#!/usr/bin/env python3
"""
Generate Quick Summary Statistics for Backtest Results
Creates a concise reference card for production deployment
"""

import json
import statistics

# Load results
with open('results/comprehensive_backtest_results_20251014_143309.json', 'r') as f:
    results = json.load(f)

def generate_production_reference():
    """Generate production reference card"""
    
    # Filter to active, high-quality models
    active_models = [r for r in results if r['total_trades'] > 50]
    
    # Top 5 by multiple criteria
    top_sharpe = sorted(active_models, key=lambda x: x['sharpe_ratio'], reverse=True)[:5]
    top_pnl = sorted(active_models, key=lambda x: x['total_pnl'], reverse=True)[:5]
    top_calmar = sorted([r for r in active_models if r['calmar_ratio'] > 0], 
                        key=lambda x: x['calmar_ratio'], reverse=True)[:5]
    
    print("\n" + "="*80)
    print("PRODUCTION REFERENCE CARD - TOP 5 MODELS BY METRIC")
    print("="*80)
    
    print("\n🏆 TOP 5 BY SHARPE RATIO (RISK-ADJUSTED)")
    print("-" * 80)
    for i, m in enumerate(top_sharpe, 1):
        print(f"{i}. {m['model_name']:25} | Sharpe: {m['sharpe_ratio']:6.2f} | "
              f"WR: {m['win_rate']:5.1f}% | PnL: ${m['total_pnl']:8.2f} | "
              f"Trades: {m['total_trades']:3d}")
    
    print("\n💰 TOP 5 BY TOTAL PNL (ABSOLUTE RETURNS)")
    print("-" * 80)
    for i, m in enumerate(top_pnl, 1):
        print(f"{i}. {m['model_name']:25} | PnL: ${m['total_pnl']:8.2f} | "
              f"Sharpe: {m['sharpe_ratio']:6.2f} | WR: {m['win_rate']:5.1f}% | "
              f"Trades: {m['total_trades']:3d}")
    
    print("\n🛡️  TOP 5 BY CALMAR RATIO (RETURN/DRAWDOWN)")
    print("-" * 80)
    for i, m in enumerate(top_calmar, 1):
        print(f"{i}. {m['model_name']:25} | Calmar: {m['calmar_ratio']:8.2f} | "
              f"DD: {m['max_drawdown']*100:6.4f}% | PnL: ${m['total_pnl']:8.2f}")
    
    # Consistent performers (all-around strong)
    consistent = [r for r in active_models if 
                  r['win_rate'] > 50 and 
                  r['profit_factor'] and r['profit_factor'] > 2 and 
                  r['calmar_ratio'] > 5 and
                  r['sharpe_ratio'] > 3]
    
    print("\n⭐ TIER 1 CONSISTENT PERFORMERS (WR>50%, PF>2, Calmar>5, Sharpe>3)")
    print("-" * 80)
    print(f"Total: {len(consistent)} models")
    
    # Sort by composite score
    for m in consistent:
        m['composite_score'] = (m['sharpe_ratio'] + m['win_rate']/10 + 
                               m['calmar_ratio']/100) / 3
    
    consistent_sorted = sorted(consistent, key=lambda x: x['composite_score'], reverse=True)[:10]
    
    for i, m in enumerate(consistent_sorted, 1):
        print(f"{i:2d}. {m['model_name']:25} | "
              f"Sharpe: {m['sharpe_ratio']:5.2f} | WR: {m['win_rate']:5.1f}% | "
              f"PnL: ${m['total_pnl']:7.2f} | Calmar: {m['calmar_ratio']:7.1f}")
    
    # Production ensemble recommendation
    print("\n" + "="*80)
    print("RECOMMENDED PRODUCTION ENSEMBLE (8 MODELS)")
    print("="*80)
    
    # 5 Tier 1 + 3 Tier 2
    tier1 = consistent_sorted[:5]
    tier2_candidates = [m for m in top_pnl if m not in tier1][:3]
    
    print("\n📊 Tier 1: Consistent Performers (70% allocation)")
    for i, m in enumerate(tier1, 1):
        allocation = 14.0  # 70% / 5 models
        print(f"{i}. {m['model_name']:25} | {allocation:4.1f}% capital | "
              f"Sharpe: {m['sharpe_ratio']:5.2f} | WR: {m['win_rate']:5.1f}%")
    
    print("\n🚀 Tier 2: High Return (30% allocation)")
    for i, m in enumerate(tier2_candidates, 1):
        allocation = 10.0  # 30% / 3 models
        print(f"{i}. {m['model_name']:25} | {allocation:4.1f}% capital | "
              f"PnL: ${m['total_pnl']:7.2f} | Sharpe: {m['sharpe_ratio']:5.2f}")
    
