You had a great month. Net profit $3,200, win rate 58%, sharp entries. But here’s the uncomfortable question: was that skill, or could a coin flip have produced similar results?
This is the question Monte Carlo simulation answers. It’s one of the most powerful tools in quantitative trading — and one of the least understood by retail traders.
What Is Monte Carlo Simulation?
Monte Carlo simulation takes your actual trade results and runs them through thousands of random reorderings. Each run shuffles the sequence of your wins and losses, creating an alternate history. After 10,000 runs, you get a distribution of possible outcomes.
The key insight: if most random reorderings of your trades still produce profit, your edge is likely real. If many reorderings produce losses, your results may have been luck.
The Simple Version
Imagine you have 100 trades:
- 55 winners averaging +$120
- 45 losers averaging -$95
You made $2,325 in reality. But what if those same 100 trades had happened in a different order?
Monte Carlo shuffles them 10,000 times. For each shuffle, it calculates:
- Final P&L
- Maximum drawdown along the way
- Longest losing streak
- Peak-to-trough decline
The result is a probability distribution — not a single number, but a range of outcomes with confidence intervals.
Why Order Matters
You might think: “If the trades are the same, won’t the final P&L be the same regardless of order?” For final P&L, yes — if you’re trading fixed size. But for drawdown and risk of ruin, order matters enormously.
Consider two sequences with the same 10 trades:
Sequence A (losses clustered):
-$200, -$180, -$150, -$120, +$300, +$250, +$200, +$150, +$120, +$100
Max drawdown: -$650 (four losses in a row at the start)
Sequence B (alternating):
+$300, -$200, +$250, -$180, +$200, -$150, +$150, -$120, +$120, +$100
Max drawdown: -$200 (single loss after a win)
Same final P&L (+$470). But Sequence A would have felt like disaster — you’d have been down $650 before recovering. Many traders would have quit, changed their strategy, or gone on tilt during that drawdown.
Monte Carlo shows you how bad things could get with the same edge, just different luck in ordering.
How to Run a Monte Carlo Simulation
Step 1: Collect Your Trade Results
You need at least 30 trades — ideally 100+. For each trade, record the net P&L (after fees and slippage). The more trades, the more reliable the simulation.
Step 2: Define Your Parameters
- Number of simulations: 10,000 is standard. More is better but slower.
- Number of trades per simulation: Match your actual count, or project forward (e.g., “what would 200 trades look like?”)
- Sampling method: With replacement (bootstrap) or without replacement (permutation). Bootstrap is more common and allows for forward projection.
Step 3: Run the Simulation
For each of 10,000 runs:
1. Randomly sample trades from your history (with replacement)
2. Calculate cumulative P&L
3. Track maximum drawdown
4. Record final P&L
Step 4: Analyze the Distribution
After all runs, you have 10,000 final P&L values and 10,000 maximum drawdowns. From these:
- Median outcome: The P&L at the 50th percentile — your “expected” result
- 95th percentile drawdown: The worst drawdown you’d see 95% of the time
- Probability of profit: What percentage of simulations ended profitable
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