Oracle对位法：链上与链下市场数据之间的关系
Oracle Counterpoint: Relationships between On-chain and Off-chain Market Data
AI总结：
研究链上市场活动与链下加密货币定价的关系，通过图模型、互信息和集成机器学习分析PoW、PoS、AMM等链上特征，发现链上数据可恢复大量定价信息并辅助检测预言机价格异常，为区块链预言机提供替代方案。
摘要：
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）定价之间的理论和经验关系。其动机是开发使用原则上可在链上验证的数据和计算来代理链下市场数据的方法，这可为区块链价格预言机提供一种替代方法。我们探讨了 PoW 挖矿、PoS 验证、区块空间市场、网络去中心化、使用量和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、互信息和集成机器学习模型对其进行分析，以探索链下定价信息能在多大程度上完全在链上恢复。我们发现链上数据中包含大量定价信息，但通常很难恢复精确价格，除非在重新训练模型的短时间尺度上。我们讨论了从链上数据恢复的即使是含噪声的信息，如何有助于检测链上预言机报告价格中的异常。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
