from __future__ import annotations

import json
from collections import defaultdict
from pathlib import Path
from typing import Any


ROOT = Path(__file__).resolve().parents[2]

ETF_NAME_HINTS = (
    "KODEX", "TIGER", "RISE", "KBSTAR", "ARIRANG", "ACE", "KOSEF", "HANARO",
    "SOL", "TIMEFOLIO", "WOORI", "PLUS", "NPLUS", "TREX", "FOCUS", "KIWOOM",
)

ROBOTICS_FALLBACK_PROXY = {
    "Sector": "로보틱스",
    "Proxy_Ticker": "0190C0",
    "Proxy_Name": "RISE 현대차고정피지컬AI",
    "Proxy_Type": "ETF",
    "Sector_Rank": 12,
    "SmartMoney_5D_KRW": 0.0,
    "Sector_Ret20D": 0.0,
}

ROBOTICS_FALLBACK_UNIVERSE = [
    {"Sector": "로보틱스", "Proxy_Ticker": "0190C0", "Proxy_Name": "RISE 현대차고정피지컬AI", "Proxy_Type": "ETF", "Constituent_Code": "005380", "Constituent_Name": "현대차", "Weight": 0.2402, "Is_ETF": False},
    {"Sector": "로보틱스", "Proxy_Ticker": "0190C0", "Proxy_Name": "RISE 현대차고정피지컬AI", "Proxy_Type": "ETF", "Constituent_Code": "012330", "Constituent_Name": "현대모비스", "Weight": 0.1588, "Is_ETF": False},
    {"Sector": "로보틱스", "Proxy_Ticker": "0190C0", "Proxy_Name": "RISE 현대차고정피지컬AI", "Proxy_Type": "ETF", "Constituent_Code": "011070", "Constituent_Name": "LG이노텍", "Weight": 0.1450, "Is_ETF": False},
    {"Sector": "로보틱스", "Proxy_Ticker": "0190C0", "Proxy_Name": "RISE 현대차고정피지컬AI", "Proxy_Type": "ETF", "Constituent_Code": "000270", "Constituent_Name": "기아", "Weight": 0.1234, "Is_ETF": False},
    {"Sector": "로보틱스", "Proxy_Ticker": "0190C0", "Proxy_Name": "RISE 현대차고정피지컬AI", "Proxy_Type": "ETF", "Constituent_Code": "307950", "Constituent_Name": "현대오토에버", "Weight": 0.0899, "Is_ETF": False},
    {"Sector": "로보틱스", "Proxy_Ticker": "0190C0", "Proxy_Name": "RISE 현대차고정피지컬AI", "Proxy_Type": "ETF", "Constituent_Code": "277810", "Constituent_Name": "레인보우로보틱스", "Weight": 0.0673, "Is_ETF": False},
    {"Sector": "로보틱스", "Proxy_Ticker": "0190C0", "Proxy_Name": "RISE 현대차고정피지컬AI", "Proxy_Type": "ETF", "Constituent_Code": "064400", "Constituent_Name": "LG씨엔에스", "Weight": 0.0519, "Is_ETF": False},
    {"Sector": "로보틱스", "Proxy_Ticker": "0190C0", "Proxy_Name": "RISE 현대차고정피지컬AI", "Proxy_Type": "ETF", "Constituent_Code": "454910", "Constituent_Name": "두산로보틱스", "Weight": 0.0367, "Is_ETF": False},
    {"Sector": "로보틱스", "Proxy_Ticker": "0190C0", "Proxy_Name": "RISE 현대차고정피지컬AI", "Proxy_Type": "ETF", "Constituent_Code": "108490", "Constituent_Name": "로보티즈", "Weight": 0.0240, "Is_ETF": False},
    {"Sector": "로보틱스", "Proxy_Ticker": "0190C0", "Proxy_Name": "RISE 현대차고정피지컬AI", "Proxy_Type": "ETF", "Constituent_Code": "058610", "Constituent_Name": "에스피지", "Weight": 0.0173, "Is_ETF": False},
]


