Michael Handschuh
fdc866fda0
We didn't experience the expected performance improvements. Locally under unit test there was aboout an order of magnitude throughput increase, but when run against the history benchmark, this new approach was 60% slower. We're reverting this for now to perform further analysis and better understand the performance profiling of the python history stack.
75 lines
3.0 KiB
Python
75 lines
3.0 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.Algorithm import *
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from QuantConnect.Indicators import *
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from datetime import datetime
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### <summary>
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### Simple indicator demonstration algorithm of MACD
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### </summary>
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### <meta name="tag" content="indicators" />
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### <meta name="tag" content="indicator classes" />
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### <meta name="tag" content="plotting indicators" />
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class MACDTrendAlgorithm(QCAlgorithm):
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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, 1, 1) #Set Start Date
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self.SetEndDate(2015, 1, 1) #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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self.AddEquity("SPY", Resolution.Daily)
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# define our daily macd(12,26) with a 9 day signal
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self.__macd = self.MACD("SPY", 12, 26, 9, MovingAverageType.Exponential, Resolution.Daily)
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self.__previous = datetime.min
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self.PlotIndicator("MACD", True, self.__macd, self.__macd.Signal)
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self.PlotIndicator("SPY", self.__macd.Fast, self.__macd.Slow)
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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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# only once per day
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if self.__previous.date() == self.Time.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["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("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("SPY")
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self.__previous = self.Time |