstest_builder +import todaysmarket_analyzer + +BANNER = """ +╔══════════════════════════════════════════════════════════╗ +β•‘ Economic Scenario Stress Test Agent β•‘ +β•šβ•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β• +""" + +STEP = " β–Ά " +OK = " βœ“ " +WARN = " ⚠ " + + +# ── Core orchestration ──────────────────────────────────────────────────────── + +def run( + scenario_description: str, + ore_workspace: Path, + scenario_id: str = "agent_scenario", + verbose: bool = False, +) -> str: + """ + Full pipeline: analyze β†’ build β†’ run ORE β†’ summarize. + + Parameters + ---------- + scenario_description : free-text user scenario + ore_workspace : path to the OREDir workspace + scenario_id : identifier embedded in the stress test XML + verbose : print extra debug info + + Returns + ------- + Markdown-formatted report string. + """ + print(BANNER) + print(f'Analyzing scenario: "{scenario_description}"\n') + + # ── Step 1: Scenario Analysis ───────────────────────────────────────── + print(STEP + "Step 1/5 Analyzing scenario with LLM …") + knowledge_base = ScenarioKnowledgeBase( + config.DATA_DIR / "scenarios.json" + ) + analysis = scenario_analyzer.analyze(scenario_description, knowledge_base) + print(OK + "Scenario analysis complete\n") + print(scenario_analyzer.format_shifts(analysis)) + print() + + shifts = analysis["shifts"] + + # ── Step 2: Parse market structure ──────────────────────────────────── + print(STEP + "Step 2/5 Parsing todaysmarket.xml …") + ore_workspace = Path(ore_workspace) + tm_xml = ore_workspace / "Input" / "todaysmarket.xml" + market = todaysmarket_analyzer.parse(tm_xml) + sector_map = todaysmarket_analyzer.load_sector_mapping() + print(OK + f"Discovered {len(market.currencies)} currencies, " + f"{len(market.discount_curves)} discount curves, " + f"{len(market.equity_curves)} equities, " + f"{len(market.default_curves)} credit names\n") + + # ── Step 3: Build stress test XML ───────────────────────────────────── + print(STEP + "Step 3/5 Generating ORE stress test XML …") + + stress_xml = ore_workspace / "Input" / "agent_stress.xml" + stresstest_builder.build( + shifts=shifts, + market=market, + sector_map=sector_map, + output_path=stress_xml, + scenario_id=scenario_id, + scenario_label=( + "Generated by Economic Scenario Stress Test Agent | " + "Matched: " + ", ".join(analysis["matched_scenarios"]) + ), + ) + print(OK + f"Written: {stress_xml}") + + ore_agent_xml = ore_workspace / "ore_agent.xml" + stresstest_builder.build_ore_config( + base_ore_xml=ore_workspace / "ore.xml", + output_ore_xml=ore_agent_xml, + stress_config_file="agent_stress.xml", + ) + print(OK + f"Written: {ore_agent_xml}\n") + + # ── Step 4: Run ORE ─────────────────────────────────────────────────── + print(STEP + "Step 4/5 Running ORE …") + csv_path = ore_runner.run( + ore_xml=ore_agent_xml, + workspace=ore_workspace, + ) + print(OK + f"ORE completed. Results: {csv_path}\n") + + # ── Step 5: Summarize ───────────────────────────────────────────────── + print(STEP + "Step 5/5 Generating impact report …") + report = impact_summarizer.summarize( + csv_path=csv_path, + scenario_description=scenario_description, + shifts=shifts, + scenario_id=scenario_id, + ) + print(OK + "Report ready\n") + + return report + + +# ── CLI ─────────────────────────────────────────────────────────────────────── + +@click.command() +@click.option( + "--scenario", "-s", + required=True, + help="Free-text description of the economic scenario to stress-test.", +) +@click.option( + "--ore-workspace", + default=str(config.ORE_WORKSPACE), + show_default=True, + help="Path to the ORE workspace directory (must contain ore.xml, Input/, Output/).", +) +@click.option( + "--scenario-id", + default="agent_scenario", + show_default=True, + help="Identifier for the stress scenario in the ORE XML (no spaces).", +) +@click.option( + "--output", + "-o", + default=None, + help="Write the Markdown report to this file (prints to stdout if not set).", +) +@click.option("--verbose", "-v", is_flag=True, help="Enable verbose output.") +def main( + scenario: str, + ore_workspace: str, + scenario_id: str, + output: str | None, + verbose: bool, +) -> None: + """Economic Scenario Stress Test Agent. + + Analyses a free-text economic scenario, maps it to historical market + parallels, generates an ORE stress test, runs it, and prints a P&L report. + """ + try: + report = run( + scenario_description=scenario, + ore_workspace=Path(ore_workspace), + scenario_id=scenario_id, + verbose=verbose, + ) + except EnvironmentError as exc: + click.echo(f"\n{WARN} Configuration error: {exc}", err=True) + sys.exit(1) + except FileNotFoundError as exc: + click.echo(f"\n{WARN} File not found: {exc}", err=True) + sys.exit(1) + except Exception as exc: # noqa: BLE001 + click.echo(f"\n{WARN} Unexpected error: {exc}", err=True) + if verbose: + import traceback + traceback.print_exc() + sys.exit(1) + + if output: + Path(output).write_text(report, encoding="utf-8") + click.echo(f"Report written to: {output}") + else: + print(report) + + +if __name__ == "__main__": + main() diff --git a/EconomicStressAgentORE/config.py b/EconomicStressAgentORE/config.py new file mode 100644 index 0000000..f4b0430 --- /dev/null +++ b/EconomicStressAgentORE/config.py @@ -0,0 +1,45 @@ +""" +config.py β€” central configuration for the Economic Scenario Stress Test Agent. +""" + +import os +from pathlib import Path + +from dotenv import load_dotenv + +load_dotenv() + +# ── Paths ───────────────────────────────────────────────────────────────────── +BASE_DIR = Path(__file__).parent +DATA_DIR = BASE_DIR / "data" # raw data files (e.g. historical scenarios) +ORE_WORKSPACE = BASE_DIR / "oredata" # workspace that contains ore.xml +ORE_INPUT_DIR = ORE_WORKSPACE / "Input" +ORE_OUTPUT_DIR = ORE_WORKSPACE / "Output" + +# Generated files written by the agent +AGENT_STRESS_XML = ORE_INPUT_DIR / "agent_stress.xml" +AGENT_ORE_XML = ORE_WORKSPACE / "ore_agent.xml" + +# ── LLM ─────────────────────────────────────────────────────────────────────── +OPENAI_API_KEY: str = os.getenv("OPENAI_API_KEY", "") +OPENAI_MODEL: str = os.getenv("OPENAI_MODEL", "gpt-4") + +# ── Sector-mapping CSV (maps equity/credit names β†’ sectors) ─────────────────── +SECTOR_MAPPING_CSV: Path = BASE_DIR / "sector_mapping.csv" + +# ── Standard tenor grids (used when generating simulation.xml & stresstest) ─── +STANDARD_RATE_TENORS: list[str] = [ + "1Y", "2Y", "3Y", "5Y", "7Y", "10Y", "15Y", "20Y", "30Y", +] + +STANDARD_CREDIT_TENORS: list[str] = [ + "6M", "1Y", "2Y", "3Y", "5Y", "7Y", "10Y", "15Y", "20Y", +] + +STANDARD_INFLATION_TENORS: list[str] = [ + "1Y", "2Y", "3Y", "5Y", "7Y", "10Y", "15Y", "20Y", "30Y", +] + +STANDARD_EQUITY_DIV_TENORS: list[str] = [ + "6M", "1Y", "2Y", "3Y", "5Y", "7Y", "10Y", +] diff --git a/EconomicStressAgentORE/data/scenarios.json b/EconomicStressAgentORE/data/scenarios.json new file mode 100644 index 0000000..67801ec --- /dev/null +++ b/EconomicStressAgentORE/data/scenarios.json @@ -0,0 +1,899 @@ +[ + { + "name": "2008 Global Financial Crisis", + "period": "Sep 2008 – Mar 2009", + "description": "Collapse of Lehman Brothers triggered a global credit crunch, massive equity sell-off, flight to quality into US Treasuries, and extreme volatility across all asset classes.", + "shifts": { + "rates": { + "EUR": { + "1Y": -0.02, + "2Y": -0.025, + "5Y": -0.015, + "10Y": -0.01, + "30Y": -0.005 + }, + "USD": { + "1Y": -0.03, + "2Y": -0.03, + "5Y": -0.02, + "10Y": -0.015, + "30Y": -0.01 + } + }, + "fx": { "EURUSD": -0.1 }, + "equity": { + "EUR": -0.45, + "USD": -0.4 + }, + "credit": { + "EUR": { + "1Y": 0.015, + "2Y": 0.02, + "3Y": 0.025, + "5Y": 0.03, + "10Y": 0.035 + }, + "USD": { "1Y": 0.02, "2Y": 0.025, "3Y": 0.03, "5Y": 0.035, "10Y": 0.04 } + } + } + }, + { + "name": "2010-2012 European Sovereign Debt Crisis", + "period": "2010 – 2012", + "description": "Greek debt crisis spread to Ireland, Portugal, Spain, and Italy. EUR weakened significantly, European equities fell, and EUR rates were cut while peripheral spreads blew out.", + "shifts": { + "rates": { + "EUR": { + "1Y": -0.01, + "2Y": -0.012, + "5Y": -0.008, + "10Y": -0.005, + "30Y": -0.003 + }, + "USD": { + "1Y": -0.008, + "2Y": -0.01, + "5Y": -0.012, + "10Y": -0.015, + "30Y": -0.01 + } + }, + "fx": { "EURUSD": -0.15 }, + "equity": { + "EUR": -0.3, + "USD": -0.12 + }, + "credit": { + "EUR": { + "1Y": 0.012, + "2Y": 0.018, + "3Y": 0.022, + "5Y": 0.025, + "10Y": 0.02 + }, + "USD": { + "1Y": 0.005, + "2Y": 0.006, + "3Y": 0.007, + "5Y": 0.008, + "10Y": 0.008 + }, + "Sovereign": { + "1Y": 0.03, + "2Y": 0.04, + "3Y": 0.05, + "5Y": 0.06, + "10Y": 0.05 + } + } + } + }, + { + "name": "2015 China Devaluation / EM Shock", + "period": "Aug – Sep 2015", + "description": "PBoC surprised markets with a CNY devaluation, triggering a global equity sell-off and flight to safety.", + "shifts": { + "rates": { + "EUR": { + "1Y": -0.002, + "2Y": -0.003, + "5Y": -0.005, + "10Y": -0.008, + "30Y": -0.006 + }, + "USD": { + "1Y": -0.005, + "2Y": -0.008, + "5Y": -0.012, + "10Y": -0.015, + "30Y": -0.01 + } + }, + "fx": { "EURUSD": 0.04 }, + "equity": { + "EUR": -0.18, + "USD": -0.12 + }, + "credit": { + "EUR": { + "1Y": 0.003, + "2Y": 0.004, + "3Y": 0.005, + "5Y": 0.005, + "10Y": 0.004 + }, + "USD": { + "1Y": 0.002, + "2Y": 0.003, + "3Y": 0.004, + "5Y": 0.004, + "10Y": 0.003 + } + } + } + }, + { + "name": "2020 COVID-19 Crash", + "period": "Feb – Mar 2020", + "description": "Global pandemic caused the fastest equity bear market in history, massive rate cuts, and a short-lived USD liquidity squeeze.", + "shifts": { + "rates": { + "EUR": { + "1Y": -0.003, + "2Y": -0.005, + "5Y": -0.007, + "10Y": -0.008, + "30Y": -0.005 + }, + "USD": { + "1Y": -0.015, + "2Y": -0.015, + "5Y": -0.012, + "10Y": -0.01, + "30Y": -0.008 + } + }, + "fx": { "EURUSD": -0.03 }, + "equity": { + "EUR": -0.35, + "USD": -0.34 + }, + "credit": { + "EUR": { + "1Y": 0.012, + "2Y": 0.018, + "3Y": 0.022, + "5Y": 0.025, + "10Y": 0.02 + }, + "USD": { + "1Y": 0.015, + "2Y": 0.02, + "3Y": 0.025, + "5Y": 0.028, + "10Y": 0.022 + } + } + } + }, + { + "name": "2022 Russia-Ukraine / Energy Crisis", + "period": "Feb – Oct 2022", + "description": "Russian invasion of Ukraine caused energy supply shock in Europe, EUR fell to parity with USD, European equities dropped, and central banks hiked rates aggressively to fight inflation.", + "shifts": { + "rates": { + "EUR": { + "1Y": 0.025, + "2Y": 0.03, + "5Y": 0.025, + "10Y": 0.02, + "30Y": 0.015 + }, + "USD": { + "1Y": 0.03, + "2Y": 0.035, + "5Y": 0.025, + "10Y": 0.018, + "30Y": 0.012 + } + }, + "fx": { "EURUSD": -0.15 }, + "equity": { + "EUR": -0.22, + "USD": -0.25 + }, + "credit": { + "EUR": { + "1Y": 0.01, + "2Y": 0.014, + "3Y": 0.016, + "5Y": 0.018, + "10Y": 0.015 + }, + "USD": { + "1Y": 0.004, + "2Y": 0.006, + "3Y": 0.007, + "5Y": 0.008, + "10Y": 0.007 + } + } + } + }, + { + "name": "1992 ERM Crisis / Black Wednesday", + "period": "Sep 1992", + "description": "Speculative attack on the British pound and Italian lira forced exits from the ERM. European currencies fell sharply against the DEM. EUR proxy (DEM) strengthened vs USD.", + "shifts": { + "rates": { + "EUR": { + "1Y": 0.01, + "2Y": 0.008, + "5Y": 0.005, + "10Y": 0.003, + "30Y": 0.002 + }, + "USD": { + "1Y": -0.003, + "2Y": -0.003, + "5Y": -0.002, + "10Y": -0.002, + "30Y": -0.001 + } + }, + "fx": { "EURUSD": 0.05 }, + "equity": { + "EUR": -0.1, + "USD": -0.03 + }, + "credit": { + "EUR": { + "1Y": 0.002, + "2Y": 0.003, + "3Y": 0.004, + "5Y": 0.004, + "10Y": 0.003 + }, + "USD": { + "1Y": 0.001, + "2Y": 0.002, + "3Y": 0.002, + "5Y": 0.002, + "10Y": 0.002 + } + } + } + }, + { + "name": "2001 Dot-com Bust", + "period": "Mar 2000 – Oct 2002", + "description": "Bursting of the tech bubble led to a prolonged equity bear market, recession fears, and aggressive Fed rate cuts.", + "shifts": { + "rates": { + "EUR": { + "1Y": -0.015, + "2Y": -0.012, + "5Y": -0.008, + "10Y": -0.005, + "30Y": -0.003 + }, + "USD": { + "1Y": -0.04, + "2Y": -0.035, + "5Y": -0.025, + "10Y": -0.015, + "30Y": -0.01 + } + }, + "fx": { "EURUSD": 0.08 }, + "equity": { + "EUR": -0.55, + "USD": -0.45, + "Tech": -0.75 + }, + "credit": { + "EUR": { + "1Y": 0.005, + "2Y": 0.007, + "3Y": 0.008, + "5Y": 0.009, + "10Y": 0.008 + }, + "USD": { + "1Y": 0.008, + "2Y": 0.01, + "3Y": 0.012, + "5Y": 0.014, + "10Y": 0.012 + } + } + } + }, + { + "name": "1997 Asian Financial Crisis", + "period": "Jul 1997 – Jan 1998", + "description": "Currency collapses across SE Asia (THB, KRW, IDR) spread contagion to global markets. Safe-haven flows benefited USD and Treasuries.", + "shifts": { + "rates": { + "EUR": { + "1Y": -0.003, + "2Y": -0.004, + "5Y": -0.005, + "10Y": -0.005, + "30Y": -0.003 + }, + "USD": { + "1Y": -0.005, + "2Y": -0.008, + "5Y": -0.01, + "10Y": -0.01, + "30Y": -0.008 + } + }, + "fx": { "EURUSD": -0.06 }, + "equity": { + "EUR": -0.15, + "USD": -0.08 + }, + "credit": { + "EUR": { + "1Y": 0.002, + "2Y": 0.003, + "3Y": 0.004, + "5Y": 0.005, + "10Y": 0.004 + }, + "USD": { + "1Y": 0.003, + "2Y": 0.005, + "3Y": 0.006, + "5Y": 0.007, + "10Y": 0.006 + } + } + } + }, + { + "name": "2023 US Regional Banking Crisis", + "period": "Mar 2023", + "description": "Collapse of SVB and Signature Bank raised fears of a wider banking crisis. Rates fell sharply as markets priced in Fed cuts; equities initially fell but recovered quickly.", + "shifts": { + "rates": { + "EUR": { + "1Y": -0.005, + "2Y": -0.008, + "5Y": -0.01, + "10Y": -0.008, + "30Y": -0.005 + }, + "USD": { + "1Y": -0.01, + "2Y": -0.015, + "5Y": -0.018, + "10Y": -0.015, + "30Y": -0.01 + } + }, + "fx": { "EURUSD": 0.03 }, + "equity": { + "EUR": -0.08, + "USD": -0.05 + }, + "credit": { + "EUR": { + "1Y": 0.003, + "2Y": 0.004, + "3Y": 0.005, + "5Y": 0.005, + "10Y": 0.004 + }, + "USD": { + "1Y": 0.006, + "2Y": 0.008, + "3Y": 0.01, + "5Y": 0.012, + "10Y": 0.01 + }, + "SeniorUnsecured": { + "1Y": 0.012, + "2Y": 0.016, + "3Y": 0.02, + "5Y": 0.024, + "10Y": 0.02 + } + } + } + }, + { + "name": "1998 LTCM / Russian Default", + "period": "Aug – Oct 1998", + "description": "Russian debt default and LTCM collapse caused a global liquidity crisis. Massive flight to quality into US Treasuries, equity sell-off, and credit spreads blew out.", + "shifts": { + "rates": { + "EUR": { + "1Y": -0.005, + "2Y": -0.008, + "5Y": -0.01, + "10Y": -0.01, + "30Y": -0.008 + }, + "USD": { + "1Y": -0.008, + "2Y": -0.012, + "5Y": -0.015, + "10Y": -0.015, + "30Y": -0.012 + } + }, + "fx": { "EURUSD": 0.06 }, + "equity": { + "EUR": -0.3, + "USD": -0.2 + }, + "credit": { + "EUR": { + "1Y": 0.008, + "2Y": 0.012, + "3Y": 0.015, + "5Y": 0.018, + "10Y": 0.015 + }, + "USD": { + "1Y": 0.012, + "2Y": 0.016, + "3Y": 0.02, + "5Y": 0.024, + "10Y": 0.02 + }, + "Sovereign": { + "1Y": 0.02, + "2Y": 0.03, + "3Y": 0.04, + "5Y": 0.05, + "10Y": 0.04 + } + } + } + }, + { + "name": "2011 US Debt-Ceiling Crisis / S&P Downgrade", + "period": "Jul – Aug 2011", + "description": "US debt-ceiling standoff and first-ever S&P downgrade of US sovereign debt. Paradoxically Treasuries rallied on flight to quality, equities sold off.", + "shifts": { + "rates": { + "EUR": { + "1Y": -0.003, + "2Y": -0.005, + "5Y": -0.008, + "10Y": -0.01, + "30Y": -0.008 + }, + "USD": { + "1Y": -0.005, + "2Y": -0.008, + "5Y": -0.012, + "10Y": -0.012, + "30Y": -0.01 + } + }, + "fx": { "EURUSD": -0.05 }, + "equity": { + "EUR": -0.25, + "USD": -0.17 + }, + "credit": { + "EUR": { + "1Y": 0.004, + "2Y": 0.006, + "3Y": 0.008, + "5Y": 0.01, + "10Y": 0.008 + }, + "USD": { + "1Y": 0.005, + "2Y": 0.007, + "3Y": 0.009, + "5Y": 0.01, + "10Y": 0.008 + } + } + } + }, + { + "name": "2018 Volmageddon / Feb Sell-off", + "period": "Feb 2018", + "description": "A sharp spike in US wage growth triggered fears of faster Fed tightening. VIX exploded, inverse-vol products blew up, equities fell sharply.", + "shifts": { + "rates": { + "EUR": { + "1Y": 0.002, + "2Y": 0.003, + "5Y": 0.005, + "10Y": 0.005, + "30Y": 0.003 + }, + "USD": { + "1Y": 0.005, + "2Y": 0.008, + "5Y": 0.01, + "10Y": 0.008, + "30Y": 0.005 + } + }, + "fx": { "EURUSD": 0.02 }, + "equity": { + "EUR": -0.1, + "USD": -0.1 + }, + "credit": { + "EUR": { + "1Y": 0.001, + "2Y": 0.002, + "3Y": 0.002, + "5Y": 0.003, + "10Y": 0.002 + }, + "USD": { + "1Y": 0.002, + "2Y": 0.002, + "3Y": 0.003, + "5Y": 0.003, + "10Y": 0.002 + } + } + } + }, + { + "name": "2013 Taper Tantrum", + "period": "May – Sep 2013", + "description": "Bernanke hinted at tapering QE. US long-term rates spiked, EM currencies and equities fell, USD strengthened.", + "shifts": { + "rates": { + "EUR": { + "1Y": 0.002, + "2Y": 0.005, + "5Y": 0.008, + "10Y": 0.01, + "30Y": 0.008 + }, + "USD": { + "1Y": 0.003, + "2Y": 0.008, + "5Y": 0.012, + "10Y": 0.013, + "30Y": 0.01 + } + }, + "fx": { "EURUSD": -0.04 }, + "equity": { + "EUR": -0.08, + "USD": -0.05 + }, + "credit": { + "EUR": { + "1Y": 0.002, + "2Y": 0.003, + "3Y": 0.003, + "5Y": 0.004, + "10Y": 0.003 + }, + "USD": { + "1Y": 0.002, + "2Y": 0.003, + "3Y": 0.004, + "5Y": 0.004, + "10Y": 0.003 + } + } + } + }, + { + "name": "2016 Brexit Referendum", + "period": "Jun 2016", + "description": "The UK voted to leave the EU. GBP collapsed, EUR weakened, European equities fell, and rates dropped as markets priced slower growth.", + "shifts": { + "rates": { + "EUR": { + "1Y": -0.003, + "2Y": -0.005, + "5Y": -0.008, + "10Y": -0.01, + "30Y": -0.008 + }, + "USD": { + "1Y": -0.005, + "2Y": -0.008, + "5Y": -0.01, + "10Y": -0.012, + "30Y": -0.01 + } + }, + "fx": { "EURUSD": -0.04 }, + "equity": { + "EUR": -0.12, + "USD": -0.05 + }, + "credit": { + "EUR": { + "1Y": 