loaders
This allows people to set their cutoff time to the time they will
actually execute 'before_trading_start'. Currently this is just passed
to the constructor of the loader; however, I would like to make this
managed by the algorithm simulation runner. This would help keep all of
the loaders in sync and lock 'before_trading_start's execution to the
time the data is queried for.
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.
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.
Allows us to run subtests while still seeing which parameter
combination caused the tests to fail. The decorator can be used to
create subtests inside a test or to parameterize an entire test method.
We cannot use something like unittest2.TestCase.subTest because our test
runner does not support that.
Previously we have capitalized input strings at different levels in
our code: in the user-facing API methods and in the asset finder.
This commit moves input string capitalization exclusively to the API
method to which the string was supplied. Specifically, the string is
capitalized by a preprocess API method decorator. The preprocess
decorator passes the input string to the newly defined
ensure_upper_case() method, which returns a TypeError if the argument
supplied is not a string.
ensure_upper_case() is defined in a new file, zipline/utils/input_validation.py.
The existing expect_types() method is also moved there.
Various tests in tests/test_assets.py are modified to account for the
fact that the asset finder method lookup_symol() no longer capitalizes
its supplied argument.
- Adds `zipline.pipeline.Pipeline`, a new user-facing class for managing
pipelines of Modeling API expressions.
- Adds `attach_pipeline` and `drain_pipeline` as API methods
- Removes `add_factor` and `add_filter` as API methods. These have been
replaced two new methods on `Pipeline`: `add`, and `apply_screen`.
- Adding a `Filter` as a column no longer implicitly truncates rows from
the Modelling API output. It simply causes a new column, of dtype
`bool` to show up in the output. Removal of rows is now handled by the
new `apply_screen` method of `Pipeline`.
- Refactors the existing Modeling API tests to reflect the new APIs.
Joe Jevnik