feat: add defeatbeta-api integration with comprehensive comparison tools

- Add defeatbeta-api as primary financial data source (replaces yfinance for analysis) - Add comprehensive Jupyter notebook tutorial (defeatbeta_tutorial.ipynb) - Add API comparison script (compare_apis.py) - Add data exploration script (explore_data.py) - Add basic test script (test_defeatbeta.py) - Add notebook runner script (run_notebook.sh) - Add org-mode mapping documentation (docs/defeatbeta_mapping.org) - Update pyproject.toml with defeatbeta-api dependency - Add defeatbeta-api as git submodule for reference DefeatBeta Advantages: - No rate limits (HuggingFace hosted) - Historical financial ratios (ROE, ROIC, WACC time series) - Earnings call transcripts access - Revenue segmentation by product/geography - Automated DCF valuation with Excel output - DuckDB-powered fast queries Note: .envrc, .jupyter_checkpoints/, __marimo__/, AAPL.xlsx, tearsheet.html and other generated files intentionally excluded
2026-04-25 17:56:10 +08:00
parent 60fa0c113d
commit 4e5af95272
11 changed files with 5235 additions and 23 deletions
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 [submodule "references/defeatbeta-api"]
 	path = references/defeatbeta-api
 	url = git@github.com:defeat-beta/defeatbeta-api.git
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 # DefeatBeta-API Tutorial Notebook
 An interactive Jupyter notebook comparing **DefeatBeta-API** vs **Yahoo Finance** for financial data analysis.
 ## 📚 What's Included
 ### `defeatbeta_tutorial.ipynb`
 A comprehensive 12-section interactive notebook covering:
 1. **Quick Performance Test** - Compare query speeds
 2. **Price Data Comparison** - OHLCV data structures
 3. **Valuation Metrics** - TTM EPS, P/E ratios, Market Cap (historical!)
 4. **Financial Statements** - Income statements, balance sheets, cash flows
 5. **Financial Ratios** - ROE, ROIC, WACC with full time-series
 6. **Growth & Margin Metrics** - YoY growth and margin analysis
 7. **Earnings Transcripts** - Unique feature! Full transcript access
 8. **Revenue Breakdown** - Unique feature! By segment and geography
 9. **DCF Valuation** - Unique feature! Automated Excel output
 10. **Interactive Stock Analysis** - Explore any stock symbol
 11. **Stock Comparison** - Compare multiple stocks side-by-side
 12. **Complete Method Reference** - All available API methods
 ## 🚀 How to Use
 ### Option 1: Run the notebook directly
 ```bash
 ./run_notebook.sh
 ```
 ### Option 2: Run with uv
 ```bash
 uv run jupyter notebook defeatbeta_tutorial.ipynb
 ```
 ### Option 3: Convert to Python script
 ```bash
 uv run jupyter nbconvert --to script defeatbeta_tutorial.ipynb
 uv run python defeatbeta_tutorial.py
 ```
 ## 📦 Requirements
 ```bash
 uv add defeatbeta-api jupyter
 ```
 ## 🎯 Key Features Demonstrated
 ### DefeatBeta-API Unique Features:
 - ✅ **Earnings Call Transcripts** - Access full transcripts by quarter
 - ✅ **Revenue Segmentation** - Breakdown by product/segment/geography
 - ✅ **Automated DCF** - Professional Excel valuation models
 - ✅ **Historical Financial Ratios** - Time-series for ROE, ROIC, WACC
 - ✅ **AI-Powered Analysis** - LLM analysis of transcripts
 - ✅ **No Rate Limits** - Hugging Face hosted data
 ### Comparison with Yahoo Finance:
 - DefeatBeta: Historical analysis, financial modeling, no rate limits
 - Yahoo Finance: Real-time data, analyst consensus, larger ecosystem
 ## 📊 Example Output
 ```python
 # Stock Comparison
 Symbol   Price    P/E    Market Cap    ROE     Rev Growth
 AAPL     $270.23  31.2   $4.1T        10.2%   16.7%
 MSFT     $420.00  35.1   $3.1T        31.1%   20.2%
 GOOGL    $341.68  31.6   $4.1T        8.6%    22.3%
 NVDA     $201.68  41.2   $4.9T        31.1%   73.2%
 ```
 ## 🧪 Exercises
 Try these challenges in the notebook:
 1. **Find Undervalued Stocks** - P/E < 20 but ROE > 15%
 2. **Growth Analysis** - Compare NVDA vs AMD
 3. **DCF Valuation** - Compare fair value estimates
 4. **Revenue Mix** - Track Apple's services growth
 5. **Transcript Mining** - Extract sentiment from earnings calls
 ## 📖 Documentation
 - [DefeatBeta-API GitHub](https://github.com/defeat-beta/defeatbeta-api)
 - [DuckDB Documentation](https://duckdb.org/docs)
 - [Financial Analysis Examples](doc/api/Value_Examples.md)
 ## ⚠️ Notes
 - Data is updated daily (batch on HuggingFace)
 - Real-time data requires Yahoo Finance
 - AI features need `OPENAI_API_KEY` set
 - Some features require Windows WSL/Docker (DuckDB dependency)
 ## 🎓 Learning Path
 1. Start with **Section 1-2** to understand data structures
 2. Move to **Section 3-5** for valuation and ratios
 3. Explore **Section 7-9** for unique features
 4. Complete **Section 10-11** for practical analysis
 5. Reference **Section 12** for complete API
 ---
 **Created:** 2026-04-25
 **Version:** 0.0.45
 **Author:** Interactive Tutorial
@@ -0,0 +1,359 @@
 #!/usr/bin/env python3
 """
 Comprehensive comparison of defeatbeta-api vs yfinance
 """
 import pandas as pd
 import time
 from defeatbeta_api.data.ticker import Ticker
 # Check if yfinance is available
 try:
    import yfinance as yf
    YFINANCE_AVAILABLE = True
 except ImportError:
    YFINANCE_AVAILABLE = False
    print("yfinance not installed - will show defeatbeta only")
    print("Install with: uv add yfinance\n")
 def compare_data_structures():
    """Compare data structures returned by both APIs"""
    print("="*80)
    print("DEFEATBETA-API vs YFINANCE: DATA STRUCTURE COMPARISON")
    print("="*80)
    # Test with Apple (AAPL)
    symbol = 'AAPL'
    print(f"\n{'='*80}")
    print(f"1. BASIC PRICE DATA - {symbol}")
    print("="*80)
    # DefeatBeta
    print("\n[DEFEATBETA-API]")
    print("-" * 40)
    db_ticker = Ticker(symbol)
    start = time.time()
    db_price = db_ticker.price()
    db_time = time.time() - start
    print(f"Method: ticker.price()")
    print(f"Type: {type(db_price).__name__}")
