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Carbon & Cashmere BTC Signal Accuracy API
x402
Carbon & Cashmere BTC Signal Accuracy API 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 metrics, calibration stats, and prediction performance for Bitcoin 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: model-level accuracy metrics (correct predictions, total predictions, accuracy rate, average confidence, edge vs coinflip in bps), calibration assessment (gap in bps, overconfident/underconfident classification), lookback period in days, best performing model summary, per-time-window accuracy breakdowns, and confidence bucket statistics. Includes a disclaimer that data is informational only.
Example use cases
Audit BTC signal reliability before trading
“Before I start acting on the Carbon & Cashmere BTC signals, pull the model accuracy metrics for the last 180 days — I want to know the overall accuracy rate, how many correct predictions were made, and what the edge over a coin flip looks like in basis points.”
Check if ML model is overconfident
“Run a calibration check on the Carbon & Cashmere Bitcoin ML model for me — I need to know if it's overconfident or underconfident, what the calibration gap is in basis points, and how that breaks down across the different confidence buckets.”
Compare signal performance across time windows
“Grab the Carbon & Cashmere BTC signal performance stats and break it down by time window for me — I want to see which prediction horizon the model performs best on and whether accuracy holds up consistently or degrades over shorter versus longer windows.”
Technical details
Endpoint
GEThttps://api.carbon-cashmere.de/v1/signal-accuracy/BTCWhen to prefer
Use this endpoint when you need to validate or audit the predictive reliability of Carbon & Cashmere's ML trading signals for BTC before acting on them, or when building a meta-layer that weights signal confidence by historical accuracy. Prefer this over the full bundle endpoint when you only need model performance stats rather than live signals.
Side effects
- Unsupported coin symbol returns 404 or empty result
- Payment not included or insufficient USDC (x402 payment required) returns 402
- API downtime returns 5xx server error
- Model has insufficient data for the lookback window, returning low sample sizes marked as inadequate
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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