How to Scrape Stock Data with Python

How to use Python to scrape data from the internet

How to Scrape Stock Data with Python?

Financial professionals looking to upgrade their skills can do so by learning how to scrape stock data with Python, a high-level, interpreted, and general-purpose programming language. Python is the most popular data scraping tool for stock data. It is also used in data mining, cybersecurity, digital forensic applications, and penetration testing.

 

Scrap Stock Data with Python

 

Python also offers the advantage of a community of contributors who volunteer in the regular improvement of the developer environment. It gives the programming language an upper hand in being up to date on the latest developments in the software world. Python language is widely used in the data scraping world due to its efficiency and reliability in carrying out tasks.

 

Benefits of Using Python for Data Scraping

 

1. Simple and reliable

The use of Python for scraping stock data is becoming prominent for a variety of reasons. First, its syntax is simple and reliable in the execution of tasks and the sharing of scripts with other users.

 

2. Inbuilt libraries

Second, Python comes with many inbuilt libraries that help save time for developers who would otherwise build their projects from scratch. Developