""" 存储上海证券交易股票列表数据 不确定其数据爬取规则，防止 IP 被封 暂时使用该方案，获取股票列表数据 —— 下载excel,收到导入到数据库 """ import pandas as pd import os import sys import csv import chardet # 用于检测文件编码 from pathlib import Path from datetime import datetime from MySQLHelper import MySQLHelper from LogHelper import LogHelper logger = LogHelper(logger_name = 'SH_Import').setup() class StockDataImporter: """股票数据导入工具（支持CSV）""" COLUMN_MAPPING = { 'A股代码': 'a_stock_code', 'B股代码': 'b_stock_code', '证券简称': 'short_name', '扩位证券简称': 'extended_name', '公司英文全称': 'eng_name', '上市日期': 'listing_date' } def __init__(self, data_dir: Path, db_config: dict): self.data_dir = data_dir self.db_config = db_config self.df = None self.csv_file = None self.encoding = 'utf-8' # 默认编码 self.delimiter = ',' # 默认分隔符 def find_csv_file(self) -> Path: """在data文件夹中查找CSV文件""" # 查找所有CSV文件 csv_files = list(self.data_dir.glob("GPLIST.csv")) if not csv_files: logger.error(f"在 {self.data_dir} 中没有找到CSV文件") return None # 如果有多个文件，选择最新的 if len(csv_files) > 1: csv_files.sort(key=os.path.getmtime, reverse=True) logger.info(f"找到多个CSV文件，选择最新的: {csv_files[0].name}") return csv_files[0] def validate_file(self, file_path: Path) -> bool: """验证CSV文件是否有效""" try: if not file_path.exists(): logger.error(f"CSV文件不存在: {file_path}") return False file_size = file_path.stat().st_size if file_size == 0: logger.error(f"CSV文件为空: {file_path}") return False return True except Exception as e: logger.error(f"文件验证失败: {e}") return False def detect_file_encoding(self, file_path: Path) -> str: """检测文件编码""" try: # 读取文件开头部分进行编码检测 with open(file_path, 'rb') as f: raw_data = f.read(10000) # 读取前10KB # 使用chardet检测编码 result = chardet.detect(raw_data) encoding = result['encoding'] confidence = result['confidence'] # 常见编码替代 encoding_map = { 'GB2312': 'GBK', 'gb2312': 'GBK', 'ISO-8859-1': 'latin1', 'ascii': 'utf-8' } # 应用映射 encoding = encoding_map.get(encoding, encoding) logger.info(f"检测到编码: {encoding} (置信度: {confidence:.2f})") return encoding or 'utf-8' except Exception as e: logger.error(f"编码检测失败: {e}, 使用默认UTF-8") return 'utf-8' def detect_csv_delimiter(self, file_path: Path) -> str: """自动检测CSV分隔符""" try: # 使用检测到的编码打开文件 with open(file_path, 'r', encoding=self.encoding) as f: # 读取前5行 lines = [f.readline() for _ in range(5) if f.readline()] # 尝试常见分隔符 delimiters = [',', '\t', ';', '|'] delimiter_counts = {} for delim in delimiters: count = 0 for line in lines: count += line.count(delim) delimiter_counts[delim] = count # 选择出现次数最多的分隔符 best_delim = max(delimiter_counts, key=delimiter_counts.get) # 如果没有任何分隔符，则使用逗号 if delimiter_counts[best_delim] == 0: logger.warning(f"无法检测到有效的分隔符，使用默认逗号分隔符") return ',' logger.info(f"检测到分隔符: {repr(best_delim)}") return best_delim except Exception as e: logger.error(f"检测分隔符失败: {e}, 使用默认逗号分隔符") return ',' def read_csv_data(self, file_path: Path) -> bool: """从CSV文件读取数据""" try: # 1. 检测文件编码 self.encoding = self.detect_file_encoding(file_path) # 2. 检测分隔符 self.delimiter = self.detect_csv_delimiter(file_path) # 3. 