500 lines
19 KiB
Python
500 lines
19 KiB
Python
"""
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存储上海证券交易股票列表数据
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不确定其数据爬取规则，防止 IP 被封
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暂时使用该方案，获取股票列表数据
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—— 下载excel,收到导入到数据库
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"""
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import pandas as pd
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import os
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import sys
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import csv
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import chardet # 用于检测文件编码
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from pathlib import Path
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from datetime import datetime
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from .MySQLHelper import MySQLHelper
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from .LogHelper import LogHelper
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logger = LogHelper(logger_name = 'execelImport').setup()
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class StockDataImporter:
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"""股票数据导入工具（支持CSV）"""
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def __init__(self, db_config: dict, column_mapping: dict, data_dir: Path):
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self.db_config = db_config
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self.column_mapping = column_mapping
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self.data_dir = data_dir
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self.df = None
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self.csv_file = None
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self.encoding = 'utf-8' # 默认编码
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self.delimiter = ',' # 默认分隔符
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self.upload_filename = None # 上传文件名
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# 更新 检讨标志
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def setUploadfile(self, filename: str):
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"""设置需要上传的文件名"""
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self.upload_filename = filename
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logger.info(f"设置上传文件名为: {filename}")
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def find_csv_file(self) -> Path:
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"""在data文件夹中查找CSV文件"""
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# 使用设置的upload_filename或默认文件名
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filename = self.upload_filename if self.upload_filename else "GPLIST.csv"
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# 查找所有匹配的文件
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csv_files = list(self.data_dir.glob(filename))
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if not csv_files:
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logger.error(f"在 {self.data_dir} 中没有找到文件: {filename}")
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return None
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# 如果有多个文件，选择最新的
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if len(csv_files) > 1:
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csv_files.sort(key=os.path.getmtime, reverse=True)
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logger.info(f"找到多个文件，选择最新的: {csv_files[0].name}")
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return csv_files[0]
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def validate_file(self, file_path: Path) -> bool:
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"""验证CSV文件是否有效"""
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try:
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if not file_path.exists():
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logger.error(f"CSV文件不存在: {file_path}")
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return False
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file_size = file_path.stat().st_size
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if file_size == 0:
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logger.error(f"CSV文件为空: {file_path}")
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return False
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return True
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except Exception as e:
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logger.error(f"文件验证失败: {e}")
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return False
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def detect_file_encoding(self, file_path: Path) -> str:
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"""检测文件编码"""
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try:
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# 读取文件开头部分进行编码检测
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with open(file_path, 'rb') as f:
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raw_data = f.read(10000) # 读取前10KB
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# 使用chardet检测编码
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result = chardet.detect(raw_data)
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encoding = result['encoding']
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confidence = result['confidence']
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# 常见编码替代
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encoding_map = {
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'GB2312': 'GBK',
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'gb2312': 'GBK',
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'ISO-8859-1': 'latin1',
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'ascii': 'utf-8'
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}
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# 应用映射
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encoding = encoding_map.get(encoding, encoding)
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logger.info(f"检测到编码: {encoding} (置信度: {confidence:.2f})")
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return encoding or 'utf-8'
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except Exception as e:
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logger.error(f"编码检测失败: {e}, 使用默认UTF-8")
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return 'utf-8'
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def detect_csv_delimiter(self, file_path: Path) -> str:
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"""自动检测CSV分隔符"""
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try:
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# 使用检测到的编码打开文件
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with open(file_path, 'r', encoding=self.encoding) as f:
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# 读取前5行
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lines = [f.readline() for _ in range(5) if f.readline()]
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# 尝试常见分隔符
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delimiters = [',', '\t', ';', '|']
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delimiter_counts = {}
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for delim in delimiters:
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count = 0
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for line in lines:
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count += line.count(delim)
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delimiter_counts[delim] = count
