482 lines
21 KiB
Python
482 lines
21 KiB
Python
# coding:utf-8
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import requests
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import pandas as pd
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from sqlalchemy import create_engine, text
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from datetime import datetime, timedelta
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from tqdm import tqdm
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from src.scripts.config import XUEQIU_HEADERS
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from src.scripts.ProxyIP import EnhancedProxyManager
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import gc
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class StockDailyDataCollector:
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"""股票日线数据采集器类"""
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def __init__(self, db_url):
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self.engine = create_engine(
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db_url,
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pool_size=5,
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max_overflow=10,
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pool_recycle=3600
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)
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self.headers = XUEQIU_HEADERS
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# 初始化代理管理器
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self.proxy_manager = EnhancedProxyManager()
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def fetch_all_stock_codes(self):
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# 从gp_code_all获取股票代码
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query_all = "SELECT gp_code FROM gp_code_all"
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df_all = pd.read_sql(query_all, self.engine)
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codes_all = df_all['gp_code'].tolist()
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# 从gp_code_zs获取股票代码
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query_zs = "SELECT gp_code FROM gp_code_zs"
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df_zs = pd.read_sql(query_zs, self.engine)
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codes_zs = df_zs['gp_code'].tolist()
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# 从gp_code_hk获取股票代码
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query_hk = "SELECT gp_code FROM gp_code_hk"
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df_hk = pd.read_sql(query_hk, self.engine)
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codes_hk = df_hk['gp_code'].tolist()
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# 合并去重
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# all_codes = list(set(codes_all + codes_zs + codes_hk))
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all_codes = list(set(codes_zs + codes_hk))
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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, {len(codes_hk)}个来自gp_code_hk, 去重后共{len(all_codes)}个")
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return all_codes
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def fetch_daily_stock_data(self, symbol, begin, count=-1):
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"""获取日线数据，count=-1表示最新一天，-2表示最近两天，-1800表示最近1800天"""
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url = f"https://stock.xueqiu.com/v5/stock/chart/kline.json?symbol={symbol}&begin={begin}&period=day&type=before&count={count}&indicator=kline,pe,pb,ps,pcf,market_capital,agt,ggt,balance"
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try:
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# 使用代理管理器发送请求
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# response = requests.get(url, headers=self.headers, timeout=20)
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response = self.proxy_manager.request_with_proxy('get', url, headers=self.headers)
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return response.json()
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except Exception as e:
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print(f"Request error for {symbol}: {e}")
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return {'error_code': -1, 'error_description': str(e)}
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def transform_data(self, data, symbol):
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try:
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items = data['data']['item']
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columns = data['data']['column']
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except KeyError as e:
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print(f"KeyError for {symbol}: {e}")
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return None
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df = pd.DataFrame(items, columns=columns)
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df['symbol'] = symbol
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required_columns = ['timestamp', 'volume', 'open', 'high', 'low', 'close',
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'chg', 'percent', 'turnoverrate', 'amount', 'symbol', 'pb', 'pe', 'ps']
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existing_columns = [col for col in required_columns if col in df.columns]
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df = df[existing_columns]
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if 'timestamp' in df.columns:
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df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms', utc=True).dt.tz_convert('Asia/Shanghai')
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return df
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def save_batch_to_database(self, batch):
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if batch:
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df_all = pd.concat(batch, ignore_index=True)
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df_all.to_sql('gp_day_data', self.engine, if_exists='append', index=False)
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def fetch_data_for_date(self, date=None):
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if date is None:
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start_date = datetime.now()
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date_str = start_date.strftime('%Y-%m-%d')
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else:
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start_date = datetime.strptime(date, '%Y-%m-%d')
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date_str = date
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# delete_query = text("DELETE FROM gp_day_data WHERE `timestamp` LIKE :date_str")
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# with self.engine.begin() as conn:
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# conn.execute(delete_query, {"date_str": f"{date_str}%"})
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stock_codes = self.fetch_all_stock_codes()
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begin = int(start_date.replace(hour=0, minute=0, second=0, microsecond=0).timestamp() * 1000)
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batch_data = []
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for idx, symbol in enumerate(tqdm(stock_codes, desc=f"Fetching and saving daily stock data for {date_str}")):
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data = self.fetch_daily_stock_data(symbol, begin)
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if data.get('error_code') == 0:
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df = self.transform_data(data, symbol)
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if df is not None:
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batch_data.append(df)
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else:
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print(f"Error fetching data for {symbol} on {date_str}: {data.get('error_description')}")
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if len(batch_data) >= 100:
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self.save_batch_to_database(batch_data)
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batch_data.clear()
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gc.collect()
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# Save remaining data
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if batch_data:
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self.save_batch_to_database(batch_data)
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gc.collect()
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self.engine.dispose()
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print(f"Daily data fetching and saving completed for {date_str}.")
