/*

* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.

* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.

*

* Licensed under the Apache License, Version 2.0 (the "License");

* you may not use this file except in compliance with the License.

* You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0

*

* Unless required by applicable law or agreed to in writing, software

* distributed under the License is distributed on an "AS IS" BASIS,

* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

* See the License for the specific language governing permissions and

* limitations under the License.

*/

using System;

using QuantConnect.Interfaces;

using QuantConnect.Securities;

using System.Collections.Generic;

using QuantConnect.Data;

namespace QuantConnect.Algorithm.CSharp

{

/// <summary>

/// Demonstration of using custom buying power model in backtesting.

/// QuantConnect allows you to model all orders as deeply and accurately as you need.

/// </summary>

/// <meta name="tag" content="trading and orders" />

/// <meta name="tag" content="transaction fees and slippage" />

/// <meta name="tag" content="custom buying power models" />

public class CustomBuyingPowerModelAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition

{

private Symbol _spy;

public override void Initialize()

{

SetStartDate(2013, 10, 01);

SetEndDate(2013, 10, 31);

var security = AddEquity("SPY", Resolution.Hour);

_spy = security.Symbol;

// set the buying power model

security.SetBuyingPowerModel(new CustomBuyingPowerModel());

}

public void OnData(Slice slice)

{

if (Portfolio.Invested)

{

return;

}

var quantity = CalculateOrderQuantity(_spy, 1m);

if (quantity % 100 != 0)

{

throw new Exception($"CustomBuyingPowerModel only allow quantity that is multiple of 100 and {quantity} was found");

}

// We normally get insufficient buying power model, but the

// CustomBuyingPowerModel always says that there is sufficient buying power for the orders

MarketOrder(_spy, quantity * 10);

}

public class CustomBuyingPowerModel : BuyingPowerModel

{

public override GetMaximumOrderQuantityResult GetMaximumOrderQuantityForTargetBuyingPower(

GetMaximumOrderQuantityForTargetBuyingPowerParameters parameters)

{

var quantity = base.GetMaximumOrderQuantityForTargetBuyingPower(parameters).Quantity;

quantity = Math.Floor(quantity / 100) * 100;

return new GetMaximumOrderQuantityResult(quantity);

}

public override HasSufficientBuyingPowerForOrderResult HasSufficientBuyingPowerForOrder(

HasSufficientBuyingPowerForOrderParameters parameters)

{

return new HasSufficientBuyingPowerForOrderResult(true);

}

}

/// <summary>

/// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.

/// </summary>

public bool CanRunLocally { get; } = true;

/// <summary>

/// This is used by the regression test system to indicate which languages this algorithm is written in.

/// </summary>

public Language[] Languages { get; } = { Language.CSharp, Language.Python };

/// <summary>

/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm

/// </summary>

public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>

{

{"Total Trades", "1"},

{"Average Win", "0%"},

{"Average Loss", "0%"},

{"Compounding Annual Return", "5672.520%"},

{"Drawdown", "22.500%"},

{"Expectancy", "0"},

{"Net Profit", "40.601%"},

{"Sharpe Ratio", "40.201"},

{"Probabilistic Sharpe Ratio", "77.339%"},

{"Loss Rate", "0%"},

{"Win Rate", "0%"},

{"Profit-Loss Ratio", "0"},

{"Alpha", "41.848"},

{"Beta", "9.224"},

{"Annual Standard Deviation", "1.164"},

{"Annual Variance", "1.355"},

{"Information Ratio", "44.459"},

{"Tracking Error", "1.04"},

{"Treynor Ratio", "5.073"},

{"Total Fees", "$30.00"},

{"Fitness Score", "0.418"},

{"Kelly Criterion Estimate", "0"},

{"Kelly Criterion Probability Value", "0"},

{"Sortino Ratio", "113.05"},

{"Return Over Maximum Drawdown", "442.81"},

{"Portfolio Turnover", "0.418"},

{"Total Insights Generated", "0"},

{"Total Insights Closed", "0"},

{"Total Insights Analysis Completed", "0"},

{"Long Insight Count", "0"},

{"Short Insight Count", "0"},

{"Long/Short Ratio", "100%"},

{"Estimated Monthly Alpha Value", "$0"},

{"Total Accumulated Estimated Alpha Value", "$0"},

{"Mean Population Estimated Insight Value", "$0"},

{"Mean Population Direction", "0%"},

{"Mean Population Magnitude", "0%"},

{"Rolling Averaged Population Direction", "0%"},

{"Rolling Averaged Population Magnitude", "0%"},

{"OrderListHash", "639761089"}

};

}

}