0.004, + "2Y": 0.006, + "3Y": 0.007, + "5Y": 0.008, + "10Y": 0.006 + }, + "USD": { + "1Y": 0.001, + "2Y": 0.002, + "3Y": 0.002, + "5Y": 0.002, + "10Y": 0.002 + } + } + } + }, + { + "name": "2022-2023 Global Rate Hiking Cycle", + "period": "2022 – 2023", + "description": "Post-COVID inflation surge led to synchronised global rate hikes by the Fed, ECB, BoE. Bond markets had historic losses, equities de-rated.", + "shifts": { + "rates": { + "EUR": { + "1Y": 0.035, + "2Y": 0.03, + "5Y": 0.025, + "10Y": 0.02, + "30Y": 0.015 + }, + "USD": { + "1Y": 0.045, + "2Y": 0.04, + "5Y": 0.03, + "10Y": 0.022, + "30Y": 0.015 + } + }, + "fx": { "EURUSD": -0.12 }, + "equity": { + "EUR": -0.18, + "USD": -0.2 + }, + "credit": { + "EUR": { + "1Y": 0.005, + "2Y": 0.007, + "3Y": 0.008, + "5Y": 0.009, + "10Y": 0.008 + }, + "USD": { + "1Y": 0.004, + "2Y": 0.005, + "3Y": 0.006, + "5Y": 0.007, + "10Y": 0.006 + } + } + } + }, + { + "name": "1970s-style Stagflation", + "period": "1973 – 1980 (composite)", + "description": "Oil-price shocks combined with wage-price spirals produced persistently high inflation and economic stagnation. Rates rose dramatically, equities underperformed in real terms.", + "shifts": { + "rates": { + "EUR": { + "1Y": 0.03, + "2Y": 0.03, + "5Y": 0.025, + "10Y": 0.02, + "30Y": 0.015 + }, + "USD": { "1Y": 0.04, "2Y": 0.04, "5Y": 0.035, "10Y": 0.03, "30Y": 0.02 } + }, + "fx": { "EURUSD": -0.08 }, + "equity": { + "EUR": -0.2, + "USD": -0.15 + }, + "credit": { + "EUR": { + "1Y": 0.006, + "2Y": 0.008, + "3Y": 0.01, + "5Y": 0.012, + "10Y": 0.01 + }, + "USD": { + "1Y": 0.008, + "2Y": 0.01, + "3Y": 0.012, + "5Y": 0.014, + "10Y": 0.012 + } + } + } + }, + { + "name": "Deflationary Recession (Japan-style)", + "period": "Composite (1990s–2010s Japan)", + "description": "Protracted deflation, zero-bound rates, equity malaise. If applied to EUR/USD: massive rate cuts, equities drift lower, EUR weakens as ECB goes ultra-loose.", + "shifts": { + "rates": { + "EUR": { + "1Y": -0.02, + "2Y": -0.02, + "5Y": -0.018, + "10Y": -0.015, + "30Y": -0.01 + }, + "USD": { + "1Y": -0.015, + "2Y": -0.015, + "5Y": -0.012, + "10Y": -0.01, + "30Y": -0.005 + } + }, + "fx": { "EURUSD": -0.1 }, + "equity": { + "EUR": -0.25, + "USD": -0.1 + }, + "credit": { + "EUR": { + "1Y": 0.005, + "2Y": 0.007, + "3Y": 0.008, + "5Y": 0.009, + "10Y": 0.008 + }, + "USD": { + "1Y": 0.003, + "2Y": 0.004, + "3Y": 0.005, + "5Y": 0.005, + "10Y": 0.004 + } + } + } + }, + { + "name": "Eurozone Break-up (Tail Risk)", + "period": "Hypothetical", + "description": "A large Eurozone member exits the single currency. Extreme EUR weakness, European equities collapse, EUR rates spike on redenomination risk, USD rates fall on safe-haven flows.", + "shifts": { + "rates": { + "EUR": { + "1Y": 0.03, + "2Y": 0.035, + "5Y": 0.03, + "10Y": 0.025, + "30Y": 0.02 + }, + "USD": { + "1Y": -0.015, + "2Y": -0.02, + "5Y": -0.025, + "10Y": -0.02, + "30Y": -0.015 + } + }, + "fx": { "EURUSD": -0.25 }, + "equity": { + "EUR": -0.5, + "USD": -0.2 + }, + "credit": { + "EUR": { + "1Y": 0.025, + "2Y": 0.035, + "3Y": 0.04, + "5Y": 0.045, + "10Y": 0.04 + }, + "USD": { + "1Y": 0.006, + "2Y": 0.008, + "3Y": 0.01, + "5Y": 0.012, + "10Y": 0.01 + }, + "Sovereign": { + "1Y": 0.05, + "2Y": 0.07, + "3Y": 0.08, + "5Y": 0.09, + "10Y": 0.08 + } + } + } + }, + { + "name": "US-China Trade War Escalation", + "period": "2018 – 2019", + "description": "Escalating tariffs between US and China caused global growth fears, equity sell-offs, and a flight to bonds.", + "shifts": { + "rates": { + "EUR": { + "1Y": -0.003, + "2Y": -0.005, + "5Y": -0.008, + "10Y": -0.01, + "30Y": -0.008 + }, + "USD": { + "1Y": -0.005, + "2Y": -0.008, + "5Y": -0.012, + "10Y": -0.012, + "30Y": -0.008 + } + }, + "fx": { "EURUSD": -0.03 }, + "equity": { + "EUR": -0.12, + "USD": -0.1 + }, + "credit": { + "EUR": { + "1Y": 0.002, + "2Y": 0.003, + "3Y": 0.004, + "5Y": 0.005, + "10Y": 0.004 + }, + "USD": { + "1Y": 0.003, + "2Y": 0.004, + "3Y": 0.005, + "5Y": 0.006, + "10Y": 0.005 + } + } + } + }, + { + "name": "Sudden Inflation Spike", + "period": "Hypothetical / 2021-style", + "description": "Unexpected jump in inflation forces central banks to tighten much faster than expected. Front-end rates spike, curves flatten, equities de-rate.", + "shifts": { + "rates": { + "EUR": { + "1Y": 0.02, + "2Y": 0.018, + "5Y": 0.012, + "10Y": 0.008, + "30Y": 0.005 + }, + "USD": { + "1Y": 0.025, + "2Y": 0.022, + "5Y": 0.015, + "10Y": 0.01, + "30Y": 0.005 + } + }, + "fx": { "EURUSD": -0.05 }, + "equity": { + "EUR": -0.15, + "USD": -0.12 + }, + "credit": { + "EUR": { + "1Y": 0.004, + "2Y": 0.005, + "3Y": 0.006, + "5Y": 0.007, + "10Y": 0.006 + }, + "USD": { + "1Y": 0.003, + "2Y": 0.004, + "3Y": 0.005, + "5Y": 0.006, + "10Y": 0.005 + } + } + } + } +] diff --git a/EconomicStressAgentORE/data/sector_mapping.csv b/EconomicStressAgentORE/data/sector_mapping.csv new file mode 100644 index 0000000..ed7fc9e --- /dev/null +++ b/EconomicStressAgentORE/data/sector_mapping.csv @@ -0,0 +1,4 @@ +type,name,currency,sector +equity,RIC:.SPX,USD,Index +equity,RIC:.STOXX50,EUR,Index +credit,Underlying1,USD,SeniorUnsecured diff --git a/EconomicStressAgentORE/historical_scenarios.py b/EconomicStressAgentORE/historical_scenarios.py new file mode 100644 index 0000000..53d34aa --- /dev/null +++ b/EconomicStressAgentORE/historical_scenarios.py @@ -0,0 +1,96 @@ +""" +Historical economic scenarios knowledge base. + +Loads scenario data from a JSON file and provides structured access +for the scenario analyser and stress-test builder. + +Each scenario has the **generic schema**:: + + shifts: + rates: { CCY: { tenor: abs_shift } } + fx: { PAIR: abs_shift } + equity: { CCY_or_sector: relative_shift } + credit: { CCY_or_sector: { tenor: abs_shift } } + +* Rates / credit: absolute change (decimal, e.g. -0.015 = -150 bps). +* FX: absolute change in spot (e.g. -0.10 means spot drops by 0.10). +* Equity: relative change (e.g. -0.25 = -25 %). +""" + +from __future__ import annotations + +import json +from pathlib import Path + + +class ScenarioKnowledgeBase: + """Immutable collection of historical stress scenarios loaded from JSON.""" + + def __init__(self, path: Path | str) -> None: + self._path = Path(path) + if not self._path.exists(): + raise FileNotFoundError(f"Scenario file not found: {self._path}") + self._scenarios: list[dict] = self._load() + + # -- loading ------------------------------------------------------------- + + def _load(self) -> list[dict]: + with open(self._path, "r") as fh: + return json.load(fh) + + # -- public accessors ---------------------------------------------------- + + @property + def scenarios(self) -> list[dict]: + """Return the raw list of scenario dicts.""" + return self._scenarios + + def __len__(self) -> int: + return len(self._scenarios) + + def __getitem__(self, index: int) -> dict: + return self._scenarios[index] + + def __iter__(self): + return iter(self._scenarios) + + # -- formatting ---------------------------------------------------------- + + @staticmethod + def _fmt_tenor_dict(d: dict[str, float], unit: str = "bps") -> str: + """Format a {tenor: shift} dict for display.""" + return " / ".join( + f"{k}: {v * 10000:+.0f}" if unit == "bps" else f"{k}: {v:+.0%}" + for k, v in d.items() + ) + + def get_scenarios_text(self) -> str: + """Return a formatted text dump of all scenarios for LLM context.""" + lines: list[str] = [] + for i, s in enumerate(self._scenarios, 1): + lines.append(f"### {i}. {s['name']} ({s['period']})") + lines.append(s["description"]) + sh = s["shifts"] + + if "fx" in sh: + for pair, v in sh["fx"].items(): + lines.append(f" FX {pair}: {v:+.2f}") + + if "equity" in sh: + for key, v in sh["equity"].items(): + lines.append(f" Equity {key}: {v:+.0%}") + + if "rates" in sh: + for ccy, tenors in sh["rates"].items(): + lines.append( + f" Rates {ccy} (bps): {self._fmt_tenor_dict(tenors)}" + ) + + if "credit" in sh: + for key, tenors in sh["credit"].items(): + lines.append( + f" Credit {key} (bps): {self._fmt_tenor_dict(tenors)}" + ) + + lines.append("") + return "\n".join(lines) diff --git a/EconomicStressAgentORE/impact_summarizer.py b/EconomicStressAgentORE/impact_summarizer.py new file mode 100644 index 0000000..011eccc --- /dev/null +++ b/EconomicStressAgentORE/impact_summarizer.py @@ -0,0 +1,220 @@ +""" +impact_summarizer.py β€” Step 4: parse ORE stresstest.csv output, compute P&L +impacts, and generate a human-readable narrative via LLM. +""" + +from __future__ import annotations + +import io +from pathlib import Path +from typing import Any + +import pandas as pd +from openai import OpenAI + +import config + +# ── CSV parsing ─────────────────────────────────────────────────────────────── + +def _read_stresstest_csv(csv_path: Path, scenario_id: str) -> pd.DataFrame: + """ + Load stresstest.csv and return rows matching *scenario_id*. + + The file has a header line that starts with ``#TradeId`` β€” pandas handles + the ``#`` by treating it as part of the first column name, so we strip it. + """ + with csv_path.open() as f: + content = f.read() + + # Remove leading # from the header line + content = content.replace("#TradeId", "TradeId", 1) + + df = pd.read_csv(io.StringIO(content)) + df.columns = [c.strip() for c in df.columns] + + # Filter to this scenario + mask = df["ScenarioLabel"].str.strip() == scenario_id + filtered = df[mask].copy() + + if filtered.empty: + raise ValueError( + f"No results found for scenario '{scenario_id}' in {csv_path}.\n" + f"Available scenarios: {df['ScenarioLabel'].unique().tolist()}" + ) + + filtered["PnL"] = filtered["Scenario NPV"] - filtered["Base NPV"] + return filtered + + +# ── Analysis helpers ────────────────────────────────────────────────────────── + +def _compute_summary(df: pd.DataFrame) -> dict[str, Any]: + """Return a dict with totals and per-trade data.""" + total_base = df["Base NPV"].sum() + total_stressed = df["Scenario NPV"].sum() + total_pnl = total_stressed - total_base + + rows = df.sort_values("PnL").to_dict(orient="records") + + return { + "total_base_npv": total_base, + "total_stressed_npv": total_stressed, + "total_pnl": total_pnl, + "trades": rows, + "top_losers": sorted(rows, key=lambda r: r["PnL"])[:3], + "top_gainers": sorted(rows, key=lambda r: r["PnL"], reverse=True)[:3], + } + + +# ── Markdown table ──────────────────────────────────────────────────────────── + +def _format_table(summary: dict[str, Any]) -> str: + """Build a Markdown results table.""" + lines = [ + "| Trade | Base NPV | Stressed NPV | P&L Impact |", + "|-------|----------|--------------|------------|", + ] + for row in summary["trades"]: + pnl_str = f"{row['PnL']:+,.0f}" + lines.append( + f"| {row['TradeId']} " + f"| {row['Base NPV']:,.0f} " + f"| {row['Scenario NPV']:,.0f} " + f"| {pnl_str} |" + ) + lines.append( + f"| **TOTAL** " + f"| **{summary['total_base_npv']:,.0f}** " + f"| **{summary['total_stressed_npv']:,.0f}** " + f"| **{summary['total_pnl']:+,.0f}** |" + ) + return "\n".join(lines) + + +# ── LLM narrative ───────────────────────────────────────────────────────────── + +_NARRATIVE_SYSTEM = """\ +You are a senior risk analyst writing a concise executive summary of a +portfolio stress test. Keep the summary to 3-4 paragraphs. Use plain prose β€” +no bullet points. Be specific about the numbers provided. +""" + +def _llm_narrative( + scenario_description: str, + shifts: dict[str, Any], + summary: dict[str, Any], +) -> str: + """Call the LLM to produce a narrative paragraph about the results.""" + if not config.OPENAI_API_KEY: + return ( + "(LLM narrative unavailable β€” OPENAI_API_KEY not set)\n\n" + f"Total P&L impact: {summary['total_pnl']:+,.0f} EUR" + ) + + client = OpenAI(api_key=config.OPENAI_API_KEY) + + # ── Build a shifts summary from the generic schema ── + shift_lines: list[str] = [] + + # FX + for pair, v in shifts.get("fx", {}).items(): + shift_lines.append(f" FX {pair}: {v:+.4f} (absolute)") + + # Equity + for key, v in shifts.get("equity", {}).items(): + shift_lines.append(f" Equity {key}: {v:+.1%}") + + # Rates + for ccy, tenors in shifts.get("rates", {}).items(): + parts = " / ".join(f"{t} {val*1e4:+.0f}bp" for t, val in tenors.items()) + shift_lines.append(f" Rates {ccy}: {parts}") + + # Credit + for key, tenors in shifts.get("credit", {}).items(): + parts = " / ".join(f"{t} {val*1e4:+.0f}bp" for t, val in tenors.items()) + shift_lines.append(f" Credit {key}: {parts}") + + shifts_text = "\n".join(shift_lines) if shift_lines else " (none)" + + prompt = f"""\ +Scenario description: {scenario_description} + +Applied market shifts: +{shifts_text} + +Portfolio results (all in EUR, base currency): + Base portfolio NPV: {summary['total_base_npv']:,.0f} + Stressed portfolio NPV: {summary['total_stressed_npv']:,.0f} + Total P&L impact: {summary['total_pnl']:+,.0f} + +Trade-level impacts (sorted by P&L): +""" + "\n".join( + f" {r['TradeId']:30s} Base: {r['Base NPV']:>14,.0f} " + f"Stressed: {r['Scenario NPV']:>14,.0f} P&L: {r['PnL']:>+14,.0f}" + for r in summary["trades"] + ) + f""" + +Top losers: {', '.join(r['TradeId'] for r in summary['top_losers'])} +Top gainers: {', '.join(r['TradeId'] for r in summary['top_gainers'])} + +Write an executive summary explaining which asset classes drove the result, +which trades were most affected, and what the overall risk interpretation is. +""" + + resp = client.chat.completions.create( + model=config.OPENAI_MODEL, + messages=[ + {"role": "system", "content": _NARRATIVE_SYSTEM}, + {"role": "user", "content": prompt}, + ], + temperature=0.4, + ) + return resp.choices[0].message.content or "" + + +# ── Main entry point ────────────────────────────────────────────────────────── + +def summarize( + csv_path: Path, + scenario_description: str, + shifts: dict[str, Any], + scenario_id: str = "agent_scenario", +) -> str: + """ + Parse *csv_path*, compute impacts, and return a Markdown report string. + + Parameters + ---------- + csv_path : path to stresstest.csv produced by ORE + scenario_description : original user text (used for narrative prompt) + shifts : the shifts dict from scenario_analyzer.analyze() + scenario_id : the StressTest id used when building the XML + + Returns + ------- + Markdown-formatted report string. + """ + df = _read_stresstest_csv(csv_path, scenario_id) + summary = _compute_summary(df) + table = _format_table(summary) + narrative = _llm_narrative(scenario_description, shifts, summary) + + header = ( + "═" * 60 + "\n" + " Portfolio Stress Test Impact Report\n" + "═" * 60 + ) + + pnl_sign = "+" if summary["total_pnl"] >= 0 else "" + pnl_label = f"TOTAL P&L: {pnl_sign}{summary['total_pnl']:,.0f} EUR" + direction = "GAIN" if summary["total_pnl"] >= 0 else "LOSS" + + report = ( + f"{header}\n\n" + f"**{pnl_label} [{direction}]**\n\n" + f"{table}\n\n" + "---\n\n" + "**Narrative Summary**\n\n" + f"{narrative}\n" + ) + return report diff --git a/EconomicStressAgentORE/ore_runner.py b/EconomicStressAgentORE/ore_runner.py new file mode 100644 index 0000000..6ed301d --- /dev/null +++ b/EconomicStressAgentORE/ore_runner.py @@ -0,0 +1,90 @@ +""" +ore_runner.py β€” Step 3: execute ORE with the agent-generated stress test config +and return the path to the output stresstest.csv. + +Uses the ORE Python bindings (``from ORE import *``). +ORE is logically "run from" the OREDir workspace directory so +that relative ``inputPath`` / ``outputPath`` in ore.xml resolve correctly. +""" + +from __future__ import annotations + +import os +import shutil +from pathlib import Path + +import config + + +# ── Public API ──────────────────────────────────────────────────────────────── + +def run( + ore_xml: Path | str | None = None, + workspace: Path | str | None = None, +) -> Path: + """ + Run ORE and return the path to the output stresstest.csv. + + Parameters + ---------- + ore_xml : path to the ORE main config file. + Defaults to ``config.AGENT_ORE_XML``. + workspace : directory from which ORE should be launched. + Defaults to ``config.ORE_WORKSPACE``. + + Returns + ------- + Path to ``Output/stresstest.csv`` inside the workspace. + """ + if ore_xml is None: + ore_xml = config.AGENT_ORE_XML + if workspace is None: + workspace = config.ORE_WORKSPACE + + ore_xml = Path(ore_xml) + workspace = Path(workspace) + + if not ore_xml.exists(): + raise FileNotFoundError(f"ORE config not found: {ore_xml}") + if not workspace.exists(): + raise FileNotFoundError(f"ORE workspace not found: {workspace}") + + # Path passed to ORE should be relative to the workspace (since ORE resolves + # Input/Output paths relative to its own working directory). + try: + ore_xml_rel = ore_xml.relative_to(workspace) + except ValueError: + ore_xml_rel = ore_xml # use absolute if not inside workspace + + # Clean the Output directory so stale results never mask a real failure. + output_dir = workspace / "Output" + if output_dir.exists(): + shutil.rmtree(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + _run_via_python_api(ore_xml_rel, workspace) + + stdout_csv = workspace / "Output" / "stresstest.csv" + if not stdout_csv.exists(): + raise RuntimeError( + f"ORE run did not produce stresstest.csv at {stdout_csv}.