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 QuantConnect.Orders;
+using QuantConnect.Interfaces;
+using System.Collections.Generic;
+using QuantConnect.Algorithm.Framework.Alphas;
+using QuantConnect.Algorithm.Framework.Execution;
+using QuantConnect.Algorithm.Framework.Portfolio;
+using QuantConnect.Algorithm.Framework.Selection;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Regression algorithm for the SpreadExecutionModel.
+    /// This algorithm shows how the execution model works to 
+    /// submit orders only when the price is on desirably tight spread.
+    /// </summary>
+    /// <meta name="tag" content="using data" />
+    /// <meta name="tag" content="using quantconnect" />
+    /// <meta name="tag" content="trading and orders" />
+    public class SpreadExecutionModelRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        /// <summary>
+        /// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
+        /// </summary>
+        public override void Initialize()
+        {
+            SetStartDate(2013, 10, 7);
+            SetEndDate(2013, 10, 11);
+
+            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 SpreadExecutionModel());
+
+            InsightsGenerated += OnInsightsGenerated;
+        }
+
+        private void OnInsightsGenerated(IAlgorithm algorithm, GeneratedInsightsCollection eventdata)
+        {
+            Log($"{Time}: {string.Join(", ", eventdata)}");
+        }
+
+        /// <summary>
+        /// Order fill event handler. On an order fill update the resulting information is passed to this method.
+        /// </summary>
+        /// <param name="orderEvent">Order event details containing details of the events</param>
+        /// <remarks>This method can be called asynchronously and so should only be used by seasoned C# experts. Ensure you use proper locks on thread-unsafe objects</remarks>
+        public override void OnOrderEvent(OrderEvent orderEvent)
+        {
+            Debug($"Purchased Stock: {orderEvent.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 => 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", "19"},
+            {"Average Win", "0.62%"},
+            {"Average Loss", "-0.19%"},
+            {"Compounding Annual Return", "398.867%"},
+            {"Drawdown", "1.500%"},
+            {"Expectancy", "1.395"},
+            {"Start Equity", "100000"},
+            {"End Equity", "102076.08"},
+            {"Net Profit", "2.076%"},
+            {"Sharpe Ratio", "10.465"},
+            {"Sortino Ratio", "21.499"},
+            {"Probabilistic Sharpe Ratio", "69.935%"},
+            {"Loss Rate", "44%"},
+            {"Win Rate", "56%"},
+            {"Profit-Loss Ratio", "3.31"},
+            {"Alpha", "0.533"},
+            {"Beta", "1.115"},
+            {"Annual Standard Deviation", "0.261"},
+            {"Annual Variance", "0.068"},
+            {"Information Ratio", "8.837"},
+            {"Tracking Error", "0.086"},
+            {"Treynor Ratio", "2.453"},
+            {"Total Fees", "$40.66"},
+            {"Estimated Strategy Capacity", "$1900000.00"},
+            {"Lowest Capacity Asset", "IBM R735QTJ8XC9X"},
+            {"Portfolio Turnover", "146.73%"},
+            {"Drawdown Recovery", "1"},
+            {"OrderListHash", "36ab6f7236250f7a064b77af9b4870c4"}
+        };
+    }
+}