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 +1,124 @@
+/*
+ * 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.Data;
+using QuantConnect.Interfaces;
+using QuantConnect.Orders;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// This regression test algorithm reproduces issue reported in GB issue https://github.com/QuantConnect/Lean/issues/2655
+    /// fixed in PR https://github.com/QuantConnect/Lean/pull/2659
+    /// </summary>
+    public class DailyResolutionSplitRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private Symbol _symbol;
+
+        public override void Initialize()
+        {
+            SetStartDate(2018, 2, 13);  //Set Start Date
+            SetEndDate(2018, 06, 01);    //Set End Date
+            SetCash(100000);             //Set Strategy Cash
+            _symbol = AddEquity("UPRO", Resolution.Daily).Symbol;
+        }
+
+        public override void OnData(Slice slice)
+        {
+            if (Time.Date == new DateTime(2018, 05, 22).Date)
+            {
+                MarketOrder(_symbol, 100);
+            }
+
+            if (Time.Date == new DateTime(2018, 05, 23).Date)
+            {
+                MarketOrder(_symbol, 100);
+            }
+
+            if (Time.Date == new DateTime(2018, 05, 24).Date)
+            {
+                MarketOrder(_symbol, 100);
+            }
+
+            if (Time.Date == new DateTime(2018, 05, 25).Date)
+            {
+                MarketOrder(_symbol, 100);
+            }
+
+            if (Time.Date == new DateTime(2018, 05, 29).Date)
+            {
+                Liquidate();
+            }
+        }
+
+        public override void OnOrderEvent(OrderEvent orderEvent)
+        {
+            Log($"{orderEvent}");
+        }
+
+        /// <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; } = false;
+
+        /// <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 => 0;
+
+        /// </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", "4"},
+            {"Average Win", "0%"},
+            {"Average Loss", "0%"},
+            {"Compounding Annual Return", "0.520%"},
+            {"Drawdown", "0.800%"},
+            {"Expectancy", "0"},
+            {"Net Profit", "0.155%"},
+            {"Sharpe Ratio", "0.242"},
+            {"Loss Rate", "0%"},
+            {"Win Rate", "0%"},
+            {"Profit-Loss Ratio", "0"},
+            {"Alpha", "0.118"},
+            {"Beta", "-5.794"},
+            {"Annual Standard Deviation", "0.022"},
+            {"Annual Variance", "0"},
+            {"Information Ratio", "-0.644"},
+            {"Tracking Error", "0.022"},
+            {"Treynor Ratio", "-0.001"},
+            {"Total Fees", "$4.00"}
+        };
+    }
+}