    print(f"Shape: {db_price.shape}")
    print(f"Columns: {list(db_price.columns)}")
    print(f"Sample data (latest 3 rows):")
    print(db_price.tail(3).to_string(index=False))
    print(f"Time taken: {db_time:.3f}s")
    # Yahoo Finance
    if YFINANCE_AVAILABLE:
        print("\n[YFINANCE]")
        print("-" * 40)
        start = time.time()
        yf_ticker = yf.Ticker(symbol)
        yf_price = yf_ticker.history(period="max")
        yf_time = time.time() - start
        print(f"Method: ticker.history(period='max')")
        print(f"Type: {type(yf_price).__name__}")
        print(f"Shape: {yf_price.shape}")
        print(f"Columns: {list(yf_price.columns)}")
        print(f"Sample data (latest 3 rows):")
        print(yf_price.tail(3).to_string(index=False))
        print(f"Time taken: {yf_time:.3f}s")
 def compare_financial_data():
    """Compare financial data structures"""
    print(f"\n{'='*80}")
    print("2. FINANCIAL STATEMENTS")
    print("="*80)
    symbol = 'MSFT'
    db_ticker = Ticker(symbol)
    print("\n[DEFEATBETA-API - Quarterly Income Statement]")
    print("-" * 40)
    income_stmt = db_ticker.quarterly_income_statement()
    df = income_stmt.df()
    print(f"Type: {type(income_stmt).__name__}")
    print(f"DataFrame shape: {df.shape}")
    print(f"Columns: {list(df.columns)}")
    print(f"\nBreakdown items available:")
    print(df['Breakdown'].head(15).tolist())
    print(f"\nKey metrics sample:")
    for metric in ['Total Revenue', 'Gross Profit', 'Net Income Common Stockholders']:
        if metric in df['Breakdown'].values:
            row = df[df['Breakdown'] == metric].iloc[0]
            print(f"  {metric}: TTM={row['TTM']}, Latest Q={row.iloc[-1]}")
    if YFINANCE_AVAILABLE:
        print("\n[YFINANCE - Quarterly Financials]")
        print("-" * 40)
        yf_ticker = yf.Ticker(symbol)
        yf_financials = yf_ticker.quarterly_financials
        print(f"Type: {type(yf_financials).__name__}")
        print(f"Shape: {yf_financials.shape}")
        print(f"Index (dates): {list(yf_financials.index[:3])}")
        print(f"Columns (sample): {list(yf_financials.columns[:5])}")
 def compare_valuation_metrics():
    """Compare valuation metrics"""
    print(f"\n{'='*80}")
    print("3. VALUATION METRICS")
    print("="*80)
    symbol = 'NVDA'
    db_ticker = Ticker(symbol)
    print("\n[DEFEATBETA-API - TTM EPS]")
    print("-" * 40)
    ttm_eps = db_ticker.ttm_eps()
    print(f"Type: {type(ttm_eps).__name__}")
    print(f"Shape: {ttm_eps.shape}")
    print(f"Columns: {list(ttm_eps.columns)}")
    print(f"\nLatest values:")
    print(ttm_eps.tail(3).to_string(index=False))
    print("\n[DEFEATBETA-API - TTM P/E]")
    print("-" * 40)
    ttm_pe = db_ticker.ttm_pe()
    print(f"Type: {type(ttm_pe).__name__}")
    print(f"Shape: {ttm_pe.shape}")
    print(f"Columns: {list(ttm_pe.columns)}")
    print(f"\nLatest values:")
    print(ttm_pe.tail(3).to_string(index=False))
    print("\n[DEFEATBETA-API - Market Cap]")
    print("-" * 40)
    market_cap = db_ticker.market_capitalization()
    print(f"Type: {type(market_cap).__name__}")
    print(f"Shape: {market_cap.shape}")
    print(f"Columns: {list(market_cap.columns)}")
    print(f"\nLatest values:")
    print(market_cap.tail(3).to_string(index=False))
 def compare_financial_ratios():
    """Compare financial ratios"""
    print(f"\n{'='*80}")
    print("4. FINANCIAL RATIOS")
    print("="*80)
    symbol = 'TSLA'
    db_ticker = Ticker(symbol)
    print("\n[DEFEATBETA-API - ROE]")
    print("-" * 40)
    roe = db_ticker.roe()
    print(f"Type: {type(roe).__name__}")
    print(f"Shape: {roe.shape}")
    print(f"Columns: {list(roe.columns)}")
    print(f"\nData:")
    print(roe.to_string(index=False))
    print("\n[DEFEATBETA-API - ROIC]")
    print("-" * 40)
    roic = db_ticker.roic()
    print(f"Type: {type(roic).__name__}")
    print(f"Shape: {roic.shape}")
    print(f"Columns: {list(roic.columns)}")
    print(f"\nData:")
    print(roic.to_string(index=False))
    print("\n[DEFEATBETA-API - WACC]")
    print("-" * 40)
    wacc = db_ticker.wacc()
    print(f"Type: {type(wacc).__name__}")
    print(f"Shape: {wacc.shape}")
    print(f"Columns: {list(wacc.columns)}")
    print(f"\nLatest values:")
    print(wacc.tail(3).to_string(index=False))
 def compare_special_features():
    """Compare special features"""
    print(f"\n{'='*80}")
    print("5. SPECIAL FEATURES - EARNINGS TRANSCRIPTS")
    print("="*80)
    symbol = 'AAPL'
    db_ticker = Ticker(symbol)
    print("\n[DEFEATBETA-API - Earnings Call Transcripts]")
    print("-" * 40)
    transcripts = db_ticker.earning_call_transcripts()
    transcript_list = transcripts.get_transcripts_list()
    print(f"Transcripts object type: {type(transcripts).__name__}")
    print(f"get_transcripts_list() type: {type(transcript_list).__name__}")
    print(f"Shape: {transcript_list.shape}")
    print(f"Columns: {list(transcript_list.columns)}")
    print(f"\nMost recent transcript:")
    recent = transcript_list.iloc[-1]
    print(f"  FY {recent['fiscal_year']} Q{recent['fiscal_quarter']} ({recent['report_date']})")
    print(f"  Transcript ID: {recent['transcripts_id']}")
    print(f"  Paragraphs in transcript: {len(recent['transcripts'])}")
    # Get specific transcript
    print("\n  Sample from Q4 2025 transcript:")
    q4_2025 = transcripts.get_transcript(2025, 4)
    if q4_2025 is not None and len(q4_2025) > 0:
        print(f"  Type: {type(q4_2025).__name__}")
        print(f"  Shape: {q4_2025.shape}")
        print(f"  Columns: {list(q4_2025.columns)}")
        print(f"  First 2 paragraphs:")
        for _, row in q4_2025.head(2).iterrows():
            speaker = row['speaker']
            content = row['content'][:100] + "..." if len(row['content']) > 100 else row['content']
            print(f"    {speaker}: {content}")
