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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).

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::simd_rng::Unseeded;
use stochastic_rs::stochastic::diffusion::ou::Ou;
use stochastic_rs::quant::pricing::heston::HestonPricer;

fn main() {
    // Mean-reverting Ornstein-Uhlenbeck path: Ou::new(theta, mu, sigma, n, x0, t, seed)
    let ou = Ou::<f64>::new(2.0, 0.0, 1.0, 1_000, Some(0.0), Some(1.0), Unseeded);
    let path = ou.sample();
    println!("OU path points: {}", path.len());

    // Heston (1993) European option, closed form. HestonPricer::new args:
    // s, v0, k, r, q, rho, kappa, theta, sigma, lambda, tau, eval, expiration
    let pricer = HestonPricer::new(
        100.0, 0.04, 100.0, 0.03, Some(0.0),
        -0.5, 2.0, 0.04, 0.3, Some(0.0),
        Some(1.0), None, None,
    );
    let (call, put) = pricer.calculate_call_put();
    println!("call={call:.4}, put={put:.4}");
}

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 `.on(Device::CudaNative).sample()`	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 `.on(Device::CudaNative).sample_par`	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 — `Normal` vs upstream `rand_distr`

Single-thread fill_slice, median of 7 runs (cargo bench --bench dist_multicore). Comparison column:

rand_distr + SimdRng — rand_distr::Normal consuming our SimdRng (same uniform stream, only the Normal algorithm differs).
rand_distr + rand::rng() — the out-of-box upstream pipeline.

n	`SimdNormal` (µs)	`rand_distr + SimdRng` (µs)	speedup	`rand_distr + rand::rng()` (µs)	speedup
4	0.008	0.013	1.73×	0.032	4.22×
8	0.014	0.026	1.78×	0.065	4.52×
16	0.029	0.051	1.79×	0.128	4.47×
64	0.109	0.208	1.90×	0.508	4.64×
256	0.432	0.840	1.94×	2.029	4.70×
4 096	6.975	13.176	1.89×	32.382	4.64×
65 536	113.458	212.406	1.87×	520.219	4.59×

Single-sample speedup vs prior release

Criterion dist.sample(rng) loop, vs the wide 1.3.0 baseline (cargo bench --bench distributions -- --baseline before):

distribution	f32 / large	f64 / large	f64 / small
`Uniform/simd`	−57% (≈ 2.3×)	−77% (≈ 4.4×)	−58% (≈ 2.4×)
`Normal/simd`	−51% (≈ 2.0×)	−75% (≈ 4.0×)	−63% (≈ 2.7×)
`Exp/simd N=64`	−3% (n.s.)	−73% (≈ 3.7×)	—
`LogNormal/simd`	−71% (≈ 3.4×)	−70% (≈ 3.4×)	−66% (≈ 2.9×)

Driven by SIMD u64→f64 / u32→f32 magic-number conversion in SimdRng (direct-write fill_uniform_f64 / fill_uniform_f32 APIs that skip the [f64; 8] return-by-value round-trip), fused Exp(λ) scaling inside fill_exp_scaled, and an 8-at-a-time main loop in fill_ziggurat so copy_from_slice inlines to stp stores instead of a memcpy call.

Opt-in: dual-stream RNG (`dual-stream-rng` feature)

[dependencies]
stochastic-rs = { version = "2.1", features = ["dual-stream-rng"] }

Unlocks SimdRngDual (two parallel xoshiro engines) and SimdNormalDual (Ziggurat unrolled 2× over the dual streams). Measured against the single-stream SimdNormal::fill_slice on Apple Silicon (cargo bench --bench dual_stream_compare --features dual-stream-rng):

n	single (`SimdNormal`)	dual (`SimdNormalDual`)	Δ
64	111.6 ns	105.5 ns	−5.5%
256	444.8 ns	418.3 ns	−6.0%
4 096	7.43 µs	6.60 µs	−11.2%
65 536	113.9 µs	106.6 µs	−6.4%
1 048 576	1.83 ms	1.70 ms	−6.8%

The win comes from hiding the 16 scalar kn / wn table-lookup latencies behind the second engine's xoshiro state update on a modern out-of-order core. Uniform fills are not bottlenecked on the engine so they see no speedup. Trade-off: SimdRngDual::from_seed does not reproduce SimdRng::from_seed's bit-exact sequence (statistical properties are identical and KS-validated).

