import sys
import os
import csv
import itertools
from decimal import Decimal

# Add project root to path
current_dir = os.path.dirname(os.path.abspath(__file__))
project_root = os.path.dirname(os.path.dirname(current_dir))
sys.path.append(project_root)

from tests.backtest.backtester import Backtester

def main():
    # Grid Parameters
    # We want to optimize:
    # 1. RANGE_WIDTH_PCT: How wide is the LP position? (e.g. 0.01 = +/-1%, 0.05 = +/-5%)
    # 2. BASE_REBALANCE_THRESHOLD_PCT: When do we hedge? (e.g. 0.10 = 10% delta drift, 0.20 = 20%)
    
    param_grid = {
        "RANGE_WIDTH_PCT": [0.005, 0.01, 0.025, 0.05],
        "BASE_REBALANCE_THRESHOLD_PCT": [0.01, 0.05]
    }
    
    keys, values = zip(*param_grid.items())
    combinations = [dict(zip(keys, v)) for v in itertools.product(*values)]
    
    results = []
    
    book_file = os.path.join(project_root, "market_data", "BNB_raw_20251230_book.csv")
    trades_file = os.path.join(project_root, "market_data", "BNB_raw_20251230_trades.csv")
    
    print(f"Starting Grid Search with {len(combinations)} combinations...")
    
    for idx, config in enumerate(combinations):
        print(f"\n--- Run {idx+1}/{len(combinations)}: {config} ---")
        
        # Initialize Backtester with overrides
        bt = Backtester(book_file, trades_file, config_overrides=config)
        
        try:
            bt.run()
            
            # Collect Metrics
            uni_fees = bt.state.uni_fees_collected
            hl_realized = bt.state.hl_realized_pnl - bt.state.hl_fees_paid
            
            # HL Unrealized
            hl_unrealized = sum(p['unrealized_pnl'] for p in bt.state.hl_positions.values())
            
            # Total PnL (Yield + Hedge Result) - Ignoring IL for now (Mock limitation)
            total_pnl = uni_fees + hl_realized + hl_unrealized
            
            result = {
                **config,
                "UNI_FEES": float(uni_fees),
                "HL_REALIZED": float(hl_realized),
                "HL_UNREALIZED": float(hl_unrealized),
                "TOTAL_PNL": float(total_pnl)
            }
            results.append(result)
            print(f"Result: {result}")
            
        except Exception as e:
            print(f"Run failed: {e}")
            import traceback
            traceback.print_exc()

    # Save Results
    out_file = os.path.join(current_dir, "optimization_results.csv")
    keys = list(combinations[0].keys()) + ["UNI_FEES", "HL_REALIZED", "HL_UNREALIZED", "TOTAL_PNL"]
    
    with open(out_file, 'w', newline='') as f:
        writer = csv.DictWriter(f, fieldnames=keys)
        writer.writeheader()
        writer.writerows(results)
        
    print(f"\nGrid Search Complete. Results saved to {out_file}")

if __name__ == "__main__":
    main()