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fundamentalanalysis.py
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#import libraries
import pandas as pd
import numpy as np
from numpy import nan
from numpy.polynomial import Polynomial as P
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.chrome.service import Service
from selenium.webdriver import ActionChains
from selenium.webdriver.common.by import By
from webdriver_manager.chrome import ChromeDriverManager
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as ec
from selenium.common.exceptions import TimeoutException
import time
from tqdm import tqdm
import pymannkendall as mk
#from multiprocessing import Manager
import random
from multiprocessing import Lock
from functools import partial
import multiprocess as mp
import os
import re
import pdb
from locale import atof, setlocale, LC_NUMERIC
setlocale(LC_NUMERIC, '')
#Set up driver
if __name__ == '__main__':
driver = webdriver.Chrome(ChromeDriverManager().install())
#data = pd.read_csv('fa_stats.csv')
'''Get tickers that pass initial finviz test'''
def get_finviz_tickers(save_path, n=100):
'''n must be greater than or equal to 20'''
#url = 'https://finviz.com/screener.ashx?v=111&f=fa_eps5years_o15,fa_roe_o15&ft=2&o=-marketcap'
url = 'https://finviz.com/screener.ashx?v=111&f=cap_midover,fa_eps5years_o15,fa_roe_o15&ft=4&o=-marketcap'
driver.get(url)
time.sleep(2)
finviz_xpath = '//*[@id="screener-views-table"]/tbody/tr[4]/td/table/tbody'
finviz = driver.find_element(By.XPATH, finviz_xpath).text
finviz = finviz.split('\n')
finviz = [s for s in finviz if not re.search(r'\d',s)]
finviz = [s for s in finviz if s.isupper()]
possible = list(pd.read_csv('listOfStockTickers.csv')['Symbol'])
tickers = [t for t in finviz if t in possible]
tickers = [t for t in finviz if t!='USA']
n-=20
k = n/20
if(n > 0):
for i in range(1, int(k+1)):
url = f'https://finviz.com/screener.ashx?v=111&f=fa_eps5years_o15,fa_roe_o15&ft=2&o=-marketcap&r={21*i}'
driver.get(url)
time.sleep(2)
finviz_xpath = '//*[@id="screener-views-table"]/tbody/tr[4]/td/table/tbody'
finviz = driver.find_element(By.XPATH, finviz_xpath).text
finviz = finviz.split('\n')
finviz = [s for s in finviz if not re.search(r'\d',s)]
finviz = [s for s in finviz if s.isupper()]
for t in finviz:
if(t in possible and t!='USA'):
tickers.append(t)
driver.close()
df = pd.DataFrame(data={'Tickers': tickers})
df.to_csv(save_path, index=False)
return
get_finviz_tickers('all_passed_tickers.csv', n=459) #get top 40 that passed the finviz test
#data = pd.read_csv('fa_stats.csv')
#buffet_data = pd.read_csv('buffet_fa.csv')
'''Get a list of the last 10 years' and current ROE of the stock'''
def update_stock_fastats(ticker, path):
print(f'COLLECTING DATA FOR:{ticker}')
print(f'SAVING TO:{path}')
data = pd.read_csv(path)
url = f'https://www.morningstar.com/search?query={ticker}'
driver.get(url)
#time.sleep(20) #!!!what is the optimal pause to ensure a crash doesn't happen? Or maybe the solution is not a pause?
#Search the ticker on the morningstar home page
#search_xpath = '//*[@id="__layout"]/div/div/div[2]/div[1]/div/header/div/div[1]/div/form/div/input'
#search = driver.find_element(By.XPATH, search_xpath)
#search.send_keys(ticker)
#search.send_keys(Keys.ENTER)
#Click on the first result, which should be the stock's info page
ticker_page_xpath = '//*[@id="__layout"]/div/div/div[2]/div[3]/main/div/div/div[1]/div/section[1]/div[2]/a'
ticker_page = wait.until(ec.visibility_of_element_located((By.XPATH, ticker_page_xpath)))
#ticker_page = wait.until(ec.visibility_of_element_located((By.XPATH, ticker_page_xpath)))
ticker_page.click()
#time.sleep(10)
#Go to the stock's valuation page
val_page = wait.until(ec.visibility_of_element_located((By.XPATH, '//*[@id="stock_tab-valuation"]')))
#val_page = driver.find_element_by_id("stock__tab-valuation")
val_page.click()
#time.sleep(10)
#Go to Operating and Efficiency
oae = wait.until(ec.visibility_of_element_located((By.ID, 'keyStatsOperatingAndEfficiency')))
#oae=driver.find_element_by_id("keyStatsOperatingAndEfficiency")
oae.click();
#time.sleep(10)
#Get ROE
#roe_xpath = '//*[@id="__layout"]/div/div/div[2]/div[3]/main/div[2]/div/div/div[1]/sal-components/div/sal-components-stocks-valuation/div/div[2]/div/div/div[4]/div/div/div/div/div[1]/table/tbody/tr[7]'
roe_xpath = '//*[@id="__layout"]/div/div/div[2]/div[3]/div/main/div/div/div[1]/section/sal-components/div/sal-components-stocks-valuation/div/div[2]/div/div/div[4]/div/div/div/div/div[1]/table/tbody/tr[7]'
try:
#roe = driver.find_element(By.XPATH, roe_xpath).text
roe = wait.until(ec.visibility_of_element_located((By.XPATH, roe_xpath))).text
except:
pdb.set_trace()
