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.
+*/
+
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using QuantConnect.Algorithm.Framework.Alphas;
+using QuantConnect.Algorithm.Framework.Portfolio;
+using QuantConnect.Algorithm.Framework.Selection;
+using QuantConnect.Data.UniverseSelection;
+using QuantConnect.Interfaces;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Example algorithm of using ETFConstituentsUniverseSelectionModel
+    /// </summary>
+    public class ETFConstituentsFrameworkAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        public override void Initialize()
+        {
+            SetStartDate(2020, 12, 1);
+            SetEndDate(2020, 12, 7);
+            SetCash(100000);
+
+            UniverseSettings.Resolution = Resolution.Daily;
+            var symbol = QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA);
+            AddUniverseSelection(new ETFConstituentsUniverseSelectionModel(symbol, UniverseSettings, ETFConstituentsFilter));
+
+            AddAlpha(new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, TimeSpan.FromDays(1)));
+
+            SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
+        }
+
+        private protected IEnumerable<Symbol> ETFConstituentsFilter(IEnumerable<ETFConstituentUniverse> constituents)
+        {
+            // Get the 10 securities with the largest weight in the index
+            return constituents.OrderByDescending(c => c.Weight).Take(8).Select(c => c.Symbol);
+        }
+
+        /// <summary>
+        /// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
+        /// </summary>
+        public bool CanRunLocally { get; } = true;
+
+        /// <summary>
+        /// This is used by the regression test system to indicate which languages this algorithm is written in.
+        /// </summary>
+        public List<Language> Languages { get; } = new() { Language.CSharp, Language.Python };
+
+        /// <summary>
+        /// Data Points count of all timeslices of algorithm
+        /// </summary>
+        public long DataPoints => 1068;
+
+        /// <summary>
+        /// Data Points count of the algorithm history
+        /// </summary>
+        public int AlgorithmHistoryDataPoints => 0;
+
+        /// <summary>
+        /// Final status of the algorithm
+        /// </summary>
+        public AlgorithmStatus AlgorithmStatus => AlgorithmStatus.Completed;
+
+        /// <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", "8"},
+            {"Average Win", "0%"},
+            {"Average Loss", "0%"},
+            {"Compounding Annual Return", "64.993%"},
+            {"Drawdown", "0.900%"},
+            {"Expectancy", "0"},
+            {"Start Equity", "100000"},
+            {"End Equity", "100918.77"},
+            {"Net Profit", "0.919%"},
+            {"Sharpe Ratio", "4.7"},
+            {"Sortino Ratio", "14.706"},
+            {"Probabilistic Sharpe Ratio", "67.293%"},
+            {"Loss Rate", "0%"},
+            {"Win Rate", "0%"},
+            {"Profit-Loss Ratio", "0"},
+            {"Alpha", "0.618"},
+            {"Beta", "-0.348"},
+            {"Annual Standard Deviation", "0.1"},
+            {"Annual Variance", "0.01"},
+            {"Information Ratio", "0.41"},
+            {"Tracking Error", "0.127"},
+            {"Treynor Ratio", "-1.358"},
+            {"Total Fees", "$7.02"},
+            {"Estimated Strategy Capacity", "$440000000.00"},
+            {"Lowest Capacity Asset", "GOOCV VP83T1ZUHROL"},
+            {"Portfolio Turnover", "13.71%"},
+            {"Drawdown Recovery", "0"},
+            {"OrderListHash", "21aeef113b8d043e018967d7c1916e5f"}
+        };
+    }
+}