examples.py

"""
LNG Terminal Valuation Skill - Example Use Cases

This file demonstrates the skill in action for different user personas:
1. Investment Analyst (M&A due diligence)
2. Energy Trader (Market intelligence)
3. Project Developer (Feasibility study)
4. Portfolio Manager (Risk assessment)
"""

from lng_analyst import LNGAnalyst
from data_collector import LNGDataCollector
from valuation_engine import LNGTerminalValuation, ValuationAssumptions, compare_terminals


def separator(title):
    """Print formatted section separator"""
    print("\n" + "="*80)
    print(f"  {title}")
    print("="*80 + "\n")


# ============================================================================
# USE CASE 1: Investment Analyst - M&A Due Diligence
# ============================================================================

def use_case_investment_analyst():
    """
    Scenario: Investment bank analyzing Cameron LNG for potential acquisition
    Client wants to know: What's it worth? What are the risks?
    """
    separator("USE CASE 1: Investment Analyst - M&A Due Diligence")
    
    print("CLIENT BRIEF:")
    print("Our client is considering acquiring Cameron LNG for $10B.")
    print("We need to value the asset and identify key risks.\n")
    
    analyst = LNGAnalyst()
    collector = LNGDataCollector()
    
    # Step 1: Get terminal data
    cameron = collector.get_terminal_by_name('Cameron LNG')
    print(f"Target: {cameron['Terminal Name']}")
    print(f"Owner: {cameron['Owner']}")
    print(f"Capacity: {cameron['Capacity (Bcf/d)']} Bcf/d\n")
    
    # Step 2: Base case valuation
    print("STEP 1: Base Case Valuation")
    print("-" * 40)
    assumptions = ValuationAssumptions(
        tolling_fee=3.00,
        utilization_rate=0.95,
        discount_rate=0.08
    )
    val = LNGTerminalValuation(cameron, assumptions)
    metrics = val.calculate_dcf()
    
    print(f"Enterprise Value: ${metrics['Enterprise Value']/1e9:.2f}B")
    print(f"NPV @ 8%: ${metrics['NPV']/1e9:.2f}B")
    print(f"EV/EBITDA: {metrics['EV/EBITDA Multiple']:.1f}x")
    print(f"Stabilized EBITDA: ${metrics['Stabilized EBITDA']/1e6:.0f}M\n")
    
    # Step 3: Scenario analysis
    print("STEP 2: Scenario Analysis (Bull/Base/Bear)")
    print("-" * 40)
    scenarios = {
        'Bear (Recession)': {
            'utilization_rate': 0.80,
            'tolling_fee': 2.50,
            'discount_rate': 0.10
        },
        'Base Case': {},
        'Bull (Energy Crisis)': {
            'utilization_rate': 1.05,
            'tolling_fee': 3.50,
            'discount_rate': 0.07
        }
    }
    
    scenario_results = val.scenario_analysis(scenarios)
    print(scenario_results.to_string(index=False))
    print()
    
    # Step 4: Investment recommendation
    print("STEP 3: Investment Decision")
    print("-" * 40)
    purchase_price = 10.0  # $10B
    base_ev = metrics['Enterprise Value'] / 1e9
    implied_return = (base_ev / purchase_price - 1) * 100
    
    print(f"Proposed Purchase Price: ${purchase_price:.1f}B")
    print(f"Our Base Case Value: ${base_ev:.1f}B")
    print(f"Implied Return: {implied_return:+.1f}%")
    
    if base_ev > purchase_price * 1.15:
        print("\n✅ RECOMMENDATION: STRONG BUY")
        print("   Asset trading at significant discount to intrinsic value")
    elif base_ev > purchase_price:
        print("\n✅ RECOMMENDATION: BUY")
        print("   Asset trading below fair value")
    elif base_ev > purchase_price * 0.90:
        print("\n⚠️  RECOMMENDATION: HOLD")
        print("   Asset fairly valued, limited upside")
    else:
        print("\n❌ RECOMMENDATION: PASS")
        print("   Asset overvalued, too much risk")
    
    print("\nKEY RISKS:")
    print("• Utilization: Breakeven = {:.0%}".format(val.calculate_breakeven_utilization()))
    print("• Tolling fee: Breakeven = ${:.2f}/MMBtu".format(val.calculate_breakeven_tolling_fee()))
    print("• Contract risk: {}% coverage".format(cameron['Contract Coverage (%)']))


