Signal Correlations

Cross-category correlation analysis of 9,150 investment signals across 23 days

9,150

Total Signals

Strong Correlations

Days of Data

Signal Categories

Thesis Links

Correlation Matrix

Pearson correlation between daily signal volumes. Click a cell to explore time-lagged correlations. Toggle between zero-lag and best-lag views.

Methodology: Daily signal counts are aggregated per category. Pearson correlation is computed on the raw daily volume time series. Time-lagged correlations shift series A by N days relative to series B (positive lag = A leads B). "Best lag" shows the lag with the highest absolute correlation in the -7 to +7 day range. With 23 days of data and 9 categories, there are 72 directional pairs. Correlations below |0.3| are generally not statistically significant at this sample size.

Investment Thesis Chain

The hypothesized causal chain: hyperscaler capex drives DC construction, which drives power demand and turbine orders. AI capex drives HBM demand, which moves memory prices. How well does the data support these links?

Daily Signal Volume

Signal ingest rate per category over the past 23 days. Trend shows last 7 days vs first 7 days.

Time-Lag Explorer

Explore how correlation changes with time lag. Positive lag = first signal leads the second. Select a pair below.

Rolling Correlations (7-day window)

How the correlation between thesis-linked pairs evolves over time. Shows whether relationships are strengthening or weakening.

Notable Correlations

All pairs with |r| ≥ 0.3 at any lag (0-7 days). Sorted by strength.