Reinforcement Learning in Quantitative Trading: A Review of Current Approaches and Breakthroughs

Reinforcement learning (RL) provides an autonomous approach to quantitative trading by allowing an agent to learn a trading policy through repeated interaction with a market environment rather than through hand-crafted rules. The aim of an RL trading agent is to select actions that maximize long-term rewards while meeting a chosen level of risk. This review examines RL applications in quantitative trading, with particular attention to value-based, policy-based, and actor–critic methods. It summarizes the reported returns, risk-adjusted performance, evaluation settings, and practical limitations of the reviewed studies. Although many RL methods report promising results in backtests and simulated environments, their outcomes are highly dependent on data selection, feature construction, transaction cost assumptions, training procedures, reward design, and validation protocols. Consequently, reported returns, Sharpe ratios, and drawdowns across studies are not directly comparable and should not be interpreted as evidence that one RL method is universally superior to another. This review shows that relevant state information, suitable reward functions, realistic risk management, and deep neural feature extraction can improve RL trading performance. However, reliable progress requires transparent and consistent reporting of data handling, time-respecting validation, execution assumptions, transaction costs, slippage, and risk-adjusted trading outcomes. We therefore argue that future RL trading research should adopt Quantitative Finance Reporting Standard (QFRS), to make trading claims data leakage free, reproducible, back-tested, economically meaningful, and comparable across studies.