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298
PyCode/CalculateCapitalization.py
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298
PyCode/CalculateCapitalization.py
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"""
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计算平均市值
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根据 conditionalselection 表格中的股票代码，查找日K数据表格
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每个月计算一次结果存放在 hk_monthly_avg_2410_2508 表格中
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"""
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import pandas as pd
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from datetime import datetime
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import logging
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from futu import *
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from pymysql import Error
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from MySQLHelper import MySQLHelper # MySQLHelper类保存为单独文件
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from typing import Optional, List, Dict, Union, Tuple
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def create_monthly_avg_table(db_config: dict, target_table: str = "monthly_close_avg") -> bool:
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"""
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创建专门存储2024年10月至2025年8月月度均值的表结构
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Args:
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db_config: 数据库配置
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target_table: 目标表名
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Returns:
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bool: 是否创建成功
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"""
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try:
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with MySQLHelper(**db_config) as db:
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create_sql = f"""
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CREATE TABLE IF NOT EXISTS {target_table} (
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id INT AUTO_INCREMENT PRIMARY KEY,
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stock_code VARCHAR(20) NOT NULL COMMENT '股票代码',
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stock_name VARCHAR(50) COMMENT '股票名称',
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ym_2410 DECIMAL(10, 3) COMMENT '2024年10月均收盘价',
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ym_2411 DECIMAL(10, 3) COMMENT '2024年11月均收盘价',
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ym_2412 DECIMAL(10, 3) COMMENT '2024年12月均收盘价',
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ym_2501 DECIMAL(10, 3) COMMENT '2025年1月均收盘价',
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ym_2502 DECIMAL(10, 3) COMMENT '2025年2月均收盘价',
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ym_2503 DECIMAL(10, 3) COMMENT '2025年3月均收盘价',
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ym_2504 DECIMAL(10, 3) COMMENT '2025年4月均收盘价',
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ym_2505 DECIMAL(10, 3) COMMENT '2025年5月均收盘价',
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ym_2506 DECIMAL(10, 3) COMMENT '2025年6月均收盘价',
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ym_2507 DECIMAL(10, 3) COMMENT '2025年7月均收盘价',
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ym_2508 DECIMAL(10, 3) COMMENT '2025年8月均收盘价',
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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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) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COMMENT='月度收盘价均值表(2024.10-2025.08)'
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"""
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db.execute_update(create_sql)
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# logging.info(f"创建/确认表 {target_table} 结构成功")
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return True
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except Exception as e:
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logging.error(f"创建表失败: {str(e)}")
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return False
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def calculate_and_save_monthly_avg(db_config: dict,
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source_table: str = "stock_quotes",
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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月的月度收盘价均值
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Args:
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db_config: 数据库配置
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source_table: 源数据表名
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target_table: 目标表名
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Returns:
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bool: 是否成功
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"""
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# 定义分析的时间范围
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month_ranges = {
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'ym_2410': ('2024-10-01', '2024-10-31'),
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'ym_2411': ('2024-11-01', '2024-11-30'),
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'ym_2412': ('2024-12-01', '2024-12-31'),
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'ym_2501': ('2025-01-01', '2025-01-31'),
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'ym_2502': ('2025-02-01', '2025-02-28'),
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'ym_2503': ('2025-03-01', '2025-03-31'),
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'ym_2504': ('2025-04-01', '2025-04-30'),
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'ym_2505': ('2025-05-01', '2025-05-31'),
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'ym_2506': ('2025-06-01', '2025-06-30'),
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'ym_2507': ('2025-07-01', '2025-07-31'),
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'ym_2508': ('2025-08-01', '2025-08-31')
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}
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try:
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# 确保表结构存在
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if not create_monthly_avg_table(db_config, target_table):
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return False
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with MySQLHelper(**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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logging.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 month_ranges.items():
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sql = f"""
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SELECT AVG(close_price) as avg_close
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FROM {source_table}
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WHERE stock_code = %s
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AND trade_date BETWEEN %s AND %s
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"""
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result = db.execute_query(sql, (stock_code, start_date, end_date))
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monthly_data[month_col] = float(result[0]['avg_close']) if result and result[0]['avg_close'] else None
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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_2410, ym_2411, ym_2412,
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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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) VALUES (
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%(stock_code)s, %(stock_name)s,
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%(ym_2410)s, %(ym_2411)s, %(ym_2412)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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)
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ON DUPLICATE KEY UPDATE
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stock_name = VALUES(stock_name),
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ym_2410 = VALUES(ym_2410),
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ym_2411 = VALUES(ym_2411),
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ym_2412 = VALUES(ym_2412),
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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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update_time = CURRENT_TIMESTAMP
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"""
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db.execute_update(upsert_sql, monthly_data)
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logging.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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logging.error(f"计算和保存月度均值失败: {str(e)}")
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return False
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# 安全转换函数
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def safe_float(v) -> Optional[float]:
