Learning financial asset-specific trading rules via deep reinforcement learning

作者：

Highlights：

• A model to learn asset-specific trading rules using DRL technique is proposed.

• Performance of DRL models in learning trading rules on single assets is studied.

• The effects of different feature extraction models and input types are studied.

• DRL models trained on OHLC data dominate the candlestick trading rules.

摘要

•A model to learn asset-specific trading rules using DRL technique is proposed.•Performance of DRL models in learning trading rules on single assets is studied.•The effects of different feature extraction models and input types are studied.•DRL models trained on OHLC data dominate the candlestick trading rules.

论文关键词：Reinforcement learning,Deep Q-learning,Single Stock trading,Trading strategy

论文评审过程：Received 9 November 2020, Revised 12 October 2021, Accepted 7 January 2022, Available online 20 January 2022, Version of Record 3 February 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.116523