\n" + "Check the ORE log at: " + str(workspace / "Output" / "log.txt") + ) + return stdout_csv + + +# ── Execution back-end ───────────────────────────────────────────────────────── + +def _run_via_python_api(ore_xml_rel: Path, workspace: Path) -> None: + """Execute ORE using the Python bindings (OREApp).""" + from ORE import OREApp, Parameters # type: ignore[import] + + orig_cwd = Path.cwd() + try: + os.chdir(workspace) + params = Parameters() + params.fromFile(str(ore_xml_rel)) + ore = OREApp(params, True) + ore.run() + finally: + os.chdir(orig_cwd) diff --git a/EconomicStressAgentORE/oredata/Input/conventions.xml b/EconomicStressAgentORE/oredata/Input/conventions.xml new file mode 100644 index 0000000..8134b78 --- /dev/null +++ b/EconomicStressAgentORE/oredata/Input/conventions.xml @@ -0,0 +1,126 @@ + + + CDS-STANDARD-CONVENTIONS + 0 + WeekendsOnly + Quarterly + Following + CDS2015 + A360 + true + true + + + EUR-DEPOSIT + true + EUR-EURIBOR + + + EUR-ON-DEPOSIT-ESTER + true + EUR-ESTER + + + USD-ON-SOFR-DEPOSIT + true + USD-SOFR + + + USD-SOFR-3M-FUTURES + USD-SOFR + + + EURIBOR-3M-FUTURES + EUR-EURIBOR-3M + + + EUR-6M-FRA + EUR-EURIBOR-6M + + + EUR-ESTER-OIS + 1 + EUR-ESTER + A360 + 1 + false + Annual + MF + MF + Backward + + + USD-SOFR-OIS + 0 + USD-SOFR + A360 + 2 + false + Annual + MF + MF + Backward + + + EUR-USD-ON-XCCY-BASIS + 2 + TARGET,US + MF + USD-SOFR + 3M + EUR-ESTER + 3M + true + true + + + EUR-USD-FX + 2 + EUR + USD + 10000 + TARGET,US + true + + + EUR-EURIBOR-3M-SWAP + TARGET + Annual + MF + 30/360 + EUR-EURIBOR-3M + + + EUR-EURIBOR-6M-SWAP + TARGET + Annual + MF + 30/360 + EUR-EURIBOR-6M + + + EURIBOR-3M-6M-BASIS + TARGET + Annual + MF + 30/360 + EUR-EURIBOR-6M + Annual + MF + 30/360 + EUR-EURIBOR-3M + true + + + EUHICPXT_INFLATIONSWAP + TARGET + MF + 30/360 + EUHICPXT + false + 3M + false + TARGET + MF + + \ No newline at end of file diff --git a/EconomicStressAgentORE/oredata/Input/curveconfig.xml b/EconomicStressAgentORE/oredata/Input/curveconfig.xml new file mode 100644 index 0000000..684d2eb --- /dev/null +++ b/EconomicStressAgentORE/oredata/Input/curveconfig.xml @@ -0,0 +1,582 @@ + + + + EUR + EUR normal cap floor volatilities + Normal + Flat + Bilinear + false + Actual/365 (Fixed) + TARGET + Following + 1Y, 2Y, 3Y, 4Y, 5Y, 6Y, 7Y, 8Y, 9Y + -0.01, 0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.1 + false + EUR-EURIBOR-6M + Yield/EUR/EUR-ESTER + 1Y, 2Y, 3Y, 4Y, 5Y, 6Y, 7Y, 8Y, 9Y + 0 + OptionletVolatilities + LinearFlat + LinearFlat + false + + 0.0000000000010000 + 0.0000000000010000 + true + 5 + 2 + 2 + 10 + + TermVolatilities + + + + EUR-ESTER + ESTER vols proxied by EURIBOR-6M vols + + + CapFloorVolatility/EUR/EUR + EUR-EURIBOR-6M + + + EUR-ESTER + 3M + + + + + + + + EUR-ESTER + EUR ESTER discount curve bootstrapped from OIS swap rates + EUR + EUR-ESTER + + + Deposit + + MM/RATE/EUR/ESTER/0D/1D + + EUR-ON-DEPOSIT-ESTER + LastRelevantPillarDate + 0 + 1 + + + OIS + + IR_SWAP/RATE/EUR/ESTER/2D/1D/1W + IR_SWAP/RATE/EUR/ESTER/2D/1D/2W + IR_SWAP/RATE/EUR/ESTER/2D/1D/3W + IR_SWAP/RATE/EUR/ESTER/2D/1D/1M + IR_SWAP/RATE/EUR/ESTER/2D/1D/2M + IR_SWAP/RATE/EUR/ESTER/2D/1D/3M + IR_SWAP/RATE/EUR/ESTER/2D/1D/4M + IR_SWAP/RATE/EUR/ESTER/2D/1D/5M + IR_SWAP/RATE/EUR/ESTER/2D/1D/6M + IR_SWAP/RATE/EUR/ESTER/2D/1D/7M + IR_SWAP/RATE/EUR/ESTER/2D/1D/8M + IR_SWAP/RATE/EUR/ESTER/2D/1D/9M + IR_SWAP/RATE/EUR/ESTER/2D/1D/10M + IR_SWAP/RATE/EUR/ESTER/2D/1D/11M + IR_SWAP/RATE/EUR/ESTER/2D/1D/1Y + IR_SWAP/RATE/EUR/ESTER/2D/1D/15M + IR_SWAP/RATE/EUR/ESTER/2D/1D/18M + IR_SWAP/RATE/EUR/ESTER/2D/1D/21M + IR_SWAP/RATE/EUR/ESTER/2D/1D/2Y + IR_SWAP/RATE/EUR/ESTER/2D/1D/3Y + IR_SWAP/RATE/EUR/ESTER/2D/1D/4Y + IR_SWAP/RATE/EUR/ESTER/2D/1D/5Y + IR_SWAP/RATE/EUR/ESTER/2D/1D/6Y + IR_SWAP/RATE/EUR/ESTER/2D/1D/7Y + IR_SWAP/RATE/EUR/ESTER/2D/1D/8Y + IR_SWAP/RATE/EUR/ESTER/2D/1D/9Y + IR_SWAP/RATE/EUR/ESTER/2D/1D/10Y + IR_SWAP/RATE/EUR/ESTER/2D/1D/11Y + IR_SWAP/RATE/EUR/ESTER/2D/1D/12Y + IR_SWAP/RATE/EUR/ESTER/2D/1D/15Y + IR_SWAP/RATE/EUR/ESTER/2D/1D/20Y + IR_SWAP/RATE/EUR/ESTER/2D/1D/25Y + IR_SWAP/RATE/EUR/ESTER/2D/1D/30Y + IR_SWAP/RATE/EUR/ESTER/2D/1D/40Y + IR_SWAP/RATE/EUR/ESTER/2D/1D/50Y + IR_SWAP/RATE/EUR/ESTER/2D/1D/60Y + + EUR-ESTER-OIS + LastRelevantPillarDate + 0 + 1 + + + Discount + LogLinear + 1 + A365 + 0.0000000000010000 + true + + 0.0000000000010000 + 0.0000000000010000 + false + 5 + 2 + 2 + 10 + + + + USD-IN-EUR + USD collateralised in EUR discount curve + USD + + + + FX Forward + + FXFWD/RATE/EUR/USD/ON + FXFWD/RATE/EUR/USD/TN + FXFWD/RATE/EUR/USD/SN + FXFWD/RATE/EUR/USD/1W + FXFWD/RATE/EUR/USD/2W + FXFWD/RATE/EUR/USD/3W + FXFWD/RATE/EUR/USD/1M + FXFWD/RATE/EUR/USD/2M + FXFWD/RATE/EUR/USD/3M + FXFWD/RATE/EUR/USD/4M + FXFWD/RATE/EUR/USD/5M + FXFWD/RATE/EUR/USD/6M + FXFWD/RATE/EUR/USD/7M + FXFWD/RATE/EUR/USD/8M + FXFWD/RATE/EUR/USD/9M + FXFWD/RATE/EUR/USD/10M + FXFWD/RATE/EUR/USD/11M + FXFWD/RATE/EUR/USD/1Y + FXFWD/RATE/EUR/USD/15M + FXFWD/RATE/EUR/USD/18M + FXFWD/RATE/EUR/USD/21M + + EUR-USD-FX + LastRelevantPillarDate + 0 + 1 + + FX/RATE/EUR/USD + + + Cross Currency Basis Swap + + CC_BASIS_SWAP/BASIS_SPREAD/USD/1D/EUR/1D/2Y + CC_BASIS_SWAP/BASIS_SPREAD/USD/1D/EUR/1D/3Y + CC_BASIS_SWAP/BASIS_SPREAD/USD/1D/EUR/1D/4Y + CC_BASIS_SWAP/BASIS_SPREAD/USD/1D/EUR/1D/5Y + CC_BASIS_SWAP/BASIS_SPREAD/USD/1D/EUR/1D/6Y + CC_BASIS_SWAP/BASIS_SPREAD/USD/1D/EUR/1D/7Y + CC_BASIS_SWAP/BASIS_SPREAD/USD/1D/EUR/1D/8Y + CC_BASIS_SWAP/BASIS_SPREAD/USD/1D/EUR/1D/9Y + CC_BASIS_SWAP/BASIS_SPREAD/USD/1D/EUR/1D/10Y + CC_BASIS_SWAP/BASIS_SPREAD/USD/1D/EUR/1D/12Y + CC_BASIS_SWAP/BASIS_SPREAD/USD/1D/EUR/1D/15Y + CC_BASIS_SWAP/BASIS_SPREAD/USD/1D/EUR/1D/20Y + CC_BASIS_SWAP/BASIS_SPREAD/USD/1D/EUR/1D/25Y + CC_BASIS_SWAP/BASIS_SPREAD/USD/1D/EUR/1D/30Y + CC_BASIS_SWAP/BASIS_SPREAD/USD/1D/EUR/1D/40Y + CC_BASIS_SWAP/BASIS_SPREAD/USD/1D/EUR/1D/50Y + + EUR-USD-ON-XCCY-BASIS + LastRelevantPillarDate + 0 + 1 + + FX/RATE/EUR/USD + USD-SOFR + EUR-ESTER + + + Discount + LogLinear + 1 + A365 + 0.0000000000010000 + true + + 0.0000000000010000 + 0.0000000000010000 + false + 5 + 2 + 2 + 10 + + + + USD-SOFR + + USD + USD-SOFR + + + Deposit + + MM/RATE/USD/SOFR/0D/1D + + USD-ON-SOFR-DEPOSIT + LastRelevantPillarDate + 0 + 1 + USD-SOFR + + + Future + + OI_FUTURE/PRICE/USD/2024-04/XCME:SRA/3M + OI_FUTURE/PRICE/USD/2024-05/XCME:SRA/3M + OI_FUTURE/PRICE/USD/2024-06/XCME:SRA/3M + OI_FUTURE/PRICE/USD/2024-07/XCME:SRA/3M + OI_FUTURE/PRICE/USD/2024-08/XCME:SRA/3M + OI_FUTURE/PRICE/USD/2024-09/XCME:SRA/3M + OI_FUTURE/PRICE/USD/2024-10/XCME:SRA/3M + OI_FUTURE/PRICE/USD/2024-11/XCME:SRA/3M + OI_FUTURE/PRICE/USD/2024-12/XCME:SRA/3M + OI_FUTURE/PRICE/USD/2025-03/XCME:SRA/3M + OI_FUTURE/PRICE/USD/2025-06/XCME:SRA/3M + OI_FUTURE/PRICE/USD/2025-09/XCME:SRA/3M + + USD-SOFR-3M-FUTURES + LastRelevantPillarDate + 0 + 1 + + + OIS + + IR_SWAP/RATE/USD/SOFR/0D/1D/2Y + IR_SWAP/RATE/USD/SOFR/0D/1D/3Y + IR_SWAP/RATE/USD/SOFR/0D/1D/4Y + IR_SWAP/RATE/USD/SOFR/0D/1D/5Y + IR_SWAP/RATE/USD/SOFR/0D/1D/6Y + IR_SWAP/RATE/USD/SOFR/0D/1D/7Y + IR_SWAP/RATE/USD/SOFR/0D/1D/8Y + IR_SWAP/RATE/USD/SOFR/0D/1D/9Y + IR_SWAP/RATE/USD/SOFR/0D/1D/10Y + IR_SWAP/RATE/USD/SOFR/0D/1D/12Y + IR_SWAP/RATE/USD/SOFR/0D/1D/15Y + IR_SWAP/RATE/USD/SOFR/0D/1D/20Y + IR_SWAP/RATE/USD/SOFR/0D/1D/25Y + IR_SWAP/RATE/USD/SOFR/0D/1D/30Y + IR_SWAP/RATE/USD/SOFR/0D/1D/40Y + IR_SWAP/RATE/USD/SOFR/0D/1D/50Y + + USD-SOFR-OIS + LastRelevantPillarDate + 0 + 1 + + + Discount + LogLinear + 1 + A365 + 0.0000000000010000 + true + + 0.0000000000010000 + 0.0000000000010000 + false + 5 + 2 + 2 + 10 + + + + EUR-EURIBOR-3M + EUR EURIBOR 3M Index Curve + EUR + EUR-ESTER + + + Deposit + + MM/RATE/EUR/2D/3M + + EUR-DEPOSIT + LastRelevantPillarDate + 0 + 1 + + + Future + + MM_FUTURE/PRICE/EUR/2024-04/XICE:FEI/3M + MM_FUTURE/PRICE/EUR/2024-05/XICE:FEI/3M + MM_FUTURE/PRICE/EUR/2024-06/XICE:FEI/3M + MM_FUTURE/PRICE/EUR/2024-07/XICE:FEI/3M + MM_FUTURE/PRICE/EUR/2024-08/XICE:FEI/3M + MM_FUTURE/PRICE/EUR/2024-09/XICE:FEI/3M + MM_FUTURE/PRICE/EUR/2024-12/XICE:FEI/3M + MM_FUTURE/PRICE/EUR/2025-03/XICE:FEI/3M + MM_FUTURE/PRICE/EUR/2025-06/XICE:FEI/3M + MM_FUTURE/PRICE/EUR/2025-09/XICE:FEI/3M + + EURIBOR-3M-FUTURES + LastRelevantPillarDate + 0 + 1 + + + Swap + + IR_SWAP/RATE/EUR/2D/3M/2Y + IR_SWAP/RATE/EUR/2D/3M/3Y + IR_SWAP/RATE/EUR/2D/3M/4Y + IR_SWAP/RATE/EUR/2D/3M/5Y + IR_SWAP/RATE/EUR/2D/3M/6Y + IR_SWAP/RATE/EUR/2D/3M/7Y + IR_SWAP/RATE/EUR/2D/3M/8Y + IR_SWAP/RATE/EUR/2D/3M/9Y + IR_SWAP/RATE/EUR/2D/3M/10Y + IR_SWAP/RATE/EUR/2D/3M/12Y + IR_SWAP/RATE/EUR/2D/3M/15Y + IR_SWAP/RATE/EUR/2D/3M/20Y + IR_SWAP/RATE/EUR/2D/3M/25Y + IR_SWAP/RATE/EUR/2D/3M/30Y + IR_SWAP/RATE/EUR/2D/3M/40Y + IR_SWAP/RATE/EUR/2D/3M/50Y + IR_SWAP/RATE/EUR/2D/3M/60Y + + EUR-EURIBOR-3M-SWAP + LastRelevantPillarDate + 0 + 1 + EUR-EURIBOR-3M + + + Discount + LogLinear + 1 + A365 + 0.0000000000010000 + true + + 0.0000000000010000 + 0.0000000000010000 + false + 5 + 2 + 2 + 10 + + + + EUR-EURIBOR-6M + EUR EURIBOR 6M Index Curve + EUR + EUR-ESTER + + + Deposit + + MM/RATE/EUR/2D/6M + + EUR-DEPOSIT + LastRelevantPillarDate + 0 + 1 + + + FRA + + FRA/RATE/EUR/1M/6M + FRA/RATE/EUR/2M/6M + FRA/RATE/EUR/3M/6M + FRA/RATE/EUR/4M/6M + FRA/RATE/EUR/5M/6M + FRA/RATE/EUR/6M/6M + FRA/RATE/EUR/9M/6M + FRA/RATE/EUR/12M/6M + + EUR-6M-FRA + LastRelevantPillarDate + 0 + 1 + EUR-EURIBOR-6M + + + Tenor Basis Two Swaps + + BASIS_SWAP/BASIS_SPREAD/3M/6M/EUR/2Y + BASIS_SWAP/BASIS_SPREAD/3M/6M/EUR/3Y + BASIS_SWAP/BASIS_SPREAD/3M/6M/EUR/4Y + BASIS_SWAP/BASIS_SPREAD/3M/6M/EUR/5Y + BASIS_SWAP/BASIS_SPREAD/3M/6M/EUR/6Y + BASIS_SWAP/BASIS_SPREAD/3M/6M/EUR/7Y + BASIS_SWAP/BASIS_SPREAD/3M/6M/EUR/8Y + BASIS_SWAP/BASIS_SPREAD/3M/6M/EUR/9Y + BASIS_SWAP/BASIS_SPREAD/3M/6M/EUR/10Y + BASIS_SWAP/BASIS_SPREAD/3M/6M/EUR/12Y + BASIS_SWAP/BASIS_SPREAD/3M/6M/EUR/15Y + BASIS_SWAP/BASIS_SPREAD/3M/6M/EUR/20Y + BASIS_SWAP/BASIS_SPREAD/3M/6M/EUR/25Y + BASIS_SWAP/BASIS_SPREAD/3M/6M/EUR/30Y + BASIS_SWAP/BASIS_SPREAD/3M/6M/EUR/40Y + BASIS_SWAP/BASIS_SPREAD/3M/6M/EUR/50Y + BASIS_SWAP/BASIS_SPREAD/3M/6M/EUR/60Y + + EURIBOR-3M-6M-BASIS + LastRelevantPillarDate + 0 + 1 + EUR-EURIBOR-6M + EUR-EURIBOR-3M + + + Discount + LogLinear + 1 + A365 + 0.0000000000010000 + true + + 0.0000000000010000 + 0.0000000000010000 + false + 5 + 2 + 2 + 10 + + + + + + Underlying1 + + USD + + + SpreadCDS + Yield/USD/USD-SOFR + Actual/365 (Fixed) + RECOVERY_RATE/RATE/Underlying1/SNRFOR/USD + + CDS/CREDIT_SPREAD/Underlying1/SNRFOR/USD/* + + CDS-STANDARD-CONVENTIONS + true + 2018-09-20 + + 0.0000000000010000 + 0.0000000000010000 + true + 10 + 1 + 2 + 5 + + false + + + + + + + RIC:.SPX + Standard & Poor's Corp + USD + USD + USD-SOFR + ForwardPrice + EQUITY/PRICE/RIC:.SPX/USD + + EQUITY_FWD/PRICE/RIC:.SPX/USD/* + + A365 + + Zero + Linear + + false + false + + + RIC:.STOXX50 + STOXX Europe 50 EUR Price Index option implied forward curve + EUR + + EUR-ESTER + OptionPremium + European + EQUITY/PRICE/RIC:.STOXX50/EUR + + EQUITY_OPTION/PRICE/RIC:.STOXX50/EUR/* + + A365 + + Zero + Linear + + false + true + + + + + EUHICPXT_ZC_Swaps + Estimation Curve for EUHICPXT + Yield/EUR/EUR-ESTER + ZC + + ZC_INFLATIONSWAP/RATE/EUHICPXT/1Y + ZC_INFLATIONSWAP/RATE/EUHICPXT/2Y + ZC_INFLATIONSWAP/RATE/EUHICPXT/3Y + ZC_INFLATIONSWAP/RATE/EUHICPXT/4Y + ZC_INFLATIONSWAP/RATE/EUHICPXT/5Y + ZC_INFLATIONSWAP/RATE/EUHICPXT/6Y + ZC_INFLATIONSWAP/RATE/EUHICPXT/7Y + ZC_INFLATIONSWAP/RATE/EUHICPXT/8Y + ZC_INFLATIONSWAP/RATE/EUHICPXT/9Y + ZC_INFLATIONSWAP/RATE/EUHICPXT/10Y + ZC_INFLATIONSWAP/RATE/EUHICPXT/12Y + ZC_INFLATIONSWAP/RATE/EUHICPXT/15Y + ZC_INFLATIONSWAP/RATE/EUHICPXT/20Y + ZC_INFLATIONSWAP/RATE/EUHICPXT/25Y + ZC_INFLATIONSWAP/RATE/EUHICPXT/30Y + ZC_INFLATIONSWAP/RATE/EUHICPXT/35Y + ZC_INFLATIONSWAP/RATE/EUHICPXT/40Y + ZC_INFLATIONSWAP/RATE/EUHICPXT/45Y + ZC_INFLATIONSWAP/RATE/EUHICPXT/50Y + + EUHICPXT_INFLATIONSWAP + true + TARGET + Actual/365 (Fixed) + 3M + Monthly + + 0.0000000000010000 + true + + 2008-01-01 + Monthly + + SEASONALITY/RATE/MULT/EUHICPXT/JAN + SEASONALITY/RATE/MULT/EUHICPXT/FEB + SEASONALITY/RATE/MULT/EUHICPXT/MAR + 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DiscountingFxForwardEngine + + FX_Analytical + + + + GarmanKohlhagen + + AnalyticEuropeanEngine + + FX_Analytical + + + + GarmanKohlhagen + + FdBlackScholesVanillaEngine + + Douglas + 100 + 100 + 0 + true + FX_Analytical + + + + BlackBachelier + + + BlackBachelierSwaptionEngine + + IR_Analytical + + + + LGM + + Bootstrap + CoterminalDealStrike + 400,3M + 0.0 + HullWhite + 0.01 + Hagan + + 0.5 + 0.20 + + Grid + + 5.0 + 30 + 5.0 + 30 + IR_FD + + + + IborCapModel + + IborCapEngine + + IR_Analytical + + + + YYCapModel + + YYCapEngine + + IR_Analytical + + + + CpiCapModel + + CpiCapEngine + + true + IR_Analytical + + + + BlackOrBachelier + + BlackIborCouponPricer + + + Black76 + + 1.0 + IR_Analytical + + + + BlackOrBachelier + + BlackOvernightIndexedCouponPricer + + IR_Analytical + + + + BlackOrBachelier + + BlackAverageONIndexedCouponPricer + + IR_Analytical + + + + Black + + BlackAnalytic + + true + IR_Analytical + + + + Black + + BlackAnalytic + + true + IR_Analytical + + + + 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IR_Semianalytical + + + + DiscountedCashflows + + DiscountingCommodityForwardEngine + + COMM_Analytical + + + + DiscountedCashflows + + CommoditySwapEngine + + COMM_Analytical + + + + Black + + AnalyticalApproximation + + 2.05 + COMM_Analytical + + + + BlackScholes + + AnalyticEuropeanEngine + + COMM_Analytical + + + + BlackScholes + + CommoditySpreadOptionEngine + + 2.05 + COMM_Analytical + + + + BlackScholes + + AnalyticEuropeanForwardEngine + + COMM_Analytical + + + + Black + + AnalyticalApproximation + + 0 + COMM_Analytical + + + + DiscountedCashflows + + MidPointIndexCdsEngine + + Underlying + + IR_Analytical + + + + Black + + BlackIndexCdsOptionEngine + + + + Underlying + IR_Semianalytical + + + + + LGM + + Bootstrap + CoterminalDealStrike + 400,3M + 0.0 + HullWhite + 0.01 + HullWhite + 0.02 + + Grid + + Automatic + 20.0 + 5.0 + 30 + 5.0 + 30 + IR_Semianalytical + + + + LGM-FlexiSwap + + 0.0 + 2.0 + Bootstrap + CoterminalDealStrike + 400,3M + 0.0 + HullWhite + 0.01 + HullWhite + 0.02 + + Grid + + Automatic + 20.0 + 5.0 + 30 + 5.0 + 30 + IR_Semianalytical + + + + SensiTrade + + SensiTradeEngine + + 1Y,2Y + 0.01 + 6M,1Y,2Y,3Y,5Y,10Y,15Y,20Y,30Y + SensiTrade + + + + GaussCopula + + 0.0 + -5.0 + 5.0 + 64 + + Y + + + + Markit2020 + + 0.35,0.3,0.35 + + Bucketing + + 124 + + DeltaWeighted + + + + N + + N + + N + 3Y,5Y + CR_Semianalytical + + + + + BrigoMercurio + + GENERIC + + MC + + 10000 + 42 + Y + Spectral + IR_MC + + + + BlackScholesMerton + + false + + ReplicatingVarianceSwapEngine + + Segment + PriceThreshold + 1E-5 + 1000 + 1000 + 1E-10 + 500 + 0.1 + -5.0 + 5.0 + FX_Semianalytical + + + + BlackScholesMerton + + ReplicatingVarianceSwapEngine + + Segment + PriceThreshold + 1E-5 + 1000 + 1000 + 1E-10 + 500 + 0.1 + -5.0 + 5.0 + EQ_Semianalytical + + + + BlackScholesMerton + + ReplicatingVarianceSwapEngine + + Segment + PriceThreshold + 1E-5 + 1000 + 1000 + 1E-10 + 500 + 0.1 + -5.0 + 5.0 + COMM_Semianalytical + + + + GarmanKohlhagen + + AnalyticEuropeanEngine + + FX_Analytical + + + + GarmanKohlhagen + + AnalyticCashSettledEuropeanEngine + + FX_Analytical + + + + GarmanKohlhagen + + AnalyticBarrierEngine + + FX_Analytical + + + + GarmanKohlhagen + + FdBlackScholesBarrierEngine + + Douglas + 100 + 100 + 0 + FX_Analytical + + + + GarmanKohlhagen + + AnalyticDoubleBarrierEngine + + FX_Analytical + + + + GarmanKohlhagen + + AnalyticDoubleBarrierBinaryEngine + + FX_Analytical + + + + + GarmanKohlhagen + + AnalyticDigitalAmericanEngine + + FX_Analytical + + + + DiscountedCashflows + + DiscountingCommodityForwardEngine + + COMM_Analytical + + + + DefaultableEquityJumpDiffusion + + 0.0 + 1.0 + true + false + false + true + true + true + + FD + + true + 6M,1Y,2Y,3Y,4Y,5Y,7Y,10Y,15Y,20Y,25Y,30Y,40Y,50Y + 24 + 400 + 1E-5 + 1.5 + Alternating + 24 + 100 + 1E-4 + 1.5 + 0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1.0,1.05,1.1,1.15,1.2,1.25,1.5,1.75,2.0 + EQ_FD + + + + Black + + 0.0 + true + + Analytic + + false + 400 + 20 + IR_Semianalytical + + + + Black + + Analytic + + false + 400 + 20 + FX_Semianalytical + + + + LGM + + Bootstrap + CoterminalDealStrike + 400,3M + 0.0 + HullWhite + 0.01 + Hagan + 0.5 + 0.02 + + Grid + + 5.0 + 30 + 5.0 + 30 + 400 + 20 + IR_FD + + + + LGM + + Bootstrap + CoterminalATM + 400,3M + 0.0 + HullWhite + 0.01 + Hagan + 0.5 + 0.02 + 3M + + Grid + + 5.0 + 30 + 5.0 + 30 + 24 + IR_FD + + + + Generic + + + USD + false + 3M(1W),1Y(1M),5Y(3M),10Y(1Y),50Y(5Y) + 0.0 + 0.0 + 0.0 + 0.0 + 0.0 + 0.0 + 0.0 + 0.0 + true + true + 400,3M + Deal + BlackScholes + DK + + GaussianCam + GaussianCam + GaussianCam + GaussianCam + GaussianCam + GaussianCam + + Generic + + + MC + 10000 + 200 + 1E-4 + 1.5 + 0.1 + 9999 + true + 2 + 24 + false + 999.9 + + 6 + 6 + 6 + FD + FD + FD + true + true + true + true + true + true + true + true + true + true + true + true + true + true + EQ_MC + FX_MC + COMM_MC + EQ_MC + FX_MC + COMM_MC + EQ_FD + FX_FD + COMM_FD + EQ_MC + FX_MC + COMM_MC + EQ_MC + FX_MC + COMM_MC + HYBRID_MC + EQ_MC + FX_MC + COMM_MC + HYBRID_MC + EQ_MC + FX_MC + COMM_MC + HYBRID_MC + IR_MC + IR_MC + HYBRID_MC + HYBRID_MC + HYBRID_MC + HYBRID_MC + + + + BlackScholesMerton + + AnalyticCashSettledEuropeanEngine + + EQ_Analytical + + + + BlackScholes + + AnalyticCashSettledEuropeanEngine + + COMM_Analytical + + + + BlackScholes + + FdBlackScholesVanillaEngine + + Douglas + 100 + 100 + 0 + true + COMM_FD + + + + GarmanKohlhagen + + AnalyticCashSettledEuropeanEngine + + FX_Analytical + + + + Accrual + + true + + AccrualRepoEngine + + IR_Analytical + + + + BlackScholes + + MCScript + + MC + 10000 + 6 + 24 + false + false + EQ_MC + + + + Black + + BlackBondOptionEngine + + 6M + IR_Analytical + + + + BlackScholesMerton + + AnalyticBarrierEngine + + EQ_Analytical + + + + BlackScholesMerton + + AnalyticDoubleBarrierEngine + + EQ_Analytical + + + + BlackScholesMerton + + AnalyticEuropeanEngine + + EQ_Analytical + + + + BlackScholesMerton + + AnalyticDigitalAmericanEngine + + EQ_Analytical + + + + BlackScholes + + Intrinsic + + 24 + EQ_FD + + + + BlackScholes + + AnalyticEuropeanEngine + + EQ_Analytical + + + + ScriptedTrade + + ScriptedTrade + + EQ_MC + + + + ScriptedTrade + + ScriptedTrade + + EQ_MC + + + + ScriptedTrade + + ScriptedTrade + + FX_MC + + + + ScriptedTrade + + ScriptedTrade + + FX_MC + + + + OneFactorCopula + + MonteCarloCBOEngine + + 1000 + 20 + 42 + + 0.2 + CR_MC + + + + Black + + MonteCarlo + + 0 + 10000 + 42 + COMM_MC + + + + + true + false + USD + GENERIC + PM:XAUUSD + PM:XAGUSD + PM:XPTUSD + PM:XPDUSD + ^RED:[0-9A-Z]{9}$ + + diff --git a/EconomicStressAgentORE/oredata/Input/sensitivity.xml b/EconomicStressAgentORE/oredata/Input/sensitivity.xml new file mode 100644 index 0000000..33e1b81 --- /dev/null +++ b/EconomicStressAgentORE/oredata/Input/sensitivity.xml @@ -0,0 +1,158 @@ + + + + Absolute + 0.0001 + Forward + + 1D, + 1W,2W,3W,1M,2M,3M,4M,5M,6M,7M,8M,9M,10M,11M,1Y,15M,18M,21M,2Y,3Y,4Y,5Y,6Y,7Y,8Y,9Y,10Y,11Y,12Y,15Y,20Y,25Y,30Y,40Y,50Y,60Y + + + DEP, + OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS + true + + EUR-ON-DEPOSIT-ESTER + EUR-ESTER-OIS + + + + + Absolute + 0.0001 + Forward + + 1D,2D,3D,1W,2W,3W,1M,2M,3M,4M,5M,6M,7M,8M,9M,10M,11M,1Y,15M,18M,21M,2Y,3Y,4Y,5Y,6Y,7Y,8Y,9Y,10Y,12Y,15Y,20Y,25Y,30Y,40Y,50Y + + + FXF,FXF,FXF,FXF,FXF,FXF,FXF,FXF,FXF,FXF,FXF,FXF,FXF,FXF,FXF,FXF,FXF,FXF,FXF,FXF,FXF,XBS,XBS,XBS,XBS,XBS,XBS,XBS,XBS,XBS,XBS,XBS,XBS,XBS,XBS,XBS,XBS + true + + EUR-USD-ON-XCCY-BASIS + USD-ON-SOFR-DEPOSIT + USD-SOFR-OIS + EUR-USD-FX + + + + + + + Absolute + 0.0001 + Forward + + 1D, + 1W,2W,3W,1M,2M,3M,4M,5M,6M,7M,8M,9M,10M,11M,1Y,15M,18M,21M,2Y,3Y,4Y,5Y,6Y,7Y,8Y,9Y,10Y,11Y,12Y,15Y,20Y,25Y,30Y,40Y,50Y,60Y + + + DEP, + OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS,OIS + true + + EUR-ON-DEPOSIT-ESTER + EUR-ESTER-OIS + + + + + Absolute + 0.0001 + Forward + 6M, 7M, 8M,9M,10M,11M,1Y,15M,18M, 2Y, 3Y, 4Y, 5Y, 6Y, 7Y, 8Y, 9Y, 10Y, 12Y, + 15Y, 20Y, 25Y, 30Y, 40Y, 50Y, 60Y + + DEP, FRA, FRA,FRA,FRA,FRA,FRA,FRA,FRA, IRS, IRS, IRS, IRS, IRS, IRS, + IRS, IRS, IRS, IRS, IRS, IRS, IRS, IRS, IRS, IRS, IRS + false + EUR-ESTER + + EUR-DEPOSIT + EUR-6M-FRA + EUR-EURIBOR-6M-SWAP + + + + + Absolute + 0.0001 + Forward + 1D, 1W, 2W, 1M, 2M, 3M, 4M, 5M, 6M, 7M, 8M, 9M, 10M, 11M, 1Y,15M, 18M, 21M, + 2Y, 3Y, 5Y, 10Y, 15Y, 20Y, 30Y + + DEP, OIS, OIS, OIS, OIS, OIS, OIS, OIS, OIS, OIS, OIS, OIS, OIS, OIS, + OIS, OIS, OIS, OIS, OIS, OIS, OIS, OIS, OIS, OIS, OIS + true + + USD-ON-SOFR-DEPOSIT + USD-SOFR-OIS + + + + + + + + Relative + 0.01 + + + + + USD + Absolute + 0.0001 + Forward + 1Y, 2Y, 3Y, 5Y, 10Y + + CDS, CDS, CDS, CDS, CDS + false + + CDS-STANDARD-CONVENTIONS + + + + + + + Absolute + 0.0001 + 1Y, 2Y, 3Y, 4Y, 5Y, 6Y, 7Y, 8Y, 9Y + -0.01, 0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.1 + EUR-EURIBOR-6M + + CapFloor + EUR-ESTER + + + + + + Absolute + 0.0001 + 6M,1Y,2Y,3Y,5Y,7Y,10Y,15Y,20Y,30Y + + ZIS,ZIS,ZIS,ZIS,ZIS,ZIS,ZIS,ZIS,ZIS,ZIS + false + + EUHICPXT_INFLATIONSWAP + + + + + + + Relative + 0.01 + Forward + + + Relative + 0.01 + Forward + + + false + true + \ No newline at end of file diff --git a/EconomicStressAgentORE/oredata/Input/simulation.xml b/EconomicStressAgentORE/oredata/Input/simulation.xml new file mode 100644 index 0000000..a6ee241 --- /dev/null +++ b/EconomicStressAgentORE/oredata/Input/simulation.xml @@ -0,0 +1,79 @@ + + + EUR + + EUR + USD + + + + 1D, 1W, 1M, 1Y, 10Y + LogLinear + FlatZero + + + + + USDEUR + + + + EUR-ESTER + EUR-EURIBOR-3M + EUR-EURIBOR-6M + USD-SOFR + + + true + ForwardVariance + + EUR-EURIBOR-6M + + 1Y, 2Y, 3Y, 4Y, 5Y, 6Y, 7Y, 8Y, 9Y, 10Y, 12Y, 15Y, 20Y, + 25Y, 30Y + + -0.015, -0.01, 0, 0.0005 + true + false + StickyStrike + StickyStrike + + + + Underlying1 + + 1Y, 2Y, 3Y, 5Y, 10Y + true + false + + TARGET + + FlatZero + + + + + EUHICPXT + + 6M,1Y,2Y,3Y,5Y,7Y,10Y,15Y,20Y + + + + true + + RIC:.SPX + RIC:.STOXX50 + + 2W, 1M, 3M, 6M, 1Y, 2Y, 3Y, 5Y, 10Y, 15Y, 20Y, 30Y + + + 0 + + + 0 + + + \ No newline at end of file diff --git a/EconomicStressAgentORE/oredata/Input/todaysmarket.xml b/EconomicStressAgentORE/oredata/Input/todaysmarket.xml new file mode 100644 index 0000000..986e4f9 --- /dev/null +++ b/EconomicStressAgentORE/oredata/Input/todaysmarket.xml @@ -0,0 +1,80 @@ + + + default + inccy + default + default + default + default + default + default + default + default + default + default + default + default + default + default + default + default + default + default + default + default + + + default + default + default + default + default + default + default + default + default + default + default + default + default + default + default + default + default + default + default + default + default + default + + + Yield/EUR/EUR-ESTER + Yield/USD/USD-IN-EUR + + + Yield/EUR/EUR-ESTER + Yield/USD/USD-SOFR + + + Yield/EUR/EUR-ESTER + Yield/EUR/EUR-EURIBOR-6M + Yield/EUR/EUR-EURIBOR-3M + Yield/USD/USD-SOFR + + + FX/EUR/USD + + + CapFloorVolatility/EUR/EUR + + + Default/USD/Underlying1 + + + Equity/USD/RIC:.SPX + Equity/EUR/RIC:.STOXX50 + + + Inflation/EUHICPXT/EUHICPXT_ZC_Swaps + + \ No newline at end of file diff --git a/EconomicStressAgentORE/oredata/ore.xml b/EconomicStressAgentORE/oredata/ore.xml new file mode 100644 index 0000000..3810927 --- /dev/null +++ b/EconomicStressAgentORE/oredata/ore.xml @@ -0,0 +1,64 @@ + + + + 2024-03-05 + Input + Output + log.txt + 31 + marketdata.csv + fixings.csv + N + curveconfig.xml + conventions.xml + todaysmarket.xml + pricingengine.xml + portfolio.xml + None + + + inccy + default + default + default + default + + + + Y + EUR + npv.csv + + + Y + flows.csv + + + Y + simulation.xml + sensitivity.xml + pricingengine.xml + scenario.csv + sensitivity.csv + crossgamma.csv + parsensi.csv + 0.000001 + + + Y + default + 240,1M + curves.csv + + + Y + simulation.xml + parstresstest.xml + sensitivity.xml + pricingengine.xml + stresstest.csv + 0.000001 + zeroStressScenarioData.xml + + + \ No newline at end of file diff --git a/EconomicStressAgentORE/requirements.txt b/EconomicStressAgentORE/requirements.txt new file mode 100644 index 0000000..d0ab6b1 --- /dev/null +++ b/EconomicStressAgentORE/requirements.txt @@ -0,0 +1,5 @@ +openai>=1.0.0 +python-dotenv>=1.0.0 +pandas>=2.0.0 +click>=8.0.0 +open-source-risk-engine>=1.8 \ No newline at end of file diff --git a/EconomicStressAgentORE/scenario_analyzer.py b/EconomicStressAgentORE/scenario_analyzer.py new file mode 100644 index 0000000..16d4925 --- /dev/null +++ b/EconomicStressAgentORE/scenario_analyzer.py @@ -0,0 +1,166 @@ +""" +scenario_analyzer.py β€” Step 1: map a free-text economic scenario to concrete +market shifts using an LLM + an in-process historical knowledge base. +""" + +from __future__ import annotations + +import json +import re +from typing import Any + +from openai import OpenAI + +import config +from historical_scenarios import ScenarioKnowledgeBase + +# ── System prompt ───────────────────────────────────────────────────────────── +_SYSTEM_PROMPT = """\ +You are a quantitative risk analyst specialising in macro stress testing. + +You are given a knowledge base of historical economic crises and their +approximate market impacts, and a user-described economic scenario. + +Your task is to: +1. Identify which historical episodes best match the described scenario + (pick 1-3 episodes). +2. Derive a *weighted average* of the market shifts from those episodes that + best represents the described scenario. You may scale the shifts up or down + to reflect the described severity. +3. Return a JSON object (and nothing else) with this exact schema: + +{ + "matched_scenarios": ["", ...], + "reasoning": "", + "shifts": { + "rates": { + "": { "": , ... } + }, + "fx": { "": }, + "equity": { + "": + }, + "credit": { + "": { "": , ... } + } + } +} + +Keys: +- rates: keyed by ISO currency (EUR, USD, …). Values are absolute changes + in swap/yield rates in decimal (e.g. -0.015 = -150 bps, +0.010 = +100 bps). + Tenors: 1Y, 2Y, 3Y, 5Y, 10Y, 30Y (at minimum include 1Y, 2Y, 5Y, 10Y, 30Y). +- fx: keyed by pair (e.g. "EURUSD"). Value is the absolute change in the spot + rate (e.g. -0.08 means EUR falls 8 big figures vs USD). +- equity: keyed by Currency (e.g. "EUR", "USD") or sector override + (e.g. "Tech", "Index"). Values are relative (fractional) changes + (e.g. -0.25 = -25 %). Always include at least the currency-level keys. +- credit: keyed by Currency or sector (e.g. "EUR", "USD", + "SeniorUnsecured", "Sovereign"). Values are dicts of tenor β†’ absolute + shift to hazard/CDS spread (positive = widening, e.g. 0.02 = +200 bps). + Tenors: 1Y, 2Y, 3Y, 5Y, 10Y. + +Return ONLY the JSON object β€” no markdown fences, no extra text. +""" + + +def _build_user_message( + scenario_description: str, + knowledge_base: ScenarioKnowledgeBase, +) -> str: + """Combine the historical knowledge base with the user's scenario.""" + kb = knowledge_base.get_scenarios_text() + return ( + f"## Historical Scenarios Knowledge Base\n\n{kb}\n\n" + f"## User-Described Scenario\n\n{scenario_description}\n\n" + "Analyse the described scenario and return the JSON object as instructed." + ) + + +def analyze( + scenario_description: str, + knowledge_base: ScenarioKnowledgeBase, +) -> dict[str, Any]: + """ + Analyse *scenario_description* and return a structured dict of market shifts. + + Returns + ------- + dict with keys: + matched_scenarios : list[str] + reasoning : str + shifts : dict (fx, equity, rates_eur, rates_usd) + """ + if not config.OPENAI_API_KEY: + raise EnvironmentError( + "OPENAI_API_KEY is not set. Please add it to your .env file." + ) + + client = OpenAI(api_key=config.OPENAI_API_KEY) + + response = client.chat.completions.create( + model=config.OPENAI_MODEL, + messages=[ + {"role": "system", "content": _SYSTEM_PROMPT}, + {"role": "user", "content": _build_user_message(scenario_description, knowledge_base)}, + ], + temperature=0.2, + response_format={"type": "json_object"}, + ) + + raw = response.choices[0].message.content or "{}" + + # Strip accidental markdown fences if the model includes them + raw = re.sub(r"^```(?:json)?\s*", "", raw.strip()) + raw = re.sub(r"```\s*$", "", raw.strip()) + + result: dict[str, Any] = json.loads(raw) + _validate(result) + return result + + +def _validate(result: dict[str, Any]) -> None: + """Raise ValueError if the returned JSON is missing required keys.""" + required_top = {"matched_scenarios", "reasoning", "shifts"} + missing = required_top - result.keys() + if missing: + raise ValueError(f"LLM response missing keys: {missing}") + + shifts = result["shifts"] + required_shifts = {"rates", "fx", "equity", "credit"} + missing_shifts = required_shifts - shifts.keys() + if missing_shifts: + raise ValueError(f"shifts missing sub-keys: {missing_shifts}") + + +def format_shifts(result: dict[str, Any]) -> str: + """Return a human-readable string of the proposed shifts.""" + s = result["shifts"] + lines = [ + "Matched scenarios:", + *[f" β€’ {name}" for name in result["matched_scenarios"]], + "", + f"Reasoning: {result['reasoning']}", + "", + "Proposed market shifts:", + ] + + # FX + for pair, v in s.get("fx", {}).items(): + lines.append(f" FX {pair}: {v:+.4f}") + + # Equity + for key, v in s.get("equity", {}).items(): + lines.append(f" Equity {key}: {v:+.1%}") + + # Rates + for ccy, tenors in s.get("rates", {}).items(): + parts = " ".join(f"{t} {v*10000:+.0f}bp" for t, v in tenors.items()) + lines.append(f" Rates {ccy}: {parts}") + + # Credit + for key, tenors in s.get("credit", {}).items(): + parts = " ".join(f"{t} {v*10000:+.0f}bp" for t, v in tenors.items()) + lines.append(f" Credit {key}: {parts}") + + return "\n".join(lines) diff --git a/EconomicStressAgentORE/stresstest_builder.py b/EconomicStressAgentORE/stresstest_builder.py new file mode 100644 index 0000000..ce22426 --- /dev/null +++ b/EconomicStressAgentORE/stresstest_builder.py @@ -0,0 +1,323 @@ +""" +stresstest_builder.py β€” Step 2: convert structured market shifts into an +ORE-compatible par stress test XML file. + +This version is **market-driven**: it accepts a ``MarketStructure`` (from +``todaysmarket_analyzer.parse()``) and a sector mapping so that every curve +present in todaysmarket.xml is automatically stressed. +""" + +from __future__ import annotations + +from pathlib import Path +from typing import Any +from xml.dom import minidom +from xml.etree import ElementTree as ET + +import config +import ORE + +from todaysmarket_analyzer import ( + CurveInfo, + MarketStructure, + SectorEntry, + load_sector_mapping, + resolve_credit_shifts, + resolve_equity_shift, +) + +# ── Tenor helpers ───────────────────────────────────────────────────────────── + +def _tenor_to_year_fraction(tenor: str, today, day_counter) -> float: + """Convert an ORE tenor string to a year-fraction relative to *today*.""" + + return day_counter.yearFraction(today, today + ORE.Period(tenor.strip())) + + +def interpolate_shift( + tenors: list[str], scenario_shifts: dict[str, float] +) -> list[float]: + """ + Linear interpolation with flat extrapolation of scenario key-tenor + shifts onto an arbitrary ORE tenor grid, using ORE's own date/period + logic and ``LinearInterpolation``. + """ + if not scenario_shifts: + return [0.0] * len(tenors) + + today = ORE.Settings.instance().evaluationDate + dc = ORE.Actual365Fixed() + + # Build sorted anchor arrays from scenario shifts + anchors = sorted( + (_tenor_to_year_fraction(t, today, dc), v) + for t, v in scenario_shifts.items() + ) + xs = [a[0] for a in anchors] + ys = [a[1] for a in anchors] + + interp = ORE.LinearInterpolation(xs, ys) + + def _lookup(t_yf: float) -> float: + if t_yf <= xs[0]: + return ys[0] + if t_yf >= xs[-1]: + return ys[-1] + return interp(t_yf) + + return [ + _lookup(_tenor_to_year_fraction(t, today, dc)) + for t in tenors + ] + + +# ── XML helpers ─────────────────────────────────────────────────────────────── + +def _shifts_str(values: list[float]) -> str: + """Format a list of shift values as a comma-separated string.""" + return ", ".join(f"{v:.6f}" for v in values) + + +def _add_curve_element( + parent: ET.Element, + tag: str, + attr: dict[str, str], + tenors: list[str], + shifts: list[float], +) -> None: + """Append a or element with shifts.""" + el = ET.SubElement(parent, tag, attr) + ET.SubElement(el, "ShiftType").text = "Absolute" + ET.SubElement(el, "Shifts").text = _shifts_str(shifts) + ET.SubElement(el, "ShiftTenors").text = ", ".join(tenors) + + +def _prettify(element: ET.Element) -> str: + """Return a pretty-printed XML string (no blank lines).""" + rough = ET.tostring(element, encoding="unicode") + reparsed = minidom.parseString(rough) + xml = reparsed.toprettyxml(indent=" ", encoding=None) + # minidom inserts blank lines when the source already has whitespace nodes + return "\n".join(line for line in xml.splitlines() if line.strip()) + + +# ── Main builder ────────────────────────────────────────────────────────────── + +def _resolve_rate_shifts( + ccy: str, shifts: dict[str, Any] +) -> dict[str, float]: + """Return the rate tenor-shift dict for a currency (empty if missing).""" + rates: dict = shifts.get("rates", {}) + return rates.get(ccy, {}) + + +def build( + shifts: dict[str, Any], + market: MarketStructure | None = None, + sector_map: dict[tuple[str, str], SectorEntry] | None = None, + output_path: Path | None = None, + scenario_id: str = "agent_scenario", + scenario_label: str | None = None, +) -> Path: + """ + Build the ORE stress test XML from *shifts* (generic schema) and the + discovered *market* structure. + + Parameters + ---------- + shifts : generic schema ``{rates, fx, equity, credit}`` + market : MarketStructure β€” if None, parsed from todaysmarket.xml + sector_map : sector mapping β€” if None, loaded from config + output_path : where to write the XML + scenario_id : ``id`` attribute of the ```` element + scenario_label : optional human-readable label embedded as a comment + """ + from todaysmarket_analyzer import parse as tm_parse # local to avoid circular + + if market is None: + market = tm_parse() + if sector_map is None: + sector_map = load_sector_mapping() + if output_path is None: + output_path = config.AGENT_STRESS_XML + + output_path = Path(output_path) + output_path.parent.mkdir(parents=True, exist_ok=True) + + # Root + root = ET.Element("StressTesting") + ET.SubElement(root, "UseSpreadedTermStructures").text = "true" + + st = ET.SubElement(root, "StressTest", {"id": scenario_id}) + if scenario_label: + st.append(ET.Comment(f" {scenario_label} ")) + + par = ET.SubElement(st, "ParShifts") + ET.SubElement(par, "IRCurves").text = "false" + ET.SubElement(par, "SurvivalProbability").text = "false" + ET.SubElement(par, "CapFloorVolatilities").text = "false" + + # ── Discount curves ──────────────────────────────────────────────── + disc_el = ET.SubElement(st, "DiscountCurves") + for curve in market.discount_curves: + rate_shifts = _resolve_rate_shifts(curve.currency, shifts) + if not rate_shifts: + continue + tenors = config.STANDARD_RATE_TENORS + interp = interpolate_shift(tenors, rate_shifts) + _add_curve_element(disc_el, "DiscountCurve", + {"ccy": curve.currency}, tenors, interp) + + # ── Index curves ─────────────────────────────────────────────────── + idx_el = ET.SubElement(st, "IndexCurves") + for curve in market.index_curves: + rate_shifts = _resolve_rate_shifts(curve.currency, shifts) + if not rate_shifts: + continue + tenors = config.STANDARD_RATE_TENORS + interp = interpolate_shift(tenors, rate_shifts) + _add_curve_element(idx_el, "IndexCurve", + {"index": curve.name}, tenors, interp) + + # ── Yield curves ─────────────────────────────────────────────────── + yc_el = ET.SubElement(st, "YieldCurves") + for curve in market.yield_curves: + rate_shifts = _resolve_rate_shifts(curve.currency, shifts) + if not rate_shifts: + continue + tenors = config.STANDARD_RATE_TENORS + interp = interpolate_shift(tenors, rate_shifts) + _add_curve_element(yc_el, "YieldCurve", + {"name": curve.name}, tenors, interp) + + # ── FX spots ─────────────────────────────────────────────────────── + fx_el = ET.SubElement(st, "FxSpots") + fx_shifts: dict[str, float] = shifts.get("fx", {}) + for pair in market.fx_pairs: + foreign = pair[:3] + domestic = pair[3:] + # Try both conventions + raw_shift = fx_shifts.get(pair, fx_shifts.get(domestic + foreign, None)) + if raw_shift is None: + continue + # ORE uses inverted convention (USDEUR) + ore_pair = domestic + foreign + if pair == ore_pair: + relative_shift = raw_shift + else: + # EURUSD scenario β†’ USDEUR in ORE: negate + relative_shift = -raw_shift + spot_el = ET.SubElement(fx_el, "FxSpot", {"ccypair": ore_pair}) + ET.SubElement(spot_el, "ShiftType").text = "Relative" + ET.SubElement(spot_el, "ShiftSize").text = f"{relative_shift:.6f}" + + # ── FxVolatilities (empty) ──────────────────────────────────────── + ET.SubElement(st, "FxVolatilities") + + # ── SwaptionVolatilities (empty) ────────────────────────────────── + ET.SubElement(st, "SwaptionVolatilities") + + # ── CapFloorVolatilities (empty) ────────────────────────────────── + ET.SubElement(st, "CapFloorVolatilities") + + # ── Equity spots ───────────────────────────────────────────────── + eq_el = ET.SubElement(st, "EquitySpots") + for curve in market.equity_curves: + rel_shift = resolve_equity_shift(curve, shifts, sector_map) + if rel_shift == 0.0: + continue + eq_spot = ET.SubElement(eq_el, "EquitySpot", {"equity": curve.name}) + ET.SubElement(eq_spot, "ShiftType").text = "Relative" + ET.SubElement(eq_spot, "ShiftSize").text = f"{rel_shift:.6f}" + + # ── EquityVolatilities (empty) ──────────────────────────────────── + ET.SubElement(st, "EquityVolatilities") + + # ── SecuritySpreads, RecoveryRates (empty) ──────────────────────── + ET.SubElement(st, "SecuritySpreads") + ET.SubElement(st, "RecoveryRates") + + # ── Survival probabilities ──────────────────────────────────────── + surv_el = ET.SubElement(st, "SurvivalProbabilities") + for curve in market.default_curves: + credit_tenor_shifts = resolve_credit_shifts(curve, shifts, sector_map) + if not credit_tenor_shifts: + continue + tenors = config.STANDARD_CREDIT_TENORS + interp = interpolate_shift(tenors, credit_tenor_shifts) + sp = ET.SubElement(surv_el, "SurvivalProbability", {"name": curve.name}) + ET.SubElement(sp, "ShiftType").text = "Absolute" + ET.SubElement(sp, "Shifts").text = _shifts_str(interp) + ET.SubElement(sp, "ShiftTenors").text = ", ".join(tenors) + + # ── Serialise ───────────────────────────────────────────────────── + xml_str = _prettify(root) + lines = xml_str.splitlines() + if lines and lines[0].startswith(" Path: + """ + Write a minimal ore_agent.xml that activates only the stress analytic and + points to the agent-generated stress test configuration. + + Parameters + ---------- + base_ore_xml : source ore.xml to base settings on + (defaults to config.ORE_WORKSPACE / "ore.xml") + output_ore_xml : where to write the agent ore config + (defaults to config.AGENT_ORE_XML) + stress_config_file : filename (relative to Input/) of the stress XML + + Returns + ------- + Path to the written file. + """ + if base_ore_xml is None: + base_ore_xml = config.ORE_WORKSPACE / "ore.xml" + if output_ore_xml is None: + output_ore_xml = config.AGENT_ORE_XML + + base_ore_xml = Path(base_ore_xml) + output_ore_xml = Path(output_ore_xml) + + tree = ET.parse(base_ore_xml) + root = tree.getroot() + + # Strip whitespace-only text/tail so _prettify produces clean output + for el in root.iter(): + if el.text and not el.text.strip(): + el.text = None + if el.tail and not el.tail.strip(): + el.tail = None + + # Disable every analytic except stress; update stress config file + analytics = root.find("Analytics") + if analytics is not None: + for analytic in analytics.findall("Analytic"): + atype = analytic.get("type", "") + if atype == "stress": + for param in analytic.findall("Parameter"): + if param.get("name") == "stressConfigFile": + param.text = stress_config_file + if param.get("name") == "active": + param.text = "Y" + else: + for param in analytic.findall("Parameter"): + if param.get("name") == "active": + param.text = "N" + + xml_str = _prettify(root) + lines = xml_str.splitlines() + if lines and lines[0].startswith("' + output_ore_xml.write_text("\n".join(lines), encoding="utf-8") + + return output_ore_xml diff --git a/EconomicStressAgentORE/test_integration.py b/EconomicStressAgentORE/test_integration.py new file mode 100644 index 0000000..50b2c2d --- /dev/null +++ b/EconomicStressAgentORE/test_integration.py @@ -0,0 +1,98 @@ +#!/usr/bin/env python3 +"""Quick integration test for the generic schema refactoring.""" + +import config +import todaysmarket_analyzer +import stresstest_builder +import ore_runner +from pathlib import Path +from historical_scenarios import ScenarioKnowledgeBase + +# 1. Test scenario loading +kb = ScenarioKnowledgeBase(config.DATA_DIR / "scenarios.json") +print(f"Loaded {len(kb)} scenarios") +print(f"First: {kb[0]['name']}") +print(f"Schema keys: {list(kb[0]['shifts'].keys())}") +print() + +# 2. Test market structure parsing +ms = todaysmarket_analyzer.parse() +print(todaysmarket_analyzer.format_market_structure(ms)) +print() + +# 3. Test sector mapping +sector_map = todaysmarket_analyzer.load_sector_mapping() +print(f"Sector map entries: {len(sector_map)}") +for k, v in sector_map.items(): + print(f" {k} -> {v}") +print() + +# 4. Test equity resolution with first scenario +shifts = kb[0]["shifts"] +for eq in ms.equity_curves: + s = todaysmarket_analyzer.resolve_equity_shift(eq, shifts, sector_map) + print(f"Equity {eq.name} ({eq.currency}): shift={s:+.0%}") + +# 5. Test credit resolution +for dc in ms.default_curves: + c = todaysmarket_analyzer.resolve_credit_shifts(dc, shifts, sector_map) + print(f"Credit {dc.name} ({dc.currency}): {c}") +print() + +# 6. Build the stress test XML +path = stresstest_builder.build(shifts=shifts, market=ms, sector_map=sector_map) +print(f"Stress XML written to: {path}") + +# 7. Validate XML +from xml.etree import ElementTree as ET +tree = ET.parse(path) +root = tree.getroot() +print(f"Root tag: {root.tag}") +stress_tests = root.findall("StressTest") +print(f"StressTests: {len(stress_tests)}") +for st in stress_tests: + disc = st.findall(".//DiscountCurve") + idx = st.findall(".//IndexCurve") + eq = st.findall(".//EquitySpot") + fx = st.findall(".//FxSpot") + surv = st.findall(".//SurvivalProbability") + print(f" Discount curves: {len(disc)}") + print(f" Index curves: {len(idx)}") + print(f" FX spots: {len(fx)}") + print(f" Equity spots: {len(eq)}") + print(f" Survival probs: {len(surv)}") + +print("\nβœ“ All imports and build OK!") + +# 8. Build ore_agent.xml (stress-only ORE config) +ore_workspace = config.ORE_WORKSPACE +ore_agent_xml = stresstest_builder.build_ore_config( + base_ore_xml=ore_workspace / "ore.xml", + stress_config_file="agent_stress.xml", +) +print(f"\nORE agent config written to: {ore_agent_xml}") + +# 9. Run ORE via Python bindings +csv_path = ore_runner.run(ore_xml=ore_agent_xml, workspace=ore_workspace) +print(f"ORE completed β€” output: {csv_path}") + +# 10. Read and display stress test results +import pandas as pd +import io + +with csv_path.open() as f: + content = f.read().replace("#TradeId", "TradeId", 1) + +df = pd.read_csv(io.StringIO(content)) +df.columns = [c.strip() for c in df.columns] +df["PnL"] = df["Scenario NPV"] - df["Base NPV"] + +print(f"\nStress test results ({len(df)} rows):") +print(f" Scenarios: {df['ScenarioLabel'].unique().tolist()}") +print(f" Total Base NPV: {df['Base NPV'].sum():>14,.0f}") +print(f" Total Stressed NPV: {df['Scenario NPV'].sum():>14,.0f}") +print(f" Total P&L: {df['PnL'].sum():>+14,.0f}") +print() +print(df[["TradeId", "Base NPV", "Scenario NPV", "PnL"]].to_string(index=False)) + +print("\nβœ“ Full pipeline (build + ORE run + output) OK!") diff --git a/EconomicStressAgentORE/todaysmarket_analyzer.py b/EconomicStressAgentORE/todaysmarket_analyzer.py new file mode 100644 index 0000000..ba4cc62 --- /dev/null +++ b/EconomicStressAgentORE/todaysmarket_analyzer.py @@ -0,0 +1,436 @@ +""" +todaysmarket_analyzer.py β€” Parse todaysmarket.xml to extract all market +entities (curves, FX pairs, equities, credit names, inflation indices). + +Returns a MarketStructure dataclass consumed by the stress test builder +and can generate a matching simulation.xml. +""" + +from __future__ import annotations + +import csv +from dataclasses import dataclass, field +from pathlib import Path +from xml.dom import minidom +from xml.etree import ElementTree as ET + +import config + + +# ── Data classes ────────────────────────────────────────────────────────────── + +@dataclass +class CurveInfo: + """A single market curve / entity extracted from todaysmarket.xml.""" + name: str # e.g. "EUR-ESTER", "Underlying1", "RIC:.SPX" + currency: str # e.g. "EUR", "USD" + curve_type: str # "discount", "index", "yield", "default", "equity", "inflation" + spec: str # full ORE spec, e.g. "Yield/EUR/EUR-ESTER" + + +@dataclass +class MarketStructure: + """Complete market topology derived from todaysmarket.xml.""" + base_currency: str = "EUR" + discount_curves: list[CurveInfo] = field(default_factory=list) + index_curves: list[CurveInfo] = field(default_factory=list) + yield_curves: list[CurveInfo] = field(default_factory=list) + default_curves: list[CurveInfo] = field(default_factory=list) + equity_curves: list[CurveInfo] = field(default_factory=list) + fx_pairs: list[str] = field(default_factory=list) + inflation_indices: list[CurveInfo] = field(default_factory=list) + capfloor_vols: list[CurveInfo] = field(default_factory=list) + + @property + def currencies(self) -> set[str]: + """Union of all currencies found across every curve.""" + ccys: set[str] = set() + for curves in (self.discount_curves, self.index_curves, + self.yield_curves, self.default_curves, + self.equity_curves, self.inflation_indices): + for c in curves: + ccys.add(c.currency) + # Also extract from FX pairs (first 3 and last 3 chars) + for pair in self.fx_pairs: + ccys.add(pair[:3]) + ccys.add(pair[3:]) + return ccys + + +# ── Sector mapping ──────────────────────────────────────────────────────────── + +@dataclass +class SectorEntry: + entity_type: str # "equity" | "credit" + name: str # ORE entity name + currency: str # optional override (empty β†’ auto-detect) + sector: str # e.g. "Tech", "SeniorUnsecured" + + +def load_sector_mapping(csv_path: Path | None = None) -> dict[tuple[str, str], SectorEntry]: + """ + Load sector_mapping.csv and return a lookup dict keyed by (type, name). + + Parameters + ---------- + csv_path : path to CSV file; defaults to config.SECTOR_MAPPING_CSV + + Returns + ------- + dict mapping (entity_type, entity_name) β†’ SectorEntry + """ + if csv_path is None: + csv_path = config.SECTOR_MAPPING_CSV + csv_path = Path(csv_path) + + mapping: dict[tuple[str, str], SectorEntry] = {} + if not csv_path.exists(): + return mapping + + with csv_path.open(newline="") as f: + reader = csv.DictReader(f) + for row in reader: + entry = SectorEntry( + entity_type=row["type"].strip(), + name=row["name"].strip(), + currency=row.get("currency", "").strip(), + sector=row["sector"].strip(), + ) + mapping[(entry.entity_type, entry.name)] = entry + return mapping + + +def resolve_equity_shift( + curve: CurveInfo, + shifts: dict, + sector_map: dict[tuple[str, str], SectorEntry], +) -> float: + """Resolve the equity shift for a given equity curve. + + Priority: sector override β†’ currency default β†’ 0. + """ + equity_shifts: dict = shifts.get("equity", {}) + entry = sector_map.get(("equity", curve.name)) + if entry and entry.sector in equity_shifts: + return equity_shifts[entry.sector] + if curve.currency in equity_shifts: + return equity_shifts[curve.currency] + return 0.0 + + +def resolve_credit_shifts( + curve: CurveInfo, + shifts: dict, + sector_map: dict[tuple[str, str], SectorEntry], +) -> dict[str, float]: + """Resolve the credit tenor shifts for a given default curve. + + Priority: sector override β†’ currency default β†’ empty dict. + """ + credit_shifts: dict = shifts.get("credit", {}) + entry = sector_map.get(("credit", curve.name)) + if entry and entry.sector in credit_shifts: + return credit_shifts[entry.sector] + if curve.currency in credit_shifts: + return credit_shifts[curve.currency] + return {} + + +# ── todaysmarket.xml parser ─────────────────────────────────────────────────── + +def _ccy_from_spec(spec: str) -> str: + """Extract currency from an ORE curve spec like 'Yield/EUR/EUR-ESTER'.""" + parts = spec.split("/") + if len(parts) >= 2: + return parts[1] + return "" + + +def _ccy_from_index_name(name: str) -> str: + """Extract currency from an index name like 'EUR-ESTER' or 'USD-SOFR'.""" + parts = name.split("-") + if parts and len(parts[0]) == 3 and parts[0].isalpha(): + return parts[0].upper() + return "" + + +def parse(todaysmarket_xml: Path | None = None) -> MarketStructure: + """ + Parse todaysmarket.xml and return the full market structure. + + Parameters + ---------- + todaysmarket_xml : path to todaysmarket.xml; defaults to + config.ORE_INPUT_DIR / "todaysmarket.xml" + """ + if todaysmarket_xml is None: + todaysmarket_xml = config.ORE_INPUT_DIR / "todaysmarket.xml" + todaysmarket_xml = Path(todaysmarket_xml) + + tree = ET.parse(todaysmarket_xml) + root = tree.getroot() + + ms = MarketStructure() + + # ── Discount curves ─────────────────────────────────────────────── + for dc_section in root.findall(".//DiscountingCurves[@id='default']"): + for dc in dc_section.findall("DiscountingCurve"): + ccy = dc.get("currency", "") + spec = (dc.text or "").strip() + name = spec.split("/")[-1] if spec else ccy + ms.discount_curves.append( + CurveInfo(name=name, currency=ccy, curve_type="discount", spec=spec) + ) + + # ── Index forwarding curves ─────────────────────────────────────── + for idx_section in root.findall(".//IndexForwardingCurves[@id='default']"): + for idx in idx_section.findall("Index"): + name = idx.get("name", "") + spec = (idx.text or "").strip() + ccy = _ccy_from_index_name(name) or _ccy_from_spec(spec) + ms.index_curves.append( + CurveInfo(name=name, currency=ccy, curve_type="index", spec=spec) + ) + + # ── Yield curves ────────────────────────────────────────────────── + for yc_section in root.findall(".//YieldCurves[@id='default']"): + for yc in yc_section.findall("YieldCurve"): + name = yc.get("name", "") + spec = (yc.text or "").strip() + ccy = _ccy_from_spec(spec) + ms.yield_curves.append( + CurveInfo(name=name, currency=ccy, curve_type="yield", spec=spec) + ) + + # ── Default curves ──────────────────────────────────────────────── + for dc_section in root.findall(".//DefaultCurves[@id='default']"): + for dc in dc_section.findall("DefaultCurve"): + name = dc.get("name", "") + spec = (dc.text or "").strip() + ccy = _ccy_from_spec(spec) + ms.default_curves.append( + CurveInfo(name=name, currency=ccy, curve_type="default", spec=spec) + ) + + # ── Equity curves ───────────────────────────────────────────────── + for eq_section in root.findall(".//EquityCurves[@id='default']"): + for eq in eq_section.findall("EquityCurve"): + name = eq.get("name", "") + spec = (eq.text or "").strip() + ccy = _ccy_from_spec(spec) + ms.equity_curves.append( + CurveInfo(name=name, currency=ccy, curve_type="equity", spec=spec) + ) + + # ── FX pairs ────────────────────────────────────────────────────── + for fx_section in root.findall(".//FxSpots[@id='default']"): + for fx in fx_section.findall("FxSpot"): + pair = fx.get("pair", "") + if pair: + ms.fx_pairs.append(pair) + + # ── Inflation indices ───────────────────────────────────────────── + for zi_section in root.findall(".//ZeroInflationIndexCurves[@id='default']"): + for zi in zi_section.findall("ZeroInflationIndexCurve"): + name = zi.get("name", "") + spec = (zi.text or "").strip() + # Inflation specs: Inflation/EUHICPXT/... β†’ ccy heuristic from name prefix + ccy = name[:2] + "R" if name[:2] in ("EU", "US", "GB") else "" + if name.startswith("EU"): + ccy = "EUR" + elif name.startswith("US"): + ccy = "USD" + elif name.startswith("GB"): + ccy = "GBP" + else: + ccy = _ccy_from_spec(spec) + ms.inflation_indices.append( + CurveInfo(name=name, currency=ccy, curve_type="inflation", spec=spec) + ) + + # ── CapFloor volatilities ───────────────────────────────────────── + for cf_section in root.findall(".