Metadata-Version: 2.1
Name: qf-lib
Version: 2.1
Summary: Quantitative Finance Library
Home-page: https://quarkfin.github.io/qf-lib/
Author: Jacek Witkowski, Marcin Borratynski, Thomas Ruxton, Dominik Picheta, Olga Kalinowska, Karolina Cynk, Jakub Czerski, Bartlomiej Czajewski
License: Apache License 2.0
Project-URL: Documentation, https://qf-lib.readthedocs.io
Project-URL: Source, https://github.com/quarkfin/qf-lib
Project-URL: Tutorials, https://quarkfin.github.io/qf-lib-info
Keywords: quantitative finance backtester
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Office/Business :: Financial
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Provides: qf_lib
Requires-Python: >=3.7.1
Description-Content-Type: text/markdown
Provides-Extra: documentation
Provides-Extra: interactive brokers
Provides-Extra: bloomberg_beap_hapi
License-File: LICENSE

# QF-Lib

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## 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**, **CoinMarketCap** or **Portara**. To check if there are any additional dependencies necessary for any of these data providers please visit [the installation guide](https://quarkfin.github.io/qf-lib-info/installation/#tips-on-how-to-install-optional-data-providers).
* Tools to prevent look-ahead bias in the backtesting environment.
* Adapted [data containers](https://quarkfin.github.io/qf-lib-info/structure/#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://quarkfin.github.io/qf-lib-info/structure/#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://quarkfin.github.io/qf-lib-info/installation/
* Configuration guide: https://quarkfin.github.io/qf-lib-info/installation/#configuration
* API documentation: https://qf-lib.readthedocs.io/


