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.Data.UniverseSelection;
+using QuantConnect.Interfaces;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// This is a regression algorithm to ensure coarse data does not enable potential look-ahead bias.
+    /// </summary>
+    public class CoarseNoLookAheadBiasAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private const int NumberOfSymbols = 1;
+        private static Dictionary<Symbol, decimal> _coarsePrices = new Dictionary<Symbol, decimal>();
+
+        public override void Initialize()
+        {
+            UniverseSettings.Resolution = Resolution.Daily;
+
+            SetStartDate(2014, 03, 24);
+            SetEndDate(2014, 04, 06);
+            SetCash(50000);
+
+            AddUniverse(CoarseSelectionFunction);
+
+            // schedule an event at 10 AM every day
+            Schedule.On(
+                DateRules.EveryDay(),
+                TimeRules.At(10, 0),
+                () =>
+                {
+                    foreach (var symbol in _coarsePrices.Keys)
+                    {
+                        if (Securities.ContainsKey(symbol))
+                        {
+                            // If the coarse price is emitted at midnight for the same date, we would have look-ahead bias
+                            // i.e. _coarsePrices[symbol] would be the closing price of the current day,
+                            // which we obviously cannot know at 10 AM :)
+                            // As the coarse data is now emitted for the previous day, there is no look-ahead bias:
+                            // _coarsePrices[symbol] and Securities[symbol].Price will have the same value (equal to the previous closing price)
+                            // for the backtesting period, so we expect this algorithm to make zero trades.
+                            if (_coarsePrices[symbol] > Securities[symbol].Price)
+                            {
+                                SetHoldings(symbol, 1m / NumberOfSymbols);
+                            }
+                            else
+                            {
+                                Liquidate(symbol);
+                            }
+                        }
+                    }
+                }
+            );
+        }
+
+        private static IEnumerable<Symbol> CoarseSelectionFunction(IEnumerable<CoarseFundamental> coarse)
+        {
+            var sortedByDollarVolume = coarse.OrderByDescending(x => x.DollarVolume);
+            var top = sortedByDollarVolume.Take(NumberOfSymbols).ToList();
+
+            // save the coarse adjusted prices in a dictionary, so we can access them in the scheduled event handler
+            _coarsePrices = top.ToDictionary(c => c.Symbol, c => c.AdjustedPrice);
+
+            return top.Select(x => x.Symbol);
+        }
+
+        /// <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 => 70951;
+
+        /// <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", "50000"},
+            {"End Equity", "50000"},
+            {"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", "-1.388"},
+            {"Tracking Error", "0.096"},
+            {"Treynor Ratio", "0"},
+            {"Total Fees", "$0.00"},
+            {"Estimated Strategy Capacity", "$0"},
+            {"Lowest Capacity Asset", ""},
+            {"Portfolio Turnover", "0%"},
+            {"Drawdown Recovery", "0"},
+            {"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"}
+        };
+    }
+}