Sentiment Driven Reinforcement Learning Trading Strategies to Enhance Market Performance

This research paper seeks to present a comprehensive examination of algorithmic trading algorithms, encompassing a range of techniques that extend from supervised learning to sentiment-aware reinforcement-based trading algorithms. The study further investigates the transformative potential of artificial intelligence (AI) in the field of stock trading, emphasizing the advantages of leveraging AI within this domain. Furthermore, the paper explores the incorporation of natural language processing (NLP) algorithms to account for the influence of news on algorithmic predictions. Subsequently, a comparative analysis of the algorithms' performance is carried out based on the profits generated by each. Lastly, the paper outlines the future implications of the subject matter and concludes by summarizing the obtained results.