A powerful data analysis tool that quantifies stock market health by calculating a 0-100 composite score based on six critical breadth components.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install einstein-research-breadth-dv
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install einstein-research-breadth-dv using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Market Breadth Analyzer?
The Market Breadth Analyzer is a specialized technical tool within the Openclaw Skills ecosystem designed to evaluate whether a market rally is broad-based or dangerously narrow. By leveraging public CSV data from TraderMonty, it eliminates the need for expensive financial API keys while providing professional-grade insights. It synthesizes complex data points like moving average participation and the Advance-Decline Line into a single, actionable health score.
This skill is essential for traders and analysts who need to distinguish between sustainable market moves and vulnerable, top-heavy rallies. By quantifying the 'internal' health of the market, it helps users spot divergences where price indexes may be rising while participation is actually thinning out.
Market Breadth Analyzer Use Cases
- Evaluating if a stock market rally is supported by the majority of stocks or just a few mega-cap leaders.
- Identifying potential market tops through negative divergence in breadth scores.
- Assessing general market risk and participation rates before entering new positions.
- Generating automated daily or weekly market health reports for financial research.
How Market Breadth Analyzer Works
- The skill triggers a Python-based analysis script that fetches the most recent Market-Breadth-Data.csv from a public repository.
- It normalizes data for six core components, including stocks above the 50-day and 200-day moving averages, and New Highs-New Lows.
- A weighted composite score is calculated, giving higher precedence to moving average participation (50%) and New Highs-New Lows (20%).
- The system compares the current score against 3-month and 6-month moving averages to determine the momentum trend.
- It generates both a structured JSON object for automated workflows and a human-readable Markdown report for immediate review.
Market Breadth Analyzer Setup
To integrate this capability into your Openclaw Skills library, ensure you have Python 3 installed and follow these steps:
# Navigate to your skills directory
cd skills/market-breadth
# Install any necessary dependencies (pandas is required for CSV processing)
pip install pandas
# Run the analyzer manually to verify setup
python3 scripts/breadth_analyzer.py
Market Breadth Analyzer Data Schema & Taxonomy
The skill organizes its findings into two primary formats to support both AI reasoning and human readability:
| File Type | Purpose | Key Metadata |
|---|---|---|
JSON |
Programmatic access | composite_score, trend, component_breakdown, moving_averages |
Markdown |
Visual Reporting | Overall Score, Trend Assessment, Takeaway Summary |
Component Weighting
- % Stocks > 50d MA: 25%
- % Stocks > 200d MA: 25%
- New Highs - New Lows: 20%
- ADL Momentum: 15%
- AAII Bullish (Inverse): 10%
- SPX distance from 200d MA: 5%
Market Breadth Analyzer Advanced Features
- Trend Analysis: Automatically detects improving or deteriorating market health by comparing current scores to historical moving averages.
- Divergence Alerts: Capable of highlighting scenarios where the S&P 500 is at highs but the Openclaw Skills breadth score is declining.
- Zero-Cost Data Pipeline: Utilizes public GitHub-hosted datasets to provide reliable financial analysis without recurring API subscription costs.
- Multi-format Outputs: Seamlessly integrates with other agents by providing clean JSON data schemas.
SKILL.md
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