`period_end` can be outside the range of data for which we have dates.
`last_close` properly gets pulled back to the last date for which we
actually have data.
We should consider whether or not we need to be storing period_end at
all.
- Previously it was returning a DataFrame because of how we applied an &
with a DataFrame mask. The error was masked by the fact that
`np.assert_array_equal` coerces inputs to arrays before comparing.
- Added `zp.utils.test_utils.check_arrays`, which checks type equality
before calling `np.assert_array_equal`.
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.
As of logbook 0.10.0, logbook no longer installs a default handler,
which means that if the application doesn't install one, log messages
disappear into the ether.
Therefore, all of our scripts with `__main__` endpoints need to push a
`logbook.StderrHandler` if they're not already pushing some other
handler.
Instead of creating a new cursor with each query, use the same cursor
throughout the lifetime of the finder instance to remove any overhead
from creating a new cursor in tight loops.
The minutely calculation of risk metrics had been removed with a
previous patch, remove vestigial references.
Remove a test which tested the behavior of updating the second minute of
a day.
Remove the logic that changed the datetime index of the risk metrics
depending on emission rate, now only trading_days are needed.
Remove `returns_frequency` parameter since both minute and daily
data frequency always use daily returns.
Fix a change in the information given to the multiple symbol error
during the recent sqlite change to the asset finder.
Instead of the string of sids, return full asset information about the
available options, since internal code relied on the full data.
Attack the startup bottleneck of creating the asset finders caches for a
large universe, which was between 1-2 seconds on development and
production machines.
Instead, allow the AssetFinder to be passed a sqlite3 file that has
already been populated and then hydrate asset objects only when an
equity is referenced for the first time.
To create aforementioned sqlite3, create an AssetFinder with an db_path
and `create_table` set to True. If `create_table` is set to False, the
prepopulated data in the sqlite file found at db_path will be used.
Default behavior is to use an in memory database.
Behavior that changes:
- Fuzzy lookup now only works on one character, that character needs to be
specified at write/metadata consumption time, since the fuzzy lookup key
is created by dropping the character from each symbol.
- Overwriting partially written metadata is no longer
supported. i.e. some unit tests allowed for inserting just the identifier,
and then later updating the symbol, end_date, etc.
Instead of building an upsert behavior at this time, this patch
changes the unit tests so that the data for each asset is only
inserted once.
Other notes:
- populate_cache is now removed, since there is no longer a two step
process of inserting metadata and then realizing that metadata into
assets. _spawn_asset is rolled into insert_metadata, so that a call to
insert_metadata both converts the metadata and makes it available in
the data store.
Since most brokers will cease accepting trades by the notice date, contracts should not be considered valid after the notice date. This commit adjusts the lookup_future_chain method to consider all contracts with notice dates on or following the current date invalid.
The previous workflow was:
- insert basic metadata for all assets
- iterate over all metadata entries. Create a sid if one does not
exist
- call insert_metadata again to update the assigned sid value.
Instead, create a sid, if missing on the initial metadata assignment and
remove the second pass of calling insert_metadata.
Also, since the sid assignment code is only done in this one context,
inline the sid assignment code so that there is less code step through
while looking for where sid assignment is done.
The datetime attribute of TradingAlgorithm was initially None, so calling get_datetime in initialize was causing an unhandled exception. This commit addresses that issue by calling on_dt_changed when initializing the TradingAlgorithm, to force all datetimes to be in line with the period_start.
Removes unused future lookup methods and consolidates everything into lookup_future_chain. Since the FutureChain object will have to hold a root symbol and dates, it should be responsible for cleaning the user input, so this is removed from the lookup method.
Adds knowledge date to future lookups. This makes our definition of valid contracts more flexible. We know about a contract if it starts trading by the knowledge date, and a contract is expired if it expires by the as_of_date.
Also fixes a bug with computing future chains, where contracts were not included in the chain on their expiration date.
This commit modifies the DataFrameSource and DataPanelSource to accept only Int64Indexes on the incoming data and moves the burden of mapping user identifiers to TradingAlgorithm.run().
The identifier cache's usage was nearly identical to using lookup_generic, so this commit removes identifier-keyed caching and modifies anything that uses it.
Move the responsibility of wrapping the main simulation loop in the
ZiplineAPI context from the algorithm modules generator setup to the
main trade simulation loop, so that different methods of invoking loop
do not need to duplicate how the context is set.
To make it easier for internal implementation of handle_data to
transition off of calling the ZiplineAPI every bar, to only invoking the
context once per simulation.
Instead of using the pandas.Series datetime index for every single
vector, get the index at the beginning of the update loop based on the
dt and then use that index to set the values.
Also, since the dt lookup is no longer needed, store the values as numpy
arrays, which are more lightweight.
Locally, this patch cuts out about 60% of the time spent in the update
method.
Scott Sanderson