MAINT: Remove unused member from cumulative risk metrics.
MAINT: Remove unused values during beta calculation.

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.
MAINT: Refactor setting of indices on risk returns containers.

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`.
MAINT: Factored out adjust_cash method in perf period

with semantics unrelated to dividends/splits
MAINT: Store sharpe values in a DataFrame instead of list.

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.
MAINT: Use DataFrame for more risk metrics.

Use metrics DataFrame for alpha, beta, as previously with sharpe.
MAINT: Some code cleanup in the blotter module
ENH: Restrict limit prices to a penny precision

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.
MAINT: Ensure the sign of the result is positive
Merge pull request #220 from quantopian/minimum_price_variation

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.
TST: Fix index to cumulative risk answer key.

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.
MAINT: Use pandas for volatility in risk metrics.

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.
MAINT: Use pandas for values directly derived from returns in risk.

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.
MAINT: Use pandas for sortino and information ratios.

Continue converting risk values that were stored as lists into
pandas structures.
MAINT: Use pandas for daily treasury values in risk.
MAINT: Removed last_return_date from risk object.

Since we are also tracking this value with latest_dt, reading the
last_return_date from the returns is no longer needed.
MAINT: Use dt in risk update method instead of last return index.

The current dt is already in scope in the update method, so use
that instead of also reading it from the algorithm_returns index.
TST: Move minute frequency risk test into risk test module.
MAINT: Reduce the number of minutes included in risk index.

Instead of midnight to midnight for each day, use the trading
environment's market open and close for each day, so that the index
is exactly the trading minutes of each day.

Reduces the amount of memory consumed, but more importantly should
make it easier to inspect the Series that use the index and check
whether the values are correctly being filled.
MAINT: Split performance module into submodules.

So that when searching code for `returns` and `update`, it is
easier to discern which performance class is affected.

Should be no functional changes.