""" 本代码用于更新日 K 数据 —— 由于行情数据限制，至少需要3个账号，进行数据更新 —— 更新内容：每个交易日日K数据，以及当日流通股数量(用于计算流通和市值) 程序流程： —— 更新 hk_stock_list table 存放全部静态数据（这是一个总表） —— 读取股票列表：三个OpenD账号的数据存放在三个 TXT 文件中 —— 根据股票代码，获取股票历史数据 —— 根据股票代码，获取流通股数量 —— 根据股票代码，新建/更新数据表 —— 更新完成需检查 ，数据是否完整 -> checktable.py 操作界面做好了之后，再融合到整体代码中 """ from futu import * from base.MySQLHelper import MySQLHelper # MySQLHelper类保存为单独文件 from base.LogHelper import LogHelper from datetime import datetime from typing import Optional, List, Dict from UpdateFutuData.ConditionalSelection import FutuStockFilter from tqdm import tqdm import pandas as pd import time # 基本用法（自动创建日期日志+控制台输出） logger = LogHelper(logger_name = 'KLine').setup() def get_market_data(market: Market) -> List[str]: """ 从Futu API获取指定市场的股票代码列表 Args: market (Market): 市场枚举值，如 Market.SH, Market.SZ Returns: List[str]: 股票代码列表 """ quote_ctx = OpenQuoteContext(host='127.0.0.1', port=11111) try: ret, data = quote_ctx.get_stock_basicinfo(market, SecurityType.STOCK) if ret == RET_OK: # 提取code列并转换为列表 codes = data['code'].astype(str).tolist() logger.info(f"获取到 {market} 市场 {len(codes)} 个股票代码") return codes else: logger.error(f"获取股票代码失败: {data}") return [] except Exception as e: logger.error(f"获取股票代码时发生异常: {str(e)}") return [] finally: quote_ctx.close() def preprocess_quote_data(df: pd.DataFrame, float_share: Optional[int] = None) -> List[Dict]: """ 预处理行情数据，转换为适合数据库存储的格式 Args: df (pd.DataFrame): 原始行情数据DataFrame float_share (Optional[int]): 流通股数量 Returns: List[Dict]: 处理后的数据列表 """ processed_data = [] for _, row in df.iterrows(): try: # 提取市场标识 market = row['code'].split('.')[0] if '.' in row['code'] else 'UNKNOWN' # 转换时间格式 trade_time = datetime.strptime(row['time_key'], '%Y-%m-%d %H:%M:%S') item = { 'stock_code': row['code'], 'stock_name': row['name'], 'trade_date': trade_time, 'open_price': float(row['open']), 'close_price': float(row['close']), 'high_price': float(row['high']), 'low_price': float(row['low']), 'pe_ratio': float(row['pe_ratio']) if pd.notna(row['pe_ratio']) else None, 'turnover_rate': float(row['turnover_rate']) if pd.notna(row['turnover_rate']) else None, 'volume': int(row['volume']) if pd.notna(row['volume']) else None, 'turnover': float(row['turnover']) if pd.notna(row['turnover']) else None, 'change_rate': float(row['change_rate']) if pd.notna(row['change_rate']) else None, 'last_close': float(row['last_close']) if pd.notna(row['last_close']) else None, 'market': market, 'float_share': float_share # 添加流通股数量字段 } processed_data.append(item) except Exception as e: logger.warning(f"处理行情数据时跳过异常行 {row.get('code', '未知')}: {str(e)}") continue return processed_data def get_float_share(quote_ctx: OpenQuoteContext, code: str) -> Optional[int]: """ 获取股票的流通股数量 Args: quote_ctx (OpenQuoteContext): Futu API连接上下文 code (str): 股票代码 Returns: Optional[int]: 流通股数量，获取失败返回None """ try: # 获取股票快照 ret, snapshot = quote_ctx.get_market_snapshot([code]) if ret == RET_OK and not snapshot.empty: # 提取流通股数量 float_share = snapshot.iloc[0].get('float_share') if pd.notna(float_share): return int(float_share) return None except Exception as e: logger.error(f"获取股票 {code} 的流通股数量失败: {str(e)}") return None def get_float_share_data(db_config: dict, table_name: str) -> Optional[Dict[str, int]]: """ 从指定表读取流通股本数据并构建字典 Args: db_config: 数据库配置 table_name: 源数据表名 Returns: Dict[str, int]: 股票代码到流通股数量的映射字典，失败返回None """ try: with MySQLHelper(**db_config) as db: # 查询流通股本数据 data = db.execute_query(f""" SELECT stock_code, float_share FROM {table_name} WHERE float_share IS NOT NULL """) if not data: logger.error(f"表 {table_name} 中没有流通股本数据") return None # 构建股票代码到流通股数量的映射字典 float_share_dict = {} for row in data: stock_code = row['stock_code'] float_share = row['float_share'] if stock_code and float_share is not None: float_share_dict[stock_code] = int(float_share) logger.info(f"成功从 {table_name} 表加载 {len(float_share_dict)} 条流通股数据") return float_share_dict except Exception as e: logger.error(f"从数据库读取流通股本数据失败: {str(e)}") return None def save_quotes_to_db(db_config: dict, quote_data: pd.DataFrame, table_name: str = 