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.Linq;
+using System.Collections.Generic;
+using QuantConnect.Data;
+using QuantConnect.Interfaces;
+using QuantConnect.Securities;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Regression algorithm illustrating how to request history data for different data normalization modes.
+    /// </summary>
+    public class HistoryWithDifferentDataNormalizationModeRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private Symbol _aaplEquitySymbol;
+        private Symbol _esFutureSymbol;
+
+        public override void Initialize()
+        {
+            SetStartDate(2013, 10, 7);
+            SetEndDate(2014, 1, 1);
+
+            _aaplEquitySymbol = AddEquity("AAPL", Resolution.Daily).Symbol;
+            _esFutureSymbol = AddFuture(Futures.Indices.SP500EMini, Resolution.Daily).Symbol;
+        }
+
+        public override void OnEndOfAlgorithm()
+        {
+            var equityDataNormalizationModes = new DataNormalizationMode[]{
+                DataNormalizationMode.Raw,
+                DataNormalizationMode.Adjusted,
+                DataNormalizationMode.SplitAdjusted
+            };
+            CheckHistoryResultsForDataNormalizationModes(_aaplEquitySymbol, StartDate, EndDate, Resolution.Daily, equityDataNormalizationModes);
+
+            var futureDataNormalizationModes = new DataNormalizationMode[]{
+                DataNormalizationMode.Raw,
+                DataNormalizationMode.BackwardsRatio,
+                DataNormalizationMode.BackwardsPanamaCanal,
+                DataNormalizationMode.ForwardPanamaCanal
+            };
+            CheckHistoryResultsForDataNormalizationModes(_esFutureSymbol, StartDate, EndDate, Resolution.Daily, futureDataNormalizationModes);
+        }
+
+        private void CheckHistoryResultsForDataNormalizationModes(Symbol symbol, DateTime start, DateTime end, Resolution resolution,
+            DataNormalizationMode[] dataNormalizationModes)
+        {
+            var historyResults = dataNormalizationModes
+                .Select(x => History(new [] { symbol }, start, end, resolution, dataNormalizationMode: x).ToList())
+                .ToList();
+
+            if (historyResults.Any(x => x.Count == 0 || x.Count != historyResults.First().Count))
+            {
+                throw new RegressionTestException($"History results for {symbol} have different number of bars");
+            }
+
+            // Check that, for each history result, close prices at each time are different for these securities (AAPL and ES)
+            for (int j = 0; j < historyResults[0].Count; j++)
+            {
+                var closePrices = historyResults.Select(hr => hr[j].Bars.First().Value.Close).ToHashSet();
+                if (closePrices.Count != dataNormalizationModes.Length)
+                {
+                    throw new RegressionTestException($"History results for {symbol} have different close prices at the same 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, Language.Python };
+
+        /// <summary>
+        /// Data Points count of all timeslices of algorithm
+        /// </summary>
+        public long DataPoints => 1028;
+
+        /// <summary>
+        /// Data Points count of the algorithm history
+        /// </summary>
+        public int AlgorithmHistoryDataPoints => 668;
+
+        /// <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", "-4.244"},
+            {"Tracking Error", "0.086"},
+            {"Treynor Ratio", "0"},
+            {"Total Fees", "$0.00"},
+            {"Estimated Strategy Capacity", "$0"},
+            {"Lowest Capacity Asset", ""},
+            {"Portfolio Turnover", "0%"},
+            {"Drawdown Recovery", "0"},
+            {"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"}
+        };
+    }
+}