Service metadata and endpoint list

Code-Snippets

curl "https://api.oanor.com/montecarlo-api/v1/meta" \
  -H "x-oanor-key: oanor_test_..."

await fetch("https://api.oanor.com/montecarlo-api/v1/meta", {
  headers: { "x-oanor-key": "oanor_test_..." }
});

$ch = curl_init("https://api.oanor.com/montecarlo-api/v1/meta");
curl_setopt($ch, CURLOPT_HTTPHEADER, ["x-oanor-key: oanor_test_..."]);
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
$out = curl_exec($ch);

import requests
requests.get(
    "https://api.oanor.com/montecarlo-api/v1/meta",
    headers={"x-oanor-key": "oanor_test_..."}
)

Beispiel-Response

Echte Antwort dieses Endpoints, erfasst beim letzten Health-Check.

{
    "data": {
        "note": "Drift/volatility/expected_return are annual decimals (0.07 = 7%). horizon in years. Runs are deterministic per seed (default 12345).",
        "source": "Computed in-process (seeded mulberry32 PRNG + Box-Muller normals)",
        "service": "montecarlo-api",
        "endpoints": {
            "GET /v1/meta": "This document.",
            "GET /v1/path": "One sample price path (spot=100, drift=0.07, volatility=0.2, horizon=1, steps=50).",
            "GET /v1/simulate": "Terminal-price distribution of an asset (spot=100, drift=0.07, volatility=0.2, horizon=1, paths=10000).",
            "GET /v1/portfolio": "Wealth projection with contributions (initial=10000, annual_contribution=6000, years=30, expected_return=0.07, volatility=0.15, goal=1000000).",
            "GET /v1/probability": "Modelled chance of reaching a target price (spot=100, target=120, drift=0.07, volatility=0.2, horizon=1)."
        },
        "description": "Live Monte-Carlo simulation for price and portfolio forecasting, computed on demand and reproducibly. The simulate endpoint runs a geometric-Brownian-motion simulation of an asset and returns the terminal-price distribution (percentiles, mean, probability of a gain); the probability endpoint returns the modelled chance of reaching a target price; the portfolio endpoint projects wealth over many years with periodic contributions (a retirement/savings projection); the path endpoint returns one sample price path. Every run is seeded, so the same inputs always give the same numbers. A forward-looking simulation engine, distinct from historical-statistics and option-pricing tools — it turns a drift and volatility into a distribution of outcomes.",
        "upstream_status": "ok"
    },
    "meta": {
        "timestamp": "2026-06-10T22:56:13.477Z",
        "request_id": "d30456f3-9b61-4085-a0ed-e90d248f7c4c"
    },
    "status": "ok",
    "message": "Meta",
    "success": true
}