Prop firm challenge EV projector
Most challenge comparison sites tell you which prop firm is cheapest. This tool tells you whether paying for the challenge has positive expected value at your edge. Monte Carlo simulation across 5,000 iterations per run, modelling FTMO, FundedNext, The 5%ers, FunderPro, Funding Pips, or any custom rule set. Outputs pass probability, expected days to pass, and EV per attempt in AUD after challenge fee and profit split.
Calculator
All values stay in your browser. Output recalculates with each input change (Monte Carlo runs in ~0.5 seconds for 5,000 iterations). Copy the URL to share or bookmark a configuration.
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How the simulation works
The calculator runs 5,000 independent simulations of one challenge attempt. Each simulation models trades as a Bernoulli process: with probability equal to your win rate, the trade wins and adds risk-per-trade multiplied by reward-to-risk to equity; otherwise the trade loses and subtracts risk-per-trade from equity. Trades are processed sequentially, with the model checking three rules after each trade:
- Total drawdown breach. If equity drops more than the total DD limit below the starting balance, the simulation ends as a fail.
- Daily drawdown breach. If equity drops more than the daily DD limit below the day-start equity, the simulation ends as a fail.
- Profit target reached. If equity gains more than the profit target above the starting balance, the simulation ends as a pass.
If none of those three conditions triggers within the time limit, the simulation ends as a fail (time-out). The aggregate pass count across 5,000 iterations divided by 5,000 produces the pass probability estimate. With 5,000 iterations the standard error is roughly plus or minus 1 percentage point at probabilities near 50 percent and tighter at the extremes.
After a passing simulation, the model runs a separate 20-day post-pass simulation under the same edge but without the profit target constraint. The total profit over 20 days, capped by drawdown breaches if they occur, becomes the simulated first-month gross payout. Multiplied by the profit split percentage, this is the after-split payout in AUD. The expected value per attempt is then computed as: pass-probability * expected-after-split-payout minus fail-probability * challenge-fee.
What each input controls
Firm preset populates the rule parameters (profit target, drawdown limits, time limit, fee, profit split) from the selected firm. Selecting "Generic / Custom" lets you enter any combination.
Account size, profit target, daily DD, total DD, time limit, fee, profit split are the firm's rules. Override any of them to model a specific firm variant the preset does not cover.
Win rate is your documented win percentage on your existing strategy, measured on at least 200 trades on the same instruments you plan to trade in the challenge. Without a documented track record, this number is a guess and the calculator output is correspondingly speculative.
Risk per trade is the percentage of the account balance you lose if a single trade hits stop loss. 0.5 percent is institutional convention; 1 percent is the standard retail upper bound; 2 percent is aggressive. Higher risk per trade increases pass probability when your edge is positive but disproportionately increases drawdown breach probability when variance hits.
Reward-to-risk ratio is the average gain on winning trades divided by the average loss on losing trades. A 1:2 R:R means winning trades make twice what losing trades lose. R:R above 1.5 with a 50 percent win rate produces a strong positive expectancy; below 1.0 with a 50 percent win rate is roughly break-even before costs.
Trades per day is the average number of independent trade decisions you take per active trading day. More trades per day produces faster equity curve velocity (more chances to hit the target, more chances to breach DD) but does not change the underlying edge.
Worked example
An Australian trader has the following documented track record over 250 trades on EUR/USD:
- Win rate: 52 percent
- Average winning trade: 1.6R
- Average losing trade: 1.0R
- Risk per trade: 0.5 percent
- Trade frequency: 4 trades per active day
They are considering an FTMO 100K challenge: 10 percent profit target, 5 percent daily DD, 10 percent total DD, 30-day time limit, AUD 810 fee, 80 percent profit split.
Plugging these into the calculator: simulated pass probability of roughly 35 percent, expected days to pass of about 18 days, average post-pass first-month profit of about AUD 6,500 at the same edge, which becomes AUD 5,200 after the 80 percent profit split.
EV per attempt = 0.35 x 5,200 - 0.65 x 810 = 1,820 - 526.5 = roughly positive AUD 1,290 per challenge attempt.
