#!/usr/bin/env python3
"""
Compare Original vs Incremental Strategies on Same Data
======================================================

This script runs both strategies on the exact same data period from btcusd_1-min_data.csv
to ensure a fair comparison.
"""

import sys
import os
import json
import pandas as pd
import numpy as np
from datetime import datetime
import matplotlib.pyplot as plt
import matplotlib.dates as mdates

# Add the parent directory to the path to import cycles modules
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

from cycles.utils.storage import Storage
from cycles.IncStrategies.inc_backtester import IncBacktester, BacktestConfig
from cycles.IncStrategies.metatrend_strategy import IncMetaTrendStrategy
from cycles.utils.data_utils import aggregate_to_minutes


def run_original_strategy_via_main(start_date: str, end_date: str, initial_usd: float, stop_loss_pct: float):
    """Run the original strategy using the main.py system."""
    print(f"\n🔄 Running Original Strategy via main.py...")
    
    # Create a temporary config file for the original strategy
    config = {
        "start_date": start_date,
        "stop_date": end_date,
        "initial_usd": initial_usd,
        "timeframes": ["15min"],
        "strategies": [
            {
                "name": "default",
                "weight": 1.0,
                "params": {
                    "stop_loss_pct": stop_loss_pct,
                    "timeframe": "15min"
                }
            }
        ],
        "combination_rules": {
            "min_strategies": 1,
            "min_confidence": 0.5
        }
    }
    
    # Save temporary config
    temp_config_file = "temp_config.json"
    with open(temp_config_file, 'w') as f:
        json.dump(config, f, indent=2)
    
    try:
        # Import and run the main processing function
        from main import process_timeframe_data
        from cycles.utils.storage import Storage
        
        storage = Storage()
        
        # Load data using absolute path
        data_file = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "data", "btcusd_1-min_data.csv")
        print(f"Loading data from: {data_file}")
        
        if not os.path.exists(data_file):
            print(f"❌ Data file not found: {data_file}")
            return None
            
        data_1min = storage.load_data(data_file, start_date, end_date)
        print(f"Loaded {len(data_1min)} minute-level data points")
        
        if len(data_1min) == 0:
            print(f"❌ No data loaded for period {start_date} to {end_date}")
            return None
        
        # Run the original strategy
        results_rows, trade_rows = process_timeframe_data(data_1min, "15min", config, debug=False)
        
        if not results_rows:
            print("❌ No results from original strategy")
            return None
            
        result = results_rows[0]
        trades = [trade for trade in trade_rows if trade['timeframe'] == result['timeframe']]
        
        return {
            'strategy_name': 'Original MetaTrend',
            'n_trades': result['n_trades'],
            'win_rate': result['win_rate'],
            'avg_trade': result['avg_trade'],
            'max_drawdown': result['max_drawdown'],
            'initial_usd': result['initial_usd'],
            'final_usd': result['final_usd'],
            'profit_ratio': (result['final_usd'] - result['initial_usd']) / result['initial_usd'],
            'total_fees_usd': result['total_fees_usd'],
            'trades': trades,
            'data_points': len(data_1min)
        }
        
    finally:
        # Clean up temporary config file
        if os.path.exists(temp_config_file):
            os.remove(temp_config_file)


def run_incremental_strategy(start_date: str, end_date: str, initial_usd: float, stop_loss_pct: float):
    """Run the incremental strategy using the new backtester."""
    print(f"\n🔄 Running Incremental Strategy...")
    
    storage = Storage()
    
    # Use absolute path for data file
    data_file = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "data", "btcusd_1-min_data.csv")
    
    # Create backtester configuration
    config = BacktestConfig(
        data_file=data_file,
        start_date=start_date,
        end_date=end_date,
        initial_usd=initial_usd,
        stop_loss_pct=stop_loss_pct,
        take_profit_pct=0.0
    )
    
    # Create strategy
    strategy = IncMetaTrendStrategy(
        name="metatrend",
        weight=1.0,
        params={
            "timeframe": "15min",
            "enable_logging": False
        }
    )
    
    # Run backtest
    backtester = IncBacktester(config, storage)
    result = backtester.run_single_strategy(strategy)
    
    result['strategy_name'] = 'Incremental MetaTrend'
    return result


def save_comparison_results(original_result: dict, incremental_result: dict, output_dir: str):
    """Save comparison results to files."""
    os.makedirs(output_dir, exist_ok=True)
    
