mirror of
https://github.com/wassname/catalyst.git
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Eddie Hebert
408c129760
Now that we use pandas Timestamp throughout, Delorean is no longer needed as a dependency.
255 lines
7.6 KiB
Markdown
255 lines
7.6 KiB
Markdown
Zipline
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=======
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Zipline is a Pythonic algorithmic trading library.
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The system is fundamentally event-driven and a close
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approximation of how live-trading systems operate.
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Currently, backtesting is well supported, but the intent is
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to develop the library for both paper and live trading,
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so that the same logic used for backtesting can be applied
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to the market.
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Zipline is currently used in production as the backtesting engine
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powering Quantopian (https://www.quantopian.com) -- a free, community-centered
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platform that allows development and real-time backtesting of trading
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algorithms in the web browser.
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Want to contribute? See our [open requests](https://github.com/quantopian/zipline/wiki/Contribution-Requests)
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and our [general guidelines](https://github.com/quantopian/zipline#contributions) below.
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Discussion and Help
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===================
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Discussion of the project is held at the Google Group,
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<zipline@googlegroups.com>,
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<https://groups.google.com/forum/#!forum/zipline>.
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Features
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========
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* Ease of use: Zipline tries to get out of your way so that you can
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focus on algorithm development. See below for a code example.
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* Zipline comes "batteries included" as many common statistics like
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moving average and linear regression can be readily accessed from
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within a user-written algorithm.
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* Input of historical data and output of performance statistics is
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based on Pandas DataFrames to integrate nicely into the existing
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Python eco-system.
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* Statistic and machine learning libraries like matplotlib, scipy,
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statsmodels, and sklearn support development, analysis and
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visualization of state-of-the-art trading systems.
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Installation
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============
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Since zipline is pure-python code it should be very easy to install
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and set up with pip:
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```
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pip install numpy # Pre-install numpy to handle dependency chain quirk
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pip install zipline
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```
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If there are problems installing the dependencies or zipline we
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recommend installing these packages via some other means. For Windows,
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the [Enthought Python Distribution](http://www.enthought.com/products/epd.php)
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includes most of the necessary dependencies. On OSX, the
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[Scipy Superpack](http://fonnesbeck.github.com/ScipySuperpack/)
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works very well.
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Dependencies
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------------
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* Python (>= 2.7.2)
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* numpy (>= 1.6.0)
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* pandas (>= 0.9.0)
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* pytz
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* Logbook
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* blist
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* requests
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* iso8601
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* [python-dateutil](https://pypi.python.org/pypi/python-dateutil) (>= 2.1)
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Quickstart
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==========
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The following code implements a simple dual moving average algorithm
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and tests it on data extracted from yahoo finance.
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```python
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from zipline import TradingAlgorithm
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from zipline.transforms import MovingAverage
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from zipline.utils.factory import load_from_yahoo
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from datetime import datetime
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import pytz
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import matplotlib.pyplot as plt
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class DualMovingAverage(TradingAlgorithm):
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"""Dual Moving Average Crossover algorithm.
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This algorithm buys apple once its short moving average crosses
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its long moving average (indicating upwards momentum) and sells
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its shares once the averages cross again (indicating downwards
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momentum).
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"""
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def initialize(self, short_window=100, long_window=400):
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# Add 2 mavg transforms, one with a long window, one
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# with a short window.
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self.add_transform(MovingAverage, 'short_mavg', ['price'],
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window_length=short_window)
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self.add_transform(MovingAverage, 'long_mavg', ['price'],
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window_length=long_window)
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# To keep track of whether we invested in the stock or not
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self.invested = False
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def handle_data(self, data):
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short_mavg = data['AAPL'].short_mavg['price']
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long_mavg = data['AAPL'].long_mavg['price']
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buy = False
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sell = False
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# Has short mavg crossed long mavg?
