244 lines
8.5 KiB
Python
244 lines
8.5 KiB
Python
#!/usr/bin/env python3
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"""
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按本周总涨幅排名东财概念板块
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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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"""获取本周的交易日期（周一到周五）"""
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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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# 生成本周所有交易日
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dates = []
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current = monday
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while current <= friday:
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if current.weekday() < 5: # 周一到周五
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dates.append(current.strftime('%Y%m%d'))
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current += timedelta(days=1)
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return dates
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def calculate_weekly_concept_performance(fetcher: TushareFetcher):
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"""计算概念板块本周总涨幅"""
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try:
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if not fetcher.pro:
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logger.error("需要Tushare Pro权限")
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return None
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logger.info("🚀 计算概念板块本周总涨幅排名...")
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# 获取本周交易日
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week_dates = get_this_week_dates()
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logger.info(f"本周交易日: {week_dates}")
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if len(week_dates) < 2:
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logger.warning("本周交易日不足，无法计算周涨幅")
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return None
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start_date = week_dates[0] # 周一
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end_date = week_dates[-1] # 最新交易日
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logger.info(f"分析周期: {start_date} 到 {end_date}")
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# 获取周一的概念板块数据（基准）
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logger.info(f"获取 {start_date} 的概念数据作为基准...")
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start_concepts = fetcher.pro.dc_index(trade_date=start_date)
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# 获取最新交易日的概念板块数据
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logger.info(f"获取 {end_date} 的概念数据...")
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end_concepts = fetcher.pro.dc_index(trade_date=end_date)
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if start_concepts.empty or end_concepts.empty:
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logger.error("无法获取概念板块数据")
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return None
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logger.info(f"周一概念数据: {len(start_concepts)} 个")
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logger.info(f"最新概念数据: {len(end_concepts)} 个")
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# 计算本周涨幅
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weekly_performance = []
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# 以最新数据为准，匹配周一数据
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for _, end_concept in end_concepts.iterrows():
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ts_code = end_concept['ts_code']
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name = end_concept['name']
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end_mv = end_concept['total_mv']
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# 查找对应的周一数据
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start_data = start_concepts[start_concepts['ts_code'] == ts_code]
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if not start_data.empty:
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start_mv = start_data.iloc[0]['total_mv']
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# 计算本周总涨幅
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if start_mv > 0:
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weekly_change = (end_mv - start_mv) / start_mv * 100
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weekly_performance.append({
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'ts_code': ts_code,
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'name': name,
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'weekly_change': weekly_change,
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'start_mv': start_mv,
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'end_mv': end_mv,
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'latest_daily_change': end_concept['pct_change'],
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'up_num': end_concept.get('up_num', 0),
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'down_num': end_concept.get('down_num', 0)
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})
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if not weekly_performance:
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logger.error("无法计算概念板块周涨幅")
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return None
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# 转换为DataFrame并按周涨幅排序
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df_weekly = pd.DataFrame(weekly_performance)
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df_weekly = df_weekly.sort_values('weekly_change', ascending=False)
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logger.info(f"成功计算 {len(df_weekly)} 个概念板块的本周涨幅")
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return df_weekly
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except Exception as e:
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logger.error(f"计算概念板块周涨幅失败: {e}")
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return None
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def display_weekly_ranking(df_weekly: pd.DataFrame):
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"""显示本周涨幅排名"""
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if df_weekly is None or df_weekly.empty:
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logger.error("无数据可显示")
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return
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print("\n" + "="*100)
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print("📈 东财概念板块本周涨幅排行榜")
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print("="*100)
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print(f"{'排名':<4} {'概念名称':<25} {'本周涨幅':<12} {'今日涨幅':<12} {'上涨股数':<8} {'下跌股数':<8} {'概念代码':<15}")
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print("-" * 100)
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for i, (_, concept) in enumerate(df_weekly.head(30).iterrows()):