    # Save original trades
    original_trades_file = os.path.join(output_dir, "original_trades.csv")
    if original_result and original_result['trades']:
        trades_df = pd.DataFrame(original_result['trades'])
        trades_df.to_csv(original_trades_file, index=False)
        print(f"Saved original trades to: {original_trades_file}")
    
    # Save incremental trades
    incremental_trades_file = os.path.join(output_dir, "incremental_trades.csv")
    if incremental_result['trades']:
        # Convert to same format as original
        trades_data = []
        for trade in incremental_result['trades']:
            trades_data.append({
                'entry_time': trade.get('entry_time'),
                'exit_time': trade.get('exit_time'),
                'entry_price': trade.get('entry_price'),
                'exit_price': trade.get('exit_price'),
                'profit_pct': trade.get('profit_pct'),
                'type': trade.get('type'),
                'fee_usd': trade.get('fee_usd')
            })
        trades_df = pd.DataFrame(trades_data)
        trades_df.to_csv(incremental_trades_file, index=False)
        print(f"Saved incremental trades to: {incremental_trades_file}")
    
    # Save comparison summary
    comparison_file = os.path.join(output_dir, "strategy_comparison.json")
    
    # Convert numpy types to Python types for JSON serialization
    def convert_numpy_types(obj):
        if hasattr(obj, 'item'):  # numpy scalar
            return obj.item()
        elif isinstance(obj, dict):
            return {k: convert_numpy_types(v) for k, v in obj.items()}
        elif isinstance(obj, list):
            return [convert_numpy_types(v) for v in obj]
        else:
            return obj
    
    comparison_data = {
        'test_date': datetime.now().isoformat(),
        'data_file': 'btcusd_1-min_data.csv',
        'original_strategy': {
            'name': original_result['strategy_name'] if original_result else 'Failed',
            'n_trades': int(original_result['n_trades']) if original_result else 0,
            'win_rate': float(original_result['win_rate']) if original_result else 0,
            'avg_trade': float(original_result['avg_trade']) if original_result else 0,
            'max_drawdown': float(original_result['max_drawdown']) if original_result else 0,
            'initial_usd': float(original_result['initial_usd']) if original_result else 0,
            'final_usd': float(original_result['final_usd']) if original_result else 0,
            'profit_ratio': float(original_result['profit_ratio']) if original_result else 0,
            'total_fees_usd': float(original_result['total_fees_usd']) if original_result else 0,
            'data_points': int(original_result['data_points']) if original_result else 0
        },
        'incremental_strategy': {
            'name': incremental_result['strategy_name'],
            'n_trades': int(incremental_result['n_trades']),
            'win_rate': float(incremental_result['win_rate']),
            'avg_trade': float(incremental_result['avg_trade']),
            'max_drawdown': float(incremental_result['max_drawdown']),
            'initial_usd': float(incremental_result['initial_usd']),
            'final_usd': float(incremental_result['final_usd']),
            'profit_ratio': float(incremental_result['profit_ratio']),
            'total_fees_usd': float(incremental_result['total_fees_usd']),
            'data_points': int(incremental_result.get('data_points_processed', 0))
        }
    }
    
    if original_result:
        comparison_data['comparison'] = {
            'profit_difference': float(incremental_result['profit_ratio'] - original_result['profit_ratio']),
            'trade_count_difference': int(incremental_result['n_trades'] - original_result['n_trades']),
            'win_rate_difference': float(incremental_result['win_rate'] - original_result['win_rate'])
        }
    
    with open(comparison_file, 'w') as f:
        json.dump(comparison_data, f, indent=2)
    print(f"Saved comparison summary to: {comparison_file}")
    
    return comparison_data


def create_comparison_plot(original_result: dict, incremental_result: dict, 
                         start_date: str, end_date: str, output_dir: str):
    """Create a comparison plot showing both strategies."""
    print(f"\n📊 Creating comparison plot...")
    
