# coding:utf-8 import requests import pandas as pd from sqlalchemy import create_engine, text from datetime import datetime from tqdm import tqdm from src.scripts.config import XUEQIU_HEADERS import gc class StockDailyDataCollector: """股票日线数据采集器类""" def __init__(self, db_url): self.engine = create_engine( db_url, pool_size=5, max_overflow=10, pool_recycle=3600 ) self.headers = XUEQIU_HEADERS def fetch_all_stock_codes(self): # 从gp_code_all获取股票代码 query_all = "SELECT gp_code FROM gp_code_all" df_all = pd.read_sql(query_all, self.engine) codes_all = df_all['gp_code'].tolist() # 从gp_code_zs获取股票代码 query_zs = "SELECT gp_code FROM gp_code_zs" df_zs = pd.read_sql(query_zs, self.engine) codes_zs = df_zs['gp_code'].tolist() # 合并去重 all_codes = list(set(codes_all + codes_zs)) print(f"获取到股票代码: {len(codes_all)}个来自gp_code_all, {len(codes_zs)}个来自gp_code_zs, 去重后共{len(all_codes)}个") return all_codes def fetch_daily_stock_data(self, symbol, begin): url = f"https://stock.xueqiu.com/v5/stock/chart/kline.json?symbol={symbol}&begin={begin}&period=day&type=before&count=-1&indicator=kline,pe,pb,ps,pcf,market_capital,agt,ggt,balance" try: response = requests.get(url, headers=self.headers, timeout=10) return response.json() except Exception as e: print(f"Request error for {symbol}: {e}") return {'error_code': -1, 'error_description': str(e)} def transform_data(self, data, symbol): try: items = data['data']['item'] columns = data['data']['column'] except KeyError as e: print(f"KeyError for {symbol}: {e}") return None df = pd.DataFrame(items, columns=columns) df['symbol'] = symbol required_columns = ['timestamp', 'volume', 'open', 'high', 'low', 'close', 'chg', 'percent', 'turnoverrate', 'amount', 'symbol', 'pb', 'pe', 'ps'] existing_columns = [col for col in required_columns if col in df.columns] df = df[existing_columns] if 'timestamp' in df.columns: df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms', utc=True).dt.tz_convert('Asia/Shanghai') return df def save_batch_to_database(self, batch): if batch: df_all = pd.concat(batch, ignore_index=True) df_all.to_sql('gp_day_data', self.engine, if_exists='append', index=False) def fetch_data_for_date(self, date=None): if date is None: start_date = datetime.now() date_str = start_date.strftime('%Y-%m-%d') else: start_date = datetime.strptime(date, '%Y-%m-%d') date_str = date delete_query = text("DELETE FROM gp_day_data WHERE `timestamp` LIKE :date_str") with self.engine.begin() as conn: conn.execute(delete_query, {"date_str": f"{date_str}%"}) stock_codes = self.fetch_all_stock_codes() begin = int(start_date.replace(hour=0, minute=0, second=0, microsecond=0).timestamp() * 1000) batch_data = [] for idx, symbol in enumerate(tqdm(stock_codes, desc=f"Fetching and saving daily stock data for {date_str}")): data = self.fetch_daily_stock_data(symbol, begin) if data.get('error_code') == 0: df = self.transform_data(data, symbol) if df is not None: batch_data.append(df) else: print(f"Error fetching data for {symbol} on {date_str}: {data.get('error_description')}") if len(batch_data) >= 100: self.save_batch_to_database(batch_data) batch_data.clear() gc.collect() # Save remaining data if batch_data: self.save_batch_to_database(batch_data) gc.collect() self.engine.dispose() print(f"Daily data fetching and saving completed for {date_str}.") def collect_stock_daily_data(db_url, date=None): collector = StockDailyDataCollector(db_url) collector.fetch_data_for_date(date) if __name__ == "__main__": db_url = 'mysql+pymysql://root:Chlry#$.8@192.168.18.199:3306/db_gp_cj' collect_stock_daily_data(db_url)