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.Algorithm.Framework.Selection;
+using QuantConnect.Data;
+using QuantConnect.Data.Market;
+using QuantConnect.Data.UniverseSelection;
+using QuantConnect.Interfaces;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Regression algorithm to test universe additions and removals with open positions
+    /// </summary>
+    /// <meta name="tag" content="regression test" />
+    public class InceptionDateSelectionRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private SecurityChanges _changes = SecurityChanges.None;
+
+        /// <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, 1);
+            SetEndDate(2013, 10, 31);
+            SetCash(100000);
+
+            UniverseSettings.Resolution = Resolution.Hour;
+
+            // select IBM once a week, empty universe the other days
+            AddUniverseSelection(new CustomUniverseSelectionModel("my-custom-universe", dt => dt.Day % 7 == 0 ? new List<string> { "IBM" } : Enumerable.Empty<string>()));
+            // Adds SPY 5 days after StartDate and keep it in Universe
+            AddUniverseSelection(new InceptionDateUniverseSelectionModel("spy-inception", new Dictionary<string, DateTime> {{"SPY", StartDate.AddDays(5)}}));
+        }
+
+        /// <summary>
+        /// OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
+        /// </summary>
+        /// <param name="slice">TradeBars dictionary object keyed by symbol containing the stock data</param>
+        public override void OnData(Slice slice)
+        {
+            if (_changes == SecurityChanges.None) return;
+
+            // we'll simply go long each security we added to the universe
+            foreach (var security in _changes.AddedSecurities)
+            {
+                SetHoldings(security.Symbol, .5);
+            }
+
+            _changes = SecurityChanges.None;
+        }
+
+        /// <summary>
+        /// Event fired each time the we add/remove securities from the data feed
+        /// </summary>
+        /// <param name="changes">Object containing AddedSecurities and RemovedSecurities</param>
+        public override void OnSecuritiesChanged(SecurityChanges changes)
+        {
+            // liquidate securities removed from our universe
+            foreach (var security in changes.RemovedSecurities)
+            {
+                Liquidate(security.Symbol, "Removed from Universe");
+            }
+
+            _changes = changes;
+        }
+
+        /// <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 => 405;
+
+        /// <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", "9"},
+            {"Average Win", "0.11%"},
+            {"Average Loss", "-0.24%"},
+            {"Compounding Annual Return", "28.358%"},
+            {"Drawdown", "1.200%"},
+            {"Expectancy", "-0.267"},
+            {"Start Equity", "100000"},
+            {"End Equity", "102119.68"},
+            {"Net Profit", "2.120%"},
+            {"Sharpe Ratio", "3.201"},
+            {"Sortino Ratio", "5.22"},
+            {"Probabilistic Sharpe Ratio", "74.902%"},
+            {"Loss Rate", "50%"},
+            {"Win Rate", "50%"},
+            {"Profit-Loss Ratio", "0.47"},
+            {"Alpha", "0.015"},
+            {"Beta", "0.478"},
+            {"Annual Standard Deviation", "0.058"},
+            {"Annual Variance", "0.003"},
+            {"Information Ratio", "-2.771"},
+            {"Tracking Error", "0.063"},
+            {"Treynor Ratio", "0.392"},
+            {"Total Fees", "$16.73"},
+            {"Estimated Strategy Capacity", "$7000000.00"},
+            {"Lowest Capacity Asset", "IBM R735QTJ8XC9X"},
+            {"Portfolio Turnover", "14.45%"},
+            {"Drawdown Recovery", "4"},
+            {"OrderListHash", "27cdeff9728c1a42239ea1b5b2c335dc"}
+        };
+    }
+}