#!/usr/bin/env python3
"""
Compare Strategy Signals Only (No Backtesting)
==============================================

This script extracts entry and exit signals from both the original and incremental
strategies on the same data and plots them for visual comparison.
"""

import sys
import os
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.metatrend_strategy import IncMetaTrendStrategy
from cycles.utils.data_utils import aggregate_to_minutes
from cycles.strategies.default_strategy import DefaultStrategy


def extract_original_signals(data_1min: pd.DataFrame, timeframe: str = "15min"):
    """Extract signals from the original strategy."""
    print(f"\n🔄 Extracting Original Strategy Signals...")
    
    # Create a mock backtester object for the strategy
    class MockBacktester:
        def __init__(self, data):
            self.original_df = data
            self.strategies = {}
            self.current_position = None
            self.entry_price = None
    
    # Initialize the original strategy
    strategy = DefaultStrategy(
        weight=1.0, 
        params={
            "timeframe": timeframe,
            "stop_loss_pct": 0.03
        }
    )
    
    # Create mock backtester and initialize strategy
    mock_backtester = MockBacktester(data_1min)
    strategy.initialize(mock_backtester)
    
    if not strategy.initialized:
        print("   ❌ Strategy initialization failed")
        return []
    
    # Get the aggregated data for the primary timeframe
    primary_data = strategy.get_primary_timeframe_data()
    if primary_data is None or len(primary_data) == 0:
        print("   ❌ No primary timeframe data available")
        return []
    
    signals = []
    
    # Process each data point in the primary timeframe
    for i in range(len(primary_data)):
        timestamp = primary_data.index[i]
        row = primary_data.iloc[i]
        
        # Get entry signal
        entry_signal = strategy.get_entry_signal(mock_backtester, i)
        if entry_signal and entry_signal.signal_type == "ENTRY":
            signals.append({
                'timestamp': timestamp,
                'type': 'ENTRY',
                'price': entry_signal.price if entry_signal.price else row['close'],
                'strategy': 'Original',
                'confidence': entry_signal.confidence,
                'metadata': entry_signal.metadata
            })
        
        # Get exit signal
        exit_signal = strategy.get_exit_signal(mock_backtester, i)
        if exit_signal and exit_signal.signal_type == "EXIT":
            signals.append({
                'timestamp': timestamp,
                'type': 'EXIT',
                'price': exit_signal.price if exit_signal.price else row['close'],
                'strategy': 'Original',
                'confidence': exit_signal.confidence,
                'metadata': exit_signal.metadata
            })
    
    print(f"   Found {len([s for s in signals if s['type'] == 'ENTRY'])} entry signals")
    print(f"   Found {len([s for s in signals if s['type'] == 'EXIT'])} exit signals")
    
    return signals


def extract_incremental_signals(data_1min: pd.DataFrame, timeframe: str = "15min"):
    """Extract signals from the incremental strategy."""
    print(f"\n🔄 Extracting Incremental Strategy Signals...")
    
    # Initialize the incremental strategy
    strategy = IncMetaTrendStrategy(
        name="metatrend",
        weight=1.0,
        params={
            "timeframe": timeframe,
            "enable_logging": False
        }
    )
    
    signals = []
    
    # Process each minute of data
    for i, (timestamp, row) in enumerate(data_1min.iterrows()):
        # Create the data structure for incremental strategy
        ohlcv_data = {
            'open': row['open'],
            'high': row['high'],
            'low': row['low'],
            'close': row['close'],
            'volume': row['volume']
        }
        
        # Update the strategy with new data (correct method signature)
        result = strategy.update_minute_data(timestamp, ohlcv_data)
        
        # Check if a complete timeframe bar was formed
        if result is not None:
            # Get entry signal
            entry_signal = strategy.get_entry_signal()
            if entry_signal and entry_signal.signal_type.upper() in ['BUY', 'ENTRY']:
                signals.append({
                    'timestamp': timestamp,
                    'type': 'BUY',
                    'price': entry_signal.price if entry_signal.price else row['close'],
                    'strategy': 'Incremental',
                    'confidence': entry_signal.confidence,
                    'reason': entry_signal.metadata.get('type', 'ENTRY') if entry_signal.metadata else 'ENTRY'
                })
            
