Jhonathan Abreu
9128ce1260
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* Add data normalization mode parameter to big History() methods * Add C# regression algorithm * Add Python regression algorithm
57 lines
3.0 KiB
Python
57 lines
3.0 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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### Regression algorithm illustrating how to request history data for different data normalization modes.
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### </summary>
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class HistoryWithDifferentDataMappingModeRegressionAlgorithm(QCAlgorithm):
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def Initialize(self):
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self.SetStartDate(2013, 10, 7)
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self.SetEndDate(2014, 1, 1)
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self.aaplEquitySymbol = self.AddEquity("AAPL", Resolution.Daily).Symbol
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self.esFutureSymbol = self.AddFuture(Futures.Indices.SP500EMini, Resolution.Daily).Symbol
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def OnEndOfAlgorithm(self):
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equityDataNormalizationModes = [
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DataNormalizationMode.Raw,
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DataNormalizationMode.Adjusted,
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DataNormalizationMode.SplitAdjusted
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]
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self.CheckHistoryResultsForDataNormalizationModes(self.aaplEquitySymbol, self.StartDate, self.EndDate, Resolution.Daily,
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equityDataNormalizationModes)
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futureDataNormalizationModes = [
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DataNormalizationMode.Raw,
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DataNormalizationMode.BackwardsRatio,
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DataNormalizationMode.BackwardsPanamaCanal,
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DataNormalizationMode.ForwardPanamaCanal
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]
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self.CheckHistoryResultsForDataNormalizationModes(self.esFutureSymbol, self.StartDate, self.EndDate, Resolution.Daily,
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futureDataNormalizationModes)
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def CheckHistoryResultsForDataNormalizationModes(self, symbol, start, end, resolution, dataNormalizationModes):
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historyResults = [self.History([symbol], start, end, resolution, dataNormalizationMode=x) for x in dataNormalizationModes]
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historyResults = [x.droplevel(0, axis=0) for x in historyResults] if len(historyResults[0].index.levels) == 3 else historyResults
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historyResults = [x.loc[symbol].close for x in historyResults]
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if any(x.size == 0 or x.size != historyResults[0].size for x in historyResults):
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raise Exception(f"History results for {symbol} have different number of bars")
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# Check that, for each history result, close prices at each time are different for these securities (AAPL and ES)
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for j in range(historyResults[0].size):
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closePrices = set(historyResults[i][j] for i in range(len(historyResults)))
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if len(closePrices) != len(dataNormalizationModes):
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raise Exception(f"History results for {symbol} have different close prices at the same time")
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