Once a company is public, its financial behavior is supposed to be made public. This information clearly depicts the financial status of the company. The stakeholders access this information to decide on the fate of their investment in the company.
Such information is widely available and accessible on various public domains. One such platform is Yahoo Finance. This is one of the most renowned web platforms in the world to access and import financial information and details of organizations around the globe.
Hence, it becomes quite imperative to understand how to execute scraping Yahoo Finance. Web scraping services across the USA understand the importance of scraping Yahoo Finance because the platform is rich with valuable finance-related information for various businesses.
Yahoo Finance displays information on an open-sourced network with massive amounts of public information. These include intel on stock exchanges, constantly varying stock prices, market behavior, data on investment instruments like SIPs and Mutual Funds, and also the latest news and updates on cryptocurrency behavior.
There are multiple ways to web scrape Yahoo Finance. Let's take a look at Yahoo Finance web scraping without coding.
Scraping Yahoo Finance Without A Code
Scraping without a code is one of the multiple ways to approach this task. Many crawler tools aid a successful scaping without using a code. These open-source software extract colossal data numbers from web pages across the internet with thousands of links accessed at a time. This data can be expressed concisely using the robust and advanced web scraping services in the USA. Such data can be expressed in any format, like Excel, at any time frame.
The most common advantage of scraping Yahoo Finance using these tools is that they require zero coding knowledge.
Let's move on to understanding scraping data from Yahoo Finance using Python.
We can efficiently utilize numerous Python modules and methods that are available on the internet for scraping data from yahoo finance using Python. To execute this process in a non-advanced mode, we will extract this information by using the Beautiful Soup Library.
Install Dependencies
In this step, we install all the dependencies we plan to utilize.
- pip install bs4
- pip install requests
- pip install pandas
Import Modules
- #import modules
- import requests
- from bs4 import BeautifulSoup
- import pandas as pd
Proof Check For Errors
This step accesses the web page URLs to rectify the errors, if any.
#get the URL using response variable
my_url = "https://finance.yahoo.com/news"
response = requests.get(my_url)
#Catching Exceptions
print("response.ok : {} , response.status_code : {}".format(response.ok , response.status_code))
print("Preview of response.text : ", response.text[:500])
Retrieve Beautiful Soup Object
Here, we design a function that retrieves HTML information of the web page as a Beautiful Soup object.
#utility function to download a webpage and return a beautiful soup doc
def get_page(url):
response = requests.get(url)
if not response.ok:
print('Status code:', response.status_code)
raise Exception('Failed to load page {}'.format(url))
page_content = response.text
doc = BeautifulSoup(page_content, 'html.parser')
return doc
#function call
doc = get_page(my_url)
Transact Information
Once the information is accessible, the same data is extracted and stored.
#appropritae tags common to news-headlines to filter out the necessary information.
a_tags = doc.find_all('a', {'class': "js-content-viewer"})
print(len(a_tags))
#print(a_tags[1])
news_list = []
#print top 10 Headlines
for i in range(1,len(a_tags)+1):
news = a_tags[i-1].text
news_list.append(news)
print("Headline "+str(i)+ ":" + news)
news_df = pd.DataFrame(news_list)
news_df.to_csv('Market_News')
Scraping Yahoo Finance for Cryptocurrencies
So far, we have decoded how to execute scraping Yahoo Finance data using Python. Now we proceed further. Yahoo Finance is a perfect space if you want to know in detail about financial instruments ranging from the stock market and money market to the crypto market.
The trading ecosystem of the crypto market is as vigorous as the stock markets. Therefore they need a strong database of the numbers that are constantly changing on the board every second. That’s when scraping for crypto news is something that becomes a job pretty necessary.
In the next section, we shall skim through the aspect of scraping data from Yahoo Finance for information on cryptocurrencies.
The process of scraping Yahoo Finance for crypto data flows through Selenium.
What Is Selenium?
Selenium is an open-source tool. It is a web-based network that aids languages like Python to test and scrape the internet. The process can be initiated using any relevant browser on your computer.
Why Selenium?