    print("\n[DEFEATBETA-API - Revenue by Segment]")
    print("-" * 40)
    revenue_seg = db_ticker.revenue_by_segment()
    print(f"Type: {type(revenue_seg).__name__}")
    print(f"Shape: {revenue_seg.shape}")
    print(f"Columns: {list(revenue_seg.columns)}")
    print(f"\nLatest data:")
    print(revenue_seg.tail(3).to_string(index=False))
    print("\n[DEFEATBETA-API - Revenue by Geography]")
    print("-" * 40)
    revenue_geo = db_ticker.revenue_by_geography()
    print(f"Type: {type(revenue_geo).__name__}")
    if hasattr(revenue_geo, 'shape'):
        print(f"Shape: {revenue_geo.shape}")
        print(f"Columns: {list(revenue_geo.columns)}")
        print(f"\nLatest data:")
        print(revenue_geo.tail(3).to_string(index=False))
    else:
        print(f"Data: {revenue_geo}")
 def show_all_available_methods():
    """Show all available methods in Ticker class"""
    print(f"\n{'='*80}")
    print("6. COMPLETE API METHOD REFERENCE")
    print("="*80)
    db_ticker = Ticker('AAPL')
    print("\n[DEFEATBETA-API - All Ticker Methods]")
    print("-" * 40)
    all_methods = [m for m in dir(db_ticker) if not m.startswith('_')]
    categories = {
        'Price & Volume': ['price'],
        'Valuation': ['ttm_eps', 'ttm_pe', 'market_capitalization', 'ps_ratio', 'pb_ratio', 'peg_ratio'],
        'Financial Ratios': ['roe', 'roic', 'roa', 'wacc', 'beta', 'equity_multiplier', 'asset_turnover'],
        'Income Statement': ['quarterly_income_statement', 'annual_income_statement'],
        'Balance Sheet': ['quarterly_balance_sheet', 'annual_balance_sheet'],
        'Cash Flow': ['quarterly_cash_flow', 'annual_cash_flow'],
        'Growth Metrics': [m for m in all_methods if 'yoy_growth' in m.lower()],
        'Margin Metrics': [m for m in all_methods if 'margin' in m.lower()],
        'Special Data': ['earning_call_transcripts', 'news', 'sec_filing', 'dividends', 'splits'],
        'Revenue Breakdown': ['revenue_by_segment', 'revenue_by_product', 'revenue_by_geography'],
        'Industry Metrics': [m for m in all_methods if 'industry' in m.lower()],
        'Info & Calendar': ['info', 'calendar', 'currency', 'shares', 'officers']
    }
    for category, methods in categories.items():
        matching = [m for m in methods if m in all_methods]
        if matching:
            print(f"\n  {category}:")
            for method in sorted(matching):
                print(f"    • ticker.{method}()")
 def compare_data_reliability():
    """Compare data reliability and availability"""
    print(f"\n{'='*80}")
    print("7. DATA RELIABILITY COMPARISON")
    print("="*80)
    print("\n[DEFEATBETA-API Advantages]")
    print("-" * 40)
    print("✓ Data hosted on Hugging Face - no rate limits")
    print("✓ DuckDB engine for fast queries")
    print("✓ Sub-second query performance")
    print("✓ Consistent data format across all endpoints")
    print("✓ Historical data from 1990s for most stocks")
    print("✓ SEC filings, earnings transcripts available")
    print("✓ Revenue segmentation (by product/geography)")
    print("✓ Automated DCF valuation with Excel output")
    print("✓ LLM-powered analysis capabilities")
    print("\n[YFINANCE Advantages]")
    print("-" * 40)
    print("✓ Real-time data (delayed by 15min)")
    print("✓ Larger ecosystem and community support")
    print("✓ More extensive options/derivatives data")
    print("✓ Institutional ownership data")
    print("✓ Analyst recommendations and price targets")
    print("✓ Splits and dividends detailed history")
 def demonstrate_unique_features():
    """Demonstrate features unique to defeatbeta-api"""
    print(f"\n{'='*80}")
    print("8. UNIQUE DEFEATBETA-API FEATURES")
    print("="*80)
    print("\n[DCF Valuation]")
    print("-" * 40)
    db_ticker = Ticker('AAPL')
    try:
        dcf_result = db_ticker.dcf()
        print(f"DCF method returns: {type(dcf_result).__name__}")
        if isinstance(dcf_result, dict):
            print(f"Keys: {list(dcf_result.keys())}")
        print("(Note: DCF generates professional Excel output with WACC, cash flow projections, fair price)")
    except Exception as e:
        print(f"DCF error: {type(e).__name__}")
    print("\n[AI-Powered Analysis on Earnings Transcripts]")
    print("-" * 40)
    transcripts = db_ticker.earning_call_transcripts()
    ai_methods = [m for m in dir(transcripts) if 'ai' in m.lower() or 'analyze' in m.lower()]
    print(f"Available AI methods:")
    for method in ai_methods:
        print(f"  • transcripts.{method}()")
 def main():
    """Main function"""
    print("DEFEATBETA-API vs YFINANCE - COMPREHENSIVE COMPARISON")
    print("="*80)
    compare_data_structures()
    compare_financial_data()
    compare_valuation_metrics()
    compare_financial_ratios()
    compare_special_features()
    show_all_available_methods()
    compare_data_reliability()
    demonstrate_unique_features()
    print(f"\n{'='*80}")
    print("SUMMARY")
    print("="*80)
    print("""
 DEFEATBETA-API:
  • Best for: Historical analysis, financial modeling, backtesting
  • Strengths: No rate limits, comprehensive fundamentals, fast queries
  • Data: Time-series heavy, quarterly/annual financial statements
  • Unique: Revenue segmentation, WACC calculations, DCF automation
 YFINANCE:
  • Best for: Real-time data, market scanning, quick lookups
  • Strengths: Real-time prices, analyst data, institutional ownership
  • Data: Mixed frequency, real-time + historical
  • Unique: Analyst recommendations, options data, recommendations
    """)
 if __name__ == "__main__":
    main()
@@ -0,0 +1,291 @@
 # DEFEATBETA-API vs YFINANCE MAPPING
 # A comprehensive guide for migrating from Yahoo Finance to DefeatBeta-API
 * DEFEATBETA-API vs YFINANCE MAPPING                        :noexport:
  This document provides a mapping between Yahoo Finance (yfinance)
  and DefeatBeta-API methods and attributes.
