from __future__ import annotations
import argparse
from pathlib import Path
import pandas as pd

from config import CLIConfig
from data import load_data
from backtest import backtest

def parse_args() -> CLIConfig:
    p = argparse.ArgumentParser(prog="bt", description="Simple supertrend backtester")
    p.add_argument("start")
    p.add_argument("end")
    p.add_argument("--timeframe-minutes", type=int, default=15)     # single TF
    p.add_argument("--timeframes-minutes", nargs="+", type=int)     # multi TF: e.g. 5 15 60 240
    p.add_argument("--stop-loss", dest="stop_losses", type=float, nargs="+", default=[0.02, 0.05])
    p.add_argument("--exit-on-bearish-flip", action="store_true")
    p.add_argument("--csv", dest="csv_path", type=Path, required=True)
    p.add_argument("--out-csv", type=Path, default=Path("summary.csv"))
    p.add_argument("--log-dir", type=Path, default=Path("./logs"))
    p.add_argument("--fee-bps", type=float, default=10.0)
    p.add_argument("--slippage-bps", type=float, default=2.0)
    a = p.parse_args()

    return CLIConfig(
        start=a.start,
        end=a.end,
        timeframe_minutes=a.timeframe_minutes,
        timeframes_minutes=a.timeframes_minutes,
        stop_losses=a.stop_losses,
        exit_on_bearish_flip=a.exit_on_bearish_flip,
        csv_path=a.csv_path,
        out_csv=a.out_csv,
        log_dir=a.log_dir,
        fee_bps=a.fee_bps,
        slippage_bps=a.slippage_bps,
    )

def main():
    cfg = parse_args()
    frames = cfg.timeframes_minutes or [cfg.timeframe_minutes]

    rows: list[dict] = []
    for tfm in frames:
        df_1min, df = load_data(cfg.start, cfg.end, tfm, cfg.csv_path)
        for sl in cfg.stop_losses:
            log_path = cfg.log_dir / f"{tfm}m_sl{sl:.2%}.csv"
            perf, equity, _ = backtest(
                df=df,
                df_1min=df_1min,
                timeframe_minutes=tfm,
                stop_loss=sl,
                exit_on_bearish_flip=cfg.exit_on_bearish_flip,
                fee_bps=cfg.fee_bps,
                slippage_bps=cfg.slippage_bps,
                log_path=log_path,
            )
            rows.append({
                "timeframe": f"{tfm}min",
                "stop_loss": sl,
                "exit_on_bearish_flip": cfg.exit_on_bearish_flip,
                "total_return": f"{perf.total_return:.2%}",
                "max_drawdown": f"{perf.max_drawdown:.2%}",
                "sharpe_ratio": f"{perf.sharpe_ratio:.2f}",
                "win_rate": f"{perf.win_rate:.2%}",
                "num_trades": perf.num_trades,
                "final_equity": f"${perf.final_equity:.2f}",
                "initial_equity": f"${perf.initial_equity:.2f}",
                "num_stop_losses": perf.num_stop_losses,
                "total_fees": perf.total_fees,
                "total_slippage_usd": perf.total_slippage_usd,
                "avg_slippage_bps": perf.avg_slippage_bps,
            })

    out = pd.DataFrame(rows)
    out.to_csv(cfg.out_csv, index=False)
    print(out.to_string(index=False))

if __name__ == "__main__":
    main()