Hands-On Machine Learning for Algorithmic Trading

Principal Component Analysis

PCA finds principal components as linear combinations of the existing features and uses these components to represent the original data. The number of components is a hyperparameter that determines the target dimensionality and needs to be equal to or smaller than the number of observations or columns, whichever is smaller.

PCA aims to capture most of the variance in the data, to make it easy to recover the original features and so that each component adds information. It reduces dimensionality by projecting the original data into the principal component space.

The PCA algorithm works by identifying a sequence of principal components, each of which aligns with the direction of maximum variance in the data after ...