Market Top Detector Skill Purpose Detect the probability of a market top formation using a quantitative 6-component scoring system (0-100). Integrates three proven market top detection methodologies: O'Neil - Distribution Day accumulation (institutional selling) Minervini - Leading stock deterioration pattern Monty - Defensive sector rotation signal Unlike the Bubble Detector (macro/multi-month evaluation), this skill focuses on tactical 2-8 week timing signals that precede 10-20% market corrections. When to Use This Skill English: User asks "Is the market topping?" or "Are we near a top?" User notices distribution days accumulating User observes defensive sectors outperforming growth User sees leading stocks breaking down while indices hold User asks about reducing equity exposure timing User wants to assess correction probability for the next 2-8 weeks Japanese: 「天井が近い？」「今は利確すべき？」 ディストリビューションデーの蓄積を懸念 ディフェンシブセクターがグロースをアウトパフォーム 先導株が崩れ始めているが指数はまだ持ちこたえている エクスポージャー縮小のタイミング判断 今後2〜8週間の調整確率を評価したい Difference from Bubble Detector Aspect Market Top Detector Bubble Detector Timeframe 2-8 weeks Months to years Target 10-20% correction Bubble collapse (30%+) Methodology O'Neil/Minervini/Monty Minsky/Kindleberger Data Price/Volume + Breadth Valuation + Sentiment + Social Score Range 0-100 composite 0-15 points Execution Workflow Phase 1: Data Collection via WebSearch Before running the Python script, collect the following data using WebSearch. Data Freshness Requirement: All data must be from the most recent 3 business days. Stale data degrades analysis quality. 1. S&P 500 Breadth (200DMA above %) AUTO-FETCHED from TraderMonty CSV (no WebSearch needed) The script fetches this automatically from GitHub Pages CSV data. Override: --breadth-200dma [VALUE] to use a manual value instead. Disable: --no-auto-breadth to skip auto-fetch entirely. 2. [REQUIRED] S&P 500 Breadth (50DMA above %) Valid range: 20-100 Primary search: "S&P 500 percent stocks above 50 day moving average" Fallback: "market breadth 50dma site:barchart.com" Record the data date 3. [REQUIRED] CBOE Equity Put/Call Ratio Valid range: 0.30-1.50 Primary search: "CBOE equity put call ratio today" Fallback: "CBOE total put call ratio current" Fallback: "put call ratio site:cboe.com" Record the data date 4. [OPTIONAL] VIX Term Structure Values: steep_contango / contango / flat / backwardation Primary search: "VIX VIX3M ratio term structure today" Fallback: "VIX futures term structure contango backwardation" Note: Auto-detected from FMP API if VIX3M quote available. CLI --vix-term overrides auto-detection. 5. [OPTIONAL] Margin Debt YoY % Primary search: "FINRA margin debt latest year over year percent" Fallback: "NYSE margin debt monthly" Note: Typically 1-2 months lagged. Record the reporting month. Phase 2: Execute Python Script Run the script with collected data as CLI arguments: python3 skills/market-top-detector/scripts/market_top_detector.py \ --api-key $FMP_API_KEY \ --breadth-50dma [ VALUE ] --breadth-50dma-date [ YYYY-MM-DD ] \ --put-call [ VALUE ] --put-call-date [ YYYY-MM-DD ] \ --vix-term [ steep_contango | contango | flat | backwardation ] \ --margin-debt-yoy [ VALUE ] --margin-debt-date [ YYYY-MM-DD ] \ --output-dir reports/ \ --context "Consumer Confidence=[VALUE]" "Gold Price=[VALUE]"

200DMA breadth is auto-fetched from TraderMonty CSV.

Override with --breadth-200dma [VALUE] if needed.

Disable with --no-auto-breadth to skip auto-fetch.

The script will: Fetch S&P 500, QQQ, VIX quotes and history from FMP API Fetch Leading ETF (ARKK, WCLD, IGV, XBI, SOXX, SMH, KWEB, TAN) data Fetch Sector ETF (XLU, XLP, XLV, VNQ, XLK, XLC, XLY) data Calculate all 6 components Generate composite score and reports Phase 3: Present Results Present the generated Markdown report to the user, highlighting: Composite score and risk zone Data freshness warnings (if any data older than 3 days) Strongest warning signal (highest component score) Historical comparison (closest past top pattern) What-if scenarios (sensitivity to key changes) Recommended actions based on risk zone Follow-Through Day status (if applicable) Delta vs previous run (if prior report exists) 6-Component Scoring System

Component Weight Data Source Key Signal 1 Distribution Day Count 25% FMP API Institutional selling in last 25 trading days 2 Leading Stock Health 20% FMP API Growth ETF basket deterioration 3 Defensive Sector Rotation 15% FMP API Defensive vs Growth relative performance 4 Market Breadth Divergence 15% Auto (CSV) + WebSearch 200DMA (auto) / 50DMA (WebSearch) breadth vs index level 5 Index Technical Condition 15% FMP API MA structure, failed rallies, lower highs 6 Sentiment & Speculation 10% FMP + WebSearch VIX, Put/Call, term structure Risk Zone Mapping Score Zone Risk Budget Action 0-20 Green (Normal) 100% Normal operations 21-40 Yellow (Early Warning) 80-90% Tighten stops, reduce new entries 41-60 Orange (Elevated Risk) 60-75% Profit-taking on weak positions 61-80 Red (High Probability Top) 40-55% Aggressive profit-taking 81-100 Critical (Top Formation) 20-35% Maximum defense, hedging API Requirements Required: FMP API key (free tier sufficient: ~33 calls per execution) Optional: WebSearch data for breadth and sentiment (improves accuracy) Output Files JSON: market_top_YYYY-MM-DD_HHMMSS.json Markdown: market_top_YYYY-MM-DD_HHMMSS.md Reference Documents references/market_top_methodology.md Full methodology with O'Neil, Minervini, and Monty frameworks Component scoring details and thresholds Historical validation notes references/distribution_day_guide.md Detailed O'Neil Distribution Day rules Stalling day identification Follow-Through Day (FTD) mechanics references/historical_tops.md Analysis of 2000, 2007, 2018, 2022 market tops Component score patterns during historical tops Lessons learned and calibration data When to Load References First use: Load market_top_methodology.md for full framework understanding Distribution day questions: Load distribution_day_guide.md Historical context: Load historical_tops.md Regular execution: References not needed - script handles scoring