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.Collections.Generic;
+using QuantConnect.Algorithm.Framework.Portfolio;
+using QuantConnect.Data;
+using QuantConnect.Interfaces;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Regression algorithm testing GH feature 3790, using SetHoldings with a collection of targets
+    /// which will be ordered by margin impact before being executed, with the objective of avoiding any
+    /// margin errors
+    /// </summary>
+    public class SetHoldingsMultipleTargetsRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private Symbol _spy;
+        private Symbol _ibm;
+
+        /// <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);
+
+            // use leverage 1 so we test the margin impact ordering
+            _spy = AddEquity("SPY", Resolution.Minute, Market.USA, false, 1).Symbol;
+            _ibm = AddEquity("IBM", Resolution.Minute, Market.USA, false, 1).Symbol;
+
+            // Order margin value has to have a minimum of 0.5% of Portfolio value, allows filtering out small trades and reduce fees.
+            // Commented so regression algorithm is more sensitive
+            //Settings.MinimumOrderMarginPortfolioPercentage = 0.005m;
+        }
+
+        /// <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 data)
+        {
+            if (!Portfolio.Invested)
+            {
+                SetHoldings(new List<PortfolioTarget> { new PortfolioTarget(_spy, 0.8m), new PortfolioTarget(_ibm, 0.2m) });
+            }
+            else
+            {
+                SetHoldings(new List<PortfolioTarget> { new PortfolioTarget(_ibm, 0.8m), new PortfolioTarget(_spy, 0.2m) });
+            }
+        }
+
+        /// <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 virtual bool CanRunLocally { get; } = true;
+
+        /// <summary>
+        /// This is used by the regression test system to indicate which languages this algorithm is written in.
+        /// </summary>
+        public virtual List<Language> Languages { get; } = new() { Language.CSharp, Language.Python };
+
+        /// <summary>
+        /// Data Points count of all timeslices of algorithm
+        /// </summary>
+        public virtual long DataPoints => 7842;
+
+        /// <summary>
+        /// Data Points count of the algorithm history
+        /// </summary>
+        public virtual 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 virtual Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
+        {
+            {"Total Orders", "11"},
+            {"Average Win", "0.00%"},
+            {"Average Loss", "-0.01%"},
+            {"Compounding Annual Return", "353.938%"},
+            {"Drawdown", "2.300%"},
+            {"Expectancy", "-0.749"},
+            {"Start Equity", "100000"},
+            {"End Equity", "101952.99"},
+            {"Net Profit", "1.953%"},
+            {"Sharpe Ratio", "11.757"},
+            {"Sortino Ratio", "0"},
+            {"Probabilistic Sharpe Ratio", "65.498%"},
+            {"Loss Rate", "75%"},
+            {"Win Rate", "25%"},
+            {"Profit-Loss Ratio", "0.00"},
+            {"Alpha", "0.96"},
+            {"Beta", "0.993"},
+            {"Annual Standard Deviation", "0.248"},
+            {"Annual Variance", "0.062"},
+            {"Information Ratio", "8.324"},
+            {"Tracking Error", "0.114"},
+            {"Treynor Ratio", "2.942"},
+            {"Total Fees", "$15.02"},
+            {"Estimated Strategy Capacity", "$2600000.00"},
+            {"Lowest Capacity Asset", "IBM R735QTJ8XC9X"},
+            {"Portfolio Turnover", "44.15%"},
+            {"Drawdown Recovery", "2"},
+            {"OrderListHash", "14d509658aa542a210a3d6d41c05cd22"}
+        };
+    }
+}