Quantifying Sub-Optimality in Routing for Automated Market Makers

Battle Count: 50

Highly relevant to quantitative trading in DeFi/crypto markets. The paper directly addresses execution optimization (a core concern in algorithmic trading), quantifies slippage and suboptimality in routing, measures the economic value of information timeliness, and identifies adversarial execution risks (sandwich attacks). The benchmark framework and algorithms are directly applicable to designing better execution strategies for on-chain trading. The findings on information staleness and pool activation are analogous to venue selection and latency optimization in traditional markets.

The bisection algorithm for optimal routing without gas is straightforward (O(log(1/epsilon)) iterations). However, the gas-aware routing requires subset enumeration which is exponential in the number of pools (O(2^|J|)), though tractable for small pool sets (4 pools in this study). The empirical pipeline requires parsing on-chain events, reconstructing transactions, computing pool states, and running optimization at scale. The sandwich detection heuristic adds additional complexity. Overall moderate complexity for the core algorithms but significant engineering effort for the full empirical pipeline.

The paper provides detailed pseudocode for both the bisection algorithm (Algorithm 1) and subset enumeration (Algorithm 2), formal theorems with proofs in the appendix, explicit data construction details (block range, filters, pool definitions), and a well-defined benchmark protocol. However, no public code repository or dataset link is explicitly provided in the extract. The methodology is fully specified and reproducible given access to Ethereum on-chain data.