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This paper introduces a novel dataset of 8.9 million on-chain events from four prominent AMM protocols (Pendle, Uniswap v3, Aave, Morpho) with fine-grained transaction type and block height annotations. To better model the event-driven price dynamics in AMMs, they propose an Uncertainty Weighted Mean Squared Error (UWM) loss function that integrates block interval regression into Time-Point Process (TPP) models. Experiments show that UWM reduces time prediction error by 56.41% on average across eight TPP architectures, while maintaining event type prediction accuracy.
AMM price prediction accuracy jumps 56% by explicitly modeling the uncertainty in block intervals, revealing the critical role of on-chain event timing.
Automated Market Makers (AMMs), as a core infrastructure of decentralized finance (DeFi), uniquely drive on-chain asset pricing through a deterministic reserve ratio mechanism. Unlike traditional markets, AMM price dynamics is triggered largely by on-chain events (e.g., swap) that change the reserve ratio, rather than by continuous responses to off-chain information. This makes event-level analysis crucial for understanding price formation mechanisms in AMMs. However, existing research generally neglects the micro-structural dynamics at the AMMs level, lacking both a comprehensive dataset covering multiple protocols with fine-grained event classification and an effective framework for event-aware modeling. To fill this gap, we construct a dataset containing 8.9 million on-chain event records from four representative AMMs protocols: Pendle, Uniswap v3, Aave and Morpho, with precise annotations of transaction type and block height timestamps. Furthermore, we propose an Uncertainty Weighted Mean Squared Error (UWM) loss function, which incorporates the block interval regression term into the traditional Time-Point Process (TPP) objective function by weighting the uncertainty with homoscedasticity. Extensive experiments on eight advanced TPP architectures demonstrate that this loss function reduces the time prediction error by an average of 56.41\% while maintaining the accuracy of event type prediction, establishing a robust benchmark for event-aware prediction in the AMMs ecosystem. This work provides the necessary data foundation and methodological framework for modeling the discreteness and event-driven characteristics of on-chain price discovery. All datasets and source code are publicly available. https://github.com/yosen-king/Deep-AMM-Events
