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...
Saved in:
Main Authors: | Deog-Yeong Park, Ki-Hoon Lee |
---|---|
Format: | article |
Language: | EN |
Published: |
IEEE
2021
|
Subjects: | |
Online Access: | https://doaj.org/article/fec1d81fddfe4844bbcbdf81d0705b41 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Reinforcement Learning Approaches to Optimal Market Making
by: Bruno Gašperov, et al.
Published: (2021) -
Applications of Multi-Agent Deep Reinforcement Learning: Models and Algorithms
by: Abdikarim Mohamed Ibrahim, et al.
Published: (2021) -
An Experimental Study on State Representation Extraction for Vision-Based Deep Reinforcement Learning
by: Junkai Ren, et al.
Published: (2021) -
Home Energy Management Algorithm Based on Deep Reinforcement Learning Using Multistep Prediction
by: Naoki Kodama, et al.
Published: (2021) -
An Adaptive Threshold for the Canny Algorithm With Deep Reinforcement Learning
by: Keong-Hun Choi, et al.
Published: (2021)