# ============================================================================
# USE CASE 2: Energy Trader - Market Intelligence
# ============================================================================

def use_case_energy_trader():
    """
    Scenario: Gas trader evaluating arbitrage opportunities
    Needs to understand: Which terminals are most profitable? Where are margins?
    """
    separator("USE CASE 2: Energy Trader - Market Intelligence")
    
    print("TRADING BRIEF:")
    print("Evaluate LNG export economics across Gulf Coast terminals.")
    print("Identify best arbitrage opportunities.\n")
    
    collector = LNGDataCollector()
    
    # Step 1: Current market conditions
    print("STEP 1: Market Snapshot")
    print("-" * 40)
    henry_hub = collector.get_current_henry_hub()
    print(f"Henry Hub Spot: ${henry_hub:.2f}/MMBtu")
    
    forward = collector.get_forward_curve(months=12)
    avg_forward = forward['Forward Price ($/MMBtu)'].mean()
    print(f"12-Month Forward Avg: ${avg_forward:.2f}/MMBtu")
    print()
    
    # Step 2: Terminal economics
    print("STEP 2: Terminal Profitability Analysis")
    print("-" * 40)
    
    # Get operating terminals
    operating = collector.search_terminals(status='Operating')
    
    # Calculate margins for each
    print(f"{'Terminal':<25} {'Capacity':<10} {'Est. Margin':<12} {'Economics'}")
    print("-" * 70)
    
    for _, terminal in operating.head(5).iterrows():
        capacity = terminal['Capacity (Bcf/d)']
        
        # Simplified margin calc: Tolling fee - Variable costs
        tolling_fee = 3.00  # Assume market rate
        variable_cost = 0.40  # Fixed opex
        margin = tolling_fee - variable_cost
        
        # Annual cash flow
        annual_cf = capacity * 1000 * 360 * margin / 1e6  # $M
        
        economics = "✅ Strong" if margin > 2.0 else "⚠️  Tight"
        
        print(f"{terminal['Terminal Name']:<25} {capacity:<10.1f} ${margin:<11.2f} {economics}")
    
    print()
    
    # Step 3: Trading recommendation
    print("STEP 3: Trading Strategy")
    print("-" * 40)
    print("BULLISH INDICATORS:")
    print("✓ Henry Hub < $4.00 = Attractive feedgas costs")
    print("✓ 100%+ utilization = Strong demand signal")
    print("✓ Forward curve in contango = Inventory value")
    print()
    print("RECOMMENDATION: Long US LNG exposure")
    print("• Buy Henry Hub calls (hedged feedgas)")
    print("• Long Asian LNG spreads (export arb)")
    print("• Monitor utilization for demand signals")


# ============================================================================
# USE CASE 3: Project Developer - Feasibility Study
# ============================================================================

def use_case_project_developer():
    """
    Scenario: Developer evaluating greenfield LNG terminal in Texas
    Needs to know: Is it economically viable? What tolling fee do I need?
    """
    separator("USE CASE 3: Project Developer - Feasibility Study")
    
    print("PROJECT BRIEF:")
    print("Greenfield 2.0 Bcf/d LNG export terminal on Texas Gulf Coast")
    print("Need feasibility analysis and required tolling fee.\n")
    
    collector = LNGDataCollector()
    
    # Project specs
    print("STEP 1: Project Specifications")
    print("-" * 40)
    capacity_bcfd = 2.0
    capacity_mtpa = capacity_bcfd * 7.5  # Rough conversion
    capex = 8000  # $8B (modular technology)
    
    print(f"Capacity: {capacity_bcfd} Bcf/d ({capacity_mtpa:.1f} mtpa)")
    print(f"Technology: Baker Hughes Modular")
    print(f"Estimated Capex: ${capex}M")
    print(f"Construction: 3.5 years")
    print()
    
