# How should you discount your backtest PnL?

## TL;DR

This paper introduces a simple framework to model and quantify in-sample overfitting in backtest PnL, enabling more accurate discounting of in-sample strategy performance.

## Contribution

It presents a novel, straightforward method to understand and measure in-sample overfitting, aiding in better PnL discounting for investment strategies.

## Key findings

- Framework effectively models overfitting
- Allows quantification of overfitting severity
- Provides a factor for PnL discounting

## Abstract

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.