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;
+using System.Collections.Generic;
+
+using QuantConnect.Data;
+using QuantConnect.Data.Market;
+using QuantConnect.Indicators;
+using QuantConnect.Interfaces;
+using QuantConnect.Securities;
+using QuantConnect.Securities.Volatility;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Algorithm illustrating the usage of the <see cref="IndicatorVolatilityModel"/> and
+    /// how to handle splits and dividends to avoid price discontinuities
+    /// </summary>
+    public class IndicatorVolatilityModelAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private const int _indicatorPeriods = 7;
+
+        private const DataNormalizationMode _dataNormalizationMode = DataNormalizationMode.Raw;
+
+        private Symbol _aapl;
+
+        private IIndicator _indicator;
+
+        private int _splitsAndDividendsCount;
+
+        private bool _volatilityChecked;
+
+        public override void Initialize()
+        {
+            SetStartDate(2014, 1, 1);
+            SetEndDate(2014, 12, 31);
+            SetCash(100000);
+
+            var equity = AddEquity("AAPL", Resolution.Daily, dataNormalizationMode: _dataNormalizationMode);
+            _aapl = equity.Symbol;
+
+            var std = new StandardDeviation(_indicatorPeriods);
+            var mean = new SimpleMovingAverage(_indicatorPeriods);
+            _indicator = std.Over(mean);
+            equity.SetVolatilityModel(new IndicatorVolatilityModel(_indicator, (_, data, _) =>
+            {
+                if (data.Price > 0)
+                {
+                    std.Update(data.Time, data.Price);
+                    mean.Update(data.Time, data.Price);
+                }
+            }));
+        }
+
+        public override void OnData(Slice slice)
+        {
+            if (slice.Splits.ContainsKey(_aapl) || slice.Dividends.ContainsKey(_aapl))
+            {
+                _splitsAndDividendsCount++;
+
+                // On a split or dividend event, we need to reset and warm the indicator up as Lean does to BaseVolatilityModel's
+                // to avoid big jumps in volatility due to price discontinuities
+                _indicator.Reset();
+                var equity = Securities[_aapl];
+                var volatilityModel = equity.VolatilityModel as IndicatorVolatilityModel;
+                volatilityModel.WarmUp(this, equity, equity.Resolution, _indicatorPeriods, _dataNormalizationMode);
+            }
+        }
+
+        public override void OnEndOfDay(Symbol symbol)
+        {
+            if (symbol != _aapl || !_indicator.IsReady)
+            {
+                return;
+            }
+
+            _volatilityChecked = true;
+
+            // This is expected only in this case, 0.05 is not a magical number of any kind.
+            // Just making sure we don't get big jumps on volatility
+            var volatility = Securities[_aapl].VolatilityModel.Volatility;
+            if (volatility <= 0 || volatility > 0.05m)
+            {
+                throw new RegressionTestException(
+                    "Expected volatility to stay less than 0.05 (not big jumps due to price discontinuities on splits and dividends), " +
+                    $"but got {volatility}");
+            }
+        }
+
+        public override void OnEndOfAlgorithm()
+        {
+            if (_splitsAndDividendsCount == 0)
+            {
+                throw new RegressionTestException("Expected to get at least one split or dividend event");
+            }
+
+            if (!_volatilityChecked)
+            {
+                throw new RegressionTestException("Expected to check volatility at least once");
+            }
+        }
+
+        private IIndicator UpdateIndicator(Security security, TradeBar bar)
+        {
+            _indicator.Update(bar);
+
+            return _indicator;
+        }
+
+        /// <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, Language.Python };
+
+        /// <summary>
+        /// Data Points count of all timeslices of algorithm
+        /// </summary>
+        public long DataPoints => 2021;
+
+        /// <summary>
+        /// Data Points count of the algorithm history
+        /// </summary>
+        public int AlgorithmHistoryDataPoints => 42;
+
+        /// <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", "-1.025"},
+            {"Tracking Error", "0.094"},
+            {"Treynor Ratio", "0"},
+            {"Total Fees", "$0.00"},
+            {"Estimated Strategy Capacity", "$0"},
+            {"Lowest Capacity Asset", ""},
+            {"Portfolio Turnover", "0%"},
+            {"Drawdown Recovery", "0"},
+            {"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"}
+        };
+    }
+}