def _parse_jsonish(value: Any) -> Any:
    if isinstance(value, (dict, list)):
        return value
    if isinstance(value, str) and value.strip():
        try:
            return json.loads(value)
        except Exception:
            return value
    return value


def _load_payload(payload: dict[str, Any]) -> tuple[dict[str, Any], dict[str, Any]]:
    data = payload.get("data") if isinstance(payload.get("data"), dict) else {}
    hctx = data.get("_harness_context") if isinstance(data.get("_harness_context"), dict) else {}
    return data, hctx


def _num(value: Any, default: float = 0.0) -> float:
    try:
        return float(value)
    except Exception:
        return default


def _txt(value: Any, default: str = "") -> str:
    if value is None:
        return default
    text = str(value).strip()
    return text if text else default


def _is_etf_like_name(name: str) -> bool:
    upper = name.upper()
    return any(hint in upper for hint in ETF_NAME_HINTS)


def _liquidity_rank(value: str) -> int:
    upper = value.upper()
    if upper in {"PREFERRED", "OK", "GOOD"}:
        return 0
    if upper in {"WATCH", "NORMAL", "TRACK"}:
        return 1
    if upper in {"CAUTION", "WARN", "RISK"}:
        return 2
    return 3


def _monitor_state(row: dict[str, Any]) -> str:
    liquidity = _txt(row.get("Liquidity_Status"), "UNKNOWN").upper()
    quote = _txt(row.get("Quote_Status"), "UNKNOWN").upper()
    spread = _txt(row.get("Spread_Status"), "UNKNOWN").upper()
    close = _num(row.get("Close"), 0.0)
    ma20 = _num(row.get("MA20"), 0.0)
    ret20d = _num(row.get("Ret20D"), 0.0)
    if quote not in {"NAVER_QUOTE_OK", "OK"} or spread not in {"OK"}:
        return "CAUTION"
    if liquidity == "PREFERRED" and close >= ma20 and ret20d > 0:
        return "BUY_REVIEW"
    if ret20d > 0 and close >= ma20:
        return "TRACK"
    return "WATCH"


def _selection_score(row: dict[str, Any], is_weighted: bool) -> float:
    liquidity = _txt(row.get("Liquidity_Status"), "UNKNOWN").upper()
    quote = _txt(row.get("Quote_Status"), "UNKNOWN").upper()
    spread = _num(row.get("Spread_Pct"), 99.0)
    ret20d = _num(row.get("Ret20D"), 0.0)
    avgtrade = _num(row.get("AvgTradeValue_20D_KRW"), 0.0)
    score = 0.0
    if is_weighted:
        score += 3.0
    if liquidity == "PREFERRED":
        score += 3.0
    elif liquidity in {"WATCH", "NORMAL", "TRACK"}:
        score += 1.5
    if quote in {"NAVER_QUOTE_OK", "OK"}:
        score += 1.0
    if spread <= 0.2:
        score += 1.0
    elif spread <= 0.5:
        score += 0.5
    if ret20d >= 0:
        score += 1.0
    if avgtrade >= 50_000_000_000:
        score += 1.0
    return round(score, 2)


def _constituent_priority_score(
    spec: dict[str, Any],
    live_row: dict[str, Any] | None,
) -> tuple[float, float, float, float, float, str]:
    weight = _num(spec.get("Weight"), 0.0)
    live_score = 0.0
    liquidity_rank = 99.0
    spread = 99.0
    ret20d = -999.0
    name = _txt(spec.get("Constituent_Name"))
    if isinstance(live_row, dict):
        live_score = _selection_score(live_row, True)
        liquidity_rank = float(_liquidity_rank(_txt(live_row.get("Liquidity_Status"), "UNKNOWN")))
        spread = _num(live_row.get("Spread_Pct"), 99.0)
        ret20d = _num(live_row.get("Ret20D"), -999.0)
        if not name:
            name = _txt(live_row.get("Name"))
    return (-weight, -live_score, liquidity_rank, spread, -ret20d, name)