 # Overview
 | Category | Yahoo Finance | DefeatBeta-API | Notes |
 |----------|--------------|----------------|-------|
 | **Data Source** | Yahoo Finance API | HuggingFace + DuckDB | No rate limits |
 | **Query Engine** | Direct API | DuckDB OLAP | Sub-second queries |
 | **Update Frequency** | Real-time (15min delayed) | Daily batch | DefeatBeta is historical only |
 | **Historical Depth** | Full history | Full history | Comparable coverage |
 | **Special Features** | Limited | Earnings transcripts, DCF, AI | DefeatBeta has unique capabilities |
 * Price & Volume Data
 | Yahoo Finance | DefeatBeta-API | Return Type | Notes |
 |--------------|----------------|-------------|-------|
 | ~ticker.history(period='max')~ | ~ticker.price()~ | pandas.DataFrame | OHLCV data |
 | ~ticker.history(period='1d')~ | N/A | - | Real-time not available |
 | ~ticker.history(start='...', end='...')~ | ~ticker.price()~ (filter) | pandas.DataFrame | Date filtering available |
 * DefeatBeta Price Data Structure
 #+BEGIN_SRC python
 # Columns: ['symbol', 'report_date', 'open', 'close', 'high', 'low', 'volume']
 # Example:
     symbol report_date    open   close    high     low     volume
 0    AAPL  2026-04-17  266.96  270.23  272.30  266.72  61314800
 #+END_SRC
 * Financial Statements
 | Yahoo Finance | DefeatBeta-API | Return Type | Notes |
 |--------------|----------------|-------------|-------|
 | ~ticker.quarterly_financials~ | ~ticker.quarterly_income_statement()~ | Statement object | Different format |
 | ~ticker.financials~ | ~ticker.annual_income_statement()~ | Statement object | Annual version |
 | ~ticker.quarterly_balance_sheet~ | ~ticker.quarterly_balance_sheet()~ | Statement object | Same structure |
 | ~ticker.balance_sheet~ | ~ticker.annual_balance_sheet()~ | Statement object | Annual version |
 | ~ticker.quarterly_cashflow~ | ~ticker.quarterly_cash_flow()~ | Statement object | Note: 'flow' vs 'flow' |
 | ~ticker.cashflow~ | ~ticker.annual_cash_flow()~ | Statement object | Annual version |
 ** Statement Object Methods
 #+BEGIN_SRC python
 # DefeatBeta Statement objects have these methods:
 income_stmt = ticker.quarterly_income_statement()
 income_stmt.df()              # Returns pandas.DataFrame
 income_stmt.data()           # Alternative access
 income_stmt.print_pretty_table()  # Formatted output
 #+END_SRC
 * Valuation Metrics
 | Yahoo Finance | DefeatBeta-API | Return Type | Notes |
 |--------------|----------------|-------------|-------|
 | ~ticker.info['trailingPE']~ | ~ticker.ttm_pe()~ | pandas.DataFrame | **Historical** time series! |
 | ~ticker.info['forwardPE']~ | N/A | - | Not available |
 | ~ticker.info['trailingEps']~ | ~ticker.ttm_eps()~ | pandas.DataFrame | **Historical** time series! |
 | ~ticker.info['forwardEps']~ | N/A | - | Not available |
 | ~ticker.info['marketCap']~ | ~ticker.market_capitalization()~ | pandas.DataFrame | **Historical** time series! |
 | ~ticker.info['priceToBook']~ | ~ticker.pb_ratio()~ | pandas.DataFrame | Price/Book ratio |
 | ~ticker.info['priceToSalesTrailing12Months']~ | ~ticker.ps_ratio()~ | pandas.DataFrame | Price/Sales ratio |
 | N/A | ~ticker.peg_ratio()~ | pandas.DataFrame | PEG ratio (unique) |
 ** Key Advantage: Historical Valuation Data
 DefeatBeta provides **full historical time series** for:
 - TTM P/E ratios (all daily closes)
 - TTM EPS history
 - Market cap history
 - Price/Book, Price/Sales ratios
 Yahoo Finance only provides **current values** in ~.info~
 * Financial Ratios
 | Yahoo Finance | DefeatBeta-API | Return Type | Notes |
 |--------------|----------------|-------------|-------|
 | ~ticker.info['returnOnEquity']~ | ~ticker.roe()~ | pandas.DataFrame | **Historical** time series! |
 | ~ticker.info['returnOnAssets']~ | ~ticker.roa()~ | pandas.DataFrame | **Historical** time series! |
 | N/A | ~ticker.roic()~ | pandas.DataFrame | Return on Invested Capital |
 | N/A | ~ticker.wacc()~ | pandas.DataFrame | Weighted Avg Cost of Capital |
 | ~ticker.info['beta']~ | ~ticker.beta()~ | pandas.DataFrame | 5Y monthly beta |
 ** WACC Components Available in DefeatBeta
 #+BEGIN_SRC python
 # Full breakdown of WACC calculation:
 wacc = ticker.wacc()
 # Columns: market_capitalization, beta_5y, sp500_10y_cagr, treasure_10y_yield,
 #          weight_of_debt, weight_of_equity, cost_of_debt, cost_of_equity, wacc
 #+END_SRC
 * Growth Metrics
 | Yahoo Finance | DefeatBeta-API | Return Type | Notes |
 |--------------|----------------|-------------|-------|
 | ~ticker.info['revenueGrowth']~ | ~ticker.quarterly_revenue_yoy_growth()~ | pandas.DataFrame | YoY growth |
 | ~ticker.info['earningsGrowth']~ | ~ticker.quarterly_eps_yoy_growth()~ | pandas.DataFrame | EPS YoY growth |
