MovingAverages.py

#Calculate moving averages and normalize the below variables and add to the dataset 'df'    
            VolumeMA = df['Volume'].rolling(window=50).mean()
            AdjClosedMA_50 = df['Adj. Close'].rolling(window=50).mean()
            AdjClosedMA_200 = df['Adj. Close'].rolling(window=200).mean()
            df['VolumeN']= ((df['Volume']-VolumeMA)/VolumeMA)*100
            df['AdjClosedN_50'] = ((df['Adj. Close']- AdjClosedMA_50)/AdjClosedMA_50)*100
            df['AdjClosedN_200'] = ((df['Adj. Close']- AdjClosedMA_200)/AdjClosedMA_200)*100
                
            #Converting numDays and minProfit to string for the purpose of exporting to csv files
            numDaysF = float(numDays)
            minProfitF = float(minProfit)
            numDaysStr = "{:.0f}".format(numDaysF)
            minProfitStr =  "{:.0f}".format(minProfitF)

            #Drop the irrelevant columns and NaN rows
            df = df.drop(['Open','High','Low','Close','Volume'],axis = 1)
            df = df[199:]
            df = df[:(len(df)-40)]

            #Export the above dataset 
            df.to_csv("C:\Users\lenovo\Desktop\Karishma\Stocks\Scripts\Datasets\\"+Tickers.iloc[n]["Symbol"]+numDaysStr+minProfitStr+".csv")