"""市场总览数据装配(与 HTTP Request 解耦)。

本模块由 `app.api.overview._build_overview` 抽离而来,目的是让「大盘复盘」
等无 Request 的调用方(定时任务、复盘服务)也能复用同一套聚合逻辑。

公共入口:
    build_market_overview(repo, as_of)
"""
from __future__ import annotations

import math
import re
from datetime import date
from typing import Any

import polars as pl

from app.services.ext_data import ExtConfig, ExtConfigStore
from app.services.screener import ScreenerService

# ================================================================
# 常量(与 overview.py 保持同步;复盘复盘仅 A 股核心指数)
# ================================================================

CORE_INDEX_NAMES = {
    "000001.SH": "上证指数",
    "399001.SZ": "深证成指",
    "399006.SZ": "创业板指",
    "000680.SH": "科创综指",
}
CORE_INDEX_SYMBOLS = tuple(CORE_INDEX_NAMES.keys())

_DIMENSION_SEP = re.compile(r"[、,，;；|/\s]+")


# ================================================================
# 通用工具
# ================================================================

def _finite(v: Any) -> float | None:
    if v is None:
        return None
    try:
        f = float(v)
    except (TypeError, ValueError):
        return None
    return f if math.isfinite(f) else None


def _json_safe(value: Any) -> Any:
    if isinstance(value, dict):
        return {k: _json_safe(v) for k, v in value.items()}
    if isinstance(value, list):
        return [_json_safe(v) for v in value]
    if isinstance(value, float) and not math.isfinite(value):
        return None
    return value


def _board(symbol: str) -> str:
    if symbol.endswith(".BJ"):
        return "北交所"
    if symbol.startswith(("300", "301")):
        return "创业板"
    if symbol.startswith(("688", "689")):
        return "科创板"
    if symbol.endswith(".SH"):
        return "沪主板"
    if symbol.endswith(".SZ"):
        return "深主板"
    return "其他"


def _score(value: float, low: float, high: float) -> int:
    if high <= low:
        return 50
    return max(0, min(100, round((value - low) / (high - low) * 100)))


# ================================================================
# 指数行情(从 kline_index_daily SQL 读取)
# ================================================================

def _index_quotes(repo, as_of: date | None = None) -> list[dict]:
    rows: list[dict] = []
    if repo:
        placeholders = ", ".join("?" for _ in CORE_INDEX_SYMBOLS)
        try:
            db_rows = repo.execute_all(
                f"""
                WITH ranked AS (
                    SELECT symbol, date, close,
                           row_number() OVER (PARTITION BY symbol ORDER BY date DESC) AS rn
                    FROM kline_index_daily
                    WHERE symbol IN ({placeholders})
                      AND (? IS NULL OR date <= ?)
                ), latest AS (
                    SELECT symbol,
                           max(CASE WHEN rn = 1 THEN date END) AS date,
                           max(CASE WHEN rn = 1 THEN close END) AS last_price,
                           max(CASE WHEN rn = 2 THEN close END) AS prev_close
                    FROM ranked
                    WHERE rn <= 2
                    GROUP BY symbol
                )
                SELECT symbol, date, last_price, prev_close
                FROM latest
                """,
                [*CORE_INDEX_SYMBOLS, as_of, as_of],
            )
        except Exception:  # noqa: BLE001
            db_rows = []
        for symbol, dt, last_price, prev_close in db_rows:
            change_amount = None
            change_pct = None
            lp = _finite(last_price)
            pc = _finite(prev_close)
            if lp is not None and pc not in (None, 0):
                change_amount = lp - pc
                change_pct = change_amount / pc * 100
            rows.append({
                "symbol": symbol,
                "name": CORE_INDEX_NAMES.get(symbol),
                "date": str(dt) if dt else None,
                "last_price": lp,
                "close": lp,
                "prev_close": pc,
                "change_amount": change_amount,
                "change_pct": change_pct,
            })

    by_symbol = {r.get("symbol"): r for r in rows}
    out = []
    for symbol in CORE_INDEX_SYMBOLS:
        r = by_symbol.get(symbol, {"symbol": symbol})
        out.append({
            "symbol": symbol,
            "name": r.get("name") or CORE_INDEX_NAMES[symbol],
            "last_price": _finite(r.get("last_price") if r.get("last_price") is not None else r.get("close")),
            "change_pct": _finite(r.get("change_pct")),
            "change_amount": _finite(r.get("change_amount")),
        })
    return out


