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.Collections.Generic;
+using System.Linq;
+using QuantConnect.Data;
+using QuantConnect.Data.UniverseSelection;
+using QuantConnect.Interfaces;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// This regression algorithm has two different Universe using the same SubscriptionDataConfig.
+    /// Reproduces GH issue 3877: 1- universe 'TestUniverse' selects and deselects SPY. 2- UserDefinedUniverse
+    /// reselects SPY, which should be marked as tradable.
+    /// </summary>
+    /// <meta name="tag" content="regression test" />
+    public class UniverseSharingSubscriptionTradableRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private Symbol _spy;
+        private int _reselectedSpy = -1;
+        private DateTime lastDataTime = DateTime.MinValue;
+
+        /// <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(2013, 10, 01);
+            SetEndDate(2013, 10, 30);
+            AddEquity("AAPL", Resolution.Daily);
+
+            UniverseSettings.Resolution = Resolution.Daily;
+            AddUniverse(SecurityType.Equity,
+                "TestUniverse",
+                Resolution.Daily,
+                Market.USA,
+                UniverseSettings,
+                time => time.Day == 1 ? new[] {"SPY"} : Enumerable.Empty<string>());
+        }
+
+        /// <summary>
+        /// OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
+        /// </summary>
+        /// <param name="data">Slice object keyed by symbol containing the stock data</param>
+        public override void OnData(Slice slice)
+        {
+            if (lastDataTime == slice.Time)
+            {
+                throw new RegressionTestException("Duplicate time for current data and last data slice");
+            }
+
+            lastDataTime = slice.Time;
+
+            if (_reselectedSpy == 0)
+            {
+                if (!Securities[_spy].IsTradable)
+                {
+                    throw new RegressionTestException($"{_spy} should be tradable");
+                }
+
+                if (!Portfolio.Invested)
+                {
+                    SetHoldings(_spy, 1);
+                }
+            }
+
+            if (_reselectedSpy == 1)
+            {
+                // SPY should be re added in the next loop
+                _reselectedSpy = 0;
+            }
+        }
+
+        public override void OnSecuritiesChanged(SecurityChanges changes)
+        {
+            if (changes.RemovedSecurities.Any())
+            {
+                // OnSecuritiesChanged is called before OnData, so SPY will still not be
+                // present
+                _reselectedSpy = 1;
+                _spy = AddEquity("SPY", Resolution.Daily).Symbol;
+
+                if (!Securities[_spy].IsTradable)
+                {
+                    throw new RegressionTestException($"{_spy} should be tradable");
+                }
+            }
+        }
+
+        /// <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 => 229;
+
+        /// <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", "84.550%"},
+            {"Drawdown", "2.000%"},
+            {"Expectancy", "0"},
+            {"Start Equity", "100000"},
+            {"End Equity", "105106.43"},
+            {"Net Profit", "5.106%"},
+            {"Sharpe Ratio", "5.253"},
+            {"Sortino Ratio", "11.491"},
+            {"Probabilistic Sharpe Ratio", "87.932%"},
+            {"Loss Rate", "0%"},
+            {"Win Rate", "0%"},
+            {"Profit-Loss Ratio", "0"},
+            {"Alpha", "0.157"},
+            {"Beta", "0.922"},
+            {"Annual Standard Deviation", "0.103"},
+            {"Annual Variance", "0.011"},
+            {"Information Ratio", "4.703"},
+            {"Tracking Error", "0.026"},
+            {"Treynor Ratio", "0.588"},
+            {"Total Fees", "$3.44"},
+            {"Estimated Strategy Capacity", "$700000000.00"},
+            {"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
+            {"Portfolio Turnover", "3.30%"},
+            {"Drawdown Recovery", "3"},
+            {"OrderListHash", "032561818d8c8c17b30d3c9b0d52fa17"}
+        };
+    }
+}