Adds a new ``downsample`` method to all computable terms. Computable
terms (Filters, Factors, and Classifiers) can be downsampled to yearly,
quarterly, monthly, or weekly frequency.
The result of ``term.downsample`` is a new term of the same
family (Filter/Factor/Classifier) as ``term``. The downsampled term
computes by delegating to the original term; repeatedly calling its
``compute`` method with length-1 date ranges.
Downsampled terms take advantage of a new ``compute_extra_rows`` Term
method, which allows terms to dynamically request that additional extra
rows of themselves be computed based on the dates for which they're
being computed. This ensures, for example, that a monthly-downsampled
term always computes at the start of a month, even when a
naively-calculated pipeline window would end in the middle of the month.
- Split out extra_rows handling into an `ExecutionPlan` subclass.
`ExecutionPlan` now requires the dates and calendar against which a
set of terms will be computed, and now defers to a term's
`compute_extra_rows` method when deciding how many extra rows are
required to compute for that term. This will allow downsampled terms
to request enough extra rows to guarantee that we can maintain consistent
calculation dates.
As a consequence of the above, `TermGraph` now only deals with logical
dependencies, not with metadata surrounding extra row calculations.
This means that TermGraph can be used to generate dependency
visualizations in interactive contexts where we don't yet have a
calendar or start/end dates.
- Refactored test_{filter,factor,classifier} to use check_terms instead
of run_graph. This makes it easier to make changes to TermGraph,
since the testing interface is now to simply provide a dict of terms.
- Refactored BasePipelineTestCase to use fixtures to create an asset
finder. This fixes a potential leak of the test's asset db, which was
not being explicitly cleaned up.
- Refactored test_technical to use BasePipelineTestCase.
- Added a new special term, `InputDates()`, which can be used to request
date labels for inputs. Like `AssetExists`, `InputDates` is provided
in the initial workspace by default.
- Added a default (failing) `_compute` method to `AssetExists` which
provides a more useful error than AttributeError.
- Adds a new class, ``LabelArray``, which is a subclass of np.ndarray.
LabelArray is conceptually similar to pandas.Categorical, in that it
stores data with many duplicate values as indices into an array of
unique values. For string data with many duplicates (e.g. time-series
of tickers or or industry classifications), this provides multiple
orders of magnitude of improvement when doing string operations,
especially string comparison/matching operations.
- Adds a new generic object "specialization" for `AdjustedArrayWindow`,
and a corresponding ObjectOverwrite adjustment.
- Adds a new ``postprocess`` method to ``zipline.pipeline.term.Term``.
This method is called on the final result of any pipeline expression
after screen filtering has occurred. The default implementation of
``postprocess`` is identity, but Classifier overrides it to coerce
string columns into pandas.Categoricals before presenting them to the
user.
- Use RestrictedDTypeMixin for dtype validation in
Filter/Factor/Classifier.
- Use new LatestMixin for Latest{Filter,Factor,Classifier} instead of
duplicating logic across all three.
- Always ignore return values in _validate.
- Consistently call super() first in validation mixins.
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.
Replace it by distinguishing between "Loadable" and "Computable".
This is useful because it's now possible to write computable terms that
don't require any inputs (e.g. an `Always` filter or an `Everything`
classifier).
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.
Scott Sanderson