What is a Monte Carlo Simulation?

If you have ever backtested a strategy and wondered whether those results actually mean anything, Monte Carlo simulation is the answer to that question.

The Problem With a Single Backtest

A backtest shows you one path. Your strategy played out over a specific sequence of trades, in a specific order, and produced a specific result. That result might look great. But it was just one version of what could have happened.

The order your trades come in matters more than most traders realize. The same win rate, the same average win, the same average loss, but with wins clustered early instead of late, produces a completely different equity curve. A different drawdown. A different psychological experience.

A backtest cannot show you any of that. It shows you one sequence. Monte Carlo shows you thousands.

What Monte Carlo Actually Does

Monte Carlo simulation takes your trading statistics and randomly generates thousands of possible trade sequences from them. Each one is a plausible future given your edge. Some sequences are lucky. Some are unlucky. Most fall somewhere in the middle.

After running all of them, you get a probability distribution of outcomes rather than a single number. Instead of “my strategy returned 40% over 200 trades,” you get something far more useful: in 90% of simulations the account was profitable, in the worst 10% the drawdown exceeded 25%, and in 2% of cases the account hit ruin.

That is the kind of information you can actually make decisions with.

What It Shows You

Running a Monte Carlo simulation gives you a realistic picture of several things a backtest cannot:

The range of equity outcomes. Not just the median result, but the full spread from the unlucky bottom 10% to the lucky top 10%. If even the pessimistic scenarios look acceptable, you have a robust strategy. If the bad scenarios are catastrophic, you have a problem worth knowing about before you trade it live.

Your realistic drawdown. The maximum drawdown in your backtest is almost certainly an underestimate. It only reflects what happened in that one sequence. Monte Carlo shows you the distribution of drawdowns across thousands of sequences, including ones where losing trades cluster together in the worst possible order.

Ruin probability. The percentage of simulations where the account loses so much it cannot recover. Even strategies with positive expectancy carry some ruin risk if sized too aggressively. Monte Carlo makes that risk visible.

Streak analysis. How long losing streaks can realistically get. Most traders underestimate this badly from a single backtest. Seeing that a 7-loss streak happens in 30% of simulations changes how you think about position sizing and mental preparation.

A Simple Example

Say you have a strategy with a 50% win rate, an average win of 1.5R, and an average loss of 1R. Solid positive expectancy. A single backtest over 200 trades might show a smooth upward curve and a 12% max drawdown.

Run 25,000 simulations of that same strategy and the picture gets more honest. The median outcome is still good. But some paths show a 25% drawdown before recovering. A small percentage of paths hit ruin at aggressive sizing. A handful of paths barely break even over the full 200 trades despite the edge being real.

None of that makes the strategy bad. It just means you now understand what you are actually signing up for, including the scenarios your backtest was lucky enough to avoid.

How to Use It in Edge Engine

Edge Engine runs up to 100,000 Monte Carlo simulations of your strategy directly in your browser. Enter your win rate, average win, and average loss, and you get equity curve distributions, drawdown analysis, streak breakdowns, and ruin probability instantly.

If you trade at a prop firm, you can go further. Model the specific rules of your evaluation, including profit targets, trailing drawdowns, and time limits, and see your actual pass rate across thousands of simulated attempts. Then switch to Funded mode to simulate what your account does after you pass.

It is the difference between hoping your strategy works and knowing the realistic range of outcomes before you risk a dollar.

Launch the simulator and run your first Monte Carlo simulation. The main simulator will always be free, no signup required.

Next Steps