The previous algorithm assumed that the group labels were integers. It
produced nonsense with LabelArrays (though sadly didn't crash because
numpy promotes None and void to object).
Classifiers are computations that represent grouping keys. They can be
used in conjuction with normalization functions like ``zscore`` or
``demean`` to perform normalizations over subsets of a dataset.
Notable changes:
- Added ``demean()`` and ``zscore()`` methods to ``Factor``.
- Added a classifier versions of ``Latest`` and ``CustomTermMixin``.
The .latest attribute of int64 dataset columns no produces a
classifier by default.
- Added ``Everything``, a classifier that maps all data to the same
value.
- Added ``zipline.lib.normalize``, which implements a naive, pure-Python
grouped normalize function. This will likely be moved to Cython in a
subsequent PR.
Renames zipline.utils.test_utils to zipline.testing
Adds zipline.testing.fixtures.ZiplineTestCase to manage setup and
teardown and adds mixins to define fixtures like an asset finder or
trading calendar.
EarningsCalendar loader.
- Moves most of AdjustedArray back into Python. The window iterator is
the only part that's performance-intensive.
- Adds a bootleg templating system for creating specialized versions of
AdjustedArrayWindow for each concrete type we care about.
- Adds support for differently dtyped terms in pipeline. This allows us
to use datetime64s which are needed in the EarningsCalendar.
- Adds EarningsCalendar dataset for the next and previous earnings
announcements in pipeline.
- Adds in memory loader for EarningsCalendar.
- Adds blaze loader for EarningsCalendar.
jfkirk