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Monte Carlo Equity Curve Simulator

Stress-test a strategy assumption by simulating thousands of possible equity paths from the same win rate, payoff ratio and fixed fractional risk.

Direct answer

This Monte Carlo simulator estimates possible terminal equity, profit probability, ruin probability and maximum drawdown from seeded random trade paths. The seed is deterministic, so the same inputs reproduce the same numbers and chart. Treat the output as a model estimate, not a prediction of future trading results.

Strategy inputs

More simulations smooth the estimate, but take longer in the browser.
Deterministic seed: same inputs, same chart.
Each trade risks a fixed equity fraction. Wins add risk × R; losses subtract risk.

Monte Carlo estimate

Median final equity—
5 / 25 / 75 / 95 percentiles—
Probability final equity is profitable—
Probability path hits ruin threshold—
Median / worst max drawdown—
Worst / best final equity—
This is a seeded model estimate, not a prediction.

Equity paths and terminal band

Thin lines are the first deterministic sample paths. The lower chart marks worst, 5th, 25th, median, 75th, 95th and best final equity.
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Risk warning. CFDs are complex instruments and carry a high risk of losing money rapidly due to leverage. A significant proportion of retail investor accounts lose money when trading CFDs; where a broker publishes an official percentage, we show it only with the source and capture date. Consider whether you understand how CFDs work and can afford the risk. Full risk disclosure.

Educational tools for non-US traders · not directed at US persons.

How it works

What this simulator does

Monte Carlo turns a compact strategy assumption into many possible trade sequences. You enter starting equity, win rate, payoff ratio, risk per trade, trade count, simulation count, ruin threshold and seed. PipGauge then samples win/loss paths and summarizes the spread of outcomes.

The path model

Each simulated path starts at your starting equity. Every trade consumes one seeded random draw. If the draw is below the win rate, equity increases by risk fraction × equity × R. Otherwise equity decreases by risk fraction × equity. Because risk is a fraction of current equity, the paths compound.

The ruin flag is set when a path falls to or below starting equity × ruin threshold. Maximum drawdown is tracked from each path's running equity peak.

What the outputs mean

  • Percentiles show the distribution of final equity after all simulated trades.
  • Profit probability is the share of paths ending above starting equity.
  • Ruin probability is the share of paths that touched your ruin threshold at any point.
  • Median and worst maximum drawdown describe the path pain, not just the final balance.

How the chart is built

The upper canvas draws the first deterministic sample paths so you can see sequence risk rather than only a table of outputs. The lower canvas marks worst final equity, 5th percentile, 25th percentile, median, 75th percentile, 95th percentile and best final equity. No external chart library is used.

Why the seed matters

The model uses a deterministic seed. That makes the tool reproducible: if you share a URL or rerun the same inputs later, the sampled paths and aggregate results stay the same. Changing the seed gives a different sampled universe under the same assumptions.

Common mistakes

  • Reading the result as a forecast. Monte Carlo is only as good as the inputs. It does not know future win rate, slippage, spread changes or execution quality.
  • Ignoring compounding drag. With percentage risk, a loss and a same-size percentage win do not perfectly cancel. That is why a fair 50% win rate at 1R can still show fewer than half the paths ending profitable.
  • Looking only at the median. The lower tail and drawdown numbers often matter more than the center of the distribution.

Frequently asked questions

What is a Monte Carlo equity curve simulator?
It is a model that generates many possible trade sequences from your win rate, payoff ratio and risk assumptions, then summarizes the range of final equity and drawdown outcomes.
Why is the seed fixed?
A fixed seed makes the output reproducible. Same inputs and same seed produce the same sample paths, percentiles and chart, which makes sharing and review easier.
Is the profit probability a prediction?
No. It is the share of modeled paths that ended above starting equity under the assumptions you entered. Real results can differ because inputs, costs and execution change.
Why can a 50% win rate at 1R show less than 50% profitable paths?
The model risks a percentage of current equity. A 1% loss followed by a 1% gain does not fully recover the account, so volatility drag can push the profitable share below 50%.
How many simulations should I use?
The default is enough for a stable browser-side estimate in most checks. More simulations can smooth the percentile estimates, but it also makes each recalculation slower.

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