Collection of notebooks about quantitative finance, with interactive python code.

Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.

NMA Computational Neuroscience course

Gaussian processes in TensorFlow

Differentiable SDE solvers with GPU support and efficient sensitivity analysis.

Rust library for quantitative finance.

Python framework for short-term ensemble prediction systems.

Generate realizations of stochastic processes in python.

📦 Python library for Stochastic Processes Simulation and Visualisation

EasyTPP: Towards Open Benchmarking Temporal Point Processes

Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem

Code for the Neural Processes website and replication of 4 papers on NPs. Pytorch implementation.

Python implementation of pricing analytics and Monte Carlo simulations for stochastic volatility models including log-normal SV model, Heston

Multifractal Detrended Fluctuation Analysis in Python

Language modeling via stochastic processes. Oral @ ICLR 2022.

Economic scenario generator for python: simulate stocks, interest rates, and other stochastic processes.

R package for statistical inference using partially observed Markov processes

Matlab Toolbox for the Numerical Solution of Stochastic Differential Equations

A library of noise processes for stochastic systems like stochastic differential equations (SDEs) and other systems that are present in scientific machine learning (SciML)

stochastic-rs is a Rust library designed for high-performance simulation and analysis of stochastic processes and models in quant finance.