# coding:utf-8 import requests import pandas as pd from sqlalchemy import create_engine, text from datetime import datetime, timedelta from tqdm import tqdm from config import XUEQIU_HEADERS class StockMinuteDataCollector: """股票分时数据采集器类""" def __init__(self, db_url): """ 初始化采集器 Parameters: ----------- db_url : str 数据库连接URL """ self.engine = create_engine(db_url) self.headers = XUEQIU_HEADERS def fetch_all_stock_codes(self): """从数据库获取所有股票代码""" query = "SELECT gp_code FROM gp_code_all" df = pd.read_sql(query, self.engine) return df['gp_code'].tolist() def update_market_cap(self, symbol, market_cap): """更新数据库中的市值信息""" query = text("UPDATE gp_code_all SET market_cap = :market_cap WHERE gp_code = :symbol") with self.engine.connect() as conn: conn.execute(query, {'market_cap': market_cap, 'symbol': symbol}) def fetch_market_cap(self, symbol): """获取股票市值信息""" url = f"https://stock.xueqiu.com/v5/stock/realtime/quotec.json?symbol={symbol}" response = requests.get(url, headers=self.headers) data = response.json() if data['error_code'] == 0: return data['data'][0]['market_capital'] else: print(f"Error fetching market cap for {symbol}: {data['error_description']}") return None def fetch_stock_data(self, symbol, begin, end): """获取股票分时数据""" url = f"https://stock.xueqiu.com/v5/stock/chart/kline.json?symbol={symbol}&begin={begin}&end={end}&period=1m&type=before&count=-284&indicator=kline,pe,pb,ps,pcf,market_capital,agt,ggt,balance" response = requests.get(url, headers=self.headers) return response.json() def save_to_database(self, data, symbol): """保存数据到数据库""" try: items = data['data']['item'] columns = data['data']['column'] except KeyError as e: print(f"KeyError for {symbol}: {e}") return df = pd.DataFrame(items, columns=columns) df['symbol'] = symbol # 数据库中有的字段 required_columns = ['timestamp', 'volume', 'open', 'high', 'low', 'close', 'chg', 'percent', 'turnoverrate', 'amount', 'symbol'] # 检查并保留实际存在的字段 existing_columns = [col for col in required_columns if col in df.columns] df = df[existing_columns] # 数据类型转换 if 'timestamp' in df.columns: df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms', utc=True).dt.tz_convert('Asia/Shanghai') df.to_sql('gp_min_data', self.engine, if_exists='append', index=False) def fetch_data_for_date(self, date=None): """ 获取指定日期或当天的数据 Parameters: ----------- date : str, optional 日期字符串,格式为'YYYY-MM-DD',如果为None则获取当天数据 """ if date is None: # 如果没有指定日期,使用当天日期 date = datetime.now().strftime('%Y-%m-%d') start_date = datetime.strptime(date, '%Y-%m-%d') end_date = start_date + timedelta(days=1) # 获取所有股票代码 stock_codes = self.fetch_all_stock_codes() # 循环请求每只股票的数据并保存,使用进度条显示进度 for symbol in tqdm(stock_codes, desc=f"Fetching and saving stock data for {date}"): begin = int(start_date.replace(hour=0, minute=0, second=0, microsecond=0).timestamp() * 1000) end = int(end_date.replace(hour=0, minute=0, second=0, microsecond=0).timestamp() * 1000) data = self.fetch_stock_data(symbol, begin, end) if data['error_code'] == 0: self.save_to_database(data, symbol) else: print(f"Error fetching data for {symbol} on {date}: {data['error_description']}") print(f"Data fetching and saving completed for {date}.") def collect_stock_minute_data(db_url, date=None): """ 快捷方法:收集股票分时数据 Parameters: ----------- db_url : str 数据库连接URL date : str, optional 日期字符串,格式为'YYYY-MM-DD',如果为None则获取当天数据 """ collector = StockMinuteDataCollector(db_url) collector.fetch_data_for_date(date) if __name__ == "__main__": # 示例调用 db_url = 'mysql+pymysql://root:Chlry#$.8@192.168.18.199:3306/db_gp_cj' # 方法1:使用快捷函数获取当天数据 collect_stock_minute_data(db_url) # 方法2:使用快捷函数获取指定日期数据 # collect_stock_minute_data(db_url, '2024-07-29') # 方法3:使用完整的类 # collector = StockMinuteDataCollector(db_url) # collector.fetch_data_for_date() # 获取当天数据 # collector.fetch_data_for_date('2024-07-29') # 获取指定日期数据