wehub-resource-sync
49 lines
2.2 KiB
C#
49 lines
2.2 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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*/
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using Python.Runtime;
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using QuantConnect.Python;
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namespace QuantConnect.Algorithm.Framework.Portfolio
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{
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/// <summary>
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/// Python wrapper for custom portfolio optimizer
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/// </summary>
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public class PortfolioOptimizerPythonWrapper : BasePythonWrapper<IPortfolioOptimizer>, IPortfolioOptimizer
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{
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/// <summary>
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/// Creates a new instance
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/// </summary>
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/// <param name="portfolioOptimizer">The python model to wrapp</param>
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public PortfolioOptimizerPythonWrapper(PyObject portfolioOptimizer)
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: base(portfolioOptimizer)
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{
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}
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/// <summary>
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/// Perform portfolio optimization for a provided matrix of historical returns and an array of expected returns
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/// </summary>
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/// <param name="historicalReturns">Matrix of annualized historical returns where each column represents a security and each row returns for the given date/time (size: K x N).</param>
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/// <param name="expectedReturns">Array of double with the portfolio annualized expected returns (size: K x 1).</param>
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/// <param name="covariance">Multi-dimensional array of double with the portfolio covariance of annualized returns (size: K x K).</param>
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/// <returns>Array of double with the portfolio weights (size: K x 1)</returns>
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public double[] Optimize(double[,] historicalReturns, double[] expectedReturns = null, double[,] covariance = null)
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{
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return InvokeMethod<double[]>(nameof(Optimize), historicalReturns, expectedReturns, covariance);
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}
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}
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}
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