# ================================================================
# 扩展数据(行业 / 概念)维度聚合
# ================================================================

def _dimension_field(config: ExtConfig, kind: str) -> str | None:
    candidates = ["概念", "concept", "theme"] if kind == "concept" else ["行业", "industry", "sector"]
    for candidate in candidates:
        needle = candidate.lower()
        for field in config.fields:
            haystack = f"{field.name} {field.label}".lower()
            if needle in haystack:
                return field.name
    return None


def _ext_files(data_dir, config: ExtConfig) -> list[str]:
    base = data_dir / "ext_data" / config.id
    if config.mode == "timeseries":
        root = base / "timeseries"
        return [str(p) for p in sorted(root.rglob("*.parquet")) if p.is_file()]
    return [str(p) for p in sorted(base.glob("*.parquet")) if p.is_file()]


def _read_ext_rows(data_dir, config: ExtConfig, dimension_field: str) -> list[dict]:
    files = _ext_files(data_dir, config)
    if not files:
        return []
    try:
        df = pl.read_parquet(files, hive_partitioning=True)
    except TypeError:
        try:
            df = pl.read_parquet(files)
        except Exception:  # noqa: BLE001
            return []
    except Exception:  # noqa: BLE001
        return []
    if df.is_empty() or dimension_field not in df.columns:
        return []

    if config.mode == "timeseries" and "date" in df.columns:
        latest = df.get_column("date").max()
        if latest is not None:
            df = df.filter(pl.col("date") == latest)

    symbol_cols = ["symbol", "code", "股票代码", "代码"]
    for mapping in (config.symbol_map, config.code_map):
        if isinstance(mapping, dict) and mapping.get("type") == "mapped" and mapping.get("col"):
            symbol_cols.append(str(mapping["col"]))
    cols = []
    for col in [dimension_field, *symbol_cols]:
        if col in df.columns and col not in cols:
            cols.append(col)
    return df.select(cols).to_dicts()


def _dimension_values(raw: Any) -> list[str]:
    if raw is None:
        return []
    values = [v.strip() for v in _DIMENSION_SEP.split(str(raw).strip()) if v.strip()]
    return values


def _symbol_keys(row: dict, config: ExtConfig) -> list[str]:
    fields = ["symbol", "code", "股票代码", "代码"]
    for mapping in (config.symbol_map, config.code_map):
        if isinstance(mapping, dict) and mapping.get("type") == "mapped" and mapping.get("col"):
            fields.append(str(mapping["col"]))

    keys: list[str] = []
    for field in fields:
        raw = row.get(field)
        if raw is None:
            continue
        text = str(raw).strip().upper()
        if not text:
            continue
        keys.append(text)
        if "." in text:
            keys.append(text.split(".", 1)[0])
    return keys


def _dimension_rank(rows: list[dict], repo, kind: str, limit: int = 5, level: int | None = None) -> dict:
    if not rows:
        return {"leading": [], "lagging": []}

    quote_map: dict[str, dict] = {}
    for row in rows:
        symbol = str(row.get("symbol") or "").strip().upper()
        if not symbol:
            continue
        quote_map[symbol] = row
        quote_map[symbol.split(".", 1)[0]] = row

    store = ExtConfigStore(repo.store.data_dir)
    groups: dict[str, dict[str, dict]] = {}
    for config in store.load_all():
        field = _dimension_field(config, kind)
        if not field:
            continue
        for ext_row in _read_ext_rows(repo.store.data_dir, config, field):
            quote = None
            for key in _symbol_keys(ext_row, config):
                quote = quote_map.get(key)
                if quote:
                    break
            if not quote:
                continue
            symbol = str(quote.get("symbol") or "")
            for value in _dimension_values(ext_row.get(field)):
                # 行业按 "-" 拆分级: "银行-银行-股份制银行" → level=2 取"银行"(二级)
                if level is not None and "-" in value:
                    parts = value.split("-")
                    value = parts[level - 1] if level <= len(parts) else parts[-1]
                groups.setdefault(value, {})[symbol] = quote

    items = []
    for name, by_symbol in groups.items():
        stocks = list(by_symbol.values())
        changes = [_finite(s.get("change_pct")) for s in stocks]
        changes = [v for v in changes if v is not None]
        if not changes:
            continue
        leader = max(stocks, key=lambda s: _finite(s.get("change_pct")) or -999)
        items.append({
            "name": name,
            "count": len(stocks),
            "avg_pct": sum(changes) / len(changes),
            "up_count": sum(1 for v in changes if v > 0),
            "down_count": sum(1 for v in changes if v < 0),
            "amount": sum(_finite(s.get("amount")) or 0 for s in stocks),
            "leader": {
                "symbol": leader.get("symbol"),
                "name": leader.get("name"),
                "change_pct": _finite(leader.get("change_pct")),
            },
        })

    leading = sorted(items, key=lambda x: x["avg_pct"], reverse=True)[:limit]
    lagging = sorted(items, key=lambda x: x["avg_pct"])[:limit]
    return {"leading": leading, "lagging": lagging}


