class类在python中获取金融数据的实例方法

#导入需要用到的模块
import numpy as np
import pandas as pd
from dateutil.parser import parse
from datetime import datetime,timedelta
#操作数据库的第三方包，使用前先安装pip install sqlalchemy
from sqlalchemy import create_engine
#tushare包设置
import tushare as ts
token='输入你在tushare上获得的token'
pro=ts.pro_api(token)
#使用python3自带的sqlite数据库
#本人创建的数据库地址为c:\zjy\db_stock\
file='sqlite:///c:\\zjy\\db_stock\\'
#数据库名称
db_name='stock_data.db'
engine = create_engine(file+db_name)
class Data(object):
  def __init__(self,
         start='20050101',
         end='20191115',
         table_name='daily_data'):
    self.start=start
    self.end=end
    self.table_name=table_name
    self.codes=self.get_code()
    self.cals=self.get_cals()    
  #获取股票代码列表  
  def get_code(self):
    codes = pro.stock_basic(list_status='L').ts_code.values
    return codes
  #获取股票交易日历
  def get_cals(self):
    #获取交易日历
    cals=pro.trade_cal(exchange='')
    cals=cals[cals.is_open==1].cal_date.values
    return cals
  #每日行情数据
  def daily_data(self,code):
    try:
      df0=pro.daily(ts_code=code,start_date=self.start,
        end_date=self.end)      
      df1=pro.adj_factor(ts_code=code,trade_date='') 
      #复权因子
      df=pd.merge(df0,df1) #合并数据
    except Exception as e:
      print(code)
      print(e)
    return df
  #保存数据到数据库
  def save_sql(self):
    for code in self.codes:
      data=self.daily_data(code)
      data.to_sql(self.table_name,engine,
         index=False,if_exists='append')
  #获取最新交易日期
  def get_trade_date(self):
    #获取当天日期时间
    pass
  #更新数据库数据
  def update_sql(self):
    pass #代码省略
  #查询数据库信息      
  def info_sql(self):

#假设你将上述代码封装成class Data
#保存在'C:\zjy\db_stock'目录下的down_data.py中
import sys
#添加到当前工作路径
sys.path.append(r'C:\zjy\db_stock')
#导入py文件中的Data类
from download_data import Data
#实例类
data=Data()
#data.save_sql() #只需运行一次即可
data.update_sql()   
data.info_sql()

>>> from pandas_datareader.data import DataReader
>>>
>>> datas = DataReader(name='AAPL', data_source='yahoo', start='2018-01-01')
>>>
>>> type(datas)

>>> datas
         Open    High     Low    Close  Adj Close \
Date
2018-01-02 170.160004 172.300003 169.259995 172.259995 172.259995
2018-01-03 172.529999 174.550003 171.960007 172.229996 172.229996
2018-01-04 172.539993 173.470001 172.080002 173.029999 173.029999
2018-01-05 173.440002 175.369995 173.050003 175.000000 175.000000
2018-01-08 174.350006 175.610001 173.929993 174.350006 174.350006
2018-01-09 174.550003 175.059998 173.410004 174.330002 174.330002
2018-01-10 173.160004 174.300003 173.000000 174.289993 174.289993
2018-01-11 174.589996 175.490005 174.490005 175.279999 175.279999
2018-01-12 176.179993 177.360001 175.649994 177.089996 177.089996

       Volume
Date
2018-01-02 25555900
2018-01-03 29517900
2018-01-04 22434600
2018-01-05 23660000
2018-01-08 20567800
2018-01-09 21584000
2018-01-10 23959900
2018-01-11 18667700
2018-01-12 25226000
>>>
>>> print(datas.to_csv())
Date,Open,High,Low,Close,Adj Close,Volume
2018-01-02,170.160004,172.300003,169.259995,172.259995,172.259995,25555900
2018-01-03,172.529999,174.550003,171.960007,172.229996,172.229996,29517900
2018-01-04,172.539993,173.470001,172.080002,173.029999,173.029999,22434600
2018-01-05,173.440002,175.369995,173.050003,175.0,175.0,23660000
2018-01-08,174.350006,175.610001,173.929993,174.350006,174.350006,20567800
2018-01-09,174.550003,175.059998,173.410004,174.330002,174.330002,21584000
2018-01-10,173.160004,174.300003,173.0,174.289993,174.289993,23959900
2018-01-11,174.589996,175.490005,174.490005,175.279999,175.279999,18667700
2018-01-12,176.179993,177.360001,175.649994,177.089996,177.089996,25226000

>>>