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Money Mind Audit — Trading Research Adjudication
x402
Money Mind Audit — Trading Research Adjudication is a paid API for AI agents from money-mind-extract.onrender.com, paid per call via x402, $300/call, status unknown (last checked 2026-10-18).
Runs a trading strategy or backtest through eleven validation gates to detect data snooping, inflated t-stats, price data fabrication, and duplicate edges across instruments, returning a verdict and diagnostic reading.
Price / call$300
Success—
StatusUnknown
How this service works
Pay-per-call adjudication for trading research. Eleven gates that killed 35 of our own concepts. Is your backtest real, is your price data real, is your t-stat inflated? USDC on Base, no account needed.
Output
Returns a JSON object with a 'reading' field containing detailed diagnostic commentary on each of the eleven gates, and a 'verdict' field with the overall pass/fail adjudication on the strategy or backtest submitted.
Example use cases
Backtest reality check before going live
“I've got a backtest showing a Sharpe of 2.4 over 3 years — before I risk any capital, can you run it through the Money Mind audit and tell me if the results are statistically credible or if I'm fooling myself with data snooping?”
Detecting p-hacking in a quant strategy pitch
“A vendor just sent me a strategy deck with impressive backtest numbers. Can you submit it to the Money Mind adjudication service and check whether the t-stats are inflated and the price data looks legitimate?”
Cross-instrument edge duplication check
“I have a book of 12 futures strategies that all look profitable — can you run the full Money Mind audit to see if any of them are really just the same edge repeated across different instruments?”
Technical details
Endpoint
POSThttps://money-mind-extract.onrender.com/audit?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need an objective, multi-gate statistical audit of a trading strategy or backtest and want a binary verdict with explanations — particularly useful before committing capital, sharing research externally, or evaluating third-party strategy claims. It is purpose-built for quantitative finance validation rather than generic statistical testing.
Side effects
- Malformed or missing 'input' body causes validation error
- Payment of 300 USDC on Base not received — request rejected with 402
- Insufficient data in the submitted book for statistical gates to evaluate
- Non-JSON body type causes parsing failure
- Service downtime on Render hosting platform returns 5xx
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"type": "json",
"example": {
"reading": "...",
"verdict": "..."
}
}Tags
x402
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