| name | analyze-stock |
| description | Top-down deep dive analysis on a US-listed stock with macro context, valuation audit, insider check, catalysts, and 3-tier entry plan with LEAPS option. Pulls live data via yfmcp. Triggers in English ("analyze X", "is X a buy", "deep dive on X", "should I buy X", "what about X stock", "research X") or Chinese ("分析 X", "X 怎么样", "X 能买吗", "深度看一下 X", "调研 X", "X 这只股票"). |
Analyze Stock — 10-Step Top-Down Master Framework
The job: deliver a fund-manager-grade analysis that connects macro context → year theme → sector position → individual thesis → entry plan. Every claim has concrete evidence. Every recommendation has size + reason.
Prime Directive
Never analyze a stock in isolation. A great stock in a bad macro window is still a sell. Always start with macro, end with sizing.
The 10 Steps (run in order)
Step 1 — Macro backdrop & event calendar (NEW: MANDATORY)
Before touching the stock, ask:
- What's the regime today? (risk-on melt-up / chop / late-cycle / risk-off / bear)
- What macro events in the next 30 days could move this stock?
- Fed meetings, FOMC minutes, CPI, NFP
- Trade summits (Trump-Xi 5/14-15, G20)
- BOJ meetings (carry trade trigger)
- OPEC (oil/inflation)
- Major regulatory (FTC, SEC, China MOFCOM)
- Geopolitical hotspots (Taiwan, Iran/Hormuz, Russia)
Tools:
WebSearch: "[stock] macro impact [next event]" e.g., "NVDA Trump-Xi summit impact"WebSearch: "Fed meeting [next month]", "BOJ meeting [next month]"
Output: 1 paragraph naming the regime + 3 bullet macro events that affect THIS stock.
Step 2 — Year theme alignment
Identify which annual narrative this stock fits. Common 2026 themes:
- K-shape divergence (winners up, losers crushed within sectors)
- AI = factory/capex mode (hyperscalers buy compute like factories buy machines)
- Power as AI bottleneck (nuclear/gas/utilities revaluation)
- Late-cycle demand destruction risk (oil/inflation pressure)
- Yen carry trade unwind risk (BOJ rate hikes triggering JPY borrowing reversal)
Question: Does this stock benefit from this year's theme, or fight it?
Step 3 — Sector position + Industry chain mechanics
Step 3a: Sector classification
- Which sector? (Use yfmcp
get_ticker_info → sector)
- Sector status: 过热 / 合理 / 未爆发 / 熊市
- Within sector, is this 龙头 / 二线 / 笨马?
- Sector ETF distance from 50DMA / 200DMA (overheated check)
Step 3b: Industry chain position (CRITICAL — different sub-sectors have different growth mechanics)
Identify which growth model this stock fits:
| Growth Model | Mechanics | Examples | Predictability |
|---|
| Capacity-bottlenecked downstream | Cannot grow faster than upstream allows | Optical modules tied to NVDA GPU schedule, OSAT tied to TSMC | 🔴 Low — "缺料"是常态 |
| Independent capacity expansion | Owns fabs, can scale on own timeline | Memory (MU/WDC), SiC fabs (WOLF), some semis | 🟢 High — capex visibility |
| Demand-elastic with structural growth | Demand >> supply, can raise price | NVIDIA GPUs, AI ASICs (AVGO/MRVL), ARM IP | 🟢 High — pricing power |
| Cyclical commodity | Boom-bust by macro | Memory DRAM/NAND cycle, copper, oil | 🟡 Medium — cycle visibility |
| Long-cycle infrastructure | Multi-year buildout, slow but visible | Power utilities, gas pipelines, data center REIT | 🟢 High — backlog-driven |
| Service/SaaS recurring | ARR-based, low capex sensitivity | Oracle DB, Cisco software, EDA (CDNS/SNPS) | 🟢 Highest — recurring rev |
Identify bottleneck specifically:
- What limits this stock's growth? (component shortage / fab capacity / customer demand / regulation)
- Is the bottleneck upstream or downstream of this stock?
- Does this stock have pricing power against the bottleneck?
