QF-Lib is a Python library that provides high quality tools for quantitative finance. A large part of the project is dedicated to backtesting investment strategies. The Backtester uses an event-driven architecture and simulates events such as daily market opening or closing. It is designed to test and evaluate any custom investment strategy.
Main features include:
- Flexible data sourcing - the project supports the possibility of an easy selection of the data source. Currently provides financial data from Bloomberg, Quandl, Haver Analytics or Portara. To check if there are any additional dependencies necessary for any of these data providers please visit the installation guide.
- Tools to prevent look-ahead bias in the backtesting environment.
- Adapted data containers, which extend the functionality of pandas
Series'andDataframes. - Summary generation - all performed studies can be summarized with a practical and informative document explaining the results. Several document templates are available in the project.
- Simple adjustment of existing settings and creation of new functionalities.
You can install qf-lib using the pip command:
pip install qf-libAlternatively, to install the library from sources, you can download the project and in the qf_lib directory (same one where you found this file after cloning the repository) execute the following command:
python setup.py installThe library uses WeasyPrint to export documents to PDF. WeasyPrint requires additional dependencies, check the platform-specific instructions for Linux, macOS and Windows installation.
In order to facilitate the GTK3+ installation process for Windows you can use
following installers. Download and run the latest
gtk3-runtime-x.x.x-x-x-x-ts-win64.exe file to install the GTK3+.
- Installation guide: https://qf-lib.readthedocs.io/en/latest/installation.html
- Configuration guide: https://qf-lib.readthedocs.io/en/latest/configuration.html
- API documentation: https://qf-lib.readthedocs.io/