Upgrade six 1.9.0 -> 1.10.0 in requirements.txt, and futures 3.0.3 ->
3.0.5, requests-futures 0.9.5 -> 0.9.7, and piprot 0.9.1 -> 0.9.6 in
requirements_dev.txt.
There was a comment in requirements.txt warning not to use
python-dateutil 2.4.0, which was necessary when we were using 2.3.x
and 2.4.0 was broken for us, but now we're using 2.4.2 which is fixed,
so the comment is no longer needed.
Upgrade Logbook to 0.12.5. This required changing a usage of
`logbook.NullHandler()` which passed `bubble=True`, since
`NullHandler` no longer supports the `bubble` argument.
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.
- Parse our requirements.txt to keep install_requires up to date.
- Create extras builds for talib and dev.
- Use pip install -e .[dev] on Travis to install Zipline before testing.
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.
Richard Frank