    # Create synthetic terminal for modeling
    import pandas as pd
    project = pd.Series({
        'Terminal Name': 'Texas Coast LNG (Proposed)',
        'Owner': 'NewCo Energy',
        'Location': 'Corpus Christi, Texas',
        'Capacity (Bcf/d)': capacity_bcfd,
        'Capacity (mtpa)': capacity_mtpa,
        'Capex ($M)': capex,
        'Status': 'Announced',
        'Technology': 'Baker Hughes Modular',
        'Contract Coverage (%)': 80,
        'Start Date': pd.Timestamp('2029-01-01')
    })
    
    # Step 2: Economic analysis
    print("STEP 2: Financial Modeling")
    print("-" * 40)
    
    assumptions = ValuationAssumptions(
        tolling_fee=3.00,
        utilization_rate=0.95,
        discount_rate=0.09,  # Higher for greenfield risk
        project_life=25
    )
    
    val = LNGTerminalValuation(project, assumptions)
    metrics = val.calculate_dcf()
    
    print(f"NPV @ 9%: ${metrics['NPV']/1e9:.2f}B")
    print(f"IRR: {metrics['IRR']*100:.1f}%" if metrics['IRR'] else "N/A")
    print(f"Payback: {metrics['Payback Period (years)']} years")
    print()
    
    # Step 3: Required returns
    print("STEP 3: Required Economics")
    print("-" * 40)
    
    breakeven_fee = val.calculate_breakeven_tolling_fee()
    print(f"Breakeven Tolling Fee: ${breakeven_fee:.2f}/MMBtu")
    
    # Check market feasibility
    benchmarks = collector.get_industry_benchmarks()
    market_low = benchmarks['tolling_fee']['low']
    market_high = benchmarks['tolling_fee']['high']
    
    print(f"Market Range: ${market_low:.2f} - ${market_high:.2f}/MMBtu")
    
    if breakeven_fee < market_low:
        print("\n✅ PROJECT HIGHLY VIABLE")
        print("   Breakeven below market floor")
    elif breakeven_fee < market_high:
        print("\n✅ PROJECT VIABLE")
        print("   Breakeven within market range")
    else:
        print("\n❌ PROJECT AT RISK")
        print("   Breakeven above market ceiling")
    
    print("\nNEXT STEPS:")
    print("• Secure 80%+ offtake contracts before FID")
    print("• Lock in EPC pricing")
    print("• Obtain FERC authorization")
    print("• Finalize project financing")


# ============================================================================
# USE CASE 4: Portfolio Manager - Risk Assessment
# ============================================================================

def use_case_portfolio_manager():
    """
    Scenario: Fund manager with $1B LNG infrastructure portfolio
    Needs to: Stress-test portfolio, identify risks, optimize allocation
    """
    separator("USE CASE 4: Portfolio Manager - Risk Assessment")
    
    print("PORTFOLIO BRIEF:")
    print("$1B diversified LNG infrastructure portfolio")
    print("Holdings: Multiple terminals across operators")
    print("Task: Stress test and optimize allocation\n")
    
    collector = LNGDataCollector()
    
    # Define portfolio
    print("STEP 1: Current Portfolio Holdings")
    print("-" * 40)
    
    portfolio_holdings = {
        'Sabine Pass LNG': 0.30,      # 30% weight
        'Cameron LNG': 0.25,           # 25% weight
        'Calcasieu Pass LNG': 0.20,   # 20% weight
        'Freeport LNG': 0.15,          # 15% weight
        'Corpus Christi LNG': 0.10    # 10% weight
    }
    
    total_value = 1.0  # $1B portfolio
    
    print(f"{'Terminal':<25} {'Weight':<10} {'Value ($M)'}")
    print("-" * 50)
    for terminal, weight in portfolio_holdings.items():
        value = weight * total_value * 1000
        print(f"{terminal:<25} {weight:<10.0%} ${value:.0f}M")
    print()
    
    # Step 2: Stress testing
    print("STEP 2: Stress Test Scenarios")
    print("-" * 40)
    
    # Get terminals and run stress scenarios
    terminals = []
    for name in portfolio_holdings.keys():
        terminal = collector.get_terminal_by_name(name)
        if terminal is not None:
            terminals.append(terminal)
    
    # Base case
    base_assumptions = ValuationAssumptions(
        utilization_rate=0.95,
        tolling_fee=3.00,
        discount_rate=0.08
    )
    
    base_comparison = compare_terminals(terminals, base_assumptions)
    base_portfolio_value = sum(
        base_comparison.iloc[i]['EV ($B)'] * list(portfolio_holdings.values())[i]
        for i in range(len(base_comparison))
    )
    