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rank = i + 1
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name = concept['name'][:23] + '..' if len(concept['name']) > 23 else concept['name']
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weekly_chg = f"{concept['weekly_change']:+.2f}%"
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daily_chg = f"{concept['latest_daily_change']:+.2f}%"
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up_num = f"{concept['up_num']:.0f}"
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down_num = f"{concept['down_num']:.0f}"
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ts_code = concept['ts_code']
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print(f"{rank:<4} {name:<25} {weekly_chg:<12} {daily_chg:<12} {up_num:<8} {down_num:<8} {ts_code:<15}")
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# 强势概念TOP15
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print(f"\n🚀 本周强势概念板块TOP15:")
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for i, (_, concept) in enumerate(df_weekly.head(15).iterrows()):
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print(f" {i+1:2d}. {concept['name']}: {concept['weekly_change']:+.2f}% (今日{concept['latest_daily_change']:+.2f}%)")
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# 弱势概念TOP10
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print(f"\n📉 本周弱势概念板块TOP10:")
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weak_concepts = df_weekly.tail(10).iloc[::-1] # 反转顺序
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for i, (_, concept) in enumerate(weak_concepts.iterrows()):
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print(f" {i+1:2d}. {concept['name']}: {concept['weekly_change']:+.2f}% (今日{concept['latest_daily_change']:+.2f}%)")
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# 统计分析
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print(f"\n📊 本周概念板块统计:")
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total_concepts = len(df_weekly)
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positive_concepts = len(df_weekly[df_weekly['weekly_change'] > 0])
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negative_concepts = len(df_weekly[df_weekly['weekly_change'] < 0])
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print(f" 总概念数量: {total_concepts}")
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print(f" 上涨概念: {positive_concepts} ({positive_concepts/total_concepts*100:.1f}%)")
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print(f" 下跌概念: {negative_concepts} ({negative_concepts/total_concepts*100:.1f}%)")
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print(f" 平均涨幅: {df_weekly['weekly_change'].mean():+.2f}%")
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print(f" 涨幅中位数: {df_weekly['weekly_change'].median():+.2f}%")
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return df_weekly
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def analyze_top_concepts_detail(fetcher: TushareFetcher, df_weekly: pd.DataFrame, top_n=5):
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"""分析TOP概念的详细趋势"""
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if df_weekly is None or df_weekly.empty:
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return
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logger.info(f"详细分析TOP{top_n}强势概念...")
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print(f"\n" + "="*80)
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print(f"📊 TOP{top_n}强势概念详细分析")
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print("="*80)
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week_dates = get_this_week_dates()
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for i, (_, concept) in enumerate(df_weekly.head(top_n).iterrows()):
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concept_code = concept['ts_code']
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concept_name = concept['name']
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print(f"\n📈 {i+1}. {concept_name} ({concept_code})")
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print(f" 本周总涨幅: {concept['weekly_change']:+.2f}%")
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# 获取每日详细数据
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daily_data = []
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for date in week_dates:
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try:
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daily_concept = fetcher.pro.dc_index(
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trade_date=date,
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ts_code=concept_code
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)
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if not daily_concept.empty:
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daily_data.append({
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'date': date,
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'pct_change': daily_concept.iloc[0]['pct_change'],
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'total_mv': daily_concept.iloc[0]['total_mv'],
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'up_num': daily_concept.iloc[0].get('up_num', 0),
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'down_num': daily_concept.iloc[0].get('down_num', 0)
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})
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except Exception as e:
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logger.debug(f"获取 {concept_code} 在 {date} 的数据失败: {e}")
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continue
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# 显示每日走势
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if daily_data:
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print(f" 每日走势:")
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for data in daily_data:
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print(f" {data['date']}: {data['pct_change']:+6.2f}% (上涨{data['up_num']:.0f}只/下跌{data['down_num']:.0f}只)")
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def main():
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"""主函数"""
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logger.info("🚀 开始计算东财概念板块本周涨幅排名...")
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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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# 1. 计算本周涨幅
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df_weekly = calculate_weekly_concept_performance(fetcher)
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# 2. 显示排名
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if df_weekly is not None:
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display_weekly_ranking(df_weekly)
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# 3. 详细分析TOP5概念
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analyze_top_concepts_detail(fetcher, df_weekly, top_n=5)
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logger.info("✅ 分析完成!")
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if __name__ == "__main__":
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main() |