"""顶底「市场温度」透明启发式（可调，非黑盒）。

温度 0–100：越高越「过热/顶部风险」，越低越「冰点/底部机会」。
四个分量（缺哪个自动剔除并重新归一权重）：
  vol_pct    量能分位：天量→高温(顶)，地量→低温(底)
  price_pct  价格分位：高位→高温，低位→低温
  sentiment  情绪：普涨/涨停潮亢奋→高温；普跌/跌停潮恐慌→低温
  etf_heat   ETF「出货热度」= (-净流入) 分位：大额净流出→高温(顶)，大额净流入(国家队)→低温(底)

权重与阈值来自 config（W_VOL/W_PRICE/W_SENTIMENT/W_ETF、HOT/COLD_THRESHOLD），
初值仅供起步，请用你自己的历史数据回测校准。
"""
from __future__ import annotations

from datetime import date
from typing import Dict, List, Optional

from .config import get_settings

settings = get_settings()


def _rolling_pct(values: List[Optional[float]], window: int) -> List[Optional[float]]:
    """滚动百分位排名(0-100)：当前值在最近 window 个有效值中的分位。"""
    out: List[Optional[float]] = []
    for i in range(len(values)):
        cur = values[i]
        if cur is None:
            out.append(None)
            continue
        lo = max(0, i - window + 1)
        base = [v for v in values[lo : i + 1] if v is not None]
        if len(base) < max(5, window // 10):  # 样本太少不给分位，避免早期噪声
            out.append(None)
            continue
        le = sum(1 for v in base if v <= cur)
        out.append(round(le / len(base) * 100, 2))
    return out


def _norm_sentiment(sr) -> Optional[float]:
    """把一条情绪原始值折算到 0-100 的「亢奋度」粗分（缺字段则 None，交给分位再平滑）。

    这里只做方向性折算：涨跌家数比越高、涨幅>5%家数越多 → 越亢奋。
    真正的相对高低由外层 _rolling_pct 处理。
    """
    if sr is None:
        return None
    parts = []
    if sr.get("up_down_ratio") is not None:
        parts.append(sr["up_down_ratio"])
    if sr.get("pct_chg_gt_5_count") is not None:
        parts.append(sr["pct_chg_gt_5_count"])
    if not parts:
        return None
    return float(sum(parts))  # 量纲无所谓，后续走分位


def compute_signals(
    bars: List[dict],
    sentiment_by_date: Dict[date, dict],
    etf_net_by_date: Dict[date, float],
    window: Optional[int] = None,
) -> List[dict]:
    """bars: [{trade_date, close, amount}, ...] 升序。返回逐日温度与标记。"""
    window = window or settings.vol_window
    dates = [b["trade_date"] for b in bars]
    closes = [b.get("close") for b in bars]
    amounts = [b.get("amount") for b in bars]

    # 情绪与 ETF 原始序列（对齐 bars 日期）
    senti_raw = [_norm_sentiment(sentiment_by_date.get(d)) for d in dates]
    etf_out_raw = [
        (-etf_net_by_date[d]) if d in etf_net_by_date and etf_net_by_date[d] is not None else None
        for d in dates
    ]

    vol_pct = _rolling_pct(amounts, window)
    price_pct = _rolling_pct(closes, window)
    senti_pct = _rolling_pct(senti_raw, window)
    etf_pct = _rolling_pct(etf_out_raw, window)

    W = {
        "vol": settings.w_vol,
        "price": settings.w_price,
        "sentiment": settings.w_sentiment,
        "etf": settings.w_etf,
    }

    out: List[dict] = []
    for i, d in enumerate(dates):
        comps = {
            "vol": vol_pct[i],
            "price": price_pct[i],
            "sentiment": senti_pct[i],
            "etf": etf_pct[i],
        }
        avail = {k: v for k, v in comps.items() if v is not None}
        if avail:
            wsum = sum(W[k] for k in avail)
            temp = round(sum(comps[k] * W[k] for k in avail) / wsum, 2) if wsum else None
        else:
            temp = None

        flags = []
        if vol_pct[i] is not None and price_pct[i] is not None:
            if vol_pct[i] >= 95 and price_pct[i] >= 80:
                flags.append("TOP_VOLUME_PRICE")   # 天量见天价
        if vol_pct[i] is not None and vol_pct[i] <= 5:
            flags.append("BOTTOM_VOLUME_DRY")       # 地量见地价
        if senti_pct[i] is not None:
            if senti_pct[i] >= 95:
                flags.append("SENTIMENT_EUPHORIA")
            elif senti_pct[i] <= 5:
                flags.append("SENTIMENT_PANIC")
        if temp is not None:
            if temp >= settings.hot_threshold:
                flags.append("OVERHEAT")
            elif temp <= settings.cold_threshold:
                flags.append("FREEZE")

        out.append(
            {
                "trade_date": d,
                "temperature": temp,
                "components": comps,
                "flags": flags,
            }
        )
    return out