Two-phase prop firm challenge simulator
Monte Carlo simulator for two-phase prop firm challenges. Composes Phase 1 + Phase 2 sequentially with phase-specific rules (target, daily DD, total DD, time limit). Tracks per-phase pass rates, conditional Phase 2 rate given Phase 1 cleared, combined pass rate, expected days to fully pass, and expected value per attempt factoring challenge fee and profit split. Designed for FTMO-style 2-step structures and any other firm running sequential phase rules.
Simulator
Pick a firm preset or set your own rules below. Output recalculates instantly. 5000 Monte Carlo iterations per run; output has roughly ±1 percentage point sampling uncertainty.
Account & Phase 1
Phase 2 & payout
Trader edge
Outputs (5000-run Monte Carlo)
What is a two-phase challenge?
The two-phase prop firm challenge is the dominant evaluation model in the retail prop firm industry. FTMO pioneered it; most major firms (E8, MyForexFunds before its 2023 collapse, FunderPro Pro tier, FundedNext 2-step, several others) run derivatives of the same structure.
Structure:
- Phase 1 (the Challenge). Larger profit target (typically 8 to 10 percent of account size) within a shorter time limit (typically 30 days). The trader must hit the target without breaching daily drawdown (typically 5 percent of starting balance, sometimes from day-start, sometimes from peak) or total drawdown (typically 8 to 12 percent from starting balance).
- Phase 2 (the Verification). Smaller profit target (typically 4 to 5 percent) over a longer window (typically 60 days). Same drawdown rules. The intent is to confirm Phase 1 was repeatable.
- Funded account. Following both passes, the trader receives a (typically simulated) live account with a profit split (typically 70 to 90 percent to the trader).
The structure is designed around the firm's economics: Phase 1 fees fund the operation (most attempts fail), Phase 2 fees fund the marketing budget, funded payouts come from the minority of traders who repeatedly extract.
How the simulator works
Each Monte Carlo iteration runs the following sequence:
- Start Phase 1. Account equity initialised at the configured account size.
- Trade-by-trade simulation. For each trade: draw a Bernoulli outcome at the configured win rate; on a win, add (risk per trade × R:R) to equity; on a loss, subtract risk per trade.
- Rule checks every trade. Total drawdown checked against running peak; daily drawdown checked against day-start equity. If breached, Phase 1 fails on this iteration.
- Profit target check. If equity reaches starting equity plus target, Phase 1 passes and we proceed to Phase 2.
- Time limit check. If Phase 1 reaches its time limit without passing or breaching, it fails (no funded account without a Phase 1 pass).
- Phase 2 (only runs if Phase 1 passes). Same trade-by-trade simulation under Phase 2 rules. Account resets to the original starting balance (most firms reset between phases).
- Post-pass payout. If both phases pass, simulate a 20-day funded trading window with the same edge. Apply the profit split to the realised profit.
- Aggregate over 5000 iterations. Compute per-phase pass rates, conditional pass rate, average days per phase, average post-pass payout, and EV per attempt.
Interpreting the output
Five output blocks:
- Phase 1 pass rate. What fraction of attempts clear Phase 1 alone. Industry benchmarks: 10 to 20 percent for a typical retail trader.
- Phase 2 (conditional). Given Phase 1 cleared, what fraction also clears Phase 2. Industry benchmarks: 40 to 60 percent (higher than Phase 1 because the target is lower).
- Combined pass rate. Product of the two. Industry benchmarks: 5 to 12 percent (most academic and broker-disclosed estimates cluster here).
- Expected days. Sum of average Phase 1 days plus probability-weighted Phase 2 days. The expected time to complete both phases.
- EV per attempt. Combined pass rate times expected payout, minus challenge fee. Positive means the challenge is statistically favourable; negative means the fee dominates.
The interpretation paragraph below the output translates these numbers into plain-English guidance: negative-edge inputs are flagged as unwinnable, marginal positive-EV results are flagged as variance-heavy, and clear positive-EV results are flagged as favourable.
Methodology
- Trade outcome model. Bernoulli at the configured win rate. Win = +R × risk; loss = -risk. Win rate and R are assumed stable across the challenge window.
- Daily drawdown check. Computed from day-start equity (FTMO convention). Some firms use peak equity; the calculator uses the more common day-start basis.
- Total drawdown check. Computed from starting equity (Phase 1) or resets to Phase 2 starting equity on entry to Phase 2. Most firms reset for Phase 2; trailing-from-peak total DD is rare.
- Time limit. Days are 'trading days' - the simulator treats each day as identical in terms of trade count. Weekend gaps and news days are not separately modelled.
- Phase 2 entry assumption. Account resets to the original starting balance. Phase 2 simulation is independent of Phase 1 equity ending. This matches FTMO and most major firm conventions.
- Post-pass payout window. 20 trading days at the trader's edge. Negative-profit windows are floored to zero (the trader does not 'pay' the firm on a losing funded month; the firm absorbs the loss against the trader's profit share for that period).
- Iterations. 5000 independent runs per simulation. Output uncertainty is approximately ±1 percentage point on pass rates.
Limitations
- Stable edge assumption. The model assumes the trader's win rate and R:R are constant across all trades. Real edge drifts with market conditions, time of day, position size scale-up, and trader psychology.
