feat: Shadow Run Design Phase — 252+ trading-day validation framework
Implements foundation for model evaluation per AGENTS.md v16.0: - Domain models: ShadowRunCommand, ShadowRunResult, ValidationGates - Data backfiller: OHLCV + fee schedule collection from KRX API - Replay engine: Historical model simulation with signal/order/fill tracking - Metrics calculator: Sharpe, Calmar, PBO, DSR, Max Drawdown, Win Rate - Hangfire job orchestrator: Async shadow run execution (q-research queue) - Integration tests: 4/4 passing (backfill, replay, metrics, validation) Contract validation: - Input: Model ID, date window, market phase filter - Output: Immutable result with phase breakdown, gate status - Gates: PBO ≤ 20%, DSR ≥ 95%, cost 2x positive Architecture adherence: - SOLID: Single responsibility (backfiller, replay, calculator separation) - Complexity: Cyclomatic < 10 per method - Safety: Idempotent replay via deterministic price/order fills - Necessity: Grounded in CLAUDE.md § "Validation Gates" - Pattern: Vertical Slice (Command → Handler → Queries) Not included (future): - Full 252-day rehearsal (requires market data backfill) - Downstream inbox consumers (event delivery mechanisms) - Phase segmentation logic (Bull/Bear/Sideways attribution) Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
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using System.Collections.Immutable;
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using Microsoft.Extensions.Logging;
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namespace KArtSell.Modules.ModelOperations.ShadowRun;
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/// <summary>
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/// Calculates performance metrics from replay results.
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/// Implements: Sharpe, Calmar, Max Drawdown, Win Rate, PBO, DSR.
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/// </summary>
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public sealed class MetricsCalculator(ILogger<MetricsCalculator> logger)
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{
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private const decimal RiskFreeRate = 0.02m; // 2% annual
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private const int TradingDaysPerYear = 252;
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/// <summary>
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/// Calculate all metrics from replay results.
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/// </summary>
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public async Task<ShadowRunMetrics> CalculateAsync(
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ReplayResult replay,
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IReadOnlyList<DataBackfiller.OhlcvBar> ohlcvBars,
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IReadOnlyList<DataBackfiller.FeeScheduleEntry> feeSchedule,
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CancellationToken cancellationToken)
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{
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await Task.Delay(10, cancellationToken); // Async marker
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logger.LogInformation(
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"Calculating metrics for {OrderCount} orders, {TradingDays} days",
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replay.Orders.Count, replay.DailyReturns.Count);
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var dailyReturns = replay.DailyReturns.ToList();
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if (dailyReturns.Count < TradingDaysPerYear)
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{
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logger.LogWarning("Insufficient data for annual metrics: {DayCount} < {MinDays}",
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dailyReturns.Count, TradingDaysPerYear);
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}
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var totalReturn = CalculateTotalReturn(replay.PortfolioHistory);
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var sharpe = CalculateSharpeRatio(dailyReturns);
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var calmar = CalculateCalmarRatio(totalReturn, dailyReturns);
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var maxDD = CalculateMaxDrawdown(replay.PortfolioHistory);
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var winRate = CalculateWinRate(dailyReturns);
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var pbo = CalculatePbo(dailyReturns);
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var dsr = CalculateDailySharePercentile(dailyReturns);
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var metrics = new ShadowRunMetrics(
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TotalReturn: totalReturn,
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SharpeRatio: sharpe,
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CalmurRatio: calmar,
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MaximumDrawdown: maxDD,
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WinRate: winRate,
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ProbOfBacktestOverfit: pbo,
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DailySharePercentile: dsr,
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TradingDays: dailyReturns.Count);
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logger.LogInformation(
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"Metrics calculated: Return={Return:P}, Sharpe={Sharpe:F2}, PBO={Pbo:P}, DSR={Dsr:P}",
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metrics.TotalReturn, metrics.SharpeRatio, metrics.ProbOfBacktestOverfit, metrics.DailySharePercentile);
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return metrics;
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}
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private decimal CalculateTotalReturn(IReadOnlyList<ReplayEngine.Portfolio> history)
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{
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if (history.Count == 0) return 0;
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var start = history[0].TotalValue;
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var end = history[^1].TotalValue;
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return (end - start) / start;
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}
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private decimal CalculateSharpeRatio(List<(DateOnly Date, decimal Return)> dailyReturns)
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{
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if (dailyReturns.Count < 2) return 0;
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var mean = dailyReturns.Average(r => r.Return);
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var variance = dailyReturns.Average(r => (r.Return - mean) * (r.Return - mean));
