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
63 lines
2.5 KiB
C#
63 lines
2.5 KiB
C#
/*
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* 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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*/
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using NUnit.Framework;
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using System.Collections.Generic;
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namespace QuantConnect.Tests.Common
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{
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[TestFixture]
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public class ExtendedDictionaryTests
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{
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[Test]
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public void RunPythonDictionaryFeatureRegressionAlgorithm()
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{
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var parameter = new RegressionTests.AlgorithmStatisticsTestParameters("PythonDictionaryFeatureRegressionAlgorithm",
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new Dictionary<string, string> {
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{"Total Trades", "3"},
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{"Average Win", "0%"},
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{"Average Loss", "0%"},
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{"Compounding Annual Return", "-100%"},
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{"Drawdown", "99.600%"},
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{"Expectancy", "0"},
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{"Net Profit", "-99.625%"},
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{"Sharpe Ratio", "-0.126"},
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{"Probabilistic Sharpe Ratio", "1.667%"},
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{"Loss Rate", "0%"},
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{"Win Rate", "0%"},
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{"Profit-Loss Ratio", "0"},
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{"Alpha", "2.999"},
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{"Beta", "-2.017"},
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{"Annual Standard Deviation", "7.952"},
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{"Annual Variance", "63.235"},
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{"Information Ratio", "-0.374"},
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{"Tracking Error", "7.968"},
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{"Treynor Ratio", "0.496"},
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{"Total Fees", "$0.00"},
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{"OrderListHash", "d303a19fe5a40b59ee99535985833000"}
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},
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Language.Python,
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AlgorithmStatus.Completed);
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AlgorithmRunner.RunLocalBacktest(parameter.Algorithm,
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parameter.Statistics,
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parameter.AlphaStatistics,
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parameter.Language,
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parameter.ExpectedFinalStatus,
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initialCash: 100000);
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}
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}
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}
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