Lightweight Python library for assembling and analysing financial data. Wraps Pandas and matplotlib for maximum ease of use in accomplishing routine tasks in working with financial data.
Detailed documentation at PyNance website.
Tested on:
- Python 2.7.8, 2.7.11, 3.4.2, 3.5.1
- matplotlib 1.4.2, 1.4.3, 1.5.1
- NumPy 1.9.0, 1.9.2, 1.10.4
- Pandas 0.14.1, 0.15.1, 0.16.0, 0.17.1
- pandas-datareader 0.1.1, 0.2.1
PyNance will also work with other versions of Python and Python packages. To verify that it works with yours, simply run the unit tests for data retrieval, then try making some charts with sample data you retrieve.
Additional dependencies for the pynance.options
module:
- lxml 3.4.2
- html5lib 0.999
- BeautifulSoup4 4.3.2
To build the documentation using Sphinx outside of a virtual Python environment,
go to ./doc
and type make html
. If you are using a virtual environment for any
dependencies, you will need to add sphinx
and numpydoc
to the virtual
environment before building the documentation. Explanation
here.
In other words, from within your virtual environment:
$ pip install sphinx
$ pip install numpydoc
Then build the documentation using make html
.
To set up your virtual environment for development:
$ mkvirtualenv -p /usr/local/bin/python pn-dev
$ python setup.py develop