Metadata-Version: 1.1
Name: zipline
Version: 0.5.8
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
        =======
        
        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
        ============
        
        Since zipline is pure-python code it should be very easy to install and
        set up with pip:
        
        ::
        
            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
        -  msgpack-python
        -  Logbook
        -  blist
        -  requests
        -  delorean
        -  iso8601
        
        Quickstart
        ==========
        
        The following code implements a simple dual moving average algorithm and
        tests it on data extracted from yahoo finance.
        
        ::
        
            from zipline.algorithm import TradingAlgorithm
            from zipline.transforms import MovingAverage
            from zipline.utils.factory import load_from_yahoo
        
            class DualMovingAverage(TradingAlgorithm):
                """Dual Moving Average algorithm.
                """
                def initialize(self, short_window=200, long_window=400):
                    # Add 2 mavg transforms, one with a long window, one
                    # with a short window.
                    self.add_transform(MovingAverage, 'short_mavg', ['price'],
                                       market_aware=True,
                                       window_length=short_window)
        
                    self.add_transform(MovingAverage, 'long_mavg', ['price'],
                                       market_aware=True,
                                       window_length=long_window)
        
                    # To keep track of whether we invested in the stock or not
                    self.invested = False
        
                    self.short_mavg = []
                    self.long_mavg = []
        
        
                def handle_data(self, data):
                    if (data['AAPL'].short_mavg['price'] > data['AAPL'].long_mavg['price']) and not self.invested:
                        self.order('AAPL', 100)
                        self.invested = True
                    elif (data['AAPL'].short_mavg['price'] < data['AAPL'].long_mavg['price']) and self.invested:
                        self.order('AAPL', -100)
                        self.invested = False
        
                    # Save mavgs for later analysis.
                    self.short_mavg.append(data['AAPL'].short_mavg['price'])
                    self.long_mavg.append(data['AAPL'].long_mavg['price'])
        
            data = load_from_yahoo()
            dma = DualMovingAverage()
            results = dma.run(data)
        
        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
        -  Brian Cappello
        -  Quantopian Team
        
        (alert us if we've inadvertantly missed listing you here!)
        
        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://github.com/bmcustodio/flake8, which is included in the
        requirements\_dev.txt.
        
        Before submitting patches or pull requests, please ensure that your
        changes pass ``flake8 zipline tests``
        
        Source
        ======
        
        The source for Zipline is hosted at
        https://github.com/quantopian/zipline.
        
        Build Status
        ============
        
        `|Build Status| <https://travis-ci.org/quantopian/zipline>`_
        
        Contact
        =======
        
        For other questions, please contact opensource@quantopian.com.
        
        .. |Build Status| image:: https://travis-ci.org/quantopian/zipline.png
        
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: 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