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# 选择出现次数最多的分隔符
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best_delim = max(delimiter_counts, key=delimiter_counts.get)
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# 如果没有任何分隔符，则使用逗号
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if delimiter_counts[best_delim] == 0:
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logger.warning(f"无法检测到有效的分隔符，使用默认逗号分隔符")
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return ','
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logger.info(f"检测到分隔符: {repr(best_delim)}")
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return best_delim
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except Exception as e:
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logger.error(f"检测分隔符失败: {e}, 使用默认逗号分隔符")
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return ','
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def read_csv_data(self, file_path: Path) -> bool:
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"""从CSV文件读取数据"""
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try:
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# 1. 检测文件编码
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self.encoding = self.detect_file_encoding(file_path)
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# 2. 检测分隔符
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self.delimiter = self.detect_csv_delimiter(file_path)
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# 3. 读取CSV文件
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logger.info(f"使用编码 '{self.encoding}' 和分隔符 '{self.delimiter}' 读取文件")
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self.df = pd.read_csv(
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file_path,
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delimiter=self.delimiter,
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dtype=str,
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encoding=self.encoding,
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on_bad_lines='warn',
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quoting=csv.QUOTE_MINIMAL,
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engine='python' # 更健壮的引擎
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)
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# 检查是否读取到数据
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if self.df.empty:
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logger.error("CSV文件没有包含有效数据")
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return False
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# 重命名列
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self.df = self.df.rename(columns=self.column_mapping)
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# 移除可能存在的空行
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self.df = self.df.dropna(how='all')
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logger.info(f"成功读取CSV数据，共 {len(self.df)} 条记录")
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return True
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except UnicodeDecodeError:
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# 尝试其他编码
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encodings_to_try = ['GBK', 'latin1', 'ISO-8859-1', 'utf-16']
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for enc in encodings_to_try:
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try:
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logger.warning(f"尝试使用 {enc} 编码读取文件")
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self.df = pd.read_csv(
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file_path,
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delimiter=self.delimiter,
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dtype=str,
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encoding=enc
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)
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self.encoding = enc
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logger.info(f"成功使用 {enc} 编码读取文件")
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return True
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except:
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continue
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logger.error("所有编码尝试均失败")
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return False
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except PermissionError:
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logger.error(f"文件被占用，请关闭后重试: {file_path}")
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return False
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except Exception as e:
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logger.error(f"读取CSV文件失败: {e}")
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return False
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def clean_stock_data(self) -> bool:
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"""清洗股票数据（基本清洗，主要处理股票代码格式）"""
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try:
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# 验证股票代码格式（如果存在stock_code列）
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if 'stock_code' in self.df.columns:
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invalid_codes = self.df[~self.df['stock_code'].astype(str).str.match(r'^\d{6}$')]
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if not invalid_codes.empty:
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logger.warning(f"发现 {len(invalid_codes)} 条无效的股票代码")
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logger.debug(f"无效代码示例: {invalid_codes['stock_code'].head().tolist()}")
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logger.info("数据清洗完成")
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return True
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except Exception as e:
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logger.error(f"数据清洗失败: {e}")
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return False
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def create_stocks_table(self, db: MySQLHelper) -> bool:
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"""创建股票信息表（包含股票代码、中文名称、英文名称、进出标志和时间戳）"""
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# 定义列类型映射
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column_type_mapping = {
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'stock_code': 'VARCHAR(6) PRIMARY KEY',
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'stock_name_chn': 'VARCHAR(50) NULL',
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'stock_name_en': 'VARCHAR(150)',
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}
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# 构建列定义SQL
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column_definitions = []
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for column_name in self.column_mapping.values():
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if column_name in column_type_mapping:
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column_definitions.append(f"{column_name} {column_type_mapping[column_name]}")
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else:
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# 对于未知列，使用VARCHAR(255)
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column_definitions.append(f"{column_name} VARCHAR(255)")
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# 添加进出标志列
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column_definitions.append("in_out TINYINT(1) DEFAULT 0 COMMENT '进出标志'")
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# 添加时间戳列
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column_definitions.append("created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP COMMENT '创建时间'")
