Imagine a strategy that wins 45% of the time, with winners twice the size of losers. On average, it makes money. But averages hide sequences. What if the losers come first — eight, nine, ten in a row? How deep could your account fall? Could you survive?
Risk of ruin is the probability that your account falls by a level you cannot tolerate — for example 50% — at some point. Monte Carlo simulation is the tool that estimates it.
What Monte Carlo simulation does
A Monte Carlo simulator takes your trading statistics and creates thousands of possible futures:
- For each simulated trade, it randomly decides win or loss based on your win rate.
- It applies your average win or loss (with some variation if you like).
- It repeats this for hundreds of trades, building an equity curve.
- It repeats the whole process thousands of times.
The result is not one prediction but a distribution: median outcome, best and worst cases, typical drawdowns, longest losing streaks and the chance of hitting your ruin level.
Try it
Our free Monte Carlo simulator runs 1,000–5,000 simulations in your browser. Enter:
- win rate,
- average win and loss (in R, multiples of risk),
- risk per trade,
- number of trades,
- the drawdown you consider "ruin".
The surprising power of position size
Run the same statistics (45% win rate, 2R average win, 1R average loss) at different risk levels and you typically see something like this:
- At 1% risk per trade, the chance of a 50% drawdown over a few hundred trades is very small.
- At 3% risk, it becomes noticeable.
- At 5% risk, a large share of paths suffer a devastating drawdown — even though the strategy has a genuine positive edge.
Same edge, same trades — only the size changed. This is why professionals obsess over risk per trade. Read position sizing explained.
Losing streaks are longer than you think
At a 45% win rate, a run of 8–10 consecutive losses is common over a few hundred trades. Traders who have not prepared for this often abandon a good strategy at exactly the wrong time. Knowing the expected streak in advance keeps you calm.
Expectancy first
Monte Carlo cannot save a strategy with negative expectancy. Expectancy per trade in R is:
win rate × average win − loss rate × average loss
With 45% winners at 2R and 55% losers at 1R: 0.9 − 0.55 = +0.35R. If expectancy is negative, more simulations just show more ways to lose. Read risk-reward ratio explained.
Where to get your inputs
- From a backtest: run your rules in the strategy builder and note win rate and average win/loss.
- From your journal: use real results from your trading journal after 50+ trades.
Real trading results are usually a little worse than backtests, so be conservative.
Limitations
- Monte Carlo assumes trades are independent and statistics stay stable. Real markets change between regimes.
- It does not model gaps or extreme events beyond your input distribution.
- Treat the results as a range of plausible outcomes, not precise forecasts.
Key takeaways
- Risk of ruin is the chance of a drawdown you cannot tolerate.
- Monte Carlo simulation shows the range of outcomes, not just the average.
- Position size changes risk of ruin dramatically, even with the same edge.
- Prepare for long losing streaks before they happen.
Frequently asked questions
What is a good risk of ruin?
Most professionals want it close to zero — under 1–2% for their chosen ruin level.
How many simulations should I run?
1,000 gives stable results; 5,000 is smoother. Results vary slightly each run because they are random.
Can Monte Carlo predict my profits?
No. It shows a range of possible outcomes based on your statistics, helping you choose size and set expectations.
Is the Monte Carlo simulator free?
Yes — our Monte Carlo simulator is free and runs in your browser.
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