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.

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

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

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

This repository contains the source code for "Stochastic data-driven model predictive control using Gaussian processes" (SDD-GP-MPC).

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

Different quantitative trading models research