Year: 2024
Type: article
Abstract: To enhance the portfolio performance of cryptocurrency and US treasuries, we introduce a pioneer approach that incorporates contrastive learning to traditional deep learning techniques. The contrastive learning process builds a contrary learning input whenever a market moves, or trader action is performed to enhance the learning supervision. We leverage contrastive learning with both Deep-Q Learning and Proximal Policy Optimization. The result shows promising performance in both downward and upward market environments.
Authors: Li, Zichao, Wang, Bingyang, Chen, Ying
Institution: Emory University
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W6892862271