Optimizing Automated Trading Systems with Deep Reinforcement Learning

In this paper, we propose a novel approach to optimize parameters for strategies in automated trading systems. Based on the framework of Reinforcement learning, our work includes the development of a learning environment, state representation, reward function, and learning algorithm for the cryptocurrency market. Considering two simple objective functions, cumulative return and Sharpe ratio, the results showed that Deep Reinforcement Learning approach with Double Deep Q-Network setting and the Bayesian Optimization approach can provide positive average returns. Among the settings being studied, Double Deep Q-Network setting with Sharpe ratio as reward function is the best Q-learning trading system. With a daily trading goal, the system shows outperformed results in terms of cumulative return, volatility and execution time when compared with the Bayesian Optimization approach. This helps traders to make quick and efficient decisions with the latest information from the market. In long-term trading, Bayesian Optimization is a method of parameter optimization that brings higher profits. Deep Reinforcement Learning provides solutions to the high-dimensional problem of Bayesian Optimization in upcoming studies such as optimizing portfolios with multiple assets and diverse trading strategies.