219 lines
7.8 KiB
Python
219 lines
7.8 KiB
Python
"""板块分析 API"""
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import logging
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from fastapi import APIRouter
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from app.config import is_market_session
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from app.data.tushare_client import tushare_client
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from app.data.eastmoney_client import get_sector_realtime_ranking
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from app.data.cache import cache
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from app.engine.recommender import get_latest_sectors
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logger = logging.getLogger(__name__)
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router = APIRouter(prefix="/api/sectors", tags=["sectors"])
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def _match_sector_name(em_name: str, ts_name: str) -> bool:
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"""东方财富板块名与 Tushare 板块名模糊匹配
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东方财富用"酿酒行业"，Tushare 可能叫"白酒"；
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东方财富用"汽车整车"，Tushare 可能叫"汽车"。
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用包含匹配（短名在长名中）或尾部去掉"行业"后完全匹配。
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"""
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# 去掉常见后缀再做比较
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em_clean = em_name.rstrip("行业").rstrip("板块").rstrip("概念").strip()
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ts_clean = ts_name.rstrip("行业").rstrip("板块").rstrip("概念").strip()
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if em_clean == ts_clean:
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return True
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# 短名包含在长名中
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short, long = (em_clean, ts_clean) if len(em_clean) <= len(ts_clean) else (ts_clean, em_clean)
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return short in long
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async def _enrich_sectors_realtime(sectors_data: list[dict]) -> list[dict]:
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"""盘中时，用东方财富实时板块数据补充涨幅和涨停数
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一次请求替代之前腾讯批量获取数千只成分股的方式。
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"""
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if not is_market_session():
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for s in sectors_data:
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s["realtime_pct_change"] = None
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s["realtime_limit_up_count"] = None
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s["is_realtime"] = False
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return sectors_data
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# 从东方财富获取实时板块排名（1次 HTTP 请求）
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try:
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em_sectors = await get_sector_realtime_ranking()
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except Exception:
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logger.warning("东方财富板块实时数据获取失败，回退到日级数据")
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for s in sectors_data:
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s["realtime_pct_change"] = None
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s["realtime_limit_up_count"] = None
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s["is_realtime"] = False
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return sectors_data
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if not em_sectors:
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for s in sectors_data:
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s["realtime_pct_change"] = None
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s["realtime_limit_up_count"] = None
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s["is_realtime"] = False
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return sectors_data
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# 构建东方财富板块名查找表（用于匹配）
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em_name_map = {s["sector_name"]: s for s in em_sectors}
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matched = 0
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for s in sectors_data:
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ts_name = s["sector_name"]
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# 尝试匹配：先精确，再模糊
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em_data = em_name_map.get(ts_name)
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if not em_data:
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# 模糊匹配
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for em_s in em_sectors:
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if _match_sector_name(em_s["sector_name"], ts_name):
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em_data = em_s
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break
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if em_data:
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matched += 1
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s["realtime_pct_change"] = em_data["pct_change"]
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s["is_realtime"] = True
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# 涨停家数仍保留 Tushare 数据（东方财富此字段不可用）
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s["realtime_limit_up_count"] = None
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# 更新领涨股（东方财富直接提供）
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if em_data.get("leading_stock_name"):
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s["leading_stocks_realtime"] = [
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{
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"ts_code": em_data.get("leading_stock_code", ""),
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"name": em_data.get("leading_stock_name", ""),
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"pct_chg": em_data.get("leading_stock_pct", 0),
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"amount": 0,
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}
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]
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else:
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s["realtime_pct_change"] = None
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s["realtime_limit_up_count"] = None
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s["is_realtime"] = False
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logger.info(f"板块实时数据: {matched}/{len(sectors_data)} 匹配成功")
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# 盘中按实时涨幅重新排序
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sectors_data.sort(key=lambda s: s.get("realtime_pct_change") or s.get("pct_change") or 0, reverse=True)
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return sectors_data
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@router.get("/hot")
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async def get_hot_sectors(limit: int = 10):
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"""获取热门板块排名（盘中自动补充实时数据）"""
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sectors = await get_latest_sectors()
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sectors_data = [
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{
