274 lines
8.9 KiB
Python
274 lines
8.9 KiB
Python
"""个股资金流向分析
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在热门板块成分股中，通过主力净流入、换手率、量比、市值
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筛选被主力资金真正介入的个股。
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"""
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import logging
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import pandas as pd
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from app.data.tushare_client import tushare_client
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from app.data import tencent_client
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from app.data.models import SectorInfo, CapitalFlow
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from app.config import settings
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from app.analysis.sector_alignment import build_hot_theme_membership
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logger = logging.getLogger(__name__)
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def _score_capital_flow(
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main_net_inflow: float,
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inflow_ratio: float,
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turnover_rate: float,
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volume_ratio: float | None,
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circ_mv: float | None,
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) -> float:
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"""资金维度评分，返回 0-100"""
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score = 0.0
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# 主力净流入金额归一化评分 (35%)
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# 这里不做跨个股归一化，而是基于绝对值打分
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if main_net_inflow > 10000: # >1亿
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score += 35
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elif main_net_inflow > 5000: # >5000万
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score += 28
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elif main_net_inflow > 2000: # >2000万
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score += 22
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elif main_net_inflow > 500: # >500万
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score += 15
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elif main_net_inflow > 0:
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score += 8
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# 主力净流入占比 (20%)
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if inflow_ratio > 15:
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score += 20
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elif inflow_ratio > 10:
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score += 16
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elif inflow_ratio > 5:
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score += 12
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elif inflow_ratio > 2:
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score += 8
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elif inflow_ratio > 0:
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score += 4
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# 换手率 (15%) - 3%-15% 最佳
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if settings.min_turnover_rate <= turnover_rate <= settings.max_turnover_rate:
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# 5%-10% 最佳
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if 5 <= turnover_rate <= 10:
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score += 15
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else:
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score += 10
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elif turnover_rate > 0:
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score += 3
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# 量比 (15%)
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if volume_ratio is not None:
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if volume_ratio > 3.0:
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score += 15
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elif volume_ratio > 2.0:
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score += 12
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elif volume_ratio > 1.5:
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score += 9
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elif volume_ratio > 1.0:
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score += 5
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# 市值适配 (15%)
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if circ_mv is not None:
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mv_b = circ_mv # 已经是亿
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if settings.min_circ_mv <= mv_b <= settings.max_circ_mv:
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if 100 <= mv_b <= 300: # 最佳区间
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score += 15
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else:
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score += 10
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elif mv_b > 0:
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score += 3
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return score
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def _score_valuation(pe: float | None, pb: float | None) -> float:
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"""估值安全评分，返回 0-100
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PE/PB 越低越安全（分数越高）。
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过高估值会被大幅惩罚，避免追高买在估值泡沫中。
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"""
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score = 0.0
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# PE_TTM 评分 (60%)
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if pe is not None and pe > 0:
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if pe <= 15:
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score += 60 # 低估
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elif pe <= 25:
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score += 50 # 合理偏低
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elif pe <= 40:
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score += 35 # 合理
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elif pe <= 60:
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score += 20 # 偏高
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elif pe <= 100:
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score += 8 # 高估
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else:
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score += 0 # 极度高估
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elif pe is not None and pe < 0:
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score += 5 # 亏损股，低分
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else:
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score += 30 # 无数据，给中间分
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# PB 评分 (40%)
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if pb is not None and pb > 0:
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if pb <= 1.5:
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score += 40 # 破净或接近，极度低估
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elif pb <= 3:
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score += 32 # 合理
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elif pb <= 5:
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score += 22 # 偏高
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elif pb <= 8:
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score += 12 # 高估
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else:
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score += 4 # 极度高估
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else:
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score += 20 # 无数据，给中间分
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return score
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async def filter_stocks_by_capital(
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hot_sectors: list[SectorInfo],
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trade_date: str = None,
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) -> list[dict]:
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"""
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从热门板块中筛选资金流入个股。
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返回: [{ts_code, name, sector, capital_score, turnover_rate, ...}]
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"""
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if not trade_date:
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trade_date = tushare_client.get_latest_trade_date()
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# 收集所有热门板块的成分股（去重）
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stock_sectors: dict[str, list[str]] = {}
