#!/usr/bin/env python3
"""
NBA Dynamic Strategy Flow Demonstration
Shows what happens when a user requests an NBA betting strategy
"""

import json

def simulate_ai_response(user_query):
    """Simulate what the Grok-4-1-Fast-Reasoning AI would generate"""

    print("🤖 AI Processing User Query:")
    print(f"   User: '{user_query}'")
    print("\n" + "="*60)

    # This is what the AI would generate based on the system prompt
    ai_generated_code = '''```python
def generate_bets(games, players, teams, params):
    """
    NBA Strategy: Bet on home teams with high win confidence
    """
    bets = []
    stake = params.get('stake', 100)

    # Focus on recent games for backtesting
    recent_games = games[-50:] if len(games) > 50 else games

    for game in recent_games:
        # Strategy logic: Bet on home team with moderate confidence
        # In a real strategy, this would analyze team performance, injuries, etc.

        bet = {
            'game_id': game.get('game_id', game.get('id', 'unknown')),
            'bet_type': 'moneyline',
            'prediction': 'home',
            'confidence': 0.58,  # Based on home court advantage
            'stake': stake
        }
        bets.append(bet)

    return bets
```'''

    print("🎯 AI Generated Strategy Code:")
    print(ai_generated_code)

    return ai_generated_code.strip('```python').strip('```').strip()

def simulate_backtesting(strategy_code):
    """Simulate what the backtesting engine would do"""

    print("\n" + "="*60)
    print("🏀 NBA Backtesting Engine Processing...")
    print("="*60)

    # Sample NBA games data (what would come from cache)
    games_data = [
        {
            'game_id': '20231201_LAL_GSW',
            'game_date': '2023-12-01',
            'home_team_name': 'Los Angeles Lakers',
            'away_team_name': 'Golden State Warriors',
            'home_score': 115,
            'away_score': 108,
            'season': '2023-24'
        },
        {
            'game_id': '20231202_BOS_NYK',
            'game_date': '2023-12-02',
            'home_team_name': 'New York Knicks',
            'away_team_name': 'Boston Celtics',
            'home_score': 125,
            'away_score': 118,
            'season': '2023-24'
        },
        {
            'game_id': '20231203_PHO_DAL',
            'game_date': '2023-12-03',
            'home_team_name': 'Dallas Mavericks',
            'away_team_name': 'Phoenix Suns',
            'home_score': 120,
            'away_score': 115,
            'season': '2023-24'
        }
    ]

    print(f"📊 Loaded {len(games_data)} NBA games from cache")
    print("🎲 Executing strategy against historical data...")

    # Execute the strategy (simulating what the backtesting engine does)
    try:
        local_scope = {}
        global_scope = {
            'games': games_data,
            'players': [],
            'teams': [],
            'len': len,
            'range': range,
            'int': int,
            'float': float,
            'str': str,
            'dict': dict,
            'list': list
        }

        exec(strategy_code, global_scope, local_scope)

        if 'generate_bets' in local_scope:
            strategy_func = local_scope['generate_bets']
            bets = strategy_func(games_data, [], [], {'stake': 100})

            print(f"✅ Strategy generated {len(bets)} betting recommendations")

            # Simulate bet evaluation (what happens in backtesting)
            print("\n📈 Evaluating bets against actual game results...")

            total_profit = 0
            winning_bets = 0

            for i, bet in enumerate(bets):
                game = next((g for g in games_data if g['game_id'] == bet['game_id']), None)
                if game:
                    home_won = game['home_score'] > game['away_score']
                    prediction_correct = (bet['prediction'] == 'home' and home_won)

                    if prediction_correct:
                        winning_bets += 1
                        total_profit += bet['stake'] * 0.91  # Approximate payout (minus vig)
                        outcome = "✅ WIN"
                    else:
                        total_profit -= bet['stake']
                        outcome = "❌ LOSS"

                    print(f"   Bet {i+1}: {bet['prediction']} vs {game['away_team_name']} @ {game['home_team_name']} - {outcome}")
                    print(f"      Profit: ${bet['stake'] * (1 if prediction_correct else -1):.2f}")
            win_rate = winning_bets / len(bets) if bets else 0

            print("\n📊 BACKTESTING RESULTS:")
            print("="*40)
            print(f"   Total Bets: {len(bets)}")
            print(f"   Winning Bets: {winning_bets}")
            print(".1f")
            print(".2f")
            print(".2f")

            return {
                'success': True,
                'total_bets': len(bets),
                'winning_bets': winning_bets,
                'win_rate': win_rate,
                'total_profit': total_profit,
                'roi': (total_profit / (len(bets) * 100)) * 100 if bets else 0
            }

    except Exception as e:
        print(f"❌ Error executing strategy: {e}")
        return {'success': False, 'error': str(e)}

def main():
    """Demonstrate the complete NBA dynamic strategy flow"""

    print("🏀 NBA Dynamic Strategy Flow Demonstration")
    print("="*60)
    print("This shows what happens when a user describes an NBA betting strategy")
    print("="*60)

    # Example user queries
    test_queries = [
        "Bet on home teams when they have won their last 3 games",
        "Create a strategy that bets against teams with injured star players",
        "Bet on underdogs when the spread is more than 8 points"
    ]

    for i, query in enumerate(test_queries, 1):
        print(f"\n{'='*20} TEST CASE {i} {'='*20}")
        print(f"User Query: '{query}'")

        # Step 1: AI generates code
        generated_code = simulate_ai_response(query)

        # Step 2: Backtesting engine processes it
        results = simulate_backtesting(generated_code)

        if results['success']:
            print("\n🎉 SUCCESS: Strategy backtested successfully!")
            print(".1f")
        else:
            print(f"\n❌ FAILED: {results.get('error', 'Unknown error')}")

    print("\n" + "="*60)
    print("🏀 NBA DYNAMIC STRATEGY SYSTEM SUMMARY")
    print("="*60)
    print("✅ AI Integration: Grok-4-1-Fast-Reasoning generates Python code")
    print("✅ Code Execution: NBA data cache provides games/players/teams")
    print("✅ Bet Evaluation: Moneyline, spread, over/under supported")
    print("✅ Results: Profit/loss, win rate, ROI calculations")
    print("✅ User Experience: Natural language → Custom strategy → Backtest results")
    print("\n🎯 The system is ready for users to describe custom NBA betting strategies!")

if __name__ == "__main__":
    main()