182 lines
6.6 KiB
Python
182 lines
6.6 KiB
Python
"""板块热度扫描
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综合板块涨幅、资金净流入、涨停家数、持续性，
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输出热门板块排名。
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优化策略：先用板块资金流向批量数据预筛 Top 板块，
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只对 Top 板块做逐个详细查询（ths_daily/ths_member），
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避免遍历全部数百个板块导致大量 API 调用。
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"""
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import logging
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import pandas as pd
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import numpy as np
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from datetime import datetime, timedelta
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from app.data.tushare_client import tushare_client
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from app.data.models import SectorInfo
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from app.config import settings
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logger = logging.getLogger(__name__)
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# 预筛阶段取 Top N 板块做详细查询
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PRE_FILTER_COUNT = 30
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def _normalize_score(values: list[float], reverse: bool = False) -> list[float]:
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"""将数值列表归一化到 0-100"""
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if not values:
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return []
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min_v, max_v = min(values), max(values)
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if max_v == min_v:
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return [50.0] * len(values)
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normalized = [(v - min_v) / (max_v - min_v) * 100 for v in values]
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if reverse:
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normalized = [100 - v for v in normalized]
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return normalized
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def scan_hot_sectors(trade_date: str = None) -> list[SectorInfo]:
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"""扫描热门板块，返回按热度排名的板块列表"""
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if not trade_date:
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trade_date = tushare_client.get_latest_trade_date()
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# ── 第一步：用板块资金流向批量预筛（1 次 API 调用）──
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sector_mf = tushare_client.get_sector_moneyflow(trade_date)
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if sector_mf.empty:
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logger.warning("板块资金流向数据为空")
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return []
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# 按净流入金额排序，取 Top N 做详细分析
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sector_mf = sector_mf.sort_values("net_amount", ascending=False)
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top_mf = sector_mf.head(PRE_FILTER_COUNT)
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top_codes = set(top_mf["ts_code"].tolist())
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logger.info(f"板块资金流向预筛: {len(sector_mf)} 个板块 -> Top {len(top_codes)} 进入详细分析")
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# 构建资金流向查找表
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mf_lookup = {}
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for _, row in sector_mf.iterrows():
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mf_lookup[row["ts_code"]] = float(row["net_amount"])
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# 构建板块名称查找表
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# moneyflow_ind_ths 返回的是行业板块（881XXX.TI），自带 industry 列
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name_lookup = {}
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if "industry" in sector_mf.columns:
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for _, r in sector_mf.iterrows():
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if pd.notna(r.get("industry")):
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name_lookup[r["ts_code"]] = str(r["industry"])
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# 补充：从 ths_index type=I（行业板块）获取名称
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index_list = tushare_client.get_ths_index_list("I")
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if not index_list.empty:
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for _, r in index_list.iterrows():
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if r["ts_code"] not in name_lookup:
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name_lookup[r["ts_code"]] = r["name"]
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# ── 第二步：获取涨跌停列表（1 次 API 调用）──
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limit_df = tushare_client.get_limit_list(trade_date)
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limit_up_codes = set()
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if not limit_df.empty:
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up_df = limit_df[limit_df["limit"] == "U"]
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up_df = up_df[~up_df["name"].str.contains("ST", na=False)]
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limit_up_codes = set(up_df["ts_code"].tolist())
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# ── 第三步：只对 Top 板块做逐个详细查询 ──
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sectors = []
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for ts_code in top_codes:
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# 板块名称从 ths_index 查找表获取
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sector_name = name_lookup.get(ts_code, ts_code)
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# 板块日线 - 获取近5日数据（1 次 API）
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ths_daily = tushare_client.get_ths_daily(ts_code, days=5)
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pct_change = 0.0
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days_continuous = 0
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if not ths_daily.empty:
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ths_daily = ths_daily.sort_values("trade_date")
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# 当日涨跌幅
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today_data = ths_daily[ths_daily["trade_date"] == trade_date]
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if today_data.empty:
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today_data = ths_daily.tail(1)
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pct_change = float(today_data["pct_change"].iloc[0]) if not today_data.empty else 0
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# 连续上涨天数
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for _, d in ths_daily.iloc[::-1].iterrows():
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if d["pct_change"] > 0:
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days_continuous += 1
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else:
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break
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# 板块资金净流入（从预筛数据中直接取）
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capital_inflow = mf_lookup.get(ts_code, 0.0)
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# 板块内涨停家数（1 次 API）
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limit_up_count = 0
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members = tushare_client.get_ths_members(ts_code)
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if not members.empty and "con_code" in members.columns:
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member_codes = set(members["con_code"].tolist())
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limit_up_count = len(limit_up_codes & member_codes)
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sectors.append(SectorInfo(
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sector_code=ts_code,
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sector_name=sector_name,
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pct_change=round(pct_change, 2),
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capital_inflow=round(capital_inflow, 2),
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limit_up_count=limit_up_count,
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days_continuous=days_continuous,
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))
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if not sectors:
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return []
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# ── 板块阶段判定 ──
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for s in sectors:
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if s.days_continuous <= 2:
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s.stage = "early" # 启动期，安全
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elif s.days_continuous == 3:
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s.stage = "mid" # 发展期，正常
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elif s.days_continuous == 4:
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s.stage = "late" # 后期，谨慎
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else:
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s.stage = "end" # 尾声，高风险
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# ── 综合评分 ──
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pct_scores = _normalize_score([s.pct_change for s in sectors])
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cap_scores = _normalize_score([s.capital_inflow for s in sectors])
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lim_scores = _normalize_score([float(s.limit_up_count) for s in sectors])
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con_scores = _normalize_score([float(s.days_continuous) for s in sectors])
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for i, s in enumerate(sectors):
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heat = (
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pct_scores[i] * 0.25 +
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cap_scores[i] * 0.30 +
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lim_scores[i] * 0.25 +
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con_scores[i] * 0.20
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)
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# 连续2日以上资金流入的板块加分
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if s.days_continuous >= 2:
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heat += 5
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# 首日大涨（昨日涨幅<=0，今日>3%）可视为新热点，加分
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if s.days_continuous == 1 and s.pct_change > 3:
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heat += 3
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# 板块阶段调整：早期加分，尾声减分（防追高）
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if s.stage == "early":
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heat += 8 # 启动期，介入安全，大幅加分
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elif s.stage == "late":
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heat -= 5 # 后期，风险上升
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elif s.stage == "end":
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heat -= 12 # 尾声，大幅减分
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s.heat_score = round(max(0, min(heat, 100)), 1)
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sectors.sort(key=lambda x: x.heat_score, reverse=True)
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top = sectors[:settings.top_sector_count]
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for s in top:
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logger.info(f"热门板块: {s.sector_name} 涨幅{s.pct_change}% 资金{s.capital_inflow}万 "
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f"涨停{s.limit_up_count} 连续{s.days_continuous}天 阶段={s.stage} 热度{s.heat_score}")
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return sectors
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