shashankvemuri · January 4, 2024 05:28 · rudhraanoop · Jun 5, 2023 · nandha027 · Jul 26, 2023
diff --git a/screener.py b/screener.py
 # Imports
 from pandas_datareader import data as pdr
 from pandas import ExcelWriter
 import yfinance as yf
 import pandas as pd
 import datetime
 import time
 import requests
 yf.pdr_override()

 # Get tickers for all S&P 500 stocks from Wikipedia
 def tickers_sp500():
    url = 'https://en.wikipedia.org/wiki/List_of_S%26P_500_companies'
    html = requests.get(url).text
    df = pd.read_html(html, header=0)[0]
    tickers = df['Symbol'].tolist()
    return tickers

 # Variables
 tickers = 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()
	# Imports
	from pandas_datareader import data as pdr
	from pandas import ExcelWriter
	import yfinance as yf
	import pandas as pd
	import datetime
	import time
	import requests
	yf.pdr_override()

	# Get tickers for all S&P 500 stocks from Wikipedia
	def tickers_sp500():
	url = 'https://en.wikipedia.org/wiki/List_of_S%26P_500_companies'
	html = requests.get(url).text
	df = pd.read_html(html, header=0)[0]
	tickers = df['Symbol'].tolist()
	return tickers

	# Variables
	tickers = 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()