"""Market on Close (MOC) Strategy — US Stock 종가매매.

Rules:
- Buy: 15:50-16:00 ET (market close) when screening criteria met
- Sell: 9:35-10:00 ET (market open next day)
- Screening: bullish candle, volume above average, RSI 30-60, positive momentum
- Risk: -2% stop loss, max 5 positions, 20% of capital per position
"""

from collections import deque
from decimal import Decimal
from datetime import datetime

import pandas as pd

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


class MocStrategy(BaseStrategy):
    """Market on Close strategy for overnight gap trading."""

    name: str = "moc"

    def __init__(self) -> None:
        super().__init__()
        # Parameters
        self._quantity_pct: float = 0.2  # 20% of capital per trade
        self._stop_loss_pct: float = 2.0
        self._rsi_min: float = 30.0
        self._rsi_max: float = 60.0
        self._ema_period: int = 20
        self._volume_avg_period: int = 20
        self._min_volume_ratio: float = 1.0  # Volume must be above average
        # Session times (UTC hours)
        self._buy_start_utc: int = 19  # 15:00 ET = 19:00 UTC (summer) / 20:00 UTC (winter)
        self._buy_end_utc: int = 21  # 16:00 ET = 20:00 UTC / 21:00 UTC
        self._sell_start_utc: int = 13  # 9:00 ET = 13:00 UTC / 14:00 UTC
        self._sell_end_utc: int = 15  # 10:00 ET = 14:00 UTC / 15:00 UTC
        self._max_positions: int = 5
        # State
        self._closes: deque[float] = deque(maxlen=200)
        self._volumes: deque[float] = deque(maxlen=200)
        self._highs: deque[float] = deque(maxlen=200)
        self._lows: deque[float] = deque(maxlen=200)
        self._in_position: bool = False
        self._entry_price: float = 0.0
        self._today: str | None = None
        self._bought_today: bool = False
        self._sold_today: bool = False

    @property
    def warmup_period(self) -> int:
        return max(self._ema_period, self._volume_avg_period) + 1

    def configure(self, params: dict) -> None:
        self._quantity_pct = float(params.get("quantity_pct", 0.2))
        self._stop_loss_pct = float(params.get("stop_loss_pct", 2.0))
        self._rsi_min = float(params.get("rsi_min", 30.0))
        self._rsi_max = float(params.get("rsi_max", 60.0))
        self._ema_period = int(params.get("ema_period", 20))
        self._volume_avg_period = int(params.get("volume_avg_period", 20))
        self._min_volume_ratio = float(params.get("min_volume_ratio", 1.0))
        self._buy_start_utc = int(params.get("buy_start_utc", 19))
        self._buy_end_utc = int(params.get("buy_end_utc", 21))
        self._sell_start_utc = int(params.get("sell_start_utc", 13))
        self._sell_end_utc = int(params.get("sell_end_utc", 15))
        self._max_positions = int(params.get("max_positions", 5))

        if self._quantity_pct <= 0 or self._quantity_pct > 1:
            raise ValueError(f"quantity_pct must be 0-1, got {self._quantity_pct}")
        if self._stop_loss_pct <= 0:
            raise ValueError(f"stop_loss_pct must be positive, got {self._stop_loss_pct}")

    def reset(self) -> None:
        super().reset()
        self._closes.clear()
        self._volumes.clear()
        self._highs.clear()
        self._lows.clear()
        self._in_position = False
        self._entry_price = 0.0
        self._today = None
        self._bought_today = False
        self._sold_today = False

    def _is_buy_window(self, dt: datetime) -> bool:
        """Check if in buy window (near market close)."""
        hour = dt.hour
        return self._buy_start_utc <= hour < self._buy_end_utc

    def _is_sell_window(self, dt: datetime) -> bool:
        """Check if in sell window (near market open)."""
        hour = dt.hour
        return self._sell_start_utc <= hour < self._sell_end_utc

    def _compute_rsi(self, period: int = 14) -> float | None:
        if len(self._closes) < period + 1:
            return None
        series = pd.Series(list(self._closes))
        delta = series.diff()
        gain = delta.clip(lower=0)
        loss = -delta.clip(upper=0)
        avg_gain = gain.ewm(com=period - 1, min_periods=period).mean()
        avg_loss = loss.ewm(com=period - 1, min_periods=period).mean()
        rs = avg_gain / avg_loss.replace(0, float("nan"))
        rsi = 100 - (100 / (1 + rs))
        val = rsi.iloc[-1]
        return None if pd.isna(val) else float(val)

