from collections import deque
from decimal import Decimal

import pandas as pd

from shared.models import Candle, Signal, OrderSide
from strategies.base import BaseStrategy


class EmaCrossoverStrategy(BaseStrategy):
    name: str = "ema_crossover"

    def __init__(self) -> None:
        super().__init__()
        self._closes: deque[float] = deque(maxlen=500)
        self._short_period: int = 9
        self._long_period: int = 21
        self._quantity: Decimal = Decimal("0.01")
        self._prev_short_above: bool | None = None

    @property
    def warmup_period(self) -> int:
        return self._long_period

    def configure(self, params: dict) -> None:
        self._short_period = int(params.get("short_period", 9))
        self._long_period = int(params.get("long_period", 21))
        self._quantity = Decimal(str(params.get("quantity", "0.01")))

        if self._short_period >= self._long_period:
            raise ValueError(
                f"EMA short_period must be < long_period, "
                f"got short={self._short_period}, long={self._long_period}"
            )
        if self._short_period < 2:
            raise ValueError(f"EMA short_period must be >= 2, got {self._short_period}")
        if self._long_period < 2:
            raise ValueError(f"EMA long_period must be >= 2, got {self._long_period}")
        if self._quantity <= 0:
            raise ValueError(f"Quantity must be positive, got {self._quantity}")

        self._init_filters(
            require_trend=True,
            adx_threshold=float(params.get("adx_threshold", 25.0)),
            min_volume_ratio=float(params.get("min_volume_ratio", 0.5)),
            atr_stop_multiplier=float(params.get("atr_stop_multiplier", 2.0)),
            atr_tp_multiplier=float(params.get("atr_tp_multiplier", 3.0)),
        )

    def reset(self) -> None:
        self._closes.clear()
        self._prev_short_above = None

    def _ema_conviction(self, short_ema: float, long_ema: float, price: float) -> float:
        """Map EMA gap to conviction (0.1-1.0). Larger gap = stronger crossover."""
        if price == 0:
            return 0.5
        gap_pct = abs(short_ema - long_ema) / price
        # Scale: 0% gap -> 0.1, 1%+ gap -> ~1.0
        return min(1.0, max(0.1, gap_pct * 100))

    def on_candle(self, candle: Candle) -> Signal | None:
        self._update_filter_data(candle)
        self._closes.append(float(candle.close))

        if len(self._closes) < self._long_period:
            return None

        series = pd.Series(list(self._closes))
        short_ema = series.ewm(span=self._short_period, adjust=False).mean().iloc[-1]
        long_ema = series.ewm(span=self._long_period, adjust=False).mean().iloc[-1]

        short_above = short_ema > long_ema

        signal = None
        if self._prev_short_above is not None:
            conviction = self._ema_conviction(short_ema, long_ema, float(candle.close))
            if not self._prev_short_above and short_above:
                signal = Signal(
                    strategy=self.name,
                    symbol=candle.symbol,
                    side=OrderSide.BUY,
                    price=candle.close,
                    quantity=self._quantity,
                    conviction=conviction,
                    reason=f"Golden Cross: short EMA ({short_ema:.2f}) crossed above long EMA ({long_ema:.2f})",
                )
            elif self._prev_short_above and not short_above:
                signal = Signal(
                    strategy=self.name,
                    symbol=candle.symbol,
                    side=OrderSide.SELL,
                    price=candle.close,
                    quantity=self._quantity,
                    conviction=conviction,
                    reason=f"Death Cross: short EMA ({short_ema:.2f}) crossed below long EMA ({long_ema:.2f})",
                )

        self._prev_short_above = short_above
        if signal is not None:
            return self._apply_filters(signal)
        return None