import random

def monte_carlo(wr, rr, risk_pct, n_trades, starting_capital, n_paths=1000):
    results = []
    ruin_count = 0
    max_drawdowns = []
    for _ in range(n_paths):
        equity = starting_capital
        peak = equity
        max_dd = 0
        for _ in range(n_trades):
            if random.random() < wr:
                equity += equity * risk_pct * rr
            else:
                equity -= equity * risk_pct
            peak = max(peak, equity)
            dd = (peak - equity) / peak
            max_dd = max(max_dd, dd)
            if equity <= starting_capital * 0.2:
                ruin_count += 1
                break
        results.append(equity)
        max_drawdowns.append(max_dd)
    results.sort()
    return {
        "median": results[len(results)//2],
        "p5": results[int(len(results)*0.05)],
        "p95": results[int(len(results)*0.95)],
        "max_dd_median": sorted(max_drawdowns)[len(max_drawdowns)//2],
        "ruin_probability": ruin_count / n_paths,
        "double_probability": sum(1 for r in results if r >= starting_capital * 2) / n_paths,
    }

Monte Carlo Simulation (1,000 paths × N trades)
─────────────────────────────────────────────
Structure: WR=60%, RR=1.0, Risk=1.1%/trade
Starting Capital: $10,000

Median Terminal Equity:  $XX,XXX
5th Percentile:          $X,XXX
95th Percentile:         $XX,XXX
Max Drawdown (median):   XX.X%
P(Ruin):                 X.X%
P(Double):               XX.X%
─────────────────────────────────────────────
Verdict: [PASS/FAIL] — structure is [robust/fragile]