并非所有流动性提供者都相同：自动做市商流动性提供中的主动-被动差距

中文摘要 AI 辅助

自动做市商中的流动性提供通常在池级别进行分析，这隐含地假设流动性提供者（LP）是同质的。这种聚合视角可能掩盖不同LP策略之间流动性提供结果的差异，尤其是在高性能区块链上运行的集中流动性AMM设计允许流动性围绕交易主动重新定位的情况下。我们开发了一个基于markout的框架，通过两种互补方法将Uniswap LP盈利能力分解为主动和被动组成部分：一种LIFO减法方法，匹配短期铸造-销毁头寸，并按流动性份额分配swap级markout；以及一个无穷小LP基准，直接从AMM价格路径估计完全被动、始终在区间内的边际LP的表现。我们将这些方法应用于以太坊、Arbitrum和Base链上的Uniswap v2、v3和v4池，发现被动盈利能力可能与聚合池级指标存在显著差异。在Uniswap v2中，流动性均匀分布且主动LP行为几乎不存在，整体和被动markout几乎相同。相比之下，集中流动性池存在系统性的主动-被动差距：被动LP往往表现低于聚合池级指标。在以太坊上的差距比在L2上更大，这与当区块时间和排序条件允许LP对流入流量做出反应时主动流动性提供更有用的情况一致。总的来说，被动LP在更高费用池中表现更好。LIFO和无穷小估计在大多数池中方向性一致，提供了分解稳健性的证据。结果表明，AMM中的逆向选择并非均匀分布在LP之间，这对LP策略、费用层级设计和DEX市场质量衡量具有重要意义。

英文摘要

Liquidity provision in automated market makers is typically analyzed at the pool level, implicitly assuming LP homogeneity. This aggregate view can hide how liquidity provision outcomes differ between LP strategies, particularly as concentrated liquidity AMM designs operating on high-performance blockchains allow liquidity to be actively repositioned around trades. We develop a markout-based framework to decompose Uniswap LP profitability into active and passive components using two complementary methods: a LIFO subtraction method that matches short-lived mint-burn positions and attributes swap-level markouts by liquidity share; and an infinitesimal LP benchmark that estimates the performance of a fully passive, always-in-range marginal LP directly from the AMM price path. We apply these methods to Uniswap v2, v3, and v4 pools on Ethereum, Arbitrum, and Base chains, and find passive profitability can materially differ from aggregate pool profitability. In Uniswap v2, with liquidity distributed evenly and active LP behavior nearly absent, the overall and passive markouts are almost the same. In contrast, concentrated-liquidity pools have a systematic active-passive gap: passive LPs tend to underperform aggregate pool-level measures. The gap is wider on Ethereum than on L2s, consistent with active liquidity provision being more useful when block times and ordering conditions allow LPs to react to incoming flow. In general, passive LPs perform better on higher fee pools. The LIFO and infinitesimal estimates are generally consistent directionally across most pools, providing evidence of the robustness of the decomposition. The results suggest that adverse selection in AMMs is not evenly distributed among LPs, with important implications for LP strategy, fee-tier design and measurement of DEX market quality.

发表机构

• Uniswap Labs（Uniswap 实验室）
• Columbia University（哥伦比亚大学）

机构由 AI 辅助整理，请以论文原文为准。