316 lines
11 KiB
Python
316 lines
11 KiB
Python
import csv
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import pandas as pd
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from MySQLHelper import MySQLHelper
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import logging
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from typing import List, Dict, Optional, Tuple
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from datetime import datetime
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def get_monthly_avg_data(db_config: dict, table_name: str) -> Optional[List[Dict]]:
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"""
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从数据库读取月度均值数据
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Args:
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db_config: 数据库配置
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table_name: 源数据表名
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Returns:
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List[Dict]: 查询结果数据集，失败返回None
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"""
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try:
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with MySQLHelper(**db_config) as db:
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# 获取表结构信息
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columns = db.execute_query(f"""
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SELECT COLUMN_NAME
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FROM INFORMATION_SCHEMA.COLUMNS
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WHERE TABLE_SCHEMA = %s AND TABLE_NAME = %s
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ORDER BY ORDINAL_POSITION
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""", (db_config['database'], table_name))
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if not columns:
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logging.error(f"表 {table_name} 不存在或没有列")
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return None
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# 获取列名列表(排除id和update_time)
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field_names = [col['COLUMN_NAME'] for col in columns
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if col['COLUMN_NAME'] not in ('id', 'update_time')]
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# 查询数据
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data = db.execute_query(f"""
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SELECT {', '.join(field_names)}
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FROM {table_name}
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ORDER BY stock_code
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""")
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if not data:
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logging.error(f"表 {table_name} 中没有数据")
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return None
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return data
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except Exception as e:
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logging.error(f"从数据库读取月度均值数据失败: {str(e)}")
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return None
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def get_float_share_data(db_config: dict, table_name: str) -> Optional[List[Dict]]:
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"""
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从conditionalselection表读取流通股本数据
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Args:
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db_config: 数据库配置
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table_name: 源数据表名
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Returns:
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List[Dict]: 查询结果数据集，失败返回None
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"""
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try:
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with MySQLHelper(**db_config) as db:
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# 查询流通股本数据
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data = db.execute_query(f"""
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SELECT stock_code, stock_name, float_share
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FROM {table_name}
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ORDER BY stock_code
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""")
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if not data:
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logging.error(f"表 {table_name} 中没有流通股本数据")
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return None
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return data
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except Exception as e:
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logging.error(f"从数据库读取流通股本数据失败: {str(e)}")
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return None
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def merge_data(monthly_data: List[Dict], float_share_data: List[Dict]) -> List[Dict]:
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"""
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合并月度均价数据和流通股本数据
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Args:
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monthly_data: 月度均价数据
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float_share_data: 流通股本数据
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Returns:
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List[Dict]: 合并后的数据集
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"""
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merged_data = []
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float_share_dict = {item['stock_code']: item['float_share'] for item in float_share_data}
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for item in monthly_data:
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merged_item = item.copy()
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merged_item['float_share'] = float_share_dict.get(item['stock_code'], 'N/A')
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merged_data.append(merged_item)
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return merged_data
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def export_to_csv(data: List[Dict], output_file: str) -> bool:
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"""
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将合并后的数据导出到CSV文件
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Args:
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data: 要导出的数据集
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output_file: 输出的CSV文件路径
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Returns:
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bool: 是否导出成功
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"""
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if not data:
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return False
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try:
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# 获取字段名(使用第一个数据的键)
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field_names = list(data[0].keys())
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# 字段名到中文的映射
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header_map = {
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'stock_code': '股票代码',
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'stock_name': '股票名称',
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'float_share': '流通股本(千股)',
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'ym_2410': '2024年10月均收盘价',
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'ym_2411': '2024年11月均收盘价',
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'ym_2412': '2024年12月均收盘价',
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'ym_2501': '2025年1月均收盘价',
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'ym_2502': '2025年2月均收盘价',
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'ym_2503': '2025年3月均收盘价',
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'ym_2504': '2025年4月均收盘价',
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'ym_2505': '2025年5月均收盘价',
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'ym_2506': '2025年6月均收盘价',
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'ym_2507': '2025年7月均收盘价',
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'ym_2508': '2025年8月均收盘价'
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}
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with open(output_file, mode='w', newline='', encoding='utf-8-sig') as csvfile:
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writer = csv.DictWriter(csvfile, fieldnames=field_names)
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# 写入中文表头
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writer.writerow({col: header_map.get(col, col) for col in field_names})
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# 写入数据
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writer.writerows(data)
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logging.info(f"成功导出 {len(data)} 条记录到CSV文件: {output_file}")
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return True
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except Exception as e:
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logging.error(f"导出到CSV失败: {str(e)}")
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return False
