Reference : An Application of Deep Reinforcement Learning to Algorithmic Trading
E-prints/Working papers : First made available on ORBi
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/246457
An Application of Deep Reinforcement Learning to Algorithmic Trading
English
Théate, Thibaut mailto [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Smart grids >]
Ernst, Damien mailto [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Smart grids >]
2020
Preprint submitted to Elsevier journal "Expert Systems with Applications".
Yes
[en] Artificial intelligence ; Deep reinforcement learning ; Algorithmic trading ; Trading policy
[en] 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 the stock market. It proposes a novel DRL trading policy 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 DRL approach 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 algorithm.
Montefiore Institute of Electrical Engineering and Computer Science - Montefiore Institute
F.R.S.-FNRS - Fonds de la Recherche Scientifique
http://hdl.handle.net/2268/246457
https://arxiv.org/abs/2004.06627

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