# ================================================================
# Top 行 / 涨跌幅分桶
# ================================================================

def _top_rows(rows: list[dict], key: str, descending: bool, limit: int = 8) -> list[dict]:
    filtered = [r for r in rows if _finite(r.get(key)) is not None]
    filtered.sort(key=lambda r: _finite(r.get(key)) or 0, reverse=descending)
    return [
        {
            "symbol": r.get("symbol"),
            "name": r.get("name"),
            "close": _finite(r.get("close")),
            "change_pct": _finite(r.get("change_pct")),
            "amount": _finite(r.get("amount")),
            "turnover_rate": _finite(r.get("turnover_rate")),
            "board": _board(str(r.get("symbol") or "")),
        }
        for r in filtered[:limit]
    ]


def _pct_band_rows(values: list[float]) -> list[dict]:
    bands = [
        ("<-5%", None, -0.05),
        ("-5~-3%", -0.05, -0.03),
        ("-3~-1%", -0.03, -0.01),
        ("-1~0%", -0.01, 0),
        ("0~1%", 0, 0.01),
        ("1~3%", 0.01, 0.03),
        ("3~5%", 0.03, 0.05),
        (">5%", 0.05, None),
    ]
    total = len(values) or 1
    out = []
    for label, low, high in bands:
        count = 0
        for v in values:
            if low is None and v < high:
                count += 1
            elif high is None and v >= low:
                count += 1
            elif low is not None and high is not None and low <= v < high:
                count += 1
        out.append({"label": label, "count": count, "pct": count / total * 100})
    return out


# ================================================================
# 主装配入口
# ================================================================

def build_market_overview(
    repo,
    as_of: date | None = None,
) -> dict:
    """装配市场总览(与原 overview._build_overview 行为一致)。

    Args:
        repo: KlineRepository(必填)。
        as_of: 指定日期,None 则取最新有数据日。
    """
    svc = ScreenerService(repo)
    as_of = as_of or svc.latest_date()
    indices = _index_quotes(repo, as_of)

    if not as_of:
        return {
            "as_of": None,
            "indices": indices,
            "breadth": {"total": 0, "up": 0, "down": 0, "flat": 0, "up_pct": 0, "down_pct": 0},
            "amount": {"total": 0, "avg": 0},
            "boards": [],
            "limit": {"limit_up": 0, "broken": 0, "failed": 0, "limit_down": 0, "max_boards": 0, "tiers": []},
            "distribution": [],
            "trend": {"above_ma5": 0, "above_ma20": 0, "above_ma60": 0, "above_ma5_pct": 0, "above_ma20_pct": 0, "above_ma60_pct": 0, "new_high": 0, "new_low": 0},
            "activity": {"avg_turnover": 0, "high_turnover": 0, "high_vol_ratio": 0, "vol_ratio": 1},
            "radar": [],
            "emotion": {"score": 50, "label": "暂无"},
            "top_gainers": [],
            "top_losers": [],
            "turnover_leaders": [],
            "active_leaders": [],
            "concept_rank": {"leading": [], "lagging": []},
            "industry_rank": {"leading": [], "lagging": []},
        }

    df = svc._load_enriched_for_date(as_of)
    if df.is_empty():
        rows: list[dict] = []
    else:
        cols = [
            "symbol", "name", "close", "change_pct", "amount", "turnover_rate", "volume",
            "vol_ratio_5d", "consecutive_limit_ups", "signal_limit_up", "signal_broken_limit_up", "signal_limit_down",
            "ma5", "ma20", "ma60", "high_60d", "low_60d", "signal_n_day_high", "signal_n_day_low",
        ]
        df = df.select([c for c in cols if c in df.columns])
        rows = df.to_dicts()