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def export_to_excel(data: List[Dict], output_file: str) -> bool:
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"""
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将合并后的数据导出为Excel文件(包含多个工作表)
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Args:
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data: 要导出的数据集
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output_file: 输出的Excel文件路径
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Returns:
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bool: 是否导出成功
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"""
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if not data:
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return False
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try:
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# 转换为DataFrame
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df = pd.DataFrame(data)
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# 设置股票代码为索引
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if 'stock_code' in df.columns:
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df.set_index('stock_code', inplace=True)
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# 创建Excel writer对象
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with pd.ExcelWriter(output_file, engine='openpyxl') as writer:
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# 1. 原始数据工作表
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df.to_excel(writer, sheet_name='合并数据')
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# 2. 统计信息工作表（仅当有数值列时）
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numeric_cols = [col for col in df.columns if col.startswith('ym_') and pd.api.types.is_numeric_dtype(df[col])]
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if numeric_cols:
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try:
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stats = df[numeric_cols].describe().loc[['mean', 'min', 'max', 'std']]
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stats.to_excel(writer, sheet_name='统计信息')
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except KeyError:
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logging.warning("无法生成完整的统计信息，数据可能不足")
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# 生成简化版统计信息
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stats = df[numeric_cols].agg(['mean', 'min', 'max', 'std'])
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stats.to_excel(writer, sheet_name='统计信息')
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# 3. 涨幅排名工作表（需要至少两个月份数据）
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if len(numeric_cols) >= 2:
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first_month = numeric_cols[0]
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last_month = numeric_cols[-1]
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try:
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df['涨幅(%)'] = (df[last_month] - df[first_month]) / df[first_month] * 100
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result_df = df[['stock_name', '涨幅(%)', 'float_share']].copy()
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result_df.dropna(subset=['涨幅(%)'], inplace=True)
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result_df.sort_values('涨幅(%)', ascending=False, inplace=True)
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result_df.to_excel(writer, sheet_name='涨幅排名')
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except Exception as e:
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logging.warning(f"无法计算涨幅: {str(e)}")
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# 4. 月度趋势工作表
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if numeric_cols:
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try:
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trend_df = df[numeric_cols].transpose()
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trend_df.index = [col.replace('ym_', '') for col in numeric_cols]
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trend_df.to_excel(writer, sheet_name='月度趋势')
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except Exception as e:
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logging.warning(f"无法生成月度趋势: {str(e)}")
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# 5. 流通股本分析工作表
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if 'float_share' in df.columns and pd.api.types.is_numeric_dtype(df['float_share']):
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try:
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float_stats = df['float_share'].describe().to_frame().T
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float_stats.to_excel(writer, sheet_name='流通股本分析')
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except Exception as e:
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logging.warning(f"无法生成流通股本分析: {str(e)}")
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logging.info(f"成功导出Excel文件: {output_file}")
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return True
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except Exception as e:
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logging.error(f"导出Excel失败: {str(e)}")
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return False
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def export_combined_data(db_config: dict,
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monthly_table: str,
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float_share_table: str,
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csv_file: str = None,
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excel_file: str = None) -> bool:
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"""
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导出合并后的数据到CSV和/或Excel
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Args:
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db_config: 数据库配置
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monthly_table: 月度均价表名
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float_share_table: 流通股本表名
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csv_file: CSV输出路径(可选)
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excel_file: Excel输出路径(可选)
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Returns:
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bool: 是否至少有一种格式导出成功
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"""
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if not csv_file and not excel_file:
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logging.error("必须指定至少一种输出格式")
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return False
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# 从数据库获取数据
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monthly_data = get_monthly_avg_data(db_config, monthly_table)
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if not monthly_data:
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logging.error("无法获取月度均价数据")
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return False
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float_share_data = get_float_share_data(db_config, float_share_table)
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if not float_share_data:
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logging.error("无法获取流通股本数据")
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return False
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# 合并数据
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merged_data = merge_data(monthly_data, float_share_data)
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# 导出结果
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csv_success = True
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excel_success = True
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if csv_file:
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csv_success = export_to_csv(merged_data, csv_file)
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if excel_file:
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excel_success = export_to_excel(merged_data, excel_file)
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return csv_success or excel_success
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# 主程序入口
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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('export_combined_data.log', encoding='utf-8'),
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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_akshare'
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}
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# 导出合并数据
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success = export_combined_data(
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db_config=db_config,
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monthly_table="hk_monthly_avg_2410_2508",
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float_share_table="conditionalselection",
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csv_file="hk_stocks_combined_data.csv",
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excel_file="hk_stocks_combined_data.xlsx"
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)
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if success:
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logging.info("数据合并导出成功完成")
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else:
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logging.error("数据合并导出过程中出现错误")
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