To allow mapping the baseline and adjustment values when backed by a
LabelArray.
This also make value attribute on Adjustment objects public, so that
update_labels() can perform the update in place.
Limit the perspective offset to 1. There is a possibility that if a
consumer of the AdjustedArrayWindow does not fetch adjustments between
the end of the data window and the vantage points beyond the end of the
window.
Until that case has a solution, e.g. having the consumer of the
AdjustedArrayWindow include the perspective offset when calculating the
query for adjustments, limit the offsets to 1.
- Refactor `test_adjusted_array` to test a range of perspective_offsets in
all tests.
- Make perspective_offset a parameter to `AdjustedArray.traverse`
instead of `AdjustedArray`.
Add a perspective offset to `AdjustedArrayWindow` and `AdjustedArray`,
so that `HistoryLoader` does not need to twiddle with offsets to support
viewing the data from the bar after end of the window, (Which is the
case when a '1d' history window is retrieved in minute mode, which is
explained in the docstring for `HistoryLoader.history`)
Presently, this simplifies the logic in
`HistoryLoader._get_adjustments_in_range`, and other incoming
AdjustmentReader's, (e.g. the roll based adjustment reader for continous
futures.) This patch should also make it easier for history and pipeline
to converge on a singular `load_adjustments` method.
TST: fix quarter normalization test
TST: change test name
BUG: remove arg
BUG: look at dict keys
TST: add test for windowing
MAINT: raise ValueError instead of asserting
TST: add assertion to check windowing
TST: parametrize test over number of quarters forward/back.
BUG: fix adjustment calculation logic for quarter crossovers.
TST: add test for previous quarter windows
BUG: fix bugs in calculating previous windows
BUG: fix missing value for datetime
TST: add test case for missing quarter
- 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.
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.