def _build_rep_item(
    row: dict[str, Any],
    spec: dict[str, Any],
    proxy: dict[str, Any],
    source_kind: str,
    original_constituent: str = "",
    original_constituent_name: str = "",
) -> dict[str, Any]:
    alignment = "ALIGNED" if (_num(row.get("Ret20D"), 0.0) >= 0) == (_num(proxy.get("Sector_Ret20D"), 0.0) >= 0) else "DIVERGING"
    item = {
        "ticker": _txt(row.get("Ticker"), _txt(spec.get("Constituent_Code"), _txt(spec.get("Ticker")))),
        "name": _txt(row.get("Name"), _txt(spec.get("Constituent_Name"), _txt(spec.get("Name")))),
        "weight": spec.get("Weight", ""),
        "close": row.get("Close", ""),
        "ma20": row.get("MA20", ""),
        "ret10d": row.get("Ret10D", ""),
        "ret20d": row.get("Ret20D", ""),
        "ret60d": row.get("Ret60D", ""),
        "avgtradevalue20d_krw": row.get("AvgTradeValue_20D_KRW", ""),
        "spread_pct": row.get("Spread_Pct", ""),
        "quote_status": _txt(row.get("Quote_Status")),
        "liquidity_status": _txt(row.get("Liquidity_Status")),
        "frg_5d": row.get("Frg_5D", ""),
        "monitor_state": _monitor_state(row),
        "proxy_alignment": alignment,
        "selection_source": source_kind,
        "selection_score": _selection_score(row, source_kind == "ETF_CONSTITUENT_WEIGHT"),
    }
    if original_constituent:
        item["original_constituent_ticker"] = original_constituent
    if original_constituent_name:
        item["original_constituent_name"] = original_constituent_name
    return item


def build_etf_representative_monitor(payload: dict[str, Any]) -> dict[str, Any]:
    data, hctx = _load_payload(payload)
    sector_flow = data.get("sector_flow") if isinstance(data.get("sector_flow"), list) else []
    core_satellite = data.get("core_satellite") if isinstance(data.get("core_satellite"), list) else []
    sector_universe = data.get("sector_universe") if isinstance(data.get("sector_universe"), list) else []
    sector_flow = [r for r in sector_flow if isinstance(r, dict)]
    core_satellite = [r for r in core_satellite if isinstance(r, dict)]
    sector_universe = [r for r in sector_universe if isinstance(r, dict)]

    etf_sectors: dict[str, dict[str, Any]] = {}
    for row in sector_flow:
        sector = _txt(row.get("Sector"))
        if not sector:
            continue
        if _txt(row.get("Proxy_Type")).upper() == "ETF":
            etf_sectors[sector] = row
    if "로보틱스" not in etf_sectors:
        etf_sectors["로보틱스"] = ROBOTICS_FALLBACK_PROXY

    sector_candidates: dict[str, list[dict[str, Any]]] = defaultdict(list)
    core_by_ticker: dict[str, dict[str, Any]] = {}
    for row in core_satellite:
        sector = _txt(row.get("Sector"))
        name = _txt(row.get("Name"))
        ticker = _txt(row.get("Ticker"))
        if not sector or not ticker:
            continue
        core_by_ticker[ticker] = row
        if _is_etf_like_name(name):
            continue
        sector_candidates[sector].append(row)

    universe_candidates: dict[str, list[dict[str, Any]]] = defaultdict(list)
    for row in sector_universe:
        sector = _txt(row.get("Sector"))
        constituent = _txt(row.get("Constituent_Code"))
        if not sector or not constituent:
            continue
        if _txt(row.get("Is_ETF")).upper() == "Y":
            continue
        if _txt(row.get("Enabled"), "Y").upper() == "N":
            continue
        if _txt(row.get("Status"), "OK").upper() not in {"OK", "ACTIVE", "LIVE"}:
            continue
        universe_candidates[sector].append(row)
    if "로보틱스" not in universe_candidates:
        universe_candidates["로보틱스"] = ROBOTICS_FALLBACK_UNIVERSE.copy()

