"""Momentum trading strategy.

Buy when price crosses above N-period SMA with increasing volume.
Sell when price crosses below SMA. Signal strength is proportional
to the distance from the SMA.
"""

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 MomentumStrategy(BaseStrategy):
    """Trend-following momentum strategy based on SMA crossover."""

    name: str = "momentum"

    async def evaluate(
        self,
        ticker: str,
        market: MarketSnapshot,
        sentiment: SentimentContext | None = None,
    ) -> TradeSignal | None:
        """Generate a signal based on SMA crossover and volume confirmation.

        Uses the 20-period SMA by default.  Signal strength is the
        normalised distance from the SMA (capped at 1.0).
        """
        if market.sma_20 is None or market.sma_20 == 0:
            return None

        price = market.current_price
        sma = market.sma_20

        # Percentage distance from SMA
        distance_pct = (price - sma) / sma

        # Need a meaningful deviation (at least 0.5%)
        if abs(distance_pct) < 0.005:
            return None

        # Determine direction
        if distance_pct > 0:
            direction = SignalDirection.LONG
        else:
            direction = SignalDirection.SHORT

        # Strength: normalise distance_pct into [0, 1]
        # 5% deviation = full strength
        strength = min(abs(distance_pct) / 0.05, 1.0)

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