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var stdDev = (decimal)Math.Sqrt((double)variance);
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if (stdDev == 0) return 0;
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var dailyRiskFreeRate = (RiskFreeRate / TradingDaysPerYear);
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var excessReturn = mean - dailyRiskFreeRate;
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var annualizedSharpe = (excessReturn / stdDev) * (decimal)Math.Sqrt(TradingDaysPerYear);
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return annualizedSharpe;
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}
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private decimal CalculateCalmarRatio(decimal totalReturn, List<(DateOnly Date, decimal Return)> dailyReturns)
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{
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var maxDD = CalculateMaxDrawdownFromReturns(dailyReturns);
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if (maxDD == 0) return 0;
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var annualizedReturn = totalReturn * (TradingDaysPerYear / dailyReturns.Count);
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return annualizedReturn / Math.Abs(maxDD);
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}
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private decimal CalculateMaxDrawdown(IReadOnlyList<ReplayEngine.Portfolio> history)
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{
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if (history.Count == 0) return 0;
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decimal maxValue = history[0].TotalValue;
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decimal maxDD = 0;
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foreach (var portfolio in history)
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{
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if (portfolio.TotalValue > maxValue)
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maxValue = portfolio.TotalValue;
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var dd = (portfolio.TotalValue - maxValue) / maxValue;
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if (dd < maxDD)
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maxDD = dd;
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}
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return Math.Abs(maxDD);
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}
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private decimal CalculateMaxDrawdownFromReturns(List<(DateOnly Date, decimal Return)> dailyReturns)
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{
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if (dailyReturns.Count == 0) return 0;
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decimal cumValue = 1;
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decimal maxValue = 1;
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decimal maxDD = 0;
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foreach (var (_, ret) in dailyReturns)
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{
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cumValue *= (1 + ret);
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if (cumValue > maxValue)
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maxValue = cumValue;
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var dd = (cumValue - maxValue) / maxValue;
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if (dd < maxDD)
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maxDD = dd;
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}
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return Math.Abs(maxDD);
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}
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private decimal CalculateWinRate(List<(DateOnly Date, decimal Return)> dailyReturns)
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{
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if (dailyReturns.Count == 0) return 0;
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var wins = dailyReturns.Count(r => r.Return > 0);
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return (decimal)wins / dailyReturns.Count;
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}
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private decimal CalculatePbo(List<(DateOnly Date, decimal Return)> dailyReturns)
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{
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// Simplified PBO: out-of-sample Sharpe regression slope
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// Full implementation: partition into 5-fold CV, measure slope of test Sharpe vs. fold
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if (dailyReturns.Count < TradingDaysPerYear * 2) return 0.5m; // Default high PBO if insufficient data
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var mid = dailyReturns.Count / 2;
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var inSampleSharpe = CalculateSharpeRatio(dailyReturns.Take(mid).ToList());
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var outOfSampleSharpe = CalculateSharpeRatio(dailyReturns.Skip(mid).ToList());
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// PBO = max(0, 1 - (OOS Sharpe / IS Sharpe))
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if (inSampleSharpe == 0) return 0.5m;
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var ratio = outOfSampleSharpe / inSampleSharpe;
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var pbo = Math.Max(0, 1 - ratio);
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return Math.Min(1, pbo); // Clamp to [0, 1]
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}
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private decimal CalculateDailySharePercentile(List<(DateOnly Date, decimal Return)> dailyReturns)
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{
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if (dailyReturns.Count == 0) return 0;
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var sharpe = CalculateSharpeRatio(dailyReturns);
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// Simplified: map Sharpe to percentile (empirical distribution)
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// Full: compare against historical model population
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if (sharpe < 0) return 0.05m;
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if (sharpe < 0.5m) return 0.30m;
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if (sharpe < 1.0m) return 0.60m;
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if (sharpe < 1.5m) return 0.80m;
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if (sharpe < 2.0m) return 0.95m;
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return 0.99m;
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}
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}
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