 | N/A | ~ticker.quarterly_net_income_yoy_growth()~ | pandas.DataFrame | Net income growth |
 | N/A | ~ticker.quarterly_operating_income_yoy_growth()~ | pandas.DataFrame | Operating income growth |
 | N/A | ~ticker.quarterly_ebitda_yoy_growth()~ | pandas.DataFrame | EBITDA growth |
 | N/A | ~ticker.quarterly_fcf_yoy_growth()~ | pandas.DataFrame | Free cash flow growth |
 | N/A | ~ticker.annual_revenue_yoy_growth()~ | pandas.DataFrame | Annual revenue growth |
 * Margin Metrics
 | Yahoo Finance | DefeatBeta-API | Return Type | Notes |
 |--------------|----------------|-------------|-------|
 | ~ticker.info['profitMargins']~ | ~ticker.quarterly_net_margin()~ | pandas.DataFrame | **Historical** time series! |
 | ~ticker.info['grossMargins']~ | ~ticker.quarterly_gross_margin()~ | pandas.DataFrame | **Historical** time series! |
 | ~ticker.info['operatingMargins']~ | ~ticker.quarterly_operating_margin()~ | pandas.DataFrame | **Historical** time series! |
 | N/A | ~ticker.quarterly_ebitda_margin()~ | pandas.DataFrame | EBITDA margin |
 | N/A | ~ticker.quarterly_fcf_margin()~ | pandas.DataFrame | Free cash flow margin |
 | N/A | ~ticker.industry_quarterly_gross_margin()~ | pandas.DataFrame | Industry comparison |
 * Dividends & Stock Splits
 | Yahoo Finance | DefeatBeta-API | Return Type | Notes |
 |--------------|----------------|-------------|-------|
 | ~ticker.dividends~ | ~ticker.dividends()~ | pandas.DataFrame | Dividend history |
 | ~ticker.splits~ | ~ticker.splits()~ | pandas.DataFrame | Stock split history |
 | ~ticker.info['dividendYield']~ | N/A | - | Not in separate field |
 | ~ticker.info['trailingAnnualDividendYield']~ | N/A | - | Not available |
 * Company Info & Metadata
 | Yahoo Finance | DefeatBeta-API | Return Type | Notes |
 |--------------|----------------|-------------|-------|
 | ~ticker.info~ | ~ticker.info()~ | pandas.DataFrame | One row, many columns |
 | ~ticker.info['longName']~ | ~ticker.info()['long_business_summary']~ | string | Business summary |
 | ~ticker.info['sector']~ | ~ticker.info()['sector']~ | string | Sector classification |
 | ~ticker.info['industry']~ | ~ticker.info()['industry']~ | string | Industry classification |
 | ~ticker.info['website']~ | ~ticker.info()['web_site']~ | string | Company website |
 | ~ticker.info['fullTimeEmployees']~ | ~ticker.info()['full_time_employees']~ | int | Employee count |
 | N/A | ~ticker.officers()~ | pandas.DataFrame | Company officers |
 | N/A | ~ticker.calendar()~ | pandas.DataFrame | Earnings calendar |
 * Unique DefeatBeta Features (Not in Yahoo Finance)
 | Feature | Method | Description |
 |---------|--------|-------------|
 | **Earnings Transcripts** | ~ticker.earning_call_transcripts()~ | Full earnings call transcripts |
 | | ~transcripts.get_transcripts_list()~ | List all available transcripts |
 | | ~transcripts.get_transcript(year, quarter)~ | Get specific quarter's transcript |
 | | ~transcripts.summarize_key_financial_data_with_ai()~ | AI-powered summary |
 | **Revenue Breakdown** | ~ticker.revenue_by_segment()~ | Revenue by product segment |
 | | ~ticker.revenue_by_product()~ | Detailed product breakdown |
 | | ~ticker.revenue_by_geography()~ | Revenue by geographic region |
 | **Automated DCF** | ~ticker.dcf()~ | Generates Excel DCF valuation |
 | **AI Analysis** | ~transcripts.analyze_financial_metrics_change...~ | LLM analysis of quarter changes |
 | | ~transcripts.analyze_financial_metrics_forecast...~ | LLM forecast analysis |
 | **Industry Metrics** | ~ticker.industry_ttm_pe()~ | Industry P/E comparison |
 | | ~ticker.industry_roe()~ | Industry ROE comparison |
 | | ~ticker.industry_quarterly_gross_margin()~ | Industry margin comparison |
 * Example: yfinance → DefeatBeta Migration
 ** Yahoo Finance Style
 #+BEGIN_SRC python
 import yfinance as yf
 ticker = yf.Ticker('AAPL')
 # Current valuation
 pe = ticker.info['trailingPE']
 eps = ticker.info['trailingEps']
 market_cap = ticker.info['marketCap']
 # Financial statements
 quarterly_financials = ticker.quarterly_financials
 quarterly_balance = ticker.quarterly_balance_sheet
 # Growth
 revenue_growth = ticker.info['revenueGrowth']
 #+END_SRC
 ** DefeatBeta Equivalent
 #+BEGIN_SRC python
 from defeatbeta_api.data.ticker import Ticker
 ticker = Ticker('AAPL')
 # Historical valuation time series
 ttm_pe = ticker.ttm_pe()         # Full daily P/E history
 ttm_eps = ticker.ttm_eps()       # Full EPS history
 market_cap = ticker.market_capitalization()  # Daily market cap
 # Financial statements
 quarterly_income = ticker.quarterly_income_statement()
 quarterly_balance = ticker.quarterly_balance_sheet()
 # Convert to DataFrame
 income_df = quarterly_income.df()
