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.
Causes doctest to not care about the module of the raised exception.
This matters because Python3 includes the module in the formatted
exception, but Python3 doesn't, so the doctest will always fail on one
or the other without this flag.
This patch lays the groundwork for a compute engine designed to
facilitate construction of factor-based universe screening and portfolio
allocation. It contains:
A new module, `zipline.modelling`, containing entities that can be used
to express computations as dependency graphs. Each node in such a graph
is an instance of the base `Term` class, defined in
`zipline.modelling.term`. Dependency graphs are executed by instances
of `FFCEngine`, defined in `zipline.modelling.engine`.
A new module, `zipline.data.ffc`, containing loaders and dataset
definitions for inputs to the modelling API.
New `TradingAlgorithm` api methods: `add_factor`, and `add_filter`.
These methods can only be called from `initialize`, and are used to
inform the algorithm that each day it should compute the given terms.
Computed factor results are made available through a new attribute of
the `data` object in `before_trading_start` and `handle_data`. Computed
filter results control which assets are available in the factor matrix
on each day.