The math is positive because the documented edge (positive expectancy with R:R 1.6 and win rate 52 percent) compounds equity fast enough to clear the 10 percent target in most simulations, and the post-split payout when they do pass is large enough to absorb the fee cost on the failed attempts. Most retail traders do not have a 52 percent / 1.6R edge documented on 250 trades. The lesson is: build the edge first, then attempt the challenge.
Why EV matters more than pass rate
The dominant mistake in prop firm content is fixating on pass probability as the metric of interest. A 30 percent pass probability sounds workable until you do the multiplication: 30 percent of attempts produce a payout, 70 percent produce a fee loss, and if the average payout is smaller than the fee multiplied by 7/3, the math is negative no matter how good the pass rate looks.
The EV framework forces you to confront the full cycle. Three traders with the same 30 percent pass probability can have wildly different EV per attempt depending on:
- The challenge fee they paid. AUD 263 (The 5%ers Bootcamp) vs AUD 810 (FTMO 100K) is a 3x difference in the downside leg of the EV equation.
- The post-split payout. A trader with 1:2 R:R and 55 percent win rate compounds equity much faster post-pass than a trader with 1:1 R:R and 55 percent win rate, even though they pass at similar rates.
- The profit split. FundedNext at 95 percent is a 19 percent uplift over FTMO at 80 percent for the same simulated trader.
The calculator forces all three of these into the same equation so the decision is grounded in expected dollars per attempt, not in feelings about the pass rate.
Firm preset rule sources
Firm presets are based on each firm's published rule documentation as of May 2026:
| Firm preset | Target | Daily DD | Total DD | Fee (AUD) | Split |
|---|---|---|---|---|---|
| FTMO 100K (combined) | 10% | 5% | 10% | 810 | 80% |
| FundedNext 100K (1-step) | 8% | 5% | 8% | 824 | 95% |
| The 5%ers 25K Bootcamp | 6% | 4% | 6% | 263 | 80% |
| FunderPro 50K (1-step) | 8% | 5% | 10% | 449 | 85% |
| Funding Pips 100K (1-step) | 8% | 5% | 10% | 810 | 90% |
AUD fees are USD prices converted at approximately 0.66 AUD per USD. Each firm runs frequent promotions; effective fee paid is often 20 to 40 percent below list. Use the actual fee you would pay, not the list price, when running the calculator.
What the model does not capture
The simulation is an order-of-magnitude tool, not a precise predictor. Real outcomes diverge from the model in several ways:
- Edge is not constant. The model assumes win rate, R:R, and risk per trade stay fixed throughout the challenge. In reality, tilt, fatigue, and market regime shifts move all three. Most live edges degrade under pressure, which means the model is optimistic.
- Execution variance. Spread, slippage, swap, and news-event widening reduce the realised R per trade vs the planned R. The model treats every trade as cleanly executed at planned prices. Build a 10 to 15 percent variance buffer into the edge inputs to compensate.
- Multi-phase challenges. The FTMO preset collapses the standard two-step structure into a single harder target. A dedicated two-phase simulator is on the roadmap.
- Consistency rules. Some firms impose minimum-trading-days requirements, maximum-trade-size caps, or news-trading bans. The model does not encode these. Read the specific firm's rules before assuming the preset captures everything.
- Trailing drawdown. Some firms (e.g. The 5%ers in funded phase) apply trailing drawdown from peak equity rather than fixed from starting balance. The model uses fixed-from-start, which is more common in challenge phase.
Related tools
- Position size calculator - exact lot size from risk percentage, stop loss, and account balance. Useful for translating the "risk per trade %" input here into actual broker order sizes.
- Total cost of trading calculator - annual broker cost across 10 ASIC-regulated brokers, useful for the post-funding broker decision once you pass.
- Best prop trading firms in Australia 2026 - full review and ranking of the firms used as presets here, including the business model explanation.
Frequently asked questions
An expected-value calculator for prop firm challenges. It runs a Monte Carlo simulation of thousands of challenge attempts using your trading edge (win rate, risk per trade, reward-to-risk ratio, trade frequency) and the specific firm's rules (profit target, daily drawdown limit, total drawdown limit, time limit). The output is the probability of passing the challenge, the expected days to pass when you do pass, and the expected value per attempt in AUD after factoring in the challenge fee, profit split, and post-pass payout. EV per attempt is the single most important number because it tells you whether the entire activity is profitable in expectation.
