AI-Driven Algorithmic Trading: A Review of Machine Learning, Deep Learning, and Reinforcement Learning

Abstract

The rapid development of Artificial Intelligence (AI) has significantly transformed algorithmic trading by enabling financial systems to analyze large volumes of market data and make automated trading decisions. This review examines the application of Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL) techniques in algorithmic trading and quantitative finance. The study reviews supervised learning approaches for price prediction, classification, and market trend forecasting, as well as unsupervised learning methods for market regime identification and portfolio clustering. Deep learning architectures, including recurrent neural networks, long short-term memory networks, convolutional neural networks, and transformer-based models, are examined for their ability to capture complex temporal and nonlinear relationships in financial markets. The review also investigates reinforcement learning approaches for portfolio optimization, trade execution, and dynamic asset allocation. Challenges related to market non-stationarity, overfitting, transaction costs, data leakage, explainability, and model robustness are discussed. The role of alternative data, high-frequency data, and real-time streaming infrastructure in AI-based trading is also considered. Future research directions involving explainable AI, causal learning, multi-agent reinforcement learning, and hybrid quantitative-AI models are identified.