---
title: stochastic-rs
description: A Rust library for stochastic process simulation, quantitative finance, statistics, copulas, distributions, and neural-network volatility models.
category: concept
status: stable
since: 2.0.0
---

# stochastic-rs

A Rust library for **stochastic process simulation, quantitative finance,
statistics, copulas, distributions, and neural-network volatility models**.
Generic over the float type (`f32` / `f64`), with SIMD acceleration on CPU
and CUDA / Metal backends where they pay off, and first-class Python
bindings via PyO3.

## What's in here

The workspace ships nine sub-crates (the `stochastic-rs` umbrella
re-exports everything):

```
stochastic-rs-core             simd_rng (foundation)
stochastic-rs-distributions    19 distributions, FloatExt + SimdFloatExt
stochastic-rs-stochastic       120+ processes, ProcessExt
stochastic-rs-copulas          bivariate + multivariate copulas
stochastic-rs-stats            estimators (MLE, Hurst, realised, …)
stochastic-rs-quant            pricing, calibration, vol surface, risk
stochastic-rs-ai               neural surrogates (feature-gated)
stochastic-rs-viz              Plotly grid plotter
stochastic-rs-py               PyO3 cdylib (210 entries)
```

## Where to start

- **New to the library?** [Quickstart](/docs/getting-started/quickstart) — install,
  simulate a path, price an option, and read a Hurst estimate, end-to-end.
- **Looking for a specific process / pricer?** Use the catalog index:
  [Processes](/docs/processes), [Distributions](/docs/distributions),
  [Quant](/docs/quant).
- **Using Python?** Start with [Python bindings overview](/docs/python).

## Navigation

The left sidebar is grouped into:

1. **Start here** — getting-started + concepts (traits, prelude, feature
   flags). Read this section once, end-to-end, before you dive into
   the catalog.
2. **Reference** — one page per process / distribution / pricer /
   calibrator / estimator / copula. Page templates are documented in
   [the docs-writing SKILL](https://github.com/dancixx/stochastic-rs/blob/main/.claude/skills/docs-writing/SKILL.md).
3. **Bindings** — Python parity table and interop notes.
4. **Guides** — long-form tutorials (Heston calibration, fBm Hurst
   estimation, vol-surface pipeline, …) and benchmark dashboard.
5. **Project** — migration, contributing, API reference.