    rows: list[dict[str, Any]] = []
    for sector, proxy in sorted(etf_sectors.items(), key=lambda item: (_num(item[1].get("Sector_Rank"), 999), -abs(_num(item[1].get("SmartMoney_5D_KRW"), 0.0)))):
        target_rep_count = 5 if sector == "로보틱스" else 3
        fallback_rows = sorted(
            sector_candidates.get(sector, []),
            key=lambda r: (
                _liquidity_rank(_txt(r.get("Liquidity_Status"), "UNKNOWN")),
                -_num(r.get("AvgTradeValue_20D_KRW"), 0.0),
                -_num(r.get("Ret20D"), 0.0),
                -_num(r.get("Ret10D"), 0.0),
            ),
        )
        # ETF 대표주는 구성비 내림차순을 1차 기준으로 고정한다.
        # live score는 동일 비중/동일 구성일 때만 보조 판단으로 사용한다.
        universe_rows = sorted(
            universe_candidates.get(sector, []),
            key=lambda r: (
                -_num(r.get("Weight"), 0.0),
                _constituent_priority_score(
                    r,
                    core_by_ticker.get(_txt(r.get("Constituent_Code")))
                    or next((x for x in fallback_rows if _txt(x.get("Ticker")) == _txt(r.get("Constituent_Code"))), None),
                ),
            ),
        )
        basket_items: list[dict[str, Any]] = []
        selected_specs: list[tuple[str, dict[str, Any]]] = [("ETF_CONSTITUENT_WEIGHT", row) for row in universe_rows[:target_rep_count]]
        selected_tickers = {_txt(row.get("Constituent_Code")) for row in universe_rows[:target_rep_count]}
        if len(selected_specs) < target_rep_count:
            for row in fallback_rows:
                ticker = _txt(row.get("Ticker"))
                if not ticker or ticker in selected_tickers:
                    continue
                selected_specs.append(("SECTOR_LIQUIDITY_FALLBACK", row))
                selected_tickers.add(ticker)
                if len(selected_specs) >= target_rep_count:
                    break
        if not selected_specs:
            selected_specs = [("SECTOR_LIQUIDITY_FALLBACK", row) for row in fallback_rows[:target_rep_count]]
        rep_source = "ETF_CONSTITUENT_WEIGHT" if universe_rows else "SECTOR_LIQUIDITY_FALLBACK"
        rep_basis_detail = "ETF_WEIGHT_PRIMARY"
        if universe_rows and len(universe_rows) < target_rep_count and len(selected_specs) >= target_rep_count:
            rep_basis_detail = "ETF_WEIGHT_PRIMARY_PLUS_SECTOR_TOPUP"
        if not universe_rows:
            rep_basis_detail = "SECTOR_LIQUIDITY_FALLBACK"
        for source_kind, spec in selected_specs:
            if source_kind == "ETF_CONSTITUENT_WEIGHT":
                ticker = _txt(spec.get("Constituent_Code"))
                rep = core_by_ticker.get(ticker)
                if rep is None:
                    rep = next((r for r in fallback_rows if _txt(r.get("Ticker")) == ticker), None)
                if rep is None:
                    rep = next((r for r in fallback_rows if _txt(r.get("Ticker")) not in selected_tickers), None)
                    if rep is not None:
                        source_kind = "SECTOR_LIQUIDITY_FALLBACK_REPLACEMENT"
            else:
                rep = spec
            if not rep:
                basket_items.append({
                    "ticker": _txt(spec.get("Constituent_Code"), _txt(spec.get("Ticker"))),
                    "name": _txt(spec.get("Constituent_Name"), _txt(spec.get("Name"))),
                    "weight": spec.get("Weight", ""),
                    "close": "DATA_MISSING — 하네스 업데이트 필요",
                    "ma20": "DATA_MISSING — 하네스 업데이트 필요",
                    "ret10d": "DATA_MISSING — 하네스 업데이트 필요",
                    "ret20d": "DATA_MISSING — 하네스 업데이트 필요",
                    "ret60d": "DATA_MISSING — 하네스 업데이트 필요",
                    "avgtradevalue20d_krw": "DATA_MISSING — 하네스 업데이트 필요",
                    "spread_pct": "DATA_MISSING — 하네스 업데이트 필요",
                    "quote_status": "DATA_MISSING — 하네스 업데이트 필요",
                    "liquidity_status": "DATA_MISSING — 하네스 업데이트 필요",
                    "frg_5d": "DATA_MISSING — 하네스 업데이트 필요",
                    "monitor_state": "DATA_MISSING",
                    "proxy_alignment": "UNKNOWN",
                    "selection_source": source_kind,
                    "selection_score": 0.0,
                    "replacement_reason": "NO_LIVE_REPLACEMENT",
                })
                continue
            basket_items.append(_build_rep_item(
                rep,
                spec,
