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.
- Fixes an error where Modeling API data known as of the close of `day
N` would be shown to algorithms during `before_trading_start` as of
the close of the same day. Algorithms should now only receive data
during `before_trading_start/handle_data` that was known as of the
simulation time at which the function would be called.
- All Term instances now have a `mask` attribute that must be a `Filter`
or an instance of `AssetExists()`. `mask` can be used to specify that
a Factor should be computed in a manner that ignores the values that
were not `True` in the mask.
- Changed the interface for `FFCLoader.load_adjusted_array` and
`Term._compute` from `(columns, mask)`, with mask as a DataFrame, to
`(columns, dates, assets, mask)`, where mask is a numpy array. This
is primarily to avoid having to reconstruct extra DataFrames when
using masks produced by non `AssetExists` filters.
- Adds `BoundColumn.latest`, which gives the most-recently-known value
of a column.
This patch lays the groundwork for a compute engine designed to
facilitate construction of factor-based universe screening and portfolio
allocation. It contains:
A new module, `zipline.modelling`, containing entities that can be used
to express computations as dependency graphs. Each node in such a graph
is an instance of the base `Term` class, defined in
`zipline.modelling.term`. Dependency graphs are executed by instances
of `FFCEngine`, defined in `zipline.modelling.engine`.
A new module, `zipline.data.ffc`, containing loaders and dataset
definitions for inputs to the modelling API.
New `TradingAlgorithm` api methods: `add_factor`, and `add_filter`.
These methods can only be called from `initialize`, and are used to
inform the algorithm that each day it should compute the given terms.
Computed factor results are made available through a new attribute of
the `data` object in `before_trading_start` and `handle_data`. Computed
filter results control which assets are available in the factor matrix
on each day.
Scott Sanderson