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https://github.com/wassname/catalyst.git
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Thomas Wiecki
159 lines
5.2 KiB
Markdown
159 lines
5.2 KiB
Markdown
Zipline
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=======
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[](https://gitter.im/quantopian/zipline?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
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[](https://pypi.python.org/pypi/zipline)
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[](https://pypi.python.org/pypi/zipline)
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[](https://travis-ci.org/quantopian/zipline)
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[](https://coveralls.io/r/quantopian/zipline)
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[](https://scrutinizer-ci.com/g/quantopian/zipline/)
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Zipline is a Pythonic algorithmic trading library. The system is
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fundamentally event-driven and a close approximation of how
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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,
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community-centered platform that allows development and real-time
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backtesting of trading algorithms in the web browser.
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[*Join our community!*](https://groups.google.com/forum/#!forum/zipline)
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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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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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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`.
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Once set up, you can install Zipline from our Quantopian channel:
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```
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conda install -c Quantopian zipline
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```
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Currently supported platforms include:
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* Windows 32-bit (can be 64-bit Windows but has to be 32-bit Anaconda)
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* OSX 64-bit
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* Linux 64-bit
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PIP
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---
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Alternatively you can install Zipline via the more traditional `pip`
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command. Since zipline is pure-python code it should be very easy to
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install and set up:
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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 or 3.3)
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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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* requests
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* [python-dateutil](https://pypi.python.org/pypi/python-dateutil) (>= 2.1)
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* ta-lib
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Quickstart
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==========
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See our [getting started tutorial](http://www.zipline.io/#quickstart).
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The following code implements a simple dual moving average algorithm.
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```python
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from zipline.api import order_target, record, symbol, history, add_history
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def initialize(context):
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# Register 2 histories that track daily prices,
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# one with a 100 window and one with a 300 day window
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add_history(100, '1d', 'price')
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add_history(300, '1d', 'price')
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context.i = 0
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def handle_data(context, data):
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# Skip first 300 days to get full windows
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context.i += 1
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if context.i < 300:
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return
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# Compute averages
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# history() has to be called with the same params
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# from above and returns a pandas dataframe.
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short_mavg = history(100, '1d', 'price').mean()
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long_mavg = history(300, '1d', 'price').mean()
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sym = symbol('AAPL')
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# Trading logic
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if short_mavg[sym] > long_mavg[sym]:
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# order_target orders as many shares as needed to
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# achieve the desired number of shares.
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order_target(sym, 100)
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elif short_mavg[sym] < long_mavg[sym]:
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order_target(sym, 0)
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# Save values for later inspection
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record(AAPL=data[sym].price,
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short_mavg=short_mavg[sym],
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long_mavg=long_mavg[sym])
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```
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You can then run this algorithm using the Zipline CLI. From the
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command line, run:
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```bash
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python run_algo.py -f dual_moving_average.py --symbols AAPL --start 2011-1-1 --end 2012-1-1 -o dma.pickle
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```
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This will download the AAPL price data from Yahoo! Finance in the
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specified time range and stream it through the algorithm and save the
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resulting performance dataframe to dma.pickle which you can then load
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and analyze from within python.
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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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