Currently, there are no convincing proxies for the fundamentals of cryptocurrency assets. We propose a new market-to-fundamental ratio, the price-to-utility (PU) ratio, utilizing unique blockchain accounting methods. We then proxy various existing fundamental-to-market ratios by Bitcoin historical data and find they have little predictive power for short-term bitcoin returns. However, PU ratio effectively predicts long-term bitcoin returns than alternative methods. Furthermore, we verify the explainability of PU ratio using machine learning. Finally, we present an automated trading strategy advised by the PU ratio that outperforms the conventional buy-and-hold and market-timing strategies. Our research contributes to explainable AI in finance from three facets: First, our market-to-fundamental ratio is based on classic monetary theory and the unique UTXO model of Bitcoin accounting rather than ad hoc; Second, the empirical evidence testifies the buy-low and sell-high implications of the ratio; Finally, we distribute the trading algorithms as open-source software via Python Package Index for future research, which is exceptional in finance research.

翻译：目前，加密货币资产缺乏令人信服的基本面代理指标。我们提出了一种利用独特区块链会计方法的市场-基本面比率——价格-效用（PU）比率。随后，我们通过比特币历史数据对多种现有的基本面-市场比率进行代理分析，发现这些比率对比特币短期收益率的预测能力较弱。然而，PU比率在预测比特币长期收益率方面优于其他方法。此外，我们利用机器学习验证了PU比率的可解释性。最后，我们提出了一种基于PU比率的自动化交易策略，该策略优于传统的买入持有策略和市场择时策略。本研究从三个层面为金融领域的可解释人工智能做出贡献：第一，我们的市场-基本面比率基于经典货币理论及比特币会计中独特的UTXO模型，而非临时性构建；第二，实证证据验证了该比率低买高卖的含义；第三，我们通过Python包索引将交易算法以开源软件形式发布以供未来研究，这在金融研究中具有独特性。