    # Recession scenario
    recession_assumptions = ValuationAssumptions(
        utilization_rate=0.70,
        tolling_fee=2.00,
        discount_rate=0.12
    )
    
    recession_comparison = compare_terminals(terminals, recession_assumptions)
    recession_portfolio_value = sum(
        recession_comparison.iloc[i]['EV ($B)'] * list(portfolio_holdings.values())[i]
        for i in range(len(recession_comparison))
    )
    
    # Energy crisis scenario
    crisis_assumptions = ValuationAssumptions(
        utilization_rate=1.10,
        tolling_fee=4.50,
        discount_rate=0.06
    )
    
    crisis_comparison = compare_terminals(terminals, crisis_assumptions)
    crisis_portfolio_value = sum(
        crisis_comparison.iloc[i]['EV ($B)'] * list(portfolio_holdings.values())[i]
        for i in range(len(crisis_comparison))
    )
    
    print(f"{'Scenario':<20} {'Portfolio Value':<15} {'Change'}")
    print("-" * 50)
    print(f"{'Base Case':<20} ${base_portfolio_value:.2f}B {'  --'}")
    print(f"{'Recession (-30%)':<20} ${recession_portfolio_value:.2f}B {(recession_portfolio_value/base_portfolio_value-1)*100:+.1f}%")
    print(f"{'Energy Crisis (+50%)':<20} ${crisis_portfolio_value:.2f}B {(crisis_portfolio_value/base_portfolio_value-1)*100:+.1f}%")
    print()
    
    # Step 3: Risk metrics
    print("STEP 3: Portfolio Risk Metrics")
    print("-" * 40)
    
    # Value at Risk (simplified)
    downside = base_portfolio_value - recession_portfolio_value
    upside = crisis_portfolio_value - base_portfolio_value
    
    print(f"Downside Risk (Recession): ${downside:.2f}B ({downside/base_portfolio_value*100:.1f}%)")
    print(f"Upside Potential (Crisis): ${upside:.2f}B ({upside/base_portfolio_value*100:.1f}%)")
    print(f"Risk/Reward Ratio: {upside/downside:.2f}x")
    print()
    
    # Step 4: Recommendations
    print("STEP 4: Portfolio Recommendations")
    print("-" * 40)
    
    if downside / base_portfolio_value < 0.20:
        print("✅ RISK LEVEL: MODERATE")
        print("   Portfolio well-positioned for downturns")
    else:
        print("⚠️  RISK LEVEL: HIGH")
        print("   Consider hedging or rebalancing")
    
    print("\nRECOMMENDATIONS:")
    print("• Maintain diversification across operators")
    print("• Consider adding international exposure")
    print("• Monitor contract expiration dates")
    print("• Hedge commodity price risk (Henry Hub options)")
    print("• Evaluate adding regasification assets (import side)")


# ============================================================================
# MAIN - Run All Use Cases
# ============================================================================

if __name__ == '__main__':
    print("\n" + "="*80)
    print("  LNG TERMINAL VALUATION SKILL - REAL-WORLD USE CASES")
    print("="*80)
    print()
    print("This demonstration shows how different professionals use the skill:")
    print("  1. Investment Analyst - M&A due diligence")
    print("  2. Energy Trader - Market intelligence")
    print("  3. Project Developer - Feasibility study")
    print("  4. Portfolio Manager - Risk assessment")
    print()
    
    input("Press Enter to start demonstration...")
    
    use_case_investment_analyst()
    input("\nPress Enter for next use case...")
    
    use_case_energy_trader()
    input("\nPress Enter for next use case...")
    
    use_case_project_developer()
    input("\nPress Enter for next use case...")
    
    use_case_portfolio_manager()
    
    print("\n" + "="*80)
    print("  DEMONSTRATION COMPLETE")
    print("="*80)
    print()
    print("All use cases completed successfully!")
    print("These examples show the skill's flexibility across different workflows.")
    print()
    print("Next steps:")
    print("  • Try interactive mode: python lng_analyst.py")
    print("  • Read the documentation: README.md")
    print("  • Customize for your needs!")
    print()