Portfolio Optimization with Reinforcement Learning

This study explores the application of deep reinforcement learning (DRL) in portfolio optimization, addressing the limitations of traditional models in dynamic financial markets. Using algorithms like Advantage Actor-Critic (A2C), Proximal Policy Optimization (PPO), Soft Actor-Critic (SAC), and Twin Delayed Deep Deterministic Policy Gradient (TD3), we evaluate their ability to balance risk and returns. Through data preprocessing, feature engineering, and advanced modeling, the study implements DRL agents to manage a portfolio of 30 stocks with low pairwise correlations. Results show SAC’s impressive risk-adjusted returns and TD3’s stable performance, though limitations such as constrained action space and tiny excess returns emphasize areas for further improvement. This work shows the potential of DRL in advancing portfolio optimization through adaptive, data-driven strategies.