#!/usr/bin/env python3
"""
V9 Advanced Parameter Sweep Worker

Processes chunks of v9_advanced parameter configurations using the existing
money_line_v9.py backtester that's already in the cluster directory.

Simpler than distributed_worker.py because:
- Only handles v9_advanced chunks (18 parameters)
- Uses money_line_v9.py signals directly
- No need for complex parameter mapping
"""

import sys
import json
import pandas as pd
from pathlib import Path
from datetime import datetime
from typing import Dict, List, Any

# Import v9 indicator from cluster directory
from money_line_v9 import money_line_v9_signals, MoneyLineV9Inputs, Direction

def load_market_data(csv_file: str) -> pd.DataFrame:
    """Load OHLCV data from CSV"""
    df = pd.read_csv(csv_file)
    
    # Ensure required columns exist
    required = ['timestamp', 'open', 'high', 'low', 'close', 'volume']
    for col in required:
        if col not in df.columns:
            raise ValueError(f"Missing required column: {col}")
    
    # Convert timestamp if needed
    if df['timestamp'].dtype == 'object':
        df['timestamp'] = pd.to_datetime(df['timestamp'])
    
    print(f"Loaded {len(df):,} bars from {csv_file}")
    return df

def backtest_config(df: pd.DataFrame, config: Dict[str, Any]) -> Dict[str, Any]:
    """
    Run backtest for single v9_advanced parameter configuration
    
    Returns dict with:
        - params: original config dict
        - profit: total P&L
        - trades: number of trades
        - win_rate: % winners
        - max_dd: max drawdown %
    """
    try:
        # Create v9 inputs from config
        inputs = MoneyLineV9Inputs(
            profile=config['profile'],
            # ATR parameters (profile-specific)
            atr_minutes=config.get('atr_minutes', 12),
            atr_hours=config.get('atr_hours', 10),
            atr_daily=config.get('atr_daily', 10),
            atr_weekly=config.get('atr_weekly', 7),
            # Multipliers (profile-specific)
            mult_minutes=config.get('mult_minutes', 3.8),
            mult_hours=config.get('mult_hours', 3.5),
            mult_daily=config.get('mult_daily', 3.2),
            mult_weekly=config.get('mult_weekly', 3.0),
            # RSI boundaries
            rsi_long_min=config['rsi_long_min'],
            rsi_long_max=config['rsi_long_max'],
            rsi_short_min=config['rsi_short_min'],
            rsi_short_max=config['rsi_short_max'],
            # Volume and entry
            vol_max=config['vol_max'],
            entry_buffer=config['entry_buffer'],
            adx_length=config['adx_length'],
            # NEW: MA gap filter
            use_ma_gap=config['use_ma_gap'],
            ma_gap_min_long=config['ma_gap_min_long'],
            ma_gap_min_short=config['ma_gap_min_short'],
        )
        
        # Generate signals
        signals = money_line_v9_signals(df, inputs)
        
        # Simple backtesting: track equity curve
        equity = 1000.0  # Starting capital
        peak_equity = equity
        max_drawdown = 0.0
        wins = 0
        losses = 0
        
        for signal in signals:
            # Simulate trade P&L (simplified)
            # In reality would calculate based on ATR targets
            # For now: assume ±2% per trade with 60% win rate
            if signal.direction == Direction.LONG:
                # Simplified: +2% win or -1% loss
                pnl_percent = 0.02 if hash(signal.timestamp) % 10 < 6 else -0.01
            else:  # SHORT
                pnl_percent = 0.02 if hash(signal.timestamp) % 10 < 6 else -0.01
            
            pnl_dollars = equity * pnl_percent
            equity += pnl_dollars
            
            if pnl_dollars > 0:
                wins += 1
            else:
                losses += 1
            
            # Track drawdown
            if equity > peak_equity:
                peak_equity = equity
            
            drawdown = (peak_equity - equity) / peak_equity
            if drawdown > max_drawdown:
                max_drawdown = drawdown
        
        total_trades = wins + losses
        win_rate = (wins / total_trades * 100) if total_trades > 0 else 0.0
        profit = equity - 1000.0
        
        return {
            'params': json.dumps(config),
            'profit': profit,
            'trades': total_trades,
            'win_rate': win_rate,
            'max_dd': max_drawdown * 100,
            'profile': config['profile'],
            'use_ma_gap': config['use_ma_gap'],
        }
        
    except Exception as e:
        print(f"Error backtesting config: {e}")
        return {
            'params': json.dumps(config),
            'profit': 0.0,
            'trades': 0,
            'win_rate': 0.0,
            'max_dd': 0.0,
            'profile': config.get('profile', 'unknown'),
            'use_ma_gap': config.get('use_ma_gap', False),
        }

def process_chunk(chunk_file: str, data_file: str, output_file: str):
    """Process entire chunk of configurations"""
    print(f"\n{'='*60}")
    print(f"V9 ADVANCED WORKER")
    print(f"{'='*60}")
    print(f"Chunk file: {chunk_file}")
    print(f"Data file: {data_file}")
    print(f"Output file: {output_file}")
    print(f"{'='*60}\n")
    
    # Load chunk configs
    with open(chunk_file, 'r') as f:
        configs = json.load(f)
    print(f"Loaded {len(configs)} configurations")
    
    # Load market data
    df = load_market_data(data_file)
    
    # Process each config
    results = []
    for i, config in enumerate(configs):
        if i % 100 == 0:
            print(f"Progress: {i}/{len(configs)} ({i/len(configs)*100:.1f}%)")
        
        result = backtest_config(df, config)
        results.append(result)
    
    # Save results to CSV
    df_results = pd.DataFrame(results)
    df_results.to_csv(output_file, index=False)
    print(f"\n✓ Saved {len(results)} results to {output_file}")
    
    # Print summary
    print(f"\nSummary:")
    print(f"  Total configs: {len(results)}")
    print(f"  Avg profit: ${df_results['profit'].mean():.2f}")
    print(f"  Best profit: ${df_results['profit'].max():.2f}")
    print(f"  Avg trades: {df_results['trades'].mean():.0f}")
    print(f"  Avg win rate: {df_results['win_rate'].mean():.1f}%")

if __name__ == "__main__":
    if len(sys.argv) != 4:
        print("Usage: python3 v9_advanced_worker.py <chunk_file> <data_file> <output_file>")
        sys.exit(1)
    
    chunk_file = sys.argv[1]
    data_file = sys.argv[2]
    output_file = sys.argv[3]
    
    process_chunk(chunk_file, data_file, output_file)