发表机构
Moscow Institute of Physics and Technology; Skolkovo Institute of Science and Technology; HSE University（莫斯科物理技术学院; 斯科尔科沃科学技术研究所; 高等经济学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种交易规模感知的动态费用框架，通过耦合做市商架构、无常损失削减费用模型和统一评估方法，在多种市场条件下提升流动性提供者收益并抑制套利利润。
AI 中文摘要
自动做市商（AMM）支持去中心化交易，但通过套利驱动的再平衡，系统性地使流动性提供者面临无常损失。虽然动态费用机制提供了一种有前景的缓解策略，但现有方法大多是被动反应式的，基于历史信号调整成本，而非将其与单笔交易带来的结构性风险明确关联。为解决这一局限，我们提出了一个基于三项核心创新的新型费用形成框架。首先，我们引入了一种耦合做市商架构，其中费用动态由次级不变量控制，使得流动性状态与交易成本能够联合演化。其次，我们开发了一种无常损失削减费用模型，该模型针对超出流动性提供者盈利区域的交易自适应地提高交易成本，有效抵消大规模套利执行带来的损失，同时为较小交易保留基准费用。第三，我们建立了一套统一的评估方法，利用性能剖面（performance profiles）系统性地比较不同市场条件下的费用算法。通过扩展异构交易者模型以推导状态依赖费用下的最优套利策略，我们在覆盖四种不同市场机制和三种代币对类别的历史数据上进行了广泛模拟。我们的结果表明，所提出的费用增强机制在波动市场中使流动性提供者收益率提高6%至24%，在平静市场中提高高达119%，同时保持非知情用户的参与度，并将知情套利盈利能力降低3%至10%。性能剖面分析证实，ILT增强算法在60%至75%的测试场景中优于基线对应算法。
英文摘要
Automated Market Makers enable decentralized trading but systematically expose liquidity providers to impermanent loss through arbitrage-driven rebalancing. While dynamic fee mechanisms offer a promising mitigation strategy, existing approaches remain largely reactive, adjusting costs based on historical signals rather than explicitly linking them to the structural risk imposed by individual trades. To address this limitation, we propose a novel fee formation framework built on three core innovations. First, we introduce a coupled market maker architecture in which fee dynamics are governed by a secondary invariant, allowing liquidity state and transaction costs to evolve jointly. Second, we develop an impermanent-loss trimming fee model that adaptively increases transaction costs for trades exceeding the liquidity providers' profitable region, effectively offsetting losses from large arbitrage executions while preserving baseline fees for smaller transactions. Third, we establish a unified evaluation methodology using performance profiles to systematically compare fee algorithms across diverse market conditions. By extending a heterogeneous trader model to derive optimal arbitrage strategies under state-dependent fees, we conduct extensive simulations on historical data spanning four distinct market regimes and three token pair categories. Our results demonstrate that the proposed fee enhancements improve liquidity provider yields by 6--24% in volatile markets and up to 119% in calm regimes, while maintaining uninformed user participation and reducing informed arbitrage profitability by 3--10%. Performance profile analysis confirms that ILT-enhanced algorithms dominate baseline counterparts across 60--75% of test scenarios.
