{"uid":"cap_52sbtAKaxH0Iavdxwg3ew","slug":"api-quantoracle-dev-a577bf1a","name":"QuantOracle: Statistical Distribution Fitting","description":"Free, fast quant finance calculators powered by a deterministic, multi-chain API. Black-Scholes, options profit, Kelly, VaR, crypto liquidation, and more.","url":"https://api.quantoracle.dev/v1/stats/distribution-fit?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"data":{"type":"array","items":{"type":"number"},"title":"Data","minItems":10,"description":"Array of data to fit distributions to"}}},"responseSchema":null,"example":{"request":{"input":{"body":{"data":[1.2,2.5,1.8,3.1,2.2,1.9,2.8,3.5,2.1,1.5,2.9,3.2,2.4,1.7,2.6,3,2.3,1.6,2.7,3.3]},"type":"http","method":"POST","bodyType":"json"}},"response":{"n":20,"ms":7.83,"fits":[{"params":{"max":3.5,"min":1.2},"distribution":"uniform","ks_statistic":0.057971,"log_likelihood":-16.66},{"params":{"std":0.656366,"mean":2.415},"distribution":"normal","ks_statistic":0.069377,"log_likelihood":-19.46},{"params":{"mu":0.842803,"sigma":0.294799},"distribution":"lognormal","ks_statistic":0.07746,"log_likelihood":-20.31}],"_meta":{"url":"https://quantoracle.dev","docs":"https://api.quantoracle.dev/docs","powered_by":"QuantOracle"},"best_fit":"uniform","descriptive":{"max":3.5,"min":1.2,"std":0.656366,"mean":2.415,"median":2.5,"kurtosis":-0.9666,"skewness":-0.1451}}},"exampleRequest":{"data":[1.2,2.5,1.8,3.1,2.2,1.9,2.8,3.5,2.1,1.5,2.9,3.2,2.4,1.7,2.6,3,2.3,1.6,2.7,3.3]},"tags":["x402"],"displayCostAmount":"0.008","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":"$0.008/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.008","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.008","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_Es4ynuW16Zoths2fvupiQ","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.008","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Fits a dataset to multiple statistical distributions (uniform, normal, lognormal, etc.) and returns the best-fit distribution along with parameters, KS statistics, log-likelihood, and descriptive statistics.","exampleAgentPrompt":"Can you fit a statistical distribution to this dataset and tell me which one fits best — uniform, normal, or lognormal? Here are my 20 data points: [1.2, 2.5, 1.8, 3.1, 2.2, 1.9, 2.8, 3.5, 2.1, 1.5, 2.9, 3.2, 2.4, 1.7, 2.6, 3.0, 2.3, 1.6, 2.7, 3.3]. I also want the KS statistic, log-likelihood, and descriptive stats like mean, median, and skewness.","exampleUseCases":null,"resultDescription":"Returns the best-fit distribution name, a ranked list of fitted distributions each with their parameters (e.g. mean/std for normal, min/max for uniform), KS statistic, log-likelihood score, and full descriptive statistics including mean, median, standard deviation, skewness, and kurtosis for the input dataset.","failureModes":["Empty or too-small data array returns an error or unreliable fit","Non-numeric values in the data array cause a parsing error","Extremely skewed or heavy-tailed data may yield poor fits across all candidate distributions","Network timeout if payload is unusually large","Payment failure via x402 if USDC balance is insufficient"],"whenToPreferThis":"Use this endpoint when you need to identify which statistical distribution best describes a numerical dataset — especially in quantitative finance contexts where knowing if returns follow a normal, lognormal, or uniform distribution matters for downstream risk modeling. Prefer this over manual fitting when you want multiple distributions compared simultaneously with KS statistics and log-likelihood scores in a single call.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-13T06:55:46.059Z","isFirstParty":false,"canonicalSlug":"api-quantoracle-dev-a577bf1a"}