"""Mean reversion strategy — buy oversold, sell overbought using RSI."""

from datetime import datetime, timezone

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


class MeanReversionStrategy(BaseStrategy):
    """Trade on the assumption that extreme RSI readings will revert to the mean.

    **Buy signal** (LONG):
        RSI < 30 (oversold).

    **Sell signal** (SHORT):
        RSI > 70 (overbought).

    Signal strength is proportional to how far the RSI is from its
    threshold, clamped to [0, 1].

    * Buy strength  = ``(30 - rsi) / 30``
    * Sell strength  = ``(rsi - 70) / 30``
    """

    name: str = "mean_reversion"

    async def evaluate(
        self,
        ticker: str,
        market: MarketSnapshot,
        sentiment: SentimentContext | None = None,
    ) -> TradeSignal | None:
        if market.rsi is None:
            return None

        rsi = market.rsi

        if rsi < 30:
            direction = SignalDirection.LONG
            raw_strength = (30 - rsi) / 30
        elif rsi > 70:
            direction = SignalDirection.SHORT
            raw_strength = (rsi - 70) / 30
        else:
            # RSI in neutral territory — no opinion.
            return None

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

        return TradeSignal(
            ticker=ticker,
            direction=direction,
            strength=strength,
            strategy_sources=[self.name],
            timestamp=datetime.now(tz=timezone.utc),
        )