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def delete_stock_history(self, symbol):
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"""删除指定股票的全部历史数据"""
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delete_query = text("DELETE FROM gp_day_data WHERE symbol = :symbol")
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try:
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with self.engine.begin() as conn:
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conn.execute(delete_query, {"symbol": symbol})
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print(f"Deleted history for {symbol}")
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return True
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except Exception as e:
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print(f"Error deleting history for {symbol}: {e}")
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return False
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def refetch_and_save_history(self, symbol, days=1800):
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"""重新获取并保存指定股票的长期历史数据"""
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print(f"Refetching last {days} days for {symbol}...")
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begin = int(datetime.now().timestamp() * 1000)
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data = self.fetch_daily_stock_data(symbol, begin, count=-days)
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if data.get('error_code') == 0:
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df = self.transform_data(data, symbol)
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if df is not None and not df.empty:
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self.save_batch_to_database([df])
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print(f"Successfully refetched and saved history for {symbol}.")
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else:
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print(f"No data transformed for {symbol} after refetch.")
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else:
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print(f"Error refetching history for {symbol}: {data.get('error_description')}")
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def check_and_fix_ex_rights_data(self):
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"""
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检查所有股票是否发生除权，如果发生，则删除历史数据并重新获取。
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新逻辑：直接用API返回的上个交易日的时间戳去数据库查询，更稳妥。
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记录：除权日期、股票代码（）、
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"""
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all_codes = self.fetch_all_stock_codes()
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ex_rights_log_data = []
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print("--- Step 1: Checking for ex-rights stocks ---")
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for symbol in tqdm(all_codes, desc="Comparing prices"):
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# 1. 从API获取最近两天的日线数据
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begin = int(datetime.now().timestamp() * 1000)
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data = self.fetch_daily_stock_data(symbol, begin, count=-2)
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api_timestamp_str = None
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api_close = None
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if data.get('error_code') == 0 and data.get('data', {}).get('item') and len(data['data']['item']) >= 2:
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try:
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# API返回的数据是按时间升序的，[-2]是上个交易日
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prev_day_data = data['data']['item'][-2]
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columns = data['data']['column']
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timestamp_index = columns.index('timestamp')
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close_index = columns.index('close')
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api_timestamp_ms = prev_day_data[timestamp_index]
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api_close = prev_day_data[close_index]
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# 将毫秒时间戳转换为'YYYY-MM-DD'格式，用于数据库查询
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api_timestamp_str = pd.to_datetime(api_timestamp_ms, unit='ms', utc=True).tz_convert('Asia/Shanghai').strftime('%Y-%m-%d')
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except (ValueError, IndexError, TypeError) as e:
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print(f"\nError parsing API data for {symbol}: {e}")
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continue # 处理下一只股票
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else:
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# 获取API数据失败或数据不足，跳过此股票
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continue
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# 如果未能从API解析出上个交易日的数据，则跳过
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if api_timestamp_str is None or api_close is None:
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continue
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# 2. 根据API返回的时间戳，从数据库查询当天的收盘价
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db_close = None
