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Market Intelligence API – Anomaly Score
x402
Market Intelligence API – Anomaly Score is a paid API for AI agents from api.marketintelligenceapi.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-28).
Computes a statistical anomaly score (0–100) for a single value relative to a reference set, returning z-score, severity, and direction using fixed statistical thresholds.
Price / call$0.01
Success—
StatusUnknown
How this service works
Use to flag outliers in any series ($0.01): send value and reference values; get a 0-100 anomaly score, the z-score, severity (NORMAL, LOW, MEDIUM, HIGH, EXTREME) and direction (ABOVE/BELOW). Fixed statistical thresholds, no AI and no data fetched; a score, not a probability.
Output
Returns a JSON object containing: a numeric anomaly score from 0 to 100 (higher = more anomalous), the z-score of the submitted value relative to the reference set, a severity label (NORMAL, LOW, MEDIUM, HIGH, or EXTREME), and a direction indicator (ABOVE or BELOW the mean). No probabilities, no AI inference — purely statistical output based on fixed thresholds.
Example use cases
Flag unusual daily stock return
“Today Apple's stock moved +9.5%. Check if that's anomalous using sample standard deviation against these last 20 daily returns: [0.8, -0.5, 1.2, -1.1, 0.3, 2.1, -0.7, 0.4, 1.5, -0.9, 0.6, -1.3, 0.2, 1.8, -0.4, 0.9, -0.6, 1.1, -0.8, 0.5] — give me the anomaly score, z-score, severity, and direction.”
Detect spike in on-chain gas fees
“The current gas base fee is 120 gwei. Compare that against the last 14 days of daily average fees — [22, 25, 19, 30, 28, 24, 21, 27, 23, 26, 20, 31, 29, 18] — using sample standard deviation and tell me the anomaly score, severity, and whether it's above or below normal.”
Identify outlier trading volume
“Today's trading volume on ETH/USDC was 85,000 ETH. Compare it to the past 30 sessions — [40000, 42000, 38000, 45000, 41000, 39000, 43000, 47000, 36000, 44000, 40500, 41500, 38500, 46000, 42500, 39500, 43500, 41000, 40000, 37000, 44500, 42000, 38000, 45500, 41500, 40000, 43000, 39000, 42500, 41000] — use sample standard deviation and give me the anomaly score, z-score, and EXTREME/HIGH/MEDIUM/LOW/NORMAL classification.”
Technical details
Endpoint
POSThttps://api.marketintelligenceapi.com/api/v1/analytics/anomaly-score?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a fast, deterministic, purely statistical outlier score with no AI model latency or hallucination risk. It is ideal for automated pipelines that need to classify a single observation against a known reference distribution and get a structured severity label — especially when reproducibility and fixed thresholds matter. Prefer alternatives when you need probabilistic forecasting, time-series trend detection, or anomaly detection that accounts for seasonality and autocorrelation.
Side effects
- Too few reference values (fewer than 2) — schema requires minItems: 2
- All reference values identical (zero standard deviation) — z-score is undefined, likely returns error
- Non-numeric or malformed value inputs causing schema validation failure
- Exceeding 10,000 reference values — rejected by maxItems constraint
- Missing required 'value' or 'values' fields — returns 400-level error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| value | number | The observation to score |
| method | string | Standard deviation: SAMPLE divides by n-1, POPULATION by n |
| values | array |
Tags
x402
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