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

Rust library for quantitative finance.

Differentiable SDE solvers with GPU support and efficient sensitivity analysis.

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

High-performance quantitative finance in Rust — 120+ stochastic processes, option pricing, calibration, fixed income, risk & copulas, with SIMD/GPU acceleration and Python bindings.

Multifractal Detrended Fluctuation Analysis in Python

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

Language modeling via stochastic processes. Oral @ ICLR 2022.

📒 A collection of notes exploring Quantitative Finance concepts with Python

R package for statistical inference using partially observed Markov processes

Matlab Toolbox for the Numerical Solution of Stochastic Differential Equations