Sector Rotation Analysis — Where Money Is Going

🔍 Pre-flight checklist — rotation creates tax + sizing events that need accounting

Rotation = realize gains in one sector, buy in another. Both halves have execution cost. Required checks:

1. Macro regime first — trigger macro-warning. Regime determines rotation type:

• 🟢 GREEN: aggressive rotation OK (sell hot to buy cheap)
• 🟡 YELLOW: defensive rotation only (sell high-beta to buy staples/utilities)
• 🔴 RED: don't rotate INTO new sectors — rotate TO CASH. Then redeploy at lower prices.

1. Tax on the trim leg — every rotation pair has a "sell X" half. Run tax-optimize on it. If held < 12 months → STCG ~25-37% federal. Often a rotation that's +5% net pre-tax is breakeven or negative post-tax. Always state the post-tax expected delta, not just the pre-tax sector spread.
2. Sizing per sector after rotation — ≤ 30% in any single sector (even Tech). Document current sector weights BEFORE recommending. If user is already 40% Tech, don't recommend adding more Tech sub-sector even if signal is green.
3. Sub-sector concentration within rotation — "rotate from Tech to Energy" doesn't mean "buy XOM at any price." Each leg needs analyze-stock-level analysis.
4. 3-tier entry on the add leg — Don't rotate at market. T1 = trigger, T2 = 50DMA, T3 = 200DMA on the destination sector ETF or stock.

"Look carefully" rule: sector ETFs hide concentration. XLK is 23% AAPL + 18% NVDA + 9% MSFT — buying XLK on a "Tech rotation" is concentrated, not diversified. Always check top-5 holdings of any sector ETF before recommending it as a rotation vehicle.

See README's Hard Rules for the full anti-pattern list.

Goal

Help user rotate from overheated to undervalued sectors while staying in the broader market. Never just "all-in" on one sector. Every quarter, identify:

1. Which sectors are overheated? (>30% above 200DMA, +50% YTD, insider distribution)
2. Which sectors are undervalued? (<5% above 200DMA, lagging YTD, insider buying)
3. Rotation pairs: trim X to add Y
4. Sector ETF map for execution

The 11 GICS Sectors (always check all)

AI Sub-sectors (zoom in) + their distinct growth mechanics

Different sub-sectors have fundamentally different supply/demand dynamics. Critical for valuation and predictability:

Key insights from this matrix

1. Same "AI" thesis, very different earnings risk profiles:

• Optical modules and OSAT are capacity-bottlenecked → "缺料" is structural, predictable disappointments
• Memory and HDD have independent capex cycles → can deliver year-on-year visibility
• Power utilities are slowest but most predictable → no earnings surprises

1. Where to find "bestpredictability for the price":

• 🟢 Power (CEG/EQT/AEP/ETR) — long-term contracts, low surprise risk
• 🟢 Memory (MU/WDC) — own capacity, sold out for years
• 🔴 Optical (LITE/COHR/FN/AAOI) — looks great but earnings keep missing on supply

1. Earnings sensitivity by sub-sector:

• High earnings risk: Optical, OSAT, Test equipment (cycle-peak), Neocloud
• Low earnings risk: Power utilities, HDD storage, Memory, Materials

1. Capacity-bottlenecked sub-sectors systematically disappoint when GPU cycle hits supply ceiling:

• Even with strong demand, "shipments < demand"
• Margin compression from input shortages
• Pattern: beat EPS, miss on operational metrics → stock drops

The 4-Step Workflow

Step 1 — Pull sector performance data

For each sector ETF (XLK, XLE, XLU, etc.), pull via mcp__yfmcp__yfinance_get_ticker_info:

• Current price
• 50DMA, 200DMA distance
• YTD %, 1Y %
• Trailing/Forward P/E (sector aggregate)

Step 2 — Compute sector heat map

For each sector, compute:

Composite score: sum metrics, output as 🟢/🟡/🔴

Step 3 — Identify rotation pairs

For each pair where one is overheated and one undervalued:

Step 4 — Recommend specific names within rotation

Within target sector, identify best laggards:

Step 4a: Within the OVER sector, pick most-overextended names to trim Step 4b: Within the UNDER sector, pick best laggards (use find-untapped-thesis style criteria)

Output format

# Sector Rotation Analysis — [Date]

## TL;DR
**Overheated**: [list with status]
**Undervalued**: [list with status]
**Top 3 rotation pairs**: trim X → add Y

## Sector Heat Map (11 GICS)
| Sector | ETF | YTD | 1Y | %200DMA | %50DMA | Status |
| Tech | XLK | XX% | XX% | +XX% | +XX% | 🔴 OVERHEATED |
| Energy | XLE | XX% | XX% | +XX% | +XX% | 🟢 UNDERVALUED |
| ...

