src/db/examples.py

"""
Example usage of the Local Database system
Demonstrates integration with all modules
"""

from datetime import datetime, timedelta
from db.local_database import LocalDatabase, DatabaseEntry, DataType
from db.adapters import (
    CalendarAdapter, 
    FundamentalAdapter, 
    NewsAdapter, 
    TechnicalAnalysisAdapter
)


def example_calendar_scraper():
    """Example: Save and retrieve calendar events"""
    print("\n" + "="*60)
    print("EXAMPLE 1: Calendar Scraper Integration")
    print("="*60)
    
    adapter = CalendarAdapter()
    today = datetime.now().date().isoformat()
    
    # Save earnings event
    print("\n📅 Saving earnings event for AAPL...")
    earnings_data = {
        'company': 'Apple Inc.',
        'time': 'After Market Close',
        'eps_forecast': 1.54,
        'last_year_eps': 1.46,
        'revenue': '89.5B',
        'market_cap': '2.8T'
    }
    
    success = adapter.save_earnings_event(
        date=today,
        ticker='AAPL',
        event_data=earnings_data,
        expiry_days=30
    )
    print(f"✓ Saved: {success}")
    
    # Save economic event
    print("\n🌍 Saving economic event...")
    economic_data = {
        'country': 'United States',
        'importance': 'high',
        'event': 'Non-Farm Payrolls',
        'actual': 199000,
        'forecast': 180000,
        'previous': 150000
    }
    
    success = adapter.save_economic_event(
        date=today,
        event_data=economic_data,
        expiry_days=7
    )
    print(f"✓ Saved: {success}")
    
    # Retrieve earnings events
    print("\n📋 Retrieving AAPL earnings events...")
    events = adapter.get_earnings_events('AAPL')
    print(f"Found {len(events)} events")
    
    for event in events:
        print(f"  Date: {event.date}, Company: {event.data.get('company')}")


def example_fundamental_analysis():
    """Example: Save and retrieve fundamental analysis"""
    print("\n" + "="*60)
    print("EXAMPLE 2: Fundamental Analysis Integration")
    print("="*60)
    
    adapter = FundamentalAdapter()
    today = datetime.now().date().isoformat()
    
    # Save financial metrics
    print("\n📊 Saving financial metrics for GOOGL...")
    metrics = {
        'market_cap': 1850000000000,
        'pe_ratio': 28.5,
        'fcf_yield': 4.08,
        'roic': 0.32,
        'revenue_growth': 0.208,
        'eps_growth': 0.25,
        'net_margin': 0.23,
        'roe': 0.28
    }
    
    success = adapter.save_financial_metrics(
        date=today,
        ticker='GOOGL',
        metrics=metrics,
        expiry_days=1
    )
    print(f"✓ Saved: {success}")
    
    # Save investment decision
    print("\n🎯 Saving investment decision for GOOGL...")
    decision = {
        'recommendation': 'BUY',
        'final_score': 0.67,
        'confidence': 1.0,
        'reasoning': [
            'Strong FCF yield of 4.08%',
            'Excellent ROIC of 32%',
            'High revenue growth of 20.8%'
        ],
        'key_metrics': {
            'fcf_yield': 4.08,
            'roic': 32.0,
            'revenue_growth': 20.8
        },
        'category_scores': {
            'fcf_yield': 0.85,
            'roic': 0.90,
            'growth': 0.80
        }
    }
    
    success = adapter.save_investment_decision(
        date=today,
        ticker='GOOGL',
        decision=decision,
        expiry_days=1
    )
    print(f"✓ Saved: {success}")
    
    # Retrieve latest metrics
    print("\n📈 Retrieving latest metrics for GOOGL...")
    entry = adapter.get_financial_metrics('GOOGL')
    if entry:
        print(f"  Date: {entry.date}")
        # Check if this is a metrics entry or decision entry
        if 'metrics' in entry.data:
            metrics_data = entry.data['metrics']
            print(f"  P/E Ratio: {metrics_data.get('pe_ratio')}")
            print(f"  FCF Yield: {metrics_data.get('fcf_yield')}%")
        elif entry.data.get('analysis_type') == 'decision':
            print(f"  Recommendation: {entry.data.get('recommendation')}")
            print(f"  Score: {entry.data.get('score')}")
        else:
            print(f"  Analysis Type: {entry.data.get('analysis_type', 'unknown')}")


def example_news_scraper():
    """Example: Save and retrieve news articles"""
    print("\n" + "="*60)
    print("EXAMPLE 3: News Scraper Integration")
    print("="*60)
    
    adapter = NewsAdapter()
    today = datetime.now().date().isoformat()
    
