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Richard Frank
191 lines
6.3 KiB
ReStructuredText
191 lines
6.3 KiB
ReStructuredText
Zipline
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=======
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|Gitter|
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|version status|
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|downloads|
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|travis status|
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|appveyor status|
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|Coverage Status|
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Zipline is a Pythonic algorithmic trading library. It is an event-driven
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system that supports both backtesting and live-trading.
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Zipline is currently used in production as the backtesting and live-trading
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engine powering `Quantopian <https://www.quantopian.com>`_ -- a free,
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community-centered, hosted platform for building and executing trading
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strategies.
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`Join our
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community! <https://groups.google.com/forum/#!forum/zipline>`_
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`Documentation <http://www.zipline.io>`_
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Want to contribute? See our `open
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requests <https://github.com/quantopian/zipline/wiki/Contribution-Requests>`_
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and our `general
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guidelines <https://github.com/quantopian/zipline#contributions>`_
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below.
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Features
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========
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- Ease of use: Zipline tries to get out of your way so that you can
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focus on algorithm development. See below for a code example.
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- Zipline comes "batteries included" as many common statistics like
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moving average and linear regression can be readily accessed from
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within a user-written algorithm.
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- Input of historical data and output of performance statistics are
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based on Pandas DataFrames to integrate nicely into the existing
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PyData eco-system.
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- Statistic and machine learning libraries like matplotlib, scipy,
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statsmodels, and sklearn support development, analysis, and
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visualization of state-of-the-art trading systems.
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Installation
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============
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Installing With ``pip``
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-----------------------
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Assuming you have all required (see note below) non-Python dependencies, you
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can install Zipline with ``pip`` via:
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.. code-block:: bash
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$ pip install zipline
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**Note:** Installing Zipline via ``pip`` is slightly more involved than the
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average Python package. Simply running ``pip install zipline`` will likely
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fail if you've never installed any scientific Python packages before.
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There are two reasons for the additional complexity:
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1. Zipline ships several C extensions that require access to the CPython C API.
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In order to build the C extensions, ``pip`` needs access to the CPython
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header files for your Python installation.
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2. Zipline depends on `numpy <http://www.numpy.org/>`_, the core library for
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numerical array computing in Python. Numpy depends on having the `LAPACK
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<http://www.netlib.org/lapack>`_ linear algebra routines available.
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Because LAPACK and the CPython headers are binary dependencies, the correct way
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to install them varies from platform to platform. On Linux, users generally
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acquire these dependencies via a package manager like ``apt``, ``yum``, or
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``pacman``. On OSX, `Homebrew <http://www.brew.sh>`_ is a popular choice
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providing similar functionality.
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See the full `Zipline Install Documentation`_ for more information on acquiring
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binary dependencies for your specific platform.
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conda
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-----
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Another way to install Zipline is via the ``conda`` package manager, which
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comes as part of `Anaconda <http://continuum.io/downloads>`_ or can be
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installed via ``pip install conda``.
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Once set up, you can install Zipline from our ``Quantopian`` channel:
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.. code-block:: bash
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conda install -c Quantopian zipline
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Currently supported platforms include:
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- GNU/Linux 64-bit
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- OSX 64-bit
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.. note::
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Windows may work; however, it is currently untested.
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Quickstart
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==========
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See our `getting started
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tutorial <http://www.zipline.io/#quickstart>`_.
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The following code implements a simple dual moving average algorithm.
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.. code:: python
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from zipline.api import (
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add_history,
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history,
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order_target,
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record,
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symbol,
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)
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def initialize(context):
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# Register 2 histories that track daily prices,
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# one with a 100 window and one with a 300 day window
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add_history(100, '1d', 'price')
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add_history(300, '1d', 'price')
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context.i = 0
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def handle_data(context, data):
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# Skip first 300 days to get full windows
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context.i += 1
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if context.i < 300:
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return
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# Compute averages
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# history() has to be called with the same params
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# from above and returns a pandas dataframe.
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short_mavg = history(100, '1d', 'price').mean()
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long_mavg = history(300, '1d', 'price').mean()
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sym = symbol('AAPL')
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# Trading logic
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if short_mavg[sym] > long_mavg[sym]:
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# order_target orders as many shares as needed to
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# achieve the desired number of shares.
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order_target(sym, 100)
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elif short_mavg[sym] < long_mavg[sym]:
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order_target(sym, 0)
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# Save values for later inspection
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record(AAPL=data[sym].price,
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short_mavg=short_mavg[sym],
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long_mavg=long_mavg[sym])
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You can then run this algorithm using the Zipline CLI. From the command
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line, run:
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.. code:: bash
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python run_algo.py -f dual_moving_average.py --symbols AAPL --start 2011-1-1 --end 2012-1-1 -o dma.pickle
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This will download the AAPL price data from Yahoo! Finance in the
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specified time range and stream it through the algorithm and save the
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resulting performance dataframe to dma.pickle which you can then load
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and analyze from within python.
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You can find other examples in the zipline/examples directory.
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Contributions
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=============
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If you would like to contribute, please see our Contribution Requests:
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https://github.com/quantopian/zipline/wiki/Contribution-Requests
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.. |Gitter| image:: https://badges.gitter.im/Join%20Chat.svg
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:target: https://gitter.im/quantopian/zipline?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge
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.. |version status| image:: https://img.shields.io/pypi/pyversions/zipline.svg
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:target: https://pypi.python.org/pypi/zipline
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.. |downloads| image:: https://img.shields.io/pypi/dd/zipline.svg
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:target: https://pypi.python.org/pypi/zipline
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.. |travis status| image:: https://travis-ci.org/quantopian/zipline.png?branch=master
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:target: https://travis-ci.org/quantopian/zipline
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.. |appveyor status| image:: https://ci.appveyor.com/api/projects/status/github/quantopian/zipline?branch=master&svg=true
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:target: https://ci.appveyor.com/project/quantopian/zipline/branch/master
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.. |Coverage Status| image:: https://coveralls.io/repos/quantopian/zipline/badge.png
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:target: https://coveralls.io/r/quantopian/zipline
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.. _`Zipline Install Documentation` : http://www.zipline.io/install.html
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