/nft-analytics

Modeling NFT projects on Ethereum to better understand hodler behaviour, sales volume trends, identifying whales, and NFT valuations.

Primary LanguageJupyter NotebookMIT LicenseMIT

NFT Analytics

One of the hardest problems of non-fungible tokens (NFT) projects is data modeling.

Modeling covers areas like understanding hodler behaviour, sales volume trends, identifying whales, and valuing NFTs relative to each other within a specific collection.

NFT Analytics is a Python framework to make that process easier. It contains code to interact with the most popular NFT marketplace on Ethereum. Ethereum was chosen as the base due to its dominant nature in the NFT space. This raw data is then modeled mathematically to distill usable information and visualize it.

Installation

  1. Create the conda environment from file
conda env create --file conda-env.yml
  1. Activate environment
conda activate nft_analytics
  1. Add environment to Jupyter kernel
python -m ipykernel install --name=nft_analytics
  1. Install jupyter lab extensions for plotly
jupyter labextension install jupyterlab-plotly
  1. To use the Infura backend (required for querying blockchain data), rename config-dummy.pyto config.py, and add in your private Infura API key
  2. Explore the various Jupyterlab Notebooks under notebooks/

APIs used

  • OpenSea public API (src/opensea_api.py)
  • Infura private API (src/infura_api.py)

Export conda environment

conda env export --no-builds | grep -v "^prefix: " > conda-env.yml

Disclaimer

This project is only for educational purposes, always do your own research before making any investment decisions.