Compute × Macro Correlate

Partial correlation for compute economics is calculated by fetching price data and computing regression-based residuals against BTC and SOL controls to isolate co-variance with a Hyperliquid macro basket. This removes confounding market drivers from the final signal.

Partial-correlation analysis with BTC and SOL controls is used to identify whether compute economics signals independently co-vary with a macro basket. It filters out the dominant market variance to determine if residual correlations meet predefined statistical thresholds.

Yes, you can monitor compute economics weekly using a two-track workflow: Track A uses DePIN proxies while Track B uses a sweep P&L track. Both tracks store findings in memory for ongoing trend confirmation.

No, the compute-macro correlation analysis does not require manual data fetching. It features operational automation that fetches Hyperliquid price data, computes residual correlations, and stores results in memory automatically on a weekly schedule.

You should not use partial correlation for time-series macro basket analysis if your dataset lacks sufficient historical price data for BTC and SOL controls, as regression-based residuals require robust baseline inputs to emit valid statistical findings.