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.
57 lines
2.4 KiB
Python
57 lines
2.4 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.Common")
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AddReference("QuantConnect.Indicators")
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from System import *
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from QuantConnect import *
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from QuantConnect.Indicators import *
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from QuantConnect.Data import *
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from QuantConnect.Data.Market import *
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from QuantConnect.Algorithm import *
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import numpy as np
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from datetime import datetime
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class IndicatorRibbonBenchmark(QCAlgorithm):
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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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def Initialize(self):
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self.SetStartDate(2010, 1, 1) #Set Start Date
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self.SetEndDate(2018, 1, 1) #Set End Date
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self.spy = self.AddEquity("SPY", Resolution.Minute).Symbol
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count = 50
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offset = 5
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period = 15
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self.ribbon = []
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# define our sma as the base of the ribbon
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self.sma = SimpleMovingAverage(period)
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for x in range(count):
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# define our offset to the zero sma, these various offsets will create our 'displaced' ribbon
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delay = Delay(offset*(x+1))
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# define an indicator that takes the output of the sma and pipes it into our delay indicator
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delayedSma = IndicatorExtensions.Of(delay, self.sma)
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# register our new 'delayedSma' for automaic updates on a daily resolution
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self.RegisterIndicator(self.spy, delayedSma, Resolution.Daily)
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self.ribbon.append(delayedSma)
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def OnData(self, data):
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# wait for our entire ribbon to be ready
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if not all(x.IsReady for x in self.ribbon): return
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for x in self.ribbon:
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value = x.Current.Value |