Assess near-term U.S. equity market top risk using six indicators, including selling pressure, weakening leaders, and defensive sector rotation. Generate a heuristic score, risk zone, and reports to inform reviews of equity exposure over two to eight weeks.
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Assesses heuristic market top risk 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 breakdown, or whether to reduce equity exposure. Focuses on 2-8 week tactical timing signals for 10-20% corrections.
实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
Assess heuristic market top risk using a 6-component scoring system (0-100). Integrates three market risk methodologies:
Unlike the Bubble Detector (macro/multi-month evaluation), this skill focuses on tactical 2-8 week timing signals that precede 10-20% market corrections.
Treat the 0-100 composite as a heuristic risk score, not an estimated event probability. A score of 70 does not mean a 70% chance of a top. Risk-budget percentages are policy heuristics, not measured outcome frequencies. Historical patterns are retrospective estimates, not point-in-time evidence. Report no comparable episode when any component is unavailable/invalid or nearest-pattern weighted RMS exceeds 15 points. This tolerance is a predeclared heuristic, not an empirically validated similarity threshold. Follow the point-in-time evaluation protocol before making calibration claims. No measured calibration results are supplied.
English:
Japanese:
Required:
$FMP_API_KEY environment variable or pass --api-key. Free tier sufficient (~33 API calls per execution).Optional:
--vix-term.Data Freshness: All manually collected data should be from the most recent 3 business days for accurate analysis.
| Aspect | Market Top Detector | Bubble Detector |
|---|---|---|
| Timeframe | 2-8 weeks | Months to years |
| Target | 10-20% correction | Bubble collapse (30%+) |
| Methodology | O'Neil/Minervini/Monty | Minsky/Kindleberger |
| Data | Price/Volume + Breadth | Valuation + Sentiment + Social |
| Score Range | 0-100 composite | 0-15 points |
Before running the Python script, collect the following data using WebSearch. Data Freshness Requirement: All data must be from the most recent 3 business days. Stale data degrades analysis quality.
1. S&P 500 Breadth (200DMA above %)
AUTO-FETCHED from TraderMonty CSV (no WebSearch needed)
The script fetches this automatically from GitHub Pages CSV data.
Override: --breadth-200dma [VALUE] to use a manual value instead.
Disable: --no-auto-breadth to skip auto-fetch entirely.
2. [REQUIRED] S&P 500 Breadth (50DMA above %)
Valid range: 20-100
Primary search: "S&P 500 percent stocks above 50 day moving average"
Fallback: "market breadth 50dma site:barchart.com"
Direct fallback when search snippets are poor: fetch `https://www.barchart.com/stocks/quotes/$S5FI/overview` and extract the embedded `lastPrice` / `tradeTime` for “S&P 500 Stocks Above 50-Day Average”.
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"
Direct fallback when Cboe CSV endpoints are stale: fetch `https://ycharts.com/indicators/cboe_equity_put_call_ratio` and parse the “Last Value” / “Latest Period” table fields. Treat this as a secondary source and cite it in freshness notes.
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.
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:
Present the generated Markdown report to the user, highlighting:
| # | 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 |
| 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 Risk) | 40-55% | Aggressive profit-taking |
| 81-100 | Critical (Very High Risk) | 20-35% | Maximum defense, hedging |
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.
Required: FMP API key (free tier sufficient: ~33 calls per execution) Optional: WebSearch data for breadth and sentiment (improves accuracy)
market_top_YYYY-MM-DD_HHMMSS.jsonmarket_top_YYYY-MM-DD_HHMMSS.mdreferences/market_top_methodology.mdreferences/distribution_day_guide.mdreferences/historical_tops.mdmarket_top_methodology.md for full framework understandingdistribution_day_guide.mdhistorical_tops.md还没有评价。欢迎分享你的使用感受。
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Plan and assess backtests, which test trading rules against historical data. Examine sensitivity to rule changes, trading costs, and data bias, then use supplied performance metrics to generate a structured evaluation with reasons that block adoption.
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Assess how broadly US stock market gains are shared across stocks using public CSV data and optional charts. Produce English reports covering market conditions, trading signals, scenarios, and positioning for different investment timeframes.
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Analyze chart images showing how broadly US stocks are participating in market trends. Assess medium-term conditions and short-term signals, then produce an English report with positioning guidance, alternative scenarios, and risks based on the visible data.
中文简介正在准备中。当前显示英语简介。
Turn VCP screening results, which identify narrowing price swings, into breakout trade plans. Calculate entry, stop-loss, target prices, and position sizes using worst-case entries, while applying portfolio risk limits and producing Alpaca-compatible order templates.
中文简介正在准备中。当前显示英语简介。
Screen US growth stocks with William O'Neil's CANSLIM method, which evaluates seven factors including earnings, price strength, trading volume, and market direction. Receive ranked candidates with composite scores, factor breakdowns, market warnings, and data quality notes.
中文简介正在准备中。当前显示英语简介。
Combine three reports on crowded positions, failed reactions to news, and weekly price reversals into one trading setup status. Identify missing or invalid evidence and determine whether the setup is ready for planning.
中文简介正在准备中。当前显示英语简介。