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.Data;
+using QuantConnect.Interfaces;
+using System;
+using System.Collections.Generic;
+using System.Linq;
+
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// This regression test algorithm reproduces issue https://github.com/QuantConnect/Lean/issues/4031 
+    /// fixed in PR https://github.com/QuantConnect/Lean/pull/4650
+    /// Adjusted data have already been all loaded by the workers so DataNormalizationMode change has no effect in the data itself
+    /// </summary>
+    public class SwitchDataModeRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private const string UnderlyingTicker = "AAPL";
+
+        private readonly Dictionary<DateTime, decimal?> _expectedCloseValues = new Dictionary<DateTime, decimal?>() {
+            { new DateTime(2014, 6, 6, 9, 57, 0), 20.83533m},
+            { new DateTime(2014, 6, 6, 9, 58, 0), 20.83565m},
+            { new DateTime(2014, 6, 6, 9, 59, 0), 648.37m},
+            { new DateTime(2014, 6, 6, 10, 0, 0), 647.86m},
+            { new DateTime(2014, 6, 6, 10, 1, 0), 646.83m},
+            { new DateTime(2014, 6, 6, 10, 2, 0), 647.79m},
+            { new DateTime(2014, 6, 6, 10, 3, 0), 646.92m}
+        };
+
+        public override void Initialize()
+        {
+            SetStartDate(2014, 6, 6);
+            SetEndDate(2014, 6, 6);
+
+            var aapl = AddEquity(UnderlyingTicker, Resolution.Minute);
+        }
+
+        public override void OnData(Slice slice)
+        {
+            if (Time.Hour == 9 && Time.Minute == 58)
+            {
+                var option = AddOption(UnderlyingTicker);
+                option.SetFilter(u => u.StandardsOnly().Strikes(-1, 1).Expiration(0, 35));
+            }
+
+            AssertValue(slice);
+        }
+
+        public override void OnEndOfAlgorithm()
+        {
+            if (_expectedCloseValues.Count > 0)
+            {
+                throw new RegressionTestException($"Not all expected data points were received.");
+            }
+        }
+
+        private void AssertValue(Slice data)
+        {
+            decimal? value;
+            if (_expectedCloseValues.TryGetValue(data.Time, out value))
+            {
+                if (data.Bars.FirstOrDefault().Value?.Close.SmartRounding() != value)
+                {
+                    throw new RegressionTestException($"Expected tradebar price, expected {value} but was {data.Bars.First().Value.Close.SmartRounding()}");
+                }
+
+                _expectedCloseValues.Remove(data.Time);
+            }
+        }
+
+        /// <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 };
+
+        /// <summary>
+        /// Data Points count of all timeslices of algorithm
+        /// </summary>
+        public long DataPoints => 7562;
+
+        /// <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", "0"},
+            {"Average Win", "0%"},
+            {"Average Loss", "0%"},
+            {"Compounding Annual Return", "0%"},
+            {"Drawdown", "0%"},
+            {"Expectancy", "0"},
+            {"Start Equity", "100000"},
+            {"End Equity", "100000"},
+            {"Net Profit", "0%"},
+            {"Sharpe Ratio", "0"},
+            {"Sortino 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%"},
+            {"Drawdown Recovery", "0"},
+            {"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"}
+        };
+    }
+}