roe = roe.replace('Return on Equity % ','')
roe = roe.split()
roe = roe[:-1]
'''Get a list of the last 10 years' and current EPSg of the stock'''
#Go to Growth
#growth = driver.find_element_by_id("keyStatsgrowthTable")
growth = wait.until(ec.visibility_of_element_located((By.ID, 'keyStatsgrowthTable')))
growth.click()
#time.sleep(10)
#Get EPSg
#epsg_xpath = '//*[@id="__layout"]/div/div/div[2]/div[3]/main/div[2]/div/div/div[1]/sal-components/div/sal-components-stocks-valuation/div/div[2]/div/div/div[3]/div/div/div/div/div[1]/table/tbody/tr[17]'
epsg_xpath = '//*[@id="__layout"]/div/div/div[2]/div[3]/div/main/div/div/div[1]/section/sal-components/div/sal-components-stocks-valuation/div/div[2]/div/div/div[3]/div/div/div/div/div/div[1]/table/tbody/tr[17]'
try:
#epsg = driver.find_element(By.XPATH, epsg_xpath).text
epsg = wait.until(ec.visibility_of_element_located((By.XPATH, epsg_xpath))).text
except:
pdb.set_trace()
epsg = epsg.replace('Year Over Year ','')
epsg = epsg.split()
'''Get a list of the past 10 years' and current stock PE'''
#pe_xpath = '//*[@id="__layout"]/div/div/div[2]/div[3]/main/div[2]/div/div/div[1]/sal-components/div/sal-components-stocks-valuation/div/div[1]/div/div/div[2]/div/div/table/tbody/tr[3]'
pe_xpath = '//*[@id="__layout"]/div/div/div[2]/div[3]/div/main/div/div/div[1]/section/sal-components/div/sal-components-stocks-valuation/div/div[1]/div/div/div[2]/div/div/table/tbody/tr[3]'
try:
#roe = driver.find_element(By.XPATH, roe_xpath).text
pe = wait.until(ec.visibility_of_element_located((By.XPATH, pe_xpath))).text
except:
pdb.set_trace()
#pe = driver.find_element(By.XPATH, pe_xpath).text
pe = pe.replace("\n", " ")
pe = pe.replace("Price/Earnings ","")
pe = pe.split()
pe_5yr = pe[-2] #5yr average according to morningstar
pe = pe[:-2] #pe of the last 10 years and current
'''Get the stock's free cash flow growth % for the past 10 years'''
#Go the Cash Flow
cf = wait.until(ec.visibility_of_element_located((By.ID, 'keyStatscashFlow')))
#cf = driver.find_element_by_id("keyStatscashFlow")
cf.click()
#time.sleep(10)
#Get the free cash flow growth
free_xpath = '//*[@id="__layout"]/div/div/div[2]/div[3]/div/main/div/div/div[1]/section/sal-components/div/sal-components-stocks-valuation/div/div[2]/div/div/div[6]/div/div/div/div/div/div[1]/table/tbody/tr[2]'
#free = driver.find_element(By.XPATH, free_xpath).text
try:
#roe = driver.find_element(By.XPATH, roe_xpath).text
free = wait.until(ec.visibility_of_element_located((By.XPATH, free_xpath))).text
except:
pdb.set_trace()
free = free.replace("Free Cash Flow Growth % YOY ","")
free = free.split()
free = free[:-1]
'''!!!Get CFs. Add get_cf_eps'''
'''Get EPS'''
'''Get the stock's EPSg for next year and the EPSg 5 year average from finviz.com'''
#Go to finviz.com
url = f'https://finviz.com/quote.ashx?t={ticker}'
driver.get(url)
time.sleep(2)
#Get next year's estimated epsg
epsg_next_xpath = '/html/body/div[4]/div/table[2]/tbody/tr[5]/td[6]'
epsg_next = driver.find_element(By.XPATH, epsg_next_xpath).text
#Get the EPSg for the last 5 years
epsg_5yavg_xpath = '/html/body/div[4]/div/table[2]/tbody/tr[7]/td[6]'
epsg_5yavg = driver.find_element(By.XPATH, epsg_5yavg_xpath).text
#pdb.set_trace()
if(data['Ticker'].str.contains(ticker).any()):
data.loc[data.index[data['Ticker']==ticker].to_list()[0], ['ROE (10yrs)','EPSg (10yrs)','Free Cash Flow Growth % (YOY)', 'PE','EPSg (next)','EPSg (5yr avg)']] = [roe, epsg, free, pe, epsg_next, epsg_5yavg]
else:
data.loc[len(data.index)] = [ticker, roe, epsg, free, pe, epsg_next, epsg_5yavg]
data.to_csv(path,index=False)
print(f'SAVED {ticker} TO CSV')
return
driver = webdriver.Chrome(ChromeDriverManager().install())
wait = WebDriverWait(driver, 30)
#update_stock_fastats('TSM', 'CFu_fa.csv')
#buffet_tickers = ['AAPL','BAC','AXP','CVX','KO','KHC','MCO','OXY','USB','ATVI','DVA','HPQ','BK']
#cfu_tickers = ['KO', 'V', 'MSFT', 'AMZN', 'TROW','CPRT','CDW','BAH','MA','COST','POOL','HD','UNH','PSA'
# ,'BBWI','WMT','ROST','LVMHF','RACE','VWAGY','QSR','MAR','QL']
#cfu_tickers_missed = ['BAH','MA','COST','POOL','HD']
for ticker in tqdm(tickers):
update_stock_fastats(ticker, 'CFu_fa.csv')
driver.close()
'''Multiprocess the scraping process'''
'''!!!Bug: if element does not appear, the process is just frozen and there is no break'''
'''!!!Does multiprocess version work?'''
def update_stock_fastats_mp(ticker, path, lock):
print(f'COLLECTING DATA FOR:{ticker}')
driver = webdriver.Chrome(ChromeDriverManager().install())
wait = WebDriverWait(driver, 30)
url = f'https://www.morningstar.com/search?query={ticker}'
driver.get(url)
#time.sleep(20) #!!!what is the optimal pause to ensure a crash doesn't happen? Or maybe the solution is not a pause?
