from selenium import webdriver

from selenium.webdriver.chrome.options import Options

from bs4 import BeautifulSoup

import time

from datetime import datetime, timedelta

import pandas as pd

import sys

class StockSplitScraper:

def __init__(self):

self.symbols_and_links = []

# URL initialization commented as it's not used here

def get_week_dates(self):

today = datetime.today()

# Calculate the next Sunday date

days_ahead = 6 - today.weekday() # Number of days until next Sunday

if days_ahead < 0: # If today is Sunday, get date for next Sunday

days_ahead += 7

sunday = today + timedelta(days=days_ahead)

# Calculate the Saturday date of the next week

saturday = sunday + timedelta(days=6)

return sunday.strftime('%Y-%m-%d'), saturday.strftime('%Y-%m-%d')

def get_page_content(self, url):

options = Options()

options.add_argument("--headless") # Run in headless mode

options.add_argument("--log-level=3") # Suppress logging

options.add_experimental_option('excludeSwitches', ['enable-logging']) # Suppress logging

self.driver = webdriver.Edge()

self.driver.get(url)

# Wait for the page to fully load

self.driver.implicitly_wait(10)

# Wait for 5 seconds before closing the web browser window

time.sleep(2)

# Get the page source

return self.driver.page_source

def parse_page_content(self, html):

soup = BeautifulSoup(html, "html.parser")

# Find all the <a> elements with the data-test attribute set to "quoteLink"

links = soup.find_all("a", {"data-test": "quoteLink"})

# Extract the symbols/tickers and links from the <a> elements

for link in links:

symbol = link.text

href = 'https://finance.yahoo.com' + link["href"]

self.symbols_and_links.append((symbol, href))

def filter_symbols_and_links(self, filter_by_length):

filtered_symbols_and_links = []

for symbol, href in self.symbols_and_links:

# Check if the user wants to filter by length and if the symbol has 3-4 characters

if filter_by_length.lower() == "yes" and len(symbol) not in [3, 4]:

continue # Skip this symbol if it doesn't meet the criteria

filtered_symbols_and_links.append((symbol, href))

if not filtered_symbols_and_links:

print("None closing program...") # Print "None" if there are no stocks with 3-4 characters

return []

else:

return filtered_symbols_and_links

def run(self):

all_symbols_and_links = [] # Using a list for collected symbols and links

see_week_splits = input("Do you want to see the stock splits for the week? (yes/no): ")

if see_week_splits.lower() == "yes":

filter_by_length = input("Do you want to filter by tickers that only have 3-4 characters? (yes/no): ")

start_date, end_date = self.get_week_dates()

for day in range(7):

self.symbols_and_links = [] # Reset the list for each day

date = datetime.strptime(start_date, '%Y-%m-%d') + timedelta(days=day)

date_str = date.strftime('%Y-%m-%d')

print("Date:", date_str)

url = f"https://finance.yahoo.com/calendar/splits?from={start_date}&to={end_date}&day={date_str}"

print(url)

html = self.get_page_content(url)

self.parse_page_content(html)

day_symbols_and_links = self.filter_symbols_and_links(filter_by_length)

# Manually check for duplicates before adding

for symbol, href in day_symbols_and_links:

if (symbol, href) not in all_symbols_and_links:

all_symbols_and_links.append((symbol, href))

self.driver.quit()

# Print the final list

for symbol, href in all_symbols_and_links:

print(f"{symbol}: {href}")

# Ask user if they want to save the data to an Excel file

save_to_excel = input("Do you want to save the stock splits data to an Excel file? (yes/no): ")

if save_to_excel.lower() == 'yes':

df = pd.DataFrame(all_symbols_and_links, columns=['Symbol', 'Link'])

filename = 'stock_splits.xlsx'

df.to_excel(filename, index=False)

print(f"Data saved to {filename}")

else:

# Your existing code to print all tickers here

pass

if __name__ == "__main__":

scraper = StockSplitScraper()

scraper.run()