Description

01. Using pandas package, pandas_datareader.data

                import pandas as pd
                import datetime                        
                import pandas_datareader.data as web      
                
                start = datetime.datetime(2017, 1, 26)     # the 2nd is the constructor
                end = datetime.datetime(2017, 2, 7)        # start and end are python type, not str
                df = web.DataReader("AAPL", "yahoo", start, end)
                print(df)
                
                print('--- 2   plotting -----------')
                import matplotlib.pyplot as plt
                from matplotlib import style
                style.use('fivethirtyeight')      # the look
                df['Volume'].plot()               # Date ~ High, time-series plot
                plt.legend()
                plt.show()  

            

review
• AAPL is the stock symbol for Apple.
• All you need is stock symbols, like MSFL, XOM, etc.
• Apparently, this api is for stock trading data.
• yahoo is Yahoo Finance for this data provider. It is the main data provider.
• All are in python environment.
• In this case, the prices increase and trading volumes were corelated at one time.

02. quandl, the financial data provider

Introduction
• Quandl is a financial data provider. Python is one way to achieve the purpose.
• One of the purpose to choose python is for integrating with others...
  • One example is as below:
  • In order to understand some financial bacground, more than 10 data are required.
  • In your code, you access all the ten data.
  • Organize and present them in a comfortable manner.
  • No much manual tasks are involved.
• You might do this for professional investments or for yourself.
• Based on the need, other specific knowledge and skills are needed
• First, I registered for a free account, and get my api-key.
• Second, I download package quandl for python on my mac.
• Now, I can use quantl module API to access quandl data with python.

How to get data
• open browser, navigate to quandl.com
• In the document section, click the link to go there
• Now, you are in https://docs.quandl.com
• In that page, click Python, using the link for time-series data or tables data
• quandl treats python as a tool, like excel, R, or API(json data)
• Now the page is https://docs.quandl.com/docs/python-time-serie,
• or, https://docs.quandl.com/docs/python-tables

    import pandas as pd
    import quandl 
    quandl.ApiConfig.api_key = "my_quandl_key"
    
    df = quandl.get('EOD/AAPL',        # quandl code
                    paginate=True,     # always for efficiency
                    rows=10)            # last 10 
    
    print(df)  
    '''
                    Open     High     ...      Adj_Close  Adj_Volume
    Date                            ...
    2019-02-05  172.86  175.080     ...         174.18  36101628.0
    2019-02-06  174.65  175.570     ...         174.24  28239591.0
    '''
            

    # You can append the following code for plotting
    close = df['Close']

    import matplotlib.pyplot as plt
    plt.plot(close, label='Close')
    
    plt.xlabel("date")
    plt.ylabel("close")
    plt.title("quandl get api test\n2/8/2019")
    plt.legend()
    
    plt.show()
    #comment: R can do also. Python can do some others. 
            

    import pandas as pd
    import quandl 
    quandl.ApiConfig.api_key = "my_quandl_key"
    
    df = quandl.get_table('ZACKS/FC',  
                    ticker='AAPL',
                    qopts={'columns':['ticker', 'per_end_date', 'eps_diluted_net']},
                    paginate=True)
    print(df.tail(5))
    '''
            ticker per_end_date  eps_diluted_net
    None
    35     AAPL   2017-12-31             3.89
    36     AAPL   2018-03-31             2.73
    37     AAPL   2018-06-30             2.34
    38     AAPL   2018-09-30             2.95
    39     AAPL   2018-12-31             4.18
    '''