#!/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()