    # Load price data for plotting
    storage = Storage()
    data_file = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "data", "btcusd_1-min_data.csv")
    data_1min = storage.load_data(data_file, start_date, end_date)
    aggregated_data = aggregate_to_minutes(data_1min, 15)
    
    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(15, 12))
    
    # Plot 1: Price with trade signals
    ax1.plot(aggregated_data.index, aggregated_data['close'], 'k-', alpha=0.7, linewidth=1, label='BTC Price')
    
    # Plot original strategy trades
    if original_result and original_result['trades']:
        original_trades = original_result['trades']
        for trade in original_trades:
            entry_time = pd.to_datetime(trade.get('entry_time'))
            exit_time = pd.to_datetime(trade.get('exit_time'))
            entry_price = trade.get('entry_price')
            exit_price = trade.get('exit_price')
            
            if entry_time and entry_price:
                # Buy signal (above price line)
                ax1.scatter(entry_time, entry_price * 1.02, color='green', marker='^', 
                           s=50, alpha=0.8, label='Original Buy' if trade == original_trades[0] else "")
            
            if exit_time and exit_price:
                # Sell signal (above price line)
                color = 'red' if trade.get('profit_pct', 0) < 0 else 'blue'
                ax1.scatter(exit_time, exit_price * 1.02, color=color, marker='v', 
                           s=50, alpha=0.8, label='Original Sell' if trade == original_trades[0] else "")
    
    # Plot incremental strategy trades
    incremental_trades = incremental_result['trades']
    if incremental_trades:
        for trade in incremental_trades:
            entry_time = pd.to_datetime(trade.get('entry_time'))
            exit_time = pd.to_datetime(trade.get('exit_time'))
            entry_price = trade.get('entry_price')
            exit_price = trade.get('exit_price')
            
            if entry_time and entry_price:
                # Buy signal (below price line)
                ax1.scatter(entry_time, entry_price * 0.98, color='lightgreen', marker='^', 
                           s=50, alpha=0.8, label='Incremental Buy' if trade == incremental_trades[0] else "")
            
            if exit_time and exit_price:
                # Sell signal (below price line)
                exit_type = trade.get('type', 'STRATEGY_EXIT')
                if exit_type == 'STOP_LOSS':
                    color = 'orange'
                elif exit_type == 'TAKE_PROFIT':
                    color = 'purple'
                else:
                    color = 'lightblue'
                
                ax1.scatter(exit_time, exit_price * 0.98, color=color, marker='v', 
                           s=50, alpha=0.8, label=f'Incremental {exit_type}' if trade == incremental_trades[0] else "")
    
    ax1.set_title(f'Strategy Comparison: {start_date} to {end_date}', fontsize=14, fontweight='bold')
    ax1.set_ylabel('Price (USD)', fontsize=12)
    ax1.legend(loc='upper left')
    ax1.grid(True, alpha=0.3)
    
    # Format x-axis
    ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
    ax1.xaxis.set_major_locator(mdates.MonthLocator())
    plt.setp(ax1.xaxis.get_majorticklabels(), rotation=45)
    
    # Plot 2: Performance comparison
    strategies = ['Original', 'Incremental']
    profits = [
        original_result['profit_ratio'] * 100 if original_result else 0, 
        incremental_result['profit_ratio'] * 100
    ]
    colors = ['blue', 'green']
    
    bars = ax2.bar(strategies, profits, color=colors, alpha=0.7)
    ax2.set_title('Profit Comparison', fontsize=14, fontweight='bold')
    ax2.set_ylabel('Profit (%)', fontsize=12)
    ax2.grid(True, alpha=0.3, axis='y')
    
    # Add value labels on bars
    for bar, profit in zip(bars, profits):
        height = bar.get_height()
        ax2.text(bar.get_x() + bar.get_width()/2., height + (0.5 if height >= 0 else -1.5),
                f'{profit:.2f}%', ha='center', va='bottom' if height >= 0 else 'top', fontweight='bold')
    
    plt.tight_layout()
    
    # Save plot
    plot_file = os.path.join(output_dir, "strategy_comparison.png")
    plt.savefig(plot_file, dpi=300, bbox_inches='tight')
    plt.close()
    print(f"Saved comparison plot to: {plot_file}")


def print_comparison_summary(original_result: dict, incremental_result: dict):
    """Print a detailed comparison summary."""
    print("\n" + "="*80)
    print("STRATEGY COMPARISON SUMMARY")
    print("="*80)
    
    if not original_result:
        print("❌ Original strategy failed to run")
        print(f"✅ Incremental strategy: {incremental_result['profit_ratio']*100:.2f}% profit")
        return
    
    print(f"\n📊 PERFORMANCE METRICS:")
    print(f"{'Metric':<20} {'Original':<15} {'Incremental':<15} {'Difference':<15}")
    print("-" * 65)
    
