from __future__ import annotations
import pandas as pd
from pathlib import Path
from trade import TradeState, enter_long, exit_long, maybe_trailing_stop
from indicators import add_supertrends, compute_meta_trend
from metrics import compute_metrics
from logging_utils import write_trade_log

DEFAULT_ST_SETTINGS = [(12, 3.0), (10, 1.0), (11, 2.0)]

def backtest(
    df: pd.DataFrame,
    df_1min: pd.DataFrame,
    timeframe_minutes: int,
    stop_loss: float,
    exit_on_bearish_flip: bool,
    fee_bps: float,
    slippage_bps: float,
    log_path: Path | None = None,
):
    df = add_supertrends(df, DEFAULT_ST_SETTINGS)
    df["meta_bull"] = compute_meta_trend(df, DEFAULT_ST_SETTINGS)

    state = TradeState(stop_loss_frac=stop_loss, fee_bps=fee_bps, slippage_bps=slippage_bps)
    equity, trades = [], []

    for i, row in df.iterrows():
        price = float(row["Close"])
        ts = pd.Timestamp(row["Timestamp"])

        if state.qty <= 0 and row["meta_bull"] == 1:
            evt = enter_long(state, price)
            if evt:
                evt.update({"t": ts.isoformat(), "reason": "bull_flip"})
                trades.append(evt)

        start = ts
        end = df["Timestamp"].iat[i + 1] if i + 1 < len(df) else ts + pd.Timedelta(minutes=timeframe_minutes)

        if state.qty > 0:
            win = df_1min[(df_1min["Timestamp"] >= start) & (df_1min["Timestamp"] < end)]
            for _, m in win.iterrows():
                hi = float(m["High"])
                lo = float(m["Low"])
                state.max_px = max(state.max_px or hi, hi)
                trail = state.max_px * (1.0 - state.stop_loss_frac)
                if lo <= trail:
                    evt = exit_long(state, trail)
                    if evt:
                        prev = trades[-1]
                        pnl = (evt["price"] - (prev.get("price") or evt["price"])) * (prev.get("qty") or 0.0)
                        evt.update({"t": pd.Timestamp(m["Timestamp"]).isoformat(), "reason": "stop", "pnl": pnl})
                        trades.append(evt)
                    break

        if state.qty > 0 and exit_on_bearish_flip and row["meta_bull"] == 0:
            evt = exit_long(state, price)
            if evt:
                prev = trades[-1]
                pnl = (evt["price"] - (prev.get("price") or evt["price"])) * (prev.get("qty") or 0.0)
                evt.update({"t": ts.isoformat(), "reason": "bearish_flip", "pnl": pnl})
                trades.append(evt)

        equity.append(state.cash + state.qty * price)

    equity_curve = pd.Series(equity, index=df["Timestamp"])
    if log_path:
        write_trade_log(trades, log_path)
    perf = compute_metrics(equity_curve, trades)
    return perf, equity_curve, trades