Liquidation Dynamics in DeFi and the Role of Transaction Fees

Battle Count: 84

The paper is highly relevant to DeFi-specific quantitative trading and MEV extraction strategies. It provides actionable insights for liquidation bot operators (optimal staged vs. lump-sum liquidation), sandwich attack profitability thresholds, and fee-level sensitivity analysis. However, it is less directly applicable to traditional quantitative trading (equities, FX, futures). The dynamic programming framework and closed-form solutions could inform algorithmic execution strategies in AMM environments.

Algorithm 1 is implementable with standard numerical computation (closed-form expressions for x_c, x_b, x_cf, profit integrals). The dynamic programming formulation is tractable due to the one-dimensional state space and closed-form solutions. However, extending to multi-asset scenarios, incorporating realistic gas costs, or implementing in a live trading environment adds significant complexity. The sandwich attack optimization (Equation 9) requires numerical maximization over attack size Delta.

The paper provides a fully specified Algorithm 1 with all inputs, intermediate computations, and outputs. Closed-form expressions for liquidation bounds (x_c, x_kb, x_cf) are given. Numerical examples (Examples 1-5) specify all parameters explicitly. However, no code repository is provided, and the dynamic programming proof is sketched in the appendix. The theoretical framework is self-contained and reproducible from the formulas given.