A Scalable Reinforcement Learning-based System Using On-Chain Data for Cryptocurrency Portfolio Management

TL;DR

This paper introduces CryptoRLPM, a scalable reinforcement learning system that leverages on-chain data for cryptocurrency portfolio management, significantly improving performance metrics over baselines.

Contribution

The study presents a novel RL-based system that effectively incorporates on-chain data, enhancing crypto portfolio management and outperforming existing methods in backtests.

Findings

CryptoRLPM outperforms baselines in ARR, DRR, and SR.

Increases in performance metrics are at least 83.14% for ARR.

The system is scalable and adaptable to different crypto portfolios.

Abstract

On-chain data (metrics) of blockchain networks, akin to company fundamentals, provide crucial and comprehensive insights into the networks. Despite their informative nature, on-chain data have not been utilized in reinforcement learning (RL)-based systems for cryptocurrency (crypto) portfolio management (PM). An intriguing subject is the extent to which the utilization of on-chain data can enhance an RL-based system's return performance compared to baselines. Therefore, in this study, we propose CryptoRLPM, a novel RL-based system incorporating on-chain data for end-to-end crypto PM. CryptoRLPM consists of five units, spanning from information comprehension to trading order execution. In CryptoRLPM, the on-chain data are tested and specified for each crypto to solve the issue of ineffectiveness of metrics. Moreover, the scalable nature of CryptoRLPM allows changes in the portfolios' cryptos at any time. Backtesting results on three portfolios indicate that CryptoRLPM outperforms all the baselines in terms of accumulated rate of return (ARR), daily rate of return (DRR), and Sortino ratio (SR). Particularly, when compared to Bitcoin, CryptoRLPM enhances the ARR, DRR, and SR by at least 83.14%, 0.5603%, and 2.1767 respectively.