Critical insight: A "great thesis" stock with the wrong growth model is still wrong. Example:
- Optical modules ride AI capex BUT are capacity-bottlenecked by GPU schedules
- Memory rides AI capex AND can expand independently
- Same upside narrative, very different earnings trajectory
Tools:
mcp__yfmcp__yfinance_get_ticker_info for sectorWebSearch: "[sector] supply chain bottleneck", "[ticker] capacity expansion", "[ticker] supply constraints"
Step 4 — Price snapshot + technicals
Pull live data via mcp__yfmcp__yfinance_get_ticker_info:
- Current price, day range, 52W range, ATH/ATL
- 50DMA, 200DMA — calculate % distance from each
- 6mo, 1Y change
- Beta, average volume
Red flags:
- 现价 +30%+ above 50DMA = 抛物线
- 现价 +50%+ above 200DMA = 极端透支
- 1Y >+200% = 概率回调
Step 5 — Full valuation audit + sub-sector value ranking
Compute via yfmcp:
- Forward P/E (most important)
- PEG (Forward P/E / EPS growth %)
- P/S, P/B
- EV/EBITDA, EV/Revenue
- OPM, Net margin, ROE, ROA
- FCF (TTM), Operating CF, Total Debt, Cash
- D/E ratio
Compare to 2-3 peers (same sub-sector, similar size). Use WebSearch if unclear who peers are.
Output table with cost-benefit ranking:
| Metric | This Stock | Peer 1 | Peer 2 | Peer 3 | Verdict |
| Forward P/E | X | Y | Z | W | Cheapest / Mid / Most expensive |
| PEG | X | Y | Z | W | |
| 1Y % | X | Y | Z | W | Most laggard / leader |
| Distance from ATH | X | Y | Z | W | |
| Capacity model | (from Step 3b) | | | | |
Rank within sub-sector:
- 🟢 Best value: Cheapest PE/PEG + clean capacity model + lagging price
- 🟡 Fair value: Middle of pack
- 🔴 Stretched: Most expensive in sub-sector at ATH
Sub-sector cost-benefit examples (showing why peer ranking matters):
- Memory: MU PE 5.3 vs WDC PE 24.8 — both AI memory but very different value
- Optical: COHR PE 43 vs LITE PE 59 — same sub-sector, COHR cheaper
- AI Power: EQT PE 12.6 vs AEP PE 19.9 vs ETR PE 23.3 — tier by valuation
- Hyperscaler: ORCL PE 21 vs MSFT PE 33 — similar AI thesis, different valuations
Step 6 — Concrete catalysts (last 30 days + next 30 days)
Past 30 days:
- Last earnings results (beat/miss, guidance)
- New contracts/customer wins
- Analyst upgrades/downgrades with specific targets
- M&A activity
Next 30 days:
- Earnings date + implied move from straddle
- Conferences (e.g., Computex, GTC, Investor Day)
- Product launches
- Macro events from Step 1
Tools:
WebSearch: "[ticker] earnings [last quarter]"WebSearch: "[ticker] news [current month]"WebSearch: "[ticker] analyst price target [current month]"
Step 7 — Insider trading (MANDATORY — use insider_ratio.py v3, openinsider primary)
Never trust yfinance "Net Shares Purchased" headline — it counts RSU as buys. Form 4 code "P" is the only real-buy signal; A/M/F/G are compensation flows. Verify any "cluster buy" claim at openinsider.com/[TICKER] — news routinely mislabels DSU/RSU grants as cluster buys.
Run (uses openinsider as primary source, 90-day default window, code-aware):
uv run --with yfinance python $(ls ~/.claude/{skills,plugins/claude-investment-skills}/review-investment-screenshot/scripts/insider_ratio.py 2>/dev/null | head -1) "TICKER" --window 90
For high-stakes calls add --source both to cross-verify against yfinance.
Verdict ladder:
| Buy/Sell ratio | Verdict |
|---|
| Buy ≥ 2× Sell | 🟢 STRONG BUY |
| Buy ≥ Sell | 🟡 Mild buy |
| Buy 0.1×-1× Sell | 🟡 Mixed |
| Buy < 10% Sell | 🔴 DISTRIBUTION |
| Buy = 0, Sell > 0 | 🔴 INSIDERS ONLY SELLING |
Always report seniority: CEO > CFO > Director > Officer. CEO buying $1M >> 5 directors selling $5M.
Step 8 — Risk dissection (bear/base/bull)
For each, give specific price target + assumption:
| Scenario | Probability | 12mo target | Trigger |
|---|
| 🟢 Bull | X% | $Y | What needs to happen |
| 🟡 Base | X% | $Y | What needs to happen |
| 🔴 Bear | X% | $Y | What needs to happen |
| 💀 Black swan | X% | $Y | E.g., yen carry, war |
Calculate weighted average price = Σ(probability × target).
