Detects market top probability using O'Neil Distribution Days, Minervini Leading Stock Deterioration, and Monty Defensive Sector Rotation. Generates a 0-100 composite score with risk zone classification. Use when user asks about market top risk, distribution days, defensive rotation, leadership brea

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: 1. O'Neil Distribution Day accumulation (institutional selling) 2. Minervini Leading stock deterioration pattern 3. 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 Prerequisites Required: FMP API Key: Set $FMP API KEY environment variable or pass api key . Free tier sufficient (~33 API calls per execution). WebSearch Access: Required to collect S&P 500 breadth (50DMA %) and CBOE Put/Call ratio data. Optional: Margin Debt Data: Enhances sentiment scoring but typically 1 2 months lagged. VIX Term Structure: Auto detected from FMP API if VIX3M quote available; manual override via vix term . Data Freshness: All manually collected data should be from the most recent 3 business days for accurate analysis. 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. Phase 2: Execute Python Script Run the script with collected data as CLI arguments: The script will: 1. Fetch S&P 500, QQQ, VIX quotes and history from FMP API 2. Fetch Leading ETF (ARKK, WCLD, IGV, XBI, SOXX, SMH, KWEB, TAN) data 3. Fetch Sector ETF (XLU, XLP, XLV, VNQ, XLK, XLC, XLY) data 4. Calculate all 6 components 5. 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 Exchange Calendar and Replay Install requirements.txt before running the detector. Freshness uses XNYS sessions rather than weekdays. as of YYYY MM DD is accepted for the live evaluation date, but historical live replay fails closed because the current quote endpoints are not point in time sources. 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

market-top-detector