Monte Carlo Simulation for Traders: What It Shows and How to Read It

Last updated: November 2026 · 3 min read · Educational content, not financial advice

A single backtest or a single formula gives you one answer. But the real future is one of many possible sequences of wins and losses, and the order matters a great deal. A Monte Carlo simulation replays your strategy thousands of times with random sequences and shows you the whole range of what could happen.

How it works

1. You provide a win rate, a reward-to-risk ratio, a risk per trade, a starting balance and a number of trades.
2. For each simulated trade, the program draws a random number. If it falls below your win rate, the trade is a win; otherwise it is a loss.
3. The account balance is updated after every trade, so each run produces a full equity curve.
4. This is repeated many times (the calculator does 1,000 runs), and the results are summarised.

Why it adds something the formulas do not

The binomial table in the calculator tells you the chance of finishing with a given number of wins. It cannot tell you about paths: how deep the account fell on the way, or how long the worst losing streak was. Those depend on the order of trades, and simulation handles them naturally.

A worked example

Take a $10,000 account, a 55% win rate, a 1:2 ratio, compounding risk and 100 trades. The table below uses 200,000 simulated runs per row, so it is more precise than the 1,000 runs the calculator uses; your own results will move slightly each time you click.

Doubling the risk raises the typical outcome a lot, but it also doubles the drawdown: the bad-case drawdown goes from about 8.7% to about 16.8%. At 5% risk the same strategy has a median drawdown of about 23.7% and a bad-case drawdown of about 38.3%. The losing streaks are the same in all three cases, because streaks depend only on the win rate; what changes is the damage each one does.

How to read the numbers

• Percentiles describe a range, not a promise. The 5th percentile means that in 5% of runs the result was worse. The median is the middle run.
• Plan around the bad case, not the middle. If your rules or your nerves cannot survive the 95th-percentile drawdown, the risk per trade is too high.
• Look at the spread. A wide gap between bad and good cases shows how much of your result will be luck.

How many runs are enough?

The more runs, the less random noise in the summary. For a probability near 50%, 1,000 runs gives a margin of error of about 3 percentage points at 95% confidence. That is plenty for judging whether one setting is far riskier than another, but not for splitting hairs between two similar ones.

The limits of Monte Carlo

• It trusts your inputs. If your true win rate is lower than the one you typed, the output is too optimistic. This is why the sample-size guide matters.
• It assumes independent trades with a fixed ratio. Real markets have streaks and regime changes, and real trades have varying sizes.
• It ignores costs and slippage unless you build them into your inputs.

A more realistic variant draws randomly from your own list of past results (in R) instead of using a fixed win rate and ratio. That keeps the true shape of your wins and losses but needs a long, honest trade history.

Related guides

This guide is for education only. It is not financial advice or a trade recommendation, and trading involves substantial risk of loss. See the Terms of Service.