astock-agent/backend/app/api/sectors.py

"""板块分析 API"""

from fastapi import APIRouter

from app.data.tushare_client import tushare_client
from app.data.cache import cache
from app.engine.recommender import get_latest_sectors

router = APIRouter(prefix="/api/sectors", tags=["sectors"])


@router.get("/hot")
async def get_hot_sectors(limit: int = 10):
    """获取最新主线主题排名。

    页面访问只读数据库里的扫描结论，不在 GET 请求中拉取外部实时行情。
    """
    sectors = await get_latest_sectors()
    trade_date = sectors[0].trade_date if sectors else ""

    sectors_data = [
        {
            "sector_code": s.sector_code,
            "sector_name": s.sector_name,
            "board_type": s.board_type,
            "theme_id": s.theme_id,
            "theme_name": s.theme_name,
            "theme_aliases": s.theme_aliases,
            "pct_change": s.pct_change,
            "capital_inflow": s.capital_inflow,
            "limit_up_count": s.limit_up_count,
            "days_continuous": s.days_continuous,
            "heat_score": s.heat_score,
            "stage": s.stage,
            # 增强分析字段
            "member_count": s.member_count,
            "leading_stocks": s.leading_stocks,
            "pct_trend": s.pct_trend,
            "turnover_avg": s.turnover_avg,
            "main_force_ratio": s.main_force_ratio,
            "trade_date": trade_date,
            "realtime_pct_change": s.realtime_pct_change,
            "realtime_limit_up_count": s.realtime_limit_up_count,
            "realtime_amount": s.realtime_amount,
            "realtime_turnover_rate": s.realtime_turnover_rate,
            "realtime_up_count": s.realtime_up_count,
            "realtime_down_count": s.realtime_down_count,
            "leading_stocks_realtime": s.leading_stocks_realtime,
            "is_realtime": s.is_realtime,
            "data_mode": s.data_mode,
            "source": s.source,
            "data_status": s.data_status,
            "source_detail": s.source_detail,
            "catalyst_score": s.catalyst_score,
            "catalyst_count": s.catalyst_count,
            "catalyst_reasons": s.catalyst_reasons,
        }
        for s in sectors[:limit]
    ]

    realtime_enabled = any(s.get("is_realtime") for s in sectors_data)
    mode = sectors[0].data_mode if realtime_enabled and sectors else "daily_snapshot"
    status = _derive_status(sectors_data)
    for s in sectors_data:
        s["data_mode"] = mode
        s["data_status"] = status
        s["structure_trade_date"] = trade_date
    return sectors_data


def _derive_status(sectors: list[dict]) -> str:
    statuses = {str(s.get("data_status") or "fresh") for s in sectors}
    if not statuses:
        return "snapshot"
    if "fresh" in statuses:
        return "fresh" if len(statuses) == 1 else "mixed"
    if "stale" in statuses:
        return "stale"
    if "fallback" in statuses:
        return "fallback"
    return next(iter(statuses))


@router.get("/rotation")
async def get_sector_rotation(days: int = 5):
    """获取近N日板块轮动数据（用于热力图）"""
    # 检查缓存
    cache_key = f"sector_rotation:{days}"
    cached = cache.get(cache_key)
    if cached is not None:
        return cached

    trade_date = tushare_client.get_latest_trade_date()

    # 获取交易日历
    trade_dates_df = tushare_client.get_trade_dates()
    today = trade_date
    past_dates = [d for d in trade_dates_df if d <= today]
    # 取最近 N 天
    recent_dates = past_dates[-days:] if len(past_dates) >= days else past_dates

    # 获取板块指数列表用于名字映射
    index_list = tushare_client.get_ths_index_list()
    name_map = {}
    if not index_list.empty:
        for _, row in index_list.iterrows():
            name_map[row["ts_code"]] = row["name"]

    all_sectors = []
    for td in recent_dates:
        df = tushare_client.get_sector_moneyflow(td)
        if df.empty:
            continue
        for _, row in df.iterrows():
            code = row.get("ts_code", "")
            # Use industry field from moneyflow data, fallback to name_map
            industry_name = row.get("industry", "") or name_map.get(code, code)
            all_sectors.append({
                "sector_code": code,
                "sector_name": industry_name,
                "trade_date": td,
                "net_amount": round(float(row.get("net_amount", 0) or 0), 2),
            })

    # 获取板块日线来补充涨跌幅
    sector_codes = list(set(s["sector_code"] for s in all_sectors))
    sector_pct_map: dict[str, dict[str, float]] = {}
    for code in sector_codes:
        df_daily = tushare_client.get_ths_daily(code, days=days + 10)
        if not df_daily.empty:
            for _, r in df_daily.iterrows():
                if r["trade_date"] in recent_dates:
                    if code not in sector_pct_map:
                        sector_pct_map[code] = {}
                    sector_pct_map[code][r["trade_date"]] = float(r.get("pct_change", 0) or 0)

    # 按板块分组
    sector_map: dict[str, dict] = {}
    for s in all_sectors:
        code = s["sector_code"]
        if code not in sector_map:
            sector_map[code] = {
                "sector_code": code,
                "sector_name": s["sector_name"],
                "daily_data": [],
            }
        pct = sector_pct_map.get(code, {}).get(s["trade_date"], 0)
        sector_map[code]["daily_data"].append({
            "trade_date": s["trade_date"],
            "pct_change": round(pct, 2),
            "net_amount": s["net_amount"],
        })

    # 按最近一天涨幅排序，取 top 20
    sorted_sectors = sorted(
        sector_map.values(),
        key=lambda x: max((d["pct_change"] for d in x["daily_data"]), default=0),
        reverse=True,
    )[:20]

    result = {"trade_date": trade_date, "dates": recent_dates, "sectors": sorted_sectors}

    # 写入缓存，TTL 300秒（5分钟）
    cache.set(cache_key, result, ttl=300)

    return result