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dagster_uniswap

This is a Dagster project scaffolded with dagster project scaffold.

This project is designed to analyze a Uniswap pool, from extracting on-chain data, decoding it, storing it, to creating metrics.

Getting started

First, install your Dagster code location as a Python package. By using the --editable flag, pip will install your Python package in "editable mode" so that as you develop, local code changes will automatically apply.

pip install -e ".[dev]"

Then, start the Dagster UI web server:

dagster dev

Open http://localhost:3000 with your browser to see the project.

You can start writing assets in dagster_uniswap/assets.py. The assets are automatically loaded into the Dagster code location as you define them.

Tools and Resources

The following tools and resources are utilized in this project:

  • Cryo: A tool by Paradigm written in Rust that enables fast extraction of data.
  • Check-the-chain: Used for data decoding.
  • Quicknode: Provides access to RPC endpoint.
  • DuckDB: Serves as the database for data storage.
  • Polars: go-to Python library for data manipulation and creating metrics.
  • Dagster: Used for orchestration.
  • The Graph: indexing protocol for blockchain data. Specifically, the Uniswap endpoint.

Code Explanation and Modification

The code extracts all blocks and event logs for events swap, burn, collect, mint, and flash from Uniswap v3 events for a defined address. Currently, the address is set to 0x11b815efB8f581194ae79006d24E0d814B7697F6 for the WETH/USDT 0.05% pool.

dagster_uniswap_assets

Assets are separated into three groups: raw_data, prepared_data, metrics.

  • raw_data: extract all blocks and event logs events swap, burn, collect, mint, and flash from Uniswap v3 events via cryo; download pool info and tick data from the Graph Uniswap endpoint.
  • prepared_data: decode raw event logs into readable format with check-the-chain; loads decoded data into DuckDB database.
  • metrics: compute metrics and insights from prepared data.

The start block and end block should be defined in the constants.py file. Some metrics are cumulative, so if you want to study a pool, you'd need to specify the START_BLOCK for when the pool was created.

To modify the code for your own use, simply change the address, start block, and end block in the constants.py file to match the pool you wish to analyze.

Future Enhancements

Plans for future enhancements include:

  • Introduction of a BI tool or similar to display metrics data.
  • Addition of more metrics, particularly related to the analysis of liquidity and liquidity providers on pools.
  • Addition of capabilities to run a job to update data on a regular basis.

Development

Adding new Python dependencies

You can specify new Python dependencies in setup.py.

Unit testing

Tests are in the dagster_uniswap_tests directory and you can run tests using pytest:

pytest dagster_uniswap_tests

Schedules and sensors

If you want to enable Dagster Schedules or Sensors for your jobs, the Dagster Daemon process must be running. This is done automatically when you run dagster dev.

Once your Dagster Daemon is running, you can start turning on schedules and sensors for your jobs.

Deploy on Dagster Cloud

The easiest way to deploy your Dagster project is to use Dagster Cloud.

Check out the Dagster Cloud Documentation to learn more.