Skip to main content

A backtester for financial algorithms.

Project description


Zipline is a financial backtester for trading algorithms written in
Python. The system is fundamentally event-driven and a close
approximation of how live-trading systems operate.

Zipline is currently used in production as the backtesting engine
powering <> -- a free, community-centered
platform that allows development and real-time backtesting of trading
algorithms in the web browser.


* 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.


Since zipline is pure-python code it should be very easy to install
and set up with pip:

```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
* msgpack-python
* iso8601
* Logbook
* blist


The following code implements a simple dual moving average algorithm
and tests it on data extracted from yahoo finance.

from zipline.algorithm import TradingAlgorithm
from zipline.transforms import MovingAverage
from zipline.utils.factory import load_from_yahoo

class DualMovingAverage(TradingAlgorithm):
"""Dual Moving Average algorithm.
def initialize(self, short_window=200, long_window=400):
# Add 2 mavg transforms, one with a long window, one
# with a short window.
self.add_transform(MovingAverage, 'short_mavg', ['price'],

self.add_transform(MovingAverage, 'long_mavg', ['price'],

# To keep track of whether we invested in the stock or not
self.invested = False

self.short_mavg = []
self.long_mavg = []

def handle_data(self, data):
if (data['AAPL'].short_mavg['price'] > data['AAPL'].long_mavg['price']) and not self.invested:
self.order('AAPL', 100)
self.invested = True
elif (data['AAPL'].short_mavg['price'] < data['AAPL'].long_mavg['price']) and self.invested:
self.order('AAPL', -100)
self.invested = False

# Save mavgs for later analysis.

data = load_from_yahoo()
dma = DualMovingAverage()
results =

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

Style Guide

To ensure that changes and patches are focused on behavior changes,
the zipline codebase adheres to PEP-8,

The maintainers check the code using the flake8 script,
<>, which is included in the

Before submitting patches or pull requests, please ensure that your
changes pass ```flake8 --ignore=E124,E125,E126 zipline tests```

Discussion and Help

Discussion of the project is held at the Google Group,


The source for Zipline is hosted at

Build Status

[![Build Status](](


For other questions, please contact <>.

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
zipline-0.5.1.tar.gz (65.7 kB) Copy SHA256 hash SHA256 Source None

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page