Zipline is a Pythonic algorithmic trading library. The system is fundamentally event-driven and a close approximation of how live-trading systems operate. Currently, backtesting is well supported, but the intent is to develop the library for both paper and live trading, so that the same logic used for backtesting can be applied to the market.
Zipline is currently used in production as the backtesting engine powering Quantopian (https://www.quantopian.com) -- a free, community-centered platform that allows development and real-time backtesting of trading algorithms in the web browser.
Want to contribute? See our open requests and our general guidelines below.
Discussion of the project is held at the Google Group, zipline@googlegroups.com, https://groups.google.com/forum/#!forum/zipline.
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Ease of use: Zipline tries to get out of your way so that you can focus on algorithm development. See below for a code example.
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Zipline comes "batteries included" as many common statistics like moving average and linear regression can be readily accessed from within a user-written algorithm.
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Input of historical data and output of performance statistics is based on Pandas DataFrames to integrate nicely into the existing Python eco-system.
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Statistic and machine learning libraries like matplotlib, scipy, statsmodels, and sklearn support development, analysis and visualization of state-of-the-art trading systems.
Since zipline is pure-python code it should be very easy to install and set up with pip:
pip install numpy # Pre-install numpy to handle dependency chain quirk
pip install zipline
If there are problems installing the dependencies or zipline we recommend installing these packages via some other means. For Windows, the Enthought Python Distribution includes most of the necessary dependencies. On OSX, the Scipy Superpack works very well.
- Python (>= 2.7.2)
- numpy (>= 1.6.0)
- pandas (>= 0.9.0)
- pytz
- Logbook
- requests
- python-dateutil (>= 2.1)
We provide experimental support for conda packages. Thus if you installed Anaconda you can try:
conda install -c https://binstar.org/twiecki zipline
Currently this only works for linux 64 bit. If you want to help extend this,
have a look at the conda subdirectory.
The following code implements a simple dual moving average algorithm and tests it on data extracted from yahoo finance.
from zipline import TradingAlgorithm
from zipline.transforms import MovingAverage
from zipline.utils.factory import load_from_yahoo
from datetime import datetime
import pytz
import matplotlib.pyplot as plt
class DualMovingAverage(TradingAlgorithm):
"""Dual Moving Average Crossover algorithm.
This algorithm buys apple once its short moving average crosses
its long moving average (indicating upwards momentum) and sells
its shares once the averages cross again (indicating downwards
momentum).
"""
def initialize(self, short_window=100, long_window=400):
# Add 2 mavg transforms, one with a long window, one
# with a short window.
self.add_transform(MovingAverage, 'short_mavg', ['price'],
window_length=short_window)
self.add_transform(MovingAverage, 'long_mavg', ['price'],
window_length=long_window)
# To keep track of whether we invested in the stock or not
self.invested = False
def handle_data(self, data):
short_mavg = data['AAPL'].short_mavg['price']
long_mavg = data['AAPL'].long_mavg['price']
buy = False
sell = False
# Has short mavg crossed long mavg?
if short_mavg > long_mavg and not self.invested:
self.order('AAPL', 100)
self.invested = True
buy = True
elif short_mavg < long_mavg and self.invested:
self.order('AAPL', -100)
self.invested = False
sell = True
# Record state variables. A column for each
# variable will be added to the performance
# DataFrame returned by .run()
self.record(short_mavg=short_mavg,
long_mavg=long_mavg,
buy=buy,
sell=sell)
# Load data
start = datetime(1990, 1, 1, 0, 0, 0, 0, pytz.utc)
end = datetime(2002, 1, 1, 0, 0, 0, 0, pytz.utc)
data = load_from_yahoo(stocks=['AAPL'], indexes={}, start=start,
end=end, adjusted=False)
# Run algorithm
dma = DualMovingAverage()
perf = dma.run(data)
# Plot results
fig = plt.figure()
ax1 = fig.add_subplot(211, ylabel='Price in $')
data['AAPL'].plot(ax=ax1, color='r', lw=2.)
perf[['short_mavg', 'long_mavg']].plot(ax=ax1, lw=2.)
ax1.plot(perf.ix[perf.buy].index, perf.short_mavg[perf.buy],
'^', markersize=10, color='m')
ax1.plot(perf.ix[perf.sell].index, perf.short_mavg[perf.sell],
'v', markersize=10, color='k')
ax2 = fig.add_subplot(212, ylabel='Portfolio value in $')
perf.portfolio_value.plot(ax=ax2, lw=2.)
ax2.plot(perf.ix[perf.buy].index, perf.portfolio_value[perf.buy],
'^', markersize=10, color='m')
ax2.plot(perf.ix[perf.sell].index, perf.portfolio_value[perf.sell],
'v', markersize=10, color='k')You can find other examples in the zipline/examples directory.
If you would like to contribute, please see our Contribution Requests: https://github.com/quantopian/zipline/wiki/Contribution-Requests
Thank you for all the help so far!
- @rday for sortino ratio, information ratio, and exponential moving average transform
- @snth
- @yinhm for integrating zipline with @yinhm/datafeed
- Jeremiah Lowin for teaching us the nuances of Sharpe and Sortino Ratios, and for implementing new order methods.
- Brian Cappello
- @verdverm (Tony Worm), Order types (stop, limit)
- @benmccann for benchmarking contributions
- @jkp and @bencpeters for bugfixes to benchmark.
- @dstephens for adding Canadian treasury curves.
- @mtrovo for adding BMF&Bovespa calendars.
- Quantopian Team
(alert us if we've inadvertantly missed listing you here!)
The following guide assumes your system has virtualenvwrapper and pip already installed.
You'll need to install some C library dependencies:
sudo apt-get install libopenblas-dev liblapack-dev gfortran
wget http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz
tar -xvzf ta-lib-0.4.0-src.tar.gz
cd ta-lib/
./configure --prefix=/usr
make
sudo make install
Suggested installation of Python library dependencies used for development:
mkvirtualenv zipline
./etc/ordered_pip.sh ./etc/requirements.txt
pip install -r ./etc/requirements_dev.txt
To ensure that changes and patches are focused on behavior changes, the zipline codebase adheres to both PEP-8, http://www.python.org/dev/peps/pep-0008/, and pyflakes, https://launchpad.net/pyflakes/.
The maintainers check the code using the flake8 script, https://bitbucket.org/tarek/flake8/wiki/Home, which is included in the requirements_dev.txt.
Before submitting patches or pull requests, please ensure that your
changes pass flake8 zipline tests and nosetests
The source for Zipline is hosted at https://github.com/quantopian/zipline.
You can compile the documentation using Sphinx:
sudo apt-get install python-sphinx
make html
For other questions, please contact opensource@quantopian.com.