    # Save news article
    print("\n📰 Saving news article for TSLA...")
    article = {
        'title': 'Tesla Announces Record Q4 Deliveries',
        'content': 'Tesla reported record vehicle deliveries...',
        'source': 'Bloomberg',
        'url': 'https://example.com/article',
        'author': 'John Doe'
    }
    
    success = adapter.save_news_article(
        date=today,
        ticker='TSLA',
        article=article,
        expiry_days=30
    )
    print(f"✓ Saved: {success}")
    
    # Save sentiment analysis
    print("\n😊 Saving sentiment analysis for TSLA...")
    sentiment = {
        'model': 'finbert-tone',
        'sentiment': 'positive',
        'score': 0.85,
        'confidence': 0.92,
        'breakdown': {
            'positive': 0.85,
            'neutral': 0.10,
            'negative': 0.05
        }
    }
    
    success = adapter.save_sentiment_analysis(
        date=today,
        ticker='TSLA',
        sentiment=sentiment,
        expiry_days=7
    )
    print(f"✓ Saved: {success}")
    
    # Retrieve news articles
    print("\n📋 Retrieving TSLA news articles...")
    articles = adapter.get_news_articles('TSLA')
    print(f"Found {len(articles)} articles")
    
    for article in articles:
        print(f"  Title: {article.data.get('title')}")
        print(f"  Source: {article.data.get('source')}")


def example_technical_analysis():
    """Example: Save and retrieve technical analysis"""
    print("\n" + "="*60)
    print("EXAMPLE 4: Technical Analysis Integration")
    print("="*60)
    
    adapter = TechnicalAnalysisAdapter()
    today = datetime.now().date().isoformat()
    
    # Save technical indicators
    print("\n📉 Saving technical indicators for NVDA...")
    indicators = {
        'rsi': 65.5,
        'macd': 12.3,
        'macd_signal': 10.8,
        'sma_50': 450.25,
        'sma_200': 420.80,
        'bollinger_upper': 480.00,
        'bollinger_lower': 430.00,
        'volume': 45000000
    }
    
    success = adapter.save_technical_indicators(
        date=today,
        ticker='NVDA',
        indicators=indicators,
        expiry_days=1
    )
    print(f"✓ Saved: {success}")
    
    # Save trading signal
    print("\n🚦 Saving trading signal for NVDA...")
    signal = {
        'signal_type': 'BUY',
        'strength': 0.75,
        'triggers': ['RSI oversold', 'MACD crossover'],
        'entry_price': 455.00,
        'stop_loss': 440.00,
        'take_profit': 480.00
    }
    
    success = adapter.save_trading_signal(
        date=today,
        ticker='NVDA',
        signal=signal,
        expiry_days=1
    )
    print(f"✓ Saved: {success}")
    
    # Retrieve technical indicators
    print("\n📊 Retrieving NVDA technical indicators...")
    indicators_list = adapter.get_technical_indicators('NVDA')
    print(f"Found {len(indicators_list)} indicator sets")
    
    for ind in indicators_list:
        print(f"  RSI: {ind.data.get('rsi')}")
        print(f"  MACD: {ind.data.get('macd')}")


def example_batch_operations():
    """Example: Batch save operations"""
    print("\n" + "="*60)
    print("EXAMPLE 5: Batch Operations")
    print("="*60)
    
    db = LocalDatabase()
    today = datetime.now().date().isoformat()
    
    # Create multiple entries
    print("\n💾 Batch saving multiple stock analyses...")
    entries = []
    
    tickers = ['AAPL', 'GOOGL', 'MSFT', 'AMZN', 'NVDA']
    
    for ticker in tickers:
        entry = DatabaseEntry(
            date=today,
            data_type=DataType.FINANCIAL_INFO.value,
            ticker=ticker,
            data={
                'analysis_type': 'quick_scan',
                'price': 100.0 + len(ticker),  # Dummy data
                'volume': 1000000 * len(ticker)
            },
            metadata={'batch_id': 'scan_001'}
        )
        entries.append(entry)
    
    count = db.save_batch(entries, expiry_days=1)
    print(f"✓ Saved {count}/{len(entries)} entries")


def example_query_operations():
    """Example: Advanced queries"""
    print("\n" + "="*60)
    print("EXAMPLE 6: Query Operations")
    print("="*60)
    
    db = LocalDatabase()
    
    # Query by date range
    print("\n🔍 Querying all financial data from last 7 days...")
    date_from = (datetime.now() - timedelta(days=7)).date().isoformat()
    
    entries = db.query(
        data_type=DataType.FINANCIAL_INFO.value,
        date_from=date_from,
        limit=10
    )
    
    print(f"Found {len(entries)} entries:")
    for entry in entries[:5]:  # Show first 5
        print(f"  {entry.date} - {entry.ticker} - {entry.data.get('analysis_type', 'N/A')}")
    
