chore: import upstream snapshot with attribution

2026-07-13 13:02:50 +08:00
commit 0fc60fdcb1
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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.Linq;
+using QuantConnect.Data;
+using QuantConnect.Interfaces;
+using System.Collections.Generic;
+using QuantConnect.Data.UniverseSelection;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Test algorithm that reproduces GH issues 3410 and 3409.
+    /// Coarse universe selection should start from the algorithm start date.
+    /// Data returned by history requests performed from the selection method should be up to date.
+    /// </summary>
+    public class CoarseSelectionTimeRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private Symbol _spy;
+        private decimal _spyPrice;
+
+        /// <summary>
+        /// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
+        /// </summary>
+        public override void Initialize()
+        {
+            SetStartDate(2014, 03, 25);
+            SetEndDate(2014, 04, 01);
+
+            _spy = AddEquity("SPY", Resolution.Daily).Symbol;
+
+            UniverseSettings.Resolution = Resolution.Daily;
+            AddUniverse(CoarseSelectionFunction);
+        }
+
+        public IEnumerable<Symbol> CoarseSelectionFunction(IEnumerable<CoarseFundamental> coarse)
+        {
+            var sortedByDollarVolume = coarse.OrderByDescending(x => x.DollarVolume);
+            var top = sortedByDollarVolume
+                .Where(fundamental => fundamental.Symbol != _spy) // ignore spy
+                .Take(1);
+
+            var historyCoarseSpyPrice = History(_spy, 1).First().Close;
+            if (_spyPrice != 0 && (historyCoarseSpyPrice == 0 ||  historyCoarseSpyPrice != _spyPrice))
+            {
+                throw new RegressionTestException($"Unexpected SPY price: {historyCoarseSpyPrice}");
+            }
+            _spyPrice = 0;
+
+            return top.Select(x => x.Symbol);
+        }
+
+        /// <summary>
+        /// OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
+        /// </summary>
+        /// <param name="slice">Slice object keyed by symbol containing the stock data</param>
+        public override void OnData(Slice slice)
+        {
+            if (slice.Count != 2)
+            {
+                throw new RegressionTestException($"Unexpected data count: {slice.Count}");
+            }
+            if (ActiveSecurities.Count != 2)
+            {
+                throw new RegressionTestException($"Unexpected ActiveSecurities count: {ActiveSecurities.Count}");
+            }
+
+            // we get the data at 4PM, selection happening at midnight
+            _spyPrice = Securities[_spy].Price;
+            if (!Portfolio.Invested)
+            {
+                SetHoldings(_spy, 1);
+                Debug("Purchased Stock");
+            }
+        }
+
+        /// <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 => 49660;
+
+        /// <summary>
+        /// Data Points count of the algorithm history
+        /// </summary>
+        public int AlgorithmHistoryDataPoints => 6;
+
+        /// <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", "36.033%"},
+            {"Drawdown", "1.300%"},
+            {"Expectancy", "0"},
+            {"Start Equity", "100000"},
+            {"End Equity", "100676.75"},
+            {"Net Profit", "0.677%"},
+            {"Sharpe Ratio", "2.646"},
+            {"Sortino Ratio", "2.77"},
+            {"Probabilistic Sharpe Ratio", "57.615%"},
+            {"Loss Rate", "0%"},
+            {"Win Rate", "0%"},
+            {"Profit-Loss Ratio", "0"},
+            {"Alpha", "-0.264"},
+            {"Beta", "1.183"},
+            {"Annual Standard Deviation", "0.103"},
+            {"Annual Variance", "0.011"},
+            {"Information Ratio", "-8.158"},
+            {"Tracking Error", "0.022"},
+            {"Treynor Ratio", "0.231"},
+            {"Total Fees", "$3.07"},
+            {"Estimated Strategy Capacity", "$930000000.00"},
+            {"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
+            {"Portfolio Turnover", "12.65%"},
+            {"Drawdown Recovery", "5"},
+            {"OrderListHash", "87438e51988f37757a2d7f97389483ea"}
+        };
+    }
+}