Create the function to get all the data

getData.py

def getData(list_of_stocks):
    for stock in list_of_stocks:
        df = DataReader(stock, 'yahoo', start, end)
        print (stock)
        
        # Current Price 
        price = si.get_live_price('{}'.format(stock))
        price = round(price, 2)
        
        # Sharpe Ratio
        x = 5000
        y = (x)
            
        stock_df = df
        stock_df['Norm return'] = stock_df['Adj Close'] / stock_df.iloc[0]['Adj Close']
         
        allocation = float(x/y)
        stock_df['Allocation'] = stock_df['Norm return'] * allocation
            
        stock_df['Position'] = stock_df['Allocation'] * x
        pos = [df['Position']]
        val = pd.concat(pos, axis=1)
        val.columns = ['WMT Pos']
        val['Total Pos'] = val.sum(axis=1)
            
        val.tail(1)            
        val['Daily Return'] = val['Total Pos'].pct_change(1)            
        Sharpe_Ratio = val['Daily Return'].mean() / val['Daily Return'].std()            
        A_Sharpe_Ratio = (252**0.5) * Sharpe_Ratio        
        A_Sharpe_Ratio = round(A_Sharpe_Ratio, 2)
        
        # News Sentiment 
        finwiz_url = 'https://finviz.com/quote.ashx?t='
        news_tables = {}
        
        url = finwiz_url + stock
        req = Request(url=url,headers={'user-agent': 'my-app/0.0.1'}) 
        response = urlopen(req)    
        html = BeautifulSoup(response, features="lxml")
        news_table = html.find(id='news-table')
        news_tables[stock] = news_table
        
        parsed_news = []
        
        # Iterate through the news
        for file_name, news_table in news_tables.items():
            for x in news_table.findAll('tr'):
                text = x.a.get_text() 
                date_scrape = x.td.text.split()
        
                if len(date_scrape) == 1:
                    time = date_scrape[0]
                    
                else:
                    date = date_scrape[0]
                    time = date_scrape[1]
        
                ticker = file_name.split('_')[0]
                
                parsed_news.append([ticker, date, time, text])
                
        vader = SentimentIntensityAnalyzer()
        
        columns = ['ticker', 'date', 'time', 'headline']
        dataframe = pd.DataFrame(parsed_news, columns=columns)
        scores = dataframe['headline'].apply(vader.polarity_scores).tolist()
        
        scores_df = pd.DataFrame(scores)
        dataframe = dataframe.join(scores_df, rsuffix='_right')
        
        dataframe['date'] = pd.to_datetime(dataframe.date).dt.date
        dataframe = dataframe.set_index('ticker')
        
        sentiment = round(dataframe['compound'].mean(), 2)
        
        # Beta
        df = DataReader(stock,'yahoo',start, end)
        dfb = DataReader('^GSPC','yahoo',start, end)
        
        rts = df.resample('M').last()
        rbts = dfb.resample('M').last()
        dfsm = pd.DataFrame({'s_adjclose' : rts['Adj Close'],
                                'b_adjclose' : rbts['Adj Close']},
                                index=rts.index)
        
        
        dfsm[['s_returns','b_returns']] = dfsm[['s_adjclose','b_adjclose']]/\
            dfsm[['s_adjclose','b_adjclose']].shift(1) -1
        dfsm = dfsm.dropna()
        covmat = np.cov(dfsm["s_returns"],dfsm["b_returns"])
                
        beta = covmat[0,1]/covmat[1,1] 
        beta = round(beta, 2)
        
        # Relative Strength Index
        df["rsi"] = talib.RSI(df["Close"])
        values = df["rsi"].tolist()[-1]
        rsi = round(value, 2)

        output = ("\nTicker: " + str(stock) + "\nCurrent Price : " + str(price) + "\nSharpe Ratio: " + str(A_Sharpe_Ratio) + "\nNews Sentiment: " + str(sentiment) + "\nRelative Strength Index: " + str(rsi) + "\nBeta Value for 1 Year: " + str(beta))
        sendMessage(output)

if __name__ == '__main__':
    getData(stock_list)