#Stochastic-processes
Showing 19 of 19 repositories tagged #stochastic-processes, ranked by stars
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 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.
📒 A collection of notes exploring Quantitative Finance concepts with Python
The most realistic keyboard typing simulator based on Markov Chains. Models authentic human behavior (errors, corrections, fatigue, speed variations) for Playwright and Selenium automation.
This repository contains the source code for "Stochastic data-driven model predictive control using Gaussian processes" (SDD-GP-MPC).
A collection of educational notebooks covering key mathematical concepts and their applications in quantitative finance
Different quantitative trading models research
Quantitative finance and derivative pricing
Bayesian Inference and parameter estimation in quant finance.
Stochastic models to price financial options
Automatic optimal sequential investment decisions. Forecasts made using advanced stochastic processes with Monte Carlo simulation. Dependency is handled with vine copulas.
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.
My personal work on the numerical projects of a book called "A First Course in Stochastic Calculus".