论文标题
价格改善的甲骨文：恒定功能做市商
Improved Price Oracles: Constant Function Market Makers
论文作者
论文摘要
自动化的做市商首先由汉森（Hanson）的对数市场评分规则（或LMSR）推广，用于预测市场，已成为分散融资的重要组成部分，称为“原始人”。一个特别有用的原始性是测量资产价格的能力，这个问题通常称为定价甲骨文问题。在本文中，我们专注于对一大批自动化营销商的分析，称为恒定功能做市商（或CFMMS），其中包括现有的流行营销商，例如UnisWap，Balancer和Curve，其年度交易量总计达到数十亿美元。我们提供足够的条件，使得在相当普遍的假设下，与这些恒定功能互动的代理商受到激励，以正确地报告资产的价格，并且他们可以以计算上有效的方式这样做。我们还得出了以前未知的其他几种有用的属性。其中包括对CFMM持有资产的总价值的下限和下限，以确保任何代理商都无法通过任何一套交易来消耗给定CFMM持有的资产储量。
Automated market makers, first popularized by Hanson's logarithmic market scoring rule (or LMSR) for prediction markets, have become important building blocks, called 'primitives,' for decentralized finance. A particularly useful primitive is the ability to measure the price of an asset, a problem often known as the pricing oracle problem. In this paper, we focus on the analysis of a very large class of automated market makers, called constant function market makers (or CFMMs) which includes existing popular market makers such as Uniswap, Balancer, and Curve, whose yearly transaction volume totals to billions of dollars. We give sufficient conditions such that, under fairly general assumptions, agents who interact with these constant function market makers are incentivized to correctly report the price of an asset and that they can do so in a computationally efficient way. We also derive several other useful properties that were previously not known. These include lower bounds on the total value of assets held by CFMMs and lower bounds guaranteeing that no agent can, by any set of trades, drain the reserves of assets held by a given CFMM.
