mirror of
https://github.com/wassname/catalyst.git
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Thomas Wiecki
251 lines
7.6 KiB
Markdown
251 lines
7.6 KiB
Markdown
Zipline
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=======
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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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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. Currently, backtesting is well
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supported, but the intent is to develop the library for both paper and
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live trading, so that the same logic used for backtesting can be
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applied 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,
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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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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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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 [tutorial](http://nbviewer.ipython.org/github/quantopian/zipline/blob/master/docs/tutorial.ipynb) to get started.
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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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# Trading logic
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if short_mavg[0] > long_mavg[0]:
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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(symbol('AAPL'), 100)
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elif short_mavg[0] < long_mavg[0]:
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order_target(symbol('AAPL'), 0)
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# Save values for later inspection
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record(AAPL=data[symbol('AAPL')].price,
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short_mavg=short_mavg[0],
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long_mavg=long_mavg[0])
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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_avg.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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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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and for implementing new order methods.
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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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- @jkp and @bencpeters for bugfixes to benchmark.
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- @dstephens for adding Canadian treasury curves.
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- @mtrovo for adding BMF&Bovespa calendars.
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- @sdrdis for bugfixes.
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- @humdings for refactoring the order methods.
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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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Finally, install zipline in develop mode (from the zipline source root dir):
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```
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python setup.py develop
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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://bitbucket.org/tarek/flake8/wiki/Home>, 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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Contact
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=======
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For other questions, please contact <opensource@quantopian.com>.
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