A Cross-Chain Event-Driven Data Infrastructure for Aave Protocol Analytics and Applications

Decentralized lending protocols, exemplified by Aave V3, have transformed financial intermediation by enabling permissionless, multi-chain borrowing and lending without intermediaries. Despite managing over $10 billion in total value locked, empirical research remains severely constrained by the lack of standardized, cross-chain event-level datasets. This paper introduces the first comprehensive, event-driven data infrastructure for Aave V3 spanning six major EVM-compatible chains (Ethereum, Arbitrum, Optimism, Polygon, Avalanche, and Base) from respective deployment blocks through October 2025. We collect and fully decode eight core event types – Supply, Borrow, Withdraw, Repay, LiquidationCall, FlashLoan, ReserveDataUpdated, and MintedToTreasury – producing over 50 million structured records enriched with block metadata and USD valuations. Using an open-source Python pipeline with dynamic batch sizing and automatic sharding (each file less than or equal to 1 million rows), we ensure strict chronological ordering and full reproducibility. The resulting publicly available dataset enables granular analysis of capital flows, interest rate dynamics, liquidation cascades, and cross-chain user behavior, providing a foundational resource for future studies on decentralized lending markets and systemic risk.

💡 Research Summary

The paper presents the first comprehensive, cross‑chain, event‑driven data infrastructure for the Aave V3 lending protocol, covering six major EVM‑compatible blockchains: Ethereum, Arbitrum, Optimism, Polygon, Avalanche, and Base. By extracting and fully decoding eight core event types—Supply, Borrow, Withdraw, Repay, LiquidationCall, FlashLoan, ReserveDataUpdated, and MintedToTreasury—from each chain’s deployment block through October 2025, the authors generate more than 50 million structured records. Each record is enriched with block metadata (block number, timestamp, gas usage, transaction hash) and a USD valuation derived from on‑chain price oracles, enabling consistent cross‑chain financial analysis.

The data pipeline is implemented in open‑source Python and emphasizes scalability, efficiency, and reproducibility. It connects to multiple RPC endpoints, queries logs in dynamically sized batches, decodes events using contract ABIs, and stores the output in CSV/Parquet files. Automatic sharding ensures that no single file exceeds one million rows, while strict chronological ordering guarantees that downstream analyses can rely on a time‑consistent dataset. All configuration files, scripts, and metadata schemas are publicly released, allowing other researchers to replicate the extraction process or extend it to additional protocols and chains.

Beyond the engineering contribution, the paper provides a detailed exposition of Aave V3’s internal mechanics. The authors describe the supply logic (aToken issuance and collateral activation), borrowing logic (real‑time collateral valuation, variable vs. stable rate selection), reserve logic (index‑based interest accrual, treasury fee extraction), and liquidation logic (health factor H, close factor κ, liquidation bonus β, and protocol fee ϕ). Formal equations for liquidation pricing and treasury accrual are presented, and the authors explain how these formulas can be directly mapped onto the decoded event records. This mapping enables precise measurement of liquidation profitability, bad‑debt incidence, and the propagation of risk across chains.

Potential research applications are extensive. Researchers can analyze capital flows to compare liquidity supply and demand dynamics across chains, model interest‑rate dynamics with time‑series econometrics, and investigate cross‑chain user behavior by linking addresses across networks. By joining LiquidationCall events with ReserveDataUpdated events, one can construct network graphs of systemic risk propagation and identify contagion pathways during market stress. The dataset also supports machine‑learning approaches for predicting loan defaults, optimizing flash‑loan strategies, or detecting anomalous activity indicative of bots or attacks.

In conclusion, the authors argue that the lack of standardized, event‑level, cross‑chain datasets has been a major bottleneck for DeFi research. Their infrastructure removes this barrier, providing a reproducible, richly annotated data source that can serve as a foundation for academic studies, risk‑management tools, and policy analysis. The open‑source nature of the pipeline invites the community to extend the framework to other protocols (e.g., Uniswap, Compound) and ultimately build an integrated, ecosystem‑wide data layer for decentralized finance.