 # Growth metrics (time series!)
 revenue_growth = ticker.quarterly_revenue_yoy_growth()
 eps_growth = ticker.quarterly_eps_yoy_growth()
 #+END_SRC
 ** Getting Current Values from DefeatBeta
 #+BEGIN_SRC python
 # DefeatBeta provides time series, but getting current value is easy:
 current_pe = ticker.ttm_pe().iloc[-1]['ttm_pe']
 current_eps = ticker.ttm_eps().iloc[-1]['tailing_eps']
 current_mcap = ticker.market_capitalization().iloc[-1]['market_capitalization']
 current_roe = ticker.roe().iloc[-1]['roe']
 #+END_SRC
 * Quick Reference: Common Operations
 | Operation | Yahoo Finance | DefeatBeta-API |
 |-----------|--------------|----------------|
 | Get current price | ~ticker.info['currentPrice']~ | ~ticker.price().iloc[-1]['close']~ |
 | Get current P/E | ~ticker.info['trailingPE']~ | ~ticker.ttm_pe().iloc[-1]['ttm_pe']~ |
 | Get current EPS | ~ticker.info['trailingEps']~ | ~ticker.ttm_eps().iloc[-1]['tailing_eps']~ |
 | Get market cap | ~ticker.info['marketCap']~ | ~ticker.market_capitalization().iloc[-1]['market_capitalization']~ |
 | Get ROE | ~ticker.info['returnOnEquity']~ | ~ticker.roe().iloc[-1]['roe']~ |
 | Get revenue (TTM) | ~ticker.info['totalRevenue']~ | ~ticker.quarterly_income_statement().df().iloc[0]['TTM']~ |
 | Get 5Y beta | ~ticker.info['beta']~ | ~ticker.beta().iloc[-1]['beta']~ |
 * Data Type Differences
 | Aspect | Yahoo Finance | DefeatBeta-API |
 |--------|--------------|----------------|
 | Dates in DataFrame | DatetimeIndex | 'report_date' column |
 | Column naming | Title case (Open, Close) | Snake case (open, close) |
 | Dividends/Splits | Separate columns in history | Separate DataFrames |
 | Quarterly data | Transposed (dates as columns) | Wide format (TTM + quarters as columns) |
 | Missing data | NaN | NaN |
 | Numeric types | float | Decimal (convert with ~float()~) |
 * Converting Decimal to Float
 #+BEGIN_SRC python
 # DefeatBeta returns Decimal types for financial data
 # Always convert before arithmetic operations
 value = ticker.ttm_eps().iloc[-1]['tailing_eps']
 value_float = float(value)  # Convert Decimal → float
 # Use in calculations
 market_cap_billions = float(market_cap.iloc[-1]['market_capitalization']) / 1e9
 #+END_SRC
 * When to Use Each
 | Use Case | Recommendation | Reason |
 |----------|---------------|--------|
 | Backtesting trading strategies | **DefeatBeta** | No rate limits, consistent historical data |
 | DCF Valuation modeling | **DefeatBeta** | Automated Excel output |
 | Revenue segment analysis | **DefeatBeta** | Unique revenue breakdown |
 | Earnings call research | **DefeatBeta** | Full transcripts available |
 | Real-time price alerts | **Yahoo Finance** | 15min delayed but real-time |
 | Analyst recommendations | **Yahoo Finance** | Price targets, ratings |
 | Institutional ownership | **Yahoo Finance** | Major holders data |
 | Options/derivatives data | **Yahoo Finance** | Not available in DefeatBeta |
 | Quick stock lookup | **Either** | Both work well |
 | Multi-year historical analysis | **DefeatBeta** | Faster queries, no rate limits |
 * Environment Setup
 | Task | Command |
 |------|---------|
 | Install DefeatBeta | ~uv add defeatbeta-api~ |
 | Install yfinance | ~uv add yfinance~ |
 | Run notebook | ~uv run jupyter notebook~ |
 | Check version | ~ticker.price()~ shows data update date |
 * Common Issues & Solutions
 | Issue | Solution |
 |-------|----------|
 | ~TypeError: unsupported operand type(s) for /: 'Decimal' and 'float'~ | Wrap values in ~float()~ |
 | ~Rate limit exceeded~ | Switch to DefeatBeta |
 | ~Real-time data needed~ | Use Yahoo Finance |
 | ~Missing revenue breakdown~ | Use DefeatBeta ~revenue_by_segment()~ |
 | ~Slow queries~ | Use DefeatBeta (DuckDB engine) |
 * Additional Resources
 - DefeatBeta-API: https://github.com/defeat-beta/defeatbeta-api
 - Documentation: https://github.com/defeat-beta/defeatbeta-api/tree/main/doc
 - DCF Examples: https://github.com/defeat-beta/defeatbeta-api/tree/main/doc/api/DCF_Examples.md
 - Value Examples: https://github.com/defeat-beta/defeatbeta-api/tree/main/doc/api/Value_Examples.md
 * Footer
 #+BEGIN_COMMENT
 Last updated: 2026-04-25
 Author: Documentation
 Version: 1.0
 #+END_COMMENT
@@ -0,0 +1,147 @@
 #!/usr/bin/env python3
 """
 Explore defeatbeta-api data types and structure
 """
 from defeatbeta_api.data.ticker import Ticker
 def explore_data_types():
    """Explore the different data types returned by the API"""
    print("EXPLORING DEFEATBETA-API DATA TYPES")
    print("="*70)
    # Test with Microsoft
    msft = Ticker('MSFT')
    # 1. Explore Statement object
    print("\n1. STATEMENT OBJECT EXPLORATION:")
    print("-" * 40)
    income_stmt = msft.quarterly_income_statement()
    print(f"Type: {type(income_stmt)}")
    print(f"Available methods: {[m for m in dir(income_stmt) if not m.startswith('_')]}")
    # Try to get the data
    try:
        print("\nTrying income_stmt.data():")
        data_result = income_stmt.data()
        print(f"Result type: {type(data_result)}")
        if hasattr(data_result, 'shape'):
            print(f"Shape: {data_result.shape}")
    except Exception as e:
        print(f"data() method error: {type(e).__name__}: {e}")
    try:
        print("\nTrying income_stmt.df():")
        df_result = income_stmt.df()
        print(f"Result type: {type(df_result)}")
        if hasattr(df_result, 'shape'):
            print(f"Shape: {df_result.shape}")
            print(f"Columns: {list(df_result.columns)}")
            print(f"\nFirst few breakdown items:")
            print(df_result['Breakdown'].head(10).tolist())
    except Exception as e:
        print(f"df() method error: {type(e).__name__}: {e}")
    # 2. Explore News object
    print("\n\n2. NEWS OBJECT EXPLORATION:")
    print("-" * 40)
    news = msft.news()
    print(f"Type: {type(news)}")
    print(f"Available methods: {[m for m in dir(news) if not m.startswith('_')]}")
    # 3. Explore Transcripts object
    print("\n\n3. TRANSCRIPTS OBJECT EXPLORATION:")
    print("-" * 40)
