from dataclasses import asdict, dataclass
from enum import Enum
from typing import Any

from app.bars.models import Timeframe


class LevelKind(str, Enum):
    MANUAL = "manual"
    MA = "ma"
    VWAP = "vwap"
    TRENDLINE = "trendline"
    HORIZONTAL = "horizontal"


class Side(str, Enum):
    SUPPORT = "support"
    RESISTANCE = "resistance"


@dataclass(slots=True)
class Level:
    id: str
    kind: LevelKind
    tf: Timeframe
    side: Side
    weight: float
    score: float
    label: str
    anchor_t: int
    anchor_p: float
    slope: float
    points: list[tuple[int, float]] | None
    touches: int
    first_t: int
    last_t: int
    provisional: bool
    hidden: bool
    period: int | None = None
    color: str | None = None
    line_width: int | None = None
    number: int | None = None
    cutoff_t: int | None = None

    def price_at(self, t: int) -> float:
        return self.anchor_p + self.slope * (t - self.anchor_t)

    def to_dict(self) -> dict[str, Any]:
        value = asdict(self)
        value["kind"] = self.kind.value
        value["tf"] = self.tf.value
        value["side"] = self.side.value
        return value

    def summary(self) -> dict[str, Any]:
        """Compact form for embedding inside a cluster.

        Clusters go out on every closed 1m bar, and a moving average carries its
        whole point history — hundreds of entries reaching back years. Embedding
        the full level duplicated the entire levels payload once a minute. The
        client already holds the full levels and joins on id.
        """
        return {
            "id": self.id,
            "kind": self.kind.value,
            "tf": self.tf.value,
            "side": self.side.value,
            "weight": self.weight,
            "label": self.label,
        }