            # Get exit signal
            exit_signal = strategy.get_exit_signal()
            if exit_signal and exit_signal.signal_type.upper() in ['SELL', 'EXIT']:
                signals.append({
                    'timestamp': timestamp,
                    'type': 'SELL',
                    'price': exit_signal.price if exit_signal.price else row['close'],
                    'strategy': 'Incremental',
                    'confidence': exit_signal.confidence,
                    'reason': exit_signal.metadata.get('type', 'EXIT') if exit_signal.metadata else 'EXIT'
                })
    
    print(f"   Found {len([s for s in signals if s['type'] == 'BUY'])} buy signals")
    print(f"   Found {len([s for s in signals if s['type'] == 'SELL'])} sell signals")
    
    return signals


def create_signals_comparison_plot(data_1min: pd.DataFrame, original_signals: list, 
                                 incremental_signals: list, start_date: str, end_date: str, 
                                 output_dir: str):
    """Create a comprehensive signals comparison plot."""
    print(f"\n📊 Creating signals comparison plot...")
    
    # Aggregate data for plotting (15min for cleaner visualization)
    aggregated_data = aggregate_to_minutes(data_1min, 15)
    
    # Create figure with subplots
    fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(20, 16))
    
    # Plot 1: Price with all signals
    ax1.plot(aggregated_data.index, aggregated_data['close'], 'k-', alpha=0.7, linewidth=1.5, label='BTC Price (15min)')
    
    # Plot original strategy signals
    original_entries = [s for s in original_signals if s['type'] == 'ENTRY']
    original_exits = [s for s in original_signals if s['type'] == 'EXIT']
    
    if original_entries:
        entry_times = [s['timestamp'] for s in original_entries]
        entry_prices = [s['price'] * 1.03 for s in original_entries]  # Position above price
        ax1.scatter(entry_times, entry_prices, color='green', marker='^', s=100, 
                   alpha=0.8, label=f'Original Entry ({len(original_entries)})', zorder=5)
    
    if original_exits:
        exit_times = [s['timestamp'] for s in original_exits]
        exit_prices = [s['price'] * 1.03 for s in original_exits]  # Position above price
        ax1.scatter(exit_times, exit_prices, color='red', marker='v', s=100, 
                   alpha=0.8, label=f'Original Exit ({len(original_exits)})', zorder=5)
    
    # Plot incremental strategy signals
    incremental_entries = [s for s in incremental_signals if s['type'] == 'BUY']
    incremental_exits = [s for s in incremental_signals if s['type'] == 'SELL']
    
    if incremental_entries:
        entry_times = [s['timestamp'] for s in incremental_entries]
        entry_prices = [s['price'] * 0.97 for s in incremental_entries]  # Position below price
        ax1.scatter(entry_times, entry_prices, color='lightgreen', marker='^', s=80, 
                   alpha=0.8, label=f'Incremental Entry ({len(incremental_entries)})', zorder=5)
    
    if incremental_exits:
        exit_times = [s['timestamp'] for s in incremental_exits]
        exit_prices = [s['price'] * 0.97 for s in incremental_exits]  # Position below price
        ax1.scatter(exit_times, exit_prices, color='orange', marker='v', s=80, 
                   alpha=0.8, label=f'Incremental Exit ({len(incremental_exits)})', zorder=5)
    
    ax1.set_title(f'Strategy Signals Comparison: {start_date} to {end_date}', fontsize=16, fontweight='bold')
    ax1.set_ylabel('Price (USD)', fontsize=12)
    ax1.legend(loc='upper left', fontsize=10)
    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.WeekdayLocator(interval=2))
    plt.setp(ax1.xaxis.get_majorticklabels(), rotation=45)
    