                proxy,
                source_kind,
                _txt(spec.get("Constituent_Code")),
                _txt(spec.get("Constituent_Name")),
            ))
        if len(basket_items) < target_rep_count:
            used_tickers = {item["ticker"] for item in basket_items}
            for rep in fallback_rows:
                ticker = _txt(rep.get("Ticker"))
                if not ticker or ticker in used_tickers:
                    continue
                basket_items.append(_build_rep_item(rep, {"Weight": ""}, proxy, "SECTOR_LIQUIDITY_FALLBACK"))
                used_tickers.add(ticker)
                if len(basket_items) >= target_rep_count:
                    break
        if not basket_items:
            continue
        primary = basket_items[0]
        basket_buy = sum(1 for r in basket_items if r.get("monitor_state") == "BUY_REVIEW")
        basket_track = sum(1 for r in basket_items if r.get("monitor_state") == "TRACK")
        basket_watch = sum(1 for r in basket_items if r.get("monitor_state") == "WATCH")
        basket_caution = sum(1 for r in basket_items if r.get("monitor_state") == "CAUTION")
        basket_aligned = sum(1 for r in basket_items if r.get("proxy_alignment") == "ALIGNED")
        basket_missing = sum(1 for r in basket_items if r.get("monitor_state") == "DATA_MISSING")
        basket_real = len(basket_items) - basket_missing
        basket_coverage_pct = round((basket_real / len(basket_items)) * 100.0, 2) if basket_items else 0.0
        basket_quality_state = "COMPLETE" if basket_missing == 0 else "PARTIAL"
        basket_state = "BUY_REVIEW" if basket_buy >= 2 and basket_aligned >= 2 else (
            "CAUTION" if basket_caution > 0 else "TRACK" if basket_track > 0 else "WATCH"
        )
        rows.append({
            "sector": sector,
            "etf_proxy_ticker": _txt(proxy.get("Proxy_Ticker")),
            "etf_proxy_name": _txt(proxy.get("Proxy_Name")),
            "etf_proxy_type": _txt(proxy.get("Proxy_Type")),
            "universe_source": _txt(proxy.get("Universe_Source"), "DEFAULT_TEMPLATE"),
            "sector_rank": proxy.get("Sector_Rank", ""),
            "sector_score": proxy.get("Sector_Score", ""),
            "sector_smart_money_5d_krw": proxy.get("SmartMoney_5D_KRW", ""),
            "sector_ret20d": proxy.get("Sector_Ret20D", ""),
            "representative_count": len(basket_items),
            "representative_ticker": primary["ticker"],
            "representative_name": primary["name"],
            "representative_basis": rep_source,
            "representative_basis_detail": rep_basis_detail,
            "constituent_weight": primary["weight"],
            "weight_sum_stocks_only": universe_rows[0].get("Weight_Sum_Stocks_Only", "") if universe_rows else "",
            "weight_sum_all": universe_rows[0].get("Weight_Sum_All", "") if universe_rows else "",
            "representative_close": primary["close"],
            "representative_ma20": primary["ma20"],
            "representative_ret10d": primary["ret10d"],
            "representative_ret20d": primary["ret20d"],
            "representative_ret60d": primary["ret60d"],
            "representative_avgtradevalue20d_krw": primary["avgtradevalue20d_krw"],
            "representative_spread_pct": primary["spread_pct"],
            "representative_quote_status": primary["quote_status"],
            "representative_liquidity_status": primary["liquidity_status"],
            "representative_frg_5d": primary["frg_5d"],
            "monitor_state": basket_state,
            "proxy_alignment": "ALIGNED" if basket_aligned >= 2 else "DIVERGING",
            "basket_buy_review_count": basket_buy,
            "basket_track_count": basket_track,
            "basket_watch_count": basket_watch,
            "basket_caution_count": basket_caution,
            "basket_aligned_count": basket_aligned,
            "basket_missing_count": basket_missing,
            "basket_real_count": basket_real,
            "basket_coverage_pct": basket_coverage_pct,
            "basket_quality_state": basket_quality_state,
            "representatives": basket_items,
            "monitor_reason": (
                f"ETF 구성비중 상위 {target_rep_count}종목이 같은 방향으로 정렬"
                if basket_state == "BUY_REVIEW"
                else "대표 종목 바스켓 추세 확인 중" if basket_state == "TRACK"
                else "유동성/추세 보수 모니터링"
            ),
        })

