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
79 lines
3.1 KiB
Python
79 lines
3.1 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 datetime import datetime
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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.Algorithm import *
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from QuantConnect.Indicators import *
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from AlgorithmPythonUtil import to_python_datetime
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class MACDTrendAlgorithm(QCAlgorithm):
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'''MACD Example Algorithm'''
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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(2004, 01, 01) #Set Start Date
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self.SetEndDate(2015, 01, 01) #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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equity = self.AddEquity("SPY", Resolution.Daily)
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self.spy = equity.Symbol
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# define our daily macd(12,26) with a 9 day signal
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self.__macd = self.MACD(self.spy, 9, 26, 9, MovingAverageType.Exponential, Resolution.Daily)
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self.__previous = datetime.min
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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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# wait for our macd to fully initialize
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if not self.__macd.IsReady: return
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pyTime = to_python_datetime(self.Time)
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# only once per day
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if self.__previous.date() == pyTime.date(): return
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# define a small tolerance on our checks to avoid bouncing
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tolerance = 0.0025;
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holdings = self.Portfolio[self.spy].Quantity
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signalDeltaPercent = (self.__macd.Current.Value - self.__macd.Signal.Current.Value)/self.__macd.Fast.Current.Value
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# if our macd is greater than our signal, then let's go long
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if holdings <= 0 and signalDeltaPercent > tolerance: # 0.01%
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# longterm says buy as well
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self.SetHoldings(self.spy, 1.0)
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# of our macd is less than our signal, then let's go short
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elif holdings >= 0 and signalDeltaPercent < -tolerance:
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self.Liquidate(self.spy)
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# plot both lines
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self.Plot("MACD", self.__macd.Current.Value)
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self.Plot("MACD", self.__macd.Signal.Current.Value)
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self.Plot(str(self.spy), self.__macd.Fast.Current.Value)
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self.Plot(str(self.spy), self.__macd.Slow.Current.Value)
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self.__previous = pyTime |