In the case that data_frequency of the algorithm is None,
allow the sim_params to provide the data_frequency.
For less redundancy when setting up an algorithm.
The __repr__ for RiskMetricsCumulative was referring to an older
structure of the class, causing an exception when printed.
Convert to printing the last values in the metrics DataFrame.
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.
When running with minutely emissions the simulator would report to the
user that it simulated 'n - 1' days (where n is the number of days
specified in the simulation params). Now the correct number of trading
days are reported as being simulated.
Previously benchmark events were emitted at 0:00 on the day the
benchmark related to: in 'minute' emission mode this meant that
the benchmarks were emitted before any intra-day trades were
processed.
See: https://github.com/quantopian/zipline/issues/241
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.
To help prevent algorithms from operating on positions that are
not in the existing universe of stocks.
Formerly, iterating over positions would return positions for stocks
which had zero shares held. (Where an explicit check in algorithm
code for `pos.amount != 0` could prevent from using a non-existent
position.)
So that the 1-Month Sharpe ratio has a curve to use during calculation,
use data from 2002, since the Treasury returns 1 Month data starting
in July, 2001.
Highlights
- Reworked risk metrics, including verification against Excel spreadsheet
- Additional order methods
- Conversion of many data structures to use pandas
- Change to behavior of stop and limit orders (@pcawthron)
- Use pandas timezone handling throughout instead of Delorean
- New commission model (@stanh)
- Adds beginning of support for Toronto stock exchange. (@dstephens)
- Python 3 compatibility.
Unit tests now pass when run with Python 3.3, and Python 3 should
now be considered officially supported.
If anything does not work under Python 3, please file as a bug.
Python 2 and 3 throw different exception types when a file does
not exist.
Catch both exception types to trigger the download, so that the
loader works under both Python versions.
The compatibility between the two versions was made easier by
letting pandas handle the heavy lifting, so pass filenames to the
pandas serialization methods, instead of dealing doing the file
handling and reading/writing within the data module.
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.
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.
The rename of walk is not provided by six, so check the import error
via an exception.
Also, callback behavior slightly changes between the two versions,
so instead iterate over the walked files and call what was formerly
a callback, directly as a function.
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.
Instead of porting these cases of type checking, remove them instead.
Slightly more Python-ic to be more generous in what is allowed, and
the conversion to make these compatible with Python 3 are more trouble
than they are worth.
Python 3 requires submodules to have more explicit pathing, so use
the dot syntax to declare submodules which are in the same directory
as another module.
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
- Use `print()` function for all print calls
- Fix strip and format calls that were on the outside of the
print function for some reason.
(Which were breaking in Python 3 because of print returning None.)
- Remove commented out print calls.
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.
Eddie Hebert