chore: import upstream snapshot with attribution

2026-07-13 13:02:50 +08:00
commit 0fc60fdcb1
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+# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
+# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from numpy import dot
+from numpy.linalg import inv
+
+### <summary>
+### Provides an implementation of a portfolio optimizer with unconstrained mean variance.'''
+### </summary>
+class UnconstrainedMeanVariancePortfolioOptimizer:
+    '''Provides an implementation of a portfolio optimizer with unconstrained mean variance.'''
+    def optimize(self, historical_returns, expected_returns = None, covariance = None):
+        '''
+        Perform portfolio optimization for a provided matrix of historical returns and an array of expected returns
+        args:
+            historical_returns: Matrix of historical returns where each column represents a security and each row returns for the given date/time (size: K x N).
+            expected_returns: Array of double with the portfolio annualized expected returns (size: K x 1).
+            covariance: Multi-dimensional array of double with the portfolio covariance of annualized returns (size: K x K).</param>
+        Returns:
+            Array of double with the portfolio weights (size: K x 1)
+        '''
+        if expected_returns is None:
+            expected_returns = historical_returns.mean()
+        if covariance is None:
+            covariance = historical_returns.cov()
+
+        return expected_returns.dot(inv(covariance))