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query = text("SELECT `close` FROM gp_day_data WHERE symbol = :symbol AND `timestamp` LIKE :date_str")
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try:
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with self.engine.connect() as conn:
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result = conn.execute(query, {"symbol": symbol, "date_str": f"{api_timestamp_str}%"}).fetchone()
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db_close = result[0] if result else None
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except Exception as e:
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print(f"\nError getting DB close for {symbol} on {api_timestamp_str}: {e}")
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continue
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# 3. 比较价格
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if db_close is not None:
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# 注意：数据库中取出的db_close可能是Decimal类型，需要转换
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if not abs(float(db_close) - api_close) < 0.001:
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print(f"\nEx-rights detected for {symbol} on {api_timestamp_str}: DB_close={db_close}, API_close={api_close}")
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ex_rights_log_data.append({
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'symbol': symbol,
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'date': datetime.now().strftime('%Y-%m-%d'),
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'db_price': float(db_close),
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'api_price': api_close,
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'log_time': datetime.now()
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})
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# 如果数据库当天没有数据，我们无法比较，所以不处理。
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# 这可能是新股或之前采集失败，不属于除权范畴。
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# 4. 对发生除权的股票进行记录和修复
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if not ex_rights_log_data:
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print("\n--- No ex-rights stocks found. Data is consistent. ---")
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self.engine.dispose()
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return
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# 在修复前，先将日志保存到数据库
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self.save_ex_rights_log(ex_rights_log_data)
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# 从日志数据中提取出需要修复的股票代码列表
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ex_rights_stocks = [item['symbol'] for item in ex_rights_log_data]
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print(f"\n--- Step 2: Found {len(ex_rights_stocks)} stocks to fix: {ex_rights_stocks} ---")
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for symbol in tqdm(ex_rights_stocks, desc="Fixing data"):
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if self.delete_stock_history(symbol):
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self.refetch_and_save_history(symbol, days=1800)
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self.engine.dispose()
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print("\n--- Ex-rights data fixing process completed. ---")
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def save_ex_rights_log(self, log_data: list):
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"""将除权日志保存到数据库"""
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if not log_data:
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return
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print(f"--- Saving {len(log_data)} ex-rights events to log table... ---")
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try:
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df = pd.DataFrame(log_data)
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# 确保列名与数据库字段匹配
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df = df.rename(columns={
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'symbol': 'stock_code',
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'date': 'change_date',
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'db_price': 'before_price',
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'api_price': 'after_price',
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'log_time': 'update_time'
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})
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df.to_sql('gp_ex_rights_log', self.engine, if_exists='append', index=False)
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print("--- Ex-rights log saved successfully. ---")
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except Exception as e:
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print(f"!!! Error saving ex-rights log: {e}")
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def fetch_single_stock_history(self, symbol, days=1800):
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"""
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获取单只股票的历史数据并保存到数据库
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:param symbol: 股票代码
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:param days: 获取的天数，默认1800天
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:return: 是否成功
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"""
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print(f"开始获取 {symbol} 最近 {days} 天的历史数据...")
