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 +1,167 @@
+/*
+ * 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 QuantConnect.Commands;
+using QuantConnect.Interfaces;
+using System.Collections.Generic;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Regression algorithm asserting the behavior of different callback commands call
+    /// </summary>
+    public class CallbackCommandRegressionAlgorithm : 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, 07);
+            SetEndDate(2013, 10, 11);
+
+            AddEquity("SPY");
+            AddEquity("BAC");
+            AddEquity("IBM");
+            AddCommand<BoolCommand>();
+            AddCommand<VoidCommand>();
+
+            var potentialCommand = new VoidCommand
+            {
+                Target = new[] { "BAC" },
+                Quantity = 10,
+                Parameters = new() { { "tag", "Signal X" } }
+            };
+            var commandLink = Link(potentialCommand);
+            Notify.Email("email@address", "Trade Command Event", $"Signal X trade\nFollow link to trigger: {commandLink}");
+
+            var commandLink2 = Link(new { Symbol = "SPY", Parameters = new Dictionary<string, int>() { { "Quantity", 10 } } });
+            Notify.Email("email@address", "Untyped Command Event", $"Signal Y trade\nFollow link to trigger: {commandLink2}");
+
+            // We need to create a project on QuantConnect to test the BroadcastCommand method
+            // and use the ProjectId in the BroadcastCommand call
+            ProjectId = 21805137;
+
+            // All live deployments receive the broadcasts below
+            var broadcastResult = BroadcastCommand(potentialCommand);
+            var broadcastResult2 = BroadcastCommand(new { Symbol = "SPY", Parameters = new Dictionary<string, int>() { { "Quantity", 10 } } });
+        }
+
+        /// <summary>
+        /// Handle generic command callback
+        /// </summary>
+        public override bool? OnCommand(dynamic data)
+        {
+            Buy(data.Symbol, data.parameters["quantity"]);
+            return true;
+        }
+
+        private class VoidCommand : Command
+        {
+            public DateTime TargetTime { get; set; }
+            public string[] Target { get; set; }
+            public decimal Quantity { get; set; }
+            public Dictionary<string, string> Parameters { get; set; }
+            public override bool? Run(IAlgorithm algorithm)
+            {
+                if (TargetTime != algorithm.Time)
+                {
+                    return null;
+                }
+
+                ((QCAlgorithm)algorithm).Order(Target[0], Quantity, tag: Parameters["tag"]);
+                return null;
+            }
+        }
+        private class BoolCommand : Command
+        {
+            public bool? Result { get; set; }
+            public override bool? Run(IAlgorithm algorithm)
+            {
+                var shouldTrade = MyCustomMethod();
+                if (shouldTrade.HasValue && shouldTrade.Value)
+                {
+                    ((QCAlgorithm)algorithm).Buy("IBM", 1);
+                }
+                return shouldTrade;
+            }
+
+            private bool? MyCustomMethod()
+            {
+                return Result;
+            }
+        }
+
+        /// <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; }
+
+        /// <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 => 3943;
+
+        /// <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", "1"},
+            {"Average Win", "0%"},
+            {"Average Loss", "0%"},
+            {"Compounding Annual Return", "271.453%"},
+            {"Drawdown", "2.200%"},
+            {"Expectancy", "0"},
+            {"Start Equity", "100000"},
+            {"End Equity", "101691.92"},
+            {"Net Profit", "1.692%"},
+            {"Sharpe Ratio", "8.854"},
+            {"Sortino Ratio", "0"},
+            {"Probabilistic Sharpe Ratio", "67.609%"},
+            {"Loss Rate", "0%"},
+            {"Win Rate", "0%"},
+            {"Profit-Loss Ratio", "0"},
+            {"Alpha", "-0.005"},
+            {"Beta", "0.996"},
+            {"Annual Standard Deviation", "0.222"},
+            {"Annual Variance", "0.049"},
+            {"Information Ratio", "-14.565"},
+            {"Tracking Error", "0.001"},
+            {"Treynor Ratio", "1.97"},
+            {"Total Fees", "$3.44"},
+            {"Estimated Strategy Capacity", "$56000000.00"},
+            {"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
+            {"Portfolio Turnover", "19.93%"},
+            {"OrderListHash", "3da9fa60bf95b9ed148b95e02e0cfc9e"}
+        };
+    }
+}