#Search the ticker on the morningstar home page
#search_xpath = '//*[@id="__layout"]/div/div/div[2]/div[1]/div/header/div/div[1]/div/form/div/input'
#search = driver.find_element(By.XPATH, search_xpath)
#search.send_keys(ticker)
#search.send_keys(Keys.ENTER)
#Click on the first result, which should be the stock's info page
ticker_page_xpath = '//*[@id="__layout"]/div/div/div[2]/div[3]/main/div/div/div[1]/div/section[1]/div[2]/a'
ticker_page = wait.until(ec.visibility_of_element_located((By.XPATH, ticker_page_xpath)))
#ticker_page = wait.until(ec.visibility_of_element_located((By.XPATH, ticker_page_xpath)))
ticker_page.click()
#time.sleep(10)
#Go to the stock's valuation page
val_page = wait.until(ec.visibility_of_element_located((By.XPATH, '//*[@id="stock_tab-valuation"]')))
#val_page = driver.find_element_by_id("stock__tab-valuation")
val_page.click()
#time.sleep(10)
#Go to Operating and Efficiency
oae = wait.until(ec.visibility_of_element_located((By.ID, 'keyStatsOperatingAndEfficiency')))
#oae=driver.find_element_by_id("keyStatsOperatingAndEfficiency")
oae.click();
#time.sleep(10)
#Get ROE
#roe_xpath = '//*[@id="__layout"]/div/div/div[2]/div[3]/main/div[2]/div/div/div[1]/sal-components/div/sal-components-stocks-valuation/div/div[2]/div/div/div[4]/div/div/div/div/div[1]/table/tbody/tr[7]'
roe_xpath = '//*[@id="__layout"]/div/div/div[2]/div[3]/div/main/div/div/div[1]/section/sal-components/div/sal-components-stocks-valuation/div/div[2]/div/div/div[4]/div/div/div/div/div[1]/table/tbody/tr[7]'
try:
#roe = driver.find_element(By.XPATH, roe_xpath).text
roe = wait.until(ec.visibility_of_element_located((By.XPATH, roe_xpath))).text
except:
pdb.set_trace()
roe = roe.replace('Return on Equity % ','')
roe = roe.split()
roe = roe[:-1]
'''Get a list of the last 10 years' and current EPSg of the stock'''
#Go to Growth
#growth = driver.find_element_by_id("keyStatsgrowthTable")
growth = wait.until(ec.visibility_of_element_located((By.ID, 'keyStatsgrowthTable')))
growth.click()
#time.sleep(10)
#Get EPSg
#epsg_xpath = '//*[@id="__layout"]/div/div/div[2]/div[3]/main/div[2]/div/div/div[1]/sal-components/div/sal-components-stocks-valuation/div/div[2]/div/div/div[3]/div/div/div/div/div[1]/table/tbody/tr[17]'
epsg_xpath = '//*[@id="__layout"]/div/div/div[2]/div[3]/div/main/div/div/div[1]/section/sal-components/div/sal-components-stocks-valuation/div/div[2]/div/div/div[3]/div/div/div/div/div/div[1]/table/tbody/tr[17]'
try:
#epsg = driver.find_element(By.XPATH, epsg_xpath).text
epsg = wait.until(ec.visibility_of_element_located((By.XPATH, epsg_xpath))).text
except:
pdb.set_trace()
epsg = epsg.replace('Year Over Year ','')
epsg = epsg.split()
'''Get a list of the past 10 years' and current stock PE'''
#pe_xpath = '//*[@id="__layout"]/div/div/div[2]/div[3]/main/div[2]/div/div/div[1]/sal-components/div/sal-components-stocks-valuation/div/div[1]/div/div/div[2]/div/div/table/tbody/tr[3]'
pe_xpath = '//*[@id="__layout"]/div/div/div[2]/div[3]/div/main/div/div/div[1]/section/sal-components/div/sal-components-stocks-valuation/div/div[1]/div/div/div[2]/div/div/table/tbody/tr[3]'
try:
#roe = driver.find_element(By.XPATH, roe_xpath).text
pe = wait.until(ec.visibility_of_element_located((By.XPATH, pe_xpath))).text
except:
pdb.set_trace()
#pe = driver.find_element(By.XPATH, pe_xpath).text
pe = pe.replace("\n", " ")
pe = pe.replace("Price/Earnings ","")
pe = pe.split()
pe_5yr = pe[-2] #5yr average according to morningstar
pe = pe[:-2] #pe of the last 10 years and current
'''Get the stock's free cash flow growth % for the past 10 years'''
#Go the Cash Flow
cf = wait.until(ec.visibility_of_element_located((By.ID, 'keyStatscashFlow')))
#cf = driver.find_element_by_id("keyStatscashFlow")
cf.click()
#time.sleep(10)
#Get the free cash flow growth
free_xpath = '//*[@id="__layout"]/div/div/div[2]/div[3]/div/main/div/div/div[1]/section/sal-components/div/sal-components-stocks-valuation/div/div[2]/div/div/div[6]/div/div/div/div/div/div[1]/table/tbody/tr[2]'
#free = driver.find_element(By.XPATH, free_xpath).text
try:
#roe = driver.find_element(By.XPATH, roe_xpath).text
free = wait.until(ec.visibility_of_element_located((By.XPATH, free_xpath))).text
except:
pdb.set_trace()
free = free.replace("Free Cash Flow Growth % YOY ","")
free = free.split()
free = free[:-1]
'''!!!Get the stock's CFO, CFI and CFF
'''
'''#Go to Financials
financials_id = 'stock_tab-financials'
financials = wait.until(ec.visibility_of_element_located((By.ID, financials_id)))
financials.click()
#Go the Cash Flow
cf_id = 'cashFlow'
cf = wait.until(ec.visibility_of_element_located((By.ID, cf_id)))
