So that algorithms that specifiy market_aware, days and delta
as args can be transitioned to just specifying a window_length kwarg.
Moving towards removing market_aware and delta completely.
The eventual goal is to remove the market_aware and delta kwargs,
but removing the kwarg completely would break the init
method of EventWindow based classes for existing algorithms.
In the meantime, this ensures that only market_aware is only ever
set to True and raises an error if it is False.
Also raises exceptions if values are set for delta, which was only
used if market_aware was False
Also, since the logic for checking window length is changed,
because in market aware mode we should always be checking the
window length, this adds some sanity checking to the window length.
Changes these tests to use market_aware==True, so that unit tests
follow the same code path as actual execution.
All use of EventWindows against data follows market_aware behavior.
These tests are the only use of market_aware==False, so heading
down the path of removing market aware completely.
Takes the value set for a variable on handle_data and records it,
e.g.:
```
def initialize(self):
self.incr = 0
self.record_variables(['incr'])
def handle_data(self, data):
self.incr += 1
```
Would record a variable of `incr`.
Emits the recorded variables as part of the daily performance.
This batch combins work from:
Thomas Wiecki <thomas.wiecki@gmail.com> (@twiecki)
fawce <fawce@quantopian.com> (@fawce)
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.
These tests ensure that there are three, not two, empty values
at the beginning of the transform.
Also, ensures that we are using a window length of 3 on the tests.
So that wordings of errors,etc. match the window length.
Reducing use of ndict on the path to fixing a memory leak.
So that pulling an instance of ndict with objgraph is more likely
to be a use of ndict that is causing the leak.
Using a class level constant here suffices for the dot access
desired on messages.
Though the addition of tracking mulitple values in the window
is powerful, the changes broke behavior of existing algorithms
by changing method signatures and names.
So temporarily reverting these changes, to be pulled back in when
a way to have the multiple fields tracked with the existing API
is written, or a cutover of the API is figured out and determined.
Eddie Hebert