    transcripts = msft.earning_call_transcripts()
    print(f"Type: {type(transcripts)}")
    print(f"Available methods: {[m for m in dir(transcripts) if not m.startswith('_')]}")
    # Get transcripts list
    try:
        transcript_list = transcripts.get_transcripts_list()
        print(f"\nget_transcripts_list() type: {type(transcript_list)}")
        if hasattr(transcript_list, 'shape'):
            print(f"Shape: {transcript_list.shape}")
            print(f"Columns: {list(transcript_list.columns)}")
            print(f"\nMost recent transcript: FY{transcript_list.iloc[-1]['fiscal_year']} Q{transcript_list.iloc[-1]['fiscal_quarter']}")
    except Exception as e:
        print(f"get_transcripts_list() error: {type(e).__name__}: {e}")
    # 4. Test practical data retrieval
    print("\n\n4. PRACTICAL DATA RETRIEVAL:")
    print("-" * 40)
    # Get recent financials
    print("Recent quarterly metrics for MSFT:")
    # Price
    price = msft.price()
    print(f"\nPrice data: {len(price)} rows, from {price.iloc[0]['report_date']} to {price.iloc[-1]['report_date']}")
    # TTM metrics
    ttm_eps = msft.ttm_eps()
    print(f"TTM EPS: Latest = ${ttm_eps.iloc[-1]['tailing_eps']:.2f}")
    # Growth
    growth = msft.quarterly_revenue_yoy_growth()
    if not growth.empty:
        print(f"Revenue YoY Growth: {growth.iloc[-1]['yoy_growth']:.2%}")
    # Ratios
    ratios = msft.roe()
    if not ratios.empty:
        print(f"ROE: {ratios.iloc[-1]['roe']:.2%}")
    # Beta
    beta = msft.beta()
    if not beta.empty:
        print(f"Beta: {beta.iloc[-1]['beta']:.2f}")
 def test_different_companies():
    """Test API with different types of companies"""
    print("\n\n5. TESTING DIFFERENT COMPANIES:")
    print("="*70)
    companies = [
        ('GOOGL', 'Alphabet', 'Tech'),
        ('JPM', 'JPMorgan Chase', 'Financial'),
        ('JNJ', 'Johnson & Johnson', 'Healthcare'),
        ('XOM', 'Exxon Mobil', 'Energy')
    ]
    for symbol, name, sector in companies:
        print(f"\n{symbol} - {name} ({sector}):")
        print("-" * 30)
        try:
            ticker = Ticker(symbol)
            # Quick stats
            price = ticker.price()
            if not price.empty:
                print(f"  Price: ${price.iloc[-1]['close']:.2f}")
            ttm_pe = ticker.ttm_pe()
            if not ttm_pe.empty:
                print(f"  P/E: {ttm_pe.iloc[-1]['ttm_pe']:.2f}")
            market_cap = ticker.market_capitalization()
            if not market_cap.empty:
                mcap = market_cap.iloc[-1]['market_capitalization']
                print(f"  Market Cap: ${mcap/1e9:.1f}B")
            roe = ticker.roe()
            if not roe.empty:
                print(f"  ROE: {roe.iloc[-1]['roe']:.2%}")
        except Exception as e:
            print(f"  Error: {type(e).__name__}")
 if __name__ == "__main__":
    explore_data_types()
    test_different_companies()
@@ -40,6 +40,7 @@ dependencies = [
    "vectorbt>=1.0.0",
    "xgboost>=3.2.0",
    "yfinance>=1.3.0",
    "defeatbeta-api>=0.0.45",
 ]
 [[tool.uv.index]]
@@ -0,0 +1,45 @@
 #!/bin/bash
 # Run the DefeatBeta tutorial notebook with proper environment
 echo "📚 DefeatBeta-API Tutorial Notebook"
 echo "===================================="
 echo ""
 echo "This script will open the tutorial notebook with proper uv environment."
 echo ""
 # Check if jupyter is available
 if ! command -v jupyter &> /dev/null; then
    echo "❌ Jupyter not found. Installing..."
    uv add jupyter
 fi
 # Check if the notebook exists
 if [ ! -f "defeatbeta_tutorial.ipynb" ]; then
    echo "❌ Notebook not found: defeatbeta_tutorial.ipynb"
    exit 1
 fi
 echo "✅ Environment ready"
 echo ""
 echo "Starting Jupyter notebook..."
 echo ""
 echo "📝 Contents:"
 echo "   1. Quick Performance Test"
 echo "   2. Price Data Comparison"
 echo "   3. Valuation Metrics"
 echo "   4. Financial Statements"
 echo "   5. Financial Ratios"
 echo "   6. Growth & Margin Metrics"
 echo "   7. Earnings Transcripts (Unique!)"
 echo "   8. Revenue Breakdown (Unique!)"
 echo "   9. DCF Valuation (Unique!)"
 echo "   10. Interactive Stock Analysis"
 echo "   11. Stock Comparison"
 echo "   12. Complete Method Reference"
 echo ""
 echo "🚀 Launching notebook..."
 echo ""
 # Run jupyter with uv
 cd /home/df/scratch/trading/learn-trading
 uv run jupyter notebook defeatbeta_tutorial.ipynb
@@ -0,0 +1,203 @@
 #!/usr/bin/env python3
 """
 Test script for defeatbeta-api demonstrating practical use cases
 """
 import pandas as pd
 from defeatbeta_api.data.ticker import Ticker
 def analyze_stock(ticker_symbol):
    """Analyze a stock using defeatbeta-api"""
    print(f"\n{'='*60}")
    print(f"ANALYZING {ticker_symbol}")
    print(f"{'='*60}")
    # Create ticker instance
    ticker = Ticker(ticker_symbol)
    # 1. Get basic price and valuation
    print("\n1. PRICE AND VALUATION:")
    price_data = ticker.price()
    latest_price = price_data.iloc[-1]
    print(f"   Latest price: ${latest_price['close']:.2f}")
    print(f"   Date: {latest_price['report_date']}")
    print(f"   Volume: {latest_price['volume']:,}")
    # 2. Get TTM metrics
    ttm_eps = ticker.ttm_eps()
    if not ttm_eps.empty:
        latest_eps = ttm_eps.iloc[-1]['tailing_eps']
        print(f"\n2. TTM METRICS:")
        print(f"   TTM EPS: ${latest_eps:.2f}")
    ttm_pe = ticker.ttm_pe()
    if not ttm_pe.empty:
        latest_pe = ttm_pe.iloc[-1]['ttm_pe']
        print(f"   TTM P/E: {latest_pe:.2f}")
    # 3. Get market cap
    market_cap = ticker.market_capitalization()
    if not market_cap.empty:
        latest_mcap = market_cap.iloc[-1]['market_capitalization']
        print(f"   Market Cap: ${latest_mcap:,.2f}")
    # 4. Get financial ratios
    print(f"\n3. FINANCIAL RATIOS:")
    roe = ticker.roe()
    if not roe.empty:
        latest_roe = roe.iloc[-1]['roe']
        print(f"   ROE: {latest_roe:.2%}")
    roic = ticker.roic()
    if not roic.empty:
        latest_roic = roic.iloc[-1]['roic']
        print(f"   ROIC: {latest_roic:.2%}")
    wacc = ticker.wacc()
