如何使用 Python 模拟 Uniswap V3 订单簿 |作者：伊恩·摩尔博士 | 2024 年 5 月 |数据驱动的投资者

本文探讨了使用 DeFiPy（一个用于 DeFi 的 Python 套件）模拟 Uniswap V3 流动性池和订单簿。通过几何布朗运动过程的模拟价格数据，使用 UniswapPy（用于 Uniswap V2/V3 操作的 Python 包）的关键组件设置了 Uniswap V3 池。模拟包括流动性调整、互换和数据捕获事件，以及 LP 参数和订单簿分布的可视化。模拟订单簿是根据报价数据构建的，可以洞察不同价格下资产的需求和供应。模拟设置有助于 DeFi 的进一步研究，包括先进系统的行为、无常损失分析以及新协议的风险分析。

Simulating a Uniswap V3 Order Book in Python: A Guide for Informed Market Analysis

用 Python 模拟 Uniswap V3 订单簿：知情市场分析指南

Introduction

介绍

Automated market makers (AMMs) play a pivotal role in decentralized exchanges (DEXs), facilitating peer-to-peer trading without the need for intermediaries. Uniswap, a pioneering DEX, introduced Uniswap V3 in May 2021, revolutionizing the AMM landscape with its concentrated liquidity market maker (CLMM) protocol. This upgrade addressed inefficiencies in liquidity distribution, leading to enhanced returns for liquidity providers.

自动做市商 (AMM) 在去中心化交易所 (DEX) 中发挥着关键作用，无需中介机构即可促进点对点交易。 Uniswap 是一家开创性的 DEX，于 2021 年 5 月推出了 Uniswap V3，凭借其集中流动性做市商 (CLMM) 协议彻底改变了 AMM 格局。此次升级解决了流动性分配效率低下的问题，从而提高了流动性提供者的回报。

Background: Uniswap V3 Concentrated Liquidity

背景：Uniswap V3 流动性集中

The CLMM protocol introduced by Uniswap V3 concentrates liquidity within the active trading band, effectively deepening the order book and making more liquidity available for trading. This optimization addresses the problem of lazy liquidity, where funds are inefficiently distributed across all price levels, resulting in potential slippage and less efficient trading.

Uniswap V3引入的CLMM协议将流动性集中在活跃交易区间内，有效加深了订单簿并为交易提供了更多流动性。这种优化解决了流动性惰性的问题，即资金在所有价格水平上的分配效率低下，从而导致潜在的滑点和交易效率较低。

Simulating Asset Prices Using Brownian Motion

使用布朗运动模拟资产价格

To simulate realistic market conditions, we utilize Brownian motion, a stochastic process widely used in modeling financial phenomena. By employing a Geometric Brownian Motion (GBM) process, we can generate asset price data with controllable drift and volatility attributes. This approach allows for experimentation with various market scenarios.

为了模拟现实的市场条件，我们利用布朗运动，这是一种广泛用于金融现象建模的随机过程。通过采用几何布朗运动（GBM）过程，我们可以生成具有可控漂移和波动属性的资产价格数据。这种方法允许对各种市场场景进行实验。

Setting Up the Uniswap V3 Pool

设置 Uniswap V3 池

Using the DeFiPy Python suite, we create a simulated Uniswap V3 pool. This framework provides essential components such as liquidity factory, join processes, and helper functions for calibrating tick intervals based on price inputs. The liquidity pool is initialized with a user-defined fee, token pair, and tick spacing.

使用 DeFiPy Python 套件，我们创建了一个模拟的 Uniswap V3 池。该框架提供了必要的组件，例如流动性工厂、连接流程以及用于根据价格输入校准报价间隔的辅助函数。流动性池使用用户定义的费用、代币对和价格变动间隔进行初始化。

Simulating Liquidity Pool Dynamics

模拟流动性池动态

The simulation comprises a series of events that mimic real-world market activity. These events include:

该模拟包括一系列模仿现实世界市场活动的事件。这些事件包括：

• Recalculating pool reserves: Adjusting reserves to reflect market price changes
• Determining tick intervals: Updating tick intervals based on price fluctuations
• Adding liquidity: Randomly depositing tokens into the pool to maintain liquidity
• Swapping tokens: Simulating token swaps with random amounts and directions
• Data capture: Collecting metrics such as pool prices, liquidity, and swap volumes

Constructing Order Book

重新计算池准备金：调整准备金以反映市场价格变化确定报价间隔：根据价格波动更新报价间隔增加流动性：随机将代币存入池中以维持流动性交换代币：以随机金额和方向模拟代币交换数据捕获：收集池价格等指标、流动性和掉期交易量构建订单簿

An order book is a record of open buy and sell orders, providing insights into market supply and demand. We construct an order book from the tick data obtained during the liquidity pool simulation. The process involves:

订单簿是未平仓买卖订单的记录，提供对市场供需的洞察。我们根据流动性池模拟期间获得的报价数据构建订单簿。该过程涉及：

• Extracting tick positions and corresponding liquidity values
• Converting tick positions to prices
• Sorting the liquidity positions by price
• Categorizing tick positions into bids (lower than center) and asks (higher than center)

Reviewing Output: Scatterplot and Depth Chart

提取即时报价头寸和相应的流动性值将即时报价头寸转换为价格按价格对流动性头寸进行排序将即时报价头寸分类为买价（低于中心）和卖价（高于中心）查看输出：散点图和深度图

The constructed order book data is visualized using a scatterplot, displaying the relationship between token price and liquidity. This visualization helps identify areas of high and low liquidity at different price levels.

构建的订单簿数据使用散点图进行可视化，显示代币价格和流动性之间的关系。这种可视化有助于识别不同价格水平下流动性高和低的区域。

Additionally, we generate a depth chart, a representation of the distribution of liquidity across price levels. The depth chart allows for analysis of market demand and supply. By observing the depth chart, traders can make informed decisions about optimal trade prices and order sizes to minimize slippage.

此外，我们还生成一个深度图，表示流动性在价格水平上的分布。深度图可以分析市场需求和供应。通过观察深度图，交易者可以就最佳交易价格和订单规模做出明智的决定，以最大限度地减少滑点。

Summary and Applications

总结与应用

This simulation provides a framework for analyzing the behavior of Uniswap V3 liquidity pools and order books. The insights gained can be used for:

该模拟提供了一个用于分析 Uniswap V3 流动性池和订单簿行为的框架。获得的见解可用于：

• Evaluating liquidity distribution and market efficiency
• Studying the impact of impermanent loss on liquidity providers
• Analyzing the risk-return profile of liquidity pools
• Optimizing trading strategies to minimize slippage and maximize returns

The code for this simulation is available on the DeFiPy GitHub repository.

评估流动性分布和市场效率研究无常损失对流动性提供者的影响分析流动性池的风险回报状况优化交易策略以最小化滑点并最大化回报此模拟的代码可在 DeFiPy GitHub 存储库中找到。