update exportdata
This commit is contained in:
@@ -16,7 +16,8 @@ from typing import Optional, List, Dict, Union, Tuple
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import csv
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from typing import List, Dict, Optional
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from datetime import datetime
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from base import LogHelper, MySQLHelper, Config
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from base import LogHelper, MySQLHelper, ConfigInfo
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class MarketDataCalculator:
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"""
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@@ -41,8 +42,8 @@ class MarketDataCalculator:
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"""
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self.db_config = db_config
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self.logger = LogHelper(logger_name=logger_name).setup()
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self.month_ranges = Config.ConfigInfo.MONTH_RANGES
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self.head_map = Config.ConfigInfo.HEADER_MAP
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self.month_ranges = ConfigInfo.MONTH_RANGES
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self.head_map = ConfigInfo.HEADER_MAP
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def create_monthly_avg_table(self, target_table: str = "monthly_close_avg") -> bool:
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"""
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@@ -69,6 +70,7 @@ class MarketDataCalculator:
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ym_2506 DECIMAL(20, 5) COMMENT '2025年06月',
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ym_2507 DECIMAL(20, 5) COMMENT '2025年07月',
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ym_2508 DECIMAL(20, 5) COMMENT '2025年08月',
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ym_2509 DECIMAL(20, 5) COMMENT '2025年09月',
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avg_all DECIMAL(20, 5) COMMENT '月间均值',
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update_time TIMESTAMP DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP COMMENT '更新时间',
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UNIQUE KEY uk_stock_code (stock_code)
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@@ -82,7 +84,7 @@ class MarketDataCalculator:
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return False
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def calculate_and_save_monthly_avg(self,
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source_table: str = "stock_quotes",
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stock_code: str = "code",
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target_table: str = "monthly_close_avg") -> bool:
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"""
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计算并保存2024年10月至2025年8月的月均流通市值
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@@ -100,79 +102,85 @@ class MarketDataCalculator:
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return False
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with MySQLHelper(**self.db_config) as db:
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# 获取所有股票代码和名称
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stock_info = db.execute_query(
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f"SELECT DISTINCT stock_code, stock_name FROM {source_table}"
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)
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if not stock_info:
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self.logger.error("没有获取到股票基本信息")
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return False
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# 为每只股票计算各月均值
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for stock in stock_info:
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stock_code = stock['stock_code']
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stock_name = stock['stock_name']
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monthly_data = {'stock_code': stock_code, 'stock_name': stock_name}
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# 计算每个月的均值
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for month_col, (start_date, end_date) in self.month_ranges.items():
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sql = """
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SELECT AVG(close_price * float_share) as avg_close
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FROM {}
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WHERE stock_code = %s
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AND trade_date BETWEEN %s AND %s
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AND close_price IS NOT NULL
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AND float_share IS NOT NULL
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""".format(source_table)
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result = db.execute_query(sql, (stock_code, start_date, end_date))
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# 保存小数点后两位，以亿为单位
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# monthly_data[month_col] = float(result[0]['avg_close']) * 1000 if result and result[0]['avg_close'] else None
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monthly_data[month_col] = round(float(result[0]['avg_close']) * 1000 / 100000000, 3) if result and result[0]['avg_close'] else None
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# 提取所有以 'ym_' 开头的键的值
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ym_values = [value for key, value in monthly_data.items() if key.startswith('ym_')]
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valid_ym_values = [value for value in ym_values if value is not None]
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# 计算全部月的均值
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if valid_ym_values:
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average = sum(valid_ym_values) / len(valid_ym_values)
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monthly_data['avg_all'] = average
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self.logger.debug(f"股票 {stock_code} 月间流通市值平均值为: {average}")
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else:
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monthly_data['avg_all'] = 0 # 给一个空值，保证数据库不报错
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self.logger.warning(f"股票 {stock_code} 没有有效的月度数据")
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# 插入或更新数据
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upsert_sql = f"""
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INSERT INTO {target_table} (
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stock_code, stock_name,
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ym_2501, ym_2502, ym_2503, ym_2504,
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ym_2505, ym_2506,ym_2507, ym_2508,
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avg_all
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) VALUES (
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%(stock_code)s, %(stock_name)s,
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%(ym_2501)s, %(ym_2502)s, %(ym_2503)s, %(ym_2504)s,
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%(ym_2505)s, %(ym_2506)s, %(ym_2507)s, %(ym_2508)s,