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begin = int(datetime.now().timestamp() * 1000)
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data = self.fetch_daily_stock_data(symbol, begin, count=-days)
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if data.get('error_code') == 0:
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df = self.transform_data(data, symbol)
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if df is not None and not df.empty:
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df.to_sql('gp_day_data', self.engine, if_exists='append', index=False)
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print(f"成功保存 {symbol} 的历史数据，共 {len(df)} 条记录")
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return True
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else:
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print(f"未能转换 {symbol} 的数据")
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return False
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else:
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print(f"获取 {symbol} 数据失败: {data.get('error_description')}")
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return False
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def fetch_and_check_ex_rights_optimized(self, date=None):
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"""
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优化版：一次遍历完成数据采集和除权检查
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获取最近2天数据，检查除权，决定是更新当天数据还是重新获取历史数据
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"""
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if date is None:
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start_date = datetime.now()
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date_str = start_date.strftime('%Y-%m-%d')
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else:
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start_date = datetime.strptime(date, '%Y-%m-%d')
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date_str = date
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print(f"开始优化版数据采集和除权检查 - {date_str}")
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# 删除今天的旧数据
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# delete_query = text("DELETE FROM gp_day_data WHERE `timestamp` LIKE :date_str")
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# with self.engine.begin() as conn:
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# conn.execute(delete_query, {"date_str": f"{date_str}%"})
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# print(f"已删除今日 {date_str} 的旧数据")
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stock_codes = self.fetch_all_stock_codes()
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begin = int(start_date.replace(hour=0, minute=0, second=0, microsecond=0).timestamp() * 1000)
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# 统计信息
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normal_update_count = 0
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ex_rights_count = 0
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error_count = 0
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skipped_count = 0 # 跳过的股票数量（停牌等原因）
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ex_rights_log_data = []
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normal_batch_data = []
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for idx, symbol in enumerate(tqdm(stock_codes, desc=f"采集和检查除权 {date_str}")):
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try:
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# 获取最近2天的数据
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data = self.fetch_daily_stock_data(symbol, begin, count=-2)
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if data.get('error_code') != 0:
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print(f"获取 {symbol} 数据失败: {data.get('error_description')}")
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error_count += 1
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continue
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df = self.transform_data(data, symbol)
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if df is None or df.empty:
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print(f"转换 {symbol} 数据失败")
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error_count += 1
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continue
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# 检查是否有足够的数据进行除权判断
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if len(df) < 2:
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# 只有一天数据，检查是否为今天的数据
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if len(df) == 1:
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latest_date = df.iloc[0]['timestamp'].strftime('%Y-%m-%d')
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if latest_date == date_str:
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# 是今天的数据，保存
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normal_batch_data.append(df)
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normal_update_count += 1
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else:
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# 不是今天的数据，可能停牌，跳过
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print(f"股票 {symbol} 最新数据日期 {latest_date} 不是今天 {date_str}，跳过")
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skipped_count += 1
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continue
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# 按时间排序，确保最新的数据在最后
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df_sorted = df.sort_values('timestamp')
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# 获取昨天和今天的数据
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latest_row = df_sorted.iloc[-1] # 最新一天（今天）
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previous_row = df_sorted.iloc[-2] # 前一天（昨天）
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# 检查最新一天是否为今天的数据
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latest_date = latest_row['timestamp'].strftime('%Y-%m-%d')
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if latest_date != date_str:
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# 最新数据不是今天的，可能停牌，跳过