cf.click()
#Expand detail view
expand_xpath = '//*[@id="__layout"]/div/div/div[2]/div[3]/div/main/div/div/div[1]/section/sal-components/div/sal-components-stocks-financials/div/div/div/div/div/div[2]/div[2]/div/div/a/span[1]'
expand = wait.until(ec.visibility_of_element_located((By.XPATH, expand_xpath)))
expand.click()
#collapse CFO row
cfo_xpath = '//*[@id="tg-3476"]/div[3]/div[2]/div[1]/div/div[13]'
try:
cfo_ = wait.until(ec.visibility_of_element_located((By.XPATH, cfo_xpath))).text #!!!can't find cfo element by xpath
except:
pdb.set_trace()
'''
'''!!!need to figure out how to get the stock's EPS
#Click 'Expand Detail View'
expand_xpath = '//*[@id="__layout"]/div/div/div[2]/div[3]/main/div[2]/div/div/div[1]/sal-components/div/sal-components-stocks-financials/div/div/div/div/div/div[2]/div[2]/div/div/a/span[1]'
expand = driver.find_element(By.XPATH, expand_xpath)
expand.click()
#Get EPS
eps_xpath = '//*[@id="tg-3009"]/div[3]/div[2]/div[1]/div/div[19]'
eps_xpath='//*[@id="tg-3009"]/div[3]/div[1]/div/div/div[17]/div/div/div'
eps = driver.find_element(By.XPATH, eps_xpath).text'''
'''Get the stock's EPSg for next year and the EPSg 5 year average from finviz.com'''
#Go to finviz.com
url = f'https://finviz.com/quote.ashx?t={ticker}'
driver.get(url)
time.sleep(2)
#Get next year's estimated epsg
epsg_next_xpath = '/html/body/div[4]/div/table[2]/tbody/tr[5]/td[6]'
epsg_next = driver.find_element(By.XPATH, epsg_next_xpath).text
#Get the EPSg for the last 5 years
epsg_5yavg_xpath = '/html/body/div[4]/div/table[2]/tbody/tr[7]/td[6]'
epsg_5yavg = driver.find_element(By.XPATH, epsg_5yavg_xpath).text
driver.close()
print(f"FINISHED DATA COLLECTION FOR: {ticker}")
'''Now that the data has been collected, save data to csv file given lock is acquirable aka no other processes saving atm'''
with lock:
sleep_value = random.random()
print(f'>process {ticker} got the lock, sleeping for {sleep_value}')
time.sleep(sleep_value)
print(f"SAVING {ticker} TO: {path}")
data = pd.read_csv(path)
if(data['Ticker'].str.contains(ticker).any()):
data.loc[data.index[data['Ticker']==ticker].to_list()[0], ['ROE (10yrs)','EPSg (10yrs)','Free Cash Flow Growth % (YOY)', 'PE','EPSg (next)','EPSg (5yr avg)']] = [roe, epsg, free, pe, epsg_next, epsg_5yavg]
else:
data.loc[len(data.index)] = [ticker, roe, epsg, free, pe, epsg_next, epsg_5yavg]
data.to_csv(path,index=False)
print(f'SAVED {ticker} TO {path}')
data = pd.read_csv(path)
print(f"DATA:{data}")
return
path = 'fa_stats.csv'
all_tickers = pd.read_csv('all_passed_tickers.csv')['Tickers']
i = int(len(all_tickers)/3-1)
tickers = all_tickers.loc[i:]
#tickers = ['AAPL','BAC','AXP','CVX','KO','KHC','MCO']
with mp.Manager() as manager:
lock = manager.Lock()
with mp.Pool(mp.cpu_count()-1) as p:
items = [(ticker, path, lock) for ticker in tickers+['PAYC']]
result = p.starmap_async(update_stock_fastats_mp, items)
result.wait()
#p.terminate()
#p.join()
''' def calc_fairvalue(beta, eps, pe_5yr_avg, pe_current, spx_pe=18.86):
Calculate the fair values for a stock given EPS, stock PEs, and
!!!vectorize this process to be instant. For loops are for noobs
INPUTS:
beta: the stocks's beta (float or string, need to add an if statement to filter)
eps: the stock's past 5yr EPS (list)
pe_5yr_avg: average of the stock's PE for the past 5 years (float)
pe_current: the stock's current PE (float)
spx_pe: the SP500's current PE ratio (float)
OUTPUT:
fair_value_spx_cons, fair_value_5y_cons, fair_value_current_cons, fair_value_spx_aggr, fair_value_5y_aggr, fair_value_current_aggr
epsg = (eps[-1]/eps[0])**(1/4)-1
eps_t1 = eps[-1]*(1+epsg)
#SPX fair value
if beta < 1:
pe = spx_pe
else:
pe = spx_pe*beta
earnings_t1 = eps_t1*pe
fair_value_spx_cons = earnings_t1/(1+0.095)
fair_value_spx_aggr = earnings_t1/(1+0.15)
#5y PE fair value
pe = pe_5yr_avg
earnings_t1 = eps_t1*pe
fair_value_5y_cons = earnings_t1/(1+0.095)
fair_value_5y_aggr = earnings_t1/(1+0.15)
#Current PE fair value
pe = pe_current
earnings_t1 = eps_t1*pe
fair_value_current_cons = earnings_t1/(1+0.095)
fair_value_current_aggr = earnings_t1/(1+0.2)
return fair_value_spx_cons, fair_value_5y_cons, fair_value_current_cons, fair_value_spx_aggr, fair_value_5y_aggr, fair_value_current_aggr'''
def random_identifier(n):
range_start = 10**(n-1)
range_end = (10**n)-1
return random.randint(range_start, range_end)
def get_cf_eps(ticker,path, skip_beta=False):
driver = webdriver.Chrome(ChromeDriverManager().install())
wait = WebDriverWait(driver, 30)
timeout = time.time() + 60*5
#print(f'SAVING TO {path}')
#data = pd.read_csv(path)
print(f"Beginning CF and EPS data extraction for {ticker}...")
