A Scalable Reinforcement Learning-based System Using On-Chain Data for
  Cryptocurrency Portfolio Management
Zhenhan Huang Fumihide Tanaka
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.