Gaussian processes in TensorFlow

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.

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

Multifractal Detrended Fluctuation Analysis in Python

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

📦 Python library for Stochastic Processes Simulation and Visualisation

EasyTPP: Towards Open Benchmarking Temporal Point Processes

Language modeling via stochastic processes. Oral @ ICLR 2022.

Predicting stock prices using Geometric Brownian Motion and the Monte Carlo method

My book: Gentle Introduction to Chaotic Dynamical Systems. Includes stochastic dynamical systems and statistical properties of numeration systems in any dimension.

This repository offers a curated collection of research papers and code examples covering advanced trading strategies and financial engineering, including quantitative and algorithmic trading. It serves as a comprehensive resource for industry professionals and enthusiasts alike, helping them stay ahead of the curve in this rapidly-evolving field.

SdePy: Numerical Integration of Ito Stochastic Differential Equations

Volatility Decomposition of Asset Price Time Series

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

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

Implementation of option pricing models using Numba that performs better. This entire project has utilized as little libraries as possible, even though certain models have their own Machine Learning Model with assessment and performance.

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

Code for "GP-ConvCNP: Better Generalization for Convolutional Conditional Neural Processes on Time Series Data"