Get historical stock prices from Yahoo! Finance

In [1]:

    

%autosave 0
%matplotlib inline
import yahoo_stocks as ys


    

    















    




Autosave disabled

In [2]:

    

# Load and align daily data for several symbols.
# First input must be a list of symbols Yahoo can recognize.
# Returns a dictionary of DataFrames.
Data = ys.load(['SPY','TSLA','^DJR'],start_date='2010-1-1',freq='d')
print( "Loaded these DataFrames:" )
[ print(x) for x in Data.keys() ]
print( )


    

    




Loading symbols
SPY
TSLA
^DJR
All symbols loaded.

Loaded these DataFrames:
Open
ShareMultiplier
Dividend
Close
AdjClose
High
Low
TotalValue
Volume

In [3]:

    

# Show a plot of daily closing prices (*not* adjusted for dividends and splits)
Data['Close'].plot(figsize=(9,3))


    

    
Out[3]:





<matplotlib.axes._subplots.AxesSubplot at 0x1149f01d0>






    

In [4]:

    

# Show recent dividends (if any)
fDiv = (Data['Dividend'] > 0).any(axis=1)
Data['Dividend'].loc[fDiv].tail()


    

    
Out[4]:






  

    

      

      
SPY
      
TSLA
      
^DJR
    
    

      
Date
      

      

      

    
  
  

    

      
2015-12-18
      
1.212
      
0.0
      
0.0
    
    

      
2016-03-18
      
1.050
      
0.0
      
0.0
    
    

      
2016-06-17
      
1.078
      
0.0
      
0.0
    
    

      
2016-09-16
      
1.082
      
0.0
      
0.0
    
    

      
2016-12-16
      
1.329
      
0.0
      
0.0
    
  

In [5]:

    

# Use get_table() to load all data for one symbol as a single DataFrame
AAPL = ys.get_table('AAPL',start_date='2010-1-1')
AAPL.head()


    

    
Out[5]:






  

    

      

      
Open
      
High
      
Low
      
Close
      
Volume
      
AdjClose
      
Dividend
      
ShareMultiplier
      
TotalValue
    
    

      
Date
      

      

      

      

      

      

      

      

      

    
  
  

    

      
2010-01-04
      
213.429998
      
214.499996
      
212.380001
      
214.009998
      
123432400.0
      
27.847252
      
0.0
      
1.0
      
214.009998
    
    

      
2010-01-05
      
214.599998
      
215.589994
      
213.249994
      
214.379993
      
150476200.0
      
27.895396
      
0.0
      
1.0
      
214.379993
    
    

      
2010-01-06
      
214.379993
      
215.230000
      
210.750004
      
210.969995
      
138040000.0
      
27.451683
      
0.0
      
1.0
      
210.969995
    
    

      
2010-01-07
      
211.750000
      
212.000006
      
209.050005
      
210.580000
      
119282800.0
      
27.400936
      
0.0
      
1.0
      
210.580000
    
    

      
2010-01-08
      
210.299994
      
212.000006
      
209.060005
      
211.980005
      
111902700.0
      
27.583106
      
0.0
      
1.0
      
211.980005
    
  

In [6]:

    

# 'AdjClose' is Yahoo's closing price adjusted for splits and dividends.
# 'TotalValue' is a different method for adjusting prices
AAPL[['Close','TotalValue','AdjClose']].plot(figsize=(9,3))


    

    
Out[6]:





<matplotlib.axes._subplots.AxesSubplot at 0x11498b5c0>






    

In [7]:

    

# Make a Yahoo! API query without actually getting the data
ys.make_url('IWM',start_date='2010-2-1')


    

    
Out[7]:





'http://real-chart.finance.yahoo.com/x?&a=1&b=1&c=2010&d=1&e=7&f=2017&g=d&s=IWM&y=0&z=30000'

In [8]:

    

# Use help() for details
help(ys)


    

    




Help on module yahoo_stocks:

NAME
    yahoo_stocks - Import historical stock/index/ETF data into pandas from Yahoo! Finance.

DESCRIPTION
    Gets these variables: ['Open','High','Low','Close','Volume','Adj Close'].
    Also calculates ['Dividend','ShareMultiplier','TotalValue'].
    
    'ShareMultiplier' is "How many shares would I have if I bought 1 share on start_date?"
    
    'TotalValue' is "How much cash would I have if I bought 1 share on start_date,
    saved all the cash dividends I received, and sold all my shares today?"

FUNCTIONS
    get_table(*args, **kwargs)
        Get raw data from Yahoo! Finance, clean it up, and return a DataFrame.
        Inputs should be same as url() function.
        Index will be DatetimeIndex of selected frequency.
        CAUTION: Only includes days on which exchange was open!
    
    load(symbol_list, start_date='2000-1-1', stop_date=datetime.date(2017, 2, 7), freq='d', verbose=True)
        Get data for multiple symbols. Returns a dictionary of DataFrames.
        Each DataFrame stores one variable (e.g 'TotalValue') for all symbols.
        symbol_list should be a list of strings, e.g. ['SPY','AAPL','^GSPC']
        Set verbose = False to disable printing to screen.
    
    make_url(symbol, start_date='2000-1-1', stop_date=datetime.date(2017, 2, 7), freq='d')
        Create url to download raw CSV data from Yahoo! Finance.
        start_date and stop_date can be any format recognized by pd.to_datetime().
        freq must be one of ['d','w','m'] meaning daily, weekly, monthly.

FILE
    /Programs/Python/YahooStocks/yahoo_stocks.py