Principal Component Analysis

Principal Component Analysis compresses a return matrix into orthogonal drivers. It is useful for diagnosing concentration, hidden common exposures, and whether a multi-asset set is less diversified than it appears.

Open Principal Component Analysis →

Features

Covariance vs correlation mode selection
Explained variance decomposition
Factor loading heatmaps
Concentration and diversification metrics to assess portfolio overlap

What It Answers

How concentrated is this basket? A dominant first component means many assets are moving together.
How many distinct drivers exist? Effective component count and the cumulative variance curve show how quickly the matrix collapses into a few dimensions.
Which assets load on each theme? The loading table shows which assets define each principal component.

Covariance vs Correlation

Covariance PCA preserves absolute volatility, so high-volatility assets dominate more easily.
Correlation PCA standardizes each asset first, making the result about co-movement structure rather than scale.

Use correlation PCA when the question is “how many independent themes are here?” Use covariance PCA when absolute risk concentration matters.

Inputs And Workflow

You can start from explicit tickers or from a Library portfolio. Portfolio mode is useful when you want to diagnose overlap inside a reusable workspace portfolio without retyping its holdings.
Daily mode is better for short-horizon structure. Monthly mode is better when you want a cleaner read on slower strategic relationships and less day-to-day noise.
After the run, you can save, share, and reopen the analysis from Workspace.
Reopening a saved or shared analysis replays the portfolio and the mode it recorded. Weighted-portfolio runs need one fixed set of weights, so a portfolio that switches holdings on a signal or changes weights over time reopens with its holdings as tickers and says why.
A portfolio whose weights come from a linked saved strategy replays exactly as it ran, but those weights are not editable here. Editing them switches the analysis to its holdings as tickers and says why.

Interpretation

PCA components are statistical constructs, not economic labels. Treat them as clues. If the first component is broad and positive across equities, that suggests a common market driver, not a complete causal story by itself.

How To Read The Output

Start with Top Component Share. If one component explains almost half the variance, the basket is less diversified than the ticker count suggests.
Then check Effective Components and the 80% threshold. These summarize how many dimensions are really carrying the portfolio’s behavior.
Finally, inspect the loading table. Assets with similar signs and magnitudes on the same component are moving together; assets with opposite signs are offsetting that theme.
Weighted-portfolio runs also show Portfolio variance by component: your weight vector projected onto the covariance components, giving the share of the portfolio's own return variance each component drives. It always uses the covariance decomposition, so in correlation display mode its components can differ from the loading table.