Practical Algorithmic Trading Using State Representation Learning and Imitative Reinforcement Learning

Practical Algorithmic Trading Using State Representation Learning and Imitative Reinforcement Learning

Algorithmic trading allows investors to avoid emotional and irrational trading decisions and helps them make profits using modern computer technology. In recent years, reinforcement learning has yielded promising results for algorithmic trading. Two prominent challenges in algorithmic trading with r...

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Bibliographic Details
Main Authors:	Deog-Yeong Park, Ki-Hoon Lee
Format:	Article
Language:	English
Published:	IEEE 2021-01-01
Series:	IEEE Access
Subjects:	Algorithmic trading deep learning state representation learning imitation learning reinforcement learning
Online Access:	https://ieeexplore.ieee.org/document/9611246/

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