.. image:: https://media.quantopian.com/logos/open_source/zipline-logo-03_.png
:target: https://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. Quantopian also offers a fully managed service for professionals <https://factset.quantopian.com>_
that includes Zipline, Alphalens, Pyfolio, FactSet data, and more.
Join our Community! <https://groups.google.com/forum/#!forum/zipline>_Documentation <https://www.zipline.io>_- Want to Contribute? See our
Development Guidelines <https://www.zipline.io/development-guidelines>_
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
Zipline currently supports Python 2.7 and Python 3.5, and may be installed via
either pip or conda.
Note: Installing Zipline is slightly more involved than the average Python
package. See the full Zipline Install Documentation_ for detailed
instructions.
For a development installation (used to develop Zipline itself), create and
activate a virtualenv, then run the etc/dev-install script.
Quickstart
See our getting started tutorial <https://www.zipline.io/beginner-tutorial>_.
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 and 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 <https://www.zipline.io/development-guidelines>_.
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 : https://www.zipline.io/install