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 QuantConnect.Interfaces;
+using System.Collections.Generic;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Example algorithm showing how to use QCAlgorithm.Train method
+    /// <meta name="tag" content="using quantconnect" />
+    /// <meta name="tag" content="training" />
+    /// </summary>
+    public class TrainingExampleAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private Queue<DateTime> _trainTimes = new();
+        public override void Initialize()
+        {
+            SetStartDate(2013, 10, 7);
+            SetEndDate(2013, 10, 14);
+
+            AddEquity("SPY", Resolution.Daily);
+
+            // Set TrainingMethod to be executed immediately
+            Train(TrainingMethod);
+
+            // Set TrainingMethod to be executed at 8:00 am every Sunday
+            Train(DateRules.Every(DayOfWeek.Sunday), TimeRules.At(8, 0), TrainingMethod);
+        }
+
+        private void TrainingMethod()
+        {
+            Log($"Start training at {Time}");
+            // Use the historical data to train the machine learning model
+            var history = History("SPY", 200, Resolution.Daily);
+
+            // ML code:
+
+
+            // let's keep this to assert in the end of the algorithm
+            _trainTimes.Enqueue(Time);
+        }
+
+        /// <summary>
+        /// Let's assert the behavior of our traning schedule
+        /// </summary>
+        public override void OnEndOfAlgorithm()
+        {
+            if (_trainTimes.Count != 2)
+            {
+                throw new RegressionTestException($"Unexpected train count: {_trainTimes.Count}");
+            }
+            if (_trainTimes.Dequeue() != StartDate
+                || _trainTimes.Dequeue() != new DateTime(2013, 10, 13, 8, 0, 0))
+            {
+                throw new RegressionTestException($"Unexpected train times!");
+            }
+        }
+
+        /// <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 };
+
+        /// <summary>
+        /// Data Points count of all timeslices of algorithm
+        /// </summary>
+        public long DataPoints => 56;
+
+        /// <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", "0"},
+            {"Average Win", "0%"},
+            {"Average Loss", "0%"},
+            {"Compounding Annual Return", "0%"},
+            {"Drawdown", "0%"},
+            {"Expectancy", "0"},
+            {"Start Equity", "100000"},
+            {"End Equity", "100000"},
+            {"Net Profit", "0%"},
+            {"Sharpe Ratio", "0"},
+            {"Sortino Ratio", "0"},
+            {"Probabilistic Sharpe Ratio", "0%"},
+            {"Loss Rate", "0%"},
+            {"Win Rate", "0%"},
+            {"Profit-Loss Ratio", "0"},
+            {"Alpha", "0"},
+            {"Beta", "0"},
+            {"Annual Standard Deviation", "0"},
+            {"Annual Variance", "0"},
+            {"Information Ratio", "-7.357"},
+            {"Tracking Error", "0.161"},
+            {"Treynor Ratio", "0"},
+            {"Total Fees", "$0.00"},
+            {"Estimated Strategy Capacity", "$0"},
+            {"Lowest Capacity Asset", ""},
+            {"Portfolio Turnover", "0%"},
+            {"Drawdown Recovery", "0"},
+            {"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"}
+        };
+    }
+}