{"uid":"cap_1V1ndjawRkV5BpI6axVs0","slug":"market-intelligence-api-anomaly-score-56536e6f","name":"Market Intelligence API – Anomaly Score","description":"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.","url":"https://api.marketintelligenceapi.com/api/v1/analytics/anomaly-score?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"value":{"type":"number","description":"The observation to score"},"method":{"enum":["SAMPLE","POPULATION"],"type":"string","default":"SAMPLE","description":"Standard deviation: SAMPLE divides by n-1, POPULATION by n"},"values":{"type":"array","items":{"type":"number","description":"Reference value"},"maxItems":10000,"minItems":2}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.01/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_kmotzuzRG_bMg5LQhm4vn","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"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.","exampleAgentPrompt":"Score today's BTC daily return of 8.2% against the last 30 days of returns — [3.1, -1.2, 0.8, 2.4, -0.5, 1.1, -2.3, 4.0, 0.3, -0.9, 1.7, -1.5, 2.8, -3.2, 0.6, 1.9, -0.7, 3.5, -1.8, 0.2, 1.3, -0.4, 2.1, -2.0, 0.9, 1.6, -1.1, 3.8, -0.6, 2.5] — using sample standard deviation and tell me the anomaly score, z-score, severity, and whether it's above or below the norm.","exampleUseCases":[{"title":"Flag unusual daily stock return","prompt":"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."},{"title":"Detect spike in on-chain gas fees","prompt":"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."},{"title":"Identify outlier trading volume","prompt":"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."}],"resultDescription":"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.","failureModes":["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"],"whenToPreferThis":"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.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-28T12:36:19.016Z","isFirstParty":false,"canonicalSlug":"market-intelligence-api-anomaly-score-56536e6f"}