Reinforcement Learning for Stock Option Trading

This work focuses on reinforcement learning and its importance in trading stock options. It is based on stock options analysis of the American expiration style, considered more challenging in trading than the European expiration style. Reinforcement learning has the potential to identify optimal trading strategies for stock options and could assist current traders in their trading strategies. In addition, automating trading and trading tasks could enable organisations to act more quickly, consistently, and cost-effectively. This work explores the potential benefits of Reinforcement Learning in stock options trading and how it can be used to modify existing techniques. It uses Reinforcement Learning algorithms to develop algorithmic trading strategies, which are difficult for human traders. This work found that Reinforcement Learning develops trading strategies to assist human traders, but more work is needed before these can be fully autonomous.