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%(avg_all)s
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)
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ON DUPLICATE KEY UPDATE
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stock_name = VALUES(stock_name),
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ym_2501 = VALUES(ym_2501),
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ym_2502 = VALUES(ym_2502),
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ym_2503 = VALUES(ym_2503),
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ym_2504 = VALUES(ym_2504),
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ym_2505 = VALUES(ym_2505),
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ym_2506 = VALUES(ym_2506),
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ym_2507 = VALUES(ym_2507),
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ym_2508 = VALUES(ym_2508),
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avg_all = VALUES(avg_all),
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update_time = CURRENT_TIMESTAMP
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sql = """
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SELECT stock_code, stock_name
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FROM stock_filter
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WHERE stock_code = %s
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"""
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db.execute_update(upsert_sql, monthly_data)
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# stock_filter表格可以当成标准表
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stock_info = db.execute_query(sql,(stock_code))
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if len(stock_info) == 0:
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return
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stock_code = stock_info[0]['stock_code']
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stock_name = stock_info[0]['stock_name']
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monthly_data = {'stock_code': stock_code, 'stock_name': stock_name}
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# 计算每个月的均值
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source_table = 'hk_' + stock_code[3:]
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for month_col, (start_date, end_date) in self.month_ranges.items():
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sql = """
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SELECT AVG(close_price * float_share) as avg_close
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FROM {}
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WHERE stock_code = %s
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AND trade_date BETWEEN %s AND %s
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AND close_price IS NOT NULL
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AND float_share IS NOT NULL
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""".format(source_table)
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result = db.execute_query(sql, (stock_code, start_date, end_date))
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# 保存小数点后两位，以亿为单位
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# monthly_data[month_col] = float(result[0]['avg_close']) * 1000 if result and result[0]['avg_close'] else None
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monthly_data[month_col] = round(float(result[0]['avg_close']) * 1000 / 100000000, 3) if result and result[0]['avg_close'] else None
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# 提取所有以 'ym_' 开头的键的值
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ym_values = [value for key, value in monthly_data.items() if key.startswith('ym_')]
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valid_ym_values = [value for value in ym_values if value is not None]
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# 计算全部月的均值
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if valid_ym_values:
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average = sum(valid_ym_values) / len(valid_ym_values)
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monthly_data['avg_all'] = average
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self.logger.debug(f"股票 {stock_code} 月间流通市值平均值为: {average}")
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else:
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monthly_data['avg_all'] = 0 # 给一个空值，保证数据库不报错
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self.logger.warning(f"股票 {stock_code} 没有有效的月度数据")
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# 插入或更新数据
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upsert_sql = f"""
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INSERT INTO {target_table} (
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stock_code, stock_name,
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ym_2501, ym_2502, ym_2503, ym_2504,
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ym_2505, ym_2506,ym_2507, ym_2508,
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ym_2509,
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avg_all
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) VALUES (
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%(stock_code)s, %(stock_name)s,
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%(ym_2501)s, %(ym_2502)s, %(ym_2503)s, %(ym_2504)s,
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%(ym_2505)s, %(ym_2506)s, %(ym_2507)s, %(ym_2508)s,
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%(ym_2509)s,
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%(avg_all)s
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)
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ON DUPLICATE KEY UPDATE
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stock_name = VALUES(stock_name),
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ym_2501 = VALUES(ym_2501),
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ym_2502 = VALUES(ym_2502),
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ym_2503 = VALUES(ym_2503),
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ym_2504 = VALUES(ym_2504),
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ym_2505 = VALUES(ym_2505),
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ym_2506 = VALUES(ym_2506),
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ym_2507 = VALUES(ym_2507),
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ym_2508 = VALUES(ym_2508),
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ym_2509 = VALUES(ym_2509),
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avg_all = VALUES(avg_all),
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update_time = CURRENT_TIMESTAMP
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"""
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db.execute_update(upsert_sql, monthly_data)
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# self.logger.info("月度均值计算和保存完成")
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return True
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except Exception as e:
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@@ -435,4 +443,6 @@ class MarketDataCalculator:
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if csv_file:
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csv_success = self.export_to_csv(monthly_data, file_path)
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return csv_success
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return csv_success
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