“Design of Stock Trading Agent Using Deep Reinforcement Learning”

Abstract

This study adopts Double Deep Q learning algorithm to design trading strategies to trade stocks of four commercial banks listed in NEPSE. The reinforcement learning agent takes discrete actions and gets negative or positive reward from the environment. CNN is utilized to form the policy network. A target network is used to mitigate instability due to Deep Q Network. The concept of experience replay is used to randomly sample the batches of experience from the memory and train the network. The performance of Double Deep Q learning agent was compared with various baseline trading strategies in terms of annualised expected trade return. The maximum annualised expected trade return obtained with traditional baseline methods was 103% for testing data of NABIL, while for the same data the reinforcement learning agent using double deep Q learning algorithm obtained annualized expected trade return of 114.44%. The experiments showed that, Double Deep Q learning agent with experience replay had higher annualised expected trade return compared to baseline trading strategies.