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 System.Linq;
+using QuantConnect.Algorithm.Framework.Portfolio;
+using QuantConnect.Data;
+using QuantConnect.Data.UniverseSelection;
+using QuantConnect.Interfaces;
+using QuantConnect.Orders.Slippage;
+using QuantConnect.Securities;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Example algorithm implementing VolumeShareSlippageModel.
+    /// </summary>
+    public class VolumeShareSlippageModelAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private List<Symbol> _longs = new();
+        private List<Symbol> _shorts = new();
+
+        public override void Initialize()
+        {
+            SetStartDate(2020, 11, 29);
+            SetEndDate(2020, 12, 2);
+            // To set the slippage model to limit to fill only 30% volume of the historical volume, with 5% slippage impact.
+            SetSecurityInitializer((security) => security.SetSlippageModel(new VolumeShareSlippageModel(0.3m, 0.05m)));
+
+            // Create SPY symbol to explore its constituents.
+            var spy = QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA);
+
+            UniverseSettings.Resolution = Resolution.Daily;
+            // Add universe to trade on the most and least weighted stocks among SPY constituents.
+            AddUniverse(Universe.ETF(spy, universeFilterFunc: Selection));
+        }
+
+        private IEnumerable<Symbol> Selection(IEnumerable<ETFConstituentUniverse> constituents)
+        {
+            var sortedByDollarVolume = constituents.OrderBy(x => x.Weight).ToList();
+            // Add the 10 most weighted stocks to the universe to long later.
+            _longs = sortedByDollarVolume.TakeLast(10)
+                .Select(x => x.Symbol)
+                .ToList();
+            // Add the 10 least weighted stocks to the universe to short later.
+            _shorts = sortedByDollarVolume.Take(10)
+                .Select(x => x.Symbol)
+                .ToList();
+
+            return _longs.Union(_shorts);
+        }
+
+        public override void OnData(Slice slice)
+        {
+            // Equally invest into the selected stocks to evenly dissipate capital risk.
+            // Dollar neutral of long and short stocks to eliminate systematic risk, only capitalize the popularity gap.
+            var targets = _longs.Select(symbol => new PortfolioTarget(symbol, 0.05m)).ToList();
+            targets.AddRange(_shorts.Select(symbol => new PortfolioTarget(symbol, -0.05m)).ToList());
+
+            // Liquidate the ones not being the most and least popularity stocks to release fund for higher expected return trades.
+            SetHoldings(targets, liquidateExistingHoldings: true);
+        }
+
+        /// <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 => 1035;
+
+        /// <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", "20.900%"},
+            {"Drawdown", "0%"},
+            {"Expectancy", "0"},
+            {"Start Equity", "100000"},
+            {"End Equity", "100190.84"},
+            {"Net Profit", "0.191%"},
+            {"Sharpe Ratio", "9.794"},
+            {"Sortino Ratio", "0"},
+            {"Probabilistic Sharpe Ratio", "0%"},
+            {"Loss Rate", "0%"},
+            {"Win Rate", "0%"},
+            {"Profit-Loss Ratio", "0"},
+            {"Alpha", "0.297"},
+            {"Beta", "-0.064"},
+            {"Annual Standard Deviation", "0.017"},
+            {"Annual Variance", "0"},
+            {"Information Ratio", "-18.213"},
+            {"Tracking Error", "0.099"},
+            {"Treynor Ratio", "-2.695"},
+            {"Total Fees", "$4.00"},
+            {"Estimated Strategy Capacity", "$4400000000.00"},
+            {"Lowest Capacity Asset", "GOOCV VP83T1ZUHROL"},
+            {"Portfolio Turnover", "4.22%"},
+            {"Drawdown Recovery", "0"},
+            {"OrderListHash", "9d2bd0df7c094c393e77f72b7739bfa0"}
+        };
+    }
+}