  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.
