The SecurityList implements a non-exposed method
`current_securities(dt)` which SecurityListRestrictions calls to
determine if an asset is restricted. Deprecate the `__iter__` and
`__contains__` methods of security lists in favor of
`current_securities(dt)`
An ABC Restrictions defines a group of restrictions responsible
for returning restriction information for sids on certain dts. An
InMemoryRestrictions is a point-in-time group of such restrictions,
with all restrictions and their dates passed in upon instantiation.
A StaticRestrictedList takes a list of sids, restricting them at all
dates
MAINT: optimization - only look at assets appearing in data
TST: simplify test
DOC: add documentation for checkpoints
MAINT: explicitly cast event date field to datetime
MAINT: add back import
TST: fix indexing to remove setting wtih copy warning
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
With the addition of the truncate function, there are cases where we'll
want to construct a BcolzMinuteBarWriter to call truncate, without
gathering all the metadata. This commit adds a write_metadata arg to its
init, which is True by default. If False is specified, no metadata is
written.
Requires adding logic to truncate to update end_session in metadata to
the truncate date.
`future_chain` will be replaced by the as yet to be implemented method,
`data.current_chain`
Also removing `FutureChain` which will be replaced by another version
which only supports indexing and iteration.
Pandas 0.18 doesn't like having null-ish values in categoricals. Fixing
this properly requires re-thinking the semantics for missing_value on
pipeline terms, so we're punting on that until after we've upgraded to
0.18.
Pandas 0.18 deprecated passing "null-ish" values to pd.categorical. The
expectation, instead, is that you use categorical's native support for
missing data, which means the user will always get NaN's for missing
entries of the categorical.
A follow-up to this change should probably drop support for custom
missing values entirely and to use LabelArray/categorical for integer
data.
Andrew Liang