A major problem of the financial industry is the ability to adapt their trading strategies at the same rate the market evolves. This paper proposes a solution using existing Reinforcement Learning libraries to help find new strategies at a practical scale. Using a wide domain of ticker symbols, an algorithm is trained in an environment that better represents reality. The supplied decision-making algorithm is tested using recorded data from the U.S stock market from 2000 through 2022. The results of this research show that existing techniques are statistically better than making decisions at random. With this result, this research shows how a practical application of Reinforcement Learning is possible through the inclusion of many more ticker symbols than previous research has done before. However, there is still work to be done to achieve acceptable returns. Potential applications of this research include informing human traders or creating automated traders.
Traxler, Philip; Aman, Sadik; Rogers, Will; and Okun, Allyn
"Investigation into a Practical Application of Reinforcement Learning for the Stock Market,"
SMU Data Science Review: Vol. 7:
3, Article 6.
Available at: https://scholar.smu.edu/datasciencereview/vol7/iss3/6
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