main.py

import argparse
import sys
from data_loader import load_data, make_synthetic_orderbook, get_real_market_data
from backtester import Backtester
from strategies.mean_reversion import MeanReversionStrategy
from strategies.momentum import MomentumStrategy
from strategies.market_making import MarketMakingStrategy
from metrics import compute_performance_metrics
from data_quality import print_data_quality_report

STRATEGIES = {
    'mean_reversion': MeanReversionStrategy,
    'momentum': MomentumStrategy,
    'market_making': MarketMakingStrategy,
}


def parse_args(argv=None):
    p = argparse.ArgumentParser(description='Professional Intraday Trading Strategy Backtester')
    
    # Data source options
    p.add_argument('--symbol', type=str, default='AAPL', help='Stock symbol to trade (default: AAPL)')
    p.add_argument('--csv', type=str, help='Path to CSV with order book data')
    p.add_argument('--synthetic', action='store_true', help='Use synthetic data instead of real market data')
    p.add_argument('--period', type=str, default='5d', choices=['1d', '5d', '1mo', '3mo'], 
                   help='Period for Yahoo Finance data (default: 5d)')
    
    # Strategy options
    p.add_argument('--strategy', type=str, default='mean_reversion', choices=STRATEGIES.keys(),
                   help='Trading strategy to use (default: mean_reversion)')
    p.add_argument('--lookback', type=int, default=60, help='Lookback period for strategy (default: 60)')
    p.add_argument('--threshold', type=float, default=0.5, help='Strategy threshold parameter (default: 0.5)')
    p.add_argument('--max-position', type=int, default=100, help='Maximum position size (default: 100)')
    
    # Backtesting parameters
    p.add_argument('--latency-ms', type=int, default=50, help='Order latency in milliseconds (default: 50)')
    p.add_argument('--fee-per-share', type=float, default=0.0005, help='Fee per share (default: 0.0005)')
    p.add_argument('--slippage-bps', type=float, default=2.0, help='Slippage in basis points (default: 2.0)')
    
    # Output options
    p.add_argument('--no-plot', action='store_true', help='Disable plotting')
    p.add_argument('--data-quality', action='store_true', help='Show detailed data quality assessment')
    
    return p.parse_args(argv)


def main(argv=None):
    args = parse_args(argv)

    print("🚀 Professional Trading Bot - Quant Research Framework")
    print("=" * 65)
    
    # Load data based on user preference
    if args.csv:
        print(f"📁 Loading data from CSV: {args.csv}")
        data = load_data(args.csv)
        print(f"✅ Loaded {len(data)} ticks from CSV")
    elif args.synthetic:
        print("🎲 Generating synthetic market data...")
        data = make_synthetic_orderbook(symbol=args.symbol)
        print(f"✅ Generated {len(data)} synthetic ticks")
    else:
        # Default: Get real market data from Yahoo Finance
        data = get_real_market_data(symbol=args.symbol, period=args.period)
    
    # Show data quality assessment
    if args.data_quality or not (args.csv or args.synthetic):
        print_data_quality_report(data)
    
    print(f"\n⚙️ Strategy Configuration:")
    print(f"  • Strategy: {args.strategy}")
    print(f"  • Lookback: {args.lookback}")
    print(f"  • Threshold: {args.threshold}")
    print(f"  • Max Position: {args.max_position}")
    print(f"  • Latency: {args.latency_ms}ms")
    print(f"  • Fees: ${args.fee_per_share:.4f}/share")
    print(f"  • Slippage: {args.slippage_bps:.1f} bps")
    
    # Build strategy
    Strategy = STRATEGIES[args.strategy]
    strategy = Strategy(
        lookback=args.lookback, 
        threshold=args.threshold, 
        max_position=args.max_position
    )

    # Run backtest
    print(f"\n🔄 Running backtest...")
    bt = Backtester(
        data=data,
        strategy=strategy,
        latency_ms=args.latency_ms,
        fee_per_share=args.fee_per_share,
        slippage_bps=args.slippage_bps,
    )
    results = bt.run()

    # Performance metrics
    metrics = compute_performance_metrics(results)
    print(f"\n📈 Performance Results:")
    print("=" * 40)
    for k, v in metrics.items():
        if isinstance(v, float):
            if 'ratio' in k.lower() or 'rate' in k.lower():
                print(f"{k.replace('_', ' ').title()}: {v:.3f}")
            elif 'drawdown' in k.lower():
                print(f"{k.replace('_', ' ').title()}: {v:.2%}")
            elif 'volatility' in k.lower():
                print(f"{k.replace('_', ' ').title()}: {v:.2%}")
            else:
                print(f"{k.replace('_', ' ').title()}: {v:.2f}")
        else:
            print(f"{k.replace('_', ' ').title()}: {v}")

    # Generate plots
    if not args.no_plot:
        print(f"\n📊 Generating performance plots...")
        try:
            from plotting import plot_results
            plot_results(results, metrics)
        except ImportError as e:
            print(f"⚠️ Plotting unavailable: {e}")
        except Exception as e:
            print(f"⚠️ Plotting failed: {e}")

    return 0


if __name__ == '__main__':
    raise SystemExit(main())