zipline README

The project's README from github.com/quantopian/zipline. As one markdown file: README.md.

.. image:: https://media.quantopian.com/logos/open_source/zipline-logo-03_.png :target: https://www.zipline.io :width: 212px :align: center :alt: Zipline

=============

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Zipline is a Pythonic algorithmic trading library. It is an event-driven system for backtesting. Zipline is currently used in production as the backtesting and live-trading engine powering Quantopian <https://www.quantopian.com>_ -- a free, community-centered, hosted platform for building and executing trading strategies. Quantopian also offers a fully managed service for professionals <https://factset.quantopian.com>_ that includes Zipline, Alphalens, Pyfolio, FactSet data, and more.

Features

Installation

Zipline currently supports Python 2.7, 3.5, and 3.6, and may be installed via either pip or conda.

Note: Installing Zipline is slightly more involved than the average Python package. See the full Zipline Install Documentation_ for detailed instructions.

For a development installation (used to develop Zipline itself), create and activate a virtualenv, then run the etc/dev-install script.

Quickstart

See our getting started tutorial <https://www.zipline.io/beginner-tutorial>_.

The following code implements a simple dual moving average algorithm.

.. code:: python

from zipline.api import order_target, record, symbol

def initialize(context):
    context.i = 0
    context.asset = symbol('AAPL')


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

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

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

    # Save values for later inspection
    record(AAPL=data.current(context.asset, 'price'),
           short_mavg=short_mavg,
           long_mavg=long_mavg)

You can then run this algorithm using the Zipline CLI. First, you must download some sample pricing and asset data:

.. code:: bash

$ zipline ingest
$ zipline run -f dual_moving_average.py --start 2014-1-1 --end 2018-1-1 -o dma.pickle --no-benchmark

This will download asset pricing data data sourced from Quandl, and stream it through the algorithm over the specified time range. Then, the resulting performance DataFrame is saved in dma.pickle, which you can load and analyze from within Python.

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

Questions?

If you find a bug, feel free to open an issue <https://github.com/quantopian/zipline/issues/new>_ and fill out the issue template.

Contributing

All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome. Details on how to set up a development environment can be found in our development guidelines <https://www.zipline.io/development-guidelines>_.

If you are looking to start working with the Zipline codebase, navigate to the GitHub issues tab and start looking through interesting issues. Sometimes there are issues labeled as Beginner Friendly <https://github.com/quantopian/zipline/issues?q=is%3Aissue+is%3Aopen+label%3A%22Beginner+Friendly%22>_ or Help Wanted <https://github.com/quantopian/zipline/issues?q=is%3Aissue+is%3Aopen+label%3A%22Help+Wanted%22>_.

Feel free to ask questions on the mailing list <https://groups.google.com/forum/#!forum/zipline>_ or on Gitter <https://gitter.im/quantopian/zipline>_.

.. note::

Please note that Zipline is not a community-led project. Zipline is maintained by the Quantopian engineering team, and we are quite small and often busy.

Because of this, we want to warn you that we may not attend to your pull request, issue, or direct mention in months, or even years. We hope you understand, and we hope that this note might help reduce any frustration or wasted time.

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.. _Zipline Install Documentation : https://www.zipline.io/install

zipline (quantopian/zipline)

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20.1K ·GitHub
Zipline
What is this?Zipline is a Pythonic algorithmic trading library — an event-driven system for backtesting trading strategies written in Python. It is used in production as the backtesting and live-trading engine behind Quantopian.
Is this a library I import into my own code, or a standalone app I run?Both halves: you write your strategy as a Python file, then drive it with the 'zipline run' command-line tool. There is no GUI — you get results back as data you analyze in Python.
How do I run my first backtest?After installing, download sample data with 'zipline ingest', then run a strategy file, e.g. 'zipline run -f dualmovingaverage.py --start 2014-1-1 --end 2018-1-1 -o dma.pickle --no-benchmark'. More examples live in the zipline/examples directory.
Where does the backtesting data come from — do I need my own price feed?Not for the quickstart: 'zipline ingest' downloads asset pricing data sourced from Quandl and streams it through your algorithm over the date range you specify.
Can I use it for live trading, or is it backtesting only?The engine itself powers Quantopian's live trading in production, but everything the README walks you through is backtesting over historical date ranges — setting up your own live trading is not covered there.
What does an algorithm look like in Zipline?A Python file with an initialize() for setup and a handler that runs on each bar. In the README's dual moving average example, a golden cross triggers ordertarget(context.asset, 100) to hold 100 shares, a death cross orders 0 to exit, and record() logs values like the current price.
Where can I see more example strategies?Besides the dual moving average demo in the README, the zipline/examples directory in the repo contains additional sample algorithms to study and adapt.
What are similar projects?bytex, nautilus_trader, backtrader, QUANTAXIS, Lean, abu, bytex, CNEquity
Which topics is it in?market-data, backtesting, quant, algorithmic-trading, pandas, quantitative-finance, data-analysis, python-library, finance, ci, developers, free
Creator
quantopian
Size
166.8 MB
Stars
20.1K
Updated
CommitOct 14Release1.4.1Oct 05
Age
13y
Commits · 90 Days