¿Qué es Backtest overfitting, in-sample, out-of-sample?

In-sample data are those used to build or choose a strategy; out-of-sample data are those it has not seen. Backtest overfitting is the selection of a strategy whose in-sample performance owes more to the particular sample than to a property that persists, so that its out-of-sample performance falls short of what was selected.