278 lines
12 KiB
Python
278 lines
12 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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输出热门板块排名及深度分析。
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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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增强分析：
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- 领涨股：每个板块中涨幅前3的成分股
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- 资金趋势：近5日板块资金净流入走势
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- 涨跌趋势：近5日板块涨跌幅走势
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- 主力资金占比
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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（含深度分析）"""
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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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# Tushare moneyflow_ind_ths 的金额单位是亿元，统一转换为万元
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_UNIT_CONV = 10000
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mf_lookup = {}
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# 同时构建主力买卖数据（用于计算主力占比）
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mf_detail = {}
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for _, row in sector_mf.iterrows():
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mf_lookup[row["ts_code"]] = float(row["net_amount"]) * _UNIT_CONV
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mf_detail[row["ts_code"]] = {
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"net_amount": float(row["net_amount"]) * _UNIT_CONV,
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"buy_elg_amount": float(row.get("buy_elg_amount", 0)) * _UNIT_CONV if pd.notna(row.get("buy_elg_amount")) else 0,
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"sell_elg_amount": float(row.get("sell_elg_amount", 0)) * _UNIT_CONV if pd.notna(row.get("sell_elg_amount")) else 0,
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"buy_lg_amount": float(row.get("buy_lg_amount", 0)) * _UNIT_CONV if pd.notna(row.get("buy_lg_amount")) else 0,
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"sell_lg_amount": float(row.get("sell_lg_amount", 0)) * _UNIT_CONV if pd.notna(row.get("sell_lg_amount")) else 0,
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}
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# 构建板块名称查找表
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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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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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# 同时收集涨停股的涨跌幅信息（用于领涨股展示）
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limit_up_info: dict[str, dict] = {}
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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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for _, row in up_df.iterrows():
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limit_up_info[row["ts_code"]] = {
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"name": row["name"],
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"pct_chg": float(row.get("pct_chg", 10)),
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"limit_times": int(row.get("limit_times", 1)),
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}
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# ── 第三步：获取全市场日线数据（用于领涨股计算）──
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daily_all = tushare_client.get_daily_all(trade_date)
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stock_basic = tushare_client.get_stock_basic()
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# ── 第四步：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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sector_name = name_lookup.get(ts_code, ts_code)
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# 板块日线 - 获取近5日数据
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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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pct_trend: list[float] = []
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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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# 近5日涨跌幅趋势
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pct_trend = [round(float(d["pct_change"]), 2) for _, d in ths_daily.iterrows()]
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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
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capital_inflow = mf_lookup.get(ts_code, 0.0)
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# 主力资金占比 = (特大单净买 + 大单净买) / (特大单买卖总额 + 大单买卖总额)
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main_force_ratio = 0.0
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detail = mf_detail.get(ts_code, {})
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total_main_amount = (detail.get("buy_elg_amount", 0) + detail.get("sell_elg_amount", 0) +
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detail.get("buy_lg_amount", 0) + detail.get("sell_lg_amount", 0))
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if total_main_amount > 0:
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main_force_ratio = round(capital_inflow / total_main_amount * 100, 1)
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# 板块成分股分析
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limit_up_count = 0
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leading_stocks: list[dict] = []
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member_count = 0
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turnover_avg = 0.0
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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 = list(members["con_code"].tolist())
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member_set = set(member_codes)
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member_count = len(member_codes)
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limit_up_count = len(limit_up_codes & member_set)
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# 领涨股：从当日全市场日级数据中筛选该板块成分股，按涨幅排序取前3
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if not daily_all.empty:
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sector_daily = daily_all[daily_all["ts_code"].isin(member_set)].copy()
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# 排除 ST
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if not stock_basic.empty:
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st_set = set(stock_basic[stock_basic["name"].str.contains("ST", na=False)]["ts_code"])
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sector_daily = sector_daily[~sector_daily["ts_code"].isin(st_set)]
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sector_daily = sector_daily.sort_values("pct_chg", ascending=False)
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# 计算板块平均换手率
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if "turnover_rate" in sector_daily.columns:
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turnover_values = sector_daily["turnover_rate"].dropna()
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if len(turnover_values) > 0:
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turnover_avg = round(float(turnover_values.mean()), 2)
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# 构建名称查找
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name_map = {}
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if not stock_basic.empty:
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for _, br in stock_basic.iterrows():
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name_map[br["ts_code"]] = br["name"]
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if "con_name" in members.columns:
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for _, m in members.iterrows():
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if pd.notna(m.get("con_name")):
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name_map[m["con_code"]] = m["con_name"]
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# 取涨幅前3
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for _, sr in sector_daily.head(3).iterrows():
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leading_stocks.append({
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"ts_code": sr["ts_code"],
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"name": name_map.get(sr["ts_code"], sr["ts_code"]),
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"pct_chg": round(float(sr["pct_chg"]), 2),
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"amount": round(float(sr.get("amount", 0)), 0),
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})
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# 涨停股也在成分股中的，补充到领涨股（如未在top3中）
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for code in (limit_up_codes & member_set):
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if code not in [s["ts_code"] for s in leading_stocks]:
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info = limit_up_info.get(code, {})
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leading_stocks.append({
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"ts_code": code,
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"name": info.get("name", code),
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"pct_chg": info.get("pct_chg", 10),
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"amount": 0,
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"limit_times": info.get("limit_times", 1),
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})
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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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member_count=member_count,
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leading_stocks=leading_stocks,
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pct_trend=pct_trend,
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turnover_avg=turnover_avg,
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main_force_ratio=main_force_ratio,
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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（结合连续天数与累计涨幅）──
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for s in sectors:
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cumulative_pct = round(sum(s.pct_trend), 2)
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if s.days_continuous <= 2 or (s.days_continuous <= 3 and cumulative_pct < 5):
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s.stage = "early"
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elif s.days_continuous == 3 and cumulative_pct >= 5 or (s.days_continuous == 4 and cumulative_pct < 8):
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s.stage = "mid"
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elif (s.days_continuous == 4 and cumulative_pct >= 8) or (s.days_continuous == 5 and cumulative_pct < 10):
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s.stage = "late"
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elif (s.days_continuous >= 5 and cumulative_pct >= 10) or s.days_continuous >= 6:
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s.stage = "end"
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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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if s.days_continuous >= 2:
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heat += 5
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if s.days_continuous == 1 and s.pct_change > 3:
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heat += 3
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if s.stage == "early":
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heat += 8
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elif s.stage == "late":
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heat -= 5
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elif s.stage == "end":
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heat -= 12
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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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leaders = ", ".join(f'{l["name"]}({l["pct_chg"]}%)' for l in s.leading_stocks[:3])
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inflow_display = f"{s.capital_inflow / 10000:.1f}亿" if s.capital_inflow >= 10000 else f"{s.capital_inflow:.0f}万"
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logger.info(f"热门板块: {s.sector_name} 涨幅{s.pct_change}% 资金{inflow_display} "
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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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f"领涨=[{leaders}]")
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return sectors
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