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.
Cite
@article{arxiv.1902.01802,
title = {How should you discount your backtest PnL?},
author = {Adam Rej and Philip Seager and Jean-Philippe Bouchaud},
journal= {arXiv preprint arXiv:1902.01802},
year = {2019}
}