Sector rotation analysis

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.
2. 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.
3. 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.
4. 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.
5. 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.

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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)

Sector	ETF	Leaders 2026
Technology	XLK / VGT	NVDA, MSFT, AVGO
Communications	XLC	GOOGL, META
Consumer Discretionary	XLY	AMZN, TSLA
Consumer Staples	XLP	COST, WMT, KO
Energy	XLE	XOM, CVX, EQT
Financials	XLF	JPM, BRK
Healthcare	XLV	LLY, UNH
Industrials	XLI	CAT, GE, RTX
Materials	XLB	LIN, FCX, NEM
Real Estate	XLRE	DLR, EQIX
Utilities	XLU	CEG, NEE, AEP

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

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

Sub-sector	Examples	Growth model	Bottleneck	Predictability	Earnings risk
AI GPU	NVDA, AMD	Demand-elastic, pricing power	TSMC capacity (already locked)	🟢 High	🟡 Medium (priced in)
AI ASIC	AVGO, MRVL	Demand-elastic + multi-customer	TSMC packaging (CoWoS)	🟢 High	🟡 Medium
AI Memory (HBM)	MU, SK Hynix	Independent capacity expansion	Own fab investment cycle	🟢 High	🟢 Lower (cycle-tied)
AI Storage (HDD)	WDC, STX	Slow capex, sold-out years out	Existing capacity	🟢🟢 Highest	🟢 Low risk
AI Optical Modules	LITE, COHR, FN, AAOI	Capacity-bottlenecked by GPU schedule	NVDA shipments	🔴 Low	🔴 High (component shortages)
AI Networking Systems	ANET, CIEN, JNPR	Mix of system + components	Various	🟡 Medium	🟡 Medium
AI Test Equipment	AMAT, LRCX, KLAC, TER	Lags fab capex by 6-12 months	Customer capex timing	🟡 Medium	🔴 High (cycle peaks)
AI OSAT (Packaging)	AMKR, ASE, SANM	Capacity-bottlenecked by TSMC	Advanced packaging	🔴 Low	🔴 High
AI Power (Utilities)	CEG, VST, AEP, ETR	Multi-year PPA buildout	Grid + nuclear permits	🟢🟢 Highest	🟢 Very low
AI Power (Gas)	EQT, ET, WMB, GEV	Long-cycle infrastructure	Pipeline capacity	🟢 High	🟢 Low
AI Cloud (Hyperscaler)	ORCL, MSFT, AMZN	Capex-driven, RPO-visible	Power, then GPU	🟢 High	🟡 Medium
AI Cloud (Neocloud)	CRWV, NBIS, IREN	Single-customer concentration	Customer payment risk	🔴 Low	🔴 High
AI Materials	APD, LIN, MP, FCX	Long-term contract structure	Mining/refining capacity	🟢 High	🟢 Low
AI Connectors	TEL, APH, GLW	Linked to GPU shipments	Various	🟡 Medium	🟡 Medium
AI Cooling/Power Infra	VRT, ETN, NVT, MOD	Equipment cycle	Manufacturing	🟡 Medium	🟡 Medium

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

2. 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

3. Earnings sensitivity by sub-sector:

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

4. 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:

Metric	Healthy	Overheated	Crisis
% above 200DMA	<15%	15-30%	>30%
YTD %	<30%	30-60%	>60%
Forward P/E	< historical avg	At historical avg	>120% of historical
Sector breadth	>65% above 50DMA	50-65%	<50%

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

Step 3 — Identify rotation pairs

For each pair where one is overheated and one undervalued:

Trim (overheated)	Add (undervalued)	Why
AI Semis (XLK)	AI Power (XLU)	Power = AI's bottleneck, cheaper, less crowded
Mag7	Energy/Materials	Concentration unwinding
Tech	Healthcare	Late-cycle rotation
Crypto-adjacent	Defensive (Staples)	Risk-off

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

Need	Tool
Sector ETF data	mcp__yfmcp__yfinance_get_ticker_info (XLK, XLE, etc.)
Sector P/E	WebSearch: "[sector] P/E ratio current vs historical"
Sub-sector ETFs	SMH (semi), XBI (biotech), KRE (banks), ITA (defense)
Internal breadth	WebSearch: "% [sector] stocks above 200DMA"
Historical heatmap	WebSearch: "S&P 500 sector returns YTD"

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)