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sector_member_codes, sector_code_map, _, _, _ = build_hot_theme_membership(hot_sectors)
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for code in sector_member_codes:
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if not code or "." not in str(code):
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continue
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stock_sectors.setdefault(code, []).append(sector_code_map.get(code, ""))
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if not stock_sectors:
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logger.warning("热门板块成分股为空")
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return []
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logger.info(f"热门板块共 {len(stock_sectors)} 只成分股待筛选")
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# 获取股票基本信息（用于过滤 ST 和次新）
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stock_basic = tushare_client.get_stock_basic()
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st_codes = set()
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new_codes = set()
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if not stock_basic.empty:
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st_codes = set(stock_basic[stock_basic["name"].str.contains("ST", na=False)]["ts_code"])
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# 上市不足 60 天
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from datetime import datetime, timedelta
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cutoff = (datetime.now() - timedelta(days=settings.min_list_days)).strftime("%Y%m%d")
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new_codes = set(stock_basic[stock_basic["list_date"] > cutoff]["ts_code"])
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# 获取当日全市场资金流向
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mf_df = tushare_client.get_moneyflow_batch(trade_date)
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# 获取当日 daily_basic（换手率、市值等）
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basic_df = tushare_client.get_daily_basic(trade_date)
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results = []
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for ts_code, sector_names in stock_sectors.items():
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# 过滤 ST 和次新
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if ts_code in st_codes or ts_code in new_codes:
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continue
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# 资金流向
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main_net = 0.0
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inflow_ratio = 0.0
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if not mf_df.empty:
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row = mf_df[mf_df["ts_code"] == ts_code]
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if not row.empty:
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r = row.iloc[0]
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main_net = (
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(r.get("buy_elg_amount", 0) or 0) - (r.get("sell_elg_amount", 0) or 0) +
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(r.get("buy_lg_amount", 0) or 0) - (r.get("sell_lg_amount", 0) or 0)
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)
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total = (
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(r.get("buy_elg_amount", 0) or 0) + (r.get("sell_elg_amount", 0) or 0) +
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(r.get("buy_lg_amount", 0) or 0) + (r.get("sell_lg_amount", 0) or 0) +
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(r.get("buy_md_amount", 0) or 0) + (r.get("sell_md_amount", 0) or 0) +
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(r.get("buy_sm_amount", 0) or 0) + (r.get("sell_sm_amount", 0) or 0)
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)
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if total > 0:
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inflow_ratio = main_net / total * 100
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# 硬性条件：主力净流入 > 0
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if main_net <= 0:
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continue
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# 换手率和市值
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turnover_rate = 0.0
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volume_ratio = None
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circ_mv = None
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pe = None
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pb = None
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if not basic_df.empty:
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b_row = basic_df[basic_df["ts_code"] == ts_code]
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if not b_row.empty:
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b = b_row.iloc[0]
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turnover_rate = float(b.get("turnover_rate", 0) or 0)
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volume_ratio = float(b["volume_ratio"]) if pd.notna(b.get("volume_ratio")) else None
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circ_mv_raw = b.get("circ_mv", 0) or 0
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circ_mv = float(circ_mv_raw) / 10000 # Tushare 单位是万元，转为亿
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pe = float(b["pe_ttm"]) if pd.notna(b.get("pe_ttm")) else None
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pb = float(b["pb"]) if pd.notna(b.get("pb")) else None
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# 换手率硬性过滤
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if turnover_rate < settings.min_turnover_rate or turnover_rate > settings.max_turnover_rate:
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continue
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# 市值硬性过滤
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if circ_mv is not None and (circ_mv < settings.min_circ_mv or circ_mv > settings.max_circ_mv):
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continue
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capital_score = _score_capital_flow(
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main_net_inflow=main_net,
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inflow_ratio=inflow_ratio,
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turnover_rate=turnover_rate,
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volume_ratio=volume_ratio,
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circ_mv=circ_mv,
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)
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valuation_score = _score_valuation(pe, pb)
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# 获取股票名称
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name = ts_code
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if not stock_basic.empty:
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name_row = stock_basic[stock_basic["ts_code"] == ts_code]
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if not name_row.empty:
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name = name_row.iloc[0]["name"]
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results.append({
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"ts_code": ts_code,
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"name": name,
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"sector": sector_names[0], # 取第一个板块
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"sectors": sector_names,
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"main_net_inflow": round(main_net, 2),
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"inflow_ratio": round(inflow_ratio, 2),
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"turnover_rate": round(turnover_rate, 2),
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"volume_ratio": volume_ratio,
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"circ_mv": round(circ_mv, 2) if circ_mv else None,
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"pe": round(pe, 2) if pe else None,
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"pb": round(pb, 2) if pb else None,
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"capital_score": round(capital_score, 1),
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"valuation_score": round(valuation_score, 1),
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})
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# 按资金评分排序
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results.sort(key=lambda x: x["capital_score"], reverse=True)
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top = results[:settings.top_stock_count]
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for r in top:
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logger.info(f"资金筛选: {r['name']}({r['ts_code']}) 板块={r['sector']} "
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f"主力净流入={r['main_net_inflow']}万 评分={r['capital_score']}")
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return top
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