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.Collections.Generic;
+using QuantConnect.Orders.Fills;
+using QuantConnect.Orders.Fees;
+using QuantConnect.Securities;
+using QuantConnect.Orders.Slippage;
+using QuantConnect.Securities.Volatility;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Algorithm asserting that when setting custom models for canonical options, a one-time warning is sent
+    /// informing the user that the contracts models are different (not the custom ones).
+    /// </summary>
+    public class OptionModelsConsistencyRegressionAlgorithm : QCAlgorithm
+    {
+        public override void Initialize()
+        {
+            var security = InitializeAlgorithm();
+            SetModels(security);
+
+            SetBenchmark(x => 0);
+        }
+
+        protected virtual Security InitializeAlgorithm()
+        {
+            SetStartDate(2015, 12, 24);
+            SetEndDate(2015, 12, 24);
+
+            var equity = AddEquity("GOOG", leverage: 4);
+            var option = AddOption(equity.Symbol);
+            option.SetFilter(u => u.Strikes(-2, +2).Expiration(0, 180));
+
+            return option;
+        }
+
+        protected virtual void SetModels(Security security)
+        {
+            security.SetFillModel(new CustomFillModel());
+            security.SetFeeModel(new CustomFeeModel());
+            security.SetBuyingPowerModel(new CustomBuyingPowerModel());
+            security.SetSlippageModel(new CustomSlippageModel());
+            security.SetVolatilityModel(new CustomVolatilityModel());
+            security.SettlementModel = new CustomSettlementModel();
+        }
+
+        public class CustomFillModel : FillModel
+        {
+        }
+
+        public class CustomFeeModel : FeeModel
+        {
+        }
+
+        public class CustomBuyingPowerModel : BuyingPowerModel
+        {
+        }
+
+        public class CustomSlippageModel : ConstantSlippageModel
+        {
+            public CustomSlippageModel() : base(0)
+            {
+            }
+        }
+
+        public class CustomVolatilityModel : BaseVolatilityModel
+        {
+        }
+
+        public class CustomSettlementModel : ImmediateSettlementModel
+        {
+        }
+
+        /// <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 virtual bool CanRunLocally => true;
+
+        /// <summary>
+        /// This is used by the regression test system to indicate which languages this algorithm is written in.
+        /// </summary>
+        public virtual Language[] Languages { get; } = { Language.CSharp, Language.Python };
+
+        /// <summary>
+        /// Data Points count of all timeslices of algorithm
+        /// </summary>
+        public virtual long DataPoints => 475777;
+
+        /// <summary>
+        /// Data Points count of the algorithm history
+        /// </summary>
+        public virtual int AlgorithmHistoryDataPoints => 0;
+
+        /// <summary>
+        /// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
+        /// </summary>
+        public virtual Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
+        {
+            {"Total Orders", "0"},
+            {"Average Win", "0%"},
+            {"Average Loss", "0%"},
+            {"Compounding Annual Return", "0%"},
+            {"Drawdown", "0%"},
+            {"Expectancy", "0"},
+            {"Net Profit", "0%"},
+            {"Sharpe Ratio", "0"},
+            {"Probabilistic Sharpe Ratio", "0%"},
+            {"Loss Rate", "0%"},
+            {"Win Rate", "0%"},
+            {"Profit-Loss Ratio", "0"},
+            {"Alpha", "0"},
+            {"Beta", "0"},
+            {"Annual Standard Deviation", "0"},
+            {"Annual Variance", "0"},
+            {"Information Ratio", "0"},
+            {"Tracking Error", "0"},
+            {"Treynor Ratio", "0"},
+            {"Total Fees", "$0.00"},
+            {"Estimated Strategy Capacity", "$0"},
+            {"Lowest Capacity Asset", ""},
+            {"Portfolio Turnover", "0%"},
+            {"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"}
+        };
+    }
+}