Enter your trading stats to calculate the probability of passing a prop firm challenge. Uses Monte Carlo simulation with 2,000 runs based on your win rate, risk:reward, and trade count.
enter win rate and risk:reward ratio
Win rate (%)
Risk:Reward ratio
Risk per trade (%)
Number of trades
Profit target (%)
Max drawdown (%)
Result (click to copy)
Using Your Probability
A directional estimate, not an exact prediction
This is a simulation based on your stated stats, not a guarantee — real trading has streaks, variance, and conditions this model doesn't capture. Use it to compare strategies relative to each other, not as a precise forecast.
Your inputs need to be honest, not hopeful
Use your actual historical win rate and average R:R from a trading journal, not your best week or your goal — an overly optimistic input makes the whole output meaningless.
A negative edge means fix the strategy first
If your expectancy is negative, no amount of position sizing or risk management will make the challenge profitable on average — the math has to work before the money management does.
How to Use the Pass Probability Calculator
Enter your win rate — use your actual historical win rate from at least 50 trades. Be honest — optimistic win rates produce misleadingly high pass probabilities.
Enter risk:reward ratio — your average R:R across winning trades. If you typically target 20 pips profit with a 10 pip stop, enter 2.0.
Set risk per trade — the percentage of account risked per trade. Lower risk = lower probability of hitting the drawdown limit but also slower progress toward the profit target.
Enter number of trades — how many trades you plan to take during the challenge. 20–50 is typical for swing traders, 50–200 for day traders.
Set firm's rules — enter the profit target % and max drawdown % from your specific firm. The calculator runs 2,000 simulations and shows what percentage pass both conditions.
Read the improvement table — the calculator shows pass rates at slightly higher win rates and R:R ratios, helping you identify the most impactful improvement to make.
🎯 Target pass rate: Aim for at least 60% pass probability before attempting a paid challenge. Below 50% means the fee is likely a losing investment on a per-attempt basis. Improve your edge first, then pay the fee.
Understanding Monte Carlo Pass Probability
The pass probability is calculated using Monte Carlo simulation — 2,000 independent random sequences of wins and losses using your exact statistics. Each simulation checks whether the account hit the profit target before breaching the max drawdown limit.
🎲 What 70% Means
70% pass rate means 7 out of 10 challenge attempts with your strategy and risk settings should pass. The other 3 will hit the drawdown limit through bad luck variance, even though your edge is real.
📉 Drawdown Kills
Most failed simulations end from hitting the max drawdown, not from failing to reach the profit target. Reducing risk per trade dramatically increases pass rate by giving your edge more room to play out.
✅ Positive Expectancy First
If the calculator shows negative expectancy (Kelly = negative), pass probability will be below 50% regardless of other factors. Fix your edge before worrying about challenge fees.
🔄 2,000 Simulations
2,000 runs gives statistically stable results — the pass rate percentage changes by only 1–3% between runs. For more precise estimates, switch to 500 curves in the Monte Carlo Simulator.
An important nuance: pass probability assumes your live win rate exactly matches your historical win rate. In practice, live trading often produces slightly lower win rates due to execution, spread, and psychological factors. Consider subtracting 5% from your historical win rate when calculating challenge probability for a more conservative estimate.
Improving Your Challenge Pass Rate
The most powerful lever: reduce risk per trade
Cutting risk from 1% to 0.5% per trade typically increases pass probability by 15–25 percentage points. This is because the max drawdown limit (10%) creates a hard ceiling — at 1% risk you breach it after 10 consecutive losses, at 0.5% you need 20 consecutive losses. Losing streaks of 10 are common; losing streaks of 20 are rare.
Increase R:R before increasing win rate
Improving your reward ratio from 1:1.5 to 1:2 is usually easier than improving win rate from 55% to 60%. R:R can often be improved simply by letting winners run further — adding a trailing stop rather than a fixed take profit. Win rate improvement requires finding genuinely better trade setups.
Optimal trade count
More trades allows your edge to express itself more consistently — variance decreases with sample size. However, more trades also means more opportunities to breach the daily drawdown limit. For most strategies, 40–80 trades over a 30-day challenge is the sweet spot — enough to demonstrate the edge, not so many that daily DD becomes unmanageable.
📊 Before every challenge: Run this calculator with your exact strategy statistics. If pass probability is below 55%, do not pay the fee — practice on a demo account for another month and re-run the calculation. The fee money is better used on a challenge you are likely to pass.
How the Simulation Works
Expectancy (R) = (Win rate × Reward) − (Loss rate × 1). Monte Carlo: 2,000 simulated trade sequences, each trade winning at your stated win rate for +Risk×R:R, or losing −Risk; a run passes if it reaches your target before breaching either a trailing (from peak) or static (from initial) drawdown limit, whichever comes first.
Worked example
55% win rate, 1:1.5 R:R: expectancy = (0.55 × 1.5) − (0.45 × 1) = 0.825 − 0.45 = +0.375R per trade — a positive edge. At 1% risk per trade over 30 trades, expected profit ≈ 0.375 × 1 × 30 = 11.25% before accounting for the chance of an early drawdown breach, which is what the Monte Carlo simulation estimates.
This model simplifies real trading in two ways: it risks a fixed % of your starting balance on every trade (not compounded on your current, growing/shrinking balance), and it checks a combined trailing-and-static drawdown condition rather than matching one specific firm's exact rule. Treat the result as directional, not exact.
Avoid These Mistakes
Using your best month's stats, not your real average
A win rate pulled from your best streak overstates your actual edge — use a full trading journal average, ideally across 50+ trades, not a cherry-picked sample.
Ignoring a negative expectancy and hoping variance saves you
If expectancy is negative, a "high enough" pass probability from lucky variance doesn't mean the strategy works — it means you got lucky once. Fix the edge before paying for another attempt.
Treating the percentage as exact
This is a simulation based on simplifying assumptions (see the worked example above), not a certified probability — a 65% estimate and a 70% estimate aren't meaningfully different in practice.
Frequently Asked Questions
What is Monte Carlo simulation?
Monte Carlo simulation runs thousands of random trade sequences using your average statistics to estimate the probability of a given outcome. With 2,000 simulations, each using your win rate and R:R randomly, it shows the percentage of scenarios where you passed the challenge without breaching drawdown.
What win rate and R:R do I need to pass?
A positive expectancy is required: (Win Rate × RR) - Loss Rate > 0. Example: 50% WR with 1:1.5 RR gives expectancy of 0.25R per trade — positive. Common passing combos: 55% WR + 1:1.5 RR, 45% WR + 1:2 RR, 60% WR + 1:1.2 RR.
Why might I fail even with a positive edge?
Even with a positive edge, variance can create losing streaks that breach the drawdown limit. With 1% risk per trade and a 10% max drawdown, you can only sustain 10 consecutive losses before failing. Reducing risk per trade gives your edge more room to play out.
References
📊 Your trading journal
Win rate and R:R inputs are only useful if they reflect your real, logged trading history — a spreadsheet or journal covering at least 50+ trades gives a far more reliable input than memory or a single good week.
📄 Your firm's official rules page
Drawdown methodology (static vs. trailing), profit target, and time limits are set by each firm — confirm current rules directly rather than assuming this simulation's assumptions match your specific plan exactly.