- News events not modelled. Spread blowouts around NFP, FOMC, and ECB releases can trigger drawdown breaches in real challenges. The simulator's stable-spread assumption underestimates Phase 1 failure rates from this source.
- Consistency rules ignored. Some firms (FTMO, Funding Pips) enforce 'no single trading day producing more than 30 percent of total profit' rules that fail otherwise-passing attempts. Not modelled.
- Soft breaches ignored. Real-world challenge failure also includes platform errors, requote rejection, news-event slippage, and 'gentleman's' breach reviews. The simulator models only rule-based breaches.
- Funded payout simplification. The 20-day post-pass window is a simplification of real funded-account economics. Real scaling plans, increasing account sizes, and per-month payout caps are not modelled.
- One trader's edge. The simulator models a single trader. It does not model the firm's aggregate economics or the survivorship-biased success stories that dominate firm marketing.
Related tools
- Prop Firm Challenge EV Projector (1-step firms) - Monte Carlo for single-phase challenges: FundedNext 1-step, FunderPro instant funding, Funding Pips 1-step.
- Risk of Ruin Calculator - probability of breaching a drawdown rule given edge and risk per trade. Pair with this simulator for the underlying survival math.
- Kelly Criterion Position Sizer - optimal bet size for log-equity growth. Half-Kelly is the institutional default for challenge accounts.
- Sharpe / Sortino / Calmar Calculator - risk-adjusted return assessment of your real trading record.
- Expectancy + Profit Factor Calculator - confirm your edge is positive before paying a challenge fee.
- How to Pass a Prop Firm Challenge - strategy and risk-management framework for the challenge phase.
- Prop Firm Tax Australia - ATO treatment of challenge fees, profit splits, and trader classification.
Frequently asked questions
A two-phase prop firm challenge requires the trader to clear two separate evaluations sequentially before being funded. Phase 1 (the 'challenge') typically requires a larger profit target (8 to 10 percent) within a shorter time limit (30 days). Phase 2 (the 'verification') requires a smaller profit target (4 to 5 percent) over a longer window (60 days), and is essentially a confirmation that the Phase 1 pass was repeatable rather than a fluke. Both phases enforce daily and total drawdown limits. FTMO is the canonical 2-step model; many other firms run derivatives of the same structure.
Phase 1 and Phase 2 have materially different rule profiles. Phase 1 has a higher target but shorter time limit, forcing larger position sizes or more trades per day. Phase 2 has a lower target with a longer window, allowing more conservative pacing. A trader who can clear Phase 1 with aggressive sizing may still fail Phase 2 if they cannot dial back the risk. The simulator runs each phase with its own rules so the combined pass probability captures the realistic conditional structure rather than treating the challenge as a single homogeneous event.
The percentage of Phase 1 passes that also clear Phase 2. If Phase 1 passes 15 percent of attempts and 60 percent of those pass Phase 2, the conditional rate is 60 percent and the combined rate is 9 percent (15 × 60 percent). The conditional rate is the more honest measure of how repeatable the trader's edge is - it controls for the fact that only Phase 1 passers ever attempt Phase 2.
R is the unit of risk: 1R = the dollar amount you risk per trade. Expectancy in R is calculated as (win rate × reward-to-risk ratio) minus (1 minus win rate). A 60 percent win rate at 1.5R reward equals 0.6 × 1.5 - 0.4 = +0.5R per trade. Anything below zero means the trader's strategy loses money in the long run; the simulator output for a negative-edge profile is dominated by variance and is not statistically meaningful. The simulator displays expectancy alongside the pass rates so the user can sanity-check their inputs.
Expected Value = (combined pass rate × expected payout on a pass) minus challenge fee. The expected payout uses a 20-day post-pass trading window simulating the first month on the funded account, takes the gross profit, multiplies by the profit split, and averages across the iterations that passed both phases. A positive EV means the challenge is statistically favourable - over many attempts, the expected per-attempt payout exceeds the fee. A negative EV means the challenge is statistically unfavourable; the fee dominates the expected payout.
The single-phase Prop Firm Challenge EV Projector models firms that run one combined evaluation (FundedNext 1-step, FunderPro instant funding, Funding Pips 1-step). This two-phase simulator models firms that run Phase 1 + Phase 2 sequentially (FTMO 2-step, E8, MyForexFunds-style structures). Use the single-phase projector for 1-step firms, this simulator for 2-step. Both share the same Monte Carlo engine and the same EV calculation principles.
Positive EV per attempt is necessary but not sufficient. Additional considerations: (1) bankroll - can you afford several failed attempts before a paying round? (2) tax treatment - profit payouts are assessable income in Australia, not capital gains; see the Prop Firm Tax Australia pillar. (3) edge stability - the simulator assumes your input win rate and R:R are stable. If the inputs are best-case estimates, the realised EV will be lower. A conservative starting point is to use 5 percent below your honest win rate estimate. (4) variance - even positive-EV challenges fail more often than they succeed; the EV is realised across many attempts, not one.
Each Monte Carlo run uses 5000 independent random simulations. The output is a statistical estimate, not a deterministic calculation. Small input changes can produce outputs that differ by a percentage point or two due to sampling noise. The trend across input ranges is the meaningful signal; the exact value at any single input set has roughly +/- 1 percentage point uncertainty. For high-confidence comparisons between firms, focus on the broad ranking rather than the exact pass rate.
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