\( \newcommand{\bm}[1]{\boldsymbol{#1}} \newcommand{\textm}[1]{\textsf{#1}} \newcommand{\textnormal}[1]{\textsf{#1}} \def\T{{\mkern-2mu\raise-1mu\mathsf{T}}} \newcommand{\R}{\mathbb{R}} % real numbers \newcommand{\E}{{\rm I\kern-.2em E}} \newcommand{\w}{\bm{w}} % bold w \newcommand{\bmu}{\bm{\mu}} % bold mu \newcommand{\bSigma}{\bm{\Sigma}} % bold mu \newcommand{\bigO}{O} %\mathcal{O} \renewcommand{\d}[1]{\operatorname{d}\!{#1}} \)

Chapter 8 Portfolio Backtesting

“I am a dreamer. I know so little of real life that I just can’t help re-living such moments as these in my dreams, for such moments are something I have very rarely experienced. I am going to dream about you the whole night, the whole week, the whole year.”

— Fyodor Dostoyevsky, White Nights

A backtest is a historical simulation of how a strategy would have performed had it been run over a past period of time. It is an essential step prior to actual live trading with real money. Nevertheless, backtesting is one of the least understood techniques in the quant’s toolbox.39 The reality is that backtesting is full of dangers and virtually impossible to execute properly. This chapter will explore portfolio backtesting in detail, so that we become aware of all the potential pitfalls.

This material will be published by Cambridge University Press as Portfolio Optimization: Theory and Application by Daniel P. Palomar. This pre-publication version is free to view and download for personal use only; not for re-distribution, re-sale, or use in derivative works. © Daniel P. Palomar 2025.

  1. A quant is a financial professional who uses complex mathematical models, computer algorithms, and statistical analysis to analyze markets, price securities, and identify trading opportunities.↩︎