This project seeks to explore the use of quantitative analysis techniques on NFTs as an asset class - methods to quantify the risk/return ratios of NFTs based on prior sales data, floor prices, etc. This includes:
- Daily Returns
- Variance / Covariance
- Beta
- Sharpe Ratios
- Python interpreter v3.9.12
- Pandas library: Data analysis and manipulation tools
- Matplotlib library: Creating static, animated, and interactive visualizations
- Python sys library: Support for system-specific parameters and functions
- Python requests library: for making REST API calls
- Python json library: for handling json data returned from API
- Python dotenv library: Support for using secure environment variables
- Python alpaca_trades_api library: for interacting with Alpaca API
- Python MCForecastTools library: for conducting data simulations
- OpenSea API
- MagicEden API
To use this application simply clone the repository and open the nft_quant.ipynb script in the Jupyter Lab application.
nft_quant.ipynb
The source code for the application is licensed under the MIT license, which you can find in the LICENSE file in this repo.