In today’s fast-paced financial landscape, staying ahead of market movements is key to success. One powerful tool at the disposal of traders, analysts, and investors alike is web crawling. Harnessing Python for this purpose opens up a world of possibilities, allowing access to real-time news updates and insights that can make or break investment decisions.
Importance of Crawling Financial News
- Timely Information: Financial markets react swiftly to news events, whether it’s earnings reports, economic indicators, or geopolitical developments. By crawling financial news websites, individuals can access up-to-the-minute information, gaining a competitive edge in decision-making.
- Market Sentiment Analysis: Understanding market sentiment is crucial for anticipating price movements. Crawling news articles enables sentiment analysis, where algorithms sift through text data to gauge public opinion. This analysis can inform trading strategies, risk management, and asset allocation.
- Identifying Opportunities and Risks: By monitoring news sources systematically, investors can identify emerging trends, potential opportunities, and looming risks. Whether it’s a merger announcement, regulatory change, or corporate scandal, being the first to know can be immensely profitable.
The Role of Sentiment Analysis
Professionals in the field of sentiment analysis play a pivotal role in interpreting the vast amount of textual data generated by financial news sources. Using natural language processing (NLP) techniques, they extract insights regarding the emotional tone, confidence, and relevance of news articles. These insights can inform trading algorithms, risk models, and investment strategies.
Example: Tesla’s Rollercoaster Ride
Let’s take the example of Tesla, a company known for its volatility and media scrutiny. Suppose a major news outlet publishes an article alleging safety concerns with Tesla’s latest vehicle model. Within minutes, the stock price plummets as investors react to the negative sentiment surrounding the brand. Traders who had anticipated this development through crawling financial news could have executed short positions or adjusted their portfolios accordingly, capitalizing on the price decline.
Code time: Crawling news from CNBC
As an example, let’s write a Python code to crawl the CNBC website for news.
First, let’s open up the website:
So, to verify how the website is crafted, we need to inspect the website (F12). This will open the HTML information for the page
From here, it’s easy to see the news headlines are defined in elements of the class “Card-title“.
We can use this information to crawl news easily using some Python libraries such as requests and BeautifulSoup
The requests library will help us to fetch the HTML page from the web, while the BeautifulSoup will help us filter the HTML the page we just fetched.
Here’s a code example with that:
import requests
from bs4 import BeautifulSoup
def extract_news(url):
response = requests.get(url)
soup = BeautifulSoup(response.content, 'html.parser')
link_elements = soup.find_all('a', class_='Card-title')
for link_element in link_elements:
title = link_element.text
href = link_element.get('href')
print("Title:", title)
print("Link:", href)
print()
url = 'https://www.cnbc.com/economy/'
extract_news(url)Results
With this simple code, we can fetch the news from that day:
- Title: Wholesale prices rose 0.2% in March, less than expected
- Link: https://www.cnbc.com/2024/04/11/ppi-inflation-report-march-2024-wholesale-prices-rose-0point2percent-in-march.html
- Title: ECB gives strong signal that cuts are on the way despite Fed uncertainty
- Link: https://www.cnbc.com/2024/04/11/european-central-bank-holds-interest-rates-hints-at-cut-ahead.html
- Title: Hot inflation data pushes market’s rate cut expectations to September
- Link: https://www.cnbc.com/2024/04/10/hot-inflation-data-pushes-markets-rate-cut-expectations-to-september.html
- Title: WTO forecasts rebound in global trade but keeps geopolitical risks in focus
- Link: https://www.cnbc.com/2024/04/10/wto-forecasts-rebound-in-global-trade-but-keeps-geopolitical-risks-in-focus.html
- Title: Consumer prices rose 3.5% from a year ago in March, more than expected
- Link: https://www.cnbc.com/2024/04/10/cpi-inflation-march-2024-consumer-prices-rose-3point5percent-from-a-year-ago-in-march.html
- Title: A crucial report Wednesday is expected to show little progress against inflation
- Link: https://www.cnbc.com/2024/04/09/a-crucial-report-wednesday-is-expected-to-show-little-progress-against-inflation.html
And the list goes on…
What to do with it?
This list in itself is interesting, as you will be able to quickly view the site’s content and open up the news you like, but if you want to go further, here are some ideas on how this list can be helpful:
- Define specific filters to this list (using Python) to make it easier to find what matters for you… For instance, you can filter only news containing the word “inflation” in the title
- Crawl the news for a bunch of different websites and filter out the news that shows up in 3 or more of them, so you know the hot topics
- Utilize natural language processing (NLP) techniques to analyze the sentiment of news titles and articles, identifying positive, negative, or neutral tones
- Extract relevant keywords from news titles and articles to identify key themes or subjects discussed in the media.
- Detect significant events or trends by analyzing patterns in news titles and URLs over time, identifying clusters of related news articles.
- Set up an alerting system to notify users in real-time about important news events or developments that meet predefined criteria
Each of these topics could be a blog post on its own, as there is a range of things to explore
To sum up, crawling financial news can be a very interesting trick to help you get up to speed on market-moving events. Also, the more information you have in today’s world, the better decisions you make, so leveraging the power of understanding the news can’t be bad!
That’s it for this post!
In the next few posts, I’m planning to go deeper into these topics that involve using the news we crawled.
I’ll see you there!