The write_data methods invokes the relevant AssetDBWriter subclass
to write data to the database. update_asset_finder is no longer
a relevant method since the AssetFinder is strictly a reader class.
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.
Test sources are now defined by the sim_params period_start and period_end, rather than by the period_start and a defined 'count' of bars. This allows us to consider the sim_params.period_end as the canonical definition of the end of a simulation.
If a SID hasn't started trading yet, pandas' convention is to use nans.
Before this change, zipline would raise an exception if there were nans in the
input data.
We now skip events where the prices contains a nan and has not been traded
before (in which case forward fill).
Fixes#446.
For more discussion see https://github.com/quantopian/zipline/pull/485.
Basically, 1000 is just a number that was supposed to be high enough if no volume was available. It turns out that number is actually very low so now we are increasing it so that volume restrictions should no matter. 1e9 of shares ought to be enough for anybody ;).
Thanks to @jlowin for pointing that out.
This makes the drift and standard deviation used in RandomWalkSource
keyword args to give the user more control over the price series that
are emitted.
The standard deviation of prices is very different at different frequencies,
users should have the ability to specify the standard deviation used.
The drift term should also be configurable to give the user more control.
This adds a new data source that emits events
with certain user-specified frequency (minute
or daily).
This allows users to backtest and debug an
algorithm in minute mode to provide a cleaner
path towards Quantopian.
This creates a data source for csv and hdf5 files, a generator to create a sample csv, and a pytables generator to go from a list of dated gzipped csv's in a directory to a pytables data source.
This does not add a unittest yet which we should write for the future.
Use six's with_metaclass to have objects that use metaclasses, in
both Python 2 and 3.
Otherwise, in Python 3 the objects were being treated as if they
did not have a metaclass, when the Python 2 syntax is used, leading
to errors because of missing attributes, etc.
Python 3 uses the `__next__` method instead of `next`,
and uses the syntax of `next(foo)` accordingly.
Add `__next__` and `next` side-by-side so both Python 2 and 3 have
a method that can be used during iteration.
Use the six module to import functions and types that are
consistent between Python 2 and 3, so that one code base can
support both versions.
- Use integer types instead of int and long.
- Use string_types instead of basestring.
- Account for iteritems, itervalues, iterkeys.
- Use six.moves for filter and zip, reduce
- Use compatible bytes for md5 hasher.
- xrange and range
Daily data should be using midnight as the timestamp,
ensure that test data created by data_gen use midnight, so that
upcoming implementations that rely on the timestamp will be compatible.
