A framework for quantitative finance In python.
A framework for quantitative finance in python.
Disclaimer: This is a very alpha project. It is not ready to be used and won't be for a while. In fact, the author is still very much learning what such a framework needs to entail. However, if you consider yourself a helpful soul contributions of any type are more then welcome. Thanks!
Some current capabilities: * Portfolio class that can import daily returns from Yahoo. * Calculation of optimal weights for Sharpe ratio and efficient frontier * Bare bones event profiler
The main documentation can be read at Read The Docs. Please start their for more information.
Any and all contributions for the project are welcome whether they be feature requests, bug reports, contributions to documentation, or patches for new features, bug fixes of other improvements. Just fork the repo, add some content and make a pull request. If you are new to Git this tutorial is nice for further details.
Also, just downloading the code and providing feedback is also extremely useful. Submit your feedback to the issues page here. Thanks in advance.
You may also join us at #quantpy on irc.freenode.net.
QuantPy may be downloaded from GitHub as::
git clone https://github.com/jsmidt/QuantPy.git
To install QuantPy type::
cd QuantPy python setup.py install
The prerequisites for Quantpy are:
Python is popular, easy to use, cross-platform, contains many helpful numerical, statistical and visualization libraries and in reality can be made as fast as C/C++ through Cython extensions. I know of no other language that meets all of these requirements.
It is a desire that QuantPy is as useful as possible, including for those who want to incorporate QuantPy into their proprietary software. The BSD license is an open source license that permits this. Please see the attached LICENSE file and http://www.linfo.org/bsdlicense.html for more information which states "Due to the extremely minimal restrictions of BSD-style licenses, software released under such licenses can be freely modified and used in proprietary (i.e., commercial) software for which the source code is kept secret."
There are a few great distributed revision control systems. Git was chosen for the simple reason that git was designed with the ability to create many anonymous untracked branches where code can be pushed and pulled from without revealing the anonymous history. We feel this design choice is important for entities with proprietary code who want to make contributions but keep their branches anonymous. Github was chosen because it seems to give the most user friendly git experience across all platforms: Windows, Mac and Linux.
Yes. Though this is an open source project, it was understood from day one that there may be a need to incorporate QuantPy into proprietary software. The above sections regarding the BSD licence and the use of Git discuss how we have addressed these concerns. We hope, however, entities repay the generosity by submitting patches for new features, bug fixes, and other improvements.
With that said, I hope you very much enjoy Quantpy. I hope it meets your needs, makes you happy and that your retrun the favor through the types of contributions mentioned above.