Zipline

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 and Help

Discussion of the project is held at the Google Group, zipline@googlegroups.com, https://groups.google.com/forum/#!forum/zipline.

Features

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.
Zipline comes "batteries included" as many common statistics like moving average and linear regression can be readily accessed from within a user-written algorithm.
Input of historical data and output of performance statistics is based on Pandas DataFrames to integrate nicely into the existing Python eco-system.
Statistic and machine learning libraries like matplotlib, scipy, statsmodels, and sklearn support development, analysis and visualization of state-of-the-art trading systems.

Installation

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.

Dependencies

Python (>= 2.7.2)
numpy (>= 1.6.0)
pandas (>= 0.9.0)
pytz
Logbook
requests
iso8601
python-dateutil (>= 2.1)

Quickstart

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.

Contributions

If you would like to contribute, please see our Contribution Requests: https://github.com/quantopian/zipline/wiki/Contribution-Requests

Credits

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
Brian Cappello
@verdverm (Tony Worm), Order types (stop, limit)
@benmccann for benchmarking contributions
Quantopian Team

(alert us if we've inadvertantly missed listing you here!)

Development Environment

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

Style Guide

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://github.com/bmcustodio/flake8, 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

Source

The source for Zipline is hosted at https://github.com/quantopian/zipline.

Documentation

You can compile the documentation using Sphinx:

sudo apt-get install python-sphinx
make html

Build Status

Contact

For other questions, please contact opensource@quantopian.com.

readevalprint/zipline