Case 03396

Skill knownasnaffy/prompthound/dataset/case_03396

A fast, offline static risk analysis CLI for AI agent skill files. Detects malicious instructions, steganographic payloads, and dangerous capability chains.

Install

npx -y skills add knownasnaffy/prompthound --skill case_03396

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Three-factor stock analysis combining DCF valuation, Livermore trend trading rules, and VIX market sentiment to generate high-confidence buy signals for US equities. Use when analyzing US stocks for investment decisions, determining optimal entry points, or evaluating buy opportunities with multi-factor confirmation.

SKILL.md

4.5 KB, 870 tokens by cl100k_base, as published. Nobody here has run it

📈 US Stock Analyzer - Three-Factor Buy Signal System

A comprehensive stock analysis system that combines Value Investing (DCF), Trend Trading (Livermore Rules), and Market Sentiment (VIX) to generate buy signals only when all three factors align.

Quick Start

# Install dependencies
pip3 install yfinance pandas numpy matplotlib pyyaml requests

# Set API key (optional, for enhanced financial data)
export FMP_API_KEY="your_key_here"

# Run analysis
python3 scripts/decision_engine.py AAPL

Three-Factor Framework

1️⃣ Value Factor (DCF) - 40% Weight

Buy Condition: Price < Intrinsic Value × (1 - Margin of Safety)
Key Metrics: Free Cash Flow, WACC, growth rates, ROE, debt levels
Output: Value score 0-100, fair value estimate

2️⃣ Trend Factor (Livermore) - 35% Weight

Buy Conditions:
- Break above resistance with volume confirmation (1.5x avg)
- Sector alignment (ETF in uptrend)
- Price > MA20 > MA60
Output: Trend score 0-100, key support/resistance levels

3️⃣ Sentiment Factor (VIX) - 25% Weight

Buy Conditions:
- VIX < 25 (non-panic)
- VIX percentile < 70% (not expensive)
- Market breadth positive (SPY uptrend)
Output: Sentiment score 0-100, market regime

Final Buy Signal

BUY = Value ≥ 60 AND Trend ≥ 60 AND Sentiment ≥ 60 AND Composite ≥ 70

Usage

Basic Analysis

from scripts.decision_engine import StockAnalyzer

analyzer = StockAnalyzer(config_path="config.yaml")
result = analyzer.analyze("TSLA")
print(result['report'])

With Chart Output

analyzer.plot_analysis(result['result'], save_path="tsla_analysis.png")

Data Sources

Data	Source	Required
Price/Volume	Yahoo Finance (yfinance)	Free
Financials	FMP API	Optional (free tier)
VIX	CBOE via Yahoo	Free
Sector ETFs	Yahoo Finance	Free

Configuration

Edit config.yaml to customize:

DCF discount rate (default: 10%)
Margin of safety threshold (default: 20%)
VIX panic threshold (default: 30)
Factor weights
Position sizing rules

Output

The system generates a formatted report including:

Executive summary with composite score
Individual factor scores with visual bars
Buy/hold signal with confidence level
Position sizing recommendation
Target price and stop loss levels
Risk assessment

File Structure

us-stock-analyzer/
├── SKILL.md                 # This file
├── config.yaml             # Configuration
├── requirements.txt        # Python dependencies
├── scripts/
│   ├── data_fetcher.py    # Data retrieval
│   ├── dcf_analyzer.py    # DCF valuation
│   ├── trend_analyzer.py  # Livermore trend analysis
│   ├── sentiment_analyzer.py # VIX sentiment
│   └── decision_engine.py # Three-factor engine
├── references/
│   └── methodology.md     # Detailed methodology
└── examples/
    └── sample_report.md   # Example output

Methodology

See references/methodology.md for:

Detailed DCF calculation methodology
Livermore trading rules implementation
VIX sentiment scoring algorithm
Position sizing formulas

Disclaimer

This tool is for informational purposes only and does not constitute investment advice. Always conduct your own research and consider consulting a financial advisor before making investment decisions.

License

MIT-0 (Public Domain)