"""이평선 정배열 점수 — 5개 조건 충족 개수 / 5 × 100."""

import pandas as pd

from .base import ScoreNode


class MaAlignment(ScoreNode):
    name  = "ma_alignment"
    label = "이평선 정배열"
    default_params = {"ma_periods": [50, 150, 200]}
    param_schema = {
        "type": "object",
        "properties": {
            "ma_periods": {"type": "array", "items": {"type": "integer"}}
        },
    }

    def compute(self, ctx, params: dict) -> pd.Series:
        ma_periods = params.get("ma_periods", self.default_params["ma_periods"])
        if len(ma_periods) != 3:
            raise ValueError("ma_periods must have 3 entries (short, medium, long)")
        ma_s, ma_m, ma_l = (int(x) for x in ma_periods)

        prices = ctx.prices
        if prices.empty:
            return pd.Series(dtype=float)

        ordered = prices.sort_values("date")
        min_history = max(252, ma_l)

        def _score(s: pd.Series) -> float:
            closes = s.astype(float).reset_index(drop=True)
            if len(closes) < min_history:
                return float("nan")
            close = closes.iloc[-1]
            ma_short  = closes.rolling(ma_s).mean().iloc[-1]
            ma_medium = closes.rolling(ma_m).mean().iloc[-1]
            ma_long   = closes.rolling(ma_l).mean().iloc[-1]
            low52 = closes.iloc[-252:].min()
            conds = [
                close > ma_short,
                ma_short  > ma_medium,
                ma_medium > ma_long,
                close > ma_long,
                close >= low52 * 1.25,
            ]
            return sum(conds) / 5 * 100.0

        raw = ordered.groupby("ticker", group_keys=False)["close"].apply(_score)
        return raw.fillna(0.0)