chore: import upstream snapshot with attribution

2026-07-13 13:02:50 +08:00
commit 0fc60fdcb1
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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.Collections.Generic;
+using QuantConnect.Securities.Option;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Regression algorithm asserting we can specify a custom option assignment
+    /// </summary>
+    public class CustomOptionAssignmentRegressionAlgorithm : OptionAssignmentRegressionAlgorithm
+    {
+        public override void Initialize()
+        {
+            SetSecurityInitializer((security) =>
+            {
+                var option = security as Option;
+                // we have to be 10% in the money to get assigned
+                option?.SetOptionAssignmentModel(new CustomOptionAssignmentModel(0.1m));
+            });
+
+            base.Initialize();
+        }
+
+        private class CustomOptionAssignmentModel : DefaultOptionAssignmentModel
+        {
+            public CustomOptionAssignmentModel(decimal requiredInTheMoneyPercent) : base (requiredInTheMoneyPercent)
+            {
+            }
+            public override OptionAssignmentResult GetAssignment(OptionAssignmentParameters parameters)
+            {
+                var result = base.GetAssignment(parameters);
+                result.Tag = "Custom Option Assignment";
+                return result;
+            }
+        }
+
+        /// <summary>
+        /// This is used by the regression test system to indicate which languages this algorithm is written in.
+        /// </summary>
+        public override List<Language> Languages { get; } = new() { Language.CSharp, Language.Python };
+
+        /// <summary>
+        /// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
+        /// </summary>
+        public override Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
+        {
+            {"Total Orders", "4"},
+            {"Average Win", "9.48%"},
+            {"Average Loss", "-16.73%"},
+            {"Compounding Annual Return", "-25.790%"},
+            {"Drawdown", "0.600%"},
+            {"Expectancy", "-0.478"},
+            {"Start Equity", "100000"},
+            {"End Equity", "99538"},
+            {"Net Profit", "-0.462%"},
+            {"Sharpe Ratio", "3.755"},
+            {"Sortino Ratio", "0"},
+            {"Probabilistic Sharpe Ratio", "67.784%"},
+            {"Loss Rate", "67%"},
+            {"Win Rate", "33%"},
+            {"Profit-Loss Ratio", "0.57"},
+            {"Alpha", "-0.008"},
+            {"Beta", "-0.096"},
+            {"Annual Standard Deviation", "0.003"},
+            {"Annual Variance", "0"},
+            {"Information Ratio", "10.577"},
+            {"Tracking Error", "0.019"},
+            {"Treynor Ratio", "-0.115"},
+            {"Total Fees", "$2.00"},
+            {"Estimated Strategy Capacity", "$4800000.00"},
+            {"Lowest Capacity Asset", "GOOCV 305RBQ20WLZZA|GOOCV VP83T1ZUHROL"},
+            {"Portfolio Turnover", "26.72%"},
+            {"Drawdown Recovery", "0"},
+            {"OrderListHash", "20f33e143b62ee896aa56f85dd2aa2e8"}
+        };
+    }
+}