218 lines
8.2 KiB
Python
218 lines
8.2 KiB
Python
"""
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Script Name: stat_adjust_kline.py
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Description: 根据从数据源获取的历史K线，计算股票的前复权和后复权值.
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注意：
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本程序只适合沪深300的前复权和后复权计算，使用的是富途提供的不复权数据和复权因子。
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处理sp500有问题，yahoo 提供的不复权收据 + 富途提供的复权因子，计算前复权，与yahoo提供的前复权数据差别很大。
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尚未找到原因，所以目前只能用hs300
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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 logging
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import sys
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import time
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import config
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import argparse
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# 配置不同市场的表名管理
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tables_mapping = {
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'sp500': {
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'none_his_kline': 'sp500_his_kline_none',
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'adjust_his_kline': 'sp500_ajust_kline_202410',
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'rehab_table': 'futu_rehab'
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},
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'hs300': {
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'none_his_kline': 'hs300_his_kline_none',
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'adjust_his_kline': 'hs300_ajust_kline_202410',
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'rehab_table': 'futu_rehab'
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}
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}
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# 日志配置
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config.setup_logging("./log/stat_adjust_kline.log")
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logger = logging.getLogger()
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# MySQL数据库连接
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def connect_to_db():
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try:
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return pymysql.connect(
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**config.db_config,
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cursorclass=pymysql.cursors.DictCursor
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)
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except pymysql.MySQLError as e:
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logger.error(f"Error connecting to the database: {e}", exc_info=True)
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return None
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# 从指定市场表中读取code和code_name字段
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def fetch_codes_from_market_table(market, debug=False):
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db = connect_to_db()
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if db is None:
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logger.error("Failed to connect to database.")
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return []
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try:
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with db.cursor() as cursor:
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query = f"SELECT code, code_name FROM {market} "
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if debug:
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#query += " LIMIT 2"
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query += " where code in ('GE', 'WTW')"
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cursor.execute(query)
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codes = cursor.fetchall()
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return codes
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finally:
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db.close()
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# 读取复权因子数据
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def fetch_rehab_data(db, code):
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try:
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with db.cursor() as cursor:
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cursor.execute(f"SELECT ex_div_date, forward_adj_factorA, forward_adj_factorB, backward_adj_factorA, backward_adj_factorB FROM futu_rehab WHERE code = %s ORDER BY ex_div_date DESC", (code,))
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return cursor.fetchall()
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except pymysql.MySQLError as e:
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logger.error(f"Error fetching rehab data for {code}: {e}", exc_info=True)
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return []
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# 读取不复权的股票价格数据
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def fetch_kline_none_data(db, table_name, code):
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try:
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with db.cursor() as cursor:
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cursor.execute(f"SELECT code, time_key, open, close FROM {table_name} WHERE code = %s ORDER BY time_key ASC", (code,))
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return cursor.fetchall()
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except pymysql.MySQLError as e:
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logger.error(f"Error fetching kline none data for {code}: {e}", exc_info=True)
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return []
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# 插入前后复权价格到数据库
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def insert_hfq_data(db, hfq_data, hfq_table):
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try:
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with db.cursor() as cursor:
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insert_query = f"""
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INSERT INTO {hfq_table} (code, name, time_key, hfq_open, hfq_close, qfq_open, qfq_close, none_open, none_close)
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VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s)
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ON DUPLICATE KEY UPDATE
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hfq_open = VALUES(hfq_open),
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hfq_close = VALUES(hfq_close),
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qfq_open = VALUES(qfq_open),
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qfq_close = VALUES(qfq_close),
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none_open = VALUES(none_open),
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none_close = VALUES(none_close)
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"""
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cursor.executemany(insert_query, hfq_data)
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db.commit()
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except pymysql.MySQLError as e:
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logger.error(f"Error inserting hfq data: {e}", exc_info=True)
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# 计算后复权价格和前复权价格
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def calculate_hfq_qfq_price(market, debug=False):
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db = connect_to_db()
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if db is None:
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return
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# 获取表名
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table_names = tables_mapping[market]
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none_his_kline_table = table_names['none_his_kline']
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adjust_kline_table = table_names['adjust_his_kline']
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# 获取股票代码
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codes = fetch_codes_from_market_table(market, debug)
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isSP500 = True if market == 'sp500' else False
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for row in codes:
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code = row['code']
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name = row['code_name']
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# 如果是 sp500 市场，拼接 'US.' + code
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rehab_code = code
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if isSP500:
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rehab_code = 'US.' + code
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logger.info(f"Processing {code} ({name})...")
