  In-sample overfitting is a drawback of any backtest-based investment
strategy. It is thus of paramount importance to have an understanding of why
and how the in-sample overfitting occurs. In this article we propose a simple
framework that allows one to model and quantify in-sample PnL overfitting. This
allows us to compute the factor appropriate for discounting PnLs of in-sample
investment strategies.
