Crypto Correlation & Sector Rotation Analysis

You are a quantitative crypto analyst who studies inter-asset correlations and sector rotation patterns in the crypto market. You have identified repeatable rotation sequences that occur during different market cycle phases.

CONTEXT: The crypto market is not monolithic — different sectors (L1s, L2s, DeFi, AI tokens, memecoins, RWA, gaming) rotate in and out of favor. Understanding these rotation patterns and correlation dynamics helps with portfolio construction, timing sector entries, and managing risk (avoiding over-concentration in correlated assets). I want a data-driven approach to tracking these dynamics.

TASK: Create a crypto sector rotation analysis system:
1. Sector classification: define the major crypto sectors and their representative indices/baskets — Layer 1 (BTC, ETH, SOL, AVAX), Layer 2 (ARB, OP, MATIC), DeFi Blue Chips (UNI, AAVE, MKR, CRV), AI/Compute (RNDR, FET, TAO), Gaming (IMX, AXS, GALA), Memecoins (DOGE, SHIB, PEPE), RWA (ONDO, MKR for RWA), and Infrastructure (LINK, GRT, FIL). Create market-cap-weighted indices for each.
2. Correlation analysis: how to calculate and interpret rolling correlations between sectors (30-day and 90-day windows). When correlations spike to 1.0 (all sectors moving together — typically in sharp selloffs), it means diversification provides no protection. When correlations drop (sectors decouple), rotation trades become viable. Provide the methodology for calculating and visualizing cross-sector correlation matrices.
3. Historical rotation sequence: document the typical crypto cycle rotation pattern — Bitcoin leads (dominance rises), then ETH catches up (ETH/BTC ratio rises), then large-cap alts (L1s, DeFi blue chips), then mid-caps, then small-caps and memecoins, then the cycle reverses. Provide historical evidence from 2020-2021 and 2024-2025 cycles with approximate timing for each phase.
4. Rotation signal detection: build a signal system for identifying sector transitions — relative strength (sector outperformance vs. BTC over 7, 14, and 30 days), volume flow analysis (which sectors are seeing increasing volume), social momentum shift (narrative analysis), and on-chain capital flow between sectors (DEX volume shift, TVL migration).
5. Portfolio rotation strategy: how to translate rotation signals into portfolio actions — when to overweight a sector (early rotation signal, before the sector has significantly outperformed), when to underweight (rotation signal fading, momentum exhaustion), and neutral positioning (when no clear rotation is occurring). Define position sizing by conviction level.
6. Risk management for rotation trades: correlation spikes during market stress, the risk of catching a "false rotation" (sector that briefly outperforms then reverses), and how to construct a portfolio that benefits from rotation while limiting downside if the rotation thesis is wrong.