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 liquidity pools and 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 a noisy trustless data source such as this can be helpful toward minimizing trust requirements of oracle designs.

翻译：我们研究了链上市场活动与链外加密货币市场（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外市场数据，从而为区块链价格预言机提供一种替代方案。我们探讨了 PoW 挖矿、PoS 验证、区块空间市场、网络去中心化、使用率与货币流通速度，以及链上流动性池与 AMM 中的关系。我们从这些市场中选取关键特征，通过图模型、互信息和集成机器学习模型进行分析，以探究链外定价信息在多大程度上可完全从链上恢复。我们发现，大量定价信息蕴含在链上数据中，但除短时间尺度内的模型重新训练外，通常难以恢复精确价格。我们讨论即使像这样的有噪声无信任数据源，如何有助于最小化预言机设计的信任需求。