Stock_Screener.py

import datetime
import pandas as pd
from pandas_datareader import data as pdr
import yfinance as yf
from pandas import ExcelWriter
import requests
from yahoo_fin import stock_info as si
import time

yf.pdr_override()

stocklist = si.tickers_nasdaq()

final = []
index = []
n = -1

exportList = pd.DataFrame(columns=['Stock', "RS_Rating", "50 Day MA", "150 Day Ma", "200 Day MA", "52 Week Low", "52 week High"])

for stock in stocklist:
    n += 1
    time.sleep(1)
    
    print ("\npulling {} from index {}".format(stock, n))
    # rsi value
    start_date = datetime.datetime.now() - datetime.timedelta(days=365)
    end_date = datetime.date.today()
    
    df = pdr.get_data_yahoo(stock, start=start_date, end=end_date)
    df = df.reset_index()
    df['Date'] = pd.to_datetime(df.Date)
    data = df.sort_values(by="Date", ascending=True).set_index("Date").last("59D")
    df = df.set_index('Date')
    rsi_period = 14
    chg = data['Close'].diff(1)
    gain = chg.mask(chg < 0, 0)
    data['gain'] = gain
    loss = chg.mask(chg > 0, 0)
    data['loss'] = loss
    avg_gain = gain.ewm(com=rsi_period - 1, min_periods=rsi_period).mean()
    avg_loss = loss.ewm(com=rsi_period - 1, min_periods=rsi_period).mean()
    data['avg_gain'] = avg_gain
    data['avg_loss'] = avg_loss
    rs = abs(avg_gain/avg_loss)
    rsi = 100-(100/(1+rs))
    rsi = rsi.reset_index()
    rsi = rsi.drop(columns=['Date'])
    rsi.columns = ['Value']
    rsi_list = rsi.Value.to_list()
    RS_Rating = rsi['Value'].mean()

    try:
        smaUsed = [50, 150, 200]
        for x in smaUsed:
            sma = x
            df["SMA_"+str(sma)] = round(df.iloc[:,4].rolling(window=sma).mean(), 2)

        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 = min(df["Adj Close"][-260:])
        high_of_52week = max(df["Adj Close"][-260:])
        
        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
        if(currentClose > moving_average_150 > moving_average_200):
            condition_1 = True
        else:
            condition_1 = False
        # Condition 2: 150 SMA and > 200 SMA
        if(moving_average_150 > moving_average_200):
            condition_2 = True
        else:
            condition_2 = False
        # Condition 3: 200 SMA trending up for at least 1 month (ideally 4-5 months)
        if(moving_average_200 > moving_average_200_20):
            condition_3 = True
        else:
            condition_3 = False
        # Condition 4: 50 SMA> 150 SMA and 50 SMA> 200 SMA
        if(moving_average_50 > moving_average_150 > moving_average_200):
            #print("Condition 4 met")
            condition_4 = True
        else:
            #print("Condition 4 not met")
            condition_4 = False
        # Condition 5: Current Price > 50 SMA
        if(currentClose > moving_average_50):
            condition_5 = True
        else:
            condition_5 = False
        # Condition 6: Current Price is at least 30% above 52 week low (Many of the best are up 100-300% before coming out of consolidation)
        if(currentClose >= (1.3*low_of_52week)):
            condition_6 = True
        else:
            condition_6 = False
        # Condition 7: Current Price is within 25% of 52 week high
        if(currentClose >= (.75*high_of_52week)):
            condition_7 = True
        else:
            condition_7 = False
        # Condition 8: IBD RS rating >70 and the higher the better
        if(RS_Rating > 70):
            condition_8 = True
        else:
            condition_8 = False

        if(condition_1 and condition_2 and condition_3 and condition_4 and condition_5 and condition_6 and condition_7 and condition_8):
            final.append(stock)
            index.append(n)
            
            dataframe = pd.DataFrame(list(zip(final, index)), columns =['Company', 'Index'])
            
            dataframe.to_csv('good_stocks.csv')
            
            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 requirements")
    except Exception as e:
        print (e)
        print("No data on "+stock)

print(exportList)

writer = ExcelWriter("ScreenOutput.xlsx")
exportList.to_excel(writer, "Sheet1")
writer.save()