catalyst/README.rst

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.

Zipline is currently used in production as the backtesting engine
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.

`Join our
community! <https://groups.google.com/forum/#!forum/zipline>`__

Want to contribute? See our `open
requests <https://github.com/quantopian/zipline/wiki/Contribution-Requests>`__
and our `general
guidelines <https://github.com/quantopian/zipline#contributions>`__
below.

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.

-  Zipline comes "batteries included" as many common statistics like
   moving average and linear regression can be readily accessed from
   within a user-written algorithm.

-  Input of historical data and output of performance statistics is
   based on Pandas DataFrames to integrate nicely into the existing
   Python eco-system.

-  Statistic and machine learning libraries like matplotlib, scipy,
   statsmodels, and sklearn support development, analysis and
   visualization of state-of-the-art trading systems.

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

The easiest way to install Zipline is via ``conda`` 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:

::

    conda install -c Quantopian zipline

Currently supported platforms include:

-  Windows 32-bit (can be 64-bit Windows but has to be 32-bit Anaconda)

-  OSX 64-bit

-  Linux 64-bit

PIP
---

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:

::

    pip install numpy   # Pre-install numpy to handle dependency chain quirk
    pip install zipline

If there are problems installing the dependencies or zipline we
recommend installing these packages via some other means. For Windows,
the `Enthought Python
Distribution <http://www.enthought.com/products/epd.php>`__ includes
most of the necessary dependencies. On OSX, the `Scipy
Superpack <http://fonnesbeck.github.com/ScipySuperpack/>`__ works very
well.

Dependencies
------------

-  Python (2.7 or 3.3)
-  numpy (>= 1.6.0)
-  pandas (>= 0.9.0)
-  pytz
-  Logbook
-  requests
-  `python-dateutil <https://pypi.python.org/pypi/python-dateutil>`__
   (>= 2.1)
-  ta-lib

Quickstart
==========

See our `getting started
tutorial <http://www.zipline.io/#quickstart>`__.

The following code implements a simple dual moving average algorithm.

.. code:: python

    from zipline.api import order_target, record, symbol, history, add_history


    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')

        context.i = 0


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

        # 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()

        sym = symbol('AAPL')

        # 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)

        # Save values for later inspection
        record(AAPL=data[sym].price,
               short_mavg=short_mavg[sym],
               long_mavg=long_mavg[sym])

You can then run this algorithm using the Zipline CLI. From the command
line, run:

.. code:: bash

    python run_algo.py -f dual_moving_average.py --symbols AAPL --start 2011-1-1 --end 2012-1-1 -o dma.pickle

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.

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

Contributions
=============

If you would like to contribute, please see our Contribution Requests:
https://github.com/quantopian/zipline/wiki/Contribution-Requests

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