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Installation

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.

Dependencies

  • Python (>= 2.7.2)
  • numpy (>= 1.6.0)
  • pandas (>= 0.9.0)
  • pytz
  • msgpack-python
  • iso8601
  • Logbook
  • blist

Develop

To run tests:

$ nosetests

To build documentation:

$ paver apidocs html
# outputs to docs/_build/html

For an interactive shell:

$ paver ishell

For coverage information:

$ paver coverage
# outputs to cover/index.html

For line count:

$ paver sloccount

For syntax checking:

$ paver pyflakes
For building distributable egg::
$ paver bdist_egg

Tooling hints

:mod:`zipline` relies heavily on scientific python components (numpy, scikit, pandas, matplotlib, ipython, etc). Tooling up can be a pain, and it often involves managing a configuration including your OS, c/c++/fortran compilers, python version, and versions of numerous modules. I've found the following tools absolutely indispensable:

  • some kind of package manager for your platform. package managers generally give you a way to search, install, uninstall, and check currently installed packages. They also do a great job of managing dependencies.
    • linux: yum/apt-get
    • mac: homebrew/macport/fink (I highly recommend homebrew: https://github.com/mxcl/homebrew)
    • windows: probably best if you use a complete distribution, like: enthought, ActiveState, or Python(x,y)
  • Python also provides good package management tools to help you manage the components you install for Python.
    • pip
    • easy_install/setuptools. I have always used setuptools, and I've been quite happy with it. Just remember that setuptools is coupled to your python version.
  • virtualenv and virtualenvwrapper are your very best friends. They complement your python package manager by allowing you to create and quickly switch between named configurations.
    • Install all the versions of Python you like to use, but install setuptools, virtualenv, and virtualenvwrapper with the very latest python. Use the latest python to install the latest setuptools, and the latest setuptools to install virtualenv and virtualenvwrapper. virtualenvwrapper allows you to specify the python version you wish to use (mkvirtualenv -p <python executable> <env name>), so you can create envs of any python denomination.

Mac OS hints

Scientific python on the Mac can be a bit confusing because of the many independent variables. You need to have several components installed, and be aware of the versions of each:

  • XCode. XCode includes the gcc and g++ compilers and architecture specific assemblers. Your version of XCode will determine which compilers and assemblers are available. The most common issue I encountered with scientific python libraries is compilation errors of underlying C code. Most scientific libraries are optimized with C routines, so this is a major hurdle. In my environment (XCode 4.0.2 with iOS components installed) I ran into problems with -arch flags asking for power pc (-arch ppc passed to the compiler). Read this stackoverflow to see how to handle similar problems: http://stackoverflow.com/questions/5256397/python-easy-install-fails-with-assembler-for-architecture-ppc-not-installed-on
  • gfortran - you need this to build numpy. With brew you can install with just: `brew install gfortran`
  • umfpack - you need this to build scipy. `brew install umfpack`
  • swig - you need this to build scipy. `brew install swig`
  • hdf5 - you need this to build tables. `brew install hdf5`
  • zeromq - you need this to run qbt. `brew install zmq`

Data Sources

The Backtest can handle multiple concurrent data sources. QBT will start a subprocess to run each datasource, and merge all events from all sources into a single serial feed, ordered by date.

Data sources have events with very different frequencies. For example, liquid stocks will trade many times per minute, while illiquid stocks may trade just once a day. In order to serialize events from all sources into a single feed, qbt loads events from all sources into memory, then sorts. The communication happens like this:

  1. QBT requests the next event from each data source, ignoring date (i.e. just next in sequence for all)
  2. Using the earliest date from all the events from all sources, QBT then asks for "next after <date>" from all sources.
  3. All datasources send all events in their history from before <date>, moving their internal pointer forward to the next unsent event.
  4. QBT merges all events in memory
  5. goto 1!