A Python library for evaluating option trading strategies.

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

Differentiable Programming Algorithms in Modern C++

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.

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

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

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

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

Collection of projects oriented around the computational finance domain.

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

Scala OrderBook Reconstructor for high-frequency order-flow data

Portfolio optimization package in Python.

Python implementation of the basic model described in Chan, Nicholas Tung, and Christian Shelton. "An electronic market-maker."

Predictive analysis of the OLMAR algorithm

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

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

robo-advisor is a quantitative analysis script written in Python that generates the least volatile portfolio given a list of stocks, with the goal of a 0% return.

This repository contains some of the implementations related to my master thesis under supervision of Prof Josef Teichmann at ETH Zurich.

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