stochastic-rs 2.1.0

A high-performance Rust library for simulating stochastic processes with first-class bindings.

stochastic-rs

A high-performance Rust library for stochastic process simulation, quantitative finance, statistics, copulas, distributions, and neural-network volatility surrogates. Generic over f32 / f64, with SIMD acceleration on CPU and CUDA / Metal / Accelerate / cubecl backends where they pay off, and first-class Python bindings via PyO3.

Documentation

📖 stochastic.rust-dd.com — full docs site (Fumadocs + Next.js, deployed on Vercel).

Local preview from source under website/:

cd website
bun install
bun run dev          # http://localhost:3000

Highlights:

• 120+ stochastic processes — diffusion, jump, fractional / rough, short-rate, HJM, LMM, fBM, Hawkes, Lévy. Generic-precision ProcessExt<T> impl, SIMD on CPU, optional CUDA / Metal for FGN / fBM.
• Pricing & calibration — closed-form (BSM, Bachelier, Black76, Bjerksund-Stensland, …), Fourier (Heston / Bates / Merton-jump / Kou / VG / CGMY / HKDE / double-Heston), Monte Carlo (basket, rainbow, cliquet, autocallable, spread), finite difference, Bermudan LSM, Heston SLV. Heston / SABR / SVJ / Lévy / rough Bergomi / double-Heston / Hull-White swaption-grid calibrators.
• Statistics & risk — Hurst (Fukasawa), MLE for 1-D diffusions with 6 transition densities, ADF / KPSS / Phillips-Perron, realised variance with BNHLS bandwidth, HMM, changepoint, particle filter, UKF. VaR / CVaR / drawdown, Sharpe / Sortino / IR / Calmar.
• Fixed income & credit — yield-curve bootstrapping, Nelson-Siegel / Svensson, multi-curve, IRS / inflation swaps, Vasicek / CIR / Hull-White / G2++ short-rate engines, Merton structural model, reduced-form survival curves, CDS pricing, JLT migration matrices.
• Microstructure — Almgren-Chriss, Kyle (1985), Bouchaud propagator, full price-time priority order book.
• Distributions & copulas — 19 SIMD distributions with closed-form pdf / cdf / cf / moments. Clayton / Frank / Gumbel / Independence bivariate; Gaussian / vine multivariate.
• Python bindings — 210 entries (198 PyO3 classes + 12 functions) spanning every sub-crate except AI surrogates. Numpy-in / numpy-out.

Installation

Rust

[dependencies]
stochastic-rs = "2.0.0"

use stochastic_rs::prelude::*;
use stochastic_rs::stochastic::diffusion::gbm::Gbm;
use stochastic_rs::quant::pricing::heston::HestonPricer;

For per-sub-crate (lean) builds, OpenBLAS / CUDA / Metal / cubecl / Accelerate feature flags, native CPU optimisation, and SIMD details, see the installation guide on the docs site.

Python

pip install stochastic-rs

Source build (requires the Rust toolchain):

pip install maturin
maturin develop --release --manifest-path stochastic-rs-py/Cargo.toml

Linux (x86_64 / aarch64) and macOS (arm64 / x86_64) wheels ship with the openblas feature on. The Windows wheel omits the 15 BLAS-backed classes; everything else (≈195 classes / 12 functions) works identically. See the Python bindings page for the parity table and the source-build path with vcpkg.

Quickstart

use stochastic_rs::prelude::*;
use stochastic_rs::stochastic::diffusion::ou::Ou;
use stochastic_rs::quant::pricing::heston::HestonPricer;
use stochastic_rs::quant::types::OptionType;

fn main() {
    // Mean-reverting OU path
    let p = Ou::<f64>::new(2.0, 0.0, 1.0, 1_000, Some(0.0), Some(1.0));
    let path = p.sample();

    // Heston European call with first- and second-order Greeks
    let pricer = HestonPricer::<f64>::new(
        100.0, 100.0, 1.0, 0.03, 0.0,
        0.04, 2.0, 0.04, 0.3, -0.5,
    );
    let price = pricer.price(OptionType::Call);
    let greeks = pricer.greeks(OptionType::Call);
    println!("call={:.4}, delta={:.4}, vega={:.4}", price, greeks.delta, greeks.vega);
}

import stochastic_rs as srs

# Mean-reverting OU path
p = srs.Ou(theta=2.0, mu=0.0, sigma=1.0, n=1000, x0=0.0, t=1.0)
path = p.sample()                       # numpy.ndarray, shape (1000,)

# Heston European call
pricer = srs.HestonPricer(
    s0=100, k=100, tau=1.0, r=0.03, q=0.0,
    v0=0.04, kappa=2.0, theta=0.04, sigma=0.3, rho=-0.5,
)
print("call =", pricer.price("call"))
g = pricer.greeks("call")
print(f"delta={g.delta:.4f}, vega={g.vega:.4f}")

More end-to-end recipes (Heston calibration, fBM Hurst estimation, vol-surface from quotes, Python interop) live in the tutorials section.

Benchmarks

FGN — CPU vs CUDA native (f32, H = 0.7)

cargo bench --features cuda-native --bench fgn_cuda_native

Single path:

n	CPU sample	CUDA sample_cuda_native(1)	Speedup
1,024	8.1 µs	46 µs	0.18×
4,096	35 µs	84 µs	0.42×
16,384	147 µs	110 µs	1.3×
65,536	850 µs	227 µs	3.7×

Batch:

n, m	CPU sample_par	CUDA sample_cuda_native	Speedup
4,096, 32	147 µs	117 µs	1.3×
4,096, 512	1.78 ms	2.37 ms	0.75×
65,536, 128	12.6 ms	10.5 ms	1.2×
65,536, 1 k	102 ms	93 ms	1.1×

CUDA wins for large n (≥ 16 k); CPU rayon dominates for medium n because of the GPU launch / transfer overhead.

Distribution sampling — multicore (cargo bench --bench dist_multicore)

sample_matrix, 1-thread vs 14-thread rayon. f64 continuous, integer discrete. Most distributions: 1024 × 1024; heavy discrete: 512 × 512.

Distribution	1T (ms)	MT (ms)	Speedup
Normal	1.78	0.34	5.28×
Cauchy	6.23	0.90	6.96×
LogNormal	5.07	0.81	6.25×
Gamma	5.20	0.72	7.19×
StudentT	7.89	1.89	4.18×
Beta	11.85	1.68	7.04×
Weibull	13.17	1.73	7.59×
AlphaStable	42.52	5.36	7.94×
Poisson	2.28	0.42	5.40×
Hypergeo (512²)	20.99	2.76	7.60×

(Full table — 18 distributions — on the benchmarks page.)

Normal single-thread fill_slice vs the upstream rand_distr baseline:

• vs rand_distr + SimdRng — ≈ 1.21× to 1.35×
• vs rand_distr + rand::rng() — ≈ 4.09× to 4.61×

Contributing

Contributions are welcome — bug reports, feature suggestions, or PRs. Open an issue or start a discussion on GitHub. Per-feature recipes (add-diffusion-process, adding-distribution, calibration-pattern, docs-writing, …) live under .claude/skills/.

License

MIT — see LICENSE.