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.
69 lines
3.1 KiB
Python
69 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 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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from System import *
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from QuantConnect import *
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from QuantConnect.Orders import *
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from QuantConnect.Algorithm import *
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from QuantConnect.Algorithm.Framework import *
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from QuantConnect.Algorithm.Framework.Selection import *
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from Alphas.RsiAlphaModel import RsiAlphaModel
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from Portfolio.EqualWeightingPortfolioConstructionModel import EqualWeightingPortfolioConstructionModel
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from Execution.VolumeWeightedAveragePriceExecutionModel import VolumeWeightedAveragePriceExecutionModel
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from datetime import timedelta
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### <summary>
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### Regression algorithm for the VolumeWeightedAveragePriceExecutionModel.
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### This algorithm shows how the execution model works to split up orders and
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### submit them only when the price is on the favorable side of the intraday VWAP.
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### </summary>
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### <meta name="tag" content="using data" />
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### <meta name="tag" content="using quantconnect" />
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### <meta name="tag" content="trading and orders" />
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class VolumeWeightedAveragePriceExecutionModelRegressionAlgorithm(QCAlgorithmFramework):
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'''Regression algorithm for the VolumeWeightedAveragePriceExecutionModel.
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This algorithm shows how the execution model works to split up orders and
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submit them only when the price is on the favorable side of the intraday VWAP.'''
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def Initialize(self):
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self.UniverseSettings.Resolution = Resolution.Minute
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self.SetStartDate(2013,10,7)
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self.SetEndDate(2013,10,11)
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self.SetCash(1000000)
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self.SetUniverseSelection(ManualUniverseSelectionModel([
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Symbol.Create('AIG', SecurityType.Equity, Market.USA),
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Symbol.Create('BAC', SecurityType.Equity, Market.USA),
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Symbol.Create('IBM', SecurityType.Equity, Market.USA),
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Symbol.Create('SPY', SecurityType.Equity, Market.USA)
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]))
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# using hourly rsi to generate more insights
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self.SetAlpha(RsiAlphaModel(14, Resolution.Hour))
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self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel())
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self.SetExecution(VolumeWeightedAveragePriceExecutionModel())
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self.InsightsGenerated += self.OnInsightsGenerated
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def OnInsightsGenerated(self, algorithm, data):
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self.Log(f"{self.Time}: {', '.join(str(x) for x in data.Insights)}")
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def OnOrderEvent(self, orderEvent):
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self.Log(f"{self.Time}: {orderEvent}") |