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QuantOracle: Statistical Distribution Fitting
x402
QuantOracle: Statistical Distribution Fitting is a paid API for AI agents from api.quantoracle.dev, paid per call via x402, $0.008/call, status unknown (last checked 2026-10-13).
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.
Price / call$0.008
Success—
StatusUnknown
How this service works
Free, fast quant finance calculators powered by a deterministic, multi-chain API. Black-Scholes, options profit, Kelly, VaR, crypto liquidation, and more.
Output
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.
Example use cases
Example prompt
“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.”
Technical details
Endpoint
POSThttps://api.quantoracle.dev/v1/stats/distribution-fit?utm_source=zero.xyzWhen to prefer
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.
Side effects
- 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
Input
{
"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"
}
}Schema · Request body
| Field | Type | Description |
|---|---|---|
| data | array | Array of data to fit distributions to |
Output
{
"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
}
}Tags
x402
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