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column_definitions.append("updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP COMMENT '更新时间'")
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# 构建完整的CREATE TABLE SQL
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columns_sql = ",\n ".join(column_definitions)
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create_table_sql = f"""
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CREATE TABLE IF NOT EXISTS hk_stock_connect (
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{columns_sql}
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) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COMMENT='股票信息表';
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"""
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try:
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db.execute_update(create_table_sql)
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logger.info("股票信息表创建成功")
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return True
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except Exception as e:
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logger.error(f"创建表失败: {e}")
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return False
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def insert_data_to_db(self, db: MySQLHelper) -> bool:
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"""将数据插入数据库（处理映射的列和进出标志）"""
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if self.df is None or self.df.empty:
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logger.error("没有有效数据可插入")
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return False
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# 获取所有映射的列名
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mapped_columns = list(self.column_mapping.values())
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# 构建INSERT SQL语句（包含in_out列，设置为1）
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columns_sql = ", ".join(mapped_columns + ['in_out'])
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placeholders = ", ".join(["%s"] * len(mapped_columns) + ["1"]) # in_out固定为1
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# 构建ON DUPLICATE KEY UPDATE部分（主键不更新，但更新in_out字段）
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update_clauses = []
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for column in mapped_columns:
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if column != 'stock_code': # 主键不更新
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update_clauses.append(f"{column} = VALUES({column})")
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# 添加in_out字段更新，确保在重复时也设置为1
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update_clauses.append("in_out = VALUES(in_out)")
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update_sql = ", ".join(update_clauses)
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insert_sql = f"""
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INSERT INTO hk_stock_connect (
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{columns_sql}
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) VALUES (
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{placeholders}
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)
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ON DUPLICATE KEY UPDATE
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{update_sql}
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"""
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# 准备参数列表（只包含映射的列，in_out由SQL固定为1）
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params_list = []
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for _, row in self.df.iterrows():
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params = []
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for column in mapped_columns:
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# 处理可能的NaN值
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value = row[column] if column in row and pd.notna(row[column]) else None
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params.append(value)
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params_list.append(tuple(params))
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# 批量执行插入
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try:
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total_rows = len(params_list)
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if total_rows == 0:
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logger.error("没有有效数据可插入")
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return False
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batch_size = 1000 # 每批插入1000条记录
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logger.info(f"开始插入数据，共 {total_rows} 条记录")
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# 分批插入，避免大事务问题
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for i in range(0, total_rows, batch_size):
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batch_params = params_list[i:i+batch_size]
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affected_rows = db.execute_many(insert_sql, batch_params)
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logger.info(f"已处理 {min(i+batch_size, total_rows)}/{total_rows} 条记录")
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logger.info(f"成功插入/更新 {total_rows} 条记录")
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return True
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except Exception as e:
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logger.error(f"插入数据失败: {e}")
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# 记录前5个参数以帮助调试
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if params_list:
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logger.debug(f"前5个参数示例: {params_list[:5]}")
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return False
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def setInOut(self, db: MySQLHelper) -> bool:
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"""设置进出标志为0，且不更新updated_at字段"""
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try:
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# # 首先检查表是否存在
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# table_check_sql = """
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# SELECT COUNT(*) AS table_exists
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# FROM information_schema.tables
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# WHERE table_schema = DATABASE()
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# AND table_name = 'hk_stock_connect'
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# """
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# result = db.execute_query(table_check_sql)
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# if not result or result[0]['table_exists'] == 0:
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# logger.warning("表 'hk_stock_connect' 不存在，跳过设置进出标志操作")
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# return True
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# 表存在，执行更新操作
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update_sql = "UPDATE hk_stock_connect SET in_out = 1, updated_at = updated_at"
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affected_rows = db.execute_update(update_sql)
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logger.info(f"成功设置 {affected_rows} 条记录的进出标志为0")
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return True
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except Exception as e:
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logger.error(f"设置进出标志失败: {e}")
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return False