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print(f"股票 {symbol} 最新数据日期 {latest_date} 不是今天 {date_str}，跳过")
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skipped_count += 1
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continue
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current_close = latest_row['close']
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previous_close = previous_row['close']
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# 查询数据库中该股票昨天的收盘价
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yesterday_date = previous_row['timestamp'].strftime('%Y-%m-%d')
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query = text("""
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SELECT `close` FROM gp_day_data
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WHERE symbol = :symbol AND DATE(`timestamp`) = :date
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LIMIT 1
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""")
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with self.engine.connect() as conn:
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result = conn.execute(query, {"symbol": symbol, "date": yesterday_date}).fetchone()
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# 判断是否除权
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is_ex_rights = False
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if result:
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db_previous_close = float(result[0])
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# 比较API返回的昨日收盘价与数据库中的收盘价
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if abs(db_previous_close - previous_close) > 0.001:
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is_ex_rights = True
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print(f"发现除权股票: {symbol}, 数据库昨收: {db_previous_close}, API昨收: {previous_close}")
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# 记录除权日志
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ex_rights_log_data.append({
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'symbol': symbol,
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'date': date_str,
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'db_price': db_previous_close,
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'api_price': previous_close,
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'log_time': datetime.now()
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})
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if is_ex_rights:
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# 除权处理：删除历史数据，重新获取1800天数据
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delete_all_query = text("DELETE FROM gp_day_data WHERE symbol = :symbol")
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with self.engine.begin() as conn:
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conn.execute(delete_all_query, {"symbol": symbol})
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# 重新获取1800天历史数据
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success = self.fetch_single_stock_history(symbol, 1800)
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if success:
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ex_rights_count += 1
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print(f"除权股票 {symbol} 历史数据重新获取成功")
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else:
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error_count += 1
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print(f"除权股票 {symbol} 历史数据重新获取失败")
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else:
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# 正常更新：只保存今天的数据
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today_data = df_sorted.tail(1) # 只取最新一天的数据
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normal_batch_data.append(today_data)
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normal_update_count += 1
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# 批量保存正常更新的数据
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if len(normal_batch_data) >= 100:
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for batch_df in normal_batch_data:
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batch_df.to_sql('gp_day_data', self.engine, if_exists='append', index=False)
|
||
normal_batch_data.clear()
|
||
gc.collect()
|
||
|
||
except Exception as e:
|
||
print(f"处理股票 {symbol} 时发生错误: {e}")
|
||
error_count += 1
|
||
continue
|
||
|
||
# 保存剩余的正常更新数据
|
||
if normal_batch_data:
|
||
for batch_df in normal_batch_data:
|
||
batch_df.to_sql('gp_day_data', self.engine, if_exists='append', index=False)
|
||
gc.collect()
|
||
|
||
# 保存除权日志
|
||
if ex_rights_log_data:
|
||
self.save_ex_rights_log(ex_rights_log_data)
|
||
|
||
# 输出统计信息
|
||
total_processed = normal_update_count + ex_rights_count + error_count + skipped_count
|
||
print(f"\n=== 采集完成统计 ===")
|
||
print(f"总处理股票数: {total_processed}")
|
||
print(f"正常更新: {normal_update_count}")
|
||
print(f"除权处理: {ex_rights_count}")
|
||
print(f"跳过股票: {skipped_count} (停牌等原因)")
|
||
print(f"错误处理: {error_count}")
|
||
print(f"除权日志: {len(ex_rights_log_data)} 条")
|
||
|
||
self.engine.dispose()
|
||
print(f"优化版数据采集和除权检查完成 - {date_str}")
|
||
|
||
def collect_stock_daily_data(db_url, date=None):
|
||
"""
|
||
原始版本：分两步执行（先采集，后检查除权）
|
||
"""
|
||
collector = StockDailyDataCollector(db_url)
|
||
collector.fetch_data_for_date(date)
|
||
collector.check_and_fix_ex_rights_data()
|
||
|
||
def collect_stock_daily_data_optimized(db_url, date=None):
|
||
"""
|
||
优化版本：一次遍历完成数据采集和除权检查
|
||
"""
|
||
collector = StockDailyDataCollector(db_url)
|
||
collector.fetch_and_check_ex_rights_optimized(date)
|
||
|
||
if __name__ == "__main__":
|
||
db_url = 'mysql+pymysql://root:Chlry#$.8@192.168.18.199:3306/db_gp_cj'
|
||
|
||
# --- 使用方式 ---
|
||
# 1. 【推荐】优化版：一次遍历完成数据采集和除权检查
|
||
collect_stock_daily_data_optimized(db_url)
|
||
|
||
# 2. 原始版本：分两步执行（先采集，后检查除权）
|
||
# collect_stock_daily_data(db_url)
|
||
|
||
# 3. 手动执行除权检查和数据修复
|
||
# collector = StockDailyDataCollector(db_url)
|
||
# collector.check_and_fix_ex_rights_data()
|
||
|
||
# 4. 单独获取某只股票的历史数据
|
||
# collector = StockDailyDataCollector(db_url)
|
||
# collector.fetch_single_stock_history('SH600000', 1800)
|