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我們搜集金融數(shù)據(jù)，通常想要的是利用爬蟲的方法。其實(shí)我們最近所學(xué)的class不僅可以進(jìn)行類調(diào)用，在獲取數(shù)據(jù)方面同樣是可行的，很多小伙伴都比較關(guān)注理財(cái)方面的情況，對(duì)金融數(shù)據(jù)的需要也是比較多的。下面就class類在python中獲取金融數(shù)據(jù)的方法為大家?guī)碇v解。

使用tushare獲取所有A股每日交易數(shù)據(jù)，保存到本地?cái)?shù)據(jù)庫，同時(shí)每日更新數(shù)據(jù)庫；根據(jù)行情數(shù)據(jù)進(jìn)行可視化和簡單的策略分析與回測(cè)。由于篇幅有限，本文著重介紹股票數(shù)據(jù)管理（下載、數(shù)據(jù)更新）的面向?qū)ο缶幊虘?yīng)用實(shí)例。

				?

									#導(dǎo)入需要用到的模塊

									import numpy as np

									import pandas as pd

									from dateutil.parser import parse

									from datetime import datetime,timedelta

									#操作數(shù)據(jù)庫的第三方包，使用前先安裝pip install sqlalchemy

									from sqlalchemy import create_engine

									#tushare包設(shè)置

									import tushare as ts

									token='輸入你在tushare上獲得的token'

									pro=ts.pro_api(token)

									#使用python3自帶的sqlite數(shù)據(jù)庫

									#本人創(chuàng)建的數(shù)據(jù)庫地址為c:\zjy\db_stock\

									file='sqlite:///c:\\zjy\\db_stock\\'

									#數(shù)據(jù)庫名稱

									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()    

									  #獲取股票代碼列表  

									  def get_code(self):

									    codes = pro.stock_basic(list_status='L').ts_code.values

									    return codes

									  #獲取股票交易日歷

									  def get_cals(self):

									    #獲取交易日歷

									    cals=pro.trade_cal(exchange='')

									    cals=cals[cals.is_open==1].cal_date.values

									    return cals

									  #每日行情數(shù)據(jù)

									  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='') 

									      #復(fù)權(quán)因子

									      df=pd.merge(df0,df1) #合并數(shù)據(jù)

									    except Exception as e:

									      print(code)

									      print(e)

									    return df

									  #保存數(shù)據(jù)到數(shù)據(jù)庫

									  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')

									  #獲取最新交易日期

									  def get_trade_date(self):

									    #獲取當(dāng)天日期時(shí)間

									    pass

									  #更新數(shù)據(jù)庫數(shù)據(jù)

									  def update_sql(self):

									    pass #代碼省略

									  #查詢數(shù)據(jù)庫信息      

									  def info_sql(self):

代碼運(yùn)行

				?

									#假設(shè)你將上述代碼封裝成class Data

									#保存在'C:\zjy\db_stock'目錄下的down_data.py中

									import sys

									#添加到當(dāng)前工作路徑

									sys.path.append(r'C:\zjy\db_stock')

									#導(dǎo)入py文件中的Data類

									from download_data import Data

									#實(shí)例類

									data=Data()

									#data.save_sql() #只需運(yùn)行一次即可

									data.update_sql()   

									data.info_sql()

實(shí)例擴(kuò)展：

Python下，pandas_datareader模塊可以用于獲取研究數(shù)據(jù)。例子如下：

				?

									>>> from pandas_datareader.data import DataReader

									>>>

									>>> datas = DataReader(name='AAPL', data_source='yahoo', start='2018-01-01')

									>>>

									>>> type(datas)

									<class 'pandas.core.frame.DataFrame'>

									>>> 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

									>>>

到此這篇關(guān)于class類在python中獲取金融數(shù)據(jù)的實(shí)例方法的文章就介紹到這了,更多相關(guān)class類怎樣在python中獲取金融數(shù)據(jù)內(nèi)容請(qǐng)搜索服務(wù)器之家以前的文章或繼續(xù)瀏覽下面的相關(guān)文章希望大家以后多多支持服務(wù)器之家！

原文鏈接：https://www.py.cn/jishu/jichu/21830.html