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 QuantConnect.Interfaces;
+using System.Collections.Generic;
+using QuantConnect.Orders;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Basic algorithm demonstrating the use of MarketOnOpen orders.
+    /// </summary>
+    public class MarketOnOpenOrderRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private Symbol _symbol;
+        private List<OrderTicket> _tickets = new();
+
+        protected virtual bool AsynchronousOrders => false;
+
+        /// <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(2021, 03, 01);
+            SetEndDate(2021, 03, 03);
+            SetCash(100000);
+
+            _symbol = AddEquity("SPY", Resolution.Hour).Symbol;
+
+            Schedule.On(DateRules.Tomorrow, TimeRules.Midnight, () =>
+            {
+                _tickets.Add(MarketOnOpenOrder(_symbol, 1, asynchronous: AsynchronousOrders));
+
+                var marketOrderTicket = MarketOrder(_symbol, 1, asynchronous: AsynchronousOrders);
+                _tickets.Add(marketOrderTicket);
+
+                if (marketOrderTicket.OrderType != OrderType.MarketOnOpen)
+                {
+                    throw new RegressionTestException($"Expected order type to be MarketOnOpen, but was {marketOrderTicket.OrderType}");
+                }
+
+                foreach (var ticket in _tickets)
+                {
+                    if (ticket.Status != OrderStatus.New && ticket.Status != OrderStatus.Submitted)
+                    {
+                        throw new RegressionTestException($"Expected tickets status to be New or Submitted, but one was {ticket.Status}");
+                    }
+                }
+            });
+        }
+
+        public override void OnEndOfAlgorithm()
+        {
+            if (_tickets.Count == 0)
+            {
+                throw new RegressionTestException("Expected to have submitted orders, but did not");
+            }
+
+            foreach (var ticket in _tickets)
+            {
+                if (ticket.Status != OrderStatus.Filled)
+                {
+                    throw new RegressionTestException($"Expected tickets status to be Filled, but one was {ticket.Status}");
+                }
+
+                if (ticket.SubmitRequest.Asynchronous != AsynchronousOrders)
+                {
+                    throw new RegressionTestException("Expected all orders to have the same asynchronous flag as the algorithm.");
+                }
+            }
+        }
+
+        /// <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 => 50;
+
+        /// <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", "2"},
+            {"Average Win", "0%"},
+            {"Average Loss", "0%"},
+            {"Compounding Annual Return", "-2.547%"},
+            {"Drawdown", "0.000%"},
+            {"Expectancy", "0"},
+            {"Start Equity", "100000"},
+            {"End Equity", "99981.16"},
+            {"Net Profit", "-0.019%"},
+            {"Sharpe Ratio", "-147.421"},
+            {"Sortino Ratio", "-147.421"},
+            {"Probabilistic Sharpe Ratio", "0%"},
+            {"Loss Rate", "0%"},
+            {"Win Rate", "0%"},
+            {"Profit-Loss Ratio", "0"},
+            {"Alpha", "-0.023"},
+            {"Beta", "0.003"},
+            {"Annual Standard Deviation", "0"},
+            {"Annual Variance", "0"},
+            {"Information Ratio", "14.89"},
+            {"Tracking Error", "0.061"},
+            {"Treynor Ratio", "-9.006"},
+            {"Total Fees", "$2.00"},
+            {"Estimated Strategy Capacity", "$1400000000.00"},
+            {"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
+            {"Portfolio Turnover", "0.26%"},
+            {"Drawdown Recovery", "0"},
+            {"OrderListHash", "56a7b3ac0475f32f06c567b494741b0d"}
+        };
+    }
+}