258 lines
9.9 KiB
Python
258 lines
9.9 KiB
Python
#!/usr/bin/env python3
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"""
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测试使用Tushare找出本周强势板块
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"""
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import sys
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from pathlib import Path
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import pandas as pd
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from datetime import datetime, timedelta
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# 添加项目根目录到路径
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current_dir = Path(__file__).parent
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sys.path.insert(0, str(current_dir))
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from src.data.tushare_fetcher import TushareFetcher
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from loguru import logger
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def get_this_week_dates():
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"""获取本周的开始和结束日期"""
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today = datetime.now()
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# 获取本周一
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monday = today - timedelta(days=today.weekday())
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# 获取本周五（或今天如果还没到周五）
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friday = monday + timedelta(days=4)
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if friday > today:
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friday = today
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return monday.strftime('%Y%m%d'), friday.strftime('%Y%m%d')
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def analyze_sector_performance(fetcher: TushareFetcher):
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"""分析行业板块表现"""
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try:
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logger.info("开始分析本周强势板块...")
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# 获取本周日期范围
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start_date, end_date = get_this_week_dates()
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logger.info(f"分析时间范围: {start_date} 到 {end_date}")
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if not fetcher.pro:
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logger.error("需要Tushare Pro权限才能获取行业数据")
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return
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# 直接使用概念和行业数据分析
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logger.info("通过个股数据分析板块表现...")
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# 获取股票基本信息（包含行业分类）
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try:
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stock_basic = fetcher.pro.stock_basic(
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exchange='',
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list_status='L',
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fields='ts_code,symbol,name,area,industry,market'
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)
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logger.info(f"获取到 {len(stock_basic)} 只股票基本信息")
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except Exception as e:
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logger.error(f"获取股票基本信息失败: {e}")
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return
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# 按行业分组分析
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industry_performance = {}
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# 取样分析（避免API请求过多）
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sample_stocks = stock_basic.sample(min(200, len(stock_basic))) # 随机取200只股票
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logger.info(f"随机抽样 {len(sample_stocks)} 只股票进行分析...")
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for _, stock in sample_stocks.iterrows():
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ts_code = stock['ts_code']
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industry = stock['industry']
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stock_name = stock['name']
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if pd.isna(industry) or industry == '':
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continue
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try:
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# 获取个股本周数据
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stock_data = fetcher.pro.daily(
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ts_code=ts_code,
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start_date=start_date,
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end_date=end_date
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)
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if not stock_data.empty and len(stock_data) >= 1:
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# 获取最新价格和前一个交易日价格
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if len(stock_data) >= 2:
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latest_close = stock_data.iloc[0]['close']
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prev_close = stock_data.iloc[1]['close']
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else:
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# 如果只有一天数据，获取开盘价作为对比
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latest_close = stock_data.iloc[0]['close']
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prev_close = stock_data.iloc[0]['open']
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if prev_close > 0:
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change_pct = (latest_close - prev_close) / prev_close * 100
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# 按行业归类
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if industry not in industry_performance:
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industry_performance[industry] = {
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'stock_changes': [],
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'stock_count': 0,
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'stock_names': []
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}
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industry_performance[industry]['stock_changes'].append(change_pct)
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industry_performance[industry]['stock_count'] += 1
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industry_performance[industry]['stock_names'].append(f"{stock_name}({change_pct:+.2f}%)")
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logger.debug(f"{stock_name} ({industry}): {change_pct:+.2f}%")
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except Exception as e:
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logger.debug(f"分析个股 {ts_code} 失败: {e}")
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continue
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# 计算各行业平均表现
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industry_results = []
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for industry, data in industry_performance.items():
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if data['stock_count'] >= 3: # 至少要有3只股票才参与排名
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avg_change = sum(data['stock_changes']) / len(data['stock_changes'])
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industry_results.append({
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'industry_name': industry,
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'avg_change_pct': avg_change,
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'stock_count': data['stock_count'],
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'best_stocks': sorted(data['stock_names'], key=lambda x: float(x.split('(')[1].split('%')[0]), reverse=True)[:3]
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})
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# 3. 分析结果
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if industry_results:
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df_performance = pd.DataFrame(industry_results)
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# 按平均涨跌幅排序
