Deep Reinforcement Learning Models for Automated Stock Trading

Abstract

This paper proposes an automated trading strategy using reinforcement learning. The stock market has become one of the largest financial institutions. These institutions embrace machine learning solutions based on artificial intelligence for market monitoring, credit quality, fraud detection, and many other areas. We desire to provide an efficient and effective solution that would overcome the manual trading drawbacks by building a Trading Bot. In this paper, we will propose a stock trading strategy that uses reinforcement learning algorithms to maximize the profit. The strategy employs three actor critic models: Advanced Actor Critic(A2C), Twin Delayed DDPG (TD3) and Soft Actor Critic (SAC). Our strategy picks the most optimal model based on the current market situation. The performance of our trading bot is evaluated and compared with Markowitz portfolio theory.