Time-driven feature-aware jointly deep reinforcement learning for financial signal representation and algorithmic trading

作者：

Highlights：

• TFJ-DRL that interactively leverages DL and RL is viable in algorithmic trading.

• The determination of financial signals introduces informative background knowledge.

• Summarizing historical trading data and changing trend aids environmental learning.

摘要

•TFJ-DRL that interactively leverages DL and RL is viable in algorithmic trading.•The determination of financial signals introduces informative background knowledge.•Summarizing historical trading data and changing trend aids environmental learning.

论文关键词：Algorithmic trading,Deep reinforcement learning,Gate,Temporal attention

论文评审过程：Received 24 January 2019, Revised 12 August 2019, Accepted 13 August 2019, Available online 14 August 2019, Version of Record 20 August 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.112872