'Compute values for `pipeline` in number of days equal to `chunksize`
and return stitched up result. Computing in chunks is useful for
pipelines computed over a long period of time.
Parameters
pipeline : Pipeline
The pipeline to run.
start_date : pd.Timestamp
The start date to run the pipeline for.
end_date : pd.Timestamp
The end date to run the pipeline for.
chunksize : int
The number of days to execute at a time.
Returns
result : pd.DataFrame
A frame of computed results.
The ``result`` columns correspond to the entries of
`pipeline.columns`, which should be a dictionary mapping strings to
instances of :class:`zipline.pipeline.term.Term`.
For each date between ``start_date`` and ``end_date``, ``result``
will contain a row for each asset that passed `pipeline.screen`.
A screen of ``None`` indicates that a row should be returned for
each asset that existed each day.
See Also
:meth:`zipline.pipeline.engine.PipelineEngine.run_pipeline`'

@abstractmethod
def run_chunked_pipeline(self, pipeline, start_date, end_date, chunksize):

    raise NotImplementedError('run_chunked_pipeline')
