Quantitative Trading through Random Perturbation Q-Network with Nonlinear Transaction Costs

Quantitative Trading through Random Perturbation Q-Network with Nonlinear Transaction Costs

In recent years, reinforcement learning (RL) has seen increasing applications in the financial industry, especially in quantitative trading and portfolio optimization when the focus is on the long-term reward rather than short-term profit. Sequential decision making and Markov decision processes are...

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Bibliographic Details
Main Authors: Tian Zhu, Wei Zhu
Format: Article
Language:English
Published: MDPI AG 2022-06-01
Series:Stats
Subjects:
deep reinforcement learning
Markov decision process
quantitative finance
random perturbation algorithm
transaction costs model
Online Access:https://www.mdpi.com/2571-905X/5/2/33

Internet

https://www.mdpi.com/2571-905X/5/2/33

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