Jhonathan Abreu
e736b28568
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* Add DataNormalizationMode parameter to QCAlgorithm.AddEquity method * Add regression algorithm * Add Python regression algorithm * Style changes * Fix test error
54 lines
2.9 KiB
Python
54 lines
2.9 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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### <summary>
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### This regression algorithm has examples of how to add an equity indicating the <see cref="DataNormalizationMode"/>
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### directly with the <see cref="QCAlgorithm.AddEquity"/> method instead of using the <see cref="Equity.SetDataNormalizationMode"/> method.
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### </summary>
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class SetEquityDataNormalizationModeOnAddEquity(QCAlgorithm):
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def Initialize(self):
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self.SetStartDate(2013, 10, 7)
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self.SetEndDate(2013, 10, 7)
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spyNormalizationMode = DataNormalizationMode.Raw
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ibmNormalizationMode = DataNormalizationMode.Adjusted
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aigNormalizationMode = DataNormalizationMode.TotalReturn
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self._priceRanges = {}
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spyEquity = self.AddEquity("SPY", Resolution.Minute, dataNormalizationMode=spyNormalizationMode)
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self.CheckEquityDataNormalizationMode(spyEquity, spyNormalizationMode)
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self._priceRanges[spyEquity] = (167.28, 168.37)
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ibmEquity = self.AddEquity("IBM", Resolution.Minute, dataNormalizationMode=ibmNormalizationMode)
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self.CheckEquityDataNormalizationMode(ibmEquity, ibmNormalizationMode)
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self._priceRanges[ibmEquity] = (135.864131052, 136.819606508)
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aigEquity = self.AddEquity("AIG", Resolution.Minute, dataNormalizationMode=aigNormalizationMode)
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self.CheckEquityDataNormalizationMode(aigEquity, aigNormalizationMode)
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self._priceRanges[aigEquity] = (48.73, 49.10)
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def OnData(self, slice):
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for equity, (minExpectedPrice, maxExpectedPrice) in self._priceRanges.items():
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if equity.HasData and (equity.Price < minExpectedPrice or equity.Price > maxExpectedPrice):
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raise Exception(f"{equity.Symbol}: Price {equity.Price} is out of expected range [{minExpectedPrice}, {maxExpectedPrice}]")
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def CheckEquityDataNormalizationMode(self, equity, expectedNormalizationMode):
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subscriptions = [x for x in self.SubscriptionManager.Subscriptions if x.Symbol == equity.Symbol]
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if any([x.DataNormalizationMode != expectedNormalizationMode for x in subscriptions]):
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raise Exception(f"Expected {equity.Symbol} to have data normalization mode {expectedNormalizationMode} but was {subscriptions[0].DataNormalizationMode}")
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