190 lines
6.2 KiB
Python
190 lines
6.2 KiB
Python
"""
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板块异动分析（Tushare 版本）
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检测板块涨跌幅、量能、资金流向异动
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"""
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import pandas as pd
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from typing import Dict, List, Optional, Tuple
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from datetime import datetime
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from app.utils.logger import logger
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from app.utils.error_handler import notify_error
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class TushareSectorAnalyzer:
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"""板块异动分析器（使用 Tushare 同花顺接口）"""
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def __init__(self, tushare_client, change_threshold: float = 2.0):
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"""
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初始化异动分析器
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Args:
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tushare_client: TushareClient 实例
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change_threshold: 涨跌幅阈值（%）
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"""
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self.change_threshold = change_threshold
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self.ts_client = tushare_client
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def detect_sector_changes(self) -> List[Dict]:
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"""
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检测异动板块
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Returns:
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异动板块列表
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"""
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try:
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# 使用 tushare 获取异动板块（一次性获取）
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df = self.ts_client.get_hot_sectors(threshold=self.change_threshold)
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if df.empty:
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logger.info("未检测到异动板块")
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return []
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# 转换为结果列表
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results = []
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for _, row in df.iterrows():
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# 成交额转换为万元
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amount_wan = row['amount'] / 10000 if row['amount'] > 0 else 0
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# 格式化成交额显示
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if amount_wan >= 100000:
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amount_str = f"{amount_wan/100000:.1f}亿"
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elif amount_wan >= 10000:
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amount_str = f"{amount_wan/10000:.1f}万"
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else:
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amount_str = f"{amount_wan:.0f}元"
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results.append({
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'name': row['name'],
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'ts_code': row['ts_code'],
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'change_pct': float(row['change_pct']),
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'change': float(row.get('change', 0)), # 涨跌额
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'close': float(row['close']),
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'amount': float(row['amount']),
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'amount_str': amount_str,
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'volume': float(row['volume']),
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'turnover_rate': float(row.get('turnover_rate', 0)), # 换手率
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'trade_date': row['trade_date'],
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'timestamp': datetime.now()
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})
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logger.info(f"检测到 {len(results)} 个异动概念板块（Tushare）")
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return results
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except Exception as e:
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error_msg = f"Tushare 检测板块异动失败: {e}"
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logger.error(error_msg)
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# 发送通知
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notify_error(
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title="A股板块监控 - Tushare 数据获取失败",
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message=f"错误: {e}\n\n可能原因：\n- Tushare token 未配置或无效\n- API 频率限制\n- 网络连接问题",
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level="warning"
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)
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return []
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def get_sector_stocks(self, ts_code: str, sector_name: str) -> List[Dict]:
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"""
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获取板块成分股
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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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成分股列表
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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"板块 {sector_name} 成分股数据为空")
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return []
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# 获取成分股的行情数据
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stock_codes = members_df['ts_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"板块 {sector_name} 成分股行情为空")
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return []
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# 合并数据
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merged = pd.merge(
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members_df,
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realtime_df,
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on='ts_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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# 转换结果
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results = []
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for _, row in merged.iterrows():
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results.append({
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'code': row['ts_code'],
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'name': row.get('name', row.get('member_name', '')),
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'price': float(row.get('close', 0)),
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'change_pct': float(row.get('pct_chg', 0)),
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'change_amount': float(row.get('change', 0)),
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'amount': float(row.get('amount', 0)),
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'volume': float(row.get('vol', 0)),
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})
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return results
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except Exception as e:
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logger.error(f"获取板块 {sector_name} 成分股失败: {e}")
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return []
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def get_hot_reason(self, sector_name: str, top_stocks: List[Dict]) -> str:
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"""
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推测异动原因（基于龙头股分析）
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Args:
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sector_name: 板块名称
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top_stocks: 龙头股列表
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Returns:
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异动原因描述
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"""
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try:
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if not top_stocks:
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return "板块整体异动"
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reasons = []
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# 检查是否有涨停股
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limit_up_count = sum(1 for s in top_stocks if s.get('change_pct', 0) >= 9.9)
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if limit_up_count > 0:
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reasons.append(f"{limit_up_count}只个股涨停")
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# 检查平均涨幅
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avg_change = sum(s.get('change_pct', 0) for s in top_stocks) / len(top_stocks)
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if avg_change >= 7:
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reasons.append("板块全线爆发")
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# 检查是否集中在某个龙头
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if len(top_stocks) >= 2:
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top1_change = top_stocks[0].get('change_pct', 0)
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top2_change = top_stocks[1].get('change_pct', 0)
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if top1_change - top2_change > 3:
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reasons.append(f"{top_stocks[0].get('name', '')}龙头领涨")
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if reasons:
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return "，".join(reasons)
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else:
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return "资金集中流入"
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except Exception as e:
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logger.error(f"推测异动原因失败: {e}")
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return "板块异动"
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