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.

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stochastic-rs 2.5.3

stochastic-rs

Documentation

Installation

Rust

Python

Quickstart

Benchmarks

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

Distribution sampling — Normal vs upstream rand_distr

Single-sample speedup vs prior release

Opt-in: dual-stream RNG (dual-stream-rng feature)

Contributing

License

Project links

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1 maintainer

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

Distribution sampling — `Normal` vs upstream `rand_distr`

Opt-in: dual-stream RNG (`dual-stream-rng` feature)

Algorithm	Hash digest
SHA256	`efb0cb6f6c8bfa34ad0d6318ef212b4bedbfaf2f2c84f72e258a4a11d1b45ba9`
MD5	`50a942172c30488879d83a1d31866f8e`
BLAKE2b-256	`5803f1a79d78507dcd9bc8ac63e5dbe7bbc92e61d0e7d939417ccb7e61bcd558`

Algorithm	Hash digest
SHA256	`4bf255ed077163b58cc0c36fa58ec52242999dc07df2ef730f194b478ced7ffd`
MD5	`1c0650ed79ac86ae2c442fb56f4deffd`
BLAKE2b-256	`f81d294a7fbb76dae66b9b24425505888657267f94f0807eeb01d52df947fe12`

Algorithm	Hash digest
SHA256	`d2878e81264247ff0bd7355e3c391b6e056d099232a74f80e707f71efb2e3603`
MD5	`d510cb243f1276aeae4529370b5422eb`
BLAKE2b-256	`4013285c273bcb8a719748271869a382559085124babacf299a2e18ae74d15a7`

Algorithm	Hash digest
SHA256	`a4222f73bcb6df2ae281242855dd39b91bd77ee43401f1ce1475bd27612c03cf`
MD5	`58cea93f46efc1ba71fc0c57c0d92190`
BLAKE2b-256	`062210cefa91b81ce4556166051f3db504b9546a29eba27519737b9d2c0eb400`

Algorithm	Hash digest
SHA256	`e9a2a00efd65504f171d9e50252273557c7534763b60a17770310df415fe5cbc`
MD5	`9cb2fb825b731ea4a4695fb7db7b32d0`
BLAKE2b-256	`eba2df09a52b9e4b9cd793ff5f299b8ddfc778e39b2fc454a62dd9f23294853d`

Algorithm	Hash digest
SHA256	`013e21f9cb26422f878e1aab3beca5ac440cd5d541982a2198b362aa1d1e9557`
MD5	`b42f0b2dd0cb7bea780bfc53de0b4113`
BLAKE2b-256	`baa744b49345dde752ca15b793a3e2fbe131b2a5b16d8f99be4c144d770d78b8`

Algorithm	Hash digest
SHA256	`218dbd59a78c4b45988db398b286b96cb3f19463a8a6741dad47966914a031a1`
MD5	`0f0be6bd343ccd18ea02d746dea2ec3f`
BLAKE2b-256	`e5181fcd799b8eaa7006664169d7598a94b072cd3bfea518163540bdd9f342c1`

Algorithm	Hash digest
SHA256	`60af4a970510e7d6f25beb5dff6061551b8dfba5073c12302f2c57abd822aff1`
MD5	`9e60252ad2a0048c837f38e0c9110545`
BLAKE2b-256	`df75d8652c0eee713fbeed6a3a56e8cdc12462e0fa0e98dc48fb440c8356a8ce`

Algorithm	Hash digest
SHA256	`9da3c0dc96c51089c304a4158057d489f183411ad5877bfda7d9d1603409790f`
MD5	`5750d598bec953e2d207498738cdb0f8`
BLAKE2b-256	`b7911bb1620917d857e895ea93f6349468c710721c51c1c4022db93f457ae1df`