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集中流动性 AMM 中的交易与流动性提供

文章 arXiv papers · 作者: Marcello Monga
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总结

本文为具有集中流动性的恒定乘积自动做市商建立模型并提出策略，涵盖包括大额订单和统计套利在内的流动性获取者，以及在一个或多个池中管理头寸的流动性提供者。研究以市场数据为建模依据，包括来自 Uniswap v3 的数据，以及对不同交易场所价格、流动性、交易成本、联动、领先滞后效应、溢出和因果关系的分析。

对于流动性获取者，研究推导出利用竞争交易场所价格作为信号、交易联动加密货币篮子的策略。对于流动性提供者，研究提出一种随机控制策略，根据池的盈利能力、头寸动态和集中度风险来选择流动性区间宽度；此外，还提出一种使用 LSTM 网络、适用于多个池的无模型方法。所提供的文本说明了数据依据和策略推导，但没有给出数值表现结果；结果取决于模型假设、数据和实施方式。

核心观点

  • 本文研究具有集中流动性的恒定乘积 AMM 中的交易与流动性提供。
  • 流动性获取策略利用竞争交易场所的价格和跨资产关系，为大额交易和统计套利提供依据。
  • 分析考察了不同交易场所的价格、流动性、交易成本、领先滞后关系、溢出和因果关系。
  • 一种随机控制方法根据池的盈利能力、头寸动态和集中度风险，设定流动性提供者的流动性区间宽度。
  • 一种无模型 LSTM 方法用于估算多个池的流动性提供策略。

标签

加密资产去中心化金融套利市场微观结构机器学习
全文
# Automated Market Making and Decentralized Finance


# Automated Market Making and Decentralized Finance









Automated market makers (AMMs) are a new type of trading venues which are revolutionising the way market participants interact. At present, the majority of AMMs are constant function market makers (CFMMs) where a deterministic trading function determines how markets are cleared. Within CFMMs, we focus on constant product market makers (CPMMs) which implements the concentrated liquidity (CL) feature. In this thesis we formalise and study the trading mechanism of CPMMs with CL, and we develop liquidity provision and liquidity taking strategies. Our models are motivated and tested with market data. We derive optimal strategies for liquidity takers (LTs) who trade orders of large size and execute statistical arbitrages. First, we consider an LT who trades in a CPMM with CL and uses the dynamics of prices in competing venues as market signals. We use Uniswap v3 data to study price, liquidity, and trading cost dynamics, and to motivate the model. Next, we consider an LT who trades a basket of crypto-currencies whose constituents co-move. We use market data to study lead-lag effects, spillover effects, and causality between trading venues. We derive optimal strategies for strategic liquidity providers (LPs) who provide liquidity in CPMM with CL. First, we use stochastic control tools to derive a self-financing and closed-form optimal liquidity provision strategy where the width of the LP's liquidity range is determined by the profitability of the pool, the dynamics of the LP's position, and concentration risk. Next, we use a model-free approach to solve the problem of an LP who provides liquidity in multiple CPMMs with CL. We do not specify a model for the stochastic processes observed by LPs, and use a long short-term memory (LSTM) neural network to approximate the optimal liquidity provision strategy.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。