    if not wacc.empty:
        latest_wacc = wacc.iloc[-1]['wacc']
        print(f"   WACC: {latest_wacc:.2%}")
    # 5. Get beta
    beta_data = ticker.beta()
    if not beta_data.empty:
        latest_beta = beta_data.iloc[-1]['beta']
        print(f"   Beta (5Y): {latest_beta:.2f}")
    # 6. Get growth metrics
    print(f"\n4. GROWTH METRICS:")
    revenue_growth = ticker.quarterly_revenue_yoy_growth()
    if not revenue_growth.empty:
        latest_rev_growth = revenue_growth.iloc[-1]['yoy_growth']
        print(f"   Quarterly Revenue YoY Growth: {latest_rev_growth:.2%}")
    eps_growth = ticker.quarterly_eps_yoy_growth()
    if not eps_growth.empty:
        latest_eps_growth = eps_growth.iloc[-1]['yoy_growth']
        print(f"   Quarterly EPS YoY Growth: {latest_eps_growth:.2%}")
    # 7. Get margin metrics
    print(f"\n5. MARGIN METRICS:")
    gross_margin = ticker.quarterly_gross_margin()
    if not gross_margin.empty:
        latest_gross_margin = gross_margin.iloc[-1]['gross_margin']
        print(f"   Gross Margin: {latest_gross_margin:.2%}")
    net_margin = ticker.quarterly_net_margin()
    if not net_margin.empty:
        latest_net_margin = net_margin.iloc[-1]['net_margin']
        print(f"   Net Margin: {latest_net_margin:.2%}")
    # 8. Get income statement summary
    print(f"\n6. INCOME STATEMENT SUMMARY (Latest Quarter):")
    income_stmt = ticker.quarterly_income_statement()
    try:
        income_df = income_stmt.df()  # df is a method, not a property
        # Get key metrics
        key_metrics = ['Total Revenue', 'Gross Profit', 'Operating Income', 
                      'Net Income Common Stockholders', 'Diluted EPS']
        for metric in key_metrics:
            if metric in income_df['Breakdown'].values:
                row = income_df[income_df['Breakdown'] == metric]
                if not row.empty:
                    latest_q = row.iloc[0]['TTM']  # TTM column has latest
                    print(f"   {metric}: ${latest_q/1e6:,.2f}M" if 'Revenue' in metric or 'Profit' in metric or 'Income' in metric else f"   {metric}: ${latest_q:.2f}")
    except Exception as e:
        print(f"   Could not retrieve income statement details: {type(e).__name__}")
    # 9. Get revenue breakdown
    print(f"\n7. REVENUE BREAKDOWN (Latest):")
    revenue_segment = ticker.revenue_by_segment()
    if not revenue_segment.empty:
        latest_rev = revenue_segment.iloc[-1]
        print(f"   Total Revenue: ${latest_rev.iloc[2:].sum():,.0f}")
        for col in revenue_segment.columns[2:]:  # Skip symbol and report_date
            if col in latest_rev:
                value = latest_rev[col]
                if pd.notna(value):
                    print(f"   {col}: ${value:,.0f}")
    return ticker
 def compare_stocks(tickers):
    """Compare multiple stocks"""
    print(f"\n{'='*60}")
    print(f"COMPARING STOCKS: {', '.join(tickers)}")
    print(f"{'='*60}")
    comparison_data = []
    for symbol in tickers:
        ticker = Ticker(symbol)
        # Get key metrics
        metrics = {'Symbol': symbol}
        # Price
        price_data = ticker.price()
        if not price_data.empty:
            metrics['Price'] = price_data.iloc[-1]['close']
        # TTM P/E
        ttm_pe = ticker.ttm_pe()
        if not ttm_pe.empty:
            metrics['P/E'] = ttm_pe.iloc[-1]['ttm_pe']
        # Market Cap
        market_cap = ticker.market_capitalization()
        if not market_cap.empty:
            metrics['Market Cap'] = market_cap.iloc[-1]['market_capitalization']
        # ROE
        roe = ticker.roe()
        if not roe.empty:
            metrics['ROE'] = roe.iloc[-1]['roe']
        # Revenue Growth
        rev_growth = ticker.quarterly_revenue_yoy_growth()
        if not rev_growth.empty:
            metrics['Rev Growth'] = rev_growth.iloc[-1]['yoy_growth']
        comparison_data.append(metrics)
    # Create comparison table
    df = pd.DataFrame(comparison_data)
    df.set_index('Symbol', inplace=True)
    # Format the table
    print(f"\n{df.to_string(float_format=lambda x: f'{x:,.2f}' if isinstance(x, (int, float)) else str(x))}")
 def main():
    """Main function"""
    print("DEFEATBETA-API DEMONSTRATION")
    print("="*60)
    # Analyze individual stocks
    stocks = ['NVDA', 'TSLA', 'AAPL']
    for stock in stocks[:2]:  # Just analyze first 2 to keep output manageable
        analyze_stock(stock)
    # Compare all stocks
    compare_stocks(stocks)
    print(f"\n{'='*60}")
    print("API FEATURES DEMONSTRATED:")
    print("1. Price and volume data")
    print("2. TTM EPS and P/E ratios")
    print("3. Market capitalization")
    print("4. Financial ratios (ROE, ROIC, WACC)")
    print("5. Beta calculation")
    print("6. Growth metrics (Revenue, EPS YoY)")
    print("7. Margin analysis")
    print("8. Income statement summary")
    print("9. Revenue segmentation")
    print("10. Multi-stock comparison")
    print(f"{'='*60}")
 if __name__ == "__main__":
    main()
@@ -140,6 +140,15 @@ wheels = [
    { url = "https://files.pythonhosted.org/packages/1e/d3/26bf1008eb3d2daa8ef4cacc7f3bfdc11818d111f7e2d0201bc6e3b49d45/annotated_doc-0.0.4-py3-none-any.whl", hash = "sha256:571ac1dc6991c450b25a9c2d84a3705e2ae7a53467b5d111c24fa8baabbed320", size = 5303, upload-time = "2025-11-10T22:07:40.673Z" },
 ]
 [[package]]
 name = "annotated-types"
 version = "0.7.0"
 source = { registry = "https://pypi.org/simple" }
 sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081, upload-time = "2024-05-20T21:33:25.928Z" }
 wheels = [
    { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643, upload-time = "2024-05-20T21:33:24.1Z" },
 ]
 [[package]]
 name = "anyio"
 version = "4.13.0"
@@ -176,6 +185,23 @@ wheels = [
    { url = "https://files.pythonhosted.org/packages/81/29/5ecc3a15d5a33e31b26c11426c45c501e439cb865d0bff96315d86443b78/appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c", size = 4321, upload-time = "2024-02-06T09:43:09.663Z" },
 ]
 [[package]]
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