| name | sector-rotation-analysis |
| description | Top-down sector heat map across 11 GICS sectors + AI sub-sectors (GPU, ASIC, Memory, Power, Cloud, Network, Materials). Identifies overheated vs undervalued sectors, leader-laggard pairs, rotation signals. Recommends specific trim-from / add-to pairs with named stocks. Triggers in English ("sector rotation", "what sector to add", "which sector is cheap", "am I too tech heavy", "sector heat map") or Chinese ("板块轮动", "该买哪个板块", "板块热力图", "我是不是 tech 太重", "板块对比"). |
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:
- 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.
- 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.
- 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.
- 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.
- 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:
- Which sectors are overheated? (>30% above 200DMA, +50% YTD, insider distribution)
- Which sectors are undervalued? (<5% above 200DMA, lagging YTD, insider buying)
- Rotation pairs: trim X to add Y
- 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
-
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
-
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
-
Earnings sensitivity by sub-sector:
- High earnings risk: Optical, OSAT, Test equipment (cycle-peak), Neocloud
- Low earnings risk: Power utilities, HDD storage, Memory, Materials
-
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
- Never recommend "rotate everything" — always paired (trim 5%, add 5%).
- Match risk levels. Don't trim defensive (Staples) to add aggressive (Crypto).
- Use ETFs only as proxy for sector. For specific names, use
find-untapped-thesis or analyze-stock.
- Sector heat is NOT predictive of next quarter — it's predictive of mean reversion over 6-12 months.
- 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)
