"""Mean reversion trading strategy.

Buy when RSI < 30 (oversold), sell when RSI > 70 (overbought).
Signal strength is proportional to RSI extremity.
"""

from __future__ import annotations

from datetime import datetime, timezone

from shared.schemas.trading import MarketSnapshot, SentimentContext, SignalDirection, TradeSignal
from shared.strategies.base import BaseStrategy


class MeanReversionStrategy(BaseStrategy):
    """Contrarian strategy based on RSI extremes."""

    name: str = "mean_reversion"

    def __init__(
        self,
        oversold_threshold: float = 30.0,
        overbought_threshold: float = 70.0,
    ) -> None:
        self.oversold_threshold = oversold_threshold
        self.overbought_threshold = overbought_threshold

    async def evaluate(
        self,
        ticker: str,
        market: MarketSnapshot,
        sentiment: SentimentContext | None = None,
    ) -> TradeSignal | None:
        """Generate a signal when RSI indicates oversold/overbought conditions."""
        if market.rsi is None:
            return None

        rsi = market.rsi

        if rsi < self.oversold_threshold:
            direction = SignalDirection.LONG
            # Strength proportional to how oversold: RSI 0 -> strength 1.0, RSI 30 -> strength 0.0
            strength = (self.oversold_threshold - rsi) / self.oversold_threshold
        elif rsi > self.overbought_threshold:
            direction = SignalDirection.SHORT
            # Strength proportional to how overbought: RSI 100 -> strength 1.0, RSI 70 -> strength 0.0
            strength = (rsi - self.overbought_threshold) / (100.0 - self.overbought_threshold)
        else:
            return None

        strength = min(max(strength, 0.0), 1.0)

        return TradeSignal(
            ticker=ticker,
            direction=direction,
            strength=round(strength, 4),
            strategy_sources=[self.name],
            sentiment_context=None,
            timestamp=datetime.now(timezone.utc),
        )