An application of deep reinforcement learning to algorithmic trading
نویسندگان
چکیده
This scientific research paper presents an innovative approach based on deep reinforcement learning (DRL) to solve the algorithmic trading problem of determining optimal position at any point in time during a activity stock markets. It proposes novel DRL strategy so as maximise resulting Sharpe ratio performance indicator broad range Denominated Trading Deep Q-Network algorithm (TDQN), this new is inspired from popular DQN and significantly adapted specific hand. The training (RL) agent entirely generation artificial trajectories limited set market historical data. In order objectively assess strategies, also novel, more rigorous assessment methodology. Following approach, promising results are reported for TDQN strategy.
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ژورنال
عنوان ژورنال: Expert Systems With Applications
سال: 2021
ISSN: ['1873-6793', '0957-4174']
DOI: https://doi.org/10.1016/j.eswa.2021.114632