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.
Want to contribute? See our development guidelines
- 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.
Assuming you have all required (see note below) non-Python dependencies, you
can install Zipline with
$ 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:
- Zipline ships several C extensions that require access to the CPython C API.
In order to build the C extensions,
pipneeds access to the CPython header files for your Python installation.
- 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
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
pip install conda.
Once set up, you can install Zipline from our
$ 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 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. From the command line, run:
$ zipline ingest $ zipline run -f dual_moving_average.py --start 2011-1-1 --end 2012-1-1 -o dma.pickle
This will download the AAPL price data from quantopian-quandl 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