    buy_review = sum(1 for r in rows if r.get("monitor_state") == "BUY_REVIEW")
    track = sum(1 for r in rows if r.get("monitor_state") == "TRACK")
    watch = sum(1 for r in rows if r.get("monitor_state") == "WATCH")
    caution = sum(1 for r in rows if r.get("monitor_state") == "CAUTION")
    aligned = sum(1 for r in rows if r.get("proxy_alignment") == "ALIGNED")
    weighted_basis = sum(1 for r in rows if r.get("representative_basis") == "ETF_CONSTITUENT_WEIGHT")
    fallback_basis = sum(1 for r in rows if r.get("representative_basis") == "SECTOR_LIQUIDITY_FALLBACK")
    complete_basket_count = sum(1 for r in rows if r.get("basket_quality_state") == "COMPLETE")
    partial_basket_count = sum(1 for r in rows if r.get("basket_quality_state") == "PARTIAL")
    basket_missing_total = sum(_num(r.get("basket_missing_count"), 0.0) for r in rows)

    result = {
        "formula_id": "ETF_REPRESENTATIVE_MONITOR_V1",
        "gate": "PASS" if rows else "DATA_MISSING",
        "etf_sector_count": len(etf_sectors),
        "tracked_count": len(rows),
        "summary": {
            "buy_review_count": buy_review,
            "track_count": track,
            "watch_count": watch,
            "caution_count": caution,
                "aligned_count": aligned,
            "weighted_basis_count": weighted_basis,
            "fallback_basis_count": fallback_basis,
            "complete_basket_count": complete_basket_count,
            "partial_basket_count": partial_basket_count,
            "basket_missing_total": basket_missing_total,
            "selected_sector_count": len({r["sector"] for r in rows}),
            "top_rep_names": [", ".join(rep["name"] for rep in r.get("representatives", [])) for r in rows[:3]],
        },
        "rows": rows,
        "source": {
            "sector_flow_rows": len(sector_flow),
            "core_satellite_rows": len(core_satellite),
            "sector_universe_rows": len(sector_universe),
            "template_source_count": sum(1 for r in rows if str(r.get("universe_source") or "").upper() == "DEFAULT_TEMPLATE"),
        },
    }
    return result