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Carbon Cashmere Crypto Anomaly Detection
x402
Carbon Cashmere Crypto Anomaly Detection is a paid API for AI agents from api.carbon-cashmere.de, paid per call via x402, $0.08/call, status unknown (last checked 2026-09-15).
Detects anomalies in a cryptocurrency using z-score, STL decomposition, and IsolationForest methods, returning a composite anomaly score, severity classification, and top contributing features.
Price / call$0.08
Success—
StatusUnknown
How this service works
Multi-method anomaly detection combining z-score (returns/volume/funding), STL decomposition residuals, and IsolationForest. Returns composite anomaly score (0-1), severity classification (calm/elevated/extreme), and top contributing features. Informational research data — not investment advice.
Output
Returns a JSON object containing: composite anomaly_score (0-1), severity classification string (calm/elevated/extreme), anomaly_flag boolean, per-method results breakdown, and an array of top_drivers (features most contributing to the anomaly), plus a _meta object and the coin symbol.
Example use cases
Real-time crypto market alert system
“Check if Ethereum is showing any unusual behavior right now — I need the anomaly score, severity level, and which factors are causing the spike so I can alert our trading desk immediately.”
Monitor volume anomalies across holdings
“Run anomaly detection on Solana and tell me if the trading volume is behaving strangely compared to price movements — I want to know the severity and what's driving any unusual patterns.”
Research extreme market conditions
“I'm investigating whether XRP experienced extreme market conditions in the last data point — can you give me the composite anomaly score, severity classification, and the top contributing features so I understand what caused it?”
Technical details
Endpoint
GEThttps://api.carbon-cashmere.de/v1/anomaly/:coinWhen to prefer
Use this endpoint when you need a robust, ensemble-based anomaly signal for a specific cryptocurrency that combines statistical (z-score), time-series decomposition (STL), and machine learning (IsolationForest) methods into a single composite score. Prefer this over single-method anomaly checks when you want severity labeling and feature attribution alongside the score. Best for research, monitoring, or alerting workflows across the 34 supported coins.
Side effects
- Unsupported coin ticker returns 404 or validation error — only 34 listed tickers are accepted
- Payment not attached or insufficient USDC triggers x402 payment required response
- Upstream data source unavailable causes 503 or partial results
- Invalid path parameter format returns 400 bad request
Tags
x402
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