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
@@ -0,0 +1,79 @@
+/*
+ * 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.
+*/
+
+using QuantConnect.Algorithm.Framework.Portfolio;
+using QuantConnect.Interfaces;
+using System;
+using System.Collections.Generic;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Regression algorithm asserting we can specify a custom portfolio optimizer with a MeanVarianceOptimizationPortfolioConstructionModel
+    /// </summary>
+    public class CustomPortfolioOptimizerRegressionAlgorithm : MeanVarianceOptimizationFrameworkAlgorithm, IRegressionAlgorithmDefinition
+    {
+        public override void Initialize()
+        {
+            base.Initialize();
+            SetPortfolioConstruction(new MeanVarianceOptimizationPortfolioConstructionModel(optimizer: new CustomPortfolioOptimizer()));
+        }
+
+        private class CustomPortfolioOptimizer : IPortfolioOptimizer
+        {
+            public double[] Optimize(double[,] historicalReturns, double[] expectedReturns = null, double[,] covariance = null)
+            {
+                var result = new double[historicalReturns.GetLength(0)];
+                Array.Fill(result, 0.5);
+                return result;
+            }
+        }
+
+        /// <summary>
+        /// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
+        /// </summary>
+        public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
+        {
+            {"Total Orders", "13"},
+            {"Average Win", "0%"},
+            {"Average Loss", "-0.14%"},
+            {"Compounding Annual Return", "773.203%"},
+            {"Drawdown", "3.300%"},
+            {"Expectancy", "-1"},
+            {"Start Equity", "100000"},
+            {"End Equity", "103012.99"},
+            {"Net Profit", "3.013%"},
+            {"Sharpe Ratio", "12.422"},
+            {"Sortino Ratio", "0"},
+            {"Probabilistic Sharpe Ratio", "62.141%"},
+            {"Loss Rate", "100%"},
+            {"Win Rate", "0%"},
+            {"Profit-Loss Ratio", "0"},
+            {"Alpha", "1.949"},
+            {"Beta", "2.094"},
+            {"Annual Standard Deviation", "0.49"},
+            {"Annual Variance", "0.24"},
+            {"Information Ratio", "14.343"},
+            {"Tracking Error", "0.287"},
+            {"Treynor Ratio", "2.906"},
+            {"Total Fees", "$39.73"},
+            {"Estimated Strategy Capacity", "$3100000.00"},
+            {"Lowest Capacity Asset", "AIG R735QTJ8XC9X"},
+            {"Portfolio Turnover", "52.21%"},
+            {"Drawdown Recovery", "2"},
+            {"OrderListHash", "a18ad75219f800ac4435bfa4f750a67d"}
+        };
+    }
+}