url = f'https://www.morningstar.com/search?query={ticker}'
driver.get(url)
#Go to ticker's page
ticker_page_xpath = '//*[@id="__layout"]/div/div/div[2]/div[3]/main/div/div/div[1]/div/section[1]/div[2]/a'
ticker_page = wait.until(ec.visibility_of_element_located((By.XPATH, ticker_page_xpath)))
#ticker_page = wait.until(ec.visibility_of_element_located((By.XPATH, ticker_page_xpath)))
ticker_page.click()
#Go to valuations page
val_page = wait.until(ec.visibility_of_element_located((By.XPATH, '//*[@id="stock_tab-valuation"]')))
val_page.click() #Go to valuation page
pe_xpath = '//*[@id="__layout"]/div/div/div[2]/div[3]/div/main/div/div/div[1]/section/sal-components/div/sal-components-stocks-valuation/div/div[1]/div/div/div[2]/div/div/table/tbody/tr[3]'
pe = wait.until(ec.visibility_of_element_located((By.XPATH, pe_xpath))).text
pe = pe.replace("\n", " ")
pe = pe.replace("Price/Earnings ","")
pe = pe.split()
pe_5yr = pe[-2] #5yr average according to morningstar
pe_10yr = pe[:-2] #pe of the last 10 years and current
pe_5yr = pe_10yr[5:-1] #pe of the last 5 years
pe_current = pe_10yr[-1] #current pe
#def reject_outliers(data, m = 2.):
# d = np.abs(data - np.median(data))
# mdev = np.median(d)
# s = d/mdev if mdev else 0.
# return data[s<m]
#pe_5yr_avg = np.mean(reject_outliers(pe_5yr))
#Go to Financials
financials_id = 'stock_tab-financials'
financials = wait.until(ec.visibility_of_element_located((By.ID, financials_id)))
financials.click()
#Get CF
if os.path.exists("/Users/cyrusleung/Downloads/Cash Flow_Annual_As Originally Reported.xls"):
os.remove("/Users/cyrusleung/Downloads/Cash Flow_Annual_As Originally Reported.xls")
print(f"Removed old {ticker} CF file")
else:
print(f"Could not find old {ticker} CF file to delete")
cf_id = 'cashFlow'
cf = wait.until(ec.visibility_of_element_located((By.ID, cf_id)))
cf.click()
try:
expand_xpath = '//*[@id="__layout"]/div/div/div[2]/div[3]/div/main/div/div/div[1]/section/sal-components/div/sal-components-stocks-financials/div/div/div/div/div/div[2]/div[2]/div/div/a/span[1]'
expand = wait.until(ec.visibility_of_element_located((By.XPATH, expand_xpath)))
expand.click()
except TimeoutException as ex:
driver.close()
return "Exception has been thrown. " + str(ex)
export_xpath = '//*[@id="__layout"]/div/div/div[2]/div[3]/div/main/div/div/div[1]/section/sal-components/div/sal-components-stocks-financials/div/div/div/div/div/div[2]/div[1]/div[2]/div/div/div/div/div/div[2]/div/button'
export = wait.until(ec.visibility_of_element_located((By.XPATH, export_xpath)))
export.click()
while not os.path.exists("/Users/cyrusleung/Downloads/Cash Flow_Annual_As Originally Reported.xls"):
print("File not in Downloads folder yet. Waiting 1 sec...")
time.sleep(1)
if time.time() > timeout:
#!!!sometimes export fails, reloading does the job so add reloading as a function
return f"Could not find {ticker} CF file within 10 min, skipping {ticker}"
#Get Income
if os.path.exists("/Users/cyrusleung/Downloads/Income Statement_Annual_As Originally Reported.xls"):
os.remove("/Users/cyrusleung/Downloads/Income Statement_Annual_As Originally Reported.xls")
print(f"Removed old {ticker} INCOME file")
else:
print(f"Could not find old {ticker} INCOME file to delete")
driver.refresh()
expand_xpath = '//*[@id="__layout"]/div/div/div[2]/div[3]/div/main/div/div/div[1]/section/sal-components/div/sal-components-stocks-financials/div/div/div/div/div/div[2]/div[2]/div/div/a'
expand = wait.until(ec.visibility_of_element_located((By.XPATH, expand_xpath)))
expand.click() #Expand to access export button
export_xpath = '//*[@id="__layout"]/div/div/div[2]/div[3]/div/main/div/div/div[1]/section/sal-components/div/sal-components-stocks-financials/div/div/div/div/div/div[2]/div[1]/div[2]/div/div/div/div/div/div[2]/div/button'
export = wait.until(ec.visibility_of_element_located((By.XPATH, export_xpath)))
export.click() #Export income statement
while not os.path.exists("/Users/cyrusleung/Downloads/Income Statement_Annual_As Originally Reported.xls"):
print("File not in Downloads folder yet. Waiting 1 sec...")
