116 lines
3.3 KiB
Python
116 lines
3.3 KiB
Python
"""
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Script Name:
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Description: 统计hs300的成分股，在区间内的涨幅。取前复权值
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Author: [Your Name]
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Created Date: YYYY-MM-DD
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Last Modified: YYYY-MM-DD
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Version: 1.0
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Modification History:
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- YYYY-MM-DD [Your Name]:
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- YYYY-MM-DD [Your Name]:
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- YYYY-MM-DD [Your Name]:
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"""
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import pymysql
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import pandas as pd
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import time
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from datetime import datetime
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import logging
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import config
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# 设置日志
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config.setup_logging()
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logger = logging.getLogger()
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# 数据库连接函数
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def connect_to_db():
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return pymysql.connect(**config.db_config)
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# 获取 2024-09-23 对应的 close 值
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def get_close_for_date(df, date):
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filtered = df[df['time_key'] == date]
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if not filtered.empty:
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return filtered.iloc[0]['close']
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else:
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logger.warning(f"No data found for date: {date}")
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return None
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# 获取年内涨幅的 c1, c3 值（最早和最晚的 close 值）
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def get_first_last_close(df):
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df_sorted = df.sort_values(by='time_key')
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c1 = df_sorted.iloc[0]['close'] # 最早的 close 值
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c3 = df_sorted.iloc[-1]['close'] # 最晚的 close 值
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return c1, c3
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# 获取最大值和最小值的 close 值
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def get_max_min_close(df):
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max_close = df['close'].max()
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min_close = df['close'].min()
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return max_close, min_close
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# 主函数
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def main():
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try:
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connection = connect_to_db()
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query = """
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SELECT code, name, time_key, close
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FROM hs300_qfq_his
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WHERE time_key >= '2021-01-01 00:00:00'
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"""
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df = pd.read_sql(query, connection)
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# 确定要查询的日期
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target_date = '2024-09-23 00:00:00'
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df['time_key'] = pd.to_datetime(df['time_key'])
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results = []
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for code, group in df.groupby('code'):
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logger.info(f"Processing code: {code}")
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# 获取 c1（最早的 close）和 c3（最晚的 close）
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c1, c3 = get_first_last_close(group)
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# 获取 c2（2024-09-23 的 close 值）
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c2 = get_close_for_date(group, target_date)
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if c1 is None or c2 is None or c3 is None:
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logger.warning(f"Skipping code {code} due to missing close values.")
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continue
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# 计算年内涨幅和自2024-09-23以来的涨幅
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year_growth_rate = (c3 / c1 - 1) if c1 else None
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growth_since_2024_09_23 = (c3 / c2 - 1) if c2 else None
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# 获取年内的最大和最小 close 值
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c4, c5 = get_max_min_close(group)
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year_volatility = (c4 / c5 - 1) if c4 and c5 else None
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results.append({
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'code': code,
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'name': group['name'].iloc[0],
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'year_growth_rate': year_growth_rate,
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'growth_since_2024_09_23': growth_since_2024_09_23,
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'year_volatility': year_volatility
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})
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time.sleep(1)
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# 将结果转换为 DataFrame 并显示
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result_df = pd.DataFrame(results)
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print(result_df)
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# 你可以选择将结果保存到 CSV 文件中
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result_df.to_csv('./result/stat_grouth_rate_since2021.csv', index=False)
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except Exception as e:
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logger.error(f"Error occurred: {e}")
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finally:
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if connection:
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connection.close()
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if __name__ == "__main__":
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main()
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