BUG: Fix numerous cumulative and period risk calculations.

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..