time.sleep(1)
if time.time() > timeout+60:
return f"Could not find {ticker} INCOME file within 10 min, skipping {ticker}"
#Analyze CF
df = pd.DataFrame(pd.read_excel("/Users/cyrusleung/Downloads/Cash Flow_Annual_As Originally Reported.xls"))
#Get CFO
cfo_row = None
if (df.loc[df[f'{ticker}_cash-flow_Annual_As_Originally_Reported']=='Cash Flow from Operating Activities, Indirect']).empty:
cfo_row = df.loc[df[f'{ticker}_cash-flow_Annual_As_Originally_Reported']==' Cash Flow from Operating Activities, Indirect'].iloc[0]
else:
cfo_row = df.loc[df[f'{ticker}_cash-flow_Annual_As_Originally_Reported']=='Cash Flow from Operating Activities, Indirect'].iloc[0]
#pdb.set_trace()
cfo = []
for year in df.columns[1:-1]:
cfo.append(float(cfo_row[year]))
#Get CFI
cfi_row = None
if (df.loc[df[f'{ticker}_cash-flow_Annual_As_Originally_Reported']=='Cash Flow from Investing Activities']).empty:
cfi_row = df.loc[df[f'{ticker}_cash-flow_Annual_As_Originally_Reported']==' Cash Flow from Investing Activities'].iloc[0]
else:
cfi_row = df.loc[df[f'{ticker}_cash-flow_Annual_As_Originally_Reported']=='Cash Flow from Investing Activities'].iloc[0]
cfi = []
for year in df.columns[1:-1]:
cfi.append(float(cfi_row[year]))
#Get CFF
cff_row = None
if (df.loc[df[f'{ticker}_cash-flow_Annual_As_Originally_Reported']=='Cash Flow from Financing Activities']).empty:
cff_row = df.loc[df[f'{ticker}_cash-flow_Annual_As_Originally_Reported']==' Cash Flow from Financing Activities'].iloc[0]
else:
cff_row = df.loc[df[f'{ticker}_cash-flow_Annual_As_Originally_Reported']=='Cash Flow from Financing Activities'].iloc[0]
cff = []
for year in df.columns[1:-1]:
cff.append(float(cff_row[year]))
#Delete the xls file after done extracting the data I want
if os.path.exists("/Users/cyrusleung/Downloads/Cash Flow_Annual_As Originally Reported.xls"):
os.remove("/Users/cyrusleung/Downloads/Cash Flow_Annual_As Originally Reported.xls")
else:
print(f"Could not find {ticker} CF file to delete")
#Analyze EPS
#income_id = 'incomeStatement'
#income = wait.until(ec.visibility_of_element_located((By.ID, income_id)))
#income.click() #Go to income statement
df = pd.DataFrame(pd.read_excel("/Users/cyrusleung/Downloads/Income Statement_Annual_As Originally Reported.xls"))
eps_row = None
try:
eps_row = df.loc[df[f'{ticker}_income-statement_Annual_As_Originally_Reported']=='Diluted EPS'].iloc[-1]
except:
return "PROBLEM: Issue with {ticker} EPS"
eps = [float(eps_row[year]) for year in df.columns[1:-1]]
#Delete the xls file after done extracting the data I want
if os.path.exists("/Users/cyrusleung/Downloads/Income Statement_Annual_As Originally Reported.xls"):
os.remove("/Users/cyrusleung/Downloads/Income Statement_Annual_As Originally Reported.xls")
else:
print(f"Could not find {ticker} INCOME file to delete")
if skip_beta:
print("Skipped Beta.")
driver.close()
print(f"FINISHED DATA COLLECTION FOR: {ticker}")
print(f"SAVING {ticker} TO: {path}")
data = pd.read_csv(path)
if (data['Ticker'].str.contains(ticker).any()):
cfo = str(cfo)
cfi = str(cfi)
cff = str(cff)
eps = str(eps)
pe_10yr = str(pe_10yr)
# pdb.set_trace()
data.loc[data.index[data['Ticker'] == ticker].to_list()[0], ['CFO', 'CFI', 'CFF', 'EPS','PE (10yr & current)']] = [cfo,cfi,cff,eps,pe_10yr]
else:
print(f"{Ticker} DNE")
data.to_csv(path, index=False)
print(f'SAVED {ticker} TO {path}')
data = pd.read_csv(path)
print(f"DATA:{data[data['Ticker'] == ticker][['CFO', 'EPS']]}")
return f"COMPLETED: {ticker}"
#Get beta
print(f"GETTING beta for {ticker} too.")
