Monte Carlo Simulator
Simulate many random trade sequences to see the range of terminal returns and worst drawdowns a system can produce.
Quick answer: A Monte Carlo simulation replays a system many times with the same statistics but a different random order of wins and losses. This tool applies your average win and loss multiplicatively over a chosen number of trades, across up to two thousand runs, and reports the median and percentile terminal returns plus the worst drawdown seen. It shows that a single backtest equity curve is just one path among many the same edge could have produced.
How to use it
Enter the win rate, the average percentage gained on a win and lost on a loss, the number of trades in a run, and how many runs to simulate (capped at 2000). Each trade multiplies equity up or down; the tool ranks the ending results and reports the median, 5th, 25th, 75th and 95th percentile terminal returns and the worst intra-run drawdown. The chart draws a sample of the simulated equity paths.
Formula
Each trade: Equity ×= ( 1 + Average win% ) on a win, or ×= ( 1 − Average loss% ) on a loss. Repeated over Trades, across Simulations runs.
Terminal return of a run = final equity ÷ starting equity − 1. Percentiles are read from the sorted terminal returns of all runs.
Limitations of the Monte Carlo Simulator
The Monte Carlo Simulator is a teaching aid, not a live risk system. It does not model the following:
- The assumption that trades are independent and identically distributed — real trades cluster and autocorrelate
- Regime change; resampling the past cannot generate conditions the sample never contained
- Fat tails and the true worst case, which a finite resample may never draw
- Trading costs, slippage and capacity, unless already embedded in the trade series
Frequently asked questions
Why is one backtest equity curve not enough?
Because that curve is one realisation of a partly random process; the exact order of wins and losses was luck. Monte Carlo shows how deep your drawdown could plausibly have been under a different ordering or resample, which is usually worse than the single historical figure.
What is the difference between reshuffling and bootstrapping a backtest?
Reshuffling keeps the same set of trades but changes their order, isolating path risk. Bootstrapping draws trades with replacement, so some repeat and others drop out, which also captures sampling risk and generally produces a wider, more conservative outcome distribution.
Why can reshuffling mislead for a trend-following backtest?
Trend-following profits depend on serial correlation — riding sustained moves. Reshuffling the trade order destroys that autocorrelation, so it can badly misstate returns and risk. Block bootstrapping, which resamples contiguous chunks, preserves more of the structure and is the usual fix.
Does Monte Carlo prove a backtested strategy works?
No. It only characterises the variability of whatever the backtest already produced. It cannot detect overfitting or look-ahead bias in the rules, and it cannot create tail events the sample never contained, so it is a lens on uncertainty, not proof of an edge.
Does Monte Carlo capture crash risk from a backtest?
Only if a crash is already in the sample. Resampling cannot invent a shock larger than anything the data contained, so a Monte Carlo built on a calm backtest window will systematically understate true tail risk. Scenario and stress testing exist precisely to cover that gap.
Runs entirely in your browser — no data leaves your device. Illustrative and educational only; real-world charges and market conditions apply in practice.