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FinanceDataReader 여러 종목의 가격을 한번에 가져오기

  • 종목별로 따로 가져와 리스트로 만들고
  • pd.concat()로 합쳐 하나의 DataFrame 으로 만들 수 있습니다




In [4]:



    


#working directory : /home/stock

# reference site :
# https://github.com/FinanceData/FinanceDataReader/wiki/Users-Guide?fbclid=IwAR2NI6fxeaM-8182lkeJv80O2CZHU3hjGt2So-zbq3V2De_mtf5NuNtv8Ck

# FinanceDataReader 설치 (런타임 유형: Python 3)

! pip install finance-datareader



    


















    






Collecting finance-datareader
  Downloading https://files.pythonhosted.org/packages/ba/da/44e2b687b6b7083bbfb16764b0f9013c33d775809828dae77ee1035cea70/finance_datareader-0.7.1-py3-none-any.whl
Collecting requests-file (from finance-datareader)
  Downloading https://files.pythonhosted.org/packages/23/9c/6e63c23c39e53d3df41c77a3d05a49a42c4e1383a6d2a5e3233161b89dbf/requests_file-1.4.3-py2.py3-none-any.whl
Requirement already satisfied: pandas>=0.19.2 in /usr/local/lib/python3.6/dist-packages (from finance-datareader) (0.22.0)
Requirement already satisfied: lxml in /usr/local/lib/python3.6/dist-packages (from finance-datareader) (4.2.5)
Requirement already satisfied: requests>=2.3.0 in /usr/local/lib/python3.6/dist-packages (from finance-datareader) (2.18.4)
Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from requests-file->finance-datareader) (1.11.0)
Requirement already satisfied: numpy>=1.9.0 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.19.2->finance-datareader) (1.14.6)
Requirement already satisfied: python-dateutil>=2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.19.2->finance-datareader) (2.5.3)
Requirement already satisfied: pytz>=2011k in /usr/local/lib/python3.6/dist-packages (from pandas>=0.19.2->finance-datareader) (2018.7)
Requirement already satisfied: idna<2.7,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.3.0->finance-datareader) (2.6)
Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.3.0->finance-datareader) (2018.11.29)
Requirement already satisfied: urllib3<1.23,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.3.0->finance-datareader) (1.22)
Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.3.0->finance-datareader) (3.0.4)
Installing collected packages: requests-file, finance-datareader
Successfully installed finance-datareader-0.7.1 requests-file-1.4.3

















In [5]:



    


import FinanceDataReader as fdr
fdr.__version__



    


















    
Out[5]:








'0.7.1'

















In [0]:



    


%matplotlib inline
import matplotlib.pyplot as plt

plt.rcParams["axes.grid"] = True
plt.rcParams["figure.figsize"] = (16,8)

한 종목의 가격 가져오기





In [7]:



    


import FinanceDataReader as fdr

# KB Finance(105560)
df = fdr.DataReader('105560', '2017-01-01', '2017-12-31')
df.head(15)



    


















    
Out[7]:











  
    

      
      Date
      Open
      High
      Low
      Close
      Volume
      Change
    

    

      Date
      
      
      
      
      
      
      
    

  
  
    

      2017-01-02
      2017-01-02
      42800
      43050
      42250
      42600
      345769
      -0.004673
    

    

      2017-01-03
      2017-01-03
      42600
      43750
      42000
      43700
      956685
      0.025822
    

    

      2017-01-04
      2017-01-04
      43900
      44400
      43450
      44150
      831295
      0.010297
    

    

      2017-01-05
      2017-01-05
      44200
      44200
      42550
      42800
      1128957
      -0.030578
    

    

      2017-01-06
      2017-01-06
      43000
      43000
      42300
      42650
      1338299
      -0.003505
    

    

      2017-01-09
      2017-01-09
      42650
      42700
      42150
      42400
      795293
      -0.005862
    

    

      2017-01-10
      2017-01-10
      43000
      43400
      42650
      43250
      1093427
      0.020047
    

    

      2017-01-11
      2017-01-11
      42700
      43700
      42700
      43350
      912080
      0.002312
    

    

      2017-01-12
      2017-01-12
      43600
      44100
      43350
      44100
      1011997
      0.017301
    

    

      2017-01-13
      2017-01-13
      43500
      44300
      43400
      43850
      861598
      -0.005669
    

    

      2017-01-16
      2017-01-16
      44100
      45250
      43950
      45150
      1474423
      0.029647
    

    

      2017-01-17
      2017-01-17
      45300
      46100
      45100
      45850
      1222286
      0.015504
    

    

      2017-01-18
      2017-01-18
      45400
      46000
      45100
      45800
      611897
      -0.001091
    

    

      2017-01-19
      2017-01-19
      45400
      46200
      45400
      45550
      467486
      -0.005459
    

    

      2017-01-20
      2017-01-20
      45300
      45850
      45300
      45400
      587244
      -0.003293

여러 종목의 종가(Close) 가져오기

  • ticker 리스트의 종가(Close) 가져와 리스트로 만들기
  • pd.concat()로 합치기




In [0]:



    


df_krx = fdr.StockListing('KRX')
tiker_list = list(df_krx.Symbol)



    











In [15]:



    


import os
os.chdir("..")
os.chdir("home")
os.getcwd()



    


















    
Out[15]:








'/home'

















In [48]:



    


df_krx.head()



    


















    
Out[48]:











  
    

      
      Symbol
      Name
      Sector
      Industry
    

  
  
    

      0
      001040
      CJ
      기타 금융업
      지주회사
    

    

      1
      011150
      CJ씨푸드
      기타 식품 제조업
      수산물(어묵,맛살)가공품 도매,원양수산업,수출입
    

    

      2
      082740
      HSD엔진
      일반 목적용 기계 제조업
      대형선박용엔진,내연발전엔진
    

    

      3
      001390
      KG케미칼
      기초 화학물질 제조업
      콘크리트혼화제, 비료, 친환경농자재, 수처리제
    

    

      4
      010060
      OCI
      기초 화학물질 제조업
      타르제품,카본블랙,무수프탈산,농약원제,석탄화학제품,정밀화학제품,플라스틱창호재 제조,판매
    

  




















In [49]:



    


# ticker 리스트의 종가(Close) 가져와 리스트로 만들기

import pandas as pd


df_list_volume = [fdr.DataReader(ticker, '2000-01-01', '2018-12-28')['Volume'] for ticker in tiker_list]

len(df_list_volume)



    


















    
Out[49]:








2264

















In [0]:



    


df_krx_name_code_list = []
for i in range(len(df_krx)):
    name_and_code= df_krx.iloc[i].Name + '(' + df_krx.iloc[i].Symbol + ')'
    df_krx_name_code_list.append(name_and_code)



    











In [2]:



    


# pd.concat()로 합치기
import pandas as pd
df_stock_data_volume=pd.concat(df_list_volume, axis=1)
df_stock_data_volume.columns = df_krx_name_code_list
#df_stock_data = df_stock_data.dropna()
df_stock_data_volume.head()



    


















    






---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-2-dca8a1e2c80e> in <module>()
      1 import pandas as pd
----> 2 df_stock_data_volume=pd.concat(df_list_volume, axis=1)
      3 df_stock_data_volume.columns = df_krx_name_code_list
      4 #df_stock_data = df_stock_data.dropna()
      5 df_stock_data_volume.head()

NameError: name 'df_list_volume' is not defined
















In [44]:



    


df_stock_data.tail()



    


















    
Out[44]:











  
    

      
      CJ(001040)
      CJ씨푸드(011150)
      HSD엔진(082740)
      KG케미칼(001390)
      OCI(010060)
      SH에너지화학(002360)
      SK네트웍스(001740)
      SK케미칼(285130)
      STX(011810)
      WISCOM(024070)
      ...
      엔케이맥스(262760)
      엘피케이(183350)
      인프라웨어테크놀러지(247300)
      제이에스피브이(250300)
      지앤이헬스케어(299480)
      툴젠(199800)
      티씨엠생명과학(228180)
      틸론(217880)
      파인이엠텍(278990)
      판도라티비(202960)
    

    

      Date
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    

  
  
    

      2018-12-21
      128715.0
      2405.0
      5370.0
      17500.0
      110500.0
      1135.0
      4910.0
      72100.0
      11500.0
      2785.0
      ...
      7550.0
      2690.0
      846.0
      1435.0
      3100.0
      87500.0
      19500.0
      5500.0
      4100.0
      1240.0
    

    

      2018-12-24
      126357.0
      2355.0
      5300.0
      17550.0
      111000.0
      1110.0
      5000.0
      71300.0
      11250.0
      2765.0
      ...
      7500.0
      2760.0
      756.0
      1435.0
      2925.0
      87700.0
      20000.0
      6290.0
      4100.0
      1240.0
    

    

      2018-12-26
      124000.0
      2265.0
      5250.0
      17300.0
      103500.0
      1075.0
      4990.0
      70500.0
      11250.0
      2790.0
      ...
      7890.0
      2900.0
      670.0
      1435.0
      2925.0
      85200.0
      20450.0
      7230.0
      4100.0
      1100.0
    

    

      2018-12-27
      120500.0
      2270.0
      5200.0
      17600.0
      104500.0
      1095.0
      5270.0
      69800.0
      11350.0
      2805.0
      ...
      7400.0
      2900.0
      603.0
      1435.0
      2925.0
      94700.0
      19050.0
      7080.0
      4100.0
      1205.0
    

    

      2018-12-28
      121500.0
      2320.0
      5280.0
      17400.0
      107000.0
      1100.0
      5200.0
      70000.0
      11750.0
      2750.0
      ...
      7380.0
      2900.0
      580.0
      1435.0
      2925.0
      100000.0
      20050.0
      6900.0
      4100.0
      1200.0
    

  



5 rows × 2264 columns



















In [0]:



    


df_stock_data_volume.to_csv('2000-2018_stock_data_volume.csv')
# df_krx.to_csv('krx_company_list_20181228.csv')



    











In [46]:



    


os.listdir()



    


















    
Out[46]:








['krx_company_list_20181228.csv', '2000-2018_stock_data.csv']

개별 종목의 2017년 수익률 비교





In [0]:



    


# 정규화: 가격의 시작을 1.0 으로

df_norm = df / df.iloc[0]
df_norm.plot()



    


















    
Out[0]:








<matplotlib.axes._subplots.AxesSubplot at 0x7fdf5faad470>

2018 FianceData.KR http://fb.com/fiancedata

Content source: Park-Youngjeung/Park-Youngjeung.github.io

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