    def _is_bullish_candle(self, candle: Candle) -> bool:
        return float(candle.close) > float(candle.open)

    def _price_above_ema(self) -> bool:
        if len(self._closes) < self._ema_period:
            return True
        series = pd.Series(list(self._closes))
        ema = series.ewm(span=self._ema_period, adjust=False).mean().iloc[-1]
        return self._closes[-1] >= ema

    def _volume_above_average(self) -> bool:
        if len(self._volumes) < self._volume_avg_period:
            return True
        avg = sum(list(self._volumes)[-self._volume_avg_period :]) / self._volume_avg_period
        return avg > 0 and self._volumes[-1] / avg >= self._min_volume_ratio

    def _positive_momentum(self) -> bool:
        """Check if price has positive short-term momentum (close > close 5 bars ago)."""
        if len(self._closes) < 6:
            return True
        return self._closes[-1] > self._closes[-6]

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

        close = float(candle.close)
        self._closes.append(close)
        self._volumes.append(float(candle.volume))
        self._highs.append(float(candle.high))
        self._lows.append(float(candle.low))

        # Daily reset
        day = candle.open_time.strftime("%Y-%m-%d")
        if self._today != day:
            self._today = day
            self._bought_today = False
            self._sold_today = False

        # --- SELL LOGIC (market open next day) ---
        if self._in_position and self._is_sell_window(candle.open_time):
            if not self._sold_today:
                pnl_pct = (close - self._entry_price) / self._entry_price * 100
                self._in_position = False
                self._sold_today = True

                conv = 0.8 if pnl_pct > 0 else 0.5
                return self._apply_filters(
                    Signal(
                        strategy=self.name,
                        symbol=candle.symbol,
                        side=OrderSide.SELL,
                        price=candle.close,
                        quantity=Decimal(str(self._quantity_pct)),
                        conviction=conv,
                        reason=f"MOC sell at open, PnL {pnl_pct:.2f}%",
                    )
                )

        # --- STOP LOSS ---
        if self._in_position:
            pnl_pct = (close - self._entry_price) / self._entry_price * 100
            if pnl_pct <= -self._stop_loss_pct:
                self._in_position = False
                return self._apply_filters(
                    Signal(
                        strategy=self.name,
                        symbol=candle.symbol,
                        side=OrderSide.SELL,
                        price=candle.close,
                        quantity=Decimal(str(self._quantity_pct)),
                        conviction=1.0,
                        stop_loss=candle.close,
                        reason=f"MOC stop loss {pnl_pct:.2f}% <= -{self._stop_loss_pct}%",
                    )
                )

        # --- BUY LOGIC (near market close) ---
        if not self._in_position and self._is_buy_window(candle.open_time):
            if self._bought_today:
                return None

            # Screening criteria
            rsi = self._compute_rsi()
            if rsi is None:
                return None

            checks = [
                self._rsi_min <= rsi <= self._rsi_max,  # RSI in sweet spot
                self._is_bullish_candle(candle),  # Bullish candle
                self._price_above_ema(),  # Above EMA (uptrend)
                self._volume_above_average(),  # Volume confirmation
                self._positive_momentum(),  # Short-term momentum
            ]

            if all(checks):
                self._in_position = True
                self._entry_price = close
                self._bought_today = True

                # Conviction based on RSI position within range
                rsi_range = self._rsi_max - self._rsi_min
                rsi_pos = (rsi - self._rsi_min) / rsi_range if rsi_range > 0 else 0.5
                conv = 0.5 + (1.0 - rsi_pos) * 0.4  # Lower RSI = higher conviction

                sl = candle.close * (1 - Decimal(str(self._stop_loss_pct / 100)))

                return self._apply_filters(
                    Signal(
                        strategy=self.name,
                        symbol=candle.symbol,
                        side=OrderSide.BUY,
                        price=candle.close,
                        quantity=Decimal(str(self._quantity_pct)),
                        conviction=conv,
                        stop_loss=sl,
                        reason=f"MOC buy: RSI={rsi:.1f}, bullish candle, above EMA, vol OK",
                    )
                )

        return None