Remove more use of lists for storing internal risk values to use
pandas structures, for easier matching of time to value.
Accordingy, convert use of -1 for getting last value,
to use current dt.
Continue on path of converting values stored inside of risk metrics
to use a DataFrame instead of storing multiple lists.
Also, the need for latest_dt in getting the current volatility for
the sharpe calculation, shows that we need to set the lastest_dt at
the beginning of the update loop.
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.
Merging branch "Minimum price variation"
Ensure limit prices conform to a minimum price variation of a penny, which works for most stocks (not, for instance, BRK). The rounding "midpoint" is custom and depends on buy/sell direction, instead of .5 of a penny.
to account for minimum price variation.
On an order to buy, between .05 below to .95 above a penny, use that penny.
On an order to sell, between .05 above to .95 below a penny, use that penny.
Eventually, all cumulative metrics, (alpha, beta, etc.) will be
stored in the same DataFrame
For easier tracking of dt to values during debugging, but should be
some performance gains as well.
So that it is easier to add new containers, factor out the creation
of the index.
Also, make the returns frequency a parameter, to make the use of
different frequencies more clear from within the risk metrics object,
rather than hot swapping in the new frequency type via the now
removed `initialize_daily_indices`.
The eigen_values, condition_number, algorithm_covariance, and
benchmark variance, which were easy to calculate alongside beta,
since they share the same inputs, but were not passed along to performance.
Remove to trim down the number of risk report members as well as
number of calcluations done.
Can add back in if there is an expressed need for eigen_values etc.,
perhaps in an 'opt-in' type configuration.
Expect the same shape of data for the supplemental data, to make
working and preparing with the supplemental data consistent with
what is passed to the algorithm.
Instead of sliding to the next trading day because of the behavior
of `searchsorted`, if dt argument is not a trading day use it as a
max value for corresponding date of the index.
Fixes a bug where if the end of the quarter is calculated with
disregard to trading days, get_index would return the first day
of the next quarter, instead of the last trading day of the intended
quarter.
For TALib functions like MACD that have output names, return a
DataFrame that for which the columns are the output names of the
function.
So that when using a TALib function, the algorithm doesn't need
to know the index position of the desired result, in favor of using
the name of the result.
e.g.
```
macd_result['AAPL'][0]
```
becomes,
```
macd_result['AAPL']['macd']
```
and
```
macd_result['AAPL'][1]
```
becomes,
```
macd_result['AAPL']['macdsignal']
```
Also, change return type of functions that return floats from a
dictionary to a Series, so that the function is always returning a
pandas type.
Correct the annualization factor from being 1/sqrt(252), since
the annualization was applied to the volatility, by including
252 in the Sharpe's numerator.
Currently, just provide a way to render to some of the data extracted.
Intended to have more thorough documentation of the spreadsheet,
explaining derivation/calculations in each sheet and column.
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.
Move to a new notebook, the AnswerKeyLink will be for a permalink
to the current version of the answer key, of which the output
won't be too noisy in git.
The annotation notebook will also be kept in source control, but
without output, since the table html output is large.
For more improved viewing experience via nbviewer.ipyhton.org,
include the output of the notebook.
When saving/updating this file, a fresh kernel and evaluation of
the entire notebook should be used so that the cell numbers stay
in order.
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.
Update answer key for cumulative risk:
- Annualization of Sharpe
- Use 10 year period
- Use of daily returns vectors instead of compounded return scalar.
No tests or risk module code are currently reading off of this sheet,
but developing it ahead of work in risk module so that the sheet can
be examined and vetted.
Instead of using a pandas Series of with dictionaries as the
values treasury curves, use a DataFrame which more naturally fits
the data type of a having a timeseries with mulitple values.
Should allow easier slicing/manipulation of the treasury curves,
e.g. getting 10 year curves would now be:
```
treasury_curves['10year']
```
Eddie Hebert