如何使用 Python 模擬 Uniswap V3 訂單簿 |作者：伊恩摩爾博士 | 2024 年 5 月 |數據驅動的投資者

本文探討了使用 DeFiPy（一個用於 DeFi 的 Python 套件）來模擬 Uniswap V3 流動性池和訂單簿。透過幾何布朗運動過程的模擬價格數據，使用 UniswapPy（用於 Uniswap V2/V3 操作的 Python 套件）的關鍵組件設定了 Uniswap V3 池。模擬包括流動性調整、互換和資料擷取事件，以及 LP 參數和訂單簿分佈的可視化。模擬訂單簿是根據報價數據建立的，可以洞察不同價格下資產的需求和供應。模擬設定有助於 DeFi 的進一步研究，包括先進系統的行為、無常損失分析以及新協議的風險分析。

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

用 Python 模擬 Uniswap V3 訂單簿：知情市場分析指南

Introduction

介紹

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.

自動做市商 (AMM) 在去中心化交易所 (DEX) 中發揮關鍵作用，無需中介機構即可促進點對點交易。 Uniswap 是一家開創性的 DEX，於 2021 年 5 月推出了 Uniswap V3，憑藉其集中流動性做市商 (CLMM) 協議徹底改變了 AMM 格局。此次升級解決了流動性分配效率低下的問題，從而提高了流動性提供者的回報。

Background: Uniswap V3 Concentrated Liquidity

背景：Uniswap V3 流動性集中

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協議將流動性集中在活躍交易區間內，有效加深了訂單簿並為交易提供了更多流動性。這種最佳化解決了流動性惰性的問題，即資金在所有價格水準上的分配效率低下，從而導致潛在的滑點和交易效率較低。

Simulating Asset Prices Using Brownian Motion

使用布朗運動模擬資產價格

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.

為了模擬現實的市場條件，我們利用布朗運動，這是一個廣泛用於金融現象建模的隨機過程。透過採用幾何布朗運動（GBM）過程，我們可以產生具有可控漂移和波動屬性的資產價格資料。這種方法允許對各種市場場景進行實驗。

Setting Up the Uniswap V3 Pool

設定 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，我們創建了一個模擬的 Uniswap V3 池。該框架提供了必要的組件，例如流動性工廠、連接流程以及用於根據價格輸入校準報價間隔的輔助函數。流動性池使用使用者定義的費用、代幣對和價格變動間隔進行初始化。

Simulating Liquidity Pool Dynamics

模擬流動性池動態

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

該模擬包括一系列模仿現實世界市場活動的事件。這些事件包括：

• 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

重新計算池準備金：調整準備金以反映市場價格變化確定報價間隔：根據價格波動更新報價間隔增加流動性：隨機將代幣存入池中以維持流動性交換代幣：以隨機金額和方​​向模擬代幣交換資料擷取：收集池價格等指標、流動性和掉期交易量建立訂單簿

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:

訂單簿是未平倉買賣訂單的記錄，提供對市場供需的洞察。我們根據流動性池模擬期間獲得的報價資料建立訂單簿。該過程涉及：

• 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

提取即時報價頭寸和相應的流動性值將即時報價頭寸轉換為價格按價格對流動性頭寸進行排序將即時報價頭寸分類為買價（低於中心）和賣價（高於中心）查看輸出：散點圖和深度圖

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.

建構的訂單簿資料使用散點圖進行視覺化，顯示代幣價格和流動性之間的關係。這種可視化有助於識別不同價格水準下流動性高和低的區域。

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，我們也產生一個深度圖，表示流動性在價格水準上的分佈。深度圖可以分析市場需求和供應。透過觀察深度圖，交易者可以就最佳交易價格和訂單規模做出明智的決定，以最大限度地減少滑點。

Summary and Applications

總結與應用

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

此模擬提供了一個用於分析 Uniswap V3 流動性池和訂單簿行為的框架。所獲得的見解可用於：

• 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.

評估流動性分佈和市場效率研究無常損失對流動性提供者的影響分析流動性池的風險回報狀況優化交易策略以最小化滑點並最大化回報此模擬的程式碼可在 DeFiPy GitHub 儲存庫中找到。