portfolio-optimisation

A program for financial portfolio management, analysis and optimisation.

Mean-Variance Portfolio Optimisation and Algorithmic Trading Strategies in MATLAB

Portfolio optimisation library for Julia. Over 50 risk measures (CVaR, EVaR, RLVaR, drawdown, OWA), hierarchical risk parity, HERC, nested clustered optimisation, risk budgeting, near-optimal centering, four Black-Litterman variants, entropy pooling, factor and high-order priors, denoising, and JuMP-backed convex and non-convex optimization.

DEPRECATED — succeeded by PortfolioOptimisers.jl (plural). Julia portfolio optimisation library; all work continues at dcelisgarza/PortfolioOptimisers.jl.

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.

A fat-tail-native quantitative finance toolkit for Python.

Multi-model framework for market regime detection and dynamic asset allocation using HMM, XGBoost, LSTM, backtesting, and RL.

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

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.

Interactive portfolio analytics platform for risk analysis, Monte Carlo simulation, optimization, rebalancing and benchmark sensitivity.

Python portfolio optimisation, Value at Risk, stress testing and financial risk analysis.

AI Powered Stock Analysis and Portfolio Optimisation Tool

Constrained portfolio rate optimisation for insurance pricing — SLSQP, FCA ENBP, efficient frontier, shadow prices, JSON audit trail

Constructing mean-variance efficient frontiers from MPT.

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

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

Provide efficient event-driven Python tools for accurate multi-asset quantitative backtesting with local data caching and detailed trade logging.

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