Some of the shortcomings of Sharpe Ratio was addressed by Marcos Lopez de Prado and David Bailey in 2014 in their Deflated Sharpe Ratio publication. The Deflated Sharpe Ratio accounts for the potential data-mining bias and different distributions created by the optimization process.
Recently, Marcos further expanded on this and created a framework to embed the Deflated Sharpe Ratio within a full statistical-inference system that accounts for non-Normality, minimum track-record requirements, power analysis, false-discovery rates and more.
You can read the updated publication here:
How to Use the Sharpe Ratio by Marcos López de Prado, Alexander Lipton, and Vincent Zoonekynd