    # 过滤真停牌（volume=0 且 change_pct=0），保留有涨跌幅的浮点误差股以对齐同花顺口径
    if rows and "volume" in rows[0]:
        rows = [r for r in rows
                if (_finite(r.get("volume")) or 0) > 0
                or (_finite(r.get("change_pct")) or 0) != 0]

    total = len(rows)
    up = sum(1 for r in rows if (_finite(r.get("change_pct")) or 0) > 0)
    down = sum(1 for r in rows if (_finite(r.get("change_pct")) or 0) < 0)
    flat = max(0, total - up - down)
    up_pct = up / total * 100 if total else 0
    down_pct = down / total * 100 if total else 0

    amounts = [_finite(r.get("amount")) or 0 for r in rows]
    total_amount = sum(amounts)
    avg_amount = total_amount / total if total else 0

    pct_values = [_finite(r.get("change_pct")) for r in rows]
    pct_values = [v for v in pct_values if v is not None]
    avg_pct = sum(pct_values) / len(pct_values) if pct_values else 0
    median_pct = sorted(pct_values)[len(pct_values) // 2] if pct_values else 0
    strong_up = sum(1 for v in pct_values if v >= 0.03)
    strong_down = sum(1 for v in pct_values if v <= -0.03)

    limit_up = sum(1 for r in rows if bool(r.get("signal_limit_up")) or (_finite(r.get("consecutive_limit_ups")) or 0) > 0)
    broken = sum(1 for r in rows if bool(r.get("signal_broken_limit_up")))
    limit_down = sum(1 for r in rows if bool(r.get("signal_limit_down")))
    max_boards = max([int(_finite(r.get("consecutive_limit_ups")) or 0) for r in rows], default=0)

    seal_rate = limit_up / (limit_up + broken) * 100 if (limit_up + broken) > 0 else 0

    def above_ma_count(ma_key: str) -> int:
        return sum(1 for r in rows if (_finite(r.get("close")) is not None and _finite(r.get(ma_key)) is not None and (_finite(r.get("close")) or 0) >= (_finite(r.get(ma_key)) or 0)))

    above_ma5 = above_ma_count("ma5")
    above_ma20 = above_ma_count("ma20")
    above_ma60 = above_ma_count("ma60")
    new_high = sum(1 for r in rows if bool(r.get("signal_n_day_high")) or (_finite(r.get("close")) is not None and _finite(r.get("high_60d")) is not None and (_finite(r.get("close")) or 0) >= (_finite(r.get("high_60d")) or 0)))
    new_low = sum(1 for r in rows if bool(r.get("signal_n_day_low")) or (_finite(r.get("close")) is not None and _finite(r.get("low_60d")) is not None and (_finite(r.get("close")) or 0) <= (_finite(r.get("low_60d")) or 0)))

    turnovers = [_finite(r.get("turnover_rate")) for r in rows]
    turnovers = [v for v in turnovers if v is not None]
    avg_turnover = sum(turnovers) / len(turnovers) if turnovers else 0
    high_turnover = sum(1 for v in turnovers if v >= 5)

    boards_map: dict[str, dict] = {}
    for r in rows:
        b = _board(str(r.get("symbol") or ""))
        item = boards_map.setdefault(b, {"board": b, "count": 0, "up": 0, "down": 0, "amount": 0.0})
        item["count"] += 1
        change = _finite(r.get("change_pct")) or 0
        if change > 0:
            item["up"] += 1
        elif change < 0:
            item["down"] += 1
        item["amount"] += _finite(r.get("amount")) or 0
    boards = sorted(boards_map.values(), key=lambda x: x["amount"], reverse=True)
    for b in boards:
        count = b["count"] or 1
        b["up_pct"] = b["up"] / count * 100

    tiers_map: dict[int, int] = {}
    for r in rows:
        n = int(_finite(r.get("consecutive_limit_ups")) or 0)
        if n > 0:
            tiers_map[n] = tiers_map.get(n, 0) + 1
    tiers = [{"boards": k, "count": v} for k, v in sorted(tiers_map.items(), key=lambda item: -item[0])]

    index_changes = [_finite(r.get("change_pct")) for r in indices]
    index_changes = [v for v in index_changes if v is not None]
    avg_index_pct = sum(index_changes) / len(index_changes) if index_changes else 0
    vol_ratios = [_finite(r.get("vol_ratio_5d")) for r in rows]
    vol_ratios = [v for v in vol_ratios if v is not None]
    avg_vol_ratio = sum(vol_ratios) / len(vol_ratios) if vol_ratios else 1
    high_vol_ratio = sum(1 for v in vol_ratios if v >= 1.5)

