| Name | qf-lib JSON |
| Version |
3.0.2
JSON |
| download |
| home_page | https://quarkfin.github.io/qf-lib-info |
| Summary | Quantitative Finance Library |
| upload_time | 2024-11-06 08:52:00 |
| maintainer | None |
| docs_url | None |
| author | Jacek Witkowski, Marcin Borratynski, Thomas Ruxton, Dominik Picheta, Olga Kalinowska, Karolina Cynk, Jakub Czerski, Bartlomiej Czajewski, Zeynep Gültuğ Aydemir, Octavian-Mihai Matei, Eirik Thorp Eythorsson, Marek Bais |
| requires_python | >=3.8.0 |
| license | Apache License 2.0 |
| keywords |
quantitative
finance
backtester
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
pandas
xarray
numpy
scipy
matplotlib
emails
Pillow
joblib
scikit-learn
seaborn
statsmodels
cvxopt
openpyxl
WeasyPrint
Jinja2
arch
requests
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# QF-Lib
[](https://pypi.org/project/qf-lib/)
[](https://pepy.tech/project/qf-lib)
[](https://github.com/quarkfin/qf-lib/blob/master/LICENSE)
[](https://app.codecov.io/gh/quarkfin/qf-lib)
[](https://qf-lib.readthedocs.io/)
[](https://github.com/quarkfin/qf-lib/actions/workflows/tests.yml)
## What is QF-lib?
**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](https://qf-lib.readthedocs.io/en/latest/installation.html#installing-optional-data-providers).
* Tools to prevent look-ahead bias in the backtesting environment.
* Adapted [data containers](https://qf-lib.readthedocs.io/en/latest/reference/structure.html#containers), which extend the functionality of pandas `Series'` and `Dataframes`.
* Summary generation - all performed studies can be summarized with a practical and informative document explaining the results. [Several document templates](https://qf-lib.readthedocs.io/en/latest/reference/structure.html#analysis) are available in the project.
* Simple adjustment of existing settings and creation of new functionalities.
## Installation
You can install `qf-lib` using the pip command:
```sh
pip install qf-lib
```
Alternatively, 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:
```sh
python setup.py install
```
## Prerequisites
The library uses [WeasyPrint](https://weasyprint.readthedocs.io) to export documents to PDF. WeasyPrint requires additional dependencies, check the
[platform-specific instructions for Linux, macOS and Windows installation](https://weasyprint.readthedocs.io/en/stable/install.html).
In order to facilitate the GTK3+ installation process for Windows you can use
[following installers](https://github.com/tschoonj/GTK-for-Windows-Runtime-Environment-Installer/releases). Download and run the latest
`gtk3-runtime-x.x.x-x-x-x-ts-win64.exe` file to install the GTK3+.
## Documentation
* 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/
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