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DOC: use sphinx docs #816
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Zipline | ||
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Zipline is a Pythonic algorithmic trading library. The system is | ||
fundamentally event-driven and a close approximation of how live-trading | ||
systems operate. | ||
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Zipline is currently used in production as the backtesting engine | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. should we mention live trading too? |
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powering `Quantopian Inc. <https://www.quantopian.com>`__ -- a free, | ||
community-centered platform that allows development and real-time | ||
backtesting of trading algorithms in the web browser. | ||
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`Join our | ||
community! <https://groups.google.com/forum/#!forum/zipline>`__ | ||
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Want to contribute? See our `open | ||
requests <https://github.com/quantopian/zipline/wiki/Contribution-Requests>`__ | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. audit this, we can do this whenever since it is the wiki |
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and our `general | ||
guidelines <https://github.com/quantopian/zipline#contributions>`__ | ||
below. | ||
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Features | ||
======== | ||
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- 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. | ||
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- Zipline comes "batteries included" as many common statistics like | ||
moving average and linear regression can be readily accessed from | ||
within a user-written algorithm. | ||
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- Input of historical data and output of performance statistics is | ||
based on Pandas DataFrames to integrate nicely into the existing | ||
Python eco-system. | ||
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- Statistic and machine learning libraries like matplotlib, scipy, | ||
statsmodels, and sklearn support development, analysis and | ||
visualization of state-of-the-art trading systems. | ||
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Installation | ||
============ | ||
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The easiest way to install Zipline is via ``conda`` which comes as part | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more.
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. I have moved pip to suggested method |
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of `Anaconda <http://continuum.io/downloads>`__ or can be installed via | ||
``pip install conda``. | ||
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Once set up, you can install Zipline from our Quantopian channel: | ||
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:: | ||
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conda install -c Quantopian zipline | ||
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Currently supported platforms include: | ||
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- Windows 32-bit (can be 64-bit Windows but has to be 32-bit Anaconda) | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Is this still true? There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. I think I am dropping windows for now. We have not tested this so we cannot make any claims |
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- OSX 64-bit | ||
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- Linux 64-bit | ||
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PIP | ||
--- | ||
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Alternatively you can install Zipline via the more traditional ``pip`` | ||
command. Since zipline is pure-python code it should be very easy to | ||
install and set up: | ||
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:: | ||
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pip install numpy # Pre-install numpy to handle dependency chain quirk | ||
pip install zipline | ||
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If there are problems installing the dependencies or zipline we | ||
recommend installing these packages via some other means. For Windows, | ||
the `Enthought Python | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. I don't think anyone we know of uses Enthought successfully with Zipline. We should probably point people on Windows to Anaconda. |
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Distribution <http://www.enthought.com/products/epd.php>`__ includes | ||
most of the necessary dependencies. On OSX, the `Scipy | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. I'm not sure we need OSX-specific instructions. |
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Superpack <http://fonnesbeck.github.com/ScipySuperpack/>`__ works very | ||
well. | ||
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Dependencies | ||
------------ | ||
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- Python (2.7 or 3.3) | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. We have many more dependencies than these now. I'm not sure it's worth listing all of them in the readme. |
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- numpy (>= 1.6.0) | ||
- pandas (>= 0.9.0) | ||
- pytz | ||
- Logbook | ||
- requests | ||
- `python-dateutil <https://pypi.python.org/pypi/python-dateutil>`__ | ||
(>= 2.1) | ||
- ta-lib | ||
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Quickstart | ||
========== | ||
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See our `getting started | ||
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 order_target, record, symbol, history, add_history | ||
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def initialize(context): | ||
# Register 2 histories that track daily prices, | ||
# one with a 100 window and one with a 300 day window | ||
add_history(100, '1d', 'price') | ||
add_history(300, '1d', 'price') | ||
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context.i = 0 | ||
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def handle_data(context, data): | ||
# Skip first 300 days to get full windows | ||
context.i += 1 | ||
if context.i < 300: | ||
return | ||
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# Compute averages | ||
# history() has to be called with the same params | ||
# from above and returns a pandas dataframe. | ||
short_mavg = history(100, '1d', 'price').mean() | ||
long_mavg = history(300, '1d', 'price').mean() | ||
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sym = symbol('AAPL') | ||
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# Trading logic | ||
if short_mavg[sym] > long_mavg[sym]: | ||
# order_target orders as many shares as needed to | ||
# achieve the desired number of shares. | ||
order_target(sym, 100) | ||
elif short_mavg[sym] < long_mavg[sym]: | ||
order_target(sym, 0) | ||
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# Save values for later inspection | ||
record(AAPL=data[sym].price, | ||
short_mavg=short_mavg[sym], | ||
long_mavg=long_mavg[sym]) | ||
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You can then run this algorithm using the Zipline CLI. From the command | ||
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 | ||
specified time range and stream it through the algorithm and save the | ||
resulting performance dataframe to dma.pickle which you can then load | ||
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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If you would like to contribute, please see our Contribution Requests: | ||
https://github.com/quantopian/zipline/wiki/Contribution-Requests | ||
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.. |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 | ||
.. |downloads| image:: https://img.shields.io/pypi/dd/zipline.svg | ||
:target: https://pypi.python.org/pypi/zipline | ||
.. |build status| image:: https://travis-ci.org/quantopian/zipline.png?branch=master | ||
:target: https://travis-ci.org/quantopian/zipline | ||
.. |Coverage Status| image:: https://coveralls.io/repos/quantopian/zipline/badge.png | ||
:target: https://coveralls.io/r/quantopian/zipline | ||
.. |Code quality| image:: https://scrutinizer-ci.com/g/quantopian/zipline/badges/quality-score.png?b=master | ||
:target: https://scrutinizer-ci.com/g/quantopian/zipline/ |
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should we still say "aprproximation", it is how our live-trading works