.. image:: https://media.quantopian.com/logos/open_source/zipline-logo-03_.png
    :target: http://www.zipline.io
    :width: 212px
    :align: center
    :alt: Zipline

=============

|Gitter|
|version status|
|travis status|
|appveyor status|
|Coverage Status|

Zipline is a Pythonic algorithmic trading library. It is an event-driven
system for backtesting. Zipline is currently used in production as the backtesting and live-trading
engine powering `Quantopian <https://www.quantopian.com>`_ -- a free,
community-centered, hosted platform for building and executing trading
strategies.

- `Join our Community! <https://groups.google.com/forum/#!forum/zipline>`_
- `Documentation <http://www.zipline.io>`_
- Want to Contribute? See our `Development Guidelines <http://zipline.io/development-guidelines.html>`_

Features
========

- **Ease of Use:** Zipline tries to get out of your way so that you can
  focus on algorithm development. See below for a code example.
- **"Batteries Included":** many common statistics like
  moving average and linear regression can be readily accessed from
  within a user-written algorithm.
- **PyData Integration:** Input of historical data and output of performance statistics are
  based on Pandas DataFrames to integrate nicely into the existing
  PyData ecosystem.
- **Statistics and Machine Learning Libraries:** You can use libraries like matplotlib, scipy,
  statsmodels, and sklearn to support development, analysis, and
  visualization of state-of-the-art trading systems.

Installation
============

Installing With ``pip``
-----------------------

Assuming you have all required (see note below) non-Python dependencies, you
can install Zipline with ``pip`` via:

.. code-block:: bash

    $ pip install zipline

**Note:** Installing Zipline via ``pip`` is slightly more involved than the
average Python package.  Simply running ``pip install zipline`` will likely
fail if you've never installed any scientific Python packages before.

There are two reasons for the additional complexity:

1. Zipline ships several C extensions that require access to the CPython C API.
   In order to build the C extensions, ``pip`` needs access to the CPython
   header files for your Python installation.

2. Zipline depends on `numpy <http://www.numpy.org/>`_, the core library for
   numerical array computing in Python.  Numpy depends on having the `LAPACK
   <http://www.netlib.org/lapack>`_ linear algebra routines available.

Because LAPACK and the CPython headers are binary dependencies, the correct way
to install them varies from platform to platform.  On Linux, users generally
acquire these dependencies via a package manager like ``apt``, ``yum``, or
``pacman``.  On OSX, `Homebrew <http://www.brew.sh>`_ is a popular choice
providing similar functionality.

See the full `Zipline Install Documentation`_ for more information on acquiring
binary dependencies for your specific platform.

conda
-----

Another way to install Zipline is via the ``conda`` package manager, which
comes as part of `Anaconda <http://continuum.io/downloads>`_ or can be
installed via ``pip install conda``.

Once set up, you can install Zipline from our ``Quantopian`` channel:

.. code-block:: bash

    $ conda install -c Quantopian zipline

Currently supported platforms include:

-  GNU/Linux 64-bit
-  OSX 64-bit
-  Windows 64-bit

.. note::

   Windows 32-bit may work; however, it is not currently included in
   continuous integration tests.

Quickstart
==========

See our `getting started tutorial <http://www.zipline.io/beginner-tutorial.html>`_.

The following code implements a simple dual moving average algorithm.

.. code:: python

    from zipline.api import order_target, record, symbol

    def initialize(context):
        context.i = 0
        context.asset = symbol('AAPL')


    def handle_data(context, data):
        # Skip first 300 days to get full windows
        context.i += 1
        if context.i < 300:
            return

        # Compute averages
        # data.history() has to be called with the same params
        # from above and returns a pandas dataframe.
        short_mavg = data.history(context.asset, 'price', bar_count=100, frequency="1d").mean()
        long_mavg = data.history(context.asset, 'price', bar_count=300, frequency="1d").mean()

        # Trading logic
        if short_mavg > long_mavg:
            # order_target orders as many shares as needed to
            # achieve the desired number of shares.
            order_target(context.asset, 100)
        elif short_mavg < long_mavg:
            order_target(context.asset, 0)

        # Save values for later inspection
        record(AAPL=data.current(context.asset, 'price'),
               short_mavg=short_mavg,
               long_mavg=long_mavg)


You can then run this algorithm using the Zipline CLI; you'll need a `Quandl <https://docs.quandl.com/docs#section-authentication>`__ API key to ingest the default data bundle.
Once you have your key, run the following from the command line:

.. code:: bash

    $ QUANDL_API_KEY=<yourkey> zipline ingest -b quandl
    $ zipline run -f dual_moving_average.py --start 2014-1-1 --end 2018-1-1 -o dma.pickle

This will download asset pricing data data from `quandl`, and stream it through the algorithm
over the specified time range. Then, the resulting performance DataFrame is saved in `dma.pickle`, which you
can load an analyze from within Python.

You can find other examples in the ``zipline/examples`` directory.

Questions?
==========

If you find a bug, feel free to `open an issue <https://github.com/quantopian/zipline/issues/new>`_ and fill out the issue template.

Contributing
============

All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome. Details on how to set up a development environment can be found in our `development guidelines <http://zipline.io/development-guidelines.html>`_.

If you are looking to start working with the Zipline codebase, navigate to the GitHub `issues` tab and start looking through interesting issues. Sometimes there are issues labeled as `Beginner Friendly <https://github.com/quantopian/zipline/issues?q=is%3Aissue+is%3Aopen+label%3A%22Beginner+Friendly%22>`_ or `Help Wanted <https://github.com/quantopian/zipline/issues?q=is%3Aissue+is%3Aopen+label%3A%22Help+Wanted%22>`_.

Feel free to ask questions on the `mailing list <https://groups.google.com/forum/#!forum/zipline>`_ or on `Gitter <https://gitter.im/quantopian/zipline>`_.



.. |Gitter| image:: https://badges.gitter.im/Join%20Chat.svg
   :target: https://gitter.im/quantopian/zipline?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge
.. |version status| image:: https://img.shields.io/pypi/pyversions/zipline.svg
   :target: https://pypi.python.org/pypi/zipline
.. |travis status| image:: https://travis-ci.org/quantopian/zipline.png?branch=master
   :target: https://travis-ci.org/quantopian/zipline
.. |appveyor status| image:: https://ci.appveyor.com/api/projects/status/3dg18e6227dvstw6/branch/master?svg=true
   :target: https://ci.appveyor.com/project/quantopian/zipline/branch/master
.. |Coverage Status| image:: https://coveralls.io/repos/quantopian/zipline/badge.png
   :target: https://coveralls.io/r/quantopian/zipline

.. _`Zipline Install Documentation` : http://www.zipline.io/install.html