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.
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.
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.
The "use_environment" decorator is too side-effectful (e.g.,
connecting to Yahoo! Finance or another data source) to be used as a
decorator to a function that gets evaluated during module load. This
causes problems, e.g., if Zipline is being used in a gevent
environment, when the trading environment created by the decorator
argument tries to use greenlets when gevent hasn't been fully
initialized.
Since the decorator is nothing more than a context-manager wrapper,
this commit removes the decorator and replaces its use with contexts,
i.e., "with" statements.
With the benchmark returns marked at midnight, the performance packet
for a day was emitted *before* any events for that day were processed.
Fix by expecting benchmarks marked at the market close, for backtests
that use minute data but emit performance results daily, so that the
benchmark handles at the end of day.
TST: Also, add test that exercises the event loop with minutely data,
(with benchmarks that are marked end of day), since that combination
was previously uncovered.