Alexandre Catarino
5361f87dd1
Regression Tests / build (push) Has been cancelled
Build & Test Lean / build (push) Has been cancelled
* Updates Equity Market Data * Updates Unit Tests * Updates Regression Tests In this commit we include regression tests with small changes (slightly different CAGR, Alpha, etc, but same number of trades) due to the data update. * Updates Regression Tests 2 The following regression tests were adapt because of verification of hard-coded market data values: - `AdjustedVolumeRegressionAlgorithm` - `HistoryWithSymbolChangesRegressionAlgorithm` - `OptionRenameRegressionAlgorithm` - `RawDataRegressionAlgorithm` - `SwitchDataModeRegressionAlgorithm` The following regression tests have more trades since adjusted prices allowed more 1-2 shares trades that were rounded down to zero before: - `AddUniverseSelectionModelCoarseAlgorithm` 23 -> 35 - `MeanVarianceOptimizationFrameworkAlgorithm` 12 -> 14 - `PortfolioRebalanceOnDateRulesRegressionAlgorithm` 298 -> 324 - `PortfolioRebalanceOnInsightChangesRegressionAlgorithm` 83 -> 86 - `ScheduledUniverseSelectionModelRegressionAlgorithm` 86 -> 90 - `SectorExposureRiskFrameworkAlgorithm` 17 -> 22 - `SetHoldingsMultipleTargetsRegressionAlgorithm` 8 -> 9 - `StandardDeviationExecutionModelRegressionAlgorithm` 196 -> 199 - `UniverseUnchangedRegressionAlgorithm` 11 -> 17 - `VolumeWeightedAveragePriceExecutionModelRegressionAlgorithm` 237 -> 238 Especial cases: - `BlackLittermanPortfolioOptimizationFrameworkAlgorithm` 18 -> 17 - BLM model sensibility - `OptionChainedAndUniverseSelectionRegressionAlgorithm` The following regression tests have different Capacity because of different volume from lowest capacity asset, except: - `OptionEquityCoveredCallRegressionAlgorithm` New lowest capacity asset is underlying - `OptionEquityCoveredPutRegressionAlgorithm` New lowest capacity asset is underlying * Revert File Update for SPWR and SPWRA * Fix Regression Tests Temporarily removes python regression test for `MeanVarianceOptimizationFrameworkAlgorithm` as the `MeanVarianceOptimizationPortfolioConstructionModel` for each version are yeilding different results. If we use C# version in `MeanVarianceOptimizationPortfolioConstructionModel.py`, the results match. * Changes Optimization Method in MinimumVariancePortfolioOptimizer [Py] Uses `trust-constr` method. See https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html
67 lines
2.8 KiB
Python
67 lines
2.8 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 QuantConnect.Data.Auxiliary import *
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from QuantConnect.Lean.Engine.DataFeeds import DefaultDataProvider
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_ticker = "GOOGL";
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_expectedRawPrices = [ 1157.93, 1158.72,
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1131.97, 1114.28, 1120.15, 1114.51, 1134.89, 567.55, 571.50, 545.25, 540.63 ]
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# <summary>
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# In this algorithm we demonstrate how to use the raw data for our securities
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# and verify that the behavior is correct.
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# </summary>
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# <meta name="tag" content="using data" />
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# <meta name="tag" content="regression test" />
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class RawDataRegressionAlgorithm(QCAlgorithm):
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def Initialize(self):
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self.SetStartDate(2014, 3, 25)
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self.SetEndDate(2014, 4, 7)
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self.SetCash(100000)
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# Set our DataNormalizationMode to raw
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self.UniverseSettings.DataNormalizationMode = DataNormalizationMode.Raw
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self._googl = self.AddEquity(_ticker, Resolution.Daily).Symbol
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# Get our factor file for this regression
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dataProvider = DefaultDataProvider()
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mapFileProvider = LocalDiskMapFileProvider()
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mapFileProvider.Initialize(dataProvider)
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factorFileProvider = LocalDiskFactorFileProvider()
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factorFileProvider.Initialize(mapFileProvider, dataProvider)
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# Get our factor file for this regression
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self._factorFile = factorFileProvider.Get(self._googl)
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def OnData(self, data):
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if not self.Portfolio.Invested:
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self.SetHoldings(self._googl, 1)
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if data.Bars.ContainsKey(self._googl):
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googlData = data.Bars[self._googl]
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# Assert our volume matches what we expected
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expectedRawPrice = _expectedRawPrices.pop(0)
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if expectedRawPrice != googlData.Close:
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# Our values don't match lets try and give a reason why
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dayFactor = self._factorFile.GetPriceScaleFactor(googlData.Time)
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probableRawPrice = googlData.Close / dayFactor # Undo adjustment
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raise Exception("Close price was incorrect; it appears to be the adjusted value"
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if expectedRawPrice == probableRawPrice else
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"Close price was incorrect; Data may have changed.")
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