Policy Gradient Learners in Trading Agricultural ETFs

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