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if short_mavg > long_mavg and not self.invested:
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self.order('AAPL', 100)
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self.invested = True
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buy = True
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elif short_mavg < long_mavg and self.invested:
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self.order('AAPL', -100)
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self.invested = False
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sell = True
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# Record state variables. A column for each
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# variable will be added to the performance
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# DataFrame returned by .run()
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self.record(short_mavg=short_mavg,
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long_mavg=long_mavg,
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buy=buy,
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sell=sell)
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# Load data
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start = datetime(1990, 1, 1, 0, 0, 0, 0, pytz.utc)
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end = datetime(2002, 1, 1, 0, 0, 0, 0, pytz.utc)
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data = load_from_yahoo(stocks=['AAPL'], indexes={}, start=start,
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end=end, adjusted=False)
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# Run algorithm
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dma = DualMovingAverage()
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perf = dma.run(data)
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# Plot results
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fig = plt.figure()
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ax1 = fig.add_subplot(211, ylabel='Price in $')
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data['AAPL'].plot(ax=ax1, color='r', lw=2.)
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perf[['short_mavg', 'long_mavg']].plot(ax=ax1, lw=2.)
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ax1.plot(perf.ix[perf.buy].index, perf.short_mavg[perf.buy],
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'^', markersize=10, color='m')
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ax1.plot(perf.ix[perf.sell].index, perf.short_mavg[perf.sell],
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'v', markersize=10, color='k')
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ax2 = fig.add_subplot(212, ylabel='Portfolio value in $')
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perf.portfolio_value.plot(ax=ax2, lw=2.)
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ax2.plot(perf.ix[perf.buy].index, perf.portfolio_value[perf.buy],
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'^', markersize=10, color='m')
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ax2.plot(perf.ix[perf.sell].index, perf.portfolio_value[perf.sell],
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'v', markersize=10, color='k')
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```
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You can find other examples in the zipline/examples directory.
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Contributions
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============
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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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Credits
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--------
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Thank you for all the help so far!
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- @rday for sortino ratio, information ratio, and exponential moving average transform
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- @snth
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- @yinhm for integrating zipline with @yinhm/datafeed
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- [Jeremiah Lowin](http://www.lowindata.com) for teaching us the nuances of Sharpe and Sortino Ratios
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- Brian Cappello
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- @verdverm (Tony Worm), Order types (stop, limit)
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- @benmccann for benchmarking contributions
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- Quantopian Team
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(alert us if we've inadvertantly missed listing you here!)
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Development Environment
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-----------------------
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The following guide assumes your system has [virtualenvwrapper](https://bitbucket.org/dhellmann/virtualenvwrapper)
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and [pip](http://www.pip-installer.org/en/latest/) already installed.
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You'll need to install some C library dependencies:
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```
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sudo apt-get install libopenblas-dev liblapack-dev gfortran
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wget http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz
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tar -xvzf ta-lib-0.4.0-src.tar.gz
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cd ta-lib/
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./configure --prefix=/usr
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make
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sudo make install
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```
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Suggested installation of Python library dependencies used for development:
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```
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mkvirtualenv zipline
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./etc/ordered_pip.sh ./etc/requirements.txt
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pip install -r ./etc/requirements_dev.txt
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```
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Style Guide
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------------
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To ensure that changes and patches are focused on behavior changes,
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the zipline codebase adheres to both PEP-8,
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<http://www.python.org/dev/peps/pep-0008/>, and pyflakes,
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<https://launchpad.net/pyflakes/>.
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The maintainers check the code using the flake8 script,
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<https://github.com/bmcustodio/flake8>, which is included in the
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requirements_dev.txt.
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Before submitting patches or pull requests, please ensure that your
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changes pass ```flake8 zipline tests``` and ```nosetests```
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Source
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======
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The source for Zipline is hosted at
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<https://github.com/quantopian/zipline>.
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Documentation
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------------
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You can compile the documentation using Sphinx:
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```
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sudo apt-get install python-sphinx
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make html
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```
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Build Status
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============
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[](https://travis-ci.org/quantopian/zipline)
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Contact
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=======
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For other questions, please contact <opensource@quantopian.com>.
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