Welcome to Mean Variance Portfolio’s documentation!¶
Mean Variance Portfolio¶
MV Port is a Python package to perform Mean-Variance Analysis. It provides a Portfolio class with a variety of methods to help on your portfolio optimization tasks.
- Free software: MIT license
- Documentation: https://mvport.readthedocs.io.
Features¶
- Easy portfolio setup
- Portfolio evaluation
- Random portfolio allocation
- Minimum Variance Portfolio optimization
- Efficient Frontier evaluation
- Tangency Portfolio for a given risk free return rate
Installation¶
To install MV Port, run this command in your terminal:
$ pip install mv-port
Check here for further information on installation.
Basic Usage¶
Instantiate a portfolio and add some stock and evaluate it given a set of weights:
>>> import mvport as mv
>>> p = mv.Portfolio()
>>> p.add_stock('AAPL', [.1,.2,.3])
>>> p.add_stock('AMZN', [.1,.3,.5])
>>> mean, variance, sharp_ratio, weights = p.evaluate([.5, .5])
>>> print '{} +- {}'.format(mean, variance)
0.25 +- 0.0225
Check here for further information on usage.
Installation¶
Stable release¶
To install Mean Variance Portfolio, run this command in your terminal:
$ pip install mvport
This is the preferred method to install Mean Variance Portfolio, as it will always install the most recent stable release.
If you don’t have pip installed, this Python installation guide can guide you through the process.
From sources¶
The sources for Mean Variance Portfolio can be downloaded from the Github repo.
You can either clone the public repository:
$ git clone git://github.com/condereis/mvport
Or download the tarball:
$ curl -OL https://github.com/condereis/mvport/tarball/master
Once you have a copy of the source, you can install it with:
$ python setup.py install
Usage¶
To use Mean Variance Portfolio in a project:
.. code:: python
>>> import mvport as mv
Instantiate a portfolio and add some stocks:
>>> p = mv.Portfolio()
>>> p.add_stock('AAPL', [.1,.2,.3])
>>> p.add_stock('AMZN', [.1,.3,.5])
Evaluate a portfolio given a set of weights:
>>> mean, variance, sharp_ratio, weights = p.evaluate([.5, .5])
>>> print '{} +- {}'.format(mean, variance)
0.25 +- 0.0225
Get the portfolio that minimizes risk for a given expected return:
>>> expected_return = 0.25
>>> mean, variance, _, w = p.get_minimum_variance_portfolio(expected_return)
>>> print 'weights: {} \n {} +- {}'.format(w, mean, variance)
weights: [[0.49999993 0.50000007]]
0.25000000746 +- 0.022500002238
Get tangency portfolio for a given risk free asset:
>>> risk_free_rate = 0.2
>>> mean, variance, _, w = p.get_tangency_portfolio(risk_free_rate)
>>> print 'weights: {} \n {} +- {}'.format(w, mean, variance)
weights: [[2.64767716e-04 9.99735232e-01]]
0.299973523228 +- 0.0399894099924
Contributing¶
Contributions are welcome, and they are greatly appreciated! Every little bit helps, and credit will always be given.
You can contribute in many ways:
Types of Contributions¶
Report Bugs¶
Report bugs at https://github.com/condereis/mvport/issues.
If you are reporting a bug, please include:
- Your operating system name and version.
- Any details about your local setup that might be helpful in troubleshooting.
- Detailed steps to reproduce the bug.
Fix Bugs¶
Look through the GitHub issues for bugs. Anything tagged with “bug” and “help wanted” is open to whoever wants to implement it.
Implement Features¶
Look through the GitHub issues for features. Anything tagged with “enhancement” and “help wanted” is open to whoever wants to implement it.
Write Documentation¶
Mean Variance Portfolio could always use more documentation, whether as part of the official Mean Variance Portfolio docs, in docstrings, or even on the web in blog posts, articles, and such.
Submit Feedback¶
The best way to send feedback is to file an issue at https://github.com/condereis/mvport/issues.
If you are proposing a feature:
- Explain in detail how it would work.
- Keep the scope as narrow as possible, to make it easier to implement.
- Remember that this is a volunteer-driven project, and that contributions are welcome :)
Get Started!¶
Ready to contribute? Here’s how to set up mvport for local development.
Fork the mvport repo on GitHub.
Clone your fork locally:
$ git clone git@github.com:your_name_here/mvport.git
Install your local copy into a virtualenv. Assuming you have virtualenvwrapper installed, this is how you set up your fork for local development:
$ mkvirtualenv mvport $ cd mvport/ $ python setup.py developCreate a branch for local development:
$ git checkout -b name-of-your-bugfix-or-feature
Now you can make your changes locally.
When you’re done making changes, check that your changes pass flake8 and the tests, including testing other Python versions with tox:
$ flake8 mvport tests $ python setup.py test or py.test $ toxTo get flake8 and tox, just pip install them into your virtualenv.
Commit your changes and push your branch to GitHub:
$ git add . $ git commit -m "Your detailed description of your changes." $ git push origin name-of-your-bugfix-or-featureSubmit a pull request through the GitHub website.
Pull Request Guidelines¶
Before you submit a pull request, check that it meets these guidelines:
- The pull request should include tests.
- If the pull request adds functionality, the docs should be updated. Put your new functionality into a function with a docstring, and add the feature to the list in README.rst.
- The pull request should work for Python 2.7, 3.4, 3.5 and 3.6, and for PyPy. Check https://travis-ci.org/condereis/mvport/pull_requests and make sure that the tests pass for all supported Python versions.
Deploying¶
A reminder for the maintainers on how to deploy. Make sure all your changes are committed (including an entry in HISTORY.rst). Then run:
$ bumpversion patch # possible: major / minor / patch
$ git push
$ git push --tags
Travis will then deploy to PyPI if tests pass.
Credits¶
Development Lead¶
- Rafael Lopes Conde dos Reis <rafael.lcreis@gmail.com>
Contributors¶
None yet. Why not be the first?