Investigating Reinforcement Learning Approaches In Stock Market Trading

The financial industry and stock market analysis have been significantly reshaped by digital advancements, fostering remarkable growth and innovation. This transformation has been accelerated by the rise of artificial intelligence (AI) and the increasing demand for sophisticated computational power across various industries. A prime example of this shift is the rapid growth of semiconductor giants like Advanced Micro Devices (AMD) and Nvidia Corporation, both of which have seen notable increases in stock prices and market share. This project investigates the application of Reinforcement Learning (RL) in automating investment strategies within financial markets. By focusing on AMD and Nvidia, the study explores the implementation of RL algorithms, including policy networks and deep Q-networks, to optimise stock trading and investment decisions. The project aims to identify robust RL approaches that enhance decision-making by maximising returns while effectively managing market complexities and volatility, through simulations of real-world scenarios and comprehensive market data.