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

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

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

Language modeling via stochastic processes. Oral @ ICLR 2022.

R package for statistical inference using partially observed Markov processes

Matlab Toolbox for the Numerical Solution of Stochastic Differential Equations

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)

The most realistic keyboard typing simulator based on Markov Chains. Models authentic human behavior (errors, corrections, fatigue, speed variations) for Playwright and Selenium automation.

Different quantitative trading models research

This repository contains the material (datasets, code, videos, spreadsheets) related to my book Stochastic Processes and Simulations - A Machine Learning Perspective.

PyCurve : Python Yield Curve is a package created in order to interpolate yield curve, create parameterized curve and create stochastic simulation.

JumpDiff: Non-parametric estimator for Jump-diffusion processes for Python

Tools to generate and study moment equations for any chemical reaction network using various moment closure approximations

Here is the exercise solution of stochastic process Ross 2nd Edition collected by the author. The answers are from the stochastic process courses of Umich, Columbia University and BJTU respectively. Due to the different assignments assigned by each teacher, the answers provided by each university are not complete, for your comprehensive reference.

PyTorch code of "Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows" (NeurIPS 2020)

Adaptive control for skid-steer robots using GP-enhanced MPPI for robust navigation and obstacle avoidance on diverse terrains.