Monte Carlo Simulation [Free Tool]
Replay your trade history thousands of times in random order to reveal the range of outcomes — including the unlucky streaks your single path hid. See how much was skill and how much was luck.
Disclaimer: Trading financial instruments involves risk, and everything on this page is provided for educational purposes only. Nothing here is financial advice or a recommendation to buy or sell any instrument.
Enter valid values.
How is this calculated?
Simulates thousands of random trade sequences based on win rate and risk-reward.
Read the full user guide
Every parameter, formula, and worked example — a complete reference for this tool.
What is Monte Carlo simulation in trading?
Monte Carlo simulation stress-tests a strategy by replaying its own historical trades in thousands of randomized orders. Your real track record is just one path through the trades you happened to take; reshuffling them shows the full spread of paths that the same trade set could have produced — including the unlucky streaks where the losses cluster early. The output is not a better prediction of the future. It is an honest look at how much of your result was skill versus the luck of the draw.
Key concepts
Randomization
Reordering or resampling historical results to generate new outcome paths.
Drawdown distribution
The spread of possible drawdowns, not just the one historically observed.
Confidence band
The range within which most simulated outcomes fall.
A worked example with real numbers
- 1
Enter the trade history
Your strategy has a 45% win rate, an average win of $100, an average loss of $60, over 100 trades.
- 2
Run thousands of reshuffles
The simulator replays those trades in thousands of different orders, each producing its own equity path.
- 3
Read the distribution
The median path looks like your history, but some unlucky orders cluster the losses early and produce far deeper drawdowns.
- 4
Judge the robustness
If many simulated paths lose heavily, your historical result may have depended on a lucky order — the strategy is more fragile than it looked.
How to use it
Enter trade history
Win rate, average win/loss and number of trades.
Run the simulation
Generate thousands of randomized paths.
Read the distribution
Median, worst-case drawdown and confidence ranges.
The formula, step by step
Simulation
Sample N trades per run from input distribution; repeat many runs
Summary statistics describe the distribution of outcomes.
Mistakes traders make
✗ Treating the historical result as the only possible outcome
✓Your trade sequence is one random draw. Monte Carlo shows the family of paths the same trades could have produced.
✗ Thinking more simulations fix bad inputs
✓Thousands of runs refine the distribution, but they cannot fix an inaccurate win rate or payoff. Garbage in, garbage out.
✗ Reading Monte Carlo as a prediction of the future
✓It maps the past's sensitivity to order and luck. It says nothing about future market regime changes.
When this tool fits — and when it does not
Use it when
- ✓After a backtest, to see how dependent the result is on trade order.
- ✓When deciding whether a drawdown figure is robust or a fluke of sequence.
- ✓Before scaling up capital, to understand the range of plausible paths.
Skip it when
- ✗Using it before you have a meaningful trade sample.
- ✗Assuming the median path is 'the expected result' and ignoring the tail.
- ✗Treating it as a replacement for out-of-sample validation.
Practical tips that actually help
Focus on the tail, not the median
The worst 5% of simulated drawdowns is the number to size for. The median flatters every strategy.
Compare reshuffle vs resample
Reshuffling keeps your exact trades; resampling with replacement tests a different assumption. Run both to bracket the uncertainty.
Pair it with out-of-sample testing
Monte Carlo tells you about luck within your trades; out-of-sample tells you about luck in the data. They answer different questions.
Related & next steps
Frequently asked questions
Does Monte Carlo make backtests more reliable?▾
It widens the picture by showing how sensitive results are to trade order and luck, but it still uses the same historical inputs and assumptions.
How many simulation runs are enough?▾
More runs mean a more stable distribution, which is why the tool defaults to thousands. The key is stability: if the summary numbers stop changing much as you add runs, you have sampled enough.
What is the difference between reshuffling and resampling?▾
Reshuffling keeps your exact trade set but changes their order. Resampling draws trades with replacement, allowing repeats. Both map out 'what could have happened', just with different assumptions about the future.
What should I look at in the simulation output?▾
Focus on the dispersion, not the median — especially the worst-case drawdowns in the tail of the distribution. If a meaningful share of simulated paths loses heavily, the historical result may have been lucky.
Is Monte Carlo the same as the training ground's out-of-sample test?▾
No. Out-of-sample testing validates on unseen historical data; Monte Carlo reshuffles the trades you already have. The two answer different questions and are strongest used together.
Ready to validate your first strategy?
Put someone else's strategy — or your own idea — through the real data. Free to start, no card required.
No credit card required · 50 free credits on signup
Monte Carlo results are statistically generated scenarios, not predictions of future performance.
Disclaimer: The tools and content on this site are provided for educational purposes only and do not constitute investment advice. Trading and investing involve a substantial risk of loss, and past performance does not guarantee future results.