193 lines
6.0 KiB
Python
193 lines
6.0 KiB
Python
"""
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龙头股筛选
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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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from .akshare_client import get_akshare_client
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class StockSelector:
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"""龙头股筛选器"""
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def __init__(self, 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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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.akshare = get_akshare_client()
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def select_leading_stocks(self, 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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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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stocks_df = self.akshare.get_concept_stocks(sector_name)
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if stocks_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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# 获取实时行情
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spot_df = self.akshare.get_stock_spot()
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if spot_df.empty:
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logger.warning("获取实时行情失败")
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return []
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# 合并数据
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merged = pd.merge(
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stocks_df[['代码', '名称']],
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spot_df,
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on='代码',
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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
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merged['最新价'] = pd.to_numeric(merged['最新价'], errors='coerce')
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merged['涨跌幅'] = pd.to_numeric(merged['涨跌幅'], errors='coerce')
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merged['涨跌额'] = pd.to_numeric(merged['涨跌额'], errors='coerce')
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merged['成交量'] = pd.to_numeric(merged['成交量'], errors='coerce')
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merged['成交额'] = pd.to_numeric(merged['成交额'], errors='coerce')
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merged['换手率'] = pd.to_numeric(merged['换手率'], errors='coerce')
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merged['振幅'] = pd.to_numeric(merged['振幅'], errors='coerce')
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merged['量比'] = pd.to_numeric(merged['量比'], errors='coerce')
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# 过滤：只保留有成交额的股票
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merged = merged[merged['成交额'] > 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['涨跌幅']
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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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results.append({
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'code': row['代码'],
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'name': row['名称'],
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'price': float(row['最新价']),
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'change_pct': float(row['涨跌幅']),
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'change_amount': float(row['涨跌额']),
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'amount': float(row['成交额']),
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'turnover': float(row['换手率']),
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'volume_ratio': float(row.get('量比', 1)),
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'amplitude': float(row.get('振幅', 0)),
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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['涨跌幅']
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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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amount = row['成交额']
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if amount >= 100000: # 10亿以上
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score += 30
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elif amount >= 50000: # 5亿以上
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score += 25
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elif amount >= 10000: # 1亿以上
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score += 20
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elif amount >= 5000: # 5000万以上
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score += 15
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elif amount >= 1000: # 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) - 适中换手率加分
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turnover = row['换手率']
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if 5 <= turnover <= 15:
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score += 10 # 适中换手率
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elif 15 < turnover <= 25:
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score += 8 # 活跃但不过热
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elif turnover > 25:
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score += 5 # 过热可能回调
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elif turnover > 0:
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score += 3 # 有成交即可
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else:
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score += 0
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return score
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