    # Plot 2: Signal frequency over time (daily counts)
    # Create daily signal counts
    daily_signals = {}
    
    for signal in original_signals:
        date = signal['timestamp'].date()
        if date not in daily_signals:
            daily_signals[date] = {'original_entry': 0, 'original_exit': 0, 'inc_entry': 0, 'inc_exit': 0}
        if signal['type'] == 'ENTRY':
            daily_signals[date]['original_entry'] += 1
        else:
            daily_signals[date]['original_exit'] += 1
    
    for signal in incremental_signals:
        date = signal['timestamp'].date()
        if date not in daily_signals:
            daily_signals[date] = {'original_entry': 0, 'original_exit': 0, 'inc_entry': 0, 'inc_exit': 0}
        if signal['type'] == 'BUY':
            daily_signals[date]['inc_entry'] += 1
        else:
            daily_signals[date]['inc_exit'] += 1
    
    if daily_signals:
        dates = sorted(daily_signals.keys())
        orig_entries = [daily_signals[d]['original_entry'] for d in dates]
        orig_exits = [daily_signals[d]['original_exit'] for d in dates]
        inc_entries = [daily_signals[d]['inc_entry'] for d in dates]
        inc_exits = [daily_signals[d]['inc_exit'] for d in dates]
        
        width = 0.35
        x = np.arange(len(dates))
        
        ax2.bar(x - width/2, orig_entries, width, label='Original Entries', color='green', alpha=0.7)
        ax2.bar(x - width/2, orig_exits, width, bottom=orig_entries, label='Original Exits', color='red', alpha=0.7)
        ax2.bar(x + width/2, inc_entries, width, label='Incremental Entries', color='lightgreen', alpha=0.7)
        ax2.bar(x + width/2, inc_exits, width, bottom=inc_entries, label='Incremental Exits', color='orange', alpha=0.7)
        
        ax2.set_title('Daily Signal Frequency', fontsize=14, fontweight='bold')
        ax2.set_ylabel('Number of Signals', fontsize=12)
        ax2.set_xticks(x[::7])  # Show every 7th date
        ax2.set_xticklabels([dates[i].strftime('%m-%d') for i in range(0, len(dates), 7)], rotation=45)
        ax2.legend(fontsize=10)
        ax2.grid(True, alpha=0.3, axis='y')
    
    # Plot 3: Signal statistics comparison
    strategies = ['Original', 'Incremental']
    entry_counts = [len(original_entries), len(incremental_entries)]
    exit_counts = [len(original_exits), len(incremental_exits)]
    
    x = np.arange(len(strategies))
    width = 0.35
    
    bars1 = ax3.bar(x - width/2, entry_counts, width, label='Entry Signals', color='green', alpha=0.7)
    bars2 = ax3.bar(x + width/2, exit_counts, width, label='Exit Signals', color='red', alpha=0.7)
    
    ax3.set_title('Total Signal Counts', fontsize=14, fontweight='bold')
    ax3.set_ylabel('Number of Signals', fontsize=12)
    ax3.set_xticks(x)
    ax3.set_xticklabels(strategies)
    ax3.legend(fontsize=10)
    ax3.grid(True, alpha=0.3, axis='y')
    
    # Add value labels on bars
    for bars in [bars1, bars2]:
        for bar in bars:
            height = bar.get_height()
            ax3.text(bar.get_x() + bar.get_width()/2., height + 0.5,
                    f'{int(height)}', ha='center', va='bottom', fontweight='bold')
    
    plt.tight_layout()
    
    # Save plot
    os.makedirs(output_dir, exist_ok=True)
    # plt.show()
    plot_file = os.path.join(output_dir, "signals_comparison.png")
    plt.savefig(plot_file, dpi=300, bbox_inches='tight')
    plt.close()
    print(f"Saved signals comparison plot to: {plot_file}")


def save_signals_data(original_signals: list, incremental_signals: list, output_dir: str):
    """Save signals data to CSV files."""
    os.makedirs(output_dir, exist_ok=True)
    
    # Save original signals
    if original_signals:
        orig_df = pd.DataFrame(original_signals)
        orig_file = os.path.join(output_dir, "original_signals.csv")
        orig_df.to_csv(orig_file, index=False)
        print(f"Saved original signals to: {orig_file}")
    
    # Save incremental signals
    if incremental_signals:
        inc_df = pd.DataFrame(incremental_signals)
        inc_file = os.path.join(output_dir, "incremental_signals.csv")
        inc_df.to_csv(inc_file, index=False)
        print(f"Saved incremental signals to: {inc_file}")
    
