Zipline

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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.

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

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

The easiest way to install Zipline is via conda 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:

  • Windows 32-bit (can be 64-bit Windows but has to be 32-bit Anaconda)
  • OSX 64-bit
  • Linux 64-bit

PIP

Alternatively you can install Zipline via the more traditional pip command. Since zipline is pure-python code it should be very easy to install and set up:

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 or 3.3)
  • numpy (>= 1.6.0)
  • pandas (>= 0.9.0)
  • pytz
  • Logbook
  • requests
  • python-dateutil (>= 2.1)
  • ta-lib

Quickstart

See our tutorial to get started.

The following code implements a simple dual moving average algorithm.

from zipline.api import order_target, record, symbol, history, add_history


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:
        return

    # 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
    record(AAPL=data[sym].price,
           short_mavg=short_mavg[sym],
           long_mavg=long_mavg[sym])

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

python run_algo.py -f dual_moving_avg.py --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.

Contributions

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

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