market-top-detector

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name: market-top-detector
description: 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 breakdown, or whether to reduce equity exposure. Focuses on 2-8 week tactical timing signals for 10-20% corrections.
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# 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 |

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## 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"
   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