I have a porfolio of indexes and I built up a python model based on spearman correlation (I used a spearman and not a pearson because, after running some test on outliers and normality checks, I have some outliers and normality checks failed) to eliminate all the indexes with correlation coefficient higher than 0.8 expecting to reduce the risk and improve sharpe ratio. I have no target variable obviously, so the reason for eliminating feature is not to run a machine learning algorithm for prediction but just to improve the performance of the portfolio. It is a portfolio construction problem as I assume for now, equal weights, no optimisation at this point.
My first question is: is this a good approach to diversify? or there is a better way? I thought of PCA but I do not want to lose interpretability Second question, are indexes with negative correlations to be removed as well? looking at the definition of a portfolio volatility, the risk should reduce with neg correlation however, returns have opposite effect on the trend and they should cancel each other out.