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 QuantConnect.Data;
+using QuantConnect.Interfaces;
+using QuantConnect.Orders.Fees;
+using QuantConnect.Securities;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Regression test algorithm where custom a <see cref="FeeModel"/> returns <see cref="OrderFee.Zero"/>
+    /// </summary>
+    public class ZeroFeeRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private Security _security;
+        // Adding this so we only trade once, so math is easier and clear
+        private bool _alreadyTraded;
+
+        /// <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
+
+            _security = AddEquity("SPY", Resolution.Minute);
+            _security.FeeModel = new ZeroFeeModel();
+        }
+
+        /// <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 slice)
+        {
+            if (!Portfolio.Invested && !_alreadyTraded)
+            {
+                _alreadyTraded = true;
+                SetHoldings(_security.Symbol, 1);
+                Debug("Purchased Stock");
+            }
+            else
+            {
+                Liquidate(_security.Symbol);
+            }
+        }
+
+        public override void OnEndOfAlgorithm()
+        {
+            Log($"TotalPortfolioValue: {Portfolio.TotalPortfolioValue}");
+            Log($"CashBook: {Portfolio.CashBook}");
+            Log($"Holdings.TotalCloseProfit: {_security.Holdings.TotalCloseProfit()}");
+
+            if (Portfolio.CashBook["USD"].Amount - _security.Holdings.LastTradeProfit != 100000)
+            {
+                throw new RegressionTestException("Unexpected USD cash amount: " +
+                    $"{Portfolio.CashBook["USD"].Amount}");
+            }
+            if (Portfolio.CashBook.ContainsKey(Currencies.NullCurrency))
+            {
+                throw new RegressionTestException("Unexpected NullCurrency cash");
+            }
+
+            var closedTrade = TradeBuilder.ClosedTrades[0];
+            if (closedTrade.TotalFees != 0)
+            {
+                throw new RegressionTestException($"Unexpected closed trades total fees {closedTrade.TotalFees}");
+            }
+            if (_security.Holdings.TotalFees != 0)
+            {
+                throw new RegressionTestException($"Unexpected closed trades total fees {closedTrade.TotalFees}");
+            }
+        }
+
+        internal class ZeroFeeModel : FeeModel
+        {
+            public override OrderFee GetOrderFee(OrderFeeParameters parameters)
+            {
+                return OrderFee.Zero;
+            }
+        }
+
+        /// <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 => 3943;
+
+        /// <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", "-0.05%"},
+            {"Compounding Annual Return", "-3.660%"},
+            {"Drawdown", "0.000%"},
+            {"Expectancy", "-1"},
+            {"Start Equity", "100000"},
+            {"End Equity", "99952.34"},
+            {"Net Profit", "-0.048%"},
+            {"Sharpe Ratio", "0"},
+            {"Sortino Ratio", "0"},
+            {"Probabilistic Sharpe Ratio", "0%"},
+            {"Loss Rate", "100%"},
+            {"Win Rate", "0%"},
+            {"Profit-Loss Ratio", "0"},
+            {"Alpha", "0"},
+            {"Beta", "0"},
+            {"Annual Standard Deviation", "0"},
+            {"Annual Variance", "0"},
+            {"Information Ratio", "-8.91"},
+            {"Tracking Error", "0.223"},
+            {"Treynor Ratio", "0"},
+            {"Total Fees", "$0.00"},
+            {"Estimated Strategy Capacity", "$18000000.00"},
+            {"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
+            {"Portfolio Turnover", "39.91%"},
+            {"Drawdown Recovery", "0"},
+            {"OrderListHash", "5bd6d98c36a3344f7383557bc375cf83"}
+        };
+    }
+}