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name: stock-research description: Comprehensive toolkit for stock analysis, research, and trading recommendations. Provides data acquisition, technical analysis, fundamental analysis, risk assessment, and professional reporting for stocks. license: MIT

Stock Research Skill

This skill provides a comprehensive framework for conducting in-depth stock analysis and generating trading recommendations. It integrates data acquisition, technical analysis, fundamental analysis, risk assessment, and professional reporting to support investment decision-making.

Purpose

To empower users with systematic approaches to stock research that go beyond surface-level information. This skill helps:

• Collect and analyze real-time and historical stock data
• Perform technical analysis using various indicators and patterns
• Conduct fundamental analysis of company financials and valuations
• Assess investment risks and calculate risk-adjusted returns
• Generate structured research reports with actionable trading recommendations
• Visualize stock data through charts and technical indicators

Core Principles

1. Data-Driven Analysis

• Base all analysis on reliable financial data from multiple sources
• Verify data accuracy through cross-referencing and validation
• Use statistical methods to identify meaningful patterns

2. Multi-Dimensional Analysis

• Combine technical, fundamental, and sentiment analysis
• Consider both quantitative metrics and qualitative factors
• Analyze stocks from multiple timeframes (short, medium, long-term)

3. Risk-Aware Decision Making

• Quantify investment risks using standard financial metrics
• Calculate risk-adjusted returns (Sharpe ratio, Sortino ratio)
• Provide clear risk disclosures and limitations

4. Structured Reporting

• Present findings in a clear, logical structure suitable for investors
• Include executive summaries, key findings, and actionable recommendations
• Use visualizations to enhance understanding of complex data

Core Components

1. Data Acquisition Module

• Real-time Data: Fetch current stock prices, volume, and market data
• Historical Data: Retrieve historical price data with adjustable timeframes
• Fundamental Data: Access financial statements, ratios, and company information
• Market Data: Obtain sector/industry comparisons and benchmark indices

2. Technical Analysis Module

• Price Action: Support/resistance levels, trend analysis, chart patterns
• Indicators: Moving averages, RSI, MACD, Bollinger Bands, Stochastic Oscillator
• Volume Analysis: Volume trends, OBV (On-Balance Volume)
• Volatility Measures: ATR (Average True Range), standard deviation

3. Fundamental Analysis Module

• Financial Statements: Income statement, balance sheet, cash flow analysis
• Valuation Metrics: P/E ratio, P/B ratio, EV/EBITDA, dividend yield
• Profitability Ratios: ROE, ROA, gross margin, operating margin
• Growth Metrics: Revenue growth, earnings growth, future estimates

4. Risk Assessment Module

• Volatility Analysis: Historical and implied volatility
• Drawdown Analysis: Maximum drawdown, recovery periods
• Correlation Analysis: Sector and market correlations
• Scenario Analysis: Best-case/worst-case scenarios

5. Trading Recommendation Module

• Recommendation Framework: Buy/Hold/Sell with conviction levels
• Price Targets: Based on technical and fundamental analysis
• Risk Management: Stop-loss levels, position sizing suggestions
• Timing Considerations: Entry/exit timing based on technical signals

Usage Instructions

Basic Usage Pattern

Copy# Example: Analyze a stock and generate report
from stock_research import StockAnalyzer

# Initialize analyzer for a stock
analyzer = StockAnalyzer(symbol="AAPL", market="US")

# Get comprehensive analysis
report = analyzer.analyze(
    timeframe="1y",
    include_technical=True,
    include_fundamental=True,
    include_risk=True
)

# Generate trading recommendation
recommendation = analyzer.get_recommendation()

# Export report
analyzer.export_report(format="html")

Step-by-Step Workflow

1. Stock Selection & Data Collection

   • Specify stock symbol and market
   • Collect real-time and historical price data
   • Gather fundamental financial data

2. Technical Analysis

   • Calculate key technical indicators
   • Identify chart patterns and trends
   • Analyze volume and momentum

3. Fundamental Analysis

   • Review financial statements and ratios
   • Compare with industry peers
   • Assess valuation metrics

4. Risk Assessment

   • Calculate volatility and drawdown metrics
   • Assess market and sector risks
   • Evaluate risk-adjusted returns

5. Integration & Synthesis

   • Combine technical and fundamental insights
   • Weight factors based on investment timeframe
   • Identify key catalysts and risks

6. Recommendation Generation

   • Determine Buy/Hold/Sell recommendation
   • Set price targets and stop-loss levels
   • Provide position sizing guidance

7. Report Generation

   • Create structured research report
   • Include charts and visualizations
   • Present clear investment thesis

Available Tools

Data Sources

• yfinance: Primary source for US and global stocks
• akshare: Alternative for Chinese stocks (A-shares)
• Alpha Vantage: For alternative data and extended history
• IEX Cloud: For real-time US stock data

Analysis Libraries

• TA-Lib: Technical analysis library with 150+ indicators
• pandas: Data manipulation and analysis
• numpy: Numerical computations
• scipy: Statistical analysis

Visualization

• matplotlib: Basic charting and plotting
• plotly: Interactive charts and dashboards
• mplfinance: Financial charts with candlestick patterns

Templates

The templates/ directory contains:

• Stock Research Report Template: Standardized structure for comprehensive stock analysis reports
• Technical Analysis Template: Framework for technical indicator analysis
• Fundamental Analysis Template: Template for financial statement analysis
• Trading Recommendation Template: Format for presenting trading recommendations
• Risk Assessment Template: Framework for evaluating investment risks

Examples

The examples/ directory contains:

• AAPL Analysis Example: Complete analysis of Apple Inc.
• TSLA Technical Analysis: Technical analysis of Tesla stock
• MSFT Fundamental Analysis: Fundamental analysis of Microsoft
• Portfolio Risk Assessment: Example of multi-stock risk analysis

Quality Standards

All stock research outputs should meet these criteria:

1. Completeness: Cover technical, fundamental, and risk dimensions
2. Accuracy: Use verified data sources and correct calculations
3. Transparency: Clearly state assumptions and limitations
4. Actionability: Provide specific, actionable recommendations
5. Risk Awareness: Include appropriate risk disclosures

Limitations & Disclaimers

• Data Limitations: Free data sources may have delays or limitations
• Market Risk: All investments carry risk of loss
• Past Performance: Historical performance does not guarantee future results
• Not Financial Advice: Outputs are for informational purposes only

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This skill is designed to complement Proteus AI's research capabilities by providing specialized tools and frameworks for systematic stock analysis and investment research.