A program for financial portfolio management, analysis and optimisation.

Mean-Variance Portfolio Optimisation and Algorithmic Trading Strategies in MATLAB

Portfolio optimisation library.

Revolutionizing Portfolio Management in the age of Generative AI using DRL and GAN

Masters dissertation numerically solving Hamilton-Jacobi-Bellman (HJB) equation in an extension of Merton's portfolio allocation problem using finite difference.

Bachelor Thesis (in progress) - Robust Portfolio Optimisation under Parameter Uncertainty in the U.S. Equity Market (S&P 100) - Robert Smith & Joaquin Rodriguez

A Python-based project exploring algorithmic trading strategies, including backtesting, real-time data integration, and predictive modelling with TensorFlow and Keras. Key topics include technical indicators, risk management, and leveraging AWS and broker APIs for automated trading

AI Powered Stock Analysis and Portfolio Optimisation Tool

A mean-variance analysis of a portfolio of risky assets, visualising the Markowitz bullet and the efficient frontier. We also compare the performance of a randomly selected portfolio within the Markowitz bullet, with that of an efficient portfolio of the same variance.

Constructing mean-variance efficient frontiers from MPT.

A machine learning pipeline that combines financial fundamentals and historical stock trends to deliver more informed stock recommendations for London-listed companies.

Optimising asset portfolios using a variational quantum eigensolver in a hybrid quantum-classical algorithm.

HMM-based regime-switching portfolio optimiser with rolling OOS backtest, mean-variance optimisation, and volatility targeting

Walk-forward portfolio optimisation backtest comparing four allocation strategies with predicted vs actual risk analysis and an interactive Streamlit dashboard.

Quant-driven portfolio optimization framework that combines market-aware synthetic data generation using Diffusion Models (DDPMs) with reinforcement learning via PPO, integrated into a comprehensive benchmark evaluation pipeline.

Research and frameworks for optimizing fintech product portfolios.

📈 Analyze stocks and optimize portfolios with AI-driven insights, technical indicators, and clear investment signals for informed decision-making.

⚡ Explore a powerful framework for evaluating the Quantum Approximate Optimization Algorithm (QAOA) with multi-optimizer support and GPU acceleration.

A Python framework for long-only equity rebalancing across SGX, HKEX, NYSE, and NSE. Features VWAP momentum and Z-score mean-reversion signals, 48-hour earnings blackout filters, and statistical correlation penalties for institutional-grade risk management.