Prop firm challenge simulator

Estimate the probability of reaching a profit target before a daily or total loss limit under entered rules and return assumptions. The simulation runs in your browser.

First passage with entered evaluation rules

The model adds independent Gaussian increments at the selected checks per trading day. A path passes after reaching the profit target and meeting the minimum trading-day count. Daily loss is measured against the previous close. Total loss uses an entered static or trailing floor. An unresolved path is censored at the chosen horizon.

For a continuous Brownian path with no daily loss rule, no minimum days and no horizon, the two-barrier pass probability is (1 − exp(2μb/σ²)) / (exp(−2μa/σ²) − exp(2μb/σ²)). At zero drift, it is b / (a + b), where a is the profit target and b is the total loss boundary. Discrete checks differ from this continuous-path reference because crossings between checks are not observed.

The selected check frequency controls observed intraday crossings. A trailing floor can follow daily closes or each check, and can stop rising at starting capital. Entered evaluation and reset fees produce an expected cost under independent attempts. The app guide covers consistency, inactivity and phase rules on timestamped strategy P&L. News-window and concurrent-position limits sit outside this model, since independent increments carry no event times and no open positions. Read the pass probability guide, app rule guide, PBO calculator and DSR calculator.

Run the first-passage simulation

Synthetic deterministic example. Percentages use initial capital as the denominator. Daily loss is checked before total loss and profit at each selected check.

Assumptions

Each trading day has the selected number of Gaussian checks. Daily loss uses the previous close; the total loss floor is static or follows the selected high-water mark. The lock caps that floor at starting capital. Fees and resets assume independent attempts with the same pass probability; expected attempts equal 1 / P(pass), and expected fees equal evaluation fee + (1 / P(pass) - 1) times reset fee. These quantities are not defined when P(pass) is zero.

Returns are independent Gaussian increments. The drift uncertainty option draws a trajectory-level drift from a normal distribution. Wilson intervals quantify finite Monte Carlo sampling at fixed inputs. They do not measure uncertainty in the estimated drift. The first twenty paths are plotted; CSV includes outcomes for every simulated path.

For consistency, inactivity and phase rules on timestamped strategy P&L, see the app guide. A news-window rule needs a timestamped event calendar and a concurrent-position cap needs a position log; Gaussian increments carry neither, so this simulator models neither.

Formula version 2; seed and entered parameters appear in the shareable URL.