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Prop Challenge Lab – Trading Journal Analyzer
x402
Prop Challenge Lab – Trading Journal Analyzer is a paid API for AI agents from agent-data.centorprat.workers.dev, paid per call via x402, $4.99/call, status unknown (last checked 2026-10-30).
Analyzes a prop-firm trading journal (CSV or MT5 JSON) against challenge rules to score consistency, calculate pass odds, and surface costly trade patterns.
Price / call$4.99
Success—
StatusUnknown
How this service works
Pay-per-call data and tools for AI agents, $0.002-0.01 per call in USDC via x402 (Base, Polygon or Solana), no account or API key; MAX tiers ($0.05-0.25) run deeper jobs. Agent toolkit: web search, search + read top pages, any page as clean markdown with a prompt-injection check, which-x402-API-to-call recommender, seller check. Data: Google Maps places with contacts, YouTube video data and transcripts, job listings (Indeed, LinkedIn, 13 boards), Google Ads Transparency, TikTok comments, Pinterest, Airbnb, Redfin, AliExpress, app stores, live sports, website and SEO checks, Shopify store tech stacks, and onchain token / address risk checks.
Output
Returns a JSON object with: net P&L, total R-sum, win rate, pass odds (fresh, robust, from-now, and zero-edge twin scenarios), number of positions, consistency grade (A–F) and score, a ranked list of costly trade archetypes with their R drain and trade count, and a shareable dashboard URL with a visual breakdown of the analysis.
Example use cases
FTMO Phase 1 pass probability check
“Here's my FTMO trading journal CSV from last month — can you run it against 2STEP_P1 rules for a $25,000 account and tell me my current pass odds and consistency score?”
Identifying costly trade patterns before funded account
“I'm about to go live on a funded account. Can you analyze my MT5 PropLabExport JSON against FUNDED rules and highlight which trade patterns are draining the most R so I can fix them first?”
1-step challenge journal review
“I'm on a 1-step prop challenge with a $50,000 account — here's my trading journal CSV. What's my win rate, R-sum, and how likely am I to pass based on what I've traded so far?”
Technical details
Endpoint
POSThttps://agent-data.centorprat.workers.dev/quant/prop-report?utm_source=zero.xyzWhen to prefer
Use this endpoint when a trader or AI agent needs to evaluate a prop-firm trading journal against specific challenge rule tiers (2STEP_P1/P2, 1STEP, TRIAL, FUNDED) and wants quantified pass odds, consistency grading, and pattern-level R attribution — rather than generic trade analytics or broker-agnostic P&L summaries. Ideal for FTMO-style challenge analysis where rule-specific drawdown and consistency metrics matter.
Side effects
- Malformed or empty CSV/JSON input returns a parse error
- Unrecognized rules tier defaults to 2STEP_P1 without warning
- Account size not provided and not inferable from journal causes computation gaps
- Insufficient trade history yields low-confidence pass odds
- Network timeout on large journals with many trades
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| file | string | FTMO trading-journal CSV text, or the PropLabExport JSON from the MT5 terminal |
| rules | string | 2STEP_P1 | 2STEP_P2 | 1STEP | TRIAL | FUNDED (default 2STEP_P1) |
| accountSize | number | account size, e.g. 10000 (the journal has none) |
Output
Schema · Response
{
"type": "json",
"example": {
"net": 101.61,
"sumR": 9.31,
"costly": [
{
"id": "swept",
"sumR": -11.56,
"title": "Stopped out, then the target was hit within 24h",
"trades": 9
}
],
"winRate": 0.459,
"passOdds": {
"fresh": 0.73,
"robust": 0.01,
"fromNow": 0.78,
"zeroEdgeTwin": 0.21
},
"positions": 61,
"consistency": {
"grade": "B",
"score": 73
},
"dashboardUrl": "https://.../quant/prop-report/view/<token>"
}
}Tags
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