stock_fundamentals/src/scripts/stock_minute_data_collector.py

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2025-04-02 13:52:34 +08:00
# 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"""
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query = "SELECT gp_code FROM gp_code_all"
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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"""
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query = text("UPDATE gp_code_all SET market_cap = :market_cap WHERE gp_code = :symbol")
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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
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db_url = 'mysql+pymysql://root:Chlry#$.8@192.168.18.199:3306/db_gp_cj'
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# 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