读取CSV文件 logger.info(f"使用编码 '{self.encoding}' 和分隔符 '{self.delimiter}' 读取文件") self.df = pd.read_csv( file_path, delimiter=self.delimiter, dtype=str, encoding=self.encoding, on_bad_lines='warn', quoting=csv.QUOTE_MINIMAL, engine='python' # 更健壮的引擎 ) # 检查是否读取到数据 if self.df.empty: logger.error("CSV文件没有包含有效数据") return False # 重命名列 self.df = self.df.rename(columns=self.COLUMN_MAPPING) # 移除可能存在的空行 self.df = self.df.dropna(how='all') logger.info(f"成功读取CSV数据，共 {len(self.df)} 条记录") return True except UnicodeDecodeError: # 尝试其他编码 encodings_to_try = ['GBK', 'latin1', 'ISO-8859-1', 'utf-16'] for enc in encodings_to_try: try: logger.warning(f"尝试使用 {enc} 编码读取文件") self.df = pd.read_csv( file_path, delimiter=self.delimiter, dtype=str, encoding=enc ) self.encoding = enc logger.info(f"成功使用 {enc} 编码读取文件") return True except: continue logger.error("所有编码尝试均失败") return False except PermissionError: logger.error(f"文件被占用，请关闭后重试: {file_path}") return False except Exception as e: logger.error(f"读取CSV文件失败: {e}") return False def clean_stock_data(self) -> bool: """清洗股票数据""" try: # 处理B股代码：将'-'转换为None self.df['b_stock_code'] = self.df['b_stock_code'].replace('-', None) # 格式化上市日期 self.df['listing_date'] = pd.to_datetime( self.df['listing_date'], format='%Y%m%d', errors='coerce' ).dt.strftime('%Y-%m-%d') # 检查日期转换是否成功 date_na_count = self.df['listing_date'].isna().sum() if date_na_count > 0: logger.warning(f"发现 {date_na_count} 条记录的上市日期格式不正确") # 提取交易所信息 self.df['exchange'] = self.df['a_stock_code'].apply( lambda x: 'SH' if str(x).startswith('60') else 'SZ' if str(x).startswith(('00', '30')) else 'OTHER' ) # 验证A股代码格式 invalid_codes = self.df[~self.df['a_stock_code'].astype(str).str.match(r'^\d{6}$')] if not invalid_codes.empty: logger.warning(f"发现 {len(invalid_codes)} 条无效的A股代码") logger.debug(f"无效代码示例: {invalid_codes['a_stock_code'].head().tolist()}") logger.info("数据清洗完成") return True except Exception as e: logger.error(f"数据清洗失败: {e}") return False def create_stocks_table(self, db: MySQLHelper) -> bool: """创建股票信息表""" create_table_sql = """ CREATE TABLE IF NOT EXISTS stocks_sh ( a_stock_code VARCHAR(6) PRIMARY KEY COMMENT 'A股代码', b_stock_code VARCHAR(6) COMMENT 'B股代码', short_name VARCHAR(50) NOT NULL COMMENT '证券简称', extended_name VARCHAR(100) COMMENT '扩位证券简称', eng_name VARCHAR(150) COMMENT '公司英文全称', listing_date DATE NOT NULL COMMENT '上市日期', exchange VARCHAR(2) NOT NULL COMMENT '交易所', created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP COMMENT '创建时间', updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP COMMENT '更新时间' ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COMMENT='沪深股票信息表'; """ try: db.execute_update(create_table_sql) logger.info("股票信息表创建成功") return True except Exception as e: logger.error(f"创建表失败: {e}") return False def insert_data_to_db(self, db: MySQLHelper) -> bool: """将数据插入数据库""" if self.df is None or self.df.empty: logger.error("没有有效数据可插入") return False # 准备SQL语句（支持重复记录更新） insert_sql = """ INSERT INTO stocks_sh ( a_stock_code, b_stock_code, short_name, extended_name, eng_name, listing_date, exchange ) VALUES ( %s, %s, %s, %s, %s, %s, %s ) ON DUPLICATE KEY UPDATE b_stock_code = VALUES(b_stock_code), short_name = VALUES(short_name), extended_name = VALUES(extended_name), eng_name = VALUES(eng_name), listing_date = VALUES(listing_date), exchange = VALUES(exchange) """ # 准备参数列表 params_list = [] for _, row in self.df.iterrows(): # 处理可能的NaN值 listing_date = row['listing_date'] if pd.notna(row['listing_date']) else '1970-01-01' params_list.append(( row['a_stock_code'], row['b_stock_code'] if pd.notna(row['b_stock_code']) else None, row['short_name'], row['extended_name'] if pd.notna(row['extended_name']) else None, row['eng_name'] if pd.notna(row['eng_name']) else None, listing_date, row['exchange'] )) # 批量执行插入 try: total_rows = len(params_list) if total_rows == 0: logger.error("没有有效数据可插入") return False batch_size = 1000 # 每批插入1000条记录 logger.info(f"开始插入数据，共 {total_rows} 条记录") # 分批插入，避免大事务问题 for i in range(0, total_rows, batch_size): batch_params = params_list[i:i+batch_size] affected_rows = db.execute_many(insert_sql, batch_params) logger.info(f"已处理 {min(i+batch_size, total_rows)}/{total_rows} 条记录") logger.info(f"成功插入/更新 {total_rows} 条记录") return True except Exception as e: logger.error(f"插入数据失败: {e}") # 记录前5个参数以帮助调试 if params_list: logger.debug(f"前5个参数示例: {params_list[:5]}") return False def verify_data_in_db(self, db: MySQLHelper, sample_size: int = 5) -> bool: """验证数据库中的数据""" try: # 检查记录总数 count_sql = "SELECT COUNT(*) AS total FROM stocks_sh" result = db.execute_query(count_sql) db_count = result[0]['total'] if result else 0 logger.info(f"数据库中共有 {db_count} 条记录") # 随机抽样检查 sample_sql = f""" SELECT a_stock_code, short_name, listing_date FROM stocks_sh ORDER BY RAND() LIMIT {sample_size} """ samples = db.execute_query(sample_sql) logger.info("\n随机抽样记录:") for idx, sample in enumerate(samples, 1): logger.info(f"{idx}. {sample['a_stock_code']}: {sample['short_name']} ({sample['listing_date']})") return True except Exception as e: logger.error(f"数据验证失败: {e}") return False def run_import(self) -> bool: """执行完整的导入流程""" logger.info(f"开始导入股票数据，数据目录: {self.data_dir}") start_time = datetime.now() # 1. 查找CSV文件 csv_file = self.find_csv_file() if not csv_file: return False # 2. 验证文件 if not self.validate_file(csv_file): return False # 3. 读取CSV数据 if not self.read_csv_data(csv_file): return False # 4. 清洗数据 if not self.clean_stock_data(): return False # 显示前5条数据 logger.info("\n前5条股票数据:") for i, row in self.df.head().iterrows(): logger.info(f"{row['a_stock_code']}: {row['short_name']} ({row['listing_date']})") # 5. 连接数据库并导入 try: with MySQLHelper(**self.db_config) as db: # 5.1 创建表 if not self.create_stocks_table(db): return False # 5.2 插入数据 if not self.insert_data_to_db(db): return False # 5.3 验证数据 if not self.verify_data_in_db(db): return False except Exception as e: logger.error(f"数据库操作异常: {e}") return False # 计算执行时间 duration = datetime.now() - start_time logger.info(f"数据处理成功完成! 总耗时: {duration.total_seconds():.2f}秒") return True if __name__ == "__main__": # 数据库配置 db_config = { 'host': 'localhost', 'user': 'root', 'password': 'bzskmysql', 'database': 'fullmarketdata_a' } # 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 importer = StockDataImporter(DATA_DIR, db_config) 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")