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Zipline, a Pythonic Algorithmic Trading Library
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Zipline is a Pythonic algorithmic trading library. It is an event-driven system that supports both backtesting and live-trading.

Zipline is currently used in production as the backtesting and live-trading engine powering Quantopian -- a free, community-centered, hosted platform for building and executing trading strategies.

Join our community!


Want to contribute? See our open requests and our general guidelines below.


  • 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 are based on Pandas DataFrames to integrate nicely into the existing PyData 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.


Installing With pip

Assuming you have all required (see note below) non-Python dependencies, you can install Zipline with pip via:

$ pip install zipline

Note: Installing Zipline via pip is slightly more involved than the average Python package. Simply running pip install zipline will likely fail if you've never installed any scientific Python packages before.

There are two reasons for the additional complexity:

  1. Zipline ships several C extensions that require access to the CPython C API. In order to build the C extensions, pip needs access to the CPython header files for your Python installation.
  2. Zipline depends on numpy, the core library for numerical array computing in Python. Numpy depends on having the LAPACK linear algebra routines available.

Because LAPACK and the CPython headers are binary dependencies, the correct way to install them varies from platform to platform. On Linux, users generally acquire these dependencies via a package manager like apt, yum, or pacman. On OSX, Homebrew is a popular choice providing similar functionality.

See the full Zipline Install Documentation for more information on acquiring binary dependencies for your specific platform.


Another way to install Zipline is via the conda package manager, which comes as part of Anaconda or can be installed via pip install conda.

Once set up, you can install Zipline from our Quantopian channel:

conda install -c Quantopian zipline

Currently supported platforms include:

  • GNU/Linux 64-bit
  • OSX 64-bit
  • Windows 64-bit


Windows 32-bit may work; however, it is not currently included in continuous integration tests.


See our getting started tutorial.

The following code implements a simple dual moving average algorithm.

from zipline.api import (

def initialize(context):
    # Register 2 histories that track daily prices,
    # one with a 100 window and one with a 300 day window
    add_history(100, '1d', 'price')
    add_history(300, '1d', 'price')
    context.i = 0

def handle_data(context, data):
    # Skip first 300 days to get full windows
    context.i += 1
    if context.i < 300:

    # Compute averages
    # history() has to be called with the same params
    # from above and returns a pandas dataframe.
    short_mavg = history(100, '1d', 'price').mean()
    long_mavg = history(300, '1d', 'price').mean()

    sym = symbol('AAPL')

    # Trading logic
    if short_mavg[sym] > long_mavg[sym]:
        # order_target orders as many shares as needed to
        # achieve the desired number of shares.
        order_target(sym, 100)
    elif short_mavg[sym] < long_mavg[sym]:
        order_target(sym, 0)

    # Save values for later inspection

You can then run this algorithm using the Zipline CLI. From the command line, run:

python -f --symbols AAPL --start 2011-1-1 --end 2012-1-1 -o dma.pickle

This will download the AAPL price data from Yahoo! Finance in the specified time range and stream it through the algorithm and save the resulting performance dataframe to dma.pickle which you can then load and analyze from within python.

You can find other examples in the zipline/examples directory.


If you would like to contribute, please see our Contribution Requests:

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