"""
Monte Carlo Simulation — Stress testing the strategy by shuffling trade sequences.
"""
import numpy as np
import pandas as pd
from typing import List

def run_monte_carlo(trades: List[dict], initial_capital: float, iterations: int = 1000):
    """
    Run Monte Carlo simulation on a list of historical trades.
    
    Args:
        trades: List of trade dictionaries with 'pnl' and 'entry_price'.
        initial_capital: Starting capital.
        iterations: Number of simulations.
    """
    if not trades:
        return {"error": "No trades to simulate"}

    pnls = [t["pnl"] for t in trades]
    results = []

    for _ in range(iterations):
        # Shuffle the order of trades
        shuffled_pnls = np.random.choice(pnls, size=len(pnls), replace=True)
        
        equity = initial_capital
        equity_curve = [initial_capital]
        max_dd = 0
        peak = initial_capital
        
        for pnl in shuffled_pnls:
            equity += pnl
            equity_curve.append(equity)
            if equity > peak:
                peak = equity
            dd = (peak - equity) / peak
            if dd > max_dd:
                max_dd = dd
        
        results.append({
            "final_equity": equity,
            "max_drawdown": max_dd * 100,
            "return_pct": (equity / initial_capital - 1) * 100
        })

    df_results = pd.DataFrame(results)
    
    stats = {
        "avg_return": df_results["return_pct"].mean(),
        "median_return": df_results["return_pct"].median(),
        "worst_case_return": df_results["return_pct"].min(),
        "best_case_return": df_results["return_pct"].max(),
        "avg_drawdown": df_results["max_drawdown"].mean(),
        "max_drawdown_95th": df_results["max_drawdown"].quantile(0.95), # Risk at 95% confidence
        "ruin_probability": (df_results["final_equity"] < initial_capital * 0.5).mean() * 100 # Prob of losing 50%
    }
    
    return stats, df_results

def print_mc_report(stats):
    print("\n" + "="*40)
    print("      MONTE CARLO STRESS TEST")
    print("="*40)
    print(f"Avg Return:      {stats['avg_return']:.2f}%")
    print(f"Median Return:   {stats['median_return']:.2f}%")
    print(f"Best Case:       {stats['best_case_return']:.2f}%")
    print(f"Worst Case:      {stats['worst_case_return']:.2f}%")
    print(f"Avg Drawdown:    {stats['avg_drawdown']:.2f}%")
    print(f"Max DD (95% CI): {stats['max_drawdown_95th']:.2f}%")
    print(f"Prob. of Ruin:   {stats['ruin_probability']:.1f}%")
    print("="*40)