245 lines
8.3 KiB
Python
245 lines
8.3 KiB
Python
"""
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龙头股筛选（Tushare 版本）
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从异动板块中筛选出龙头股
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"""
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import pandas as pd
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from typing import Dict, List
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from datetime import datetime
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from app.utils.logger import logger
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class TushareStockSelector:
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"""龙头股筛选器（使用 Tushare）"""
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def __init__(self, tushare_client, top_n: int = 3):
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"""
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初始化筛选器
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Args:
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tushare_client: TushareClient 实例
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top_n: 返回前 N 只龙头股
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"""
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self.top_n = top_n
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self.ts_client = tushare_client
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def select_leading_stocks(self, ts_code: str, sector_name: str) -> List[Dict]:
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"""
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筛选板块龙头股
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Args:
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ts_code: 板块指数代码
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sector_name: 板块名称
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Returns:
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龙头股列表（已排序）
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"""
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try:
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# 获取成分股
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members_df = self.ts_client.get_sector_members(ts_code)
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if members_df.empty:
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logger.warning(f"获取板块 {sector_name} 成分股失败")
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return []
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# ths_member 返回的是 con_code（成分股代码），需要用这个来查行情
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stock_codes = members_df['con_code'].tolist()
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# 限制数量，避免请求过多
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if len(stock_codes) > 50:
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stock_codes = stock_codes[:50]
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# 获取实时行情
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realtime_df = self.ts_client.get_realtime_data(stock_codes)
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if realtime_df.empty:
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logger.warning(f"获取板块 {sector_name} 成分股行情失败")
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return []
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# 获取每日指标（换手率、量比）
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from datetime import datetime
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trade_date = datetime.now().strftime('%Y%m%d')
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basic_df = self.ts_client.get_stock_daily_basic(stock_codes, trade_date)
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# 合并数据 - 注意：ths_member 的 con_code 对应 daily 的 ts_code
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members_df = members_df.rename(columns={'con_code': 'stock_code'})
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realtime_df = realtime_df.rename(columns={'ts_code': 'stock_code'})
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if not basic_df.empty:
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basic_df = basic_df.rename(columns={'ts_code': 'stock_code'})
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merged = pd.merge(
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members_df[['stock_code', 'con_name']],
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realtime_df,
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on='stock_code',
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how='inner'
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)
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merged = pd.merge(
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merged,
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basic_df[['stock_code', 'turnover_rate', 'volume_ratio']],
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on='stock_code',
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how='left'
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)
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else:
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merged = pd.merge(
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members_df[['stock_code', 'con_name']],
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realtime_df,
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on='stock_code',
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how='inner'
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)
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if merged.empty:
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return []
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# 数据类型转换 - daily 接口返回 pct_chg 不是 pct_change
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merged['close'] = pd.to_numeric(merged['close'], errors='coerce')
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merged['pct_chg'] = pd.to_numeric(merged['pct_chg'], errors='coerce')
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merged['change'] = pd.to_numeric(merged['change'], errors='coerce')
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merged['vol'] = pd.to_numeric(merged['vol'], errors='coerce')
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# 注意：daily 接口的 amount 单位是千元，需要转换为元
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merged['amount'] = pd.to_numeric(merged['amount'], errors='coerce') * 1000
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# 换手率和量比填充默认值
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if 'turnover_rate' in merged.columns:
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merged['turnover_rate'] = pd.to_numeric(merged['turnover_rate'], errors='coerce').fillna(0)
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else:
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merged['turnover_rate'] = 0.0
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if 'volume_ratio' in merged.columns:
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merged['volume_ratio'] = pd.to_numeric(merged['volume_ratio'], errors='coerce').fillna(1.0)
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else:
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merged['volume_ratio'] = 1.0
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# 过滤：只保留有成交额的股票
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merged = merged[merged['amount'] > 0].copy()
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if merged.empty:
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return []
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# 计算综合评分
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merged['score'] = merged.apply(self._calculate_score, axis=1)
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# 排序：按综合得分
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merged = merged.sort_values('score', ascending=False)
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# 取前 N 只
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top_stocks = merged.head(self.top_n)
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# 转换结果
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results = []
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for _, row in top_stocks.iterrows():
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# 计算涨速等级
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change_pct = row['pct_chg']
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if change_pct >= 5:
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speed_level = "⚡⚡⚡ 极快"
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elif change_pct >= 3:
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speed_level = "⚡⚡ 快速"
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elif change_pct >= 1:
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speed_level = "⚡ 较快"
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else:
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speed_level = "🐌 平稳"
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# 计算振幅
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amplitude = 0.0
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if 'high' in row and 'low' in row and row['low'] > 0:
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amplitude = (row['high'] - row['low']) / row['low'] * 100
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results.append({
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'code': row['stock_code'],
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'name': row['con_name'],
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'price': float(row['close']),
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'change_pct': float(row['pct_chg']),
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'change_amount': float(row['change']),
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'amount': float(row['amount']),
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'turnover': float(row.get('turnover_rate', 0)),
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'volume_ratio': float(row.get('volume_ratio', 1.0)),
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'amplitude': amplitude,
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'score': float(row['score']),
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'speed_level': speed_level,
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})
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logger.info(f"板块 {sector_name} 龙头股筛选完成，Top {len(results)}")
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return results
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except Exception as e:
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logger.error(f"筛选龙头股失败 {sector_name}: {e}")
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return []
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def _calculate_score(self, row: pd.Series) -> float:
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"""
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计算综合得分
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评分维度：
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- 涨跌幅 (40%)
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- 成交额 (30%)
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- 涨速 (20%)
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- 换手率 (10%)
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Args:
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row: 股票数据行
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Returns:
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综合得分
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"""
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score = 0.0
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# 1. 涨跌幅得分 (40分) - 涨幅越高得分越高
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change_pct = row['pct_chg']
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if change_pct >= 7:
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score += 40 # 涨停级别
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elif change_pct >= 5:
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score += 35
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elif change_pct >= 3:
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score += 30
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elif change_pct >= 2:
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score += 25
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elif change_pct >= 1:
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score += 20
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elif change_pct > 0:
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score += 15
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else:
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score += max(0, 10 + change_pct * 5) # 下跌也有基础分
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# 2. 成交额得分 (30分) - 成交额越大得分越高
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# 注意：amount 已在 select_leading_stocks 中从千元转换为元
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amount = row['amount'] # 单位是元
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if amount >= 1000000000: # 10亿以上
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score += 30
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elif amount >= 500000000: # 5亿以上
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score += 25
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elif amount >= 100000000: # 1亿以上
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score += 20
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elif amount >= 50000000: # 5000万以上
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score += 15
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elif amount >= 10000000: # 1000万以上
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score += 10
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else:
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score += 5
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# 3. 涨速得分 (20分) - 简化用涨幅代替
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if change_pct >= 5:
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score += 20
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elif change_pct >= 3:
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score += 15
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elif change_pct >= 1:
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score += 10
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else:
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score += 5
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# 4. 换手率得分 (10分) - 使用真实换手率数据
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turnover_rate = row.get('turnover_rate', 0)
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if turnover_rate >= 15:
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score += 10 # 换手率极高，资金活跃
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elif turnover_rate >= 10:
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score += 9
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elif turnover_rate >= 7:
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score += 8
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elif turnover_rate >= 5:
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score += 7
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elif turnover_rate >= 3:
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score += 6
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elif turnover_rate >= 1:
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score += 4
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elif turnover_rate >= 0.5:
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score += 2
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else:
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score += 1 # 换手率较低
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return score
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