This is a fork of Microsoft Qlib with some bug fixes and new features. Thanks for the Microsoft Qlib team for their great work.

Motivation of this fork

I'm currently using qlib to resarch and execute some factor strategies on my own. However, some features I need are not avaliable in the original qlib also there are some bugs. So I forked the original qlib and added some features I need. I hope this fork can help some people who are also using qlib. I will try to merge some non-debateable features/bugfix to the original qlib. But will keep some features that might be controversial in the fork only.

Features

Feature	Description	Status
Cross Sectional Factor	Add cross-sectional factors such as cross-sectional ranking; average etc	Done (Linux only)
Orthogonalization preprocesser	Add preprocessers to do Schimit and Symetric Orthogonalization	Done
Support non-adjusted data	Add support for non-adjusted data	Done
Enhanced plotting I	Use rangebreak to allow Datetime axis in plottings	Merged
Enhanced plotting II	Add support for plotting factor returns	Done
Enhanced plotting III	1) Custom benchmark; 2) stratifying fix; 3) Colorbar enhancement	Merging
Topk backtest engine I	Allow sell in limit-up case and allow buy in limit-down case	Merged
Topk backtest engine II	Sell names which are removed from instrument population (expired, delisted, removed from index etc)	Done
Ops Today	Return Calendar days since BASE_DAY(1970-01-01)	Done
Customize float data precision	Allow customize the precision of dumped data (to float64)	Done

Note:

• Done: The feature is implemented in this fork but will not be merged to the original qlib. Either because it is rejected by the original qlib team or because it is not suitable for the original qlib.
• Merged: The feature is merged to the original qlib.
• Merging: PR opened to the original qlib but not accepted yet.
• Developing: The feature is under development.

Quick Start

This quick start guide tries to demonstrate

1. It's very easy to build a complete Quant research workflow and try your ideas with Qlib.
2. Though with public data and simple models, machine learning technologies work very well in practical Quant investment.

Here is a quick demo shows how to install Qlib, and run LightGBM with qrun.

Installation

This table demonstrates the supported Python version of Qlib:

	install with pip	install from source	plot
Python 3.7	❌	✔️	✔️
Python 3.8	❌	✔️	✔️
Python 3.9	❌	✔️	✔️

Note:

1. Conda is suggested for managing your Python environment.
2. Please pay attention that installing cython in Python 3.6 will raise some error when installing Qlib from source. If users use Python 3.6 on their machines, it is recommended to upgrade Python to version 3.7 or use conda's Python to install Qlib from source.
3. QlibRequires tables package, hdf5 in tables does not support python3.9.
4. This fork is not available on pip.

Install from package manager

Current the fork is not available in pip or conda

Install from source

Users can install the latest forked version Qlib by the source code according to the following steps:

• Before installing Qlib from source, users need to install some dependencies:

  pip install numpy
  pip install --upgrade  cython

• Clone the repository and install Qlib as follows.

  git clone https://github.com/qianyun210603/qlib.git && cd qlib
  pip install .

  Note: You can install Qlib with python setup.py install as well. But it is not the recommended approach. It will skip pip and cause obscure problems. For example, only the command pip install . can overwrite the stable version installed by pip install pyqlib, while the command python setup.py install can't.

Tips: If you fail to install Qlib or run the examples in your environment, comparing your steps and the CI workflow may help you find the problem.

More details

Please refer the readme and documentation of the original qlib.

Licence

The forked version inherits the licence of the original qlib. See LICENSE for details.