  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.