## AI Sub-sector Detail
| Sub-sector | YTD | 1Y | Status | Top idea |
| GPU/ASIC | +XX% | +XX% | 🔴 | Trim into strength |
| Power | +XX% | +XX% | 🟢 | Add EQT, AEP |
| Memory | +XX% | +XX% | 🟡 | Selective: MU only |

## Rotation Pairs (Top 3)

### Pair #1: [SECTOR_OVER] → [SECTOR_UNDER]
- **Trim from over**: [list specific stocks with quantities]
- **Add to under**: [list specific stocks with entry levels]
- **Net portfolio change**: [dollar impact, beta change]
- **Why this pair**: [thesis]

### Pair #2 ...
### Pair #3 ...

## Macro Backdrop
[1 paragraph from macro-risk-check, key signals]

## Recommended Actions Today
1. [Specific trim order]
2. [Specific add order]
3. [Hold others]

## Watch list (next 30 days)
- Sectors approaching turning point
- Sectors approaching overheat threshold

Hard rules

1. Never recommend "rotate everything" — always paired (trim 5%, add 5%).
2. Match risk levels. Don't trim defensive (Staples) to add aggressive (Crypto).
3. Use ETFs only as proxy for sector. For specific names, use find-untapped-thesis or analyze-stock.
4. Sector heat is NOT predictive of next quarter — it's predictive of mean reversion over 6-12 months.
5. Don't fight macro. If macro is RED, "rotate" might mean "rotate to cash + bonds."

Common patterns (2024-2026 examples)

Pattern A: Tech overheats → Defensive rotation

• 2021/Q4: Tech XLK +30% → Healthcare XLV started outperforming
• Outcome: 2022 Tech -33%, Healthcare -2%
• Lesson: Watch when only one sector is up

Pattern B: Energy undervalued → Catalyst-driven rally

• 2020/Q3: Energy XLE down -50% → 2021/Q4 Russia + recovery
• Outcome: XLE +60% in 2022 vs SPX -19%
• Lesson: Cycle bottoms have biggest re-rating

Pattern C: AI mega-cap → AI infrastructure

• 2025-2026: NVDA +500% → power/utilities catch up
• Now: CEG, VST, EQT outperform NVDA in next 6mo
• Lesson: After mega-runs, the supply chain catches up

Pattern D: K-shape divergence (winner-take-all within sectors)

• Within Tech: NVDA wins, software/SaaS lose
• Within Industrials: Defense wins, traditional loses
• Lesson: Sector ≠ Stock; pick winners within winning sector

When to invoke

• User asks: "What sector should I rotate to"
• User asks: "Where's the next move"
• User asks: "Am I too tech-heavy"
• Quarterly review (mandatory)
• After 1 sector hits +25% in a month

Companion skills

• Run macro-risk-check first for regime context
• Run find-untapped-thesis after picking target sector (for specific names)
• Run portfolio-audit to see actual current sector mix
• Run analyze-stock for deep dive on top picks

Tool cheat-sheet

Sector "Cheat Sheet" — Quick reference

When VIX > 25:

• Trim: XLK, XLY, XLC (high beta)
• Add: XLU, XLP, XLV (defensive)

When 30Y > 5%:

• Trim: REITs (XLRE), Utilities (XLU sometimes)
• Add: Banks (XLF), Energy (XLE)

When USD/JPY < 153 (yen carry unwind):

• Trim: All semis (heavy Japanese ownership)
• Add: Domestic-only (XLP, XLV)

When OPEC + 1973 risk:

• Trim: Growth, Tech, EVs
• Add: Energy (XLE), Materials (XLB), Defense (ITA)

When Trump-Xi summit positive:

• Trim: Defensive
• Add: China-exposed (BABA, JD), AI semis (NVDA China upside)