Martin Molinero
37 lines
1.9 KiB
Python
37 lines
1.9 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 numpy import dot
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from numpy.linalg import inv
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### <summary>
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### Provides an implementation of a portfolio optimizer with unconstrained mean variance.'''
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### </summary>
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class UnconstrainedMeanVariancePortfolioOptimizer:
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'''Provides an implementation of a portfolio optimizer with unconstrained mean variance.'''
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def Optimize(self, historicalReturns, expectedReturns = None, covariance = None):
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'''
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Perform portfolio optimization for a provided matrix of historical returns and an array of expected returns
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args:
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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).
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expectedReturns: Array of double with the portfolio annualized expected returns (size: K x 1).
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covariance: Multi-dimensional array of double with the portfolio covariance of annualized returns (size: K x K).</param>
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Returns:
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Array of double with the portfolio weights (size: K x 1)
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'''
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if expectedReturns is None:
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expectedReturns = historicalReturns.mean()
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if covariance is None:
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covariance = historicalReturns.cov()
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return expectedReturns.dot(inv(covariance)) |