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Money Mind Drawdown Adjudication
x402
Money Mind Drawdown Adjudication is a paid API for AI agents from money-mind-extract.onrender.com, paid per call via x402, $0.31/call, status unknown (last checked 2026-10-18).
Analyzes whether a trading strategy's drawdown reflects a genuinely broken strategy or just a normal bad patch, using statistical comparison against shuffled returns.
Price / call$0.31
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 providing a detailed statistical interpretation of the drawdown and a 'verdict' field giving a clear adjudication: whether the drawdown indicates a genuinely broken strategy or falls within the expected range of shuffled/randomized returns.
Example use cases
Drawdown reality check during live trading
“My quant strategy is down 18% over the last 6 weeks and I'm not sure if I should shut it off — can you run these live return series through the drawdown gate and tell me if this looks like a busted strategy or just an unlucky stretch?”
Post-backtest drawdown validation
“I built a backtest that has a 22% max drawdown and I want to know if that's a real structural weakness or something that shows up just by chance — here are the equity curve returns, can you adjudicate the drawdown against shuffled versions of the same returns?”
Pre-deployment strategy stress test
“Before I go live with this strategy, I want to stress-test the worst historical drawdown period — run the drawdown adjudication on these returns and tell me whether the reading suggests the drawdown was a regime break or just statistical noise.”
Technical details
Endpoint
POSThttps://money-mind-extract.onrender.com/drawdown?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a pay-per-call, no-account statistical adjudication of whether a trading strategy drawdown is structurally significant versus noise, particularly when you want to compare against shuffled returns as a null hypothesis. Prefer this over general backtesting tools when you specifically need a binary verdict on drawdown validity, and over full quant platforms when you want lightweight, one-off validation without a subscription.
Side effects
- Missing or malformed body with drawdown/returns data returns validation error
- Insufficient data points for statistical comparison may yield inconclusive verdict
- Payment failure via x402/USDC on Base blocks access before analysis
- Render.com cold start may cause initial latency or timeout on first call
- Malformed input schema (missing required 'type', 'method', or 'body' fields) returns 400 error
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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