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

ArXiv ID: 2307.01599 “View on arXiv”

Authors: Unknown

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.

Keywords: On-chain data, Reinforcement learning (RL), Crypto portfolio management, Blockchain metrics, Cryptocurrency

Complexity vs Empirical Score

  • Math Complexity: 3.5/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Street Traders
  • Why: The paper introduces a novel RL-based system incorporating on-chain data for cryptocurrency portfolio management, backed by concrete backtesting results on three portfolios with specific performance metrics (ARR, DRR, Sortino ratio) and a detailed five-unit system architecture. The mathematical complexity is moderate, focusing on feature selection via Pearson correlation and RL framework application without heavy theoretical derivations or advanced formulas.
  flowchart TD
    A["Research Goal: Impact of On-Chain Data on RL Crypto Portfolios"] --> B["Data Input: On-Chain Blockchain Metrics"]
    B --> C["Methodology: CryptoRLPM System<br>5 Units: Info Comprehension to Execution"]
    C --> D["Key Innovation: Metric Specification & Scalable Portfolio"]
    D --> E["Computational Process: RL-based Training & Backtesting"]
    E --> F["Key Findings: Outperformance vs. Baselines"]
    F --> G["Outcome: Significant Boost in ARR, DRR, and Sortino Ratio"]