Monte-Carlo engine · win-rate × R:R vs. whatever drawdown rules you set below. Defaults match a typical FTMO-style challenge (10% target / 6% max loss / 3% daily loss) — edit them to model any firm's ruleset.
simulation output
Pass probability
—
hits target before any breach
Fail · max loss
—
equity ≤ max loss floor
Fail · daily loss
—
single day exceeds limit
Timeout
—
no breach, no target yet
sample equity paths (40 of N runs)
passmax lossdaily losstimeout
./verdict.sh
Awaiting first run
Expectancy / trade—
Avg. days to target (passes only)—
Full-Kelly optimal risk (reference only)—
Assumptions: trades are modeled as independent Bernoulli outcomes at the stated win rate — no streak clustering, no correlation between setups. Max loss and daily loss are static thresholds measured from initial balance / start-of-day equity — this matches most standard (non-trailing) prop-firm challenge rules; check your own firm's account type if it uses a trailing drawdown instead, since that would need a different model. Position size is held constant at the chosen risk-per-trade throughout (no compounding of lot size with equity, no martingale). Challenge rules (target / max loss / daily loss) are fully editable above — the defaults shown just reflect one common structure. This is a probabilistic estimate from simulated randomness, not a guarantee of live trading outcomes — real markets add slippage, spread, execution risk, and behavioral error that this model does not.