An Application of Deep Reinforcement Learning to Algorithmic Trading - Damien Ernst

This research paper presents a novel deep reinforcement learning (DRL) solution to the decision-making problem behind algorithmic trading in the stock markets: selecting the appropriate trading action (buy, hold or sell shares) without human intervention. Naturally, the core objective is to achieve an appreciable profit while efficiently mitigating the trading risk. This specific task is particularly complex due to the sequential nature of the problem as well as the stochastic and adversarial aspects of the environment. Moreover, a huge amount of both quantitative and qualitative information, which is generally not available, influences the dynamics of this environment. Until now, DRL algorithms mainly focused on well-known environment with specific properties, such as games.