Industry-wide challenge pass rates sit between 10 and 20 percent across major firms. Combined with challenge fees of AUD 250 to 1,000 per attempt, the math is brutal for traders with an unproven edge. A trader with a 50 percent win rate and a 1:1 reward-to-risk ratio at 0.5 percent risk per trade typically projects a pass probability below 10 percent on a standard 8 to 10 percent profit target challenge, putting the expected value of attempting deeply negative. The same trader at 55 percent win rate and 1:1.5 reward-to-risk often projects positive EV because the underlying edge compounds quickly enough to clear the target before the drawdown rules trip. The lesson: prop firm profitability is downstream of demonstrable edge, not the other way around.
The simulation models trade outcomes as a Bernoulli process (win or loss) with constant probability and fixed reward-to-risk per trade. Drawdown rules are checked tick by tick. With 5,000 iterations per run, the standard error on the pass probability is roughly plus or minus 1 percentage point. The model assumes constant edge throughout the challenge (no learning curve, no tilt-induced variance) and a fixed number of trades per day (no opportunity flexibility). Real prop firm outcomes are noisier than the model because of execution variance, news event slippage, and psychological factors. Treat the output as an order-of-magnitude estimate, not a guarantee. The model is most accurate when your win rate and reward-to-risk are estimated from a documented track record of at least 200 trades on the same instruments you intend to trade in the challenge.
Firm presets are based on each firm's published rule documentation as of May 2026. FTMO 100K: 10 percent profit target, 5 percent daily drawdown, 10 percent total drawdown, 30 day time limit, AUD 810 fee, 80 percent profit split. FundedNext 100K 1-step: 8 percent target, 5 percent daily, 8 percent total, no formal time limit, AUD 824 fee, 95 percent split. The 5%ers 25K Bootcamp: 6 percent combined target, 4 percent daily, 6 percent total, AUD 263 fee, 80 percent split. FunderPro 50K: 8 percent target, 5 percent daily, 10 percent total, AUD 449 fee, 85 percent split. Funding Pips 100K: 8 percent target, 5 percent daily, 10 percent total, AUD 810 fee, 90 percent split. Verify current rules and fees on the firm's website before paying, as challenge structures and pricing change. AUD fees are USD prices converted at approximately 0.66 AUD per USD.
EV per attempt has two components: the upside from passing (pass probability multiplied by post-split payout) and the downside from failing (fail probability multiplied by challenge fee). When the challenge fee is high relative to the expected first-month payout, even a 30 to 40 percent pass probability can produce negative EV. The fix is either to reduce the challenge fee weight (target a lower-cost firm like The 5%ers Bootcamp at AUD 263) or improve the trading edge so the expected first-month payout grows. Many traders correctly identify positive pass probability and incorrectly conclude the math works; the EV calculation forces you to confront the full cycle including the cost of the failed attempts.
The FTMO preset uses the combined version of the two-step challenge collapsed into a single harder target (10 percent profit target). For most edge estimates this is a reasonable approximation because passing both phases requires consistently beating the harder Phase 1. A separate two-step simulator is on the roadmap and will compose the two phases sequentially, applying the Phase 2 rules only after a Phase 1 pass. For now, the single-step approximation runs slightly conservative on pass probability and slightly optimistic on time-to-pass.
All monetary values in the calculator are AUD. Challenge fees that are originally priced in USD by the firm are converted at approximately 0.66 AUD per USD (the recent AUD/USD rate). Post-split payouts are also reported in AUD. If you pay challenge fees with a crypto stablecoin or USD bank transfer, the actual AUD cost may differ from the displayed value depending on conversion fees and exchange rate movement between fee payment and challenge completion.
After a simulated pass, the calculator continues to trade the funded account for 20 trading days (approximately one calendar month) using the same trader edge inputs but without the profit target constraint. The total profit over the 20 days is the simulated first-month gross payout, which is then multiplied by the firm's profit split percentage to get the after-split payout in AUD. Daily and total drawdown limits still apply during the post-pass phase, so a trader who breaches DD in the first month loses access to the funded account and the simulated payout caps at the profit accumulated before the breach. The post-pass simulation is conservative because it assumes the trader stops at 20 days; in reality a successful trader continues to trade and accumulates further payouts.
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