Algorithmic Trading

Algorithmic trading uses computer programs to execute trades automatically, aiming to maximize profits and minimize risks in financial markets. Current research heavily focuses on applying and improving machine learning models, including reinforcement learning (e.g., DQN, A3C, TD3), neural networks (often combined with HMMs), and other techniques like contrastive learning, to predict price movements and optimize trading strategies across various asset classes (stocks, cryptocurrencies, commodities). This field is significant due to its potential to improve market efficiency and investment returns, driving ongoing investigation into model robustness, risk management, and the ethical implications of increasingly sophisticated automated trading systems.