text: # Imports
  from pandas_datareader import data as pdr
  from yahoo_fin import stock_info as si
  from pandas import ExcelWriter
  import yfinance as yf
  import pandas as pd
  import datetime
  import time
  yf.pdr_override()
  
  # Variables
  tickers = si.tickers_sp500()
  tickers = [item.replace(".", "-") for item in tickers] # Yahoo Finance uses dashes instead of dots
  index_name = '^GSPC' # S&P 500
  start_date = datetime.datetime.now() - datetime.timedelta(days=365)
  end_date = datetime.date.today()
  exportList = pd.DataFrame(columns=['Stock', "RS_Rating", "50 Day MA", "150 Day Ma", "200 Day MA", "52 Week Low", "52 week High"])
  returns_multiples = []
  
  # Index Returns
  index_df = pdr.get_data_yahoo(index_name, start_date, end_date)
  index_df['Percent Change'] = index_df['Adj Close'].pct_change()
  index_return = (index_df['Percent Change'] + 1).cumprod()[-1]
  
  # Find top 30% performing stocks (relative to the S&P 500)
  for ticker in tickers:
      # Download historical data as CSV for each stock (makes the process faster)
      df = pdr.get_data_yahoo(ticker, start_date, end_date)
      df.to_csv(f'{ticker}.csv')
  
      # Calculating returns relative to the market (returns multiple)
      df['Percent Change'] = df['Adj Close'].pct_change()
      stock_return = (df['Percent Change'] + 1).cumprod()[-1]
      
      returns_multiple = round((stock_return / index_return), 2)
      returns_multiples.extend([returns_multiple])
      
      print (f'Ticker: {ticker}; Returns Multiple against S&P 500: {returns_multiple}\n')
      time.sleep(1)
  
  # Creating dataframe of only top 30%
  rs_df = pd.DataFrame(list(zip(tickers, returns_multiples)), columns=['Ticker', 'Returns_multiple'])
  rs_df['RS_Rating'] = rs_df.Returns_multiple.rank(pct=True) * 100
  rs_df = rs_df[rs_df.RS_Rating >= rs_df.RS_Rating.quantile(.70)]
  
  # Checking Minervini conditions of top 30% of stocks in given list
  rs_stocks = rs_df['Ticker']
  for stock in rs_stocks:
      try:
          df = pd.read_csv(f'{stock}.csv', index_col=0)
          sma = [50, 150, 200]
          for x in sma:
              df["SMA_"+str(x)] = round(df['Adj Close'].rolling(window=x).mean(), 2)
          
          # Storing required values
          currentClose = df["Adj Close"][-1]
          moving_average_50 = df["SMA_50"][-1]
          moving_average_150 = df["SMA_150"][-1]
          moving_average_200 = df["SMA_200"][-1]
          low_of_52week = round(min(df["Low"][-260:]), 2)
          high_of_52week = round(max(df["High"][-260:]), 2)
          RS_Rating = round(rs_df[rs_df['Ticker']==stock].RS_Rating.tolist()[0])
          
          try:
              moving_average_200_20 = df["SMA_200"][-20]
          except Exception:
              moving_average_200_20 = 0
  
          # Condition 1: Current Price > 150 SMA and > 200 SMA
          condition_1 = currentClose > moving_average_150 > moving_average_200
          
          # Condition 2: 150 SMA and > 200 SMA
          condition_2 = moving_average_150 > moving_average_200
  
          # Condition 3: 200 SMA trending up for at least 1 month
          condition_3 = moving_average_200 > moving_average_200_20
          
          # Condition 4: 50 SMA> 150 SMA and 50 SMA> 200 SMA
          condition_4 = moving_average_50 > moving_average_150 > moving_average_200
             
          # Condition 5: Current Price > 50 SMA
          condition_5 = currentClose > moving_average_50
             
          # Condition 6: Current Price is at least 30% above 52 week low
          condition_6 = currentClose >= (1.3*low_of_52week)
             
          # Condition 7: Current Price is within 25% of 52 week high
          condition_7 = currentClose >= (.75*high_of_52week)
          
          # If all conditions above are true, add stock to exportList
          if(condition_1 and condition_2 and condition_3 and condition_4 and condition_5 and condition_6 and condition_7):
              exportList = exportList.append({'Stock': stock, "RS_Rating": RS_Rating ,"50 Day MA": moving_average_50, "150 Day Ma": moving_average_150, "200 Day MA": moving_average_200, "52 Week Low": low_of_52week, "52 week High": high_of_52week}, ignore_index=True)
              print (stock + " made the Minervini requirements")
      except Exception as e:
          print (e)
          print(f"Could not gather data on {stock}")
  
  exportList = exportList.sort_values(by='RS_Rating', ascending=False)
  print('\n', exportList)
  writer = ExcelWriter("ScreenOutput.xlsx")
  exportList.to_excel(writer, "Sheet1")
  writer.save()
  