    concept_rank = _dimension_rank(rows, repo, "concept")
    industry_rank = _dimension_rank(rows, repo, "industry", level=2)

    strong_diff_pct = (strong_up - strong_down) / total * 100 if total else 0
    high_vol_pct = high_vol_ratio / total * 100 if total else 0
    strong_down_pct = strong_down / total * 100 if total else 0
    tier2_count = sum(t["count"] for t in tiers if t["boards"] >= 2)
    mainline_items = [*concept_rank["leading"][:3], *industry_rank["leading"][:3]]
    mainline_avg = max([_finite(item.get("avg_pct")) or 0 for item in mainline_items], default=0)
    mainline_cover_pct = max([(_finite(item.get("count")) or 0) / total * 100 for item in mainline_items], default=0) if total else 0
    mainline_score = round(_score(mainline_avg, -0.005, 0.03) * 0.65 + _score(mainline_cover_pct, 1, 12) * 0.35) if mainline_items else 50

    radar = [
        {"key": "index", "label": "指数", "value": _score(avg_index_pct, -2.5, 2.5)},
        {"key": "profit", "label": "赚钱", "value": round(_score(up_pct, 20, 80) * 0.45 + _score(avg_pct, -0.02, 0.02) * 0.25 + _score(median_pct, -0.02, 0.02) * 0.20 + _score(strong_diff_pct, -8, 8) * 0.10)},
        {"key": "money", "label": "量能", "value": round(_score(avg_vol_ratio, 0.6, 1.8) * 0.70 + _score(high_vol_pct, 2, 12) * 0.30)},
        {"key": "speculation", "label": "投机", "value": round(_score(limit_up, 5, 90) * 0.25 + _score(seal_rate, 30, 85) * 0.35 + _score(max_boards, 1, 8) * 0.25 + _score(tier2_count, 0, 30) * 0.15)},
        {"key": "resilience", "label": "抗跌", "value": 100 - round(_score(down_pct, 20, 80) * 0.55 + _score(strong_down_pct, 1, 12) * 0.45)},
        {"key": "mainline", "label": "主线", "value": mainline_score},
    ]
    emotion_score = round(sum(r["value"] for r in radar) / len(radar)) if radar else 50
    if emotion_score >= 70:
        emotion_label = "强势"
    elif emotion_score >= 55:
        emotion_label = "偏暖"
    elif emotion_score >= 45:
        emotion_label = "震荡"
    elif emotion_score >= 30:
        emotion_label = "偏冷"
    else:
        emotion_label = "冰点"

    return _json_safe({
        "as_of": str(as_of),
        "indices": indices,
        "breadth": {
            "total": total,
            "up": up,
            "down": down,
            "flat": flat,
            "up_pct": up_pct,
            "down_pct": down_pct,
            "avg_pct": avg_pct,
            "median_pct": median_pct,
            "strong_up": strong_up,
            "strong_down": strong_down,
        },
        "amount": {"total": total_amount, "avg": avg_amount},
        "boards": boards,
        "limit": {"limit_up": limit_up, "broken": broken, "failed": 0, "limit_down": limit_down, "max_boards": max_boards, "seal_rate": seal_rate, "tiers": tiers},
        "distribution": _pct_band_rows(pct_values),
        "trend": {
            "above_ma5": above_ma5,
            "above_ma20": above_ma20,
            "above_ma60": above_ma60,
            "above_ma5_pct": above_ma5 / total * 100 if total else 0,
            "above_ma20_pct": above_ma20 / total * 100 if total else 0,
            "above_ma60_pct": above_ma60 / total * 100 if total else 0,
            "new_high": new_high,
            "new_low": new_low,
        },
        "activity": {
            "avg_turnover": avg_turnover,
            "high_turnover": high_turnover,
            "high_vol_ratio": high_vol_pct,
            "vol_ratio": avg_vol_ratio,
        },
        "radar": radar,
        "emotion": {"score": emotion_score, "label": emotion_label},
        "top_gainers": _top_rows(rows, "change_pct", True),
        "top_losers": _top_rows(rows, "change_pct", False),
        "turnover_leaders": _top_rows(rows, "amount", True),
        "active_leaders": _top_rows(rows, "turnover_rate", True),
        "concept_rank": concept_rank,
        "industry_rank": industry_rank,
    })