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

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.

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

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

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

Portfolio optimization package in Python.

Scala OrderBook Reconstructor for high-frequency order-flow data

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.

Application of the Bellman Ford (shortest-path) Algorithm to detect real time arbitrage opportunities in currency trading.

End-to-End Python implementation of "FedSight AI" multi-agent system for Federal Funds Target Rate prediction (NeurIPS 2025 Workshop). Simulates FOMC deliberations using LLMs with Chain-of-Draft reasoning and In-Context Learning. Integrates structured macro indicators with unstructured narratives (Beige Book, Dot Plots).