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"""安全转换为float，处理N/A和空值"""
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try:
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return float(v) if pd.notna(v) and str(v).upper() != 'N/A' else None
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except (ValueError, TypeError):
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return None
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def safe_int(v) -> Optional[int]:
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"""安全转换为int，处理N/A和空值"""
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try:
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return int(v) if pd.notna(v) and str(v).upper() != 'N/A' else None
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except (ValueError, TypeError):
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return None
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def safe_parse_date(date_str, date_format='%Y-%m-%d'):
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"""
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安全解析日期字符串
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:param date_str: 日期字符串
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:param date_format: 日期格式
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:return: 解析后的datetime对象或None
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"""
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if not date_str or pd.isna(date_str) or str(date_str).strip() == '':
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return None
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try:
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return datetime.strptime(str(date_str), date_format)
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except ValueError:
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logging.warning(f"无法解析日期字符串: {date_str}")
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return None
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def validate_market_data(dataset: list) -> list:
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"""
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验证市场数据有效性
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Args:
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dataset (list): 原始数据集
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Returns:
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list: 通过验证的数据集
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"""
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validated_data = []
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for item in dataset:
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try:
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# 必要字段检查
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if not item.get('code') or not item.get('name'):
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logging.warning(f"跳过无效数据: 缺少必要字段 code或name")
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continue
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# 筛选股票名称
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if item.get('name')[-1] == 'R':
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continue
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# 数值范围验证
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if item.get('lot_size') is not None and item['lot_size'] < 0:
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logging.warning(f"股票 {item['code']} 的lot_size为负值: {item['lot_size']}")
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item['lot_size'] = None
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validated_data.append(item)
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except Exception as e:
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logging.warning(f"数据验证失败，跳过记录 {item.get('code')}: {str(e)}")
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continue
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return validated_data
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def get_market_data(market: Market) -> List[str]:
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"""
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从Futu API获取指定市场的股票代码列表
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Args:
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market (Market): 市场枚举值，如 Market.SH, Market.SZ
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Returns:
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List[str]: 股票代码列表
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"""
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quote_ctx = OpenQuoteContext(host='127.0.0.1', port=11111)
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try:
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ret, data = quote_ctx.get_stock_basicinfo(market, SecurityType.STOCK)
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if ret == RET_OK:
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# 提取code列并转换为列表
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codes = data['code'].astype(str).tolist()
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logging.info(f"获取到 {market} 市场 {len(codes)} 个股票代码")
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return codes
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else:
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logging.error(f"获取股票代码失败: {data}")
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return []
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except Exception as e:
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logging.error(f"获取股票代码时发生异常: {str(e)}")
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return []
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finally:
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quote_ctx.close()
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def get_stock_codes() -> List[str]:
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"""从conditionalselection表获取所有股票代码"""
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try:
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with MySQLHelper(**db_config) as db:
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sql = f"SELECT DISTINCT stock_code,stock_name FROM conditionalselection"
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results = db.execute_query(sql)
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return [
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row['stock_code']
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for row in results
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if row['stock_code'] and (row.get('stock_name', '') and row['stock_name'][-1] != 'R') # 排除 name 以 R 结尾的票
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]
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except Exception as e:
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logging.error(f"获取股票代码失败: {str(e)}")
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return []
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if __name__ == "__main__":
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# 配置日志
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(levelname)s - %(message)s',
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handlers=[
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logging.FileHandler('Debug.log', encoding='utf-8'), # 关键在这里
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logging.StreamHandler()
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]
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)
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# 数据库配置
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db_config = {
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'host': 'localhost',
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'user': 'root',
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'password': 'bzskmysql',
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'database': 'klinedata_1d_hk'
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}
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# market_data = get_market_data(Market.HK)
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market_data = get_stock_codes() # 使用按照价格和流通股数量筛选的那个表格
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for code in market_data:
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tablename = 'hk_' + code[3:]
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# 计算并保存月度均值
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success = calculate_and_save_monthly_avg(
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db_config=db_config,
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source_table=tablename,
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target_table="hk_monthly_avg_2410_2508"
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)
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if success:
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logging.info("月度均值处理成功完成")
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else:
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logging.error("处理过程中出现错误")
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