* Updated cython build scripts
* Updated setup.py to to install catalyst package
* Updated momentum example to use catalyst package
* catalyst executable now supports loading pipelines from multiple bundles
BLD: Move setuptools_scm recipe earlier
in build process. We order alphabetically.
BLD: Update to newest Cython version
BLD: Update version of matplotlib
BLD: Pin matplotlib dependency
This format is intended for storing data for all sids of an asset type,
e.g. equities or futures for a session. bcolz is not used to avoid the overhead
of creating the directories and files for each asset (which numbers around ~8000
for active equities) can be removed since the update is meant to be read at
once, instead of supporting the random access pattern needed by the simulation.
This patch only adds the reader/writer pair, with the management of finding the
paths to delta files and the application of the updates to the bcolz write left
to internal loader code.
Also, the update reader interface is intentionally constrained to the data for
an entire session to allow for an implementation that allows for mid-session updates.
For example, to prevent `alpha` from internally calling
`beta` a second time, pass the previously-calculated
`beta` value in.
Requires empyrical 0.1.10 from pypi
Adds the data bundle concept which makes it easy for users to register
loading functions to build out minute and daily data along with an
assets db and adjustments db. By default we have provided a `quandl`
bundle which pulls from the public domain WIKI dataset. Users may
register new bundles by decorating an ingest function with
`zipline.data.bundles.register(<name>)`. This also provides a
`yahoo_equities` function for creating an ingestion function that will
load a static set of assets from yahoo.
The cli is now structured as a couple of subcommands and has been
changed to `python -m zipline`. The old behavior of `run_algo.py` has
been moved to the `run` subcommand. This is almost entirely the same
except that it now takes the name of the data bundle to use, defaulting
to `quandl`.
The next subcommand is `ingest` which takes the name of
a data bundle to ingest. This will run the loading machinery and write
the data to a specified location that `run` can find.
There is also a `clean` subcommand which deletes the data that was
written with `ingest`.
Extensions have also been added to zipline. This is an experimental
feature where users can provide an extra set of python files to run at
the start of the process. These can be used to configure aspects of
zipline. Right now the only thing that is supported in an extension file
is the registration of a new data bundle.
Changes BcolzDailyBarWriter to not be an abc, data is passed as an
iterator of (sid, dataframe) pairs to the write method.
Changes the AssetsDBWriter to be a single class which accepts an engine
at construction time and has a `write` method for writing dataframes for
the various tables. We no longer support writing the various other data
types, callers should coerce their data into a dataframe themselves. See
zipline.assets.synthetic for some helpers to do this.
Adds many new fixtures and updates some existing fixtures to use the new
ones:
WithDefaultDateBounds
A fixture that provides the suite a START_DATE and END_DATE. This is
meant to make it easy for other fixtures to synchronize their date
ranges without depending on eachother in strange ways. For example,
WithBcolzMinuteBarReader and WithBcolzDailyBarReader by default should
both have data for the same dates, so they may use depend on
WithDefaultDates without forcing a dependency between them.
WithTmpDir, WithInstanceTmpDir
Provides the suite or individual test case a temporary directory.
WithBcolzDailyBarReader
Provides the suite a BcolzDailyBarReader which reads from bcolz data
written to a temporary directory. The data will be read from
dataframes and then converted to bcolz files with
BcolzDailyBarWriter.write
WithBcolzDailyBarReaderFromCSVs
Provides the suite a BcolzDailyBarReader which reads from bcolz data
written to a temporary directory. The data will be read from a
collection of CSV files and then converted into the bcolz data through
BcolzDailyBarWriter.write_csvs
WithBcolzMinuteBarReader
Provides the suite a BcolzMinuteBarReader which reads from bcolz data
written to a temporary directory. The data will be read from
dataframes and then converted to bcolz files with
BcolzMinuteBarWriter.write
WithAdjustmentReader
Provides the suite a SQLiteAdjustmentReader which reads from an in
memory sqlite database. The data will be read from dataframes and then
converted into sqlite with SQLiteAdjustmentWriter.write
WithDataPortal
Provides each test case a DataPortal object with data from temporary
resources.
Add a cap of 5 sliding windows (one per OHCLV column) to the history
loader's cache of sliding windos.
This prevents unbounded growth on algorithms that call history with a
highly varied list of equities.
To follow is splitting the cache up by column and by sid, so that the
loader does not re-prefetch sids which have already been read with
sufficient data; however this patch is enough to fix the issue where an
algo with high rotation can add up a megabyte per day of memory on
algorithms which rotate on a 5% dollar volume pipeline. With this cap
those algorithms have more plateaus with regard to memory consumption.
This patch requires new dependency of `cachetools` library.
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.
Victor Grau Serrat