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.
Return -1 when there is a zero value for a spot price.
Intended for use by the incoming data portal changes. When the data
portal will see a -1 value, the portal will seek back a trading day
until a non-negative value is returned.
Volumes were incorrectly having the thousands factor applied, however
the volume is written as is (without the factor, since it volume is an
int, not float value.)
Fix by adding a special case for volume which returns the price as is.
Adds tests asserting that we resolve conflicts in accordance with the
following rules when we have multiple assets holding the same symbol at
the same time:
If multiple SIDs exist for symbol S at time T, return the candidate
SID whose start_date is highest. (200 cases)
If multiple SIDs exist for symbol S at time T, the best candidate
SIDs share the highest start_date, return the SID with the highest
end_date. (34 cases)
It is the opinion of the author (ssanderson) that we should consider
this malformed input and fail here. But this is the current indended
behavior of the code, and I accidentally broke it while refactoring.
These will serve as regression tests until the time comes that we
decide to enforce this as an error.
See https://github.com/quantopian/zipline/issues/837 for more
details.
Fixes the case where a delta has an asof_date of the last requested
day and an index error would occur. This guards against this
specifically to make the delta be effective through the end of the
requested window.
Adds a test case for this behavior.
Rather than a list that's ordered the same as the received columns.
Most nontrivial loaders were constructing dicts internally and then
converting back to lists, only to have the engine convert **back again**
into a dict. This cuts out the middleman, and prevents bugs due to
incorrect ordering of the output arrays.
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