Application of Deep Reinforcement Learning on Automated Stock Trading

Abstract

How to make right decisions in stock trading is a vital and challenging task for investors. Since deep reinforcement learning (DRL) has outperformed human beings in many fields such as playing Atari Games, can a DRL agent automatically make trading decisions and achieve long-term stable profits? In this paper, we try to solve this challenge by applying Deep Q-network (DQN) and Deep Recurrent Q-network (DRQN) in stock trading and try to build an end-to-end daily stock trading system which can decide to buy or to sell automatically at each trading day. The S...P500 ETF is selected as our trading asset and its daily trading data are used as the state of the trading environment. The agent's performance is evaluated by comparing with benchmarks of Buy and Hold (BH) and Random action-selected DQN trader. Experiment results show that our DQN trader outperforms the two benchmarks and DRQN trader is even better than DQN trader mainly because the recurrence framework can discover and exploit profitable patterns hidden in time-related sequence.