Why Simulations Crush Replay Mode

For the last few years, "Market Replay" has been the gold standard for backtesting. Tools that allow you to replay price action bar-by-bar are incredibly useful for practicing execution. But they have a fatal flaw: Replaying the past is only half the battle.

To truly trust your edge, you need to simulate the future.

The Limit of Replay Mode

Replay mode assumes the future will look exactly like the past. If you replay the 2022 bear market, you learn how to trade that specific bear market. But markets never repeat exactly.

• Volatility changes: What if the VIX is 15 instead of 30?
• Correlations shift: What if stocks and bonds fall together?
• Liquidity dries up: What if your slippage doubles?

Replay mode breeds a false sense of confidence because you are practicing on a static dataset.

Enter Monte Carlo Simulations

Monte Carlo simulations take your actual trade metrics (win rate, risk-to-reward ratio, trade distribution) and shuffle them thousands of times to show you the range of possible outcomes.

This is the standard for professional risk management. It answers the question: "If I trade my strategy for the next 1000 trades, what are all the possible equity curves I might see?"

What Simulations Reveal

1. Max Drawdown Risk: You might think your max drawdown is 10%. A simulation might reveal that there is a 5% chance of a 25% drawdown simply due to bad luck (sequence of returns risk).
2. Ruin Probability: What are the odds you blow up your account? If this number is anything above 0%, you are sizing too large.
3. Streak Analysis: Is 5 losses in a row normal for your system? Or 10? Simulations help you weather losing streaks because you know they are statistically probable.

Code Snippet: Simple Monte Carlo Logic

Here's a simplified view of how we calculate your equity curve projection. We don't just rely on your average; we test the variance.

def simulate_equity(starting_balance, trades, iterations=1000):
    results = []
    for i in range(iterations):
        # Shuffle the order of trades to test sequence risk
        shuffled_trades = random.shuffle(trades)
        equity = starting_balance
        path = [equity]
        
        for trade in shuffled_trades:
            equity += trade.pnl
            path.append(equity)
            
        results.append(path)
    return results

Plan, Don't Just Play

TradePlanner.ai integrates these simulations directly into your journal. Before you size up, run the sim.

• Swing Traders: Use it to model gap risk and overnight exposure.
• Day Traders: Use it to model the impact of a string of "tilt" days.

Don't just play the market. Plan for it. By understanding the full range of mathematical possibilities, you can trade with true confidence, knowing that your edge is robust enough to handle whatever the market throws at you next.