Uniswap price and liquidity research with Jupyter and Python

Trading Strategy has published its first data research notebook for Uniswap v3 price and liquidity analysis.

What is data research and Jupyter?

Jupyter is the most popular data research tool in the world. With interactive notebooks that mix text notes, Python code and graphs, one can create repeatable analyses that are easy to read and build upon. Market analysts and quant finance experts can create their own analyses, scenarios and strategies using these tools.

Uniswap price and liquidity data

Recipient of a Uniswap grant, Trading Strategy has created data tooling around Uniswap v3 market data. These market data feeds can be used for market research and automated trading. The related infrastructure code is open source and available in the web3-ethereum-defi Python package repository.

There are two example notebooks available:

Both consume raw data from the Ethereum JSON-RPC endpoint and transform it into trading-friendly OHLCV candles or time series using Pandas.

An example price chart for ETH/USDC pair on Uniswap v3

Example notebooks come with easy-to-read Python code which allows data researchers to dive into decentralised exchange data with a low barrier to entry:

All produced charts are interactive, providing pan and zoom features using Plotly.

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Trading Strategy is an algorithmic trading protocol for decentralised markets, enabling automated trading on decentralised exchanges (DEXs). Learn more about algorithmic trading here.