Martin-Molinero
03f56481d4
Regression Tests / build (push) Has been cancelled
Build & Test Lean / build (push) Has been cancelled
* Python research import improvements - Improve start.py for research env - Remove unrequired imports * Centralize algorithm imports * Add regression test GH action * Unit test python import clean up * Join research and main imports * More python import clean up * Fix failing skipped regression algorithm
59 lines
2.7 KiB
Python
59 lines
2.7 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 AlgorithmImports 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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### <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(QCAlgorithm):
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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}")
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