# 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.

from clr import AddReference

AddReference("System")

AddReference("QuantConnect.Algorithm")

AddReference("QuantConnect.Indicators")

AddReference("QuantConnect.Common")

from System import *

from QuantConnect import *

from QuantConnect.Data import *

from QuantConnect.Algorithm import *

from QuantConnect.Indicators import *

class WarmupHistoryAlgorithm(QCAlgorithm):

'''This algorithm demonstrates using the history provider to

retrieve data to warm up indicators before data is received'''

def Initialize(self):

'''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''

self.SetStartDate(2014,5,2) #Set Start Date

self.SetEndDate(2014,5,2) #Set End Date

self.SetCash(100000) #Set Strategy Cash

# Find more symbols here: http://quantconnect.com/data

forex = self.AddForex("EURUSD", Resolution.Second)

forex = self.AddForex("NZDUSD", Resolution.Second)

fast_period = 60

slow_period = 3600

self.fast = self.EMA("EURUSD", fast_period)

self.slow = self.EMA("EURUSD", slow_period)

# "slow_period + 1" because rolling window waits for one to fall off the back to be considered ready

# History method returns a dict with a pandas.DataFrame

history = self.History(["EURUSD", "NZDUSD"], slow_period + 1)

# prints out the tail of the dataframe

self.Log(str(history.loc["EURUSD"].tail()))

self.Log(str(history.loc["NZDUSD"].tail()))

for index, row in history.loc["EURUSD"].iterrows():

datapoint = IndicatorDataPoint(index, row["close"])

self.fast.Update(datapoint)

self.slow.Update(datapoint)

self.Log("FAST {0} READY. Samples: {1}".format("IS" if self.fast.IsReady else "IS NOT", self.fast.Samples))

self.Log("SLOW {0} READY. Samples: {1}".format("IS" if self.slow.IsReady else "IS NOT", self.slow.Samples))

def OnData(self, data):

'''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.'''

if self.fast.Current.Value > self.slow.Current.Value:

self.SetHoldings("EURUSD", 1)

else:

self.SetHoldings("EURUSD", -1)