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Carbon & Cashmere Signal Accuracy API – ETH
x402
Carbon & Cashmere Signal Accuracy API – ETH is a paid API for AI agents from api.carbon-cashmere.de, paid per call via x402, $0.02/call, status unknown (last checked 2026-09-14).
Returns ML model signal accuracy statistics, calibration data, and prediction performance metrics for Ethereum (ETH) over a 180-day lookback window.
Price / call$0.02
Success—
StatusUnknown
How this service works
Professional crypto market intelligence. Real-time prices, signals, and market scoring.
Output
A JSON object containing: coin identifier, model-level accuracy stats (correct predictions, total predictions, accuracy rate, avg confidence, edge vs coinflip in basis points), calibration classification (e.g. overconfident) with gap in bps, lookback period in days, best performing model summary, per-time-window accuracy breakdown, and confidence bucket accuracy breakdown.
Example use cases
Evaluate ETH signal before trading
“Before I act on any ETH signals today, pull the Carbon & Cashmere model accuracy stats for Ethereum — I need to know the overall accuracy rate, calibration classification, and edge over coinflip so I can decide if the signal is actually worth following right now.”
Monitor ETH model drift over time
“Grab the latest Carbon & Cashmere ML performance metrics for ETH — I want to check the per-time-window accuracy breakdown and confidence bucket stats to see if the model has drifted or degraded compared to what I was seeing last month.”
Compare confidence vs actual accuracy
“Pull the ETH signal calibration data from Carbon & Cashmere — specifically the confidence bucket breakdown and calibration gap in basis points. I want to know whether the model is overconfident or underconfident so I can adjust how much weight I put on high-confidence predictions.”
Technical details
Endpoint
GEThttps://api.carbon-cashmere.de/v1/signal-accuracy/ETHWhen to prefer
Use this endpoint when you need quantified, backtested accuracy and calibration data for the ETH ML signal model specifically — not raw price data or live signals. Ideal for evaluating whether the signal is trustworthy before acting on it, or for monitoring model drift over time. Prefer this over general market data endpoints when the question is 'how good is this signal?' rather than 'what is the signal?'
Side effects
- Coin symbol not supported — endpoint is hardcoded to ETH; other tickers may 404
- Insufficient sample size for certain time windows — labeled in response as inadequate
- Payment not processed — x402 payment required at $0.02 USDC per call; unpaid requests will be rejected
- Stale data — if model has not run recently, accuracy stats may reflect older period
- Malformed response if upstream ML pipeline has an error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| properties | string |
Output
Schema · Response
{
"type": "json",
"example": {
"coin": "BTC",
"_meta": {
"disclaimer": "Informational research data only — not investment advice."
},
"models": [
{
"model": "v3_tsfresh",
"correct": 469,
"accuracy": 0.6228,
"avg_confidence": 0.7274,
"total_predictions": 753,
"edge_vs_coinflip_bps": 1228.4
}
],
"calibration": {
"gap_bps": 1046,
"classification": "overconfident",
"avg_confidence_primary_model": 0.7274,
"actual_accuracy_primary_model": 0.6228
},
"lookback_days": 180,
"best_performing_model": {
"name": "v3_tsfresh",
"accuracy": 0.6228,
"sample_size": 753
},
"time_windows_v3_tsfresh": [
{
"accuracy": 0.6429,
"window_days": 7,
"total_predictions": 42,
"sample_size_adequacy": "adequate"
}
],
"confidence_buckets_v3_tsfresh": [
{
"total": 180,
"accuracy": 0.65,
"confidence_bucket": "high"
}
]
}
}Tags
x402
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