backtest-report-generator

# Backtest Report Generator ## Overview Transforms raw backtest results into professional reports with full statistical analysis, equity curves, drawdown visualization, Monte Carlo simulation, and distribution analysis. Outputs as HTML (interactive) or PDF. --- ## 1. Report Data Structure ```python import pandas as pd import numpy as np from scipy import stats from datetime import datetime from typing import Optional def compute_tearsheet( equity_curve: pd.Series, returns: pd.Series, trades_df: Optional[pd.DataFrame] = None, benchmark_returns: Optional[pd.Series] = None, risk_free_rate: float = 0.04, ) -> dict: """Compute comprehensive strategy tearsheet metrics.""" annual_factor = 252 total_return = (equity_curve.iloc[-1] / equity_curve.iloc[0]) - 1 years = len(returns) / annual_factor cagr = (1 + total_return) ** (1 / max(years, 0.01)) - 1 # Drawdown analysis peak = equity_curve.cummax() dd = (equity_curve - peak) / peak max_dd = dd.min() dd_durations = [] in_dd = False start = None for i, d in enumerate(dd): if d < 0 and not in_dd: in_dd = True start = i elif d == 0 and in_dd: in_dd = False dd_durations.append(i - start) # Risk metrics vol = returns.std() * np.sqrt(annual_factor) sharpe = (returns.mean() * annual_factor - risk_free_rate) / max(vol, 1e-10) downside_ret = returns[returns < 0] sortino = (returns.mean() * annual_factor - risk_free_rate) / (downside_ret.std() * np.sqrt(annual_factor)) if len(downside_ret) > 0 else 0 calmar = cagr / abs(max_dd) if max_dd != 0 else 0 var_95 = returns.quantile(0.05) cvar_95 = returns[returns <= var_95].mean() # Win/loss analysis from trades trade_stats = {} if trades_df is not None and not trades_df.empty: closed = trades_df[trades_df["pnl_pips"].notna()] wins = closed[closed["pnl_pips"] > 0] losses = closed[closed["pnl_pips"] <= 0] trade_stats = { "total_trades": len(closed), "win_rate": round(len(wins) / max(len(closed), 1) * 100, 1), "avg_win": round(wins["pnl_pips"].mean(), 1) if len(wins) > 0 else 0, "avg_loss": round(losses["pnl_pips"].mean(), 1) if len(losses) > 0 else 0, "profit_factor": round(wins["pnl_usd"].sum() / abs(losses["pnl_usd"].sum()), 2) if len(losses) > 0 and losses["pnl_usd"].sum() != 0 else float("inf"), "expectancy_pips": round(closed["pnl_pips"].mean(), 2), "largest_win": round(wins["pnl_pips"].max(), 1) if len(wins) > 0 else 0, "largest_loss": round(losses["pnl_pips"].min(), 1) if len(losses) > 0 else 0, "avg_hold_bars": "from timestamps", } return { "summary": { "total_return": round(total_return * 100, 2), "cagr": round(cagr * 100, 2), "sharpe": round(sharpe, 3), "sortino": round(sortino, 3), "calmar": round(calmar, 3), "volatility": round(vol * 100, 2), "max_drawdown": round(max_dd * 100, 2), "avg_drawdown_duration": round(np.mean(dd_durations), 0) if dd_durations else 0, "max_drawdown_duration": max(dd_durations) if dd_durations else 0, "var_95": round(var_95 * 100, 4), "cvar_95": round(cvar_95 * 100, 4), }, "trade_stats": trade_stats, "period": f"{equity_curve.index[0]} → {equity_curve.index[-1]}", "bars": len(returns), "years": round(years, 2), } ``` --- ## 2. Monte Carlo Simulation ```python def monte_carlo_simulation( returns: pd.Series, n_simulations: int = 1000, n_periods: int = 252, initial_capital: float = 10000, confidence_levels: list[float] = [0.05, 0.25, 0.50, 0.75, 0.95], ) -> dict: """ Bootstrap Monte Carlo — resample from actual returns to generate distribution of possible outcomes. Tests strategy robustness. """ np.random.seed(42) all_paths = np.zeros((n_simulations, n_periods)) for sim in range(n_simulations): sampled = np.random.choice(returns.values, size=n_periods, replace=True) equity = initial_capital * np.cumprod(1 + sampled) all_paths[sim] = equity final_values = all_paths[:, -1] max_drawdowns = [] for path in all_paths: peak = np.maximum.accumulate(path) dd = (path - peak) / peak max_drawdowns.append(dd.min()) percentiles = {f"p{int(cl*100)}": round(np.percentile(final_values, cl * 100), 2) for cl in confidence_levels} dd_percentiles = {f"p{int(cl*100)}": round(np.percentile(max_drawdowns, cl * 100) * 100, 2) for cl in