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 QuantConnect.Interfaces;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Test algorithm verifying OnEndOfDay callbacks are called as expected. See GH issue 2865.
+    /// </summary>
+    public class OnEndOfDayRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private Symbol _spySymbol;
+        private Symbol _bacSymbol;
+        private Symbol _ibmSymbol;
+        private int _onEndOfDaySpyCallCount;
+        private int _onEndOfDayBacCallCount;
+        private int _onEndOfDayIbmCallCount;
+
+        /// <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, 07);
+            SetEndDate(2013, 10, 11);
+            SetCash(100000);
+
+            _spySymbol = QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA);
+            _bacSymbol = QuantConnect.Symbol.Create("BAC", SecurityType.Equity, Market.USA);
+            _ibmSymbol = QuantConnect.Symbol.Create("IBM", SecurityType.Equity, Market.USA);
+
+            AddUniverse("my-universe-name", time =>
+            {
+                if (time.Day == 8)
+                {
+                    return new List<string> { _spySymbol.Value, _ibmSymbol.Value };
+                }
+                return new List<string> { _spySymbol.Value };
+            });
+        }
+
+        /// <summary>
+        /// We expect it to be called for the universe selected <see cref="Symbol"/>
+        /// and the post initialize manually added equity <see cref="Symbol"/>
+        /// </summary>
+        public override void OnEndOfDay(Symbol symbol)
+        {
+            if (symbol == _spySymbol)
+            {
+                if (_onEndOfDaySpyCallCount == 0)
+                {
+                    // just the first time
+                    SetHoldings(_spySymbol, 0.5);
+                    AddEquity("BAC");
+                }
+                _onEndOfDaySpyCallCount++;
+            }
+            else if (symbol == _bacSymbol)
+            {
+                if (_onEndOfDayBacCallCount == 0)
+                {
+                    // just the first time
+                    SetHoldings(_bacSymbol, 0.5);
+                }
+                _onEndOfDayBacCallCount++;
+            }
+            else if (symbol == _ibmSymbol)
+            {
+                _onEndOfDayIbmCallCount++;
+            }
+            Log($"OnEndOfDay({symbol}) called: {UtcTime}." +
+                $" SPY count: {_onEndOfDaySpyCallCount}." +
+                $" IBM count: {_onEndOfDayIbmCallCount}." +
+                $" BAC count: {_onEndOfDayBacCallCount}");
+        }
+
+        /// <summary>
+        /// Assert expected behavior
+        /// </summary>
+        public override void OnEndOfAlgorithm()
+        {
+            if (_onEndOfDaySpyCallCount != 5)
+            {
+                throw new RegressionTestException($"OnEndOfDay(SPY) unexpected count call {_onEndOfDaySpyCallCount}");
+            }
+            if (_onEndOfDayBacCallCount != 4)
+            {
+                throw new RegressionTestException($"OnEndOfDay(BAC) unexpected count call {_onEndOfDayBacCallCount}");
+            }
+            if (_onEndOfDayIbmCallCount != 1)
+            {
+                throw new RegressionTestException($"OnEndOfDay(IBM) unexpected count call {_onEndOfDayIbmCallCount}");
+            }
+        }
+
+        /// <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 => 7868;
+
+        /// <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", "2"},
+            {"Average Win", "0%"},
+            {"Average Loss", "0%"},
+            {"Compounding Annual Return", "489.968%"},
+            {"Drawdown", "1.200%"},
+            {"Expectancy", "0"},
+            {"Start Equity", "100000"},
+            {"End Equity", "102295.22"},
+            {"Net Profit", "2.295%"},
+            {"Sharpe Ratio", "15.661"},
+            {"Sortino Ratio", "0"},
+            {"Probabilistic Sharpe Ratio", "78.318%"},
+            {"Loss Rate", "0%"},
+            {"Win Rate", "0%"},
+            {"Profit-Loss Ratio", "0"},
+            {"Alpha", "1.604"},
+            {"Beta", "0.954"},
+            {"Annual Standard Deviation", "0.223"},
+            {"Annual Variance", "0.05"},
+            {"Information Ratio", "22.254"},
+            {"Tracking Error", "0.068"},
+            {"Treynor Ratio", "3.656"},
+            {"Total Fees", "$22.11"},
+            {"Estimated Strategy Capacity", "$5600000.00"},
+            {"Lowest Capacity Asset", "NB R735QTJ8XC9X"},
+            {"Portfolio Turnover", "19.96%"},
+            {"Drawdown Recovery", "1"},
+            {"OrderListHash", "17eb374f011ccb57a28cef4b9a4585d8"}
+        };
+    }
+}