chore: import upstream snapshot with attribution

2026-07-13 13:02:50 +08:00
commit 0fc60fdcb1
5008 changed files with 910633 additions and 0 deletions
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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 AlgorithmImports import *
+from MeanVarianceOptimizationFrameworkAlgorithm import MeanVarianceOptimizationFrameworkAlgorithm
+
+### <summary>
+### Regression algorithm asserting we can specify a custom portfolio
+### optimizer with a MeanVarianceOptimizationPortfolioConstructionModel
+### </summary>
+### <meta name="tag" content="using data" />
+### <meta name="tag" content="using quantconnect" />
+### <meta name="tag" content="trading and orders" />
+class CustomPortfolioOptimizerRegressionAlgorithm(MeanVarianceOptimizationFrameworkAlgorithm):
+    def initialize(self):
+        super().initialize()
+        self.set_portfolio_construction(MeanVarianceOptimizationPortfolioConstructionModel(timedelta(days=1), PortfolioBias.LONG_SHORT, 1, 63, Resolution.DAILY, 0.02, CustomPortfolioOptimizer()))
+
+class CustomPortfolioOptimizer:
+    def optimize(self, historical_returns, expected_returns, covariance):
+        return [0.5]*(np.array(historical_returns)).shape[1]