Free tool · Monte Carlo

Prop Firm Challenge Simulator — Know Your Pass Rate Before You Pay

A challenge fee buys you one draw from a probability distribution. This prop firm simulator runs thousands of Monte Carlo attempts of an FTMO-style two-step challenge — or a one-step or trailing-drawdown rule set — against your real trading statistics, and tells you the pass rate, the expected number of attempts and what getting funded is likely to cost in total.

Challenge rules

Preset (typical / approximate values — rules change, verify with the firm)

Steps

Phase 1 profit target (%)

Phase 2 profit target (%)

Max drawdown (%)

Drawdown type

Daily loss limit (%)

Your trading

Win rate (%) 55%

Average payoff R (avg win ÷ avg loss)

Risk per trade (%) 1%

Trades per day

Max trading days (0 = unlimited)

Costs & simulation

Challenge fee (€)

Reset / retry fee (€)

Simulations

Overall pass rate (funded per paid attempt)

—

Phase 1 × Phase 2, each attempt paid

Phase 1 pass rate

—

reach the target without a breach

Phase 2 pass rate

—

second step, same limits

Expected attempts until funded

—

1 ÷ overall pass rate

Expected total cost

—

fee + (attempts − 1) × reset fee

Why attempts fail (share of failed attempts)

Cause	Share
Daily loss limit hit	—
Max drawdown hit	—
Target not reached in time	—

Not affiliated with any prop firm. Presets are typical / approximate values — rules change, verify with the firm. Trailing drawdown uses an end-of-day high-water mark; unlimited-time runs are capped at 260 simulated trading days (≈ one year), unresolved runs count as "target not reached in time".

Feed it your real statistics, not guesses

The ReziFX extension captures planned trades from the TradingView position tool into a journal — win rate, average R and drawdown are computed automatically. The dashboard's Monte Carlo runs 100,000 simulations with scenario saving, so you can pressure-test a challenge before paying for it.

Try ReziFX free Risk of ruin calculator

Risk-per-trade sensitivity (1,000 sims per row)

Risk per trade	Overall pass rate	Expected total cost

More risk per trade does not simply mean more passing

The intuition "I'll just size up and hit the target faster" is half right and, in the simulations, often fully wrong. Raising risk per trade does not straightforwardly raise the pass rate — it shifts probability mass from one failure mode to another: fewer attempts die of "target not reached in time", and more die on the daily loss limit or the maximum drawdown. The sensitivity table above makes this visible for your own statistics: pass rates typically rise with risk up to a point, then roll over as drawdown breaches take over. Where that sweet spot sits depends mainly on your win rate and on how tight the daily limit is relative to your risk per trade.

This is also why an FTMO-style two-step challenge is harder than multiplying two target percentages suggests: every attempt faces the same loss limits in both phases, and a Phase 2 failure sends you back to a fresh, paid Phase 1. The expected-cost figure treats each paid attempt as one Phase 1 × Phase 2 chain — expected attempts = 1 ÷ (p1 × p2), expected total cost = fee + (attempts − 1) × reset fee. Small differences in per-phase pass rates compound into large differences in what funding actually costs.

The model's assumptions are deliberately simple and stated openly: constant win rate and payoff, independent trades, fixed trades per day, high-water mark updated at end of day for trailing rules. Real challenges add consistency rules, news restrictions and intraday trailing variants — so treat the numbers as structure, not prophecy, and verify the current rule set with the firm before paying.

Frequently asked

What percentage of traders pass prop firm challenges?

Firms rarely publish audited pass rates, and the few numbers that circulate publicly are low. Simulations like this one show why: a challenge combines a profit target, a daily loss limit and a maximum drawdown, so even a strategy with a positive expectancy fails a meaningful share of attempts purely through sequence risk. Run your own statistics through the simulator to see the mechanics rather than trusting any quoted figure.

What is the difference between static and trailing drawdown?

A static drawdown is measured from your starting balance: with a 10% static limit on a 100k account, your equity may never fall below 90k, no matter how high it went before. A trailing drawdown follows your high-water mark: with a 3% trailing limit, the fail line rises as your balance makes new highs, so profits you have already made can be lost to a breach. Trailing rules are markedly harder to trade under — this simulator updates the high-water mark at end of day, which is a common futures-style convention.

What risk per trade should I use for a challenge?

There is no universal answer, and this tool does not give investment advice. What the sensitivity table shows is the mechanical trade-off: at low risk you rarely breach the loss limits but may run out of time; at high risk you reach the target faster in the runs that survive, but far more runs die on the daily loss or maximum drawdown first. The risk level with the best overall pass rate depends on your win rate, payoff ratio and the firm's specific limits — which is exactly what the table lets you compare for your own numbers.

How accurate is this prop firm simulator?

It is as accurate as its assumptions: constant win rate and payoff ratio, independent trades, a fixed number of trades per day, and the rule set you configure. Real firms add details this model omits — news-trading restrictions, consistency rules, weekend holding bans, intraday trailing conventions — and real trading statistics drift over time. Treat the output as a structural estimate of how rule sets interact with your statistics, not as a prediction, and always verify the current rules with the firm.

Educational tool. Not financial advice — trading involves substantial risk of loss.