mirror of
https://github.com/wassname/catalyst.git
synced 2026-07-02 03:14:57 +08:00
Eddie Hebert
204eae7274
In a previous patch, the transform argument had been changed from days to window_length. The README example was thus not able to be run, updating algo so that it runs against current versions of transforms.
165 lines
5.0 KiB
Markdown
165 lines
5.0 KiB
Markdown
Zipline
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=======
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Zipline is a financial backtester for trading algorithms written in
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Python. The system is fundamentally event-driven and a close
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approximation of how live-trading systems operate.
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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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- @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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- 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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