Deviations from Tradition: Stylized Facts in the Era of DeFi

Battle Count: 88

Highly relevant to quantitative trading in DeFi. The paper provides fundamental insights into DEX market microstructure that directly impact trading strategy design, execution algorithms, and risk management. Key findings on MEV strategies (sandwich attacks, JIT liquidity), return autocorrelation patterns, order flow dynamics, and liquidity droughts are directly actionable for traders operating on DEXs. The identification of deviations from TradFi stylized facts is critical for adapting traditional quantitative strategies to DeFi environments. The paper also provides groundwork for building realistic DEX simulators essential for strategy backtesting.

Moderate complexity. The statistical methods used (ACF analysis, power-law fitting, bootstrap, KDE, FPCA) are standard in econophysics. However, the data pipeline requires blockchain-specific knowledge (accessing Ethereum nodes, parsing smart contract events, understanding Uniswap v3 tick mechanics). The MEV detection algorithms (JIT, sandwich) require careful implementation with specific logic for block-level event ordering. The event-time vs. tick-time vs. clock-time framework adds conceptual complexity. No ML model training is required, but the analysis spans multiple dimensions (pool types, fee tiers, time scales).

Data is publicly available on the Ethereum blockchain. The paper provides specific references to the eth_defi Python package and TheGraph subgraph for data extraction. Algorithms for JIT and sandwich detection are provided in pseudo-code (Algorithm 1 and 2). GitHub repository for data extraction is referenced. However, the full analysis code is not explicitly provided in a dedicated repository. The methodology is well-documented with clear parameter choices (24 pools, 2023-2024 period, specific fee tiers).