chore: import upstream snapshot with attribution
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wehub-resource-sync
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/*
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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.Optimizer.Parameters;
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using System;
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using System.Collections.Generic;
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using System.Globalization;
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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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/// Per-parameter sensitivity analysis via 1-D slices through the backtest cloud with a piecewise linear fit.
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/// </summary>
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internal static class OptimizationSlicing
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{
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public static ParameterReport AnalyzeParameter(
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OptimizationParameter parameter,
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IReadOnlyList<OptimizationBacktestMetrics> backtests,
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OptimizationBacktestMetrics best)
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{
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var name = parameter.Name;
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var owning = backtests.Where(b => b.Parameters.ContainsKey(name)).ToList();
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var otherParamNames = owning
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.SelectMany(b => b.Parameters.Keys)
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.Where(k => k != name)
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.Distinct()
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.OrderBy(k => k, StringComparer.Ordinal)
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.ToList();
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// Group backtests by other-parameter values; each group is one 1-D slice.
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IEnumerable<IGrouping<string, OptimizationBacktestMetrics>> grouped = otherParamNames.Count == 0
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? new[] { owning.GroupBy(_ => "").FirstOrDefault() }
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.Where(g => g != null)
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.Cast<IGrouping<string, OptimizationBacktestMetrics>>()
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: owning.GroupBy(b => SliceKey(b, otherParamNames));
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var slices = new List<SliceFit>();
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foreach (var group in grouped)
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{
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var slice = BuildSlice(group.ToList(), name, otherParamNames);
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if (slice != null) slices.Add(slice);
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}
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var hasBest = best.Parameters.TryGetValue(name, out var bestValue);
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var (searchedMin, searchedMax, step) = ExtractGridSpec(parameter, owning, name);
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var bestAtEdge = hasBest && IsAtSearchedEdge(bestValue, searchedMin, searchedMax, step);
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var meanRange = slices.Count > 0 ? slices.Average(s => s.SharpeRange) : 0m;
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var maxRange = slices.Count > 0 ? slices.Max(s => s.SharpeRange) : 0m;
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var maxDerivPerStep = slices.Count > 0
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? slices.Max(s => s.MaxAbsDerivative) * (step ?? 1m)
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: 0m;
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return new ParameterReport
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{
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Name = name,
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SearchedMin = searchedMin,
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SearchedMax = searchedMax,
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Step = step,
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MeanWithinSliceSharpeRange = meanRange,
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MaxWithinSliceSharpeRange = maxRange,
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MaxAbsDerivativePerStep = maxDerivPerStep,
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BestValue = bestValue,
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BestAtSearchedEdge = bestAtEdge,
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Slices = slices
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};
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}
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private static SliceFit BuildSlice(
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List<OptimizationBacktestMetrics> backtests,
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string varyingParamName,
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IReadOnlyList<string> otherParamNames)
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{
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// Defensively collapse duplicate parameter values by averaging Sharpes.
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var points = backtests
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.GroupBy(b => b.Parameters[varyingParamName])
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.Select(g => (X: g.Key, Y: g.Average(b => b.SharpeRatio)))
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.OrderBy(p => p.X)
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.ToList();
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if (points.Count == 0) return null;
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var xs = points.Select(p => p.X).ToList();
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var ys = points.Select(p => p.Y).ToList();
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var sharpeRange = ys.Count >= 2 ? ys.Max() - ys.Min() : 0m;
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// Piecewise linear: one segment per adjacent pair; slope is sensitivity per parameter unit.
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var segments = new List<LinearSegment>();
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decimal maxAbsDerivative = 0m;
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for (var i = 0; i < points.Count - 1; i++)
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{
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var dx = xs[i + 1] - xs[i];
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var slope = (ys[i + 1] - ys[i]) / dx;
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segments.Add(new LinearSegment
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{
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XLo = xs[i],
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XHi = xs[i + 1],
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A = ys[i],
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B = slope
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});
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var absSlope = Math.Abs(slope);
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if (absSlope > maxAbsDerivative) maxAbsDerivative = absSlope;
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}
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var fixedParams = new Dictionary<string, decimal>();
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if (otherParamNames.Count > 0)
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{
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var first = backtests[0];
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foreach (var p in otherParamNames)
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{
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if (first.Parameters.TryGetValue(p, out var v)) fixedParams[p] = v;
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}
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}
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return new SliceFit
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{
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FixedParameters = fixedParams,
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SharpeRange = sharpeRange,
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MaxAbsDerivative = maxAbsDerivative,
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Segments = segments
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};
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}
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private static (decimal Min, decimal Max, decimal? Step) ExtractGridSpec(
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OptimizationParameter parameter,
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IReadOnlyList<OptimizationBacktestMetrics> owning,
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string name)
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{
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if (parameter is OptimizationStepParameter step)
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{
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return (step.MinValue, step.MaxValue, step.Step);
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}
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// Fallback for non-step parameters: infer min/max/step from measured values.
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var values = owning.Select(b => b.Parameters[name]).Distinct().OrderBy(v => v).ToList();
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if (values.Count == 0) return (0m, 0m, null);
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if (values.Count == 1) return (values[0], values[0], null);
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var min = values[0];
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var max = values[^1];
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var gaps = new List<decimal>();
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for (var i = 1; i < values.Count; i++) gaps.Add(values[i] - values[i - 1]);
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return (min, max, gaps.Min());
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}
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private static bool IsAtSearchedEdge(decimal value, decimal min, decimal max, decimal? step)
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{
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var tol = ((step ?? 1m) / 2m) + 1e-9m;
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return Math.Abs(value - min) <= tol || Math.Abs(value - max) <= tol;
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}
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private static string SliceKey(OptimizationBacktestMetrics backtest, IReadOnlyList<string> otherParamNames)
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{
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return string.Join("|", otherParamNames.Select(p =>
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(backtest.Parameters.TryGetValue(p, out var v) ? v.ToString(CultureInfo.InvariantCulture) : "NaN")));
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}
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}
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}
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