from dataclasses import dataclass

from app.analysis.confluence import Cluster


@dataclass(slots=True)
class Alert:
    cluster: Cluster
    message: str


class AlertEngine:
    def __init__(self, min_score: float, cooldown_seconds: int = 900):
        self.min_score = min_score
        self.cooldown_seconds = cooldown_seconds
        self._fired_at: dict[str, int] = {}

    def evaluate(
        self,
        clusters: list[Cluster],
        current_price: float,
        atr15: float,
        now: int,
        symbol: str,
    ) -> list[Alert]:
        tolerance = 0.5 * atr15
        if tolerance <= 0:
            return []
        alerts: list[Alert] = []
        active_ids = {cluster.id for cluster in clusters}
        for cluster_id, fired_at in list(self._fired_at.items()):
            cluster = next((item for item in clusters if item.id == cluster_id), None)
            separated = cluster is None or abs(cluster.center - current_price) > 2 * tolerance
            if separated and now - fired_at >= self.cooldown_seconds:
                del self._fired_at[cluster_id]
            elif cluster_id not in active_ids and now - fired_at >= self.cooldown_seconds:
                del self._fired_at[cluster_id]

        for cluster in clusters:
            if (
                cluster.score < self.min_score
                or abs(cluster.center - current_price) > tolerance
                or cluster.id in self._fired_at
            ):
                continue
            self._fired_at[cluster.id] = now
            direction = "BEARISH" if cluster.side.value == "resistance" else "BULLISH"
            timeframes = ", ".join(dict.fromkeys(member.tf.value for member in cluster.members))
            message = (
                f"{direction} ZONE {symbol} {current_price:.2f}\n"
                f"{cluster.side.value.title()} confluence {cluster.score:g} "
                f"@ {cluster.low:.2f}-{cluster.high:.2f}\n{timeframes}"
            )
            alerts.append(Alert(cluster, message))
        return alerts