

In [22]:

    
import matplotlib.pyplot as plt
%matplotlib inline
import pandas as pd
import io
import requests
import time
import seaborn as sns
import string



In [3]:

    
##This function is modified to retrived most historical stock data from NASDAQ and NYSX
##This function cannot retrieve historical index data since Google Finance does not support downding this type of data
##This function return dataframe
month_exe = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
def google_stocks(symbol, startdate, enddate = None):
    
    startdate = month_exe[startdate[0]] + '%20' + str(startdate[1]) + '%2C%20' + str(startdate[2])

    enddate = month_exe[enddate[0]] + '%20' + str(enddate[1]) + '%2C%20' + str(enddate[2])
    
    stock_url = 'https://finance.google.com/finance/historical?q=' + symbol + \
                '&startdate=' + startdate + '&enddate=' + enddate + '&output=csv'
 
    raw_response = requests.get(stock_url).content
 
    stock_data = pd.read_csv(io.StringIO(raw_response.decode('utf-8')))
 
    return stock_data



In [5]:

    
#https://finance.google.com/finance/historical?q=AAPL&startdate=Jul%201%2C%202016&enddate=Jul%201%2C%202017&output=csv

Retrieve Stock Historical Data



In [143]:

    
startdate = (7,1,2016)
enddate   = (7,1,2017)



In [144]:

    
Symbol = ['NASDAQ:GOOG','NYSE:UVV','NYSE:BP','NYSE:CLD','NYSE:BHP','NYSE:RIO']



In [145]:

    
for i in Symbol:
    symbol = i
    df_name = symbol.split(':')[1] + '_hist'
    exec('{} = google_stocks(symbol, startdate, enddate)'.format(df_name))



In [146]:

    
BP_hist.iloc[0:10]



In [147]:

    
BP_hist.Date = pd.to_datetime(BP_hist.Date)



In [148]:

    
BP_hist.Low.iloc[0:10]









    Out[148]:





0    34.47
1    34.81
2    35.18
3    35.04
4    34.86
5    34.54
6    34.51
7    34.51
8    34.78
9    35.97
Name: Low, dtype: float64



In [158]:

    
goog = sns.tsplot(GOOG_hist.Close)



In [159]:

    
uvv = sns.tsplot(UVV_hist.Close)



In [204]:

    
for i in ['CLD','BHP','RIO']:
    exec('{}_hist["company"] = i'.format(i))



In [205]:

    
coal_mining = CLD_hist.append(BHP_hist).append(RIO_hist)



In [221]:

    
coal_mining.head()



In [234]:

    
cld = sns.tsplot(CLD_hist.Close)



In [141]:

    
bhp = sns.tsplot(BHP_hist.Close)



In [142]:

    
rio = sns.tsplot(RIO_hist.Close)



In [1]:

    
#https://finance.google.com/finance/historical?q=INDEXSP:.INX&startdate=Jul+1%2C+2016&enddate=Jul+1%2C+2017&output=csv

	Date	Open	High	Low	Close	Volume
0	30-Jun-17	34.76	34.82	34.47	34.65	4807251
1	29-Jun-17	35.27	35.30	34.81	34.86	3545048
2	28-Jun-17	35.25	35.44	35.18	35.27	4845269
3	27-Jun-17	35.12	35.39	35.04	35.15	4930949
4	26-Jun-17	35.04	35.09	34.86	34.88	3762404
5	23-Jun-17	34.59	34.91	34.54	34.84	3501179
6	22-Jun-17	34.64	34.80	34.51	34.60	4961404
7	21-Jun-17	34.76	34.98	34.51	34.65	7100841
8	20-Jun-17	35.31	35.31	34.78	34.98	8766518
9	19-Jun-17	36.00	36.26	35.97	36.04	6064749

	Date	Open	High	Low	Close	Volume	company
0	30-Jun-17	3.66	3.66	3.50	3.53	929278	CLD
1	29-Jun-17	3.66	3.78	3.58	3.63	1157099	CLD
2	28-Jun-17	3.56	3.71	3.55	3.64	1463069	CLD
3	27-Jun-17	3.57	3.75	3.49	3.52	1248118	CLD
4	26-Jun-17	3.27	3.60	3.17	3.53	1752988	CLD