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Carbon Cashmere Signal Accuracy – Per-Coin ML Model Backtesting
x402
Carbon Cashmere Signal Accuracy – Per-Coin ML Model Backtesting is a paid API for AI agents from api.carbon-cashmere.de, paid per call via x402, $0.1/call, status unknown (last checked 2026-09-14).
Returns historical directional accuracy metrics for ML signal models on a specific cryptocurrency, including per-model hit rates, edge over coin-flip in basis points, calibration checks, and confidence-bucket breakdowns across 7d/30d/90d windows.
Price / call$0.1
Success—
StatusUnknown
How this service works
Historical directional accuracy of our ML signal models for a specific coin. Per-model hit rate, edge-vs-coinflip in bps, calibration check, confidence-bucket breakdown over 7d/30d/90d windows. Verifiability layer for signal endpoints. Informational only.
Output
Returns a structured breakdown of historical ML signal accuracy for the requested coin: per-model hit rates, directional edge over random chance in basis points, a calibration quality check, and confidence-bucket performance breakdowns — all sliced across 7-day, 30-day, and 90-day lookback windows. Useful for verifying signal reliability before acting on recommendations.
Example use cases
Audit ETH signal reliability before trading
“Before I start trading based on those Ethereum signals, can you pull up the historical accuracy metrics? I want to know the hit rates, how much edge the models have over a coin flip, and whether the calibration checks out across the different time windows.”
Compare ML model quality across coins
“I'm trying to decide which coins to trust the ML signals on — can you check the directional accuracy stats for both Solana and Bitcoin and tell me which one has better hit rates and edge over random chance in the 30d and 90d windows?”
Verify signal confidence buckets for BTC
“I noticed the signal model is flagging high-confidence calls for BTC lately — can you pull the confidence-bucket breakdown from the historical accuracy data to see if the high-confidence predictions actually perform better than the lower-confidence ones?”
Technical details
Endpoint
GEThttps://api.carbon-cashmere.de/v1/signal-accuracy/:coinWhen to prefer
Use this endpoint when you need to audit or verify the trustworthiness of ML trading signals from Carbon Cashmere before acting on them, or when comparing model quality across coins. It is the verifiability layer specifically tied to Carbon Cashmere's own signal ensemble — prefer it over generic backtesting tools when working within that ecosystem.
Side effects
- Unsupported coin symbol returns 404 or empty result
- Insufficient historical data for short windows on newly listed coins
- Stale model data if retraining pipeline is delayed
- Rate limiting or payment failure returns 402 or 429
- Model name not recognized if referencing deprecated versions
Tags
x402
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