Algorithmic trading on financial time series using Deep Reinforcement Learning

Abstract

Abstract The use of technology in financial markets has led to extensive changes in conventional trading structures.Today, most orders that reach exchanges are created by algorithmic trading agents. Today, machine learning-based methods play an important role in building automated trading systems. The increasing complexity and dynamism of financial markets are among the key challenges of these methods. The most widely used machine learning approach is supervised learning, but in interactive environments, the use of supervised learning alone has limitations such as difficulty in defining appropriate labels and lack of modeling of the dynamic nature of the market. Due to the good performance of deep reinforcement learning-based approaches, we will use these approaches to solve the mentioned problems. In this paper, we presented a deep reinforcement learning framework for trading in the financial market, a set of input features and indicators selected and tailored to the purpose of the problem, reward function, appropriate models based on fully connected, convolutional and hybrid networks. The proposed top models traded under real market conditions such as transaction costs and then were evaluated. In addition to outperforming the buy and hold strategy, these models achieved excellent cumulative returns while having appropriate risk metrics.