Selenium has touchpoints to locate various elements on a web page while scraping the information. These include clickable buttons, scrolling, page refresh, screenshot, and form filling.
Here is a summary of the entire process of scraping Yahoo Finance using Selenium.
- Download and install before completing the setup.
if 'google.colab' in str(get_ipython()):
print('Google CoLab Installation')
!apt update --quiet
!apt install chromium-chromedriver --quiet
- Install and import libraries
print('Library Import')
if 'google.colab' not in str(get_ipython()):
print('Not running on CoLab')
from selenium.webdriver.chrome.options import Options
from selenium.webdriver.chrome.service import Service
import os
else:
print('Running on CoLab')
print('Common Library Import')
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
import pandas as pd
import time
- Create a web driver
if 'google.colab' in str(get_ipython()):
print('Running on CoLab')
def get_driver(url):
"""Return web driver"""
colab_options = webdriver.ChromeOptions()
colab_options.add_argument('--no-sandbox')
colab_options.add_argument('--disable-dev-shm-usage')
colab_options.add_argument('--headless')
colab_options.add_argument('--start-maximized')
colab_options.add_argument('--start-fullscreen')
colab_options.add_argument('--single-process')
driver = webdriver.Chrome(options=colab_options)
driver.get(url)
return driver
else:
print('Not running on CoLab')
def get_driver(url):
"""Return web driver"""
chrome_options = Options()
chrome_options.add_argument('--no-sandbox')
chrome_options.add_argument('--disable-dev-shm-usage')
chrome_options.add_argument('--headless')
chrome_options.add_argument('--start-maximized')
chrome_options.add_argument('--start-fullscreen')
chrome_options.add_argument('--single-process')
serv = Service(os.getcwd()+'/chromedriver')
driver = webdriver.Chrome(options=chrome_options, service=serv)
driver.get(url)
return driver
- Explore and locate elements
header = driver.find_elements(By.TAG_NAME, value= 'th')
print(header[0].text)
print(header[2].text)
- Extract and bind the data into a Python list
def parse_multiple_pages(driver, total_crypto):
"""Loop through each row, perform Next button click at the end of page
return total_crypto numbers of rows
"""
table_data = []
page_num = 1
is_scraping = True
header_list = get_table_header(driver)
while is_scraping:
table_rows = get_table_rows(driver)
print('Found {} rows on Page : {}'.format(table_rows, page_num))
print('Parsing Page : {}'.format(page_num))
table_data += [parse_table_rows(i, driver, header_list) for i in range (1, table_rows + 1)]
total_count = len(table_data)
print('Total rows scraped : {}'.format(total_count))
if total_count >= total_crypto:
print('Done Parsing..')
is_scraping = False
else:
print('Clicking Next Button')
element = WebDriverWait(driver, 20).until(EC.presence_of_element_located((By.XPATH, '//*[@id="scr-res-table"]/div[2]/button[3]')))
element.click()
page_num += 1
return table_data
- Save all the scraping data as CSV file
def parse_multiple_pages(driver, total_crypto):
"""Loop through each row, perform Next button click at the end of page
return total_crypto numbers of rows
"""
table_data = []
page_num = 1
is_scraping = True
header_list = get_table_header(driver)
while is_scraping:
table_rows = get_table_rows(driver)
print('Found {} rows on Page : {}'.format(table_rows, page_num))
print('Parsing Page : {}'.format(page_num))
table_data += [parse_table_rows(i, driver, header_list) for i in range (1, table_rows + 1)]
total_count = len(table_data)
print('Total rows scraped : {}'.format(total_count))
if total_count >= total_crypto:
print('Done Parsing..')
is_scraping = False
else:
print('Clicking Next Button')
element = WebDriverWait(driver, 20).until(EC.presence_of_element_located((By.XPATH, '//*[@id="scr-res-table"]/div[2]/button[3]')))
element.click()
page_num += 1
return table_data
Here’s how we can scrape data from Yahoo Finance using various tools and utilize that information on multiple finance decisions. These tools benefit everyone engaged in financial research, transactions, and even personal investment.
If you are looking for web scraping services to gather data from Yahoo Finance, feel free to contact us. We would be more than happy to help you!