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.Alphas;
+using QuantConnect.Algorithm.Framework.Execution;
+using QuantConnect.Algorithm.Framework.Portfolio;
+using QuantConnect.Algorithm.Framework.Selection;
+using QuantConnect.Data;
+using QuantConnect.Interfaces;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Test algorithm using <see cref="QCAlgorithm.AddAlphaModel(IAlphaModel)"/>
+    /// </summary>
+    public class AddAlphaModelAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private Symbol _spy;
+        private Symbol _fb;
+        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);  //Set Start Date
+            SetEndDate(2013, 10, 11);    //Set End Date
+            SetCash(100000);             //Set Strategy Cash
+
+            UniverseSettings.Resolution = Resolution.Daily;
+
+            _spy = QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA);
+            _fb = QuantConnect.Symbol.Create("FB", SecurityType.Equity, Market.USA);
+            _ibm = QuantConnect.Symbol.Create("IBM", SecurityType.Equity, Market.USA);
+            SetUniverseSelection(new ManualUniverseSelectionModel(_spy, _fb, _ibm));
+            SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
+            SetExecution(new ImmediateExecutionModel());
+
+            AddAlpha(new OneTimeAlphaModel(_spy));
+            AddAlpha(new OneTimeAlphaModel(_fb));
+            AddAlpha(new OneTimeAlphaModel(_ibm));
+
+            InsightsGenerated += OnInsightsGeneratedVerifier;
+        }
+
+        private void OnInsightsGeneratedVerifier(IAlgorithm algorithm,
+            GeneratedInsightsCollection insightsCollection)
+        {
+            if (insightsCollection.Insights.Count(insight => insight.Symbol == _fb) != 1
+                || insightsCollection.Insights.Count(insight => insight.Symbol == _spy) != 1
+                || insightsCollection.Insights.Count(insight => insight.Symbol == _ibm) != 1)
+            {
+                throw new RegressionTestException("Unexpected insights were emitted");
+            }
+        }
+
+        private class OneTimeAlphaModel : AlphaModel
+        {
+            private readonly Symbol _symbol;
+            private bool _triggered;
+
+            public OneTimeAlphaModel(Symbol symbol)
+            {
+                _symbol = symbol;
+            }
+
+            public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice data)
+            {
+                if (!_triggered)
+                {
+                    _triggered = true;
+                    yield return Insight.Price(
+                        _symbol,
+                        Resolution.Daily,
+                        1,
+                        InsightDirection.Down
+                        );
+                }
+            }
+        }
+
+        /// <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 => 58;
+
+        /// <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", "0.86%"},
+            {"Average Loss", "-0.27%"},
+            {"Compounding Annual Return", "206.404%"},
+            {"Drawdown", "1.700%"},
+            {"Expectancy", "1.781"},
+            {"Start Equity", "100000"},
+            {"End Equity", "101441.92"},
+            {"Net Profit", "1.442%"},
+            {"Sharpe Ratio", "4.836"},
+            {"Sortino Ratio", "10.481"},
+            {"Probabilistic Sharpe Ratio", "59.374%"},
+            {"Loss Rate", "33%"},
+            {"Win Rate", "67%"},
+            {"Profit-Loss Ratio", "3.17"},
+            {"Alpha", "4.164"},
+            {"Beta", "-1.322"},
+            {"Annual Standard Deviation", "0.321"},
+            {"Annual Variance", "0.103"},
+            {"Information Ratio", "-0.795"},
+            {"Tracking Error", "0.532"},
+            {"Treynor Ratio", "-1.174"},
+            {"Total Fees", "$14.78"},
+            {"Estimated Strategy Capacity", "$120000000.00"},
+            {"Lowest Capacity Asset", "IBM R735QTJ8XC9X"},
+            {"Portfolio Turnover", "41.18%"},
+            {"Drawdown Recovery", "0"},
+            {"OrderListHash", "713c956deb193bed2290e9f379c0f9f9"}
+        };
+    }
+}