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"sector_code": s.sector_code,
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"sector_name": s.sector_name,
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"pct_change": s.pct_change,
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"capital_inflow": s.capital_inflow,
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"limit_up_count": s.limit_up_count,
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"days_continuous": s.days_continuous,
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"heat_score": s.heat_score,
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"stage": s.stage,
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# 增强分析字段
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"member_count": s.member_count,
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"leading_stocks": s.leading_stocks,
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"pct_trend": s.pct_trend,
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"turnover_avg": s.turnover_avg,
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"main_force_ratio": s.main_force_ratio,
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}
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for s in sectors[:limit]
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]
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sectors_data = await _enrich_sectors_realtime(sectors_data)
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return sectors_data
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@router.get("/rotation")
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async def get_sector_rotation(days: int = 5):
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"""获取近N日板块轮动数据（用于热力图）"""
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# 检查缓存
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cache_key = f"sector_rotation:{days}"
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cached = cache.get(cache_key)
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if cached is not None:
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return cached
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trade_date = tushare_client.get_latest_trade_date()
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# 获取交易日历
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trade_dates_df = tushare_client.get_trade_dates()
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today = trade_date
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past_dates = [d for d in trade_dates_df if d <= today]
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# 取最近 N 天
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recent_dates = past_dates[-days:] if len(past_dates) >= days else past_dates
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# 获取板块指数列表用于名字映射
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index_list = tushare_client.get_ths_index_list()
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name_map = {}
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if not index_list.empty:
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for _, row in index_list.iterrows():
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name_map[row["ts_code"]] = row["name"]
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all_sectors = []
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for td in recent_dates:
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df = tushare_client.get_sector_moneyflow(td)
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if df.empty:
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continue
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for _, row in df.iterrows():
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code = row.get("ts_code", "")
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# Use industry field from moneyflow data, fallback to name_map
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industry_name = row.get("industry", "") or name_map.get(code, code)
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all_sectors.append({
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"sector_code": code,
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"sector_name": industry_name,
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"trade_date": td,
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"net_amount": round(float(row.get("net_amount", 0) or 0), 2),
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})
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# 获取板块日线来补充涨跌幅
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sector_codes = list(set(s["sector_code"] for s in all_sectors))
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sector_pct_map: dict[str, dict[str, float]] = {}
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for code in sector_codes:
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df_daily = tushare_client.get_ths_daily(code, days=days + 10)
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if not df_daily.empty:
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for _, r in df_daily.iterrows():
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if r["trade_date"] in recent_dates:
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if code not in sector_pct_map:
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sector_pct_map[code] = {}
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sector_pct_map[code][r["trade_date"]] = float(r.get("pct_change", 0) or 0)
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# 按板块分组
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sector_map: dict[str, dict] = {}
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for s in all_sectors:
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code = s["sector_code"]
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if code not in sector_map:
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sector_map[code] = {
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"sector_code": code,
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"sector_name": s["sector_name"],
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"daily_data": [],
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}
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pct = sector_pct_map.get(code, {}).get(s["trade_date"], 0)
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sector_map[code]["daily_data"].append({
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"trade_date": s["trade_date"],
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"pct_change": round(pct, 2),
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"net_amount": s["net_amount"],
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})
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# 按最近一天涨幅排序，取 top 20
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sorted_sectors = sorted(
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sector_map.values(),
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key=lambda x: max((d["pct_change"] for d in x["daily_data"]), default=0),
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reverse=True,
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)[:20]
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result = {"trade_date": trade_date, "dates": recent_dates, "sectors": sorted_sectors}
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# 写入缓存，TTL 300秒（5分钟）
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cache.set(cache_key, result, ttl=300)
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return result
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