'stock_quotes', float_share: Optional[int] = None) -> bool: """ 将行情数据保存到数据库 Args: db_config (dict): 数据库配置 quote_data (pd.DataFrame): 行情数据DataFrame table_name (str): 目标表名(默认为'stock_quotes') float_share (Optional[int]): 流通股数量 Returns: bool: 是否成功保存 """ # 预处理数据 processed_data = preprocess_quote_data(quote_data, float_share) if not processed_data: logger.error("没有有效数据需要保存") return False # 动态生成SQL插入语句 insert_sql = f""" INSERT INTO {table_name} ( stock_code, stock_name, trade_date, open_price, close_price, high_price, low_price, pe_ratio, turnover_rate, volume, turnover, change_rate, last_close, market, float_share ) VALUES ( %(stock_code)s, %(stock_name)s, %(trade_date)s, %(open_price)s, %(close_price)s, %(high_price)s, %(low_price)s, %(pe_ratio)s, %(turnover_rate)s, %(volume)s, %(turnover)s, %(change_rate)s, %(last_close)s, %(market)s, %(float_share)s ) ON DUPLICATE KEY UPDATE stock_name = VALUES(stock_name), open_price = VALUES(open_price), close_price = VALUES(close_price), high_price = VALUES(high_price), low_price = VALUES(low_price), pe_ratio = VALUES(pe_ratio), turnover_rate = VALUES(turnover_rate), volume = VALUES(volume), turnover = VALUES(turnover), change_rate = VALUES(change_rate), last_close = VALUES(last_close), float_share = VALUES(float_share) """ try: with MySQLHelper(**db_config) as db: # 检查表是否存在，不存在则创建 if not db.table_exists(table_name): create_table_sql = f""" CREATE TABLE IF NOT EXISTS {table_name} ( id INT AUTO_INCREMENT PRIMARY KEY, stock_code VARCHAR(20) NOT NULL COMMENT '股票代码', stock_name VARCHAR(50) COMMENT '股票名称', trade_date DATETIME NOT NULL COMMENT '交易日期时间', open_price DECIMAL(10, 3) COMMENT '开盘价', close_price DECIMAL(10, 3) COMMENT '收盘价', high_price DECIMAL(10, 3) COMMENT '最高价', low_price DECIMAL(10, 3) COMMENT '最低价', pe_ratio DECIMAL(10, 3) COMMENT '市盈率', turnover_rate DECIMAL(10, 6) COMMENT '换手率(%)', volume BIGINT COMMENT '成交量(股)', turnover DECIMAL(20, 2) COMMENT '成交额(元)', change_rate DECIMAL(10, 6) COMMENT '涨跌幅(%)', last_close DECIMAL(10, 3) COMMENT '昨收价', market VARCHAR(10) COMMENT '市场标识', float_share BIGINT COMMENT '流通股数量', create_time TIMESTAMP DEFAULT CURRENT_TIMESTAMP COMMENT '创建时间', UNIQUE KEY uk_code_date (stock_code, trade_date), KEY idx_trade_date (trade_date), KEY idx_stock_code (stock_code) ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COMMENT='股票行情数据表' """ db.execute_update(create_table_sql) logger.info(f"创建了新表: {table_name}") affected_rows = db.execute_many(insert_sql, processed_data) logger.info(f"成功插入/更新 {affected_rows} 条行情记录到表 {table_name}") return True except Exception as e: logger.error(f"保存行情数据到表 {table_name} 失败: {str(e)}") return False def read_single_account_stock_codes(file_path='data\missing_tables_小航富途.txt'): """基础读取方法 - 按行读取所有内容""" try: with open(file_path, 'r', encoding='utf-8') as f: lines = f.readlines() # 去除每行末尾的换行符，并过滤空行 codes = [line.strip() for line in lines if line.strip()] return codes except FileNotFoundError: print(f"文件 {file_path} 不存在") return [] except Exception as e: print(f"读取文件失败: {str(e)}") return [] def get_stock_codes() -> List[str]: """从conditionalselection表获取所有股票代码""" try: with MySQLHelper(**db_config) as db: sql = f"SELECT DISTINCT stock_code FROM stock_filter" results = db.execute_query(sql) return [row['stock_code'] for row in results if row['stock_code']] except Exception as e: logger.error(f"获取股票代码失败: {str(e)}") return [] def write_missing_codes_to_txt(missing_codes: list, filename: str = "config\missing_codes.txt"): """将缺失的股票代码追加到TXT文件，如果文件不存在则创建""" try: # 确保目录存在 directory = os.path.dirname(filename) if directory and not os.path.exists(directory): os.makedirs(directory) logger.info(f"创建目录: {directory}") # # 检查文件是否存在 # file_exists = os.path.exists(filename) with open(filename, 'a', encoding='utf-8') as f: for code in missing_codes: f.write(f"\n{code}") logger.info(f"已将 {len(missing_codes)} 个缺失表对应的股票代码写入 {filename}") except Exception as e: logger.error(f"写入TXT文件失败: {str(e)}") # 数据库配置 db_config = { 'host': 'localhost', 'user': 'root', 'password': 'bzskmysql', 'database': 'hk_kline_1d' }