Thomas Wiecki
b976c1252b
I wrote this a little while ago as I noticed that a lot of time is spent computing risk statistics. This is done over the complete history over and over again while this could be done just by using the previously computed value (iteratively). We didn't go forward back then because for minute trade data the difference was not significant enough. However, now with zipline standalone I think most people will use daily (because that's what's available) and it makes a huge difference (speed-up of a couple of 100%). Unfortunately, we can't just replace the existing one with an iterative as for the final cumulative stats the batch is still better. So that's not as nice, but the performance increase is big enough for me to issue this PR (zipline is actually painfully slow with daily data). There is a unittest that compares that both produce exactly the same outputs. Speed measurements (for 500 trading days, daily source): with iterative: real 26.617 user 12.909 sys 6.112 pcpu 71.46 prior: real 44.176 user 31.030 sys 11.381 pcpu 96.00
Zipline
Zipline is a realtime stream processing system.
System Setup
Running
Initial virtualenv setup::
$ mkvirtualenv zipline
$ workon zipline
# Go get coffee, the following will compile a heap of C/C++ code
$ ./etc/ordered_pip.sh etc/requirements.txt
# And optionally:
$ ./etc/ordered_pip.sh etc/requirements_dev.txt
Develop
To run tests::
$ nosetests
Tooling hints
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 OS: 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 ), 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
Style Guide
To ensure that changes and patches are focused on behavior changes, the zipline codebase adheres to PEP-8, http://www.python.org/dev/peps/pep-0008/.
The maintainers check the code using the flake8 script, https://github.com/jcrocholl/pep8/, which is included in the requirements_dev.txt.
Before submitting patches or pull requests, please ensure that your changes pass flake8 zipline tests
Discussion and Help
Discussion of the project is held at the Google Group, zipline@googlegroups.com, https://groups.google.com/forum/#!forum/zipline.
Source
The source for Zipline is hosted at https://github.com/quantopian/zipline.
Contact
For other questions, please contact opensource@quantopian.com.