Abstract
Designing a profitable trading strategy plays a critical role in algorithmic trading, where the algorithm can manage and execute automated trading decisions. Determining a specific trading rule for trading at a particular time is a critical research problem in financial market trading. However, an intelligent, and a dynamic algorithmic trading driven by the current patterns of a given price time-series may help deal with this issue. Thus, Reinforcement Learning (RL) can achieve optimal dynamic algorithmic trading by considering the price time-series as its environment. A comprehensive representation of the environment states is indeed vital for proposing a dynamic algorithmic trading using RL. Therefore, we propose a representation of the environment states using the Directional Change (DC) event approach with a dynamic DC threshold. We refer to the proposed algorithmic trading approach as the DCRL trading strategy. In addition, the proposed DCRL trading strategy was trained using the Q-learning algorithm to find an optimal trading rule. We evaluated the DCRL trading strategy on real stock market data (S&P500, NASDAQ, and Dow Jones, for five years period from 2015-2020), and the results demonstrate that the DCRL state representation policies obtained more substantial trading returns and improved the Sharpe Ratios in a volatile stock market. In addition, a series of performance analyses demonstrate the robust performance and extensive applicability of the proposed DCRL trading strategy.