https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2326253
Most firms and portfolio managers rely on backtests (or historical simulations of performance) to select investment strategies and allocate them capital. Standard statistical techniques designed to prevent regression over-fitting, such as hold-out, tend to be unreliable and inaccurate in the context of investment backtests. We propose a framework that estimates the probability of backtest over-fitting (PBO) specifically in the context of investment simulations, through a numerical method that we call combinatorially symmetric cross-validation (CSCV). We show that CSCV produces accurate estimates of the probability that a particular backtest is over-fit.
Backtest Overfitting: An Interactive Example: http://datagrid.lbl.gov/backtest/