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https://github.com/jsmidt/QuantPy

A framework for quantitative finance In python.
https://github.com/jsmidt/QuantPy

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A framework for quantitative finance In python.

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QuantPy

==========



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



Documentation:

--------------



The main documentation can be read at [Read The Docs](https://quantpy.readthedocs.org/en/latest/).  Please start their for more information.



Contributions Welcome.

----------------------



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](https://help.github.com/articles/fork-a-repo), add some content and [make

a pull request] (https://help.github.com/articles/be-social).  If you are new

to Git [this tutorial](http://learn.github.com/p/intro.html) 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](https://github.com/jsmidt/QuantPy/issues?state=open).  Thanks in advance.



You may also join us at #quantpy on irc.freenode.net.



How To Install.

---------------



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:



* pandas (> 0.10.0)

* matplotlib (> 0.1.0)

* PyGObject (> 3.8.2, only needed for GUI)



Why Python?

-----------



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. 



Why BSD license?

----------------



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."



Why Git and GitHub?

-------------------



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. 



Is Non-Commercial/Proprietary Use allowed?

------------------------------------------



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.