from typing import Optional

import pandas as pd

from .models import ScoreDetail, ScoreResult


def _daily_short_score(row: pd.Series) -> int:
    """单日短期动力打分。"""
    oi = float(row["oi"])
    oi_chg = float(row["oi_chg"])
    close = float(row["close"])
    pre_close = float(row["pre_close"])

    oi_change_pct = abs(oi_chg / oi) if oi != 0 else 0
    price_up = close >= pre_close

    if oi_change_pct < 0.01:
        return 60 if price_up else 40

    oi_increasing = oi_chg > 0
    if oi_increasing and price_up:
        return 100
    if oi_increasing and not price_up:
        return 0
    if not oi_increasing and price_up:
        return 70
    return 30


def calc_short_term(df: pd.DataFrame, window: int = 7) -> tuple[float, list]:
    recent = df.iloc[-window:].copy()
    scores = []
    details = []
    for _, row in recent.iterrows():
        score = _daily_short_score(row)
        scores.append(score)
        details.append({
            "trade_date": str(row["trade_date"]),
            "close": float(row["close"]),
            "pre_close": float(row["pre_close"]),
            "oi": float(row["oi"]),
            "oi_chg": float(row["oi_chg"]),
            "score": score,
        })
    return sum(scores) / len(scores), details


def calc_medium_term(df: pd.DataFrame, window: int = 15) -> tuple[float, dict]:
    if len(df) < window + 1:
        raise ValueError(f"数据不足,需要至少 {window + 1} 行")

    recent = df.iloc[-window:].copy()
    close_now = float(df.iloc[-1]["close"])
    close_before = float(df.iloc[-window - 1]["close"])

    price_return = (close_now - close_before) / close_before if close_before != 0 else 0
    price_score = max(0.0, min(100.0, 50.0 + price_return * 500))

    long_up = 0
    long_down = 0
    for _, row in recent.iterrows():
        if row["oi_chg"] > 0:
            if row["close"] >= row["pre_close"]:
                long_up += 1
            else:
                long_down += 1

    fund_score = 80 if long_up > long_down else (20 if long_up < long_down else 50)
    score = price_score * 0.6 + fund_score * 0.4

    detail = {
        "price_return_pct": round(price_return * 100, 2),
        "price_signal": round(price_score, 1),
        "long_up_days": long_up,
        "long_down_days": long_down,
        "fund_signal": fund_score,
    }
    return score, detail


def calc_long_term(df: pd.DataFrame, window: int = 30) -> tuple[float, dict]:
    if len(df) < window + 1:
        raise ValueError(f"数据不足,需要至少 {window + 1} 行")

    recent_oi = df.iloc[-window:]["oi"]
    avg_oi = recent_oi.mean()
    oi_before = float(df.iloc[-window - 1]["oi"])

    change_pct = (avg_oi - oi_before) / oi_before if oi_before != 0 else 0

    if change_pct > 0.10:
        score = 90
    elif change_pct > 0.05:
        score = 70
    elif change_pct > -0.05:
        score = 50
    elif change_pct > -0.10:
        score = 30
    else:
        score = 10

    detail = {
        "avg_oi": round(float(avg_oi), 0),
        "oi_before": round(oi_before, 0),
        "change_pct": round(change_pct * 100, 2),
    }
    return score, detail


def _interpret(composite: float) -> str:
    if composite >= 80:
        return "强烈看多区域 — 价格与资金共振,趋势多头的温床"
    if composite >= 50:
        return "偏多/震荡偏强 — 上涨但资金犹豫,或空头离场反弹"
    if composite >= 40:
        return "偏空/震荡偏弱 — 多头止损,或缺乏资金的阴跌"
    return "强烈看空区域 — 资金主动且持续地打压价格"


def score_daily(df: pd.DataFrame, trade_date: Optional[str] = None) -> ScoreResult:
    """对 DataFrame 中指定日期或最新一条记录打分。"""
    if len(df) < 31:
        raise ValueError(f"数据量不足(仅 {len(df)} 行),需要至少 31 行")

    if trade_date:
        trade_date_str = str(trade_date)
        mask = df["trade_date"].astype(str) == trade_date_str
        if not mask.any():
            raise ValueError(f"指定日期 {trade_date_str} 不在数据中")
        pos = mask.idxmax()
        df = df.iloc[:pos + 1].copy()
        if len(df) < 31:
            raise ValueError(f"指定日期 {trade_date_str} 之前数据不足(仅 {len(df)} 行),需要至少 31 行")

    latest = df.iloc[-1]

    short, short_details = calc_short_term(df, 7)
    medium, medium_detail = calc_medium_term(df, 15)
    long_, long_detail = calc_long_term(df, 30)

    composite = short * 0.4 + medium * 0.35 + long_ * 0.25
    signal = _interpret(composite)

    return ScoreResult(
        ts_code=str(latest["ts_code"]),
        trade_date=str(latest["trade_date"]),
        close=float(latest["close"]),
        oi=float(latest["oi"]),
        oi_chg=float(latest["oi_chg"]),
        short_term=round(short, 1),
        medium_term=round(medium, 1),
        long_term=round(long_, 1),
        composite=round(composite, 1),
        signal=signal,
        detail=ScoreDetail(
            short_details=short_details,
            medium_detail=medium_detail,
            long_detail=long_detail,
        ),
    )