//CapFloorVolatilities[@id='default']"): + for cf in cf_section.findall("CapFloorVolatility"): + key = cf.get("key", "") + spec = (cf.text or "").strip() + ccy = _ccy_from_index_name(key) or _ccy_from_spec(spec) + ms.capfloor_vols.append( + CurveInfo(name=key, currency=ccy, curve_type="capfloor_vol", spec=spec) + ) + + # Derive base currency from first discount curve or default to EUR + if ms.discount_curves: + ms.base_currency = ms.discount_curves[0].currency + else: + ms.base_currency = "EUR" + + return ms + + +# ── Simulation XML generation ───────────────────────────────────────────────── + +def _prettify(element: ET.Element) -> str: + """Return a pretty-printed XML string (no XML declaration).""" + rough = ET.tostring(element, encoding="unicode") + reparsed = minidom.parseString(rough) + pretty = reparsed.toprettyxml(indent=" ") + # Remove the XML declaration added by minidom + lines = pretty.splitlines() + if lines and lines[0].startswith(" Path: + """ + Generate a simulation.xml from the discovered MarketStructure. + + Uses standard tenor grids from config for each curve type. + + Parameters + ---------- + market : MarketStructure from parse() + output_path : where to write; defaults to config.ORE_INPUT_DIR / "simulation.xml" + """ + if output_path is None: + output_path = config.ORE_INPUT_DIR / "simulation.xml" + output_path = Path(output_path) + + root = ET.Element("Simulation") + mkt = ET.SubElement(root, "Market") + + # ── BaseCurrency ────────────────────────────────────────────────── + ET.SubElement(mkt, "BaseCurrency").text = market.base_currency + + # ── Currencies ──────────────────────────────────────────────────── + ccys_el = ET.SubElement(mkt, "Currencies") + for ccy in sorted(market.currencies): + ET.SubElement(ccys_el, "Currency").text = ccy + + # ── YieldCurves (global config) ─────────────────────────────────── + yc_el = ET.SubElement(mkt, "YieldCurves") + cfg = ET.SubElement(yc_el, "Configuration", {"curve": ""}) + ET.SubElement(cfg, "Tenors").text = ", ".join(config.STANDARD_RATE_TENORS) + ET.SubElement(cfg, "Interpolation").text = "LogLinear" + ET.SubElement(cfg, "Extrapolation").text = "FlatZero" + + # ── FxRates ─────────────────────────────────────────────────────── + fx_el = ET.SubElement(mkt, "FxRates") + pairs_el = ET.SubElement(fx_el, "CurrencyPairs") + for pair in market.fx_pairs: + # ORE simulation uses inverted convention (USDEUR instead of EURUSD) + foreign = pair[:3] + domestic = pair[3:] + sim_pair = domestic + foreign # e.g. EURUSD β†’ USDEUR + ET.SubElement(pairs_el, "CurrencyPair").text = sim_pair + + # ── Indices ─────────────────────────────────────────────────────── + indices_el = ET.SubElement(mkt, "Indices") + for idx in market.index_curves: + ET.SubElement(indices_el, "Index").text = idx.name + + # ── BenchmarkCurves (empty) ─────────────────────────────────────── + ET.SubElement(mkt, "BenchmarkCurves") + + # ── CapFloorVolatilities ────────────────────────────────────────── + if market.capfloor_vols: + cf_el = ET.SubElement(mkt, "CapFloorVolatilities") + ET.SubElement(cf_el, "Simulate").text = "true" + ET.SubElement(cf_el, "ReactionToTimeDecay").text = "ForwardVariance" + keys_el = ET.SubElement(cf_el, "Keys") + for cv in market.capfloor_vols: + ET.SubElement(keys_el, "Key").text = cv.name + ET.SubElement(cf_el, "Expiries", {"key": cv.name}).text = ( + "1Y, 2Y, 3Y, 4Y, 5Y, 6Y, 7Y, 8Y, 9Y, 10Y, 12Y, 15Y, 20Y, 25Y, 30Y" + ) + ET.SubElement(cf_el, "Strikes", {"key": cv.name}).text = ( + "-0.015, -0.01, 0, 0.0005" + ) + ET.SubElement(cf_el, "AdjustOptionletPillars").text = "true" + ET.SubElement(cf_el, "UseCapAtm").text = "false" + ET.SubElement(cf_el, "SmileDynamics", {"key": ""}).text = "StickyStrike" + for cv in market.capfloor_vols: + ET.SubElement(cf_el, "SmileDynamics", {"key": cv.name}).text = "StickyStrike" + + # ── DefaultCurves ───────────────────────────────────────────────── + if market.default_curves: + dc_el = ET.SubElement(mkt, "DefaultCurves") + names_el = ET.SubElement(dc_el, "Names") + for dc in market.default_curves: + ET.SubElement(names_el, "Name").text = dc.name + ET.SubElement(dc_el, "Tenors").text = ", ".join(config.STANDARD_CREDIT_TENORS) + ET.SubElement(dc_el, "SimulateSurvivalProbabilities").text = "true" + ET.SubElement(dc_el, "SimulateRecoveryRates").text = "false" + cals = ET.SubElement(dc_el, "Calendars") + ET.SubElement(cals, "Calendar", {"name": ""}).text = "TARGET" + ET.SubElement(dc_el, "Extrapolation").text = "FlatZero" + + # ── ZeroInflationIndexCurves ────────────────────────────────────── + if market.inflation_indices: + zi_el = ET.SubElement(mkt, "ZeroInflationIndexCurves") + names_el = ET.SubElement(zi_el, "Names") + for zi in market.inflation_indices: + ET.SubElement(names_el, "Name").text = zi.name + ET.SubElement(zi_el, "Tenors").text = ", ".join(config.STANDARD_INFLATION_TENORS) + + # ── Equities ────────────────────────────────────────────────────── + if market.equity_curves: + eq_el = ET.SubElement(mkt, "Equities") + ET.SubElement(eq_el, "SimulateDividendYield").text = "true" + names_el = ET.SubElement(eq_el, "Names") + for eq in market.equity_curves: + ET.SubElement(names_el, "Name").text = eq.name + ET.SubElement(eq_el, "DividendTenors").text = ", ".join( + config.STANDARD_EQUITY_DIV_TENORS + ) + + # ── CreditStates (empty) ───────────────────────────────────────── + cs = ET.SubElement(mkt, "CreditStates") + ET.SubElement(cs, "NumberOfFactors").text = "0" + acs = ET.SubElement(mkt, "AggregationScenarioDataCreditStates") + ET.SubElement(acs, "NumberOfFactors").text = "0" + + # ── Write ───────────────────────────────────────────────────────── + xml_str = _prettify(root) + output_path.write_text(xml_str, encoding="utf-8") + return output_path + + +# ── Pretty-print MarketStructure ────────────────────────────────────────────── + +def format_market_structure(ms: MarketStructure) -> str: + """Return a human-readable summary of the discovered market structure.""" + lines = [ + f"Base currency: {ms.base_currency}", + f"Currencies: {', '.join(sorted(ms.currencies))}", + "", + f"Discount curves ({len(ms.discount_curves)}):", + ] + for c in ms.discount_curves: + lines.append(f" {c.currency:4s} {c.name:30s} {c.spec}") + lines.append(f"\nIndex curves ({len(ms.index_curves)}):") + for c in ms.index_curves: + lines.append(f" {c.currency:4s} {c.name:30s} {c.spec}") + if ms.yield_curves: + lines.append(f"\nYield curves ({len(ms.yield_curves)}):") + for c in ms.yield_curves: + lines.append(f" {c.currency:4s} {c.name:30s} {c.spec}") + lines.append(f"\nFX pairs ({len(ms.fx_pairs)}):") + for p in ms.fx_pairs: + lines.append(f" {p}") + if ms.equity_curves: + lines.append(f"\nEquity curves ({len(ms.equity_curves)}):") + for c in ms.equity_curves: + lines.append(f" {c.currency:4s} {c.name:30s} {c.spec}") + if ms.default_curves: + lines.append(f"\nDefault curves ({len(ms.default_curves)}):") + for c in ms.default_curves: + lines.append(f" {c.currency:4s} {c.name:30s} {c.spec}") + if ms.inflation_indices: + lines.append(f"\nInflation indices ({len(ms.inflation_indices)}):") + for c in ms.inflation_indices: + lines.append(f" {c.currency:4s} {c.name:30s} {c.spec}") + return "\n".join(lines) From 1d33ae1414e53d0a3dab684953f8bf6bb00510d8 Mon Sep 17 00:00:00 2001 From: Matthias Groncki Date: Sat, 28 Feb 2026 16:30:22 +0100 Subject: [PATCH 23/25] smaller improvements --- EconomicStressAgentORE/.env.example | 5 +- EconomicStressAgentORE/README.md | 216 +++++++++++++++----- EconomicStressAgentORE/config.py | 3 +- EconomicStressAgentORE/impact_summarizer.py | 75 ++++--- EconomicStressAgentORE/scenario_analyzer.py | 2 +- 5 files changed, 219 insertions(+), 82 deletions(-) diff --git a/EconomicStressAgentORE/.env.example b/EconomicStressAgentORE/.env.example index c552cc2..0bd2e4a 100644 --- a/EconomicStressAgentORE/.env.example +++ b/EconomicStressAgentORE/.env.example @@ -2,4 +2,7 @@ OPENAI_API_KEY=sk-... # OpenAI model to use -OPENAI_MODEL=gpt-4 +OPENAI_MODEL=gpt-5.2 + +# LLM temperature (0.0 = deterministic, 1.0 = creative) +OPENAI_TEMPERATURE=0.2 diff --git a/EconomicStressAgentORE/README.md b/EconomicStressAgentORE/README.md index 538648c..b46f1c0 100644 --- a/EconomicStressAgentORE/README.md +++ b/EconomicStressAgentORE/README.md @@ -35,6 +35,65 @@ User describes scenario β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ ``` +## Known Shortcomings & Shortcuts + +This project is a proof-of-concept built for learning purposes. Several design +decisions were taken as shortcuts and would need to be rethought for any +production use. + +### 1. Historical scenarios are mock data + +The 20 historical episodes in `data/scenarios.json` are AI-generated +approximations, not rigorously sourced market data. In a real system these +would be replaced by: + +- Curated, auditable data sets of actual market moves (sourced from market + data providers or internal risk databases). +- Proper versioning and provenance tracking so every shift can be traced + back to an observed market event. + +### 2. The LLM produces the final shifts directly β€” not auditable + +Currently the LLM receives the historical scenarios, picks the closest +matches, and returns a _weighted average_ of market shifts in a single step. +This is problematic because: + +- **Non-reproducible**: the same prompt can yield different numbers across + runs or model versions, making results impossible to audit. +- **Not explainable**: there is no transparent formula an auditor or + regulator can inspect β€” the blending logic is a black box inside the model. + +A better architecture would split the task in two: + +1. **LLM step (qualitative)**: the model selects the _N_ closest historical + scenarios and proposes a _severity multiplier_ (e.g. "80% of the 2008 + crisis combined with 50% of the Dot-com bust"). This output is + human-readable and auditable. +2. **Deterministic step (quantitative)**: a macro-economic model or a simple rule-based mapping takes the selected scenarios and severity parameters + as inputs and produces the final shift vector. Because the model is + versioned code with fixed parameters, the output is fully reproducible, + testable, and auditable. + +This separation keeps the LLM's role limited to _judgement_ (scenario +matching and severity assessment) while all _numerical_ work is done by +auditable, deterministic code. + +### 3. Other simplifications + +- **Single stress scenario per run** β€” no support for running a grid of + severities or multiple scenarios in one invocation. +- **No volatility or correlation shifts** β€” only spot-level shifts are + applied; a real stress test would also shock implied vols, correlations, + and basis spreads. +- **Flat extrapolation of tenor grids** β€” shift curves are linearly + interpolated and flat-extrapolated, which may be too simplistic for + far-out-of-grid tenors. +- **No validation against market bounds** β€” the LLM-generated shifts are + not sanity-checked (e.g. negative rates below a floor, FX moves beyond + historical extremes). +- **Toy portfolio** β€” the included ORE workspace has only a handful of + trades; + ## Setup ### 1. Install dependencies @@ -54,17 +113,17 @@ cp .env.example .env `.env` keys: -| Key | Description | Required | -| ---------------- | ----------------------------- | -------- | -| `OPENAI_API_KEY` | Your OpenAI API key | Yes | -| `OPENAI_MODEL` | Model name (default: `gpt-4`) | No | +| Key | Description | Required | +| ---------------- | ------------------------------- | -------- | +| `OPENAI_API_KEY` | Your OpenAI API key | Yes | +| `OPENAI_MODEL` | Model name (default: `gpt-5.2`) | No | ### 3. ORE workspace -The agent expects the ORE workspace at `./OREDir/` containing: +The agent expects the ORE workspace at `./oredata/` containing: ``` -OREDir/ +oredata/ β”œβ”€β”€ ore.xml ← main ORE config β”œβ”€β”€ Input/ β”‚ β”œβ”€β”€ portfolio.xml @@ -75,13 +134,13 @@ OREDir/ β”‚ β”œβ”€β”€ simulation.xml β”‚ β”œβ”€β”€ sensitivity.xml β”‚ └── pricingengine.xml -└── Output/ ← auto-created by ORE +└── Output/ ← cleaned and recreated on each run ``` The agent injects two files at runtime: -- `OREDir/Input/agent_stress.xml` β€” generated stress scenario -- `OREDir/ore_agent.xml` β€” stripped-down ore.xml (stress analytic only) +- `oredata/Input/agent_stress.xml` β€” generated stress scenario +- `oredata/ore_agent.xml` β€” stripped-down ore.xml (stress analytic only) ## Usage @@ -92,7 +151,7 @@ python agent.py --scenario "A sudden European banking crisis with contagion fear # With all options python agent.py \ --scenario "A sudden European banking crisis with contagion fears" \ - --ore-workspace ./OREDir \ + --ore-workspace ./oredata \ --scenario-id my_scenario \ --output report.md \ --verbose @@ -100,66 +159,115 @@ python agent.py \ ## Architecture -| File | Purpose | -| ------------------------- | --------------------------------------------- | -| `agent.py` | CLI orchestrator β€” ties all steps together | -| `scenario_analyzer.py` | LLM call β€” maps text to market shifts | -| `stresstest_builder.py` | XML generator β€” writes ORE stress test config | -| `ore_runner.py` | ORE execution (Python API or subprocess) | -| `impact_summarizer.py` | CSV parser + LLM narrative report | -| `historical_scenarios.py` | Knowledge base of 20 historical episodes | -| `config.py` | Paths, model settings, curve grids | - -## Market entities handled - -| Asset class | Scenario key | ORE name | -| ------------- | ------------ | ------------------------------ | -| FX | `EURUSD` | `EURUSD` (absolute shift) | -| Euro equities | `SX5E` | `RIC:.STOXX50` | -| US equities | `SPX` | `RIC:.SPX` | -| EUR rates | `rates_eur` | EUR-ESTER + EUR discount curve | -| USD rates | `rates_usd` | USD-SOFR + USD discount curve | - -Rate shifts are specified at **1Y, 2Y, 5Y, 10Y, 30Y** and interpolated onto -the full ORE tenor grid using piecewise-linear interpolation. +| File | Purpose | +| -------------------------- | ------------------------------------------------------- | +| `agent.py` | CLI orchestrator β€” ties all steps together | +| `scenario_analyzer.py` | LLM call β€” maps text to market shifts | +| `stresstest_builder.py` | XML generator β€” writes ORE stress test config | +| `ore_runner.py` | ORE execution via Python API | +| `impact_summarizer.py` | CSV parser + LLM narrative report | +| `historical_scenarios.py` | `ScenarioKnowledgeBase` class β€” loads `scenarios.json` | +| `todaysmarket_analyzer.py` | Parses todaysmarket.xml to discover curves and equities | +| `config.py` | Paths, model settings, tenor grids | +| `data/scenarios.json` | 20 historical episodes with structured shifts | + +## Market shift schema + +Shifts are specified in a generic, multi-currency schema: + +| Asset class | Schema key | Value convention | ORE mapping | +| ----------- | ------------ | ---------------------------------------- | ------------------------------- | +| Rates | `rates.CCY` | Absolute (decimal, e.g. -0.015 = -150bp) | Discount + Index curves per ccy | +| FX | `fx.PAIR` | Absolute change in spot | FxSpot (inverted for ORE) | +| Equity | `equity.KEY` | Relative (e.g. -0.25 = -25%) | EquitySpot | +| Credit | `credit.KEY` | Absolute per tenor (widening positive) | SurvivalProbability | + +Rate and credit shifts are interpolated onto the ORE tenor grid using +`ORE.LinearInterpolation` with flat extrapolation. + +Equity and credit names are resolved via an optional sector mapping +(`sector_mapping.csv`) that maps trade-level names to scenario-level keys. ## Example output ``` +python agent.py -s "world wide AI mass lay offs for high income jobs" + ╔══════════════════════════════════════════════════════════╗ β•‘ Economic Scenario Stress Test Agent β•‘ β•šβ•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β• -Analyzing scenario: "A sudden European banking crisis with contagion fears" +Analyzing scenario: "world wide AI mass lay offs for high income jobs" - β–Ά Step 1/4 Analyzing scenario with LLM … + β–Ά Step 1/5 Analyzing scenario with LLM … βœ“ Scenario analysis complete Matched scenarios: - β€’ 2010-2012 European Sovereign Debt Crisis - β€’ 2023 US Regional Banking Crisis - -EURUSD : -0.0700 (absolute) -SX5E : -19.0% -SPX : -8.5% -EUR rates: 1Y -8bp 2Y -10bp 5Y -9bp 10Y -7bp 30Y -4bp -USD rates: 1Y -8bp 2Y -12bp 5Y -14bp 10Y -12bp 30Y -8bp - - β–Ά Step 2/4 Generating ORE stress test XML … - β–Ά Step 3/4 Running ORE … - β–Ά Step 4/4 Generating impact report … + β€’ 2001 Dot-com Bust (Mar 2000 – Oct 2002) + β€’ 2015 China Devaluation / EM Shock (Aug – Sep 2015) + β€’ 2011 US Debt-Ceiling Crisis / S&P Downgrade (Jul – Aug 2011) + +Reasoning: A worldwide AI-driven wave of layoffs in high-income (white-collar/tech-heavy) jobs is most analogous to a tech-led equity drawdown with growth fears and policy easing (dot-com), with an added global risk-off/flight-to-quality component (2015, 2011). Severity is set to moderate-to-severe but below 2008: large equity hit (especially tech), meaningful rate declines, and moderate credit widening; USD benefits as a relative safe haven so EURUSD falls. + +Proposed market shifts: + FX EURUSD: -0.0126 + Equity EUR: -30.8% + Equity USD: -25.2% + Equity Tech: -45.0% + Rates EUR: 1Y -105bp 2Y -99bp 3Y -80bp 5Y -84bp 10Y -84bp 30Y -63bp + Rates USD: 1Y -210bp 2Y -196bp 3Y -161bp 5Y -154bp 10Y -126bp 30Y -84bp + Credit EUR: 1Y +35bp 2Y +49bp 3Y +60bp 5Y +67bp 10Y +56bp + Credit USD: 1Y +49bp 2Y +63bp 3Y +77bp 5Y +88bp 10Y +74bp + + β–Ά Step 2/5 Parsing todaysmarket.xml … + βœ“ Discovered 2 currencies, 2 discount curves, 2 equities, 1 credit names + + β–Ά Step 3/5 Generating ORE stress test XML … + βœ“ Written: /Users/matthiasgroncki/quant-dev/IPythonScripts/EconomicStressAgentORE/oredata/Input/agent_stress.xml + βœ“ Written: /Users/matthiasgroncki/quant-dev/IPythonScripts/EconomicStressAgentORE/oredata/ore_agent.xml + + β–Ά Step 4/5 Running ORE … +Loading inputs OK +Requested analytics STRESS +StressTestAnalytic: Build Market OK +StressTestAnalytic: Build Portfolio OK +Risk: Stress Test Report OK +Writing reports... OK +Writing cubes... OK +run time: 0.180000 sec +ORE done. + βœ“ ORE completed. Results: /Users/matthiasgroncki/quant-dev/IPythonScripts/EconomicStressAgentORE/oredata/Output/stresstest.csv + + β–Ά Step 5/5 Generating impact report … + βœ“ Report ready ════════════════════════════════════════════════════════════ Portfolio Stress Test Impact Report ════════════════════════════════════════════════════════════ -**TOTAL P&L: +92,000 EUR [GAIN]** + TOTAL P&L: -6,614,962 EUR [β–Ό LOSS] + +β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” +β”‚ Trade β”‚ Base NPV β”‚ Stressed NPV β”‚ P&L Impact β”‚ +β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€ +β”‚ EUR6MSwap β”‚ 5,924,804 β”‚ 4,415,089 β”‚ -1,509,715 β”‚ +β”‚ EquityCFD_USD β”‚ 76,647 β”‚ -1,359,666 β”‚ -1,436,313 β”‚ +β”‚ EquityCFD_EUR β”‚ 1,263,244 β”‚ -172,789 β”‚ -1,436,033 β”‚ +β”‚ XccySwap β”‚ 268,878 β”‚ -1,027,997 β”‚ -1,296,875 β”‚ +β”‚ Cap β”‚ 4,988,927 β”‚ 4,045,146 β”‚ -943,781 β”‚ +β”‚ ZeroCouponInflationSwapEUR β”‚ -20,433 β”‚ -21,311 β”‚ -878 β”‚ +β”‚ CDS β”‚ -64,059 β”‚ -55,424 β”‚ +8,635 β”‚ +β”œβ•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β”Όβ•β•β•β•β•β•β•β•β•β•β•β•β”Όβ•β•β•β•β•β•β•β•β•β•β•β•β•β•β”Όβ•β•β•β•β•β•β•β•β•β•β•β•β”€ +β”‚ TOTAL β”‚ 12,438,009 β”‚ 5,823,047 β”‚ -6,614,962 β”‚ +β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ + +──────────────────────────────────────────────────────────── + Narrative Summary +──────────────────────────────────────────────────────────── + +The β€œAI mass layoffs in high‑income jobs” scenario produces a severe risk‑off shock across equities and a strong rally in core rates, alongside widening credit spreads and a modest EURUSD move. Equities fall sharply (EUR equities -30.8%, USD equities -25.2%, and Tech -45.0%), while rates decline materially (EUR down 63–105bp across 1Y–30Y; USD down 84–210bp across 1Y–30Y). Credit spreads widen in both regions (EUR +35–67bp out to 10Y; USD +49–88bp out to 10Y). Under these combined shifts, portfolio NPV drops from EUR 12,438,009 to EUR 5,823,047, for a total P&L impact of -EUR 6,614,962 (about -53% of base NPV). + +Losses are concentrated in equity and rates/FX-sensitive positions. The two equity CFD positions are the largest equity-driven detractors: EquityCFD_USD loses -EUR 1,436,313 (from EUR 76,647 to -EUR 1,359,666) and EquityCFD_EUR loses -EUR 1,436,033 (from EUR 1,263,244 to -EUR 172,789), consistent with the large equity drawdowns in both currencies. The rates rally and cross-currency dynamics also contribute heavily: the EUR6MSwap loses -EUR 1,509,715 (from EUR 5,924,804 to EUR 4,415,089) and the XccySwap loses -EUR 1,296,875 (from EUR 268,878 to -EUR 1,027,997), indicating significant exposure to curve moves and/or basis/FX interactions (EURUSD shifts by -0.0126 in absolute terms). The Cap position also detracts -EUR 943,781 (from EUR 4,988,927 to EUR 4,045,146), suggesting the scenario’s large rate move and volatility/convexity effects are adverse for this structure despite the directionally lower yields. -| Trade | Base NPV | Stressed NPV | P&L Impact | -|-------------|------------|--------------|-------------| -| CDS | -64,059 | -63,293 | +766 | -| XccySwap | 268,878 | 230,964 | -37,914 | -| EUR6MSwap | 5,924,804 | 5,867,079 | -57,725 | -| ... | | | | -| **TOTAL** | ... | ... | **+92,000** | +Offsets are limited and primarily credit-related. The CDS position gains EUR 8,635 (from -EUR 64,059 to -EUR 55,424), consistent with credit spread widening improving the value of protection, but the magnitude is small relative to the equity and rates-driven losses. The ZeroCouponInflationSwapEUR is essentially flat (-EUR 878), providing negligible diversification in this shock. Overall, the risk interpretation is that the portfolio is materially exposed to a synchronized risk-off regime where equities reprice lower and rates rally sharply; credit hedging is present but insufficient in size to counteract the dominant equity and rates/cross-currency losses, leaving the portfolio vulnerable to macro shocks that combine growth fears, policy-rate cuts, and widening spreads. ``` diff --git a/EconomicStressAgentORE/config.py b/EconomicStressAgentORE/config.py index f4b0430..57034c4 100644 --- a/EconomicStressAgentORE/config.py +++ b/EconomicStressAgentORE/config.py @@ -22,7 +22,8 @@ # ── LLM ─────────────────────────────────────────────────────────────────────── OPENAI_API_KEY: str = os.getenv("OPENAI_API_KEY", "") -OPENAI_MODEL: str = os.getenv("OPENAI_MODEL", "gpt-4") +OPENAI_MODEL: str = os.getenv("OPENAI_MODEL", "gpt-5.2") +OPENAI_TEMPERATURE: float = float(os.getenv("OPENAI_TEMPERATURE", "0.2")) # ── Sector-mapping CSV (maps equity/credit names β†’ sectors) ─────────────────── SECTOR_MAPPING_CSV: Path = BASE_DIR / "sector_mapping.csv" diff --git a/EconomicStressAgentORE/impact_summarizer.py b/EconomicStressAgentORE/impact_summarizer.py index 011eccc..37ccb6e 100644 --- a/EconomicStressAgentORE/impact_summarizer.py +++ b/EconomicStressAgentORE/impact_summarizer.py @@ -69,25 +69,48 @@ def _compute_summary(df: pd.DataFrame) -> dict[str, Any]: # ── Markdown table ──────────────────────────────────────────────────────────── def _format_table(summary: dict[str, Any]) -> str: - """Build a Markdown results table.""" - lines = [ - "| Trade | Base NPV | Stressed NPV | P&L Impact |", - "|-------|----------|--------------|------------|", + """Build a nicely aligned plain-text table with box-drawing characters.""" + # Determine column widths dynamically + trade_ids = [str(r["TradeId"]) for r in summary["trades"]] + ["TOTAL"] + base_vals = [f"{r['Base NPV']:,.0f}" for r in summary["trades"]] + [ + f"{summary['total_base_npv']:,.0f}" ] - for row in summary["trades"]: - pnl_str = f"{row['PnL']:+,.0f}" - lines.append( - f"| {row['TradeId']} " - f"| {row['Base NPV']:,.0f} " - f"| {row['Scenario NPV']:,.0f} " - f"| {pnl_str} |" + stress_vals = [f"{r['Scenario NPV']:,.0f}" for r in summary["trades"]] + [ + f"{summary['total_stressed_npv']:,.0f}" + ] + pnl_vals = [f"{r['PnL']:+,.0f}" for r in summary["trades"]] + [ + f"{summary['total_pnl']:+,.0f}" + ] + + w_trade = max(len("Trade"), max(len(t) for t in trade_ids)) + 2 + w_base = max(len("Base NPV"), max(len(v) for v in base_vals)) + 2 + w_stress = max(len("Stressed NPV"), max(len(v) for v in stress_vals)) + 2 + w_pnl = max(len("P&L Impact"), max(len(v) for v in pnl_vals)) + 2 + + def hline(left: str, mid: str, right: str, fill: str = "─") -> str: + return f"{left}{fill * w_trade}{mid}{fill * w_base}{mid}{fill * w_stress}{mid}{fill * w_pnl}{right}" + + def row(c1: str, c2: str, c3: str, c4: str, bold: bool = False) -> str: + s = ( + f"β”‚ {c1:<{w_trade - 2}} " + f"β”‚ {c2:>{w_base - 2}} " + f"β”‚ {c3:>{w_stress - 2}} " + f"β”‚ {c4:>{w_pnl - 2}} β”‚" ) - lines.append( - f"| **TOTAL** " - f"| **{summary['total_base_npv']:,.0f}** " - f"| **{summary['total_stressed_npv']:,.0f}** " - f"| **{summary['total_pnl']:+,.0f}** |" - ) + return s + + lines: list[str] = [] + lines.append(hline("β”Œ", "┬", "┐")) + lines.append(row("Trade", "Base NPV", "Stressed NPV", "P&L Impact")) + lines.append(hline("β”œ", "β”Ό", "─")) + + for tid, bv, sv, pv in zip(trade_ids[:-1], base_vals[:-1], stress_vals[:-1], pnl_vals[:-1]): + lines.append(row(tid, bv, sv, pv)) + + lines.append(hline("β”œ", "β”Ό", "─", "═")) + lines.append(row("TOTAL", base_vals[-1], stress_vals[-1], pnl_vals[-1])) + lines.append(hline("β””", "β”΄", "β”˜")) + return "\n".join(lines) @@ -167,7 +190,7 @@ def _llm_narrative( {"role": "system", "content": _NARRATIVE_SYSTEM}, {"role": "user", "content": prompt}, ], - temperature=0.4, + temperature=config.OPENAI_TEMPERATURE, ) return resp.choices[0].message.content or "" @@ -199,22 +222,24 @@ def summarize( table = _format_table(summary) narrative = _llm_narrative(scenario_description, shifts, summary) + width = 60 header = ( - "═" * 60 + "\n" - " Portfolio Stress Test Impact Report\n" - "═" * 60 + "═" * width + "\n" + + " Portfolio Stress Test Impact Report\n" + + "═" * width ) pnl_sign = "+" if summary["total_pnl"] >= 0 else "" pnl_label = f"TOTAL P&L: {pnl_sign}{summary['total_pnl']:,.0f} EUR" - direction = "GAIN" if summary["total_pnl"] >= 0 else "LOSS" + direction = "β–² GAIN" if summary["total_pnl"] >= 0 else "β–Ό LOSS" report = ( f"{header}\n\n" - f"**{pnl_label} [{direction}]**\n\n" + f" {pnl_label} [{direction}]\n\n" f"{table}\n\n" - "---\n\n" - "**Narrative Summary**\n\n" + + "─" * width + "\n" + " Narrative Summary\n" + + "─" * width + "\n\n" f"{narrative}\n" ) return report diff --git a/EconomicStressAgentORE/scenario_analyzer.py b/EconomicStressAgentORE/scenario_analyzer.py index 16d4925..c8e779a 100644 --- a/EconomicStressAgentORE/scenario_analyzer.py +++ b/EconomicStressAgentORE/scenario_analyzer.py @@ -104,7 +104,7 @@ def analyze( {"role": "system", "content": _SYSTEM_PROMPT}, {"role": "user", "content": _build_user_message(scenario_description, knowledge_base)}, ], - temperature=0.2, + temperature=config.OPENAI_TEMPERATURE, response_format={"type": "json_object"}, ) From 8e552d027f0b8d86b22e05090630ceb169aaff5e Mon Sep 17 00:00:00 2001 From: Matthias Groncki Date: Sat, 28 Feb 2026 17:25:07 +0100 Subject: [PATCH 24/25] build simulation and sensitivity xml for stresstest --- EconomicStressAgentORE/README.md | 2 + EconomicStressAgentORE/agent.py | 1 + EconomicStressAgentORE/stresstest_builder.py | 74 ++++++++++-- EconomicStressAgentORE/test_integration.py | 1 + .../todaysmarket_analyzer.py | 114 ++++++++++++++++++ 5 files changed, 179 insertions(+), 13 deletions(-) diff --git a/EconomicStressAgentORE/README.md b/EconomicStressAgentORE/README.md index b46f1c0..86fa9fc 100644 --- a/EconomicStressAgentORE/README.md +++ b/EconomicStressAgentORE/README.md @@ -93,6 +93,8 @@ auditable, deterministic code. historical extremes). - **Toy portfolio** β€” the included ORE workspace has only a handful of trades; +- **Construction of simulation/stresstest/sensitivity config** is incomplete and can handle + only a few risk factors (no volatilities, no commodity risk factors) and allow only a small range or products. ## Setup diff --git a/EconomicStressAgentORE/agent.py b/EconomicStressAgentORE/agent.py index 35c33af..14046de 100644 --- a/EconomicStressAgentORE/agent.py +++ b/EconomicStressAgentORE/agent.py @@ -101,6 +101,7 @@ def run( base_ore_xml=ore_workspace / "ore.xml", output_ore_xml=ore_agent_xml, stress_config_file="agent_stress.xml", + market=market, ) print(OK + f"Written: {ore_agent_xml}\n") diff --git a/EconomicStressAgentORE/stresstest_builder.py b/EconomicStressAgentORE/stresstest_builder.py index ce22426..bfdf745 100644 --- a/EconomicStressAgentORE/stresstest_builder.py +++ b/EconomicStressAgentORE/stresstest_builder.py @@ -101,6 +101,13 @@ def _prettify(element: ET.Element) -> str: return "\n".join(line for line in xml.splitlines() if line.strip()) +def _add_param(parent: ET.Element, name: str, value: str) -> ET.Element: + """Add a ``value`` child element.""" + p = ET.SubElement(parent, "Parameter", name=name) + p.text = value + return p + + # ── Main builder ────────────────────────────────────────────────────────────── def _resolve_rate_shifts( @@ -263,11 +270,17 @@ def build_ore_config( base_ore_xml: Path | None = None, output_ore_xml: Path | None = None, stress_config_file: str = "agent_stress.xml", + market: MarketStructure | None = None, ) -> Path: """ Write a minimal ore_agent.xml that activates only the stress analytic and points to the agent-generated stress test configuration. + If *market* is provided, ``simulation_agent.xml`` and + ``sensitivity_agent.xml`` are generated from the market structure and + the stress analytic is pointed at them. This removes the dependency on + hand-maintained simulation.xml / sensitivity.xml files. + Parameters ---------- base_ore_xml : source ore.xml to base settings on @@ -275,11 +288,16 @@ def build_ore_config( output_ore_xml : where to write the agent ore config (defaults to config.AGENT_ORE_XML) stress_config_file : filename (relative to Input/) of the stress XML + market : MarketStructure from todaysmarket_analyzer.parse(); + when provided, agent simulation & sensitivity XMLs + are auto-generated Returns ------- Path to the written file. """ + from todaysmarket_analyzer import generate_simulation_xml, generate_sensitivity_xml + if base_ore_xml is None: base_ore_xml = config.ORE_WORKSPACE / "ore.xml" if output_ore_xml is None: @@ -288,6 +306,17 @@ def build_ore_config( base_ore_xml = Path(base_ore_xml) output_ore_xml = Path(output_ore_xml) + # ── Generate simulation & sensitivity XMLs if market is available ── + sim_file = "simulation.xml" + sensi_file = "sensitivity.xml" + + if market is not None: + input_dir = output_ore_xml.parent / "Input" + sim_path = generate_simulation_xml(market, input_dir / "simulation_agent.xml") + sensi_path = generate_sensitivity_xml(market, input_dir / "sensitivity_agent.xml") + sim_file = sim_path.name + sensi_file = sensi_path.name + tree = ET.parse(base_ore_xml) root = tree.getroot() @@ -300,19 +329,38 @@ def build_ore_config( # Disable every analytic except stress; update stress config file analytics = root.find("Analytics") - if analytics is not None: - for analytic in analytics.findall("Analytic"): - atype = analytic.get("type", "") - if atype == "stress": - for param in analytic.findall("Parameter"): - if param.get("name") == "stressConfigFile": - param.text = stress_config_file - if param.get("name") == "active": - param.text = "Y" - else: - for param in analytic.findall("Parameter"): - if param.get("name") == "active": - param.text = "N" + if analytics is None: + analytics = ET.SubElement(root, "Analytics") + + has_stress = False + for analytic in analytics.findall("Analytic"): + atype = analytic.get("type", "") + if atype == "stress": + has_stress = True + for param in analytic.findall("Parameter"): + if param.get("name") == "stressConfigFile": + param.text = stress_config_file + if param.get("name") == "active": + param.text = "Y" + if param.get("name") == "marketConfigFile": + param.text = sim_file + if param.get("name") == "sensitivityConfigFile": + param.text = sensi_file + else: + for param in analytic.findall("Parameter"): + if param.get("name") == "active": + param.text = "N" + + # If no stress analytic exists in the source ore.xml, add one + if not has_stress: + stress_el = ET.SubElement(analytics, "Analytic", type="stress") + _add_param(stress_el, "active", "Y") + _add_param(stress_el, "marketConfigFile", sim_file) + _add_param(stress_el, "stressConfigFile", stress_config_file) + _add_param(stress_el, "sensitivityConfigFile", sensi_file) + _add_param(stress_el, "pricingEnginesFile", "pricingengine.xml") + _add_param(stress_el, "scenarioOutputFile", "stresstest.csv") + _add_param(stress_el, "outputThreshold", "0.000001") xml_str = _prettify(root) lines = xml_str.splitlines() diff --git a/EconomicStressAgentORE/test_integration.py b/EconomicStressAgentORE/test_integration.py index 50b2c2d..3886556 100644 --- a/EconomicStressAgentORE/test_integration.py +++ b/EconomicStressAgentORE/test_integration.py @@ -69,6 +69,7 @@ ore_agent_xml = stresstest_builder.build_ore_config( base_ore_xml=ore_workspace / "ore.xml", stress_config_file="agent_stress.xml", + market=market, ) print(f"\nORE agent config written to: {ore_agent_xml}") diff --git a/EconomicStressAgentORE/todaysmarket_analyzer.py b/EconomicStressAgentORE/todaysmarket_analyzer.py index ba4cc62..2e0e375 100644 --- a/EconomicStressAgentORE/todaysmarket_analyzer.py +++ b/EconomicStressAgentORE/todaysmarket_analyzer.py @@ -399,6 +399,120 @@ def generate_simulation_xml( return output_path +# ── Sensitivity XML generation ──────────────────────────────────────────────── + +def generate_sensitivity_xml( + market: MarketStructure, + output_path: Path | None = None, +) -> Path: + """ + Generate a minimal sensitivity.xml from the discovered MarketStructure. + + This produces a zero-shift sensitivity config (ShiftSize = 0.0001) that + covers all market entities required by the stress test analytic. + No ParConversion blocks are emitted β€” this is a simplified "zero" config + sufficient for the stress test engine to resolve all risk factors. + + Parameters + ---------- + market : MarketStructure from parse() + output_path : where to write; defaults to + config.ORE_INPUT_DIR / "sensitivity_agent.xml" + """ + if output_path is None: + output_path = config.ORE_INPUT_DIR / "sensitivity_agent.xml" + output_path = Path(output_path) + + rate_tenors = ", ".join(config.STANDARD_RATE_TENORS) + credit_tenors = ", ".join(config.STANDARD_CREDIT_TENORS) + inflation_tenors = ", ".join(config.STANDARD_INFLATION_TENORS) + + root = ET.Element("SensitivityAnalysis") + + # ── Discount curves ─────────────────────────────────────────────── + dc_el = ET.SubElement(root, "DiscountCurves") + for dc in market.discount_curves: + curve = ET.SubElement(dc_el, "DiscountCurve", ccy=dc.currency) + ET.SubElement(curve, "ShiftType").text = "Absolute" + ET.SubElement(curve, "ShiftSize").text = "0.0001" + ET.SubElement(curve, "ShiftScheme").text = "Forward" + ET.SubElement(curve, "ShiftTenors").text = rate_tenors + + # ── Index curves ────────────────────────────────────────────────── + ic_el = ET.SubElement(root, "IndexCurves") + for idx in market.index_curves: + curve = ET.SubElement(ic_el, "IndexCurve", index=idx.name) + ET.SubElement(curve, "ShiftType").text = "Absolute" + ET.SubElement(curve, "ShiftSize").text = "0.0001" + ET.SubElement(curve, "ShiftScheme").text = "Forward" + ET.SubElement(curve, "ShiftTenors").text = rate_tenors + + # ── Yield curves (empty) ────────────────────────────────────────── + ET.SubElement(root, "YieldCurves") + + # ── FX spots ────────────────────────────────────────────────────── + fx_el = ET.SubElement(root, "FxSpots") + for pair in market.fx_pairs: + foreign = pair[:3] + domestic = pair[3:] + sim_pair = domestic + foreign # ORE convention + spot = ET.SubElement(fx_el, "FxSpot", ccypair=sim_pair) + ET.SubElement(spot, "ShiftType").text = "Relative" + ET.SubElement(spot, "ShiftSize").text = "0.01" + + # ── Credit curves ───────────────────────────────────────────────── + if market.default_curves: + cc_el = ET.SubElement(root, "CreditCurves") + for dc in market.default_curves: + curve = ET.SubElement(cc_el, "CreditCurve", name=dc.name) + ET.SubElement(curve, "Currency").text = dc.currency + ET.SubElement(curve, "ShiftType").text = "Absolute" + ET.SubElement(curve, "ShiftSize").text = "0.0001" + ET.SubElement(curve, "ShiftScheme").text = "Forward" + ET.SubElement(curve, "ShiftTenors").text = credit_tenors + + # ── CapFloor volatilities ───────────────────────────────────────── + if market.capfloor_vols: + cfv_el = ET.SubElement(root, "CapFloorVolatilities") + for cv in market.capfloor_vols: + vol = ET.SubElement(cfv_el, "CapFloorVolatility", key=cv.name) + ET.SubElement(vol, "ShiftType").text = "Absolute" + ET.SubElement(vol, "ShiftSize").text = "0.0001" + ET.SubElement(vol, "ShiftExpiries").text = ( + "1Y, 2Y, 3Y, 5Y, 7Y, 10Y, 15Y, 20Y" + ) + ET.SubElement(vol, "ShiftStrikes").text = ( + "-0.01, 0, 0.01, 0.02, 0.03, 0.04, 0.05" + ) + ET.SubElement(vol, "Index").text = cv.name + + # ── Zero inflation index curves ─────────────────────────────────── + if market.inflation_indices: + zi_el = ET.SubElement(root, "ZeroInflationIndexCurves") + for zi in market.inflation_indices: + curve = ET.SubElement(zi_el, "ZeroInflationIndexCurve", index=zi.name) + ET.SubElement(curve, "ShiftType").text = "Absolute" + ET.SubElement(curve, "ShiftSize").text = "0.0001" + ET.SubElement(curve, "ShiftTenors").text = inflation_tenors + + # ── Equity spots ────────────────────────────────────────────────── + if market.equity_curves: + eq_el = ET.SubElement(root, "EquitySpots") + for eq in market.equity_curves: + spot = ET.SubElement(eq_el, "EquitySpot", equity=eq.name) + ET.SubElement(spot, "ShiftType").text = "Relative" + ET.SubElement(spot, "ShiftSize").text = "0.01" + ET.SubElement(spot, "ShiftScheme").text = "Forward" + + # ── Global flags ────────────────────────────────────────────────── + ET.SubElement(root, "ComputeGamma").text = "false" + ET.SubElement(root, "UseSpreadedTermStructures").text = "true" + + xml_str = _prettify(root) + output_path.write_text(xml_str, encoding="utf-8") + return output_path + + # ── Pretty-print MarketStructure ────────────────────────────────────────────── def format_market_structure(ms: MarketStructure) -> str: From 265da426f3d0060784f07c2b3eb10c22e87f8095 Mon Sep 17 00:00:00 2001 From: Matthias Groncki Date: Sat, 28 Feb 2026 20:43:39 +0100 Subject: [PATCH 25/25] update readme --- EconomicStressAgentORE/README.md | 69 +++++----- EconomicStressAgentORE/data/scenarios.json | 88 +++++++++++++ EconomicStressAgentORE/ore_runner.py | 2 +- .../oredata/Input/portfolio.xml | 119 +----------------- 4 files changed, 125 insertions(+), 153 deletions(-) diff --git a/EconomicStressAgentORE/README.md b/EconomicStressAgentORE/README.md index 86fa9fc..7d267cf 100644 --- a/EconomicStressAgentORE/README.md +++ b/EconomicStressAgentORE/README.md @@ -148,11 +148,11 @@ The agent injects two files at runtime: ```bash # Basic usage -python agent.py --scenario "A sudden European banking crisis with contagion fears" +python agent.py --scenario "Giant monsters emerge from the ocean and destroy two major capitals in Europe and Asia simultaneously, triggering martial law, insurance system collapse, and a global flight to safety" # With all options python agent.py \ - --scenario "A sudden European banking crisis with contagion fears" \ + --scenario "python agent.py --scenario "Giant monsters emerge from the ocean and destroy two major capitals in Europe and Asia simultaneously, triggering martial law, insurance system collapse, and a global flight to safety" \ --ore-workspace ./oredata \ --scenario-id my_scenario \ --output report.md \ @@ -193,33 +193,31 @@ Equity and credit names are resolved via an optional sector mapping ## Example output ``` -python agent.py -s "world wide AI mass lay offs for high income jobs" - ╔══════════════════════════════════════════════════════════╗ β•‘ Economic Scenario Stress Test Agent β•‘ β•šβ•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β• -Analyzing scenario: "world wide AI mass lay offs for high income jobs" +Analyzing scenario: "Giant monsters emerge from the ocean and destroy two major capitals in Europe and Asia simultaneously, triggering martial law, insurance system collapse, and a global flight to safety" β–Ά Step 1/5 Analyzing scenario with LLM … βœ“ Scenario analysis complete Matched scenarios: - β€’ 2001 Dot-com Bust (Mar 2000 – Oct 2002) - β€’ 2015 China Devaluation / EM Shock (Aug – Sep 2015) - β€’ 2011 US Debt-Ceiling Crisis / S&P Downgrade (Jul – Aug 2011) + β€’ 2008 Global Financial Crisis (Sep 2008 – Mar 2009) + β€’ 2020 COVID-19 Crash (Feb – Mar 2020) + β€’ Eurozone Break-up (Tail Risk) (Hypothetical) -Reasoning: A worldwide AI-driven wave of layoffs in high-income (white-collar/tech-heavy) jobs is most analogous to a tech-led equity drawdown with growth fears and policy easing (dot-com), with an added global risk-off/flight-to-quality component (2015, 2011). Severity is set to moderate-to-severe but below 2008: large equity hit (especially tech), meaningful rate declines, and moderate credit widening; USD benefits as a relative safe haven so EURUSD falls. +Reasoning: Simultaneous destruction of major capitals with martial law and insurance-system collapse implies an extreme, sudden global risk-off/liquidity shock (GFC/COVID-like) plus acute Europe-specific tail risk and EUR dislocation (Eurozone break-up proxy). Weighted blend emphasizes severe credit stress and safe-haven bid, with EUR underperformance; scaled up to reflect catastrophic severity beyond typical historical episodes. Proposed market shifts: - FX EURUSD: -0.0126 - Equity EUR: -30.8% - Equity USD: -25.2% - Equity Tech: -45.0% - Rates EUR: 1Y -105bp 2Y -99bp 3Y -80bp 5Y -84bp 10Y -84bp 30Y -63bp - Rates USD: 1Y -210bp 2Y -196bp 3Y -161bp 5Y -154bp 10Y -126bp 30Y -84bp - Credit EUR: 1Y +35bp 2Y +49bp 3Y +60bp 5Y +67bp 10Y +56bp - Credit USD: 1Y +49bp 2Y +63bp 3Y +77bp 5Y +88bp 10Y +74bp + FX EURUSD: -0.1875 + Equity EUR: -63.7% + Equity USD: -48.0% + Rates EUR: 1Y -8bp 2Y -9bp 3Y -9bp 5Y -6bp 10Y -4bp 30Y -2bp + Rates USD: 1Y -225bp 2Y -240bp 3Y -240bp 5Y -225bp 10Y -180bp 30Y -135bp + Credit EUR: 1Y +270bp 2Y +375bp 3Y +435bp 5Y +495bp 10Y +465bp + Credit USD: 1Y +225bp 2Y +300bp 3Y +360bp 5Y +420bp 10Y +375bp + Credit Sovereign: 1Y +675bp 2Y +945bp 3Y +1080bp 5Y +1215bp 10Y +1080bp β–Ά Step 2/5 Parsing todaysmarket.xml … βœ“ Discovered 2 currencies, 2 discount curves, 2 equities, 1 credit names @@ -229,6 +227,7 @@ Proposed market shifts: βœ“ Written: /Users/matthiasgroncki/quant-dev/IPythonScripts/EconomicStressAgentORE/oredata/ore_agent.xml β–Ά Step 4/5 Running ORE … +Running ORE with config: ore_agent.xml from workspace: /Users/matthiasgroncki/quant-dev/IPythonScripts/EconomicStressAgentORE/oredata Loading inputs OK Requested analytics STRESS StressTestAnalytic: Build Market OK @@ -247,29 +246,29 @@ ORE done. Portfolio Stress Test Impact Report ════════════════════════════════════════════════════════════ - TOTAL P&L: -6,614,962 EUR [β–Ό LOSS] - -β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” -β”‚ Trade β”‚ Base NPV β”‚ Stressed NPV β”‚ P&L Impact β”‚ -β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€ -β”‚ EUR6MSwap β”‚ 5,924,804 β”‚ 4,415,089 β”‚ -1,509,715 β”‚ -β”‚ EquityCFD_USD β”‚ 76,647 β”‚ -1,359,666 β”‚ -1,436,313 β”‚ -β”‚ EquityCFD_EUR β”‚ 1,263,244 β”‚ -172,789 β”‚ -1,436,033 β”‚ -β”‚ XccySwap β”‚ 268,878 β”‚ -1,027,997 β”‚ -1,296,875 β”‚ -β”‚ Cap β”‚ 4,988,927 β”‚ 4,045,146 β”‚ -943,781 β”‚ -β”‚ ZeroCouponInflationSwapEUR β”‚ -20,433 β”‚ -21,311 β”‚ -878 β”‚ -β”‚ CDS β”‚ -64,059 β”‚ -55,424 β”‚ +8,635 β”‚ -β”œβ•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β”Όβ•β•β•β•β•β•β•β•β•β•β•β•β”Όβ•β•β•β•β•β•β•β•β•β•β•β•β•β•β”Όβ•β•β•β•β•β•β•β•β•β•β•β•β”€ -β”‚ TOTAL β”‚ 12,438,009 β”‚ 5,823,047 β”‚ -6,614,962 β”‚ -β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ + TOTAL P&L: -20,452,247 EUR [β–Ό LOSS] + +β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” +β”‚ Trade β”‚ Base NPV β”‚ Stressed NPV β”‚ P&L Impact β”‚ +β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€ +β”‚ XccySwap β”‚ 268,878 β”‚ -18,241,162 β”‚ -18,510,040 β”‚ +β”‚ EquityCFD_USD β”‚ 76,647 β”‚ -3,119,320 β”‚ -3,195,967 β”‚ +β”‚ EquityCFD_EUR β”‚ 1,263,244 β”‚ -1,709,064 β”‚ -2,972,308 β”‚ +β”‚ EUR6MSwap β”‚ 5,924,804 β”‚ 5,774,330 β”‚ -150,474 β”‚ +β”‚ CDS β”‚ -6,405,864 β”‚ -2,029,322 β”‚ +4,376,542 β”‚ +β”œβ•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β”Όβ•β•β•β•β•β•β•β•β•β•β•β•β”Όβ•β•β•β•β•β•β•β•β•β•β•β•β•β•β”Όβ•β•β•β•β•β•β•β•β•β•β•β•β•β”€ +β”‚ TOTAL β”‚ 1,127,710 β”‚ -19,324,538 β”‚ -20,452,247 β”‚ +β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ ──────────────────────────────────────────────────────────── Narrative Summary ──────────────────────────────────────────────────────────── -The β€œAI mass layoffs in high‑income jobs” scenario produces a severe risk‑off shock across equities and a strong rally in core rates, alongside widening credit spreads and a modest EURUSD move. Equities fall sharply (EUR equities -30.8%, USD equities -25.2%, and Tech -45.0%), while rates decline materially (EUR down 63–105bp across 1Y–30Y; USD down 84–210bp across 1Y–30Y). Credit spreads widen in both regions (EUR +35–67bp out to 10Y; USD +49–88bp out to 10Y). Under these combined shifts, portfolio NPV drops from EUR 12,438,009 to EUR 5,823,047, for a total P&L impact of -EUR 6,614,962 (about -53% of base NPV). +Under the β€œglobal flight to safety” shockβ€”EURUSD down 0.1875 (absolute), equities down 63.7% in EUR and 48.0% in USD, sharp USD rate rallies (down 225–240bp out to 5Y and down 180bp at 10Y), and severe spread widening (EUR credit +270bp to +495bp, USD credit +225bp to +420bp, sovereign credit +675bp to +1,215bp)β€”the portfolio moves from a base NPV of EUR 1,127,710 to a stressed NPV of EUR -19,324,538. This is a total P&L impact of EUR -20,452,247, indicating the portfolio is highly exposed to combined FX dislocation, equity crash risk, and cross-currency/rates basis dynamics under extreme systemic stress. + +The loss is overwhelmingly driven by the cross-currency and equity risk factors. The XccySwap contributes EUR -18,510,040 of the total drawdown, with its valuation swinging from EUR 268,878 to EUR -18,241,162, consistent with a regime where USD funding stress and large EURUSD moves dominate outcomes. Equity risk is the next major driver: EquityCFD_USD loses EUR -3,195,967 (EUR 76,647 to EUR -3,119,320) and EquityCFD_EUR loses EUR -2,972,308 (EUR 1,263,244 to EUR -1,709,064), reflecting the -48.0% and -63.7% equity shocks compounded by the EURUSD drop for USD-denominated exposure when reported in EUR. -Losses are concentrated in equity and rates/FX-sensitive positions. The two equity CFD positions are the largest equity-driven detractors: EquityCFD_USD loses -EUR 1,436,313 (from EUR 76,647 to -EUR 1,359,666) and EquityCFD_EUR loses -EUR 1,436,033 (from EUR 1,263,244 to -EUR 172,789), consistent with the large equity drawdowns in both currencies. The rates rally and cross-currency dynamics also contribute heavily: the EUR6MSwap loses -EUR 1,509,715 (from EUR 5,924,804 to EUR 4,415,089) and the XccySwap loses -EUR 1,296,875 (from EUR 268,878 to -EUR 1,027,997), indicating significant exposure to curve moves and/or basis/FX interactions (EURUSD shifts by -0.0126 in absolute terms). The Cap position also detracts -EUR 943,781 (from EUR 4,988,927 to EUR 4,045,146), suggesting the scenario’s large rate move and volatility/convexity effects are adverse for this structure despite the directionally lower yields. +Offsetting gains come primarily from credit protection. The CDS position generates EUR +4,376,542 (from EUR -6,405,864 to EUR -2,029,322), benefiting from the very large credit and sovereign spread widening (up to +495bp in EUR credit and +1,215bp in sovereign spreads at 5Y). Rates contribute only marginally: the EUR6MSwap loses EUR -150,474 (EUR 5,924,804 to EUR 5,774,330), consistent with relatively small EUR curve shifts (single-digit bp rally across tenors) compared with the much larger moves in USD rates and credit. -Offsets are limited and primarily credit-related. The CDS position gains EUR 8,635 (from -EUR 64,059 to -EUR 55,424), consistent with credit spread widening improving the value of protection, but the magnitude is small relative to the equity and rates-driven losses. The ZeroCouponInflationSwapEUR is essentially flat (-EUR 878), providing negligible diversification in this shock. Overall, the risk interpretation is that the portfolio is materially exposed to a synchronized risk-off regime where equities reprice lower and rates rally sharply; credit hedging is present but insufficient in size to counteract the dominant equity and rates/cross-currency losses, leaving the portfolio vulnerable to macro shocks that combine growth fears, policy-rate cuts, and widening spreads. +Overall, the stress test indicates a concentrated tail risk profile: the portfolio’s protection via CDS is meaningful but insufficient against the dominant cross-currency swap exposure and equity beta in a simultaneous FX break and equity crash. The result suggests the portfolio is effectively short extreme funding/FX dislocation (via the XccySwap) and long risk assets (via the equity CFDs), with credit hedges providing partial convexity but not enough to prevent a large negative NPV under systemic shock. The key risk interpretation is that diversification breaks down in this scenario and the portfolio’s P&L is governed by a small number of positions whose sensitivities amplify precisely when liquidity and basis risks are most stressed. ``` diff --git a/EconomicStressAgentORE/data/scenarios.json b/EconomicStressAgentORE/data/scenarios.json index 67801ec..0ba7a2e 100644 --- a/EconomicStressAgentORE/data/scenarios.json +++ b/EconomicStressAgentORE/data/scenarios.json @@ -895,5 +895,93 @@ } } } + }, + { + "name": "9/11 Terror Attack", + "period": "Sep 2001", + "description": "Coordinated terrorist attacks on the World Trade Center and Pentagon caused an immediate market shutdown and a sharp sell-off on reopening. The Fed cut rates 50bp within days. Flight to US Treasuries intensified, equities fell sharply, and credit spreads widened across aviation, insurance, and broader corporate sectors.", + "shifts": { + "rates": { + "EUR": { + "1Y": -0.008, + "2Y": -0.007, + "5Y": -0.005, + "10Y": -0.003, + "30Y": -0.002 + }, + "USD": { + "1Y": -0.018, + "2Y": -0.015, + "5Y": -0.01, + "10Y": -0.006, + "30Y": -0.003 + } + }, + "fx": { "EURUSD": 0.02 }, + "equity": { + "EUR": -0.14, + "USD": -0.12 + }, + "credit": { + "EUR": { + "1Y": 0.005, + "2Y": 0.007, + "3Y": 0.009, + "5Y": 0.01, + "10Y": 0.008 + }, + "USD": { + "1Y": 0.008, + "2Y": 0.011, + "3Y": 0.013, + "5Y": 0.015, + "10Y": 0.012 + } + } + } + }, + { + "name": "2011 Tōhoku Earthquake / Fukushima", + "period": "Mar 2011", + "description": "Magnitude-9.0 earthquake and subsequent tsunami devastated northeastern Japan, triggering a nuclear crisis at Fukushima Daiichi. The Nikkei fell ~20% in the first two weeks. Global risk-off sentiment pushed European and US equities lower, rates declined on growth concerns, and credit spreads widened modestly. Safe-haven flows initially strengthened the yen before G7 coordinated intervention reversed the move.", + "shifts": { + "rates": { + "EUR": { + "1Y": -0.004, + "2Y": -0.005, + "5Y": -0.006, + "10Y": -0.007, + "30Y": -0.005 + }, + "USD": { + "1Y": -0.006, + "2Y": -0.008, + "5Y": -0.009, + "10Y": -0.008, + "30Y": -0.005 + } + }, + "fx": { "EURUSD": 0.01 }, + "equity": { + "EUR": -0.1, + "USD": -0.06 + }, + "credit": { + "EUR": { + "1Y": 0.003, + "2Y": 0.004, + "3Y": 0.005, + "5Y": 0.006, + "10Y": 0.005 + }, + "USD": { + "1Y": 0.003, + "2Y": 0.004, + "3Y": 0.005, + "5Y": 0.005, + "10Y": 0.004 + } + } + } } ] diff --git a/EconomicStressAgentORE/ore_runner.py b/EconomicStressAgentORE/ore_runner.py index 6ed301d..9b65f55 100644 --- a/EconomicStressAgentORE/ore_runner.py +++ b/EconomicStressAgentORE/ore_runner.py @@ -78,7 +78,7 @@ def run( def _run_via_python_api(ore_xml_rel: Path, workspace: Path) -> None: """Execute ORE using the Python bindings (OREApp).""" from ORE import OREApp, Parameters # type: ignore[import] - + print(f"Running ORE with config: {ore_xml_rel} from workspace: {workspace}") orig_cwd = Path.cwd() try: os.chdir(workspace) diff --git a/EconomicStressAgentORE/oredata/Input/portfolio.xml b/EconomicStressAgentORE/oredata/Input/portfolio.xml index cee4011..6cb58a4 100644 --- a/EconomicStressAgentORE/oredata/Input/portfolio.xml +++ b/EconomicStressAgentORE/oredata/Input/portfolio.xml @@ -90,51 +90,7 @@ - - Swap - - CPTY_A - CPTY_A - - - - - Floating - false - EUR - - 100000000 - - A360 - MF - - EUR-EURIBOR-6M - 2 - - 0.0 - - - 0.03 - - false - - - - 20240301 - 20260301 - 3M - TARGET - MF - MF - Backward - false - - - - - - - + Swap @@ -193,7 +149,7 @@ true USD - 1000000 + 100000000 A360 F @@ -305,75 +261,4 @@ - - InflationSwap - - CPTY - NS - - party - 2024-03-05 - - - - - ZeroCouponFixed - true - EUR - - 184500.0000 - - A360 - MF - - - 2024-01-15 - 2029-01-15 - 0D - EUR - MF - Zero - - - - - 0.016500 - - Compounded - - - - CPI - false - EUR - - 184500 - - true - - - A360 - F - - - EUR - - 2029-01-15 - - - - - EUHICPXT - - 0 - - 114.4 - 2024-01-15 - 3M - Linear - true - - - - \ No newline at end of file