modify scripts

cron/daily_snap.sh

@@ -0,0 +1,19 @@
+#!/bin/bash
+
+# 项目基础路径
+PROJ_DIR=$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)
+
+echo "项目基础路径: ${PROJ_DIR}"
+
+export DB_HOST=127.0.0.1
+cd "${PROJ_DIR}" || exit 1
+
+# 从命令行读取参数，并传递给python脚本
+if [ $# -gt 0 ]; then
+    python3 -m src.static.daily_snap_em --list="$@" --notify
+else
+    python3 -m src.static.daily_snap_em --list="cn" --notify
+fi
+
+
+

src/static/bak_stat_growth_rate.py

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