Abstract
This paper analyzes the use of Reinforcement Learning in trading Agricultural ETFs. The first section of the paper examines the performance of using a naive trading strategy to be used in a later comparison with Reinforcement Learning strategies. Here, returns were very volatile, with agents both severely under- and over-performing index benchmarks. The second part of this paper examines the use of the Monte Carlo Policy Gradient algorithm in trading. Finally, results of performing Dynamic Time Warping are presented for side-by-side comparisons between naive trading and trading using reinforcement learning algorithms. Overall, reinforcement learning produces more volatile results with a positive Sharpe ratio as compared to naive trading, with negative average returns.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.