    # Create summary
    summary = {
        'test_date': datetime.now().isoformat(),
        'original_strategy': {
            'total_signals': len(original_signals),
            'entry_signals': len([s for s in original_signals if s['type'] == 'ENTRY']),
            'exit_signals': len([s for s in original_signals if s['type'] == 'EXIT'])
        },
        'incremental_strategy': {
            'total_signals': len(incremental_signals),
            'entry_signals': len([s for s in incremental_signals if s['type'] == 'BUY']),
            'exit_signals': len([s for s in incremental_signals if s['type'] == 'SELL'])
        }
    }
    
    import json
    summary_file = os.path.join(output_dir, "signals_summary.json")
    with open(summary_file, 'w') as f:
        json.dump(summary, f, indent=2)
    print(f"Saved signals summary to: {summary_file}")


def print_signals_summary(original_signals: list, incremental_signals: list):
    """Print a detailed signals comparison summary."""
    print("\n" + "="*80)
    print("SIGNALS COMPARISON SUMMARY")
    print("="*80)
    
    # Count signals by type
    orig_entries = len([s for s in original_signals if s['type'] == 'ENTRY'])
    orig_exits = len([s for s in original_signals if s['type'] == 'EXIT'])
    inc_entries = len([s for s in incremental_signals if s['type'] == 'BUY'])
    inc_exits = len([s for s in incremental_signals if s['type'] == 'SELL'])
    
    print(f"\n📊 SIGNAL COUNTS:")
    print(f"{'Signal Type':<20} {'Original':<15} {'Incremental':<15} {'Difference':<15}")
    print("-" * 65)
    print(f"{'Entry Signals':<20} {orig_entries:<15} {inc_entries:<15} {inc_entries - orig_entries:<15}")
    print(f"{'Exit Signals':<20} {orig_exits:<15} {inc_exits:<15} {inc_exits - orig_exits:<15}")
    print(f"{'Total Signals':<20} {len(original_signals):<15} {len(incremental_signals):<15} {len(incremental_signals) - len(original_signals):<15}")
    
    # Signal timing analysis
    if original_signals and incremental_signals:
        orig_times = [s['timestamp'] for s in original_signals]
        inc_times = [s['timestamp'] for s in incremental_signals]
        
        print(f"\n📅 TIMING ANALYSIS:")
        print(f"{'Metric':<20} {'Original':<15} {'Incremental':<15}")
        print("-" * 50)
        print(f"{'First Signal':<20} {min(orig_times).strftime('%Y-%m-%d %H:%M'):<15} {min(inc_times).strftime('%Y-%m-%d %H:%M'):<15}")
        print(f"{'Last Signal':<20} {max(orig_times).strftime('%Y-%m-%d %H:%M'):<15} {max(inc_times).strftime('%Y-%m-%d %H:%M'):<15}")
    
    print("\n" + "="*80)


def main():
    """Main signals comparison function."""
    print("🚀 Comparing Strategy Signals (No Backtesting)")
    print("=" * 80)
    
    # Configuration
    start_date = "2025-01-01"
    end_date = "2025-01-10"
    timeframe = "15min"
    
    print(f"📅 Test Period: {start_date} to {end_date}")
    print(f"⏱️  Timeframe: {timeframe}")
    print(f"📊 Data Source: btcusd_1-min_data.csv")
    
    try:
        # Load data
        storage = Storage()
        data_file = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "data", "btcusd_1-min_data.csv")
        
        print(f"\n📂 Loading data from: {data_file}")
        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 False
        
        # Extract signals from both strategies
        original_signals = extract_original_signals(data_1min, timeframe)
        incremental_signals = extract_incremental_signals(data_1min, timeframe)
        
        # Print comparison summary
        print_signals_summary(original_signals, incremental_signals)
        
        # Save signals data
        output_dir = "results/signals_comparison"
        save_signals_data(original_signals, incremental_signals, output_dir)
        
        # Create comparison plot
        create_signals_comparison_plot(data_1min, original_signals, incremental_signals, 
                                     start_date, end_date, output_dir)
        
        print(f"\n📁 Results saved to: {output_dir}/")
        print(f"   - signals_comparison.png")
        print(f"   - original_signals.csv")
        print(f"   - incremental_signals.csv")
        print(f"   - signals_summary.json")
        
        return True
        
    except Exception as e:
        print(f"\n❌ Error during signals comparison: {e}")
        import traceback
        traceback.print_exc()
        return False


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