url = f'https://finviz.com/quote.ashx?t={ticker}&p=d'
driver.get(url)
time.sleep(2)
beta_xpath = '/html/body/div[4]/div/table[2]/tbody/tr[7]/td[12]/b'
beta = wait.until(ec.visibility_of_element_located((By.XPATH, beta_xpath))).text
#fair_value_spx_cons, fair_value_5y_cons, fair_value_current_cons, fair_value_spx_aggr, fair_value_5y_aggr, fair_value_current_aggr = calc_fairvalue(beta, eps, pe_5yr_avg, pe_current)
driver.close()
print(f"FINISHED DATA COLLECTION FOR: {ticker}")
print(f"SAVING {ticker} TO: {path}")
data = pd.read_csv(path)
if(data['Ticker'].str.contains(ticker).any()):
cfo = str(cfo)
cfi = str(cfi)
cff = str(cff)
eps = str(eps)
pe_10yr = str(pe_10yr)
#pdb.set_trace()
data.loc[data.index[data['Ticker']==ticker].to_list()[0], ['CFO', 'CFI', 'CFF', 'EPS', 'PE (10yr & current)','Beta']] = [cfo, cfi, cff, eps, pe_10yr, beta]
#data.loc[data['Ticker']==ticker,['CFO', 'CFI', 'CFF', 'EPS', 'PE (10yr & current)','Beta']] = [[cfo, cfi, cff, eps, pe_10yr.tolist(), beta]]
else:
print(f"{Ticker} DNE")
data.to_csv(path,index=False)
print(f'SAVED {ticker} TO {path}')
data = pd.read_csv(path)
print(f"DATA:{data[data['Ticker']==ticker][['CFO','EPS']]}")
return f"COMPLETED: {ticker}"
path = 'fa_stats.csv'
all_tickers = pd.read_csv(path)['Ticker']
#i = int(len(all_tickers)/3-1)
#tickers1 = all_tickers.loc[:i]
#tickers2 = all_tickers.loc[i:2*i]
#tickers3 = all_tickers.loc[2*i:]
missing_eps_tickers = ['KHC','TTE','AMT','IBN'] #Ticker H also has some problem too, Index error. List index out of bound
#all_tickers.index()
#list(tickers3).index('EC')
#tickers1[tickers1=='GSK'].index[0]
#list(tickers3)[list(tickers3).index('AVGO'):]
for ticker in tqdm(list(all_tickers)[list(all_tickers).index('AVGO'):]):
get_cf_eps(ticker,path, skip_beta=True)
def get_beta(ticker):
#Get stock beta
url = f'https://finance.yahoo.com/quote/{ticker}?p={ticker}&.tsrc=fin-srch'
driver.get(url)
beta_xpath = '//*[@id="quote-summary"]/div[2]/table/tbody/tr[2]/td[2]'
beta = wait.until(ec.visibility_of_element_located((By.XPATH, beta_xpath))).text
beta = float(beta)
return beta
'''---------------Part 2: Analyzing the Data------------------'''
'''Helper functions'''
def floatify_ind(list_val):
'''Convert the individual elements in the list into my standard'''
if(list_val=='—'):
return '-'
if(list_val=='-'):
return '-'
if(list_val=='––'):
return '-'
if(list_val[-1]=='%'):
return float(list_val[:-1])
if(list_val=='nan'):
return '-'
else:
return atof(list_val)
def floatify_beta(beta):
if isinstance(beta, float):
return beta
if(beta=='—'):
return '-'
if(beta=='-'):
return '-'
if(beta=='––'):
return '-'
if(beta=='nan'):
return '-'
else:
return atof(beta)
def floatify(row_val):
return list(map(floatify_ind, list(map(lambda s: s.replace("'",""), row_val.strip('][').split(', ')))))
def floatify_eps(row_val):
def floatify_helper(list_val):
'''Convert the individual elements in the list into my standard'''
if (list_val == '—'):
return '-'
if (list_val == '-'):
return '-'
if (list_val == '––'):
return '-'
if (list_val == 'nan'):
return '-'
else:
try:
return atof(list_val)
except:
return '-'
return list(map(floatify_helper, list(map(lambda s: s.replace("'",""), row_val.strip('][').split(', ')))))
#data['EPSg (next)'] = data['EPSg (next)'].map(floatify_ind)
#data['EPSg (5yr avg)'] = data['EPSg (5yr avg)'].map(floatify_ind)
'''ROE test
ROE_5y: max score of 2, if it fails 1 year then score of 1, else score of 0
ROE_10y: fails nonce: 3, fails once: 2, fails twice: 1; else 0
'''
#ROE_5y
def roe_fail(n):
if(n=='-'): return True
if(n==''): return True
if(n<12): return True
return False
def roe5y(roes):
roe_5y = 2
fail_count = sum(list(map(roe_fail, roes[5:])))
roe_5y -= fail_count
if(roe_5y <= 0): return 0
return roe_5y
#ROE_10y
def roe10y(roes):
roe_10y = 3
fail_count = sum(list(map(roe_fail, roes)))
roe_10y -= fail_count
if(roe_10y <= 0): return 0
return roe_10y
'''EPS test
EPSg_5y: fails nonce: 2, fails once: 1, else 0
EPSg_10y: fails nonce: 2, fails once: 1, else 0
'''
#EPS_5y
def epsg_fail(n):
if(n=='-'): return True
if(n<12): return True
return False
def epsg5y(epsgs):
epsg_5y = 2
fail_count = sum(list(map(epsg_fail, epsgs[6:])))
epsg_5y -= fail_count
if(epsg_5y <= 0): return 0
return epsg_5y
#EPSg_10y
def epsg10y(epsgs):
epsg_10y = 2
fail_count = sum(list(map(epsg_fail, epsgs)))
epsg_10y -= fail_count
if(epsg_10y <= 0): return 0
return epsg_10y
'''Free cash flow test
Requirements for a pass:
1. at least 5 out of 10 years were positive
2. The sum of all free cash flow growths are positive for all 10 years
3. (for hard) the free cash flow growth is increasing
'''
def trenddetector(array_of_data, order=1):
result = np.polyfit(list(map(float, range(len(array_of_data)))), array_of_data, order)
slope = result[-2]
return float(slope)
#cfo_wmt = [25591,23257,28564,27389,31350,28337,27753,25255,36074,24181]
def free_pos(f):
if(f=='-'): return False
return True
def free_sum(frees):
sum = 0
for f in frees:
if(f=='-'): sum+=0
else: sum+=f
return sum
def free_soft(frees):
if((sum(list(map(free_pos, frees))) >= 5)
and free_sum(frees) >= 0):
return 1
return 0
'''def free_hard(frees):
if((len(list(filter(free_pos, frees))) >= 5)
and free_sum(frees) >= 0
and trenddetector(frees) >= 0):
return 0.5
return 0'''
'''CF test, total = 3
CFO: subtract 1 for every negative
CFI: subtract 1 for every positive
CFF: if majority positive, subtract 1 for every negative; if majority negative, subtract 1 for every positive; caveat: if transitioning (but how to define transition)
!!!figure out bonus of if CFO is positive trend by some X% growth per year
'''
def cfo_test(cfo):
'''
Return 0 if fail nothing, return minus however many points for every failed case
Failed case is if cfo is negative
Add 0.5 if passes the positive 12%+ CFO trend test
'''
def helper(x):
if x == '-':
return -1
if x>=0:
return 0
else:
return -1
'''sum_before = sum(list(map(helper, cfo)))
if '-' in cfo:
return sum_before
if any(map(lambda x: True if isinstance(x,str) else False,cfo)):
return sum_before
if sum(np.polynomial.polynomial.polyfit(list(range(1,len(cfo)+1)),cfo,1)/cfo > 0.12) == 5:
return sum_before+0.5
else:
return sum_before'''
return sum(list(map(helper, cfo)))
def cfi_test(cfi):
'''
Return 0 if fail nothing, return minus however many points for every failed case
Failed case is if cfi is positive
'''
def helper(x):
if x == '-':
return -1
if x<0:
return 0
else:
return -1
return sum(list(map(helper, cfi)))
def cff_test(cff):
'''
Return 0 if fail nothing, return minus however many points for every failed case
Failed case is if cfi is positive
cff: list
!!!consider another if case being the company is transitioning from growth to mature or mature to growth
'''
def helper(x):
if x>=0:
return 1
else:
return -1
pos_count = np.sum(np.array(list(map(helper,cff))) >= 0, axis=0) #How many pos cff
neg_count = np.sum(np.array(list(map(helper,cff))) < 0, axis=0) #How many neg cff
if pos_count < neg_count:
return -pos_count
else:
return -neg_count
def cfi_test(cfi):
'''
CFI should be negative no matter the type of business.
Subtract one point for every positive cfi year
'''
def helper(x):
if isinstance(x, str):
print(x, type(x))
if x>=0:
return -1
else:
return 0
return sum(list(map(helper, cfi)))
def calc_fairvalue(data_path, spx_pe=18.86):
'''Calculate the fair values for a stock given EPS, stock PEs, and
!!!vectorize this process to be instant. For loops are for noobs
INPUTS:
beta: the stocks's beta (float or string, need to add an if statement to filter)
eps: the stock's past 5yr EPS (list)
pe_5yr_avg: average of the stock's PE for the past 5 years (float)
pe_current: the stock's current PE (float)
spx_pe: the SP500's current PE ratio (float)
OUTPUT:
fair_value_spx_cons, fair_value_5y_cons, fair_value_current_cons, fair_value_spx_aggr, fair_value_5y_aggr, fair_value_current_aggr
'''
#Clean data first
data = pd.read_csv(data_path)
data['ROE (10yrs)'] = data['ROE (10yrs)'].map(floatify)
data['EPSg (10yrs)'] = data['EPSg (10yrs)'].map(floatify)
data['Free Cash Flow Growth % (YOY)'] = data['Free Cash Flow Growth % (YOY)'].map(floatify)
data['PE'] = data['PE'].map(floatify)
data['CFO'] = data['CFO'].map(floatify)
data['CFI'] = data['CFI'].map(floatify)
data['CFF'] = data['CFF'].map(floatify)
data['EPS'] = data['EPS'].map(floatify_eps)
data['PE (10yr & current)'] = data['PE (10yr & current)'].map(floatify)
data['Beta'] = data['Beta'].map(floatify_beta)
beta = data['Beta']
eps = data['EPS']
pe_10yrAndCurrent = data['PE']
data.to_csv(data_path,index=False)
#Get the average of the stock's PE for the last 5yrs
#This works.
def get_pe_5yravg(lst):
if np.nan in lst[-6:-1] or '-' in lst[-6:-1]:
return np.nan
else:
return np.mean(lst[-6:-1])
pe_5yr_avg = pe_10yrAndCurrent.map(lambda k: get_pe_5yravg(k))
#Get the stock's current PE ratio
#This works.
def get_pe_current(lst):
if isinstance(lst[-1],float):
return lst[-1]
return np.nan
pe_current = pe_10yrAndCurrent.map(lambda k: get_pe_current(k))
'''def get_epsg(lst):
if isinstance(lst[-1], float) and isinstance(lst[0], float) and lst[0] != 0.0:
if (lst[-1] / lst[0]) <= 0:
return np.nan
return (lst[-1]/lst[0])**(1/4)-1
else:
return np.nan
epsg = list(map(get_epsg, eps))'''
#Project the stock's eps one year from now
#This works.
def get_eps_t1(lst):
def get_epsg(lst):
if isinstance(lst[-1], float) and isinstance(lst[0], float) and lst[0] != 0.0:
if (lst[-1] / lst[0]) <= 0:
return np.nan
return (lst[-1] / lst[0]) ** (1 / 4) - 1
else:
return np.nan
epsg = get_epsg(lst)
if isinstance(lst[-1], float) and lst[-1] > 0:
return lst[-1] * (1 + epsg)
else:
return np.nan
eps_t1 = list(map(get_eps_t1, eps))
#Calculate fair value with SPX PE
#This works.
def get_earnings_t1(beta, eps_t1):
if not isinstance(beta, float): #Catch case is beta is nan or '-'