Deep Reinforcement Learning Based Stock Trading Strategy Considering Opportunity Loss

Abstract

In recent years, research on financial transactions using deep reinforcement learning, which is one of machine learning, has been actively conducted. In these studies, various approaches have been taken, such as those that consider the number of financial instruments bought and sold, compound interest calculation, and those that use stock price charts for input, but enough profit has not been made in all periods. It is considered that this is because the opportunity loss cannot be taken into consideration. Therefore, in this study, we build a model to learn the optimal buying and selling timings to make a profit in stock investment by incorporating the opportunity loss for each action into the reward in deep reinforcement learning, and show its effectiveness.