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def verify_data_in_db(self, db: MySQLHelper, sample_size: int = 5) -> bool:
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"""验证数据库中的数据"""
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try:
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# 检查记录总数
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count_sql = "SELECT COUNT(*) AS total FROM hk_stock_connect"
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result = db.execute_query(count_sql)
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db_count = result[0]['total'] if result else 0
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logger.info(f"数据库中共有 {db_count} 条记录")
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# 获取映射的列名用于显示
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mapped_columns = list(self.column_mapping.values())
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# 构建查询列（使用所有映射的列和in_out字段）
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select_columns = mapped_columns + ['in_out'] if mapped_columns else ["*"]
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columns_sql = ", ".join(select_columns)
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# 随机抽样检查
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sample_sql = f"""
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SELECT {columns_sql}
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FROM hk_stock_connect
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ORDER BY RAND()
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LIMIT {sample_size}
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"""
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samples = db.execute_query(sample_sql)
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logger.info("\n随机抽样记录:")
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for idx, sample in enumerate(samples, 1):
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# 动态构建日志消息，显示所有映射的列和in_out字段
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sample_info = []
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for column in select_columns:
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if column in sample and sample[column] is not None:
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sample_info.append(f"{column}: {sample[column]}")
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logger.info(f"{idx}. {' | '.join(sample_info) if sample_info else 'No data to display'}")
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return True
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except Exception as e:
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logger.error(f"数据验证失败: {e}")
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return False
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def run_import(self) -> bool:
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"""执行完整的导入流程"""
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logger.info(f"开始导入股票数据，数据目录: {self.data_dir}")
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start_time = datetime.now()
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# 1. 查找CSV文件
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csv_file = self.find_csv_file()
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if not csv_file:
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return False
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# 2. 验证文件
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if not self.validate_file(csv_file):
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return False
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# 3. 读取CSV数据
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if not self.read_csv_data(csv_file):
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return False
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# 4. 清洗数据
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if not self.clean_stock_data():
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return False
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# 显示前5条数据（动态显示可用列）
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logger.info("\n前5条股票数据:")
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for i, row in self.df.head().iterrows():
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# 动态构建显示信息
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display_info = []
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if 'stock_code' in row:
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display_info.append(f"代码: {row['stock_code']}")
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if 'stock_name_chn' in row:
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display_info.append(f"名称: {row['stock_name_chn']}")
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if 'stock_name_en' in row:
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display_info.append(f"英文: {row['stock_name_en']}")
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logger.info(f"{i+1}. {' | '.join(display_info)}")
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# 5. 连接数据库并导入
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try:
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with MySQLHelper(**self.db_config) as db:
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# 5.1 创建表
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if not self.create_stocks_table(db):
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return False
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# 更新检讨结果
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self.setInOut(db)
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# 5.2 插入数据
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if not self.insert_data_to_db(db):
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return False
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# 5.3 验证数据
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if not self.verify_data_in_db(db):
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return False
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except Exception as e:
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logger.error(f"数据库操作异常: {e}")
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return False
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# 计算执行时间
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duration = datetime.now() - start_time
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logger.info(f"数据处理成功完成! 总耗时: {duration.total_seconds():.2f}秒")
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return True
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if __name__ == "__main__":
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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',
|
||
'password': 'bzskmysql',
|
||
'database': 'hk_kline_1d'
|
||
}
|
||
|
||
# 列映射配置
|
||
COLUMN_MAPPING = {
|
||
'证券代码': 'stock_code',
|
||
'中文简称': 'stock_name_chn',
|
||
'英文简称': 'stock_name_en',
|
||
}
|
||
|
||
# 获取当前脚本所在目录
|
||
current_dir = Path(__file__).parent if "__file__" in locals() else Path.cwd()
|
||
|
||
# 设置数据目录
|
||
DATA_DIR = current_dir / "data"
|
||
|
||
# 确保data目录存在
|
||
DATA_DIR.mkdir(exist_ok=True, parents=True)
|
||
|
||
# 安装依赖 (如果chardet未安装)
|
||
try:
|
||
import chardet
|
||
except ImportError:
|
||
logger.info("安装chardet库以支持编码检测...")
|
||
import subprocess
|
||
subprocess.check_call([sys.executable, "-m", "pip", "install", "chardet"])
|
||
import chardet
|
||
|
||
# 创建导入器并执行导入（使用新的参数顺序）
|
||
importer = StockDataImporter(db_config, COLUMN_MAPPING, DATA_DIR)
|
||
|
||
|
||
|
||
# 2.2w设置上传文件名（可以注释掉使用默认文件名）
|
||
importer.setUploadfile("港股通标的证券名单.csv")
|
||
|
||
if importer.run_import():
|
||
logger.info("股票数据导入成功!")
|
||
else:
|
||
logger.error("股票数据导入失败，请检查日志了解详情")
|