The calculations that are expected to change are:
- cumulative.beta
- cumulative.alpha
- cumulative.information
- cumulative.sharpe
- period.sortino
* Explanation of how risk calculations are changing
** Risk Fixes for Both Period and Cumulative
*** Downside Risk
Use sample instead of population for standard deviation.
Add a rounding factor, so that if the two values are close for a given
dt, that they do not count as a downside value, which would throw off
the denominator of the standard deviation of the downside diffs.
*** Standard Deviation Type
Across the board the standard deviation has been standardized to using
a 'sample' calculation, whereas before cumulative risk was monstly using
'population'. Using `ddof=1` with `np.std` calculates as if the values
are a sample.
** Cumulative Risk Fixes
*** Beta
Use the daily algorithm returns and benchmarks instead of annualized
mean returns.
*** Volatility
Use sample instead of population with standard deviation.
The volatility is an input to other calculations so this change affects
Sharpe and Information ratio calculations.
*** Information Ratio
The benchmark returns input is changed from annualized benchmark returns
to the annualized mean returns.
*** Alpha
The benchmark returns input is changed from annualized benchmark returns
to the annualized mean returns.
** Period Risk Fixes
*** Sortino
Use the downside risk of the daily return vs. the mean algorithm returns
for the minimum acceptable return instead of the treasury return.
The above required adding the calculation of the mean algorithm returns
for period risk.
Also, use algorithm_period_returns and tresaury_period_return as the
cumulative Sortino does, instead of using algorithm returns for both
inputs into the Sortino calculation.
* Other Supporting Changes
** answer_key
Add new mappings for downside risk and Sortino as well as
re-address the index mappings because of changes to the answer key
spread sheet.
** test_risk_cumulative
Change the decimal precision to expect higher precision.
The calculations are now more aligned with the answer key, so we can
expect higher precision. In particular now that the standard deviation
type matches everywhere in both the Python implementation and the answer
sheet, the precision of the first value no longer has to be glossed over.
** test_events_through_risk
Change the results which are used as a canary for risk changes,
since we do expect Sharpe to change with this change..
The input into max drawdown was incorrect, causing the bad results.
i.e. the `compounded_log_returns` were not values representative of
the algorithms total return at a given time, though
`calculate_max_drawdown` was treating the values as if they were.
Instead, use the `algorithm_period_returns` series, which does provide
the total return.
Update risk answer key with an Excel calculation of max drawdown
to help corroborate the calculations.
Also, remove `compounded_log_returns`, (which actually had stopped
being the `compounded_log_returns` at some point), since the max
drawdown was the only calculation using the values in that series.
This could perhaps be labelled BUG, as well.
Change the Sharpe (and algorithm volatiilty) value used to compare
algorithms/backtests so that it is annualized and uses daily returns.
Previously, the Sharpe metric was using the same calculation style
as the fixed size periods, i.e. 3 Month, 6 Month, etc., which can
use the geometric mean when comparing against the risk free.
Change the Sharpe calculation to use the arithmetic mean differenc
against the risk free rate, using daily (non-compounded) values.
Also, use annualized mean returns.
Indexes to risk answers were pointing to a previous version.
Also, provide the risk cumulative answers as a pd.Series,
so that it is easier to compare to values produced by risk class.
Add indexes for Sharpe, returns and other values needed for
reading answers for cumulative risk metrics.
Prepare for unit test and matching change of implementation.
Refactor the reading of values from the Excel spreadsheet so that
parsers are configurable by index.
Needed so that we can parse columns that have dates, in addition
to floats as previously.
Instead of using the indexes defined in the answer key class
to index back into the answer key object, populate the answers
so that they are available as members of the answer key object.
Update period risk test to use new answer key structure.
Also, remove the rounding behavior from the answer sheet, leaving
the rounding to the consumer of the answer key values, so that
the values can be retrieved from the spreadsheet during answer
key __init__ without knowledge of the decimal point that the calling
code expects.
Correspondingly, change period risk tests to use
np.testing.assert_almost_equal when doing floating point comparison.
Read tests.risk.answer_key module into an IPython notebook, to start
a base to which answer key values and explanations can be added and
display without access to Excel.
For now, the notebook just provides the latest download link for
the spreadsheet.
For maintainer use, requires AWS credentials for the account where
the `zipline-test-data` bucket is hosted.
Script does the following steps which used to be manual:
- Create a key name based on the md5 of the answer key file.
- Upload the answer key to S3 bucket.
- Make the file publically downloadable over HTTP.
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.
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.
Eddie Hebert