    # Profit comparison
    orig_profit = original_result['profit_ratio'] * 100
    inc_profit = incremental_result['profit_ratio'] * 100
    profit_diff = inc_profit - orig_profit
    print(f"{'Profit %':<20} {orig_profit:<15.2f} {inc_profit:<15.2f} {profit_diff:<15.2f}")
    
    # Final USD comparison
    orig_final = original_result['final_usd']
    inc_final = incremental_result['final_usd']
    usd_diff = inc_final - orig_final
    print(f"{'Final USD':<20} ${orig_final:<14.2f} ${inc_final:<14.2f} ${usd_diff:<14.2f}")
    
    # Trade count comparison
    orig_trades = original_result['n_trades']
    inc_trades = incremental_result['n_trades']
    trade_diff = inc_trades - orig_trades
    print(f"{'Total Trades':<20} {orig_trades:<15} {inc_trades:<15} {trade_diff:<15}")
    
    # Win rate comparison
    orig_wr = original_result['win_rate'] * 100
    inc_wr = incremental_result['win_rate'] * 100
    wr_diff = inc_wr - orig_wr
    print(f"{'Win Rate %':<20} {orig_wr:<15.2f} {inc_wr:<15.2f} {wr_diff:<15.2f}")
    
    # Average trade comparison
    orig_avg = original_result['avg_trade'] * 100
    inc_avg = incremental_result['avg_trade'] * 100
    avg_diff = inc_avg - orig_avg
    print(f"{'Avg Trade %':<20} {orig_avg:<15.2f} {inc_avg:<15.2f} {avg_diff:<15.2f}")
    
    # Max drawdown comparison
    orig_dd = original_result['max_drawdown'] * 100
    inc_dd = incremental_result['max_drawdown'] * 100
    dd_diff = inc_dd - orig_dd
    print(f"{'Max Drawdown %':<20} {orig_dd:<15.2f} {inc_dd:<15.2f} {dd_diff:<15.2f}")
    
    # Fees comparison
    orig_fees = original_result['total_fees_usd']
    inc_fees = incremental_result['total_fees_usd']
    fees_diff = inc_fees - orig_fees
    print(f"{'Total Fees USD':<20} ${orig_fees:<14.2f} ${inc_fees:<14.2f} ${fees_diff:<14.2f}")
    
    print("\n" + "="*80)
    
    # Determine winner
    if profit_diff > 0:
        print(f"🏆 WINNER: Incremental Strategy (+{profit_diff:.2f}% better)")
    elif profit_diff < 0:
        print(f"🏆 WINNER: Original Strategy (+{abs(profit_diff):.2f}% better)")
    else:
        print(f"🤝 TIE: Both strategies performed equally")
    
    print("="*80)


def main():
    """Main comparison function."""
    print("🚀 Comparing Original vs Incremental Strategies on Same Data")
    print("=" * 80)
    
    # Configuration
    start_date = "2025-01-01"
    end_date = "2025-05-01"
    initial_usd = 10000
    stop_loss_pct = 0.03  # 3% stop loss
    
    print(f"📅 Test Period: {start_date} to {end_date}")
    print(f"💰 Initial Capital: ${initial_usd:,}")
    print(f"🛑 Stop Loss: {stop_loss_pct*100:.1f}%")
    print(f"📊 Data Source: btcusd_1-min_data.csv")
    
    try:
        # Run both strategies
        original_result = run_original_strategy_via_main(start_date, end_date, initial_usd, stop_loss_pct)
        incremental_result = run_incremental_strategy(start_date, end_date, initial_usd, stop_loss_pct)
        
        # Print comparison summary
        print_comparison_summary(original_result, incremental_result)
        
        # Save results
        output_dir = "results/strategy_comparison"
        comparison_data = save_comparison_results(original_result, incremental_result, output_dir)
        
        # Create comparison plot
        create_comparison_plot(original_result, incremental_result, start_date, end_date, output_dir)
        
        print(f"\n📁 Results saved to: {output_dir}/")
        print(f"   - strategy_comparison.json")
        print(f"   - strategy_comparison.png")
        print(f"   - original_trades.csv")
        print(f"   - incremental_trades.csv")
        
        return True
        
    except Exception as e:
        print(f"\n❌ Error during comparison: {e}")
        import traceback
        traceback.print_exc()
        return False


if __name__ == "__main__":
    success = main()
    sys.exit(0 if success else 1)