Metadata-Version: 1.1
Name: zipline
Version: 0.7.0
Summary: A backtester for financial algorithms.
Home-page: https://github.com/quantopian/zipline
Author: Quantopian Inc.
Author-email: opensource@quantopian.com
License: Apache 2.0
Description: Zipline
        =======
        
        |version status| |downloads| |build status| |Coverage Status|
        
        Zipline is a Pythonic algorithmic trading library. The system is
        fundamentally event-driven and a close approximation of how live-trading
        systems operate. Currently, backtesting is well supported, but the
        intent is to develop the library for both paper and live trading, so
        that the same logic used for backtesting can be applied to the market.
        
        Zipline is currently used in production as the backtesting engine
        powering Quantopian (https://www.quantopian.com) -- a free,
        community-centered platform that allows development and real-time
        backtesting of trading algorithms in the web browser.
        
        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.
        
        Discussion and Help
        ===================
        
        Discussion of the project is held at the Google Group,
        zipline@googlegroups.com,
        https://groups.google.com/forum/#!forum/zipline.
        
        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 (>= 2.7.2)
        -  numpy (>= 1.6.0)
        -  pandas (>= 0.9.0)
        -  pytz
        -  Logbook
        -  requests
        -  `python-dateutil <https://pypi.python.org/pypi/python-dateutil>`__
           (>= 2.1)
        
        Quickstart
        ==========
        
        See our
        `tutorial <http://nbviewer.ipython.org/github/quantopian/zipline/blob/master/docs/tutorial.ipynb>`__
        to get started.
        
        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()
        
                # Trading logic
                if short_mavg > long_mavg:
                    # order_target orders as many shares as needed to
                    # achieve the desired number of shares.
                    order_target(symbol('AAPL'), 100)
                elif short_mavg < long_mavg:
                    order_target(symbol('AAPL'), 0)
        
                # Save values for later inspection
                record(AAPL=data[symbol('AAPL')].price,
                       short_mavg=short_mavg[0],
                       long_mavg=long_mavg[0])
        
        You can then run this algorithm using the Zipline CLI. From the command
        line, run:
        
        .. code:: bash
        
            python run_algo.py -f dual_moving_avg.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
        
        Credits
        -------
        
        Thank you for all the help so far!
        
        -  @rday for sortino ratio, information ratio, and exponential moving
           average transform
        -  @snth
        -  @yinhm for integrating zipline with @yinhm/datafeed
        -  `Jeremiah Lowin <http://www.lowindata.com>`__ for teaching us the
           nuances of Sharpe and Sortino Ratios, and for implementing new order
           methods.
        -  Brian Cappello
        -  @verdverm (Tony Worm), Order types (stop, limit)
        -  @benmccann for benchmarking contributions
        -  @jkp and @bencpeters for bugfixes to benchmark.
        -  @dstephens for adding Canadian treasury curves.
        -  @mtrovo for adding BMF&Bovespa calendars.
        -  @sdrdis for bugfixes.
        -  @humdings for refactoring the order methods.
        -  Quantopian Team
        
        (alert us if we've inadvertantly missed listing you here!)
        
        Development Environment
        -----------------------
        
        The following guide assumes your system has
        `virtualenvwrapper <https://bitbucket.org/dhellmann/virtualenvwrapper>`__
        and `pip <http://www.pip-installer.org/en/latest/>`__ already installed.
        
        You'll need to install some C library dependencies:
        
        ::
        
            sudo apt-get install libopenblas-dev liblapack-dev gfortran
        
            wget http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz
            tar -xvzf ta-lib-0.4.0-src.tar.gz
            cd ta-lib/
            ./configure --prefix=/usr
            make
            sudo make install
        
        Suggested installation of Python library dependencies used for
        development:
        
        ::
        
            mkvirtualenv zipline
            ./etc/ordered_pip.sh ./etc/requirements.txt
            pip install -r ./etc/requirements_dev.txt
        
        Finally, install zipline in develop mode (from the zipline source root
        dir):
        
        ::
        
            python setup.py develop
        
        Style Guide
        -----------
        
        To ensure that changes and patches are focused on behavior changes, the
        zipline codebase adheres to both PEP-8,
        http://www.python.org/dev/peps/pep-0008/, and pyflakes,
        https://launchpad.net/pyflakes/.
        
        The maintainers check the code using the flake8 script,
        https://bitbucket.org/tarek/flake8/wiki/Home, which is included in the
        requirements\_dev.txt.
        
        Before submitting patches or pull requests, please ensure that your
        changes pass ``flake8 zipline tests`` and ``nosetests``
        
        Source
        ======
        
        The source for Zipline is hosted at
        https://github.com/quantopian/zipline.
        
        Documentation
        -------------
        
        You can compile the documentation using Sphinx:
        
        ::
        
            sudo apt-get install python-sphinx
            make html
        
        Contact
        =======
        
        For other questions, please contact opensource@quantopian.com.
        
        .. |version status| image:: https://pypip.in/v/zipline/badge.png
           :target: https://pypi.python.org/pypi/zipline
        .. |downloads| image:: https://pypip.in/d/zipline/badge.png
           :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
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.3
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Office/Business :: Financial
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: System :: Distributed Computing