    # Expected ensemble performance
    print("\n" + "="*80)
    print("EXPECTED ENSEMBLE PERFORMANCE")
    print("="*80)
    
    tier1_sharpe = statistics.mean([m['sharpe_ratio'] for m in tier1])
    tier1_wr = statistics.mean([m['win_rate'] for m in tier1])
    tier1_pnl = statistics.mean([m['total_pnl'] for m in tier1])
    
    tier2_sharpe = statistics.mean([m['sharpe_ratio'] for m in tier2_candidates])
    tier2_wr = statistics.mean([m['win_rate'] for m in tier2_candidates])
    tier2_pnl = statistics.mean([m['total_pnl'] for m in tier2_candidates])
    
    ensemble_sharpe = 0.7 * tier1_sharpe + 0.3 * tier2_sharpe
    ensemble_wr = 0.7 * tier1_wr + 0.3 * tier2_wr
    ensemble_pnl = 0.7 * tier1_pnl + 0.3 * tier2_pnl
    
    print(f"\nTier 1 Average: Sharpe {tier1_sharpe:.2f}, WR {tier1_wr:.1f}%, PnL ${tier1_pnl:.2f}")
    print(f"Tier 2 Average: Sharpe {tier2_sharpe:.2f}, WR {tier2_wr:.1f}%, PnL ${tier2_pnl:.2f}")
    print(f"\nWeighted Ensemble: Sharpe {ensemble_sharpe:.2f}, WR {ensemble_wr:.1f}%, PnL ${ensemble_pnl:.2f}")
    
    # Monthly return projection
    monthly_return_pct = (ensemble_pnl / 90) * 30  # Scale to 30 days
    annual_return_pct = monthly_return_pct * 12
    
    print(f"\nProjected Returns (90-day backtest scaled):")
    print(f"  Monthly: {monthly_return_pct:.1f}% (on $10K = ${monthly_return_pct * 100:.2f}/month)")
    print(f"  Annual: {annual_return_pct:.1f}% (not compounded)")
    
    # Risk metrics
    ensemble_dd = statistics.mean([m['max_drawdown'] for m in tier1 + tier2_candidates])
    print(f"\nRisk Metrics:")
    print(f"  Expected Max Drawdown: {ensemble_dd*100:.3f}%")
    print(f"  Expected Calmar Ratio: {(monthly_return_pct/30)/(ensemble_dd*100):.1f}")
    
    print("\n" + "="*80)
    print("PRODUCTION RISK LIMITS")
    print("="*80)
    print("""
1. Per-Model Max Drawdown: 1.0% (kill switch)
2. Per-Model Min Win Rate: 55% (rolling 100 trades)
3. Per-Model Min Sharpe: 2.0 (rolling 50 trades)
4. Ensemble Max Drawdown: 2.0% (flatten all)
5. Daily Loss Limit: -3% (halt trading for 24h)
6. Trade Frequency: 10-30 trades/day per model
7. Position Sizing: 2% risk per trade
8. Max Correlation: 0.7 between models
""")
    
    print("="*80)
    print("DEPLOYMENT CHECKLIST")
    print("="*80)
    print("""
☐ Week 1: Out-of-sample validation (Jan-Mar 2025 data)
☐ Week 2: Production risk framework implementation
☐ Week 3: Ensemble system build + unit tests
☐ Week 4: Paper trading (target: Sharpe >2.0, WR >55%)
☐ Week 5: Limited live ($10K, Tier 1 only)
☐ Week 6: Daily monitoring (require >3% weekly return)
☐ Week 7: Add Tier 2 ($5K additional)
☐ Week 8: Scale to $50K if 10%+ return, <3% DD
☐ Week 9: Full production ($100K, 8-model ensemble)
☐ Week 10: Automated monitoring dashboard
☐ Week 11: Monthly retraining cycle begin
☐ Week 12: Operations playbook documentation
""")

def generate_model_matrix():
    """Generate model selection matrix"""
    
    print("\n" + "="*80)
    print("MODEL SELECTION MATRIX")
    print("="*80)
    
    models = [r for r in results if r['total_trades'] > 50]
    
    # Categorize by characteristics
    categories = {
        'High Sharpe (>7)': [m for m in models if m['sharpe_ratio'] > 7],
        'High PnL (>$80)': [m for m in models if m['total_pnl'] > 80],
        'Low Drawdown (<0.01%)': [m for m in models if m['max_drawdown'] < 0.0001],
        'High Win Rate (>58%)': [m for m in models if m['win_rate'] > 58],
        'Low Frequency (<20/day)': [m for m in models if m['trade_frequency'] < 20],
        'Balanced (50-60% WR, 100-500 trades)': [m for m in models if 
            50 < m['win_rate'] < 60 and 100 < m['total_trades'] < 500]
    }
    
    for category, category_models in categories.items():
        print(f"\n{category}: {len(category_models)} models")
        for m in sorted(category_models, key=lambda x: x['sharpe_ratio'], reverse=True)[:3]:
            print(f"  • {m['model_name']:25} | Sharpe: {m['sharpe_ratio']:5.2f} | "
                  f"WR: {m['win_rate']:5.1f}% | PnL: ${m['total_pnl']:7.2f}")

if __name__ == '__main__':
    generate_production_reference()
    generate_model_matrix()
    
    print("\n" + "="*80)
    print("SUMMARY GENERATION COMPLETE")
    print("="*80 + "\n")