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https://github.com/wassname/catalyst.git
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Thomas Wiecki
171 lines
5.2 KiB
Markdown
171 lines
5.2 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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```pip install zipline```
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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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* msgpack-python
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* Logbook
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* blist
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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.algorithm 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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class DualMovingAverage(TradingAlgorithm):
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"""Dual Moving Average algorithm.
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"""
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def initialize(self, short_window=200, 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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market_aware=True,
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window_length=short_window)
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self.add_transform(MovingAverage, 'long_mavg', ['price'],
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market_aware=True,
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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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self.short_mavg = []
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self.long_mavg = []
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def handle_data(self, data):
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if (data['AAPL'].short_mavg['price'] > data['AAPL'].long_mavg['price']) and not self.invested:
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self.order('AAPL', 100)
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self.invested = True
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elif (data['AAPL'].short_mavg['price'] < data['AAPL'].long_mavg['price']) and self.invested:
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self.order('AAPL', -100)
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self.invested = False
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# Save mavgs for later analysis.
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self.short_mavg.append(data['AAPL'].short_mavg['price'])
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self.long_mavg.append(data['AAPL'].long_mavg['price'])
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data = load_from_yahoo()
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dma = DualMovingAverage()
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results = dma.run(data)
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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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- Quantopian Team
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(alert us if we've inadvertantly missed listing you here!)
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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```
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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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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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