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Carbon & Cashmere Signal Accuracy API – SOL
x402
Carbon & Cashmere Signal Accuracy API – SOL is a paid API for AI agents from api.carbon-cashmere.de, paid per call via x402, $0.02/call, status unknown (last checked 2026-09-15).
Returns ML model signal accuracy, prediction statistics, calibration data, and edge-vs-coinflip metrics for Solana (SOL) crypto trading signals.
Price / call$0.02
Success—
StatusUnknown
How this service works
Professional crypto market intelligence. Real-time prices, signals, and market scoring.
Output
Returns a JSON object with ML model accuracy (e.g. 62.28%), total predictions, average confidence, edge vs coinflip in basis points, calibration classification (e.g. overconfident), confidence bucket breakdowns, time-window accuracy slices, and the best-performing model name — all for Solana over a 180-day lookback window.
Example use cases
Validate SOL signal reliability before trading
“Before I start acting on any Solana signals, can you pull the Carbon & Cashmere accuracy stats for SOL? I want to know the overall model accuracy, how many predictions it's made over the last 180 days, and whether it's actually giving me a real edge over a coinflip or just noise.”
Check if SOL model is overconfident
“I've been second-guessing the SOL signals lately — can you check the Carbon & Cashmere calibration data for Solana? Specifically tell me whether the model is overconfident or well-calibrated, and show me how accuracy holds up across the different confidence buckets.”
Compare SOL prediction edge across time windows
“I want to backtest how reliable the Carbon & Cashmere ML signals have been for SOL across different time windows. Pull the accuracy slices and edge-over-coinflip in basis points so I can see if the model performs better over shorter or longer horizons before I size up my position.”
Technical details
Endpoint
GEThttps://api.carbon-cashmere.de/v1/signal-accuracy/SOLWhen to prefer
Use this endpoint when you need to evaluate the historical accuracy and reliability of ML-driven crypto trading signals specifically for SOL (Solana). It is ideal for backtesting confidence, assessing model calibration, and quantifying predictive edge before acting on signals. Choose this over generic price endpoints when you need statistical validation of signal quality, not just current price data.
Side effects
- Invalid or unsupported coin symbol returns an error or empty result
- Payment not received results in HTTP 402 rejection
- Endpoint may return limited data if model has insufficient prediction history for SOL
- Network timeout if the service is temporarily unavailable
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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