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.
This is a step towards the goal of uniting Quantopian scripts
and zipline.
To make the syntax of zipline identical to Quantopian
we break out the API methods (like order) and turn them into
functions. To access the algo object we add a thread local reference
to the current algorithm that is accessed in the API functions.
TradingAlgorithm now takes either a string or two functions
(initialize and handle_data) that it executes.
Use api method decorator for methods available in algoscript.
Ported appropriate algorithm tests from internal code.
Use date sorted sources instead, instead of sorting with second
argument of Event, etc. since the `heapq.merge` behavior is using
the second part of the tuple, thus requiring a richer set of comparison
methods, which would only be used in the test context.
Use `date_sorted_sources` instead, so that sorting is done on algo time
and source id.
Python 3 removes the `.message` attribute, so use `str` instead.
Also, the divide by zero message has changed slightly between versions,
so just check for the exception type, instead of also checking the message.
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
Note that the calendar test is decorated with @nottest (as per the other calendar test functions). I've run the test to confirm the calendar works. The differences between the env (Yahoo Finance of GSPTSE) and the calendar are illustrated in the tradingcalendar_tse file and are confirmed to be errors on Yahoo Finance's part.
The code that was consuming noop_environment now uses a
real trading environment.
As more behavior relies on an accurate trading calendar, maintaining
the noop environment was a constraint that was more overhead than it
is worth.
Passing the exchange time timestamp to is_market_hours was ending
up with odd behavior due to conversion back to UTC when checking
the is_trading_day boolean.
Use the early closes to populate a DataFrame which includes
the open and close minute for each day.
To be used by the environment instead of calculating each value
mid-backtest.
Remove the lists of DailyReturn objects in favor of using pd.Series
to store the return values.
Should make it easier to inspect the values when stepping through,
make the windowing of data to a certain range more facile by using,
and have some performance increases due to removing object creation
and member access.
Instead of using all calendar days between start and end in test
sources, use the trading calendar for test sources.
Needed for an incoming refactoring of market open and close,
where the opens and closes are indexed by market days.
Most of the functions in date_utils can be done via pandas.
The other functions are no longer used for loading, etc. so remove
the date_utils module to reduce the total surface area of Zipline core.
Instead of using a pandas Series of with dictionaries as the
values treasury curves, use a DataFrame which more naturally fits
the data type of a having a timeseries with mulitple values.
Should allow easier slicing/manipulation of the treasury curves,
e.g. getting 10 year curves would now be:
```
treasury_curves['10year']
```
For consistency, datetimes returned by the trading calendar should
always show HHMMSS of midnight UTC. Not only is this useful for
consistency, but it also allows us to check if a particular date() is
in an array of these datetimes, because they will hash to the same
thing. For example:
early_closes = get_early_closes()
... later ...
if current_bar_datetime.date() in early_closes:
... today closes early ...
If if the datetimes returned by the trading calendar functions don't
have 00:00:00 for HHMMSS, then the "in" check above will fail because
the date and the datetimes in early_closes won't hash to the same
thing.
Fix warnings when compiling the docs.
Removes the documentation of the default types, which already gets
included automatically and was wrong because not kept in sync with the
function signature.
Changed, the formatting to the Sphinx formatting.
This looks much better in the compiled documents, but does make the
source a bit harder to read.
Before the change to the RollingPanel, window_length
specified the number of days that should be in a window.
The previous commit broke this if data was minute resolution.
By passing bar='minute' to the batch_transform we internally
multiply the window_length by 60*6.5 to have a full day.
Also adds a (still rudamentary) test for batch_transform
with minute data.
The check() function in zipline.utils.test_utils was only comparing
lists up to the length of the shortest list. This fix uses
izip_longest instead of izip so it compares up to the length of the
longest list, which among other things means that it will now
correctly report when one list is empty and the other is not.
Eddie Hebert