    # Query specific ticker
    print("\n🔍 Querying all data for AAPL...")
    aapl_entries = db.query(ticker='AAPL', limit=5)
    print(f"Found {len(aapl_entries)} AAPL entries")


def example_database_stats():
    """Example: Database statistics"""
    print("\n" + "="*60)
    print("EXAMPLE 7: Database Statistics")
    print("="*60)
    
    db = LocalDatabase()
    stats = db.get_stats()
    
    print(f"\n📊 Database Overview:")
    print(f"  Total Entries: {stats.get('total_entries', 0):,}")
    print(f"  Total Size: {stats.get('total_size_mb', 0)} MB")
    print(f"  Expired Entries: {stats.get('expired_entries', 0)}")
    
    by_type = stats.get('by_type', {})
    if by_type:
        print(f"\n📁 By Data Type:")
        for data_type, count in by_type.items():
            print(f"  {data_type}: {count:,}")


def example_integration_workflow():
    """Example: Complete workflow combining all modules"""
    print("\n" + "="*60)
    print("EXAMPLE 8: Complete Integration Workflow")
    print("="*60)
    
    ticker = 'AAPL'
    today = datetime.now().date().isoformat()
    
    # 1. Check calendar for upcoming events
    print(f"\n1️⃣ Checking calendar for {ticker}...")
    calendar_adapter = CalendarAdapter()
    earnings = calendar_adapter.get_earnings_events(ticker)
    print(f"   Found {len(earnings)} upcoming earnings events")
    
    # 2. Save fundamental analysis
    print(f"\n2️⃣ Saving fundamental analysis for {ticker}...")
    fundamental_adapter = FundamentalAdapter()
    
    decision = {
        'recommendation': 'HOLD',
        'final_score': 0.31,
        'confidence': 0.85,
        'reasoning': ['Strong ROIC but low FCF yield'],
        'key_metrics': {'roic': 73.8, 'fcf_yield': 2.36}
    }
    
    fundamental_adapter.save_investment_decision(today, ticker, decision)
    print("   ✓ Decision saved")
    
    # 3. Check news sentiment
    print(f"\n3️⃣ Saving news sentiment for {ticker}...")
    news_adapter = NewsAdapter()
    
    sentiment = {
        'model': 'finbert-tone',
        'sentiment': 'neutral',
        'score': 0.55
    }
    
    news_adapter.save_sentiment_analysis(today, ticker, sentiment)
    print("   ✓ Sentiment saved")
    
    # 4. Save technical signal
    print(f"\n4️⃣ Saving technical signal for {ticker}...")
    technical_adapter = TechnicalAnalysisAdapter()
    
    signal = {
        'signal_type': 'HOLD',
        'strength': 0.60,
        'triggers': ['Neutral RSI', 'No clear pattern']
    }
    
    technical_adapter.save_trading_signal(today, ticker, signal)
    print("   ✓ Signal saved")
    
    # 5. Comprehensive analysis
    print(f"\n5️⃣ Retrieving comprehensive analysis for {ticker}...")
    db = LocalDatabase()
    all_data = db.query(ticker=ticker, date_from=today)
    
    print(f"\n📊 Complete {ticker} Analysis ({today}):")
    print(f"   Total data points: {len(all_data)}")
    
    for entry in all_data:
        data_type = entry.data_type
        if data_type == 'financial_info':
            rec = entry.data.get('recommendation', 'N/A')
            print(f"   💰 Fundamental: {rec}")
        elif data_type == 'news':
            sent = entry.data.get('sentiment', 'N/A')
            print(f"   📰 News Sentiment: {sent}")
        elif data_type == 'technical_analysis':
            sig = entry.data.get('signal_type', 'N/A')
            print(f"   📉 Technical Signal: {sig}")


def run_all_examples():
    """Run all examples"""
    print("\n" + "="*60)
    print("LOCAL DATABASE SYSTEM - EXAMPLE USAGE")
    print("="*60)
    
    try:
        example_calendar_scraper()
        example_fundamental_analysis()
        example_news_scraper()
        example_technical_analysis()
        example_batch_operations()
        example_query_operations()
        example_database_stats()
        example_integration_workflow()
        
        print("\n" + "="*60)
        print("✓ All examples completed successfully!")
        print("="*60 + "\n")
        
    except Exception as e:
        print(f"\n❌ Error running examples: {e}")
        import traceback
        traceback.print_exc()


if __name__ == "__main__":
    run_all_examples()