A Python library for evaluating option trading strategies.

Companion code for "Modern Computational Finance: AAD and Parallel Simulations" (Antoine Savine, Wiley, 2018)

Implement, demonstrate, reproduce and extend the results of the Risk articles 'Differential Machine Learning' (2020) and 'PCA with a Difference' (2021) by Huge and Savine, and cover implementation details left out from the papers.

Differentiable Programming Algorithms in Modern C++

state of the art C++ pseudo-random number generator library for sequential and parallel Monte Carlo simulations

Complement the article 'Differential Machine Learning' (Huge & Savine, 2020), including mathematical proofs and important implementation details for production

Stock Market Prediction on High-Frequency Data Using soft computing based AI models

using the Inverse-Transform method to speed up options pricing simulations in R

An R Package for Monte Carlo Option Pricing Algorithm for Jump Diffusion Models with Correlational Companies

Predictive analysis of the OLMAR algorithm

Collection of projects oriented around the computational finance domain.

Scala OrderBook Reconstructor for high-frequency order-flow data

A simple algorithmic trading bot based on machine learning methods.

Portfolio optimization package in Python.

functions and scripts for the course Computational Finance a.c. 2016/2017

A collection of assignment submissions from the 2021/22 MSc Computational Finance Course.

Mathematical Finance

JASA is a high-performance auction simulator written in JAVA. It is designed for performing experiments in agent-based computational economics.

Prediction of Occurrence of Circuits in Stock Prices using Machine Learning

Efficient financial risk estimation via computer experiment design (regression + variance-reduced sampling)