wehub-resource-sync
104 lines
4.5 KiB
C#
104 lines
4.5 KiB
C#
/*
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* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
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* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*
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*/
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using QuantConnect.Logging;
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using System.Collections.Generic;
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using System.Linq;
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namespace QuantConnect.Optimizer.Analysis
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{
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/// <summary>
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/// Builds an aggregate <see cref="OptimizationAnalysis"/> from a completed optimization's per-backtest metrics; optimization-side analogue of the Engine ResultsAnalyzer.
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/// </summary>
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public class OptimizationAnalyzer
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{
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/// <summary>
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/// Runs the full optimization-analysis pipeline.
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/// </summary>
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/// <param name="parameters">Completed backtest metrics plus the parameter grid spec.</param>
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/// <returns>The populated <see cref="OptimizationAnalysis"/>, or null when no usable backtests remain.</returns>
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public OptimizationAnalysis Run(OptimizationAnalysisRunParameters parameters)
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{
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var allBacktests = parameters?.CompletedBacktests ?? new List<OptimizationBacktestMetrics>();
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var backtests = allBacktests.Where(b => b?.TotalPerformance?.PortfolioStatistics != null).ToList();
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if (backtests.Count == 0)
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{
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Log.Trace("OptimizationAnalyzer.Run(): no completed backtests with parsable Sharpe ratios; skipping analysis");
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return null;
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}
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var sharpes = backtests.Select(b => b.SharpeRatio).ToList();
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var overall = new SharpeSummary
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{
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Mean = sharpes.Average(),
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StdDev = StdDev(sharpes),
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Min = sharpes.Min(),
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Max = sharpes.Max(),
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Median = Median(sharpes)
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};
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// Sharpe is the universal yardstick regardless of the optimization's Criterion.
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var best = backtests.OrderByDescending(b => b.SharpeRatio).First();
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var bestSummary = new BacktestSummary
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{
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BacktestId = best.BacktestId,
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Parameters = new Dictionary<string, decimal>(best.Parameters),
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SharpeRatio = best.SharpeRatio
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};
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var paramReports = parameters.OptimizationParameters
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.Select(p => OptimizationSlicing.AnalyzeParameter(p, backtests, best))
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.ToList();
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var clusters = OptimizationClustering.Build(backtests, parameters.OptimizationParameters);
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var modes = OptimizationModes.Find(backtests, parameters.OptimizationParameters);
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var failed = OptimizationFailedBacktests.Build(allBacktests);
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return new OptimizationAnalysis
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{
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BacktestCountTotal = allBacktests.Count,
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BacktestCountUsed = backtests.Count,
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OverallSharpe = overall,
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Best = bestSummary,
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Parameters = paramReports,
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Clusters = clusters,
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Modes = modes,
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FailedBacktests = failed
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};
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}
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// ── Aggregate helpers ────────────────────────────────────────────────────
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private static decimal StdDev(IReadOnlyCollection<decimal> values)
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{
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if (values.Count < 2) return 0m;
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var mean = values.Average();
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var s = values.Sum(v => (v - mean) * (v - mean));
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// System.Math has no decimal Sqrt; cross into double for the root and back.
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return (decimal)System.Math.Sqrt((double)(s / (values.Count - 1)));
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}
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private static decimal Median(IEnumerable<decimal> values)
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{
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var sorted = values.OrderBy(v => v).ToList();
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if (sorted.Count == 0) return 0m;
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return sorted.Count % 2 == 1
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? sorted[sorted.Count / 2]
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: 0.5m * (sorted[sorted.Count / 2 - 1] + sorted[sorted.Count / 2]);
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}
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}
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}
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