The tradingcalendar module has been replaced by the new exchange
calendars and trading schedules. Issues a ZiplineDeprecationWarning at
tradingcalendar module scope to be triggered on imports.
Some unit tests for test_tradingcalendar failed on 2015-03-01, because the
addition of 365 days put the end date at 2016-02-29; when the replaces
the year on that date it fails because there is no 2017-02-29.
Instead use relativedelta with a year argument which accounts for leap
years.
Fixes the following test failure:
```
======================================================================
ERROR: test_day_after_thanksgiving (tests.test_tradingcalendar.TestTradingCalendar)
----------------------------------------------------------------------
Traceback (most recent call last):
File "./tests/test_tradingcalendar.py", line 211, in test_day_after_thanksgiving
tradingcalendar.end.replace(year=tradingcalendar.end.year + 1)
File "tslib.pyx", line 297, in pandas.tslib.Timestamp.replace (pandas/tslib.c:7325)
ValueError: day is out of range for month
----------------------------------------------------------------------
Ran 1 test in 0.001s
```
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.
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.
other details:
- also fixed grammatical errors in loader's status messages.
- converting the treasury curves to an ordered dict.
- moved to using a lambda for clarity as per @ehebert
- initializing calendar end dates to be midnight of current date in
- US/Eastern. Yahoo data isn't available until midnight eastern.
- added LSE reference rrules calendar (thanks to Edward Johns)
- added tests to verify LSE environment matches rrule calendar
- added a test to verify global environment behavior can be set.
- moved DailyReturn class to trading to eliminate circularity from
risk <-> trading.
- updated TradingEnvironment to be a context manager. This allows users
to run algorithms in individually isolated environments in one python
process. This is useful for managing multiple algorithms in a single
ipython notebook.
- added comments to explain behavior and useage of the global environment
Global state for the financial simulation environment is accessed through the
zipline.finance.trading module, which now contains a module variable:
environment.
Parameters are passed into an algorithm as a keyword argument, sim_params.
SimulationParameters creates a trading day index for the test period that
can be used to find trading days, calculate distance between trading days,
and other common operations. The sim params index is just selected from the
global state.
================
Details:
- adding delorean to the requirements.
- made index symbol a parameter for loading the benchmark data. changed
messagepack storage to be symbol specific.
- ported risk, performance, algorithm, transforms, batch transforms
and associated tests to use simulation parameters and global environment
- factory and sim factory use global state and sim params
- factory method parameter names now reflect the class expected
- Removes New Year's on Saturday, in that case there is no New Year's observed.
- Adds Monday after a July 4th Sunday
- Adds Friday before a July 4th Saturday
- Adds Monday after a Christmas Sunday
- Adds Friday before a Christmas Saturday
The trading day index is all business days in range minus the
non trading days we are already calculating.
Also, uses trading calendar indexes for batch transform, since the
batch transform was the only use of non_trading_days.
Instead of constantly adding and removing holidays to do market
day delta math, uses pandas DatetimeIndex to get the index of the dates
and uses the index difference to calculate market days.
The test factory was creating non-market days.
i.e. the date range spanned the weekend.
Using pandas' BDay frequency so that only business days are created.
This specific date range doesn't have holidays, so not accounting
for holidays in the factory.
Also, widens the range of the trading calendar to cover the test dates
generated by the factory which include 1990.
Previously the trading calendar began with 2002, meaning that holiday
and weekend adjustments with the data exercised by the factory did
not trigger when run with data in 1990.
This does increase the memory footprint of the tradingcalendar module.
However, only by a couple MB, so taking the hit there to enable
correct behavior.
Mostly whitespace, line width and other spacing changes.
Also, removes use of deprecated has_key in favor of `in`
Going forward new patches should pass running `flake8` before
submission.
Andrew Daniels