We investigate the theoretical and empirical relationships between activity in on-chain markets and pricing in off-chain cryptocurrency markets (e.g., ETH/USD prices). The motivation is to develop methods for proxying off-chain market data using data and computation that is in principle verifiable on-chain and could provide an alternative approach to blockchain price oracles. We explore relationships in PoW mining, PoS validation, block space markets, network decentralization, usage and monetary velocity, and on-chain Automated Market Makers (AMMs). We select key features from these markets, which we analyze through graphical models, mutual information, and ensemble machine learning models to explore the degree to which off-chain pricing information can be recovered entirely on-chain. We find that a large amount of pricing information is contained in on-chain data, but that it is generally hard to recover precise prices except on short time scales of retraining the model. We discuss how even noisy information recovered from on-chain data could help to detect anomalies in oracle-reported prices on-chain.

翻译：我们研究了链上市场活动与链下加密货币市场（如ETH/USD价格）定价之间的理论与实证关系。其动机在于开发一种方法，利用原则上可在链上验证的数据与计算来代理链下市场数据，从而为区块链价格预言机提供替代方案。我们探讨了PoW挖矿、PoS验证、区块空间市场、网络去中心化、使用与货币流通速度，以及链上自动做市商（AMM）中的关系。我们从这些市场中选取关键特征，通过图模型、互信息以及集成机器学习模型进行分析，以探究链下定价信息在多大程度上可完全通过链上数据还原。研究发现，大量定价信息包含于链上数据中，但除短时间尺度的模型重新训练外，通常难以恢复精确价格。我们讨论了即使从链上数据恢复的含噪信息，也可能有助于检测链上预言机报告价格中的异常。