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.
88 lines
3.6 KiB
Python
88 lines
3.6 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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import clr
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clr.AddReference("System")
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clr.AddReference("QuantConnect.Algorithm")
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clr.AddReference("QuantConnect.Indicators")
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clr.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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import decimal as d
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### <summary>
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### In this example we look at the canonical 15/30 day moving average cross. This algorithm
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### will go long when the 15 crosses above the 30 and will liquidate when the 15 crosses
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### back below the 30.
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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="moving average cross" />
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### <meta name="tag" content="strategy example" />
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class MovingAverageCrossAlgorithm(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(2009, 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")
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# create a 15 day exponential moving average
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self.fast = self.EMA("SPY", 15, Resolution.Daily)
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# create a 30 day exponential moving average
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self.slow = self.EMA("SPY", 30, Resolution.Daily)
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self.previous = None
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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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# a couple things to notice in this method:
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# 1. We never need to 'update' our indicators with the data, the engine takes care of this for us
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# 2. We can use indicators directly in math expressions
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# 3. We can easily plot many indicators at the same time
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# wait for our slow ema to fully initialize
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if not self.slow.IsReady:
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return
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# only once per day
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if self.previous is not None and self.previous.date() == self.Time.date():
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return
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# define a small tolerance on our checks to avoid bouncing
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tolerance = 0.00015
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holdings = self.Portfolio["SPY"].Quantity
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# we only want to go long if we're currently short or flat
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if holdings <= 0:
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# if the fast is greater than the slow, we'll go long
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if self.fast.Current.Value > self.slow.Current.Value * d.Decimal(1 + tolerance):
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self.Log("BUY >> {0}".format(self.Securities["SPY"].Price))
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self.SetHoldings("SPY", 1.0)
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# we only want to liquidate if we're currently long
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# if the fast is less than the slow we'll liquidate our long
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if holdings > 0 and self.fast.Current.Value < self.slow.Current.Value:
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self.Log("SELL >> {0}".format(self.Securities["SPY"].Price))
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self.Liquidate("SPY")
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self.previous = self.Time |