A high-performance Rust library for simulating stochastic processes, with applications in quantitative finance, statistical modeling, and synthetic data generation.
stochastic-rs is a Rust library for simulating and analyzing stochastic processes. It provides a wide range of models and tools for quantitative finance, statistical modeling, and synthetic data generation, with a focus on high performance through SIMD and parallel computing.
Quantitative developers, financial engineers, and researchers who need high-performance simulation of stochastic models for pricing, risk analysis, or statistical research.
It offers an extensive collection of models, high performance via Rust and optional GPU acceleration, and seamless Python integration, positioning itself as a modern, open-source alternative to libraries like QuantLib.
stochastic-rs is a Rust library designed for high-performance simulation and analysis of stochastic processes and models in quant finance.
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Includes over 85 stochastic models spanning diffusions, jumps, and volatility, providing broad coverage for quantitative finance simulations as listed in the README.
Leverages SIMD-optimized distributions and parallel sampling via rayon, with benchmarks showing up to 7.9x speedup on multi-core CPUs for tasks like distribution sampling.
Offers full Python bindings with numpy array returns, allowing easy adoption in Python workflows while maintaining Rust's performance benefits, as demonstrated in the usage examples.
Optional CUDA-native feature enables GPU-accelerated sampling, with benchmarks indicating speedups for large datasets (e.g., 3.7x for single paths at n=65,536).
Optional features like OpenBLAS and CUDA require manual system installations and specific build commands, which can be error-prone, especially on Windows as noted in the README.
The roadmap admits missing module documentation and zero tests for key areas like calendars and bonds, potentially hindering usability and reliability for new users.
Roadmap highlights stability concerns such as panic-prone .unwrap() calls, inconsistent data structures (e.g., Vec<Vec<f64>>), and mixed linear algebra libraries, indicating ongoing refactoring needs.
stochastic-rs is an open-source alternative to the following products: