Ricardo Andrés Marino Rojas
3712786301
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* Solve bug and add regression test The bug was raised because, when trying to use C# MeanVarianceOptimizationPortfolioConstructionModel from a Python algorithm, there wasn't a constructor that accepted a portfolio optimizer as a PyObject. Additionally, there wasn't also a Python Wrapper to wrapp that portfolio optimizer. - Add PortfolioOptimizerPythonWrapper.cs - Add constructor in MeanVarianceOptimizationPortfolioConstructionModel.cs that accepts portfolio optimizer as a PyObject - Add regression algorithms to cover the changes * Improve constructor overload implementation * Change implementation to follow API pattern * Enhance implementation and add unit tests * Enhance implementation and add more unit tests * Enhance implementation
32 lines
1.6 KiB
Python
32 lines
1.6 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 MeanVarianceOptimizationFrameworkAlgorithm import MeanVarianceOptimizationFrameworkAlgorithm
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### <summary>
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### Regression algorithm asserting we can specify a custom portfolio
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### optimizer with a MeanVarianceOptimizationPortfolioConstructionModel
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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 CustomPortfolioOptimizerRegressionAlgorithm(MeanVarianceOptimizationFrameworkAlgorithm):
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def Initialize(self):
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super().Initialize()
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self.SetPortfolioConstruction(MeanVarianceOptimizationPortfolioConstructionModel(timedelta(days=1), PortfolioBias.LongShort, 1, 63, Resolution.Daily, 0.02, CustomPortfolioOptimizer()))
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class CustomPortfolioOptimizer:
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def Optimize(self, historicalReturns, expectedReturns, covariance):
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return [0.5]*(np.array(historicalReturns)).shape[1]
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