from __future__ import annotations

import argparse
import dataclasses
from pathlib import Path
from typing import Optional

import pandas as pd

from backtester.data_loader import load_csv
from backtester.indicators.money_line import MoneyLineInputs
from backtester.simulator import TradeConfig, simulate_money_line


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser(description="Run Money Line backtests against CSV OHLCV data.")
    parser.add_argument("--csv", type=Path, required=True, help="Path to CSV file with timestamp, open, high, low, close, volume columns")
    parser.add_argument("--symbol", type=str, required=True, help="Symbol label (e.g., SOL-PERP)")
    parser.add_argument("--timeframe", type=str, default="5", help="Timeframe label for reference (default: 5)")
    parser.add_argument("--start", type=str, default=None, help="Optional ISO timestamp for start filter (e.g., 2024-01-01)")
    parser.add_argument("--end", type=str, default=None, help="Optional ISO timestamp for end filter")
    parser.add_argument("--position-size", type=float, default=1000.0, dest="position_size", help="Notional size in USD for each simulated trade")
    parser.add_argument("--max-bars", type=int, default=None, help="Maximum bars to hold a trade before force exit")
    parser.add_argument("--export-trades", type=Path, default=None, help="Optional path to write detailed trade results as CSV")
    return parser.parse_args()


def main() -> None:
    args = parse_args()

    data_slice = load_csv(
        path=args.csv,
        symbol=args.symbol,
        timeframe=args.timeframe,
        start=args.start,
        end=args.end,
    )

    df = data_slice.data
    if not isinstance(df.index, pd.DatetimeIndex):
        df = df.copy()
        df.index = pd.to_datetime(df.index)

    config = TradeConfig(position_size=args.position_size, max_bars_per_trade=args.max_bars)
    inputs = MoneyLineInputs()

    result = simulate_money_line(df=df, symbol=data_slice.symbol, inputs=inputs, config=config)

    print("=== Backtest Summary ===")
    print(f"Symbol: {data_slice.symbol}")
    print(f"Rows processed: {len(df)}")
    print(f"Trades: {len(result.trades)}")
    print(f"Total PnL: ${result.total_pnl:,.2f}")
    print(f"Average PnL: ${result.average_pnl:,.2f}")
    print(f"Win rate: {result.win_rate * 100:.2f}%")
    print(f"Max drawdown: ${result.max_drawdown:,.2f}")

    if args.export_trades:
        trades_df = pd.DataFrame(
            [
                {k: v for k, v in dataclasses.asdict(trade).items() if k != "_exit_index"}
                for trade in result.trades
            ]
        )
        trades_df.to_csv(args.export_trades, index=False)
        print(f"Detailed trades exported to {args.export_trades}")


if __name__ == "__main__":
    main()