Alexandre Catarino
58f0caf647
Some python algorithms suffered corrections to run under the new python framework (pythonnet). Others were deleted because some features will be supported in futures implementations. Adds a method in AlgorithmPythonUtil to transform C# DateTime into Python datetime
62 lines
2.8 KiB
Python
62 lines
2.8 KiB
Python
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
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# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from clr import AddReference
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AddReference("System")
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AddReference("QuantConnect.Algorithm")
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AddReference("QuantConnect.Indicators")
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AddReference("QuantConnect.Common")
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from System import *
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from QuantConnect import *
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from QuantConnect.Data import *
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from QuantConnect.Algorithm import *
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from QuantConnect.Indicators import *
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class WarmupHistoryAlgorithm(QCAlgorithm):
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'''This algorithm demonstrates using the history provider to
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retrieve data to warm up indicators before data is received'''
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def Initialize(self):
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'''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
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self.SetStartDate(2014,5,2) #Set Start Date
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self.SetEndDate(2014,5,2) #Set End Date
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self.SetCash(100000) #Set Strategy Cash
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# Find more symbols here: http://quantconnect.com/data
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forex = self.AddForex("EURUSD", Resolution.Second)
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self.__symbol = forex.Symbol
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self.__fastPeriod = 60
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self.__slowPeriod = 3600
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self.__fast = self.EMA(self.__symbol, self.__fastPeriod)
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self.__slow = self.EMA(self.__symbol, self.__slowPeriod)
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# "self.__slowPeriod + 1" because rolling window waits for one to fall off the back to be considered ready
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history = map(lambda x: x[self.__symbol], self.History(self.__slowPeriod + 1))
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for bar in history:
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datapoint = IndicatorDataPoint(bar.EndTime, bar.Close)
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self.__fast.Update(datapoint)
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self.__slow.Update(datapoint)
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self.Log("FAST IS {0} READY. Samples: {1}".format("" if self.__fast.IsReady else "NOT", self.__fast.Samples))
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self.Log("SLOW IS {0} READY. Samples: {1}".format("" if self.__slow.IsReady else "NOT", self.__slow.Samples))
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def OnData(self, data):
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'''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.'''
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if self.__fast.Current.Value > self.__slow.Current.Value:
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self.SetHoldings(self.__symbol, 1)
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else:
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self.SetHoldings(self.__symbol, -1) |