Step 9 — Entry plan (3 tiers)
| 价位 | 性质 | 仓位 % |
|---|
| 现价 | 试仓 | 30% |
| 50DMA | 健康回调 | 30% |
| 200DMA | 库重 | 40% |
Position size cap: any single stock max 8-10% of portfolio, max 5% for high beta/parabolic names.
Step 10 — LEAPS recommendation (if applicable)
For each stock, also check:
mcp__yfmcp__yfinance_get_option_dates- For 2027/1 and 2028/1 expiries: pull option chain
- Filter: OI > 1000, IV < 80%, ATM to +20% OTM
Recommend 1-2 strikes with:
- Mid price
- Breakeven
- 2x scenario
- Max loss (premium)
LEAPS over stock when: high conviction + want leverage + IV reasonable.
Stock over LEAPS when: dividend yield matters + tax efficiency + uncertain timeline.
Output format
# [TICKER] Deep Dive — [Date]
## TL;DR (Verdict)
[One paragraph: action + size + reason]
## Step 1 — Macro Context
- Regime: [tag]
- Macro events 30d: [list with dates]
## Step 2 — Year Theme Fit
[Which 2026 narrative? Yes/No fit?]
## Step 3 — Sector Position
[Sector / leader-laggard / overheated?]
## Step 4 — Price + Technicals
| Metric | Value | Signal |
## Step 5 — Valuation
[Table vs peers]
## Step 6 — Catalysts
- Past 30d: [list]
- Next 30d: [list with dates]
## Step 7 — Insider
[Run insider_ratio.py output + verdict]
## Step 8 — Scenarios
[Bear/base/bull table]
## Step 9 — Entry Plan
[3-tier table with $ and %]
## Step 10 — LEAPS
[Recommended strikes + R/R]
## What I'd do today
[Specific action: buy X shares at $Y / wait for Z / hedge with W]
Hard rules
- Always start with macro. A perfectly priced stock in a bad regime is still wrong.
- Never skip insider check. Use insider_ratio.py — yfinance summary is RSU-polluted.
- Concrete evidence over narrative. "AI is good" ≠ thesis. "Microsoft signed $19.4B 5-year contract on 9/22/2025" = thesis.
- Position size has a CAP. No single stock >10%, no high-beta name >5%.
- 3-tier entry mandatory. No "buy at market" recommendations.
- If insider distribution + parabolic price → 🔴 even if business is great. ADI/TER pattern.
- Report what's NOT priced in. ORCL pattern: China=0 in NVDA case = pure upside.
- Always check 30-day macro events. Trump-Xi, BOJ, FOMC, OPEC.
Common patterns to recognize
| Pattern | Example | Signal |
|---|
| 已涨爆 + 内部人卖 + 距 ATH < 5% | ADI, TER, AVGO 4/2026 | 🔴 顶部分发 |
| 估值便宜 + 大跌 -50% + 反转早期 | ORCL 5/2026, NOK 2024 | 🟢 narrative reversal |
| 1Y 落后 + PE 低 + 真 thesis | EQT, AEP, HBM 2026 | 🟢 未爆发 |
| 业绩好 + 但指引平 | TER 4/2026, AMD 2/2026 | 🔴 priced in，跌 |
| Insider 集中买 (3+ 高管 1 周内) | CEVA 2026, COHR 2024 | 🟢 STRONG BUY signal |
| 高 beta + 客户集中 | APLD (CRWV 60%) | 🔴 单点失败风险 |
When user asks "is X a buy?"
Run all 10 steps. Don't shortcut. The user is asking for a full analysis, not an opinion.
Tool cheat-sheet
| Need | Tool |
|---|
| Live price + valuation | mcp__yfmcp__yfinance_get_ticker_info |
| Historical prices | mcp__yfmcp__yfinance_get_price_history |
| Option chain | mcp__yfmcp__yfinance_get_option_chain |
| News | mcp__yfmcp__yfinance_get_ticker_news + WebSearch |
| Insider | `$(ls ~/.claude/{skills,plugins/claude-investment-skills}/review-investment-screenshot/scripts/insider_ratio.py 2>/dev/null |
| Max pain | `$(ls ~/.claude/{skills,plugins/claude-investment-skills}/review-investment-screenshot/scripts/max_pain.py 2>/dev/null |
| Option walls | `$(ls ~/.claude/{skills,plugins/claude-investment-skills}/review-investment-screenshot/scripts/option_walls.py 2>/dev/null |
| Macro events | WebSearch |
