stochastic-rs

A high-performance Rust library for simulating stochastic processes, with first-class bindings. Built for quantitative finance, statistical modeling and synthetic data generation.

Features

• 85+ stochastic models — diffusions, jump processes, stochastic volatility, interest rate models, autoregressive models, noise generators, and probability distributions
• Copulas — bivariate, multivariate, and empirical copulas with correlation utilities
• Quant toolbox — option pricing, bond analytics, calibration, loss models, order book, and trading strategies
• Statistics — MLE, kernel density estimation, fractional OU estimation, and CIR parameter fitting
• SIMD-optimized — fractional Gaussian noise, fractional Brownian motion, and all probability distributions use wide SIMD for fast sample generation
• Parallel sampling — sample_par(m) generates m independent paths in parallel via rayon
• Generic precision — most models support both f32 and f64
• Bindings — full stochastic model coverage with numpy integration; all models return numpy arrays

Installation

Rust

[dependencies]
stochastic-rs = "1.0.0"

Bindings

pip install stochastic-rs

For development builds from source (requires maturin):

pip install maturin
maturin develop --release

Usage

Rust

use stochastic_rs::stochastic::process::fbm::FBM;
use stochastic_rs::stochastic::volatility::heston::Heston;
use stochastic_rs::traits::ProcessExt;

fn main() {
    // Fractional Brownian Motion
    let fbm = FBM::new(0.7, 1000, None);
    let path = fbm.sample();

    // Parallel batch sampling
    let paths = fbm.sample_par(1000);

    // Heston stochastic volatility
    let heston = Heston::new(0.05, 2.0, 0.04, 0.3, -0.7, 1000, Some(100.0), Some(0.04), None, None);
    let [price, variance] = heston.sample();
}

Bindings

All models return numpy arrays. Use dtype="f32" or dtype="f64" (default) to control precision.

import stochastic_rs as sr

# Basic processes
fbm = sr.PyFBM(0.7, 1000)
path = fbm.sample()           # shape (1000,)
paths = fbm.sample_par(500)   # shape (500, 1000)

# Stochastic volatility
heston = sr.PyHeston(mu=0.05, kappa=2.0, theta=0.04, sigma=0.3, rho=-0.7, n=1000)
price, variance = heston.sample()

# Models with callable parameters
hw = sr.PyHullWhite(theta=lambda t: 0.04 + 0.01*t, alpha=0.1, sigma=0.02, n=1000)
rates = hw.sample()

# Jump processes with custom jump distributions
import numpy as np
merton = sr.PyMerton(
    alpha=0.05, sigma=0.2, lambda_=3.0, theta=0.01,
    distribution=lambda: np.random.normal(0, 0.1),
    n=1000,
)
log_prices = merton.sample()

Benchmarks

Distribution sampling performance: stochastic-rs SIMD vs rand_distr. All distributions use an internal SIMD PRNG (xoshiro256++/xoshiro128++ on wide SIMD types) for maximum throughput. For Normal and Exp, the const generic buffer size (N=32 / N=64) is also compared. Measured with Criterion on Apple M-series, --release.

1K samples (small dataset)

100K samples (large dataset)

Contributing

Contributions are welcome — bug reports, feature suggestions, or PRs. Open an issue or start a discussion on GitHub.

License

MIT — see LICENSE.