Quantitative Trading

Quantitative trading uses computational methods to automate financial market decisions, aiming to maximize profits and minimize risk. Current research heavily focuses on applying and improving machine learning techniques, particularly deep reinforcement learning (e.g., DDPG, SAC) and transformer architectures, often incorporating contextual information like market sentiment to enhance prediction accuracy and trading strategy optimization. These advancements are significantly impacting the financial industry by enabling more sophisticated and efficient trading strategies, while also presenting opportunities for further research into model robustness, interpretability, and risk management.