Intern Spotlight: HRT AI Labs Project

This post details the investigation into applying reinforcement learning to optimal execution problems in algorithmic trading. It describes modeling the problem as an optimal execution task where an RL agent uses short-term return predictions to trade towards a predefined target position. The post contrasts Soft Actor-Critic (SAC) and Proximal Policy Optimization (PPO), highlighting SAC's tendency to learn non-smooth policies and PPO's enhanced training stability due to on-policy data collection. A Memory-Augmented PPO is introduced to address partial observability. The evaluation compares the trading profit and loss (PnL) of these algorithms against a rule-based baseline under varying signal accuracy levels, showing Memory-Augmented PPO as the top performer.