Project: Abstraction Table for Concentrated Liquidity Protocols

Objective:

Develop a comprehensive abstraction layer that consolidates data from various concentrated liquidity protocols into a single, queryable database. This project aims to provide clean, structured datasets to end users, particularly research analysts in the DeFi space, facilitating in-depth analysis and insights into liquidity provision behaviors, pool dynamics, and protocol performance.

Methodology:

1. Data Collection:

• Identify key concentrated liquidity protocols (e.g., Uniswap V3, SushiSwap, Balancer) and their data availability. • Use APIs, blockchain explorers (Etherscan, BscScan), and The Graph for raw data extraction.

2. Data Modeling: • Design a relational database schema to effectively model the complexities of concentrated liquidity mechanisms. • Key tables: Protocols, Pools, Transactions, Liquidity Positions, and Tokens. • Establish relationships between tables to reflect the real-world interactions among protocols, pools, and tokens.

3. Data Cleaning and Transformation: • Implement scripts (Python) to clean and transform raw data into a structured format. • Use dbt (Data Build Tool) for data transformation within the database, ensuring data quality and consistency.

4. Abstraction Layer Development: • Create SQL views and stored procedures to abstract complex queries, making it easier for end users to access and analyze the data without deep technical knowledge.

5. Documentation and Usability: • Document the database schema, data sources, and abstraction layer usage comprehensively. • Provide examples and templates for common queries that answer typical research questions.

6. Technology Stack: • Database: PostgreSQL for relational data storage. • Scripting: Python for data collection, cleaning, and interaction with external sources. • Data Transformation: dbt for managing data transformation pipelines. • API Clients: requests and aiohttp for fetching data from external sources.

Solution: • Develop a normalized PostgreSQL database containing consolidated data from multiple concentrated liquidity protocols. • Created an abstraction layer that simplifies complex data queries, enabling research analysts to perform their analyses with minimal SQL knowledge. • The database includes over 20 liquidity pools, 1000+ transactions, and detailed liquidity position records, providing a rich dataset for liquidity behavior analysis. • Sample queries and reports demonstrate how to analyze liquidity concentration, pool performance over time, and the impact of fee tiers on trading volume and slippage.