Stock Market Trading Agent Using On-Policy Reinforcement Learning Algorithms

Abstract

Stock market has been a complex system which has been difficult to predict for humans, thereby, making the trading decisions difficult to take. It will be useful for traders if there is a model agent which can learn the stock market trends and suggest trading decisions which in turn maximizes the profits. Inorder to develop this agent we have formulated the problem as a Markov Decision Process (MDP) and created a stock trading environment which serves as a platform for this agent to trade the stocks. In this paper, we introduce a Reinforcement Learning based approach to develop a trading agent which performs trading actions on the environment and learns according to the rewards in terms of profit or loss it receives. We have applied different On-policy Reinforcement Learning Algorithms such as Vanilla Policy Gradient (VPG), Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO) on the environment to obtain the profits while trading stocks for 3 companies viz. Apple, Microsoft and Nike. The performance of these algorithms in order to maximize the profits have been evaluated and the results and conclusions have been elaborated.