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.Linq;
+using System.Collections.Generic;
+using QuantConnect.Data;
+using QuantConnect.Interfaces;
+using QuantConnect.Securities.Equity;
+using QuantConnect.Securities;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// This regression algorithm has examples of how to add an securities indicating the <see cref="DataNormalizationMode"/>
+    /// with the <see cref="QCAlgorithm.AddSecurity"/> method.
+    /// </summary>
+    public class SetDataNormalizationModeOnAddSecurityAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private readonly DataNormalizationMode _spyNormalizationMode = DataNormalizationMode.Raw;
+        private readonly DataNormalizationMode _ibmNormalizationMode = DataNormalizationMode.Adjusted;
+        private readonly DataNormalizationMode _aigNormalizationMode = DataNormalizationMode.TotalReturn;
+
+        private readonly DataNormalizationMode _esNormalizationMode = DataNormalizationMode.BackwardsRatio;
+        private readonly DataNormalizationMode _btcustdNormalizationMode = DataNormalizationMode.ForwardPanamaCanal;
+
+        private Dictionary<Equity, Tuple<decimal, decimal>> _priceRanges = new();
+
+        public override void Initialize()
+        {
+            SetStartDate(2013, 10, 7);
+            SetEndDate(2013, 10, 7);
+
+            var spyEquity = AddSecurity(SecurityType.Equity, "SPY", Resolution.Minute, dataNormalizationMode: _spyNormalizationMode);
+            CheckEquityDataNormalizationMode(spyEquity, _spyNormalizationMode);
+            _priceRanges.Add(spyEquity as Equity, new Tuple<decimal, decimal>(167.28m, 168.37m));
+
+            var ibmEquity = AddSecurity(SecurityType.Equity, "IBM", Resolution.Minute, dataNormalizationMode: _ibmNormalizationMode);
+            CheckEquityDataNormalizationMode(ibmEquity, _ibmNormalizationMode);
+            _priceRanges.Add(ibmEquity as Equity, new Tuple<decimal, decimal>(135.864131052m, 136.819606508m));
+
+            var aigEquity = AddSecurity(SecurityType.Equity, "AIG", Resolution.Minute, dataNormalizationMode: _aigNormalizationMode);
+            CheckEquityDataNormalizationMode(aigEquity, _aigNormalizationMode);
+            _priceRanges.Add(aigEquity as Equity, new Tuple<decimal, decimal>(48.73m, 49.10m));
+
+            var esFutures = AddSecurity(SecurityType.Future, "ES", Resolution.Minute, dataNormalizationMode: _esNormalizationMode);
+            CheckEquityDataNormalizationMode(esFutures, _esNormalizationMode);
+
+            var btsustdCryto = AddSecurity(SecurityType.Crypto,  "BTCUSDT", Resolution.Minute, dataNormalizationMode: _btcustdNormalizationMode);
+            CheckEquityDataNormalizationMode(btsustdCryto, _btcustdNormalizationMode);
+        }
+
+        public override void OnData(Slice slice)
+        {
+            foreach (var kvp in _priceRanges)
+            {
+                var security = kvp.Key;
+                var minExpectedPrice = kvp.Value.Item1;
+                var maxExpectedPrice = kvp.Value.Item2;
+
+                if (security.HasData && (security.Price < minExpectedPrice || security.Price > maxExpectedPrice))
+                {
+                    throw new RegressionTestException($"{security.Symbol}: Price {security.Price} is  out of expected range [{minExpectedPrice}, {maxExpectedPrice}]");
+                }
+            }
+        }
+
+        private void CheckEquityDataNormalizationMode(Security security, DataNormalizationMode expectedNormalizationMode)
+        {
+            var subscriptions = SubscriptionManager.Subscriptions.Where(x => x.Symbol == security.Symbol);
+            if (subscriptions.Any(x => x.DataNormalizationMode != expectedNormalizationMode))
+            {
+                throw new RegressionTestException($"Expected {security.Symbol} to have data normalization mode {expectedNormalizationMode} but was {subscriptions.First().DataNormalizationMode}");
+            }
+        }
+
+        /// <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 => 5072;
+
+        /// <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", "0"},
+            {"Tracking Error", "0"},
+            {"Treynor Ratio", "0"},
+            {"Total Fees", "$0.00"},
+            {"Estimated Strategy Capacity", "$0"},
+            {"Lowest Capacity Asset", ""},
+            {"Portfolio Turnover", "0%"},
+            {"Drawdown Recovery", "0"},
+            {"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"}
+        };
+    }
+}