Zipline, a Pythonic Algorithmic Trading Library
.. image:: https://media.quantopian.com/logos/open_source/zipline-logo-03_.png :target: https://www.zipline.io :width: 212px :align: center :alt: Zipline
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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
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>_
Development Guidelines <https://www.zipline.io/development-guidelines>_
Zipline currently supports Python 2.7, 3.5, and 3.6, 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
For a development installation (used to develop Zipline itself), create and
activate a virtualenv, then run the
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. First, you must download some sample pricing and asset data:
.. code:: bash
$ zipline ingest $ zipline run -f dual_moving_average.py --start 2014-1-1 --end 2018-1-1 -o dma.pickle --no-benchmark
This will download asset pricing data data sourced 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
If you find a bug, feel free to
open an issue <https://github.com/quantopian/zipline/issues/new>_ and fill out the issue template.
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
Please note that Zipline is not a community-led project. Zipline is maintained by the Quantopian engineering team, and we are quite small and often busy.
Because of this, we want to warn you that we may not attend to your pull request, issue, or direct mention in months, or even years. We hope you understand, and we hope that this note might help reduce any frustration or wasted time.
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Zipline Install Documentation : https://www.zipline.io/install