confidence_levels} return { "n_simulations": n_simulations, "n_periods": n_periods, "initial_capital": initial_capital, "final_value_percentiles": percentiles, "median_final": round(np.median(final_values), 2), "mean_final": round(np.mean(final_values), 2), "prob_profit": round((final_values > initial_capital).mean() * 100, 1), "prob_double": round((final_values > initial_capital * 2).mean() * 100, 1), "prob_ruin_50pct": round((final_values < initial_capital * 0.5).mean() * 100, 1), "max_drawdown_percentiles": dd_percentiles, "worst_case_dd": round(min(max_drawdowns) * 100, 2), "paths_summary": "Use all_paths array for visualization", } ``` --- ## 3. HTML Report Generator ```python def generate_html_report(tearsheet: dict, monte_carlo: dict, strategy_name: str = "Strategy") -> str: """Generate a standalone HTML report with charts using Chart.js.""" return f"""<!DOCTYPE html> <html><head><meta charset="utf-8"><title>{strategy_name} Backtest Report</title> <script src="https://cdn.jsdelivr.net/npm/chart.js"></script> <style> body {{ font-family: -apple-system, sans-serif; margin: 40px; background: #0f0f1a; color: #e0e0e0; }} .card {{ background: #1a1a2e; border-radius: 12px; padding: 24px; margin: 16px 0; }} .metric {{ display: inline-block; margin: 12px 24px; text-align: center; }} .metric .value {{ font-size: 28px; font-weight: bold; }} .metric .label {{ font-size: 12px; color: #888; }} .green {{ color: #00e676; }} .red {{ color: #ff5252; }} .yellow {{ color: #ffd740; }} h1 {{ color: #7c4dff; }} h2 {{ color: #448aff; border-bottom: 1px solid #333; padding-bottom: 8px; }} table {{ width: 100%; border-collapse: collapse; }} th, td {{ padding: 8px 12px; text-align: left; border-bottom: 1px solid #333; }} th {{ color: #888; }} </style></head><body> <h1>{strategy_name} — Backtest Report</h1> <p>Generated: {datetime.utcnow().strftime('%Y-%m-%d %H:%M UTC')}</p> <div class="card"><h2>Performance Summary</h2> <div class="metric"><div class="value {'green' if tearsheet['summary']['total_return'] > 0 else 'red'}">{tearsheet['summary']['total_return']}%</div><div class="label">Total Return</div></div> <div class="metric"><div class="value">{tearsheet['summary']['cagr']}%</div><div class="label">CAGR</div></div> <div class="metric"><div class="value">{tearsheet['summary']['sharpe']}</div><div class="label">Sharpe</div></div> <div class="metric"><div class="value">{tearsheet['summary']['sortino']}</div><div class="label">Sortino</div></div> <div class="metric"><div class="value red">{tearsheet['summary']['max_drawdown']}%</div><div class="label">Max DD</div></div> <div class="metric"><div class="value">{tearsheet['summary']['volatility']}%</div><div class="label">Volatility</div></div> </div> <div class="card"><h2>Trade Statistics</h2> <table><tr><th>Metric</th><th>Value</th></tr> {''.join(f"<tr><td>{k}</td><td>{v}</td></tr>" for k, v in tearsheet.get('trade_stats', {}).items())} </table></div> <div class="card"><h2>Monte Carlo Analysis ({monte_carlo['n_simulations']} simulations)</h2> <div class="metric"><div class="value green">{monte_carlo['prob_profit']}%</div><div class="label">Probability of Profit</div></div> <div class="metric"><div class="value">{monte_carlo['median_final']}</div><div class="label">Median Final Value</div></div> <div class="metric"><div class="value red">{monte_carlo['prob_ruin_50pct']}%</div><div class="label">Prob of 50% Loss</div></div> <div class="metric"><div class="value red">{monte_carlo['worst_case_dd']}%</div><div class="label">Worst DD</div></div> </div> <div class="card"><h2>Risk Metrics</h2> <table><tr><th>Metric</th><th>Value</th></tr> <tr><td>VaR (95%)</td><td>{tearsheet['summary']['var_95']}%</td></tr> <tr><td>CVaR (95%)</td><td>{tearsheet['summary']['cvar_95']}%</td></tr> <tr><td>Calmar Ratio</td><td>{tearsheet['summary']['calmar']}</td></tr> <tr><td>Avg DD Duration</td><td>{tearsheet['summary']['avg_drawdown_duration']} bars</td></tr> </table></div> <div class="card" style="background:#2a1a1a; border: 1px solid #ff5252;"> <strong style="color:#ff5252;">⚠ DISCLAIMER:</strong> Past performance does not guarantee future results.