Prop Firm Challenge Simulator

Enter a challenge's rules and your own trading statistics, and this simulator plays 10,000 challenges in your browser to estimate how often you would pass, what would end the attempt when you do not, and how many trading days a pass usually takes. Below it, the statistics that decide those odds, with worked numbers.

Challenges to try this on

Real 100K plans from firms we have an affiliate agreement with, with numbers from our firm data. Load rules copies the phase 1 target, max loss, drawdown type and daily limit into the simulator and keeps your trading inputs. Minimum trading days, news rules and the finer points of each drawdown are not modelled, so check the firm page before you buy.

Blue Guardian: our firm data describes the max loss as “6% trailing, locks at the starting balance once 6% up (+1% buffer)”. The simulator’s lock option stops the floor exactly at the starting balance and does not model the buffer, so check the firm page for the exact lock level.

The statistical play: what actually moves your pass odds

A challenge is a race between two lines: the profit target above you and the max-loss floor below, with the daily loss limit, a clock and sometimes a consistency rule as extra ways to lose. Your statistics per trade decide which line you are likely to reach first. The illustrative scenarios quoted here use a $100K account, an 8% target, a 10% static max loss, a 5% daily limit, 3 trades a day and no time limit unless stated, and were produced by this page's own simulator code with seed 2026.

1. Expectancy per trade decides the direction

Expectancy is the average result of one trade, measured in R (multiples of the amount risked):

At a 45% win rate and 1.5:1, that is 0.45 × 1.5 − 0.55 = +0.125R per trade. Risking 1% of $100K ($1,000), the average trade adds $125, so at that average pace an 8% target ($8,000) takes about 64 trades. Runs that pass usually get there sooner, because a lucky streak helps them, and the slow runs are the ones more likely to hit the max loss first; that is why the simulator's median above is shorter. Spread, commission and slippage are paid on every trade and come straight out of expectancy, so a strategy that looks slightly positive before costs can be negative after them. This is the break-even win rate for common reward:risk ratios (1 ÷ (1 + reward:risk)):

2. Risk per trade relative to drawdown dominates

Divide the max loss by the risk per trade and you get your room in losing trades: a 10% limit at 1% risk is 10R, at 2% it is 5R. With a 55% chance of losing each trade, five losses in a row happen with probability 0.55⁵ = 5.0% for any given run of five trades, ten in a row 0.25%. With a positive edge after costs, halving risk raises the pass rate. With zero or negative edge it does not.

What that room is worth depends on the sign of your edge. With a positive expectancy, a smaller risk passes more often because the edge gets more trades to show, but it takes longer. With a negative expectancy the order flips: fewer, larger trades give the losing average less time to work. With zero edge the risk size mostly changes how fast you finish, not how often you pass, until the daily limit starts to bite.

Illustrative only: 10,000 simulated runs per row, fixed-size wins and losses, no trading costs. ± about 1 percentage point of sampling error.

Read the table from the middle. The positive-edge strategy passes 96.6% of runs at 0.5% risk and 78.8% at 1.5%, while the median time to pass drops from 31 to 6 trading days. The negative-edge strategy passes 30.5% at 0.5% risk and 45.6% at 1.5%: a larger bet on a losing average, not a better strategy. At 2% risk both the zero-edge and the positive-edge rows fail mostly on the daily limit, which is the next point.

3. The target-to-drawdown ratio sets the baseline

For a zero-edge strategy (50% win rate, 1:1) with fixed risk and no daily limit, the chance of reaching the target before the max loss is a standard gambler's-ruin result:

So an 8% target with a 10% static max loss starts at 55.6% for a coin flip before costs (the simulator returns 56.0% at 1% risk), while a 10% target with a 6% max loss starts at 37.5%. Any edge is added on top of this baseline, which is why comparing plans on target versus max loss often matters more than a few dollars of price.

4. Reward:risk and win rate are one number, not two

A higher reward:risk lowers the win rate you need, but in practice wider targets are hit less often, so the two only matter through expectancy and how noisy it is. The spread of one trade's result is (1 + reward:risk) × √(win rate × (1 − win rate)) in R. At 45% and 1.5:1 that is 1.24R around an average of +0.125R; at 60% and 0.8:1 it is 0.88R around +0.08R. The second strategy has a lower average but a steadier path, which can matter on an account with a tight daily limit. Put both into the simulator with your own risk and daily limit to see which one your rules favour.

5. Trailing versus static drawdown

A static max loss is a fixed floor below the starting balance. A trailing max loss moves up behind your highest balance, so a run of profits followed by a normal pullback can breach an account that a static rule would have left alone. End-of-day trailing only moves the floor at the close, which is friendlier to trades that run into profit intraday and give some of it back before the close.

Moving from a static to a trailing 10% limit takes the same strategy from 85.7% to 78.1% (end-of-day) and 75.8% (every closed trade). The simulator tracks closed-trade balance, so a firm that trails open equity intraday is stricter than the trailing option here.

6. The daily loss limit is a hidden constraint

The daily limit caps how many losses you can take in one day. At 1% risk and a 3% daily limit, three full losses in a row reach the limit, and spreads or commission take them through it; with a 55% chance of losing each trade, that happens on 16.6% of 3-trade days. In the table above, tightening the daily limit from 5% to 3% drops the pass rate from 85.7% to 29.4%. A practical check is trades per day × risk per trade: if a full losing day reaches the daily limit, a normal bad day can end the challenge.

7. Time limits and consistency caps

Small risk plus a positive edge needs many trades. With a 30-trading-day limit and 0.5% risk, 52.3% of runs in the table ran out of days before reaching the target, even though the same strategy passes 96.6% with unlimited time. A consistency cap, such as best day at most 30% of total profit, rarely causes a breach by itself but keeps you trading after the target: the median time to pass rose from 11 to 22 days, and the extra days add extra chances to hit the max loss. See strategy when the consistency cap is low.

What to take from this

• Measure your win rate and reward:risk on a large sample of your own trades after costs, not your best month.
• If your honest expectancy is not clearly positive after costs, no risk setting fixes that. Larger risk can raise the odds of one lucky pass, but the losing average is still there on the funded account.
• Size so that a full losing day stays inside the daily limit with room to spare.
• Compare plans on target versus max loss and on drawdown type before comparing price.
• Two-step challenges need both phases: at the example settings the chance of passing both is about 85.7% × 87.9% = 75.3%.

How the simulator works

• Each trade wins with probability equal to your win rate. A win adds risk × reward:risk and a loss subtracts the risk. There are no partial exits or break-even trades.
• Risk is a fixed percentage of the starting balance (no compounding). Trades are independent.
• After each trade the simulator checks the max loss, then the daily limit (from the day's starting balance, as a % of the starting account size), then the target. Reaching a limit exactly counts as a breach. If both limits break on the same trade, it is counted as a max-loss breach.
• Trailing modes move the floor to the highest closed-trade balance (or end-of-day balance) minus the max loss. With the lock option the floor never rises above the starting balance.
• The consistency cap counts a pass only when the best day's profit is at most the cap × total profit; otherwise trading continues.
• Minimum trading days, weekend and news rules, and trading costs are not modelled. Lower the win rate or reward:risk to include costs.
• Random numbers come from a seeded generator (mulberry32), so the same inputs and seed always give the same result. Very slow settings stop early after 60 million simulated trades and say so.

Frequently asked questions