A multi-stock TALib transform was returning the same value for
all stocks, specifically the value for the first stock in the panel.
Index into the datapanel using `sid` instead of using the `[0:]`
index which was used when only supporting one sid.
The TALib transform only supported operating on the first value
of a given batch transform panel row.
Instead of returning the one value, even if an panel with multiple
sids was provided, return a dictionary that maps stock to TALib
result.
Prepare for making the zipline_wrapper operate on multiple sids,
as the needed nested logic will get cramped within the nested function.
Also, should help clearly define the inputs of the zipline_wrapper
function that are needed before it is passed to the BatchTransform
constructor.
Set the `compute_only_full` to False so that the 'is window full' logic
is delegated to the TALib's lookback function.
If the window is not full to the `timeperiod` or other lookback setting,
then TALib returns a `np.nan`.
Also, fix the bars/data_frequency not being passed to the BatchTransform
init.
This further shows need to create a minute test for TALib transforms.
Add a `bars` keyword arg, as is used with BatchTransform.
Also, instead of overwriting the window_length kwarg with timeperiod,
always use the lookback value from the created TALib function,
as timeperiod will be an input into that value if it exists.
Calculate `window_length` in minute mode so that there are enough
days to cover the minutes in the timeperiod.
For the creation of a TALib transform use timeperiod intsead of
window_length, to be more in the style of TALib usage, since all
TALib functions may not ending up using BatchTransform, so start
the practice of adhering to TALib conventions to make porting and
explanation easier.
Python 3 requires using dot syntax for relative imports,
otherwise the import is treated as an absolute import, i.e.
an import of a module from outside of the project.
By using dot syntax now, imports should be compatible with both
Python 2.7 and Python 3.
In the batch_transform we were incrementing the trading_days counter if there
is a new day event. Thus with a window_length of 1 and daily bars you will
update the batch_transform on the first day which is correct. But with minutes
you update with the first minute bar of the day which is not correct.
This is fixed by calculating the market_close explicity and seeing whether the
event.dt is on or past it.
I also added a unittest to test the correct behavior of this.
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.
When setting timeperiod in the talib function it subtracts by 1. We then used this subtracted value to set the window_length in the batch_transform which was then not passing a big enough panel. Ultimately this caused the talib transforms to always return nans.
This also makes the unittest more stringent by explicitly comparing the output of the wrapped TALib moving average to pandas rolling_mean().
Finally, this also allows passing of window_length instead of timeperiod to allow usage of the same interface as before.
So that TALib is still available, but smooth out the ability to
run tests with some issues that bear investigating.
- Ignore MAVP during tests.
- Temporarily use a "regular" member instead of __doc__ string.
(TODO: look into using `type` to generate the class)
- During tests wait until a window exists.
In the previous implementation of batch transform it happened
that a window_length of `0` caused the transform to update on every
bar, for the time being that behavior should be retained,
though the new rolling implementation more correctly aligns to the
term of 'period' so a period of 1 would achieve the same effect.
When moving BatchTransform off of EventWindow as a base object,
the checking of window length was lost, restore that check using
the same function as EventWindow.
The use of np.allclose introduced a severe performance penalty,
caused by the creation of two `np.array`s for each check.
Instead create and use a similar check which maintains tolerance
to floating point rounding, but operates only on scalars.
Instead of creating a list of benchmarks in the risk module,
stream benchmarks through the system as events, starting from the
algorithm generator.
Works towards more easily setting arbritrary pricing data as
a a benchmark, as well as working towards live minutely benchmarks.
The batch_transform decorator wipes out the doc string of the function
it wraps. Decorate the creator with functools.wraps to preserve function
metadata.
- perf modified to let non-performance related events flow through.
- changes to support streaming non-trading data through batch transforms
and for mixing in sids with just custom data.
- allowing CUSTOM events to flow through to transforms.
- Added logic to maintain pre-specified sid filter.
- repeated calls with the same data window do not update batch transform
windows.
- repeated calls with the same data and same supplemental parameters do
not update batch transform results
- repeated calls with the same data and different supplemental params
do update batch transform results
- removed use_panel
- default for refresh_period is now 0
- refresh_period will only affect the recreation of the datapanel
- user's transform method is invoked on every call to batch transform
- the underlying dequeue shouldn't be modified, so forwarding to the
user function is a bit misleading
- if we want to provide a dequeue we should consider another class
or an EventWindow decorator.
Adds a check to see if the s_squared value is near 0.
When the number was very near 0, a very small negative floating
point, the sqrt throws a 'math domain error', this prevents that
case.
The call to `Panel.dropna` after the fillna was deleting all values,
if a stock stopped trading mid run and thus provided volume 0.
i.e. if any sid had 0 non-null values the entire panel of frames
would be truncated.
It's possible to avoid the collapse via by adding the `how='all'` flag
to `dropna`, however with the current tick based creation of the panel,
the `dropna` with `how='all'` should be functionally equivalent to
not dropping at all.
The dropna has been dropped in favor of leaving the drop to algorithm
code.
The recent change to the creation of the data panel ended up with
a panel with the dtype of 'object', which was causing numpy ufuncs
like `log` to crash out on an `AttributeError`.
This forces all frames in the panel to use a dtype of 'float',
we may want to look at seeting a dtype on a frame by frame basis,
e.g. 'volume' may more accurately be 'int'.
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
Eddie Hebert