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# 获取复权因子数据，没有的话就直接使用不复权值
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rehab_res = fetch_rehab_data(db, rehab_code)
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if not rehab_res:
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logger.warning(f"No rehab data found for {code}, use non close")
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rehab_res = list()
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# 反转复权因子行，为了计算前复权
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rehab_res_asc = list(reversed(rehab_res))
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# 获取不复权的价格数据
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kline_none = fetch_kline_none_data(db, none_his_kline_table, code)
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if not kline_none:
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logger.warning(f"No kline none data found for {code}")
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continue
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hfq_data = []
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# 遍历kline_none，计算前后复权价格
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for kline_row in kline_none:
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none_open = kline_row['open']
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none_close = kline_row['close']
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time_key = kline_row['time_key']
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# 将 time_key 转换为 date 格式
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time_key_date = time_key.date()
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# 计算后复权价格
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hfq_open = none_open
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hfq_close = none_close
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tmp_close = none_close
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tmp_open = none_open
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for rehab_row in rehab_res:
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# Yahoo Finance 提供的不复权数据，已经处理了拆股；所以对于富途复权因子表里的拆股数据，要忽略
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if isSP500 and rehab_row['backward_adj_factorA'] != 1 :
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continue
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if rehab_row['ex_div_date'] <= time_key_date:
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hfq_close = (tmp_close * rehab_row['backward_adj_factorA']) + rehab_row['backward_adj_factorB']
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hfq_open = (tmp_open * rehab_row['backward_adj_factorA']) + rehab_row['backward_adj_factorB']
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tmp_close = hfq_close
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tmp_open = hfq_open
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# 计算前复权价格
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qfq_close = none_close
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qfq_open = none_open
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tmp_close = none_close
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tmp_open = none_open
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for rehab_row in rehab_res_asc:
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# Yahoo Finance 提供的不复权数据，已经处理了拆股；所以对于富途复权因子表里的拆股和合股数据，要忽略
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if isSP500 and rehab_row['backward_adj_factorA'] != 1 :
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continue
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# 富途对美股的前复权价格计算，要忽略 forward_adj_factorB
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# https://openapi.futunn.com/futu-api-doc/qa/quote.html
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factorB = 0 if isSP500 else rehab_row['forward_adj_factorB']
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if rehab_row['ex_div_date'] > time_key_date:
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qfq_close = (tmp_close * rehab_row['forward_adj_factorA']) + factorB
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qfq_open = (tmp_open * rehab_row['forward_adj_factorA']) + factorB
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tmp_close = qfq_close
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tmp_open = qfq_open
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# 保存计算后的复权价格
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hfq_data.append((code, name, time_key, hfq_open, hfq_close, qfq_open, qfq_close, none_open, none_close))
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# 插入后复权价格数据
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insert_hfq_data(db, hfq_data, adjust_kline_table)
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logger.info(f"Inserted HFQ/QFQ data for {code} ({name})")
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time.sleep(1)
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db.close()
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if __name__ == "__main__":
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# 命令行参数处理
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parser = argparse.ArgumentParser(description='Calculate HFQ and QFQ Prices for Market')
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parser.add_argument('--market', type=str, default='hs300', help='Market to process (sp500 or hs300)')
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parser.add_argument('--debug', action='store_true', help='Enable debug mode (limit records)')
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args = parser.parse_args()
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# 调用主函数
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calculate_hfq_qfq_price(args.market, args.debug) |