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
@@ -0,0 +1,157 @@
+/*
+ * 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.Collections.Generic;
+using System.Linq;
+
+using QuantConnect.Data;
+using QuantConnect.Interfaces;
+using QuantConnect.Util;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Algorithm asserting that the <see cref="DataNormalizationMode.ScaledRaw"/> data normalization mode is allowed history requests and
+    /// that prices are adjusted to the last factor before the history end date.
+    /// </summary>
+    public class ScaledRawHistoryAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private Symbol _aapl;
+
+        private DateTime _lastSplitOrDividendDate;
+
+        public override void Initialize()
+        {
+            SetStartDate(2013, 1, 1);
+            SetEndDate(2014, 12, 31);
+            SetCash(100000);
+            SetBenchmark(x => 0);
+
+            _aapl = AddEquity("AAPL", Resolution.Daily).Symbol;
+        }
+
+        public override void OnData(Slice slice)
+        {
+            if (slice.Splits.ContainsKey(_aapl))
+            {
+                _lastSplitOrDividendDate = slice.Splits[_aapl].Time;
+            }
+
+            if (slice.Dividends.ContainsKey(_aapl))
+            {
+                _lastSplitOrDividendDate = slice.Dividends[_aapl].Time;
+            }
+        }
+
+        public override void OnEndOfAlgorithm()
+        {
+            if (_lastSplitOrDividendDate == DateTime.MinValue)
+            {
+                throw new RegressionTestException("No split or dividend was found in the algorithm.");
+            }
+
+            var start = Time.AddMonths(-18);
+            var end = Time;
+            var rawHistory = History(new[] { _aapl }, start, end, dataNormalizationMode: DataNormalizationMode.Raw).ToList();
+            var scaledRawHistory = History(new[] { _aapl }, start, end, dataNormalizationMode: DataNormalizationMode.ScaledRaw).ToList();
+
+            if (rawHistory.Count == 0 || scaledRawHistory.Count != rawHistory.Count)
+            {
+                throw new RegressionTestException($@"Expected history results to not be empty and have the same count. Raw: {rawHistory.Count
+                    }, ScaledRaw: {scaledRawHistory.Count}");
+            }
+
+            for (var i = 0; i < rawHistory.Count; i++)
+            {
+                var rawBar = rawHistory[i].Bars[_aapl];
+                var scaledRawBar = scaledRawHistory[i].Bars[_aapl];
+
+                if (rawBar.Time < _lastSplitOrDividendDate)
+                {
+                    if (rawBar.Open == scaledRawBar.Open || rawBar.High == scaledRawBar.High || rawBar.Low == scaledRawBar.Low || rawBar.Close == scaledRawBar.Close)
+                    {
+                        throw new RegressionTestException($@"Expected history results to be different at {rawBar.Time
+                            } before the last split or dividend date {_lastSplitOrDividendDate}");
+                    }
+                }
+                else if (rawBar.Open != scaledRawBar.Open || rawBar.High != scaledRawBar.High || rawBar.Low != scaledRawBar.Low || rawBar.Close != scaledRawBar.Close)
+                {
+                    throw new RegressionTestException($@"Expected history results to be the same at {rawBar.Time} after the last split or dividend date {_lastSplitOrDividendDate}");
+                }
+            }
+        }
+
+        /// <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 => 515;
+
+        /// <summary>
+        /// Data Points count of the algorithm history
+        /// </summary>
+        public int AlgorithmHistoryDataPoints => 760;
+
+        /// <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"}
+        };
+    }
+}