A Contrastive Deep Learning Approach to Cryptocurrency Portfolio with US Treasuries

Zichao Li; Bingyang Wang; Ying Chen

doi:10.5281/zenodo.13357988

Authors

Zichao Li Canoakbit Alliance Inc
Bingyang Wang Emory University
Ying Chen MyMap AI

DOI:

https://doi.org/10.5281/zenodo.13357988

ARK:

https://n2t.net/ark:/40704/JCTAM.v1n3a01

Keywords:

Cryptocurrency, Supervised Learning, Digital Asset, Bitcoin, Ethereum, Litecoin, Machine Learning, Crypto, Contrastive Learning

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.

Author Biographies

Zichao Li, Canoakbit Alliance Inc

Canoakbit Alliance Inc, Canada.

Bingyang Wang, Emory University

Emory University, USA.

Ying Chen, MyMap AI

MyMap AI, USA.

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