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.Linq;
+using System.Collections.Generic;
+using QuantConnect.Algorithm.Framework.Alphas;
+using QuantConnect.Algorithm.Framework.Execution;
+using QuantConnect.Algorithm.Framework.Portfolio;
+using QuantConnect.Algorithm.Framework.Selection;
+using QuantConnect.Orders;
+using QuantConnect.Interfaces;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Regression algorithm for the VolumeWeightedAveragePriceExecutionModel.
+    /// This algorithm shows how the execution model works to split up orders and submit them only when
+    /// the price is on the favorable side of the intraday VWAP.
+    /// </summary>
+    public class VolumeWeightedAveragePriceExecutionModelRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        public override void Initialize()
+        {
+            UniverseSettings.Resolution = Resolution.Minute;
+
+            SetStartDate(2013, 10, 07);
+            SetEndDate(2013, 10, 11);
+            SetCash(1000000);
+
+            SetUniverseSelection(new ManualUniverseSelectionModel(
+                QuantConnect.Symbol.Create("AIG", SecurityType.Equity, Market.USA),
+                QuantConnect.Symbol.Create("BAC", SecurityType.Equity, Market.USA),
+                QuantConnect.Symbol.Create("IBM", SecurityType.Equity, Market.USA),
+                QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA)
+            ));
+
+            // using hourly rsi to generate more insights
+            SetAlpha(new RsiAlphaModel(14, Resolution.Hour));
+            SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
+            SetExecution(new VolumeWeightedAveragePriceExecutionModel());
+
+            InsightsGenerated += (algorithm, data) => Log($"{Time}: {string.Join(" | ", data.Insights.Select(insight => insight))}");
+        }
+
+        public override void OnOrderEvent(OrderEvent orderEvent)
+        {
+            Log($"{Time}: {orderEvent}");
+        }
+
+        /// <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 => 15643;
+
+        /// <summary>
+        /// Data Points count of the algorithm history
+        /// </summary>
+        public int AlgorithmHistoryDataPoints => 56;
+
+        /// <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", "239"},
+            {"Average Win", "0.05%"},
+            {"Average Loss", "-0.01%"},
+            {"Compounding Annual Return", "434.257%"},
+            {"Drawdown", "1.300%"},
+            {"Expectancy", "1.938"},
+            {"Start Equity", "1000000"},
+            {"End Equity", "1021655.71"},
+            {"Net Profit", "2.166%"},
+            {"Sharpe Ratio", "11.638"},
+            {"Sortino Ratio", "0"},
+            {"Probabilistic Sharpe Ratio", "70.184%"},
+            {"Loss Rate", "31%"},
+            {"Win Rate", "69%"},
+            {"Profit-Loss Ratio", "3.26"},
+            {"Alpha", "0.85"},
+            {"Beta", "1.059"},
+            {"Annual Standard Deviation", "0.253"},
+            {"Annual Variance", "0.064"},
+            {"Information Ratio", "10.466"},
+            {"Tracking Error", "0.092"},
+            {"Treynor Ratio", "2.778"},
+            {"Total Fees", "$399.15"},
+            {"Estimated Strategy Capacity", "$470000.00"},
+            {"Lowest Capacity Asset", "AIG R735QTJ8XC9X"},
+            {"Portfolio Turnover", "130.79%"},
+            {"Drawdown Recovery", "1"},
+            {"OrderListHash", "7a14c40f79d36294f931cd4b1f9e7179"}
+        };
+    }
+}