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Agent Souk – Prop-Firm Challenge Pass Probability (Block-Bootstrap Monte Carlo)
x402
Agent Souk – Prop-Firm Challenge Pass Probability (Block-Bootstrap Monte Carlo) is a paid API for AI agents from api.agentsouk.dev, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-13).
Estimates the probability of passing a prop-firm trading challenge and computes risk statistics by running block-bootstrap Monte Carlo simulations over a trader's historical daily returns.
Price / call$0.01
Success—
StatusUnknown
How this service works
Prop-firm challenge pass probability and risk statistics from daily returns (block-bootstrap Monte Carlo, no LLM)
Output
Returns Monte Carlo-derived statistics including the estimated probability of passing the prop-firm challenge (hitting the profit target within the allowed days without breaching any loss limit), probability of busting via overall drawdown, probability of busting via single-day loss rule, and additional risk metrics derived from the simulated return paths — all computed deterministically without any LLM.
Example use cases
Evaluating strategy before funding application
“I want to apply for a prop-firm funded account. Here are my daily returns as fractions over the past 90 days: [0.004, -0.008, 0.002, ...]. Run 20,000 Monte Carlo simulations with a 10% profit target, 5% daily loss rule, 10% max loss, and a 60-day challenge window. What's my probability of passing?”
Comparing two position sizing approaches
“I have two sets of daily returns from the same strategy — one at normal size and one at half size. Can you simulate my prop-firm pass probability for both using a 10% profit target, 5% daily loss limit, 10% overall drawdown cap, and 42 trading days, so I can see which sizing gives better odds?”
Stress-testing returns under stricter challenge rules
“My trading history as daily fractions is [0.005, -0.003, 0.007, ...] over 120 days. What happens to my pass probability if the daily loss limit drops from 5% to 2%? Keep the profit target at 8%, max loss at 8%, and run 15,000 simulations over a 30-day challenge window.”
Technical details
Endpoint
POSThttps://api.agentsouk.dev/v1/x402/lst_01M2ND5C6502HRV8VA9PMG40W6?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a statistically rigorous, reproducible estimate of prop-firm challenge pass probability based on actual historical daily return data. It uses block-bootstrap resampling to preserve autocorrelation structure in returns (unlike simple i.i.d. Monte Carlo), making it more realistic for trading strategies with momentum or mean-reversion. Prefer it over LLM-based analysis or simple Sharpe-ratio estimates when you need calibrated probabilities with explicit challenge rule parameters (profit target, daily loss cap, overall drawdown cap, time limit).
Side effects
- Fewer than 20 daily returns provided — minimum array length not met
- daily_returns values outside [-1, 1] range cause validation errors
- simulations count exceeds 50,000 limit
- max_days exceeds 365 limit
- Inconsistent or all-zero returns array may produce degenerate probability estimates
- Network timeout if simulation count is very high and server load is elevated
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| seed | integer | |
| scale | number | Multiplies every return (position size) |
| target | number | Profit target, fraction of the starting balance |
| max_days | integer | Trading days the challenge may take |
| max_loss | number | Overall loss limit (cumulative at or below -max_loss busts) |
| daily_loss | number | Single-day loss limit (a day at or below -daily_loss busts); 0 = off |
| simulations | integer | |
| block_length | number | Mean block length; 1 = i.i.d.; may exceed the number of days |
| daily_returns | array | Daily results as fractions of the starting balance, in order (0.004 = +0.4 %) |
| trading_days_per_year | integer |
Tags
x402
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