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;
+using System.Globalization;
+using System.Collections.Generic;
+using QuantConnect.Data;
+using QuantConnect.Benchmarks;
+using QuantConnect.Interfaces;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Regression algorithm to demonstrate the use of SetBenchmark() with custom data
+    /// </summary>
+    public class CustomDataBenchmarkRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        public override void Initialize()
+        {
+            SetStartDate(2017, 8, 18);
+            SetEndDate(2017, 8, 21);
+            SetCash(100000);
+
+            AddEquity("SPY", Resolution.Hour);
+            var customSymbol = AddData<ExampleCustomData>("ExampleCustomData", Resolution.Hour).Symbol;
+            SetBenchmark(customSymbol);
+        }
+
+        public override void OnData(Slice slice)
+        {
+            if (!Portfolio.Invested)
+            {
+                SetHoldings("SPY", 1);
+            }
+        }
+
+        public override void OnEndOfAlgorithm()
+        {
+            var securityBenchmark = (SecurityBenchmark)Benchmark;
+            if (securityBenchmark.Security.Price == 0)
+            {
+                throw new RegressionTestException("Security benchmark price was not expected to be zero");
+            }
+        }
+
+        /// <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 => 114;
+
+        /// <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", "29.610%"},
+            {"Drawdown", "0.600%"},
+            {"Expectancy", "0"},
+            {"Start Equity", "100000"},
+            {"End Equity", "100281.67"},
+            {"Net Profit", "0.282%"},
+            {"Sharpe Ratio", "7.023"},
+            {"Sortino Ratio", "0"},
+            {"Probabilistic Sharpe Ratio", "0%"},
+            {"Loss Rate", "0%"},
+            {"Win Rate", "0%"},
+            {"Profit-Loss Ratio", "0"},
+            {"Alpha", "0.094"},
+            {"Beta", "-0.016"},
+            {"Annual Standard Deviation", "0.007"},
+            {"Annual Variance", "0"},
+            {"Information Ratio", "-6.047"},
+            {"Tracking Error", "0.439"},
+            {"Treynor Ratio", "-3.13"},
+            {"Total Fees", "$2.21"},
+            {"Estimated Strategy Capacity", "$180000000.00"},
+            {"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
+            {"Portfolio Turnover", "24.86%"},
+            {"Drawdown Recovery", "0"},
+            {"OrderListHash", "8c07dafc84c73401fa0c7709b6baf802"}
+        };
+
+        public class ExampleCustomData : BaseData
+        {
+            public decimal Open { get; set; }
+            public decimal High { get; set; }
+            public decimal Low { get; set; }
+            public decimal Close { get; set; }
+
+            public override SubscriptionDataSource GetSource(SubscriptionDataConfig config, DateTime date, bool isLiveMode)
+            {
+                var source = "https://www.dl.dropboxusercontent.com/s/d83xvd7mm9fzpk0/path_to_my_csv_data.csv?dl=0";
+                return new SubscriptionDataSource(source, SubscriptionTransportMedium.RemoteFile, FileFormat.Csv);
+            }
+
+            public override BaseData Reader(SubscriptionDataConfig config, string line, DateTime date, bool isLiveMode)
+            {
+                var csv = line.Split(",");
+                var data = new ExampleCustomData()
+                {
+                    Symbol = config.Symbol,
+                    Time = DateTime.ParseExact(csv[0], DateFormat.DB, CultureInfo.InvariantCulture).AddHours(20),
+                    Value = csv[4].ToDecimal(),
+                    Open = csv[1].ToDecimal(),
+                    High = csv[2].ToDecimal(),
+                    Low = csv[3].ToDecimal(),
+                    Close = csv[4].ToDecimal()
+                };
+
+                return data;
+            }
+        }
+    }
+}