from __future__ import annotations

import argparse
import json
import math
from collections import defaultdict
from datetime import date, timedelta
from pathlib import Path

import pandas as pd

from qfr.data.tushare_client import load_tushare_config, pro_api


def median_amount(cfg, ts_code: str, start: str, end: str) -> float:
    api = pro_api(cfg)
    df = api.fund_daily(ts_code=ts_code, start_date=start, end_date=end, fields="trade_date,amount")
    if df is None or df.empty or "amount" not in df.columns:
        return 0.0
    amt = pd.to_numeric(df["amount"], errors="coerce").dropna()
    if amt.empty:
        return 0.0
    return float(amt.median())


def classify_by_keyword(kw: str) -> str:
    # very rough tagging for universe constraints / reporting
    equity_kws = {
        "半导体",
        "芯片",
        "通信",
        "5G",
        "通信设备",
        "军工",
        "机器人",
        "工业母机",
        "智能制造",
        "消费电子",
        "AI",
        "算力",
        "软件",
        "创新药",
        "医药",
        "新能源",
        "光伏",
        "锂电",
        "电池",
        "新材料",
        "稀土",
    }
    commodity_kws = {"黄金", "白银", "有色", "稀土", "矿业", "原油", "油", "煤", "化工", "豆粕", "农业"}
    rates_kws = {"国债", "政金债", "债", "短债", "中债"}

    if kw in rates_kws:
        return "rates_cn"
    if kw in commodity_kws:
        return "commodity_cn"
    if kw in equity_kws:
        return "equity_cn_sector"
    return "equity_cn_sector"


def main() -> None:
    ap = argparse.ArgumentParser()
    ap.add_argument("--config", default="configs/etf_universe.json")
    ap.add_argument("--out", default=None)
    ap.add_argument("--per_keyword", type=int, default=2)
    ap.add_argument("--min_median_amount", type=float, default=0.0)
    ap.add_argument(
        "--keywords",
        default=(
            "半导体,芯片,通信,5G,通信设备,军工,机器人,工业母机,智能制造,消费电子,AI,算力,软件,创新药,医药,新能源,光伏,锂电,电池,"
            "矿业,有色,稀土,新材料,黄金,白银,原油,煤,化工,豆粕,农业,国债,政金债"
        ),
    )
    args = ap.parse_args()

    cfg = load_tushare_config()
    api = pro_api(cfg)

    conf_path = Path(args.config)
    conf = json.loads(conf_path.read_text(encoding="utf-8"))

    assets = conf.get("assets", [])
    have = {a["ts_code"] for a in assets}

    kw_list = [k.strip() for k in str(args.keywords).split(",") if k.strip()]

    fb = api.fund_basic(market="E", status="L", fields="ts_code,name")
    if fb is None or fb.empty:
        raise RuntimeError("fund_basic returned empty")

    fb = fb.dropna(subset=["ts_code", "name"]).copy()

    end = date.today().strftime("%Y%m%d")
    start = (date.today() - timedelta(days=180)).strftime("%Y%m%d")

    buckets: dict[str, list[tuple[str, str]]] = defaultdict(list)
    for _, r in fb.iterrows():
        ts_code = str(r["ts_code"]).strip()
        name = str(r["name"]).strip()
        for kw in kw_list:
            if kw in name:
                buckets[kw].append((ts_code, name))
                break

    chosen: list[tuple[str, str, str, float, str]] = []

    for kw in kw_list:
        cands = buckets.get(kw, [])
        if not cands:
            continue

        scored: list[tuple[float, str, str]] = []
        for ts_code, name in cands:
            if ts_code in have:
                continue
            try:
                m = median_amount(cfg, ts_code, start, end)
            except Exception:
                m = 0.0
            if not math.isfinite(m) or m <= 0:
                continue
            if m < float(args.min_median_amount):
                continue
            scored.append((m, ts_code, name))

        scored.sort(reverse=True)
        for m, ts_code, name in scored[: int(args.per_keyword)]:
            cls = classify_by_keyword(kw)
            chosen.append((kw, ts_code, name, m, cls))

    for kw, ts_code, name, m, cls in chosen:
        assets.append({"ts_code": ts_code, "asset_class": cls, "name": name})
        have.add(ts_code)

    conf["assets"] = assets

    out_path = Path(args.out) if args.out else conf_path
    out_path.write_text(json.dumps(conf, ensure_ascii=True, indent=2) + "\n", encoding="utf-8")

    print(f"added {len(chosen)} ETFs")
    for kw, ts_code, name, m, cls in chosen[:80]:
        print(f"{kw}\t{ts_code}\t{m:.0f}\t{cls}\t{name}")


if __name__ == "__main__":
    main()