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df_performance = df_performance.sort_values('avg_change_pct', ascending=False)
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logger.info("\n" + "="*80)
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logger.info("📈 本周强势板块排行榜（基于抽样股票分析）")
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logger.info("="*80)
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print(f"{'排名':<4} {'行业名称':<20} {'平均涨跌幅':<12} {'样本数量':<8} {'代表个股':<30}")
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print("-" * 80)
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for i, (_, row) in enumerate(df_performance.head(15).iterrows()):
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rank = i + 1
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industry_name = row['industry_name'][:18] + '..' if len(row['industry_name']) > 18 else row['industry_name']
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change_pct = f"{row['avg_change_pct']:+.2f}%"
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stock_count = f"{row['stock_count']}只"
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best_stock = row['best_stocks'][0] if row['best_stocks'] else "无数据"
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print(f"{rank:<4} {industry_name:<20} {change_pct:<12} {stock_count:<8} {best_stock:<30}")
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# 输出强势板块（涨幅前5）
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top_sectors = df_performance.head(5)
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logger.info(f"\n🚀 本周TOP5强势板块:")
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for i, (_, sector) in enumerate(top_sectors.iterrows()):
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logger.info(f" {i+1}. {sector['industry_name']}: {sector['avg_change_pct']:+.2f}% (样本{sector['stock_count']}只)")
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for j, stock in enumerate(sector['best_stocks'][:3]):
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logger.info(f" └─ {stock}")
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# 输出弱势板块（跌幅前5）
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weak_sectors = df_performance.tail(5)
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logger.info(f"\n📉 本周TOP5弱势板块:")
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for i, (_, sector) in enumerate(weak_sectors.iterrows()):
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logger.info(f" {i+1}. {sector['industry_name']}: {sector['avg_change_pct']:+.2f}% (样本{sector['stock_count']}只)")
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else:
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logger.warning("未获取到有效的行业表现数据")
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except Exception as e:
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logger.error(f"分析行业表现失败: {e}")
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def get_sector_top_stocks(fetcher: TushareFetcher, industry_code: str, industry_name: str, limit: int = 5):
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"""获取指定板块的强势个股"""
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try:
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logger.info(f"获取 {industry_name} 板块的强势个股...")
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if not fetcher.pro:
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return
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# 获取该行业的成分股
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try:
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constituents = fetcher.pro.index_member(index_code=industry_code)
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if constituents.empty:
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logger.warning(f"{industry_name} 行业无成分股数据")
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return
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stock_codes = constituents['con_code'].tolist()[:20] # 取前20只股票测试
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logger.info(f"{industry_name} 行业共 {len(constituents)} 只成分股，分析前 {len(stock_codes)} 只")
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except Exception as e:
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logger.error(f"获取 {industry_name} 成分股失败: {e}")
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return
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# 获取本周日期
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start_date, end_date = get_this_week_dates()
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# 分析各股票本周表现
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stock_performance = []
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for stock_code in stock_codes[:10]: # 限制分析数量
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try:
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# 获取个股本周数据
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stock_data = fetcher.pro.daily(
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ts_code=stock_code,
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start_date=start_date,
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end_date=end_date
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)
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if not stock_data.empty and len(stock_data) >= 2:
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latest_close = stock_data.iloc[0]['close']
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week_start_close = stock_data.iloc[-1]['close']
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week_change = (latest_close - week_start_close) / week_start_close * 100
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# 获取股票名称
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stock_name = fetcher.get_stock_name(stock_code.split('.')[0])
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stock_performance.append({
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'stock_code': stock_code,
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'stock_name': stock_name,
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'week_change_pct': week_change,
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'latest_close': latest_close
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})
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except Exception as e:
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logger.debug(f"分析个股 {stock_code} 失败: {e}")
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continue
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# 输出该板块强势个股
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if stock_performance:
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df_stocks = pd.DataFrame(stock_performance)
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df_stocks = df_stocks.sort_values('week_change_pct', ascending=False)
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logger.info(f"\n📊 {industry_name} 板块强势个股 TOP{limit}:")
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for _, stock in df_stocks.head(limit).iterrows():
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logger.info(f" {stock['stock_name']} ({stock['stock_code']}): {stock['week_change_pct']:+.2f}%")
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except Exception as e:
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logger.error(f"获取 {industry_name} 板块个股失败: {e}")
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def main():
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"""主函数"""
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logger.info("开始测试Tushare获取本周强势板块...")
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# 初始化Tushare数据获取器
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token = "0ed6419a00d8923dc19c0b58fc92d94c9a0696949ab91a13aa58a0cc"
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fetcher = TushareFetcher(token=token)
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# 分析板块表现
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analyze_sector_performance(fetcher)
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# 分析某个强势板块的个股（示例）
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# get_sector_top_stocks(fetcher, "801010.SI", "农林牧渔")
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logger.info("测试完成!")
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if __name__ == "__main__":
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main() |