Fix the spreadsheet to apply a factor of COUNT / COUNT - 1
to the COVAR value.
Also, go back to using the C[1][1] index instead of calculating
var independently.
Use recent change to benchmark variance in the beta calculation,
instead of referring to the 4th quadrant of the covariance.
Also, read answers from answer key for corroboration of beta values.
The np.cov call needs a ddof of 0 to match the answer key, which uses
Excel's VAR.
When switching np.cov to use a ddof of 0, the benchmark variance is
no longer the 4th quadrant of the cov result, so use np.var directly.
Add reference to updated answer key with benchmark variance cells,
and use the new cells as the reference for the benchmark variance
test.
The values changed from the original hardcoded values, due to the
change to close over close benchmarks.
To help tentpole the risk results with an alternative implementation.
Also, the spreadsheet provided is what the original answers were based
from, reading from said spreadsheet should help prevent drift.
So that the answer key does not onerous on the SCM repo size, add a
utility to download the answer key automatically.
Prevent re-download on every test suite run if the local answer key
matches the latest version.
The risk tests originally were based on a spread sheet, with the
results of returns etc copy and pasted into the `test_risk` module.
Include the spreadsheet and read the values directly using a Python
Excel spreadsheet library.
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.
This reverts commit e3a9ca27b1, reversing
changes made to 3d8bdeb429.
Conflicts:
zipline/gens/tradesimulation.py
The aforementioned change needs a revert because it caused a 'doubling'
of orders, since the portfolio is not updated until after handle_data
is called a second time after an order has been processed.
The flexibility of fill_delay is still desired, but remove for now,
favoring reverting back to existing behavior over trying ot fix the
fill_delay logic.
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.
To support mulitple sids the TALib transforms now return a dict,
instead of a float. Accordingly, the TALib example script now needs
to index into the transform result.
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.
Provide a default value for data_frequency, choosing 'daily',
so that the fill_delay is set even when a data_frequency value
is not in kwargs.
This does open up a place for disjointedness if the sim_params that
is passed to run does not match the data_frequency set during initialize.
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.
The loader module printed some warning messages, these could
be changed to use a logger, but for now convert to use the print
function for compatibility with Python 3.
Eddie Hebert