An Application of Deep Reinforcement Learning to Algorithmic Trading
This work addresses the problem of optimizing trading strategies for stock market participants, but it appears incremental as it adapts an existing DQN algorithm to a specific domain.
The paper tackled the algorithmic trading problem of determining optimal trading positions by proposing a novel deep reinforcement learning strategy called TDQN, which achieved promising results in maximizing the Sharpe ratio across stock markets using a new performance assessment methodology.
This scientific research paper presents an innovative approach based on deep reinforcement learning (DRL) to solve the algorithmic trading problem of determining the optimal trading position at any point in time during a trading activity in stock markets. It proposes a novel DRL trading strategy so as to maximise the resulting Sharpe ratio performance indicator on a broad range of stock markets. Denominated the Trading Deep Q-Network algorithm (TDQN), this new trading strategy is inspired from the popular DQN algorithm and significantly adapted to the specific algorithmic trading problem at hand. The training of the resulting reinforcement learning (RL) agent is entirely based on the generation of artificial trajectories from a limited set of stock market historical data. In order to objectively assess the performance of trading strategies, the research paper also proposes a novel, more rigorous performance assessment methodology. Following this new performance assessment approach, promising results are reported for the TDQN strategy.