It means to remove a set of factors from the signal - or the returns of the signal, which would impact its performance or exposure, and are not meant to be captured by the signal.
Imagine a "I have no preference" signal which gives a weight of 1/n to each asset of the portfolio - you have 100 stocks in your basket, you give 1% to each. If you would compute its performance, you would see that this portfolio would roughly make 10% average returns over 20 years. That seems good right?
Well no it's not, because these 10% don't come from anything meaningful that your signal did. It came because you gained exposure to the market, and that exposure "carried" your signal performance.
> What do the need to do to the software to “residualize” it?
Well residualizing overall just means that you want to regress your signals on a set of factors that would carry its performance for no justified reason. In practical terms, you could start by doing a linear regression of your signal against such factors (say beta, country, sector) and only keeping the remaining residuals (alpha).
Imagine a "I have no preference" signal which gives a weight of 1/n to each asset of the portfolio - you have 100 stocks in your basket, you give 1% to each. If you would compute its performance, you would see that this portfolio would roughly make 10% average returns over 20 years. That seems good right?
Well no it's not, because these 10% don't come from anything meaningful that your signal did. It came because you gained exposure to the market, and that exposure "carried" your signal performance.
> What do the need to do to the software to “residualize” it?
Well residualizing overall just means that you want to regress your signals on a set of factors that would carry its performance for no justified reason. In practical terms, you could start by doing a linear regression of your signal against such factors (say beta, country, sector) and only keeping the remaining residuals (alpha).