Crypto matrix
Crypto assets plus QQQ, IGV and gold references.
Daily dependency map
Correlations
Map shared market exposure, find relationships that persist across horizons, and identify correlation breaks worth deeper relative-value research.
Research only. Correlation is a screening input, not a long/short instruction.
- Last refresh
- 12 Jul 2026, 02:37 UTC
- Coverage
- 102 crypto + 32 macro references
- Windows
- 7 days · 1 month · 1 year
- Mode
- Read-only research
Complete dataset
Full correlation matrices
Both published tables use aligned returns from the same daily snapshot. Scroll horizontally and vertically; hover or focus a cell for its observation count.
Macro matrix
Bitcoin, commodities, equity indices and selected listed equities.
Pair laboratory
Interrogate a relationship
Select two assets. The diagnostic distinguishes persistent co-movement from a short-term break; it does not infer which leg is rich or cheap.
Research state—
7D correlation—
1M correlation—
1Y correlation—
Persistence score—Correlation-only triage, 0–100
Correlation persistenceCointegration Stable hedge ratioSpread z-score Costs + liquidityOut-of-sample validation
Daily shortlist
Persistent pair candidates
Ranked by weighted 7D/1M/1Y correlation with a penalty for cross-window instability. High rank means “research next,” not “trade now.”
| Pair | 7D | 1M | 1Y | Range | Persistence | State |
|---|
Regime monitor
Largest short-horizon breaks
Pairs whose 7-day correlation has fallen furthest below the 1-month relationship. This can reflect a catalyst, changing beta, data mismatch, or a temporary dislocation.
Research basis
Why this is useful—and where correlation stops
Correlation identifies duplicated exposure and narrows thousands of possible pairs. Classic pairs research instead forms pairs from historical relative-price behaviour and tests convergence after divergence. Cointegration theory adds the stricter requirement that a linear combination of non-stationary prices is stationary.
Two assets can show high return correlation while their price spread drifts indefinitely. Structural breaks, changing regimes, funding, and execution costs can erase an apparent relationship.
Gatev, Goetzmann & RouwenhorstPairs formed by historical normalized-price distance; convergence is tested after divergence. Engle & GrangerCointegration and error correction provide the basis for a mean-reverting equilibrium relation. Campos, Ericsson & HendryStructural change can distort stationarity and cointegration inference. Bailey et al.Repeated strategy selection creates backtest-overfitting risk; validation must account for multiple trials.
Implementation path
From dependency map to testable signal
- 01 · Live nowCandidate discovery
Multi-horizon Pearson return correlation, aligned-observation counts, persistence ranking, and break detection.
- 02 · Next data layerEquilibrium model
Aligned log prices, cointegration tests, rolling OLS hedge ratio, residual stationarity, and structural-break checks.
- 03 · Signal layerSpread state
Rolling residual z-score, estimated half-life, fixed entry/exit bands, and a neutral “no signal” state.
- 04 · Validation layerTradeability
Walk-forward tests, purged splits, fee/funding/slippage/borrow model, capacity filters, and false-discovery controls.
Proposed publish rule
Only show a directional research signal when cointegration passes, beta is stable, the spread is sufficiently displaced, expected convergence exceeds modeled costs, both legs are liquid, and the model remains valid out of sample. Otherwise publish “candidate,” “monitor,” or “no signal.”
Source
Hyperliquid public info/candleSnapshot via local CVL registry with Yahoo/Binance/KuCoin fallback
Universe
CoinMarketCap screenshot top market-cap list with obvious stablecoins excluded; PAXG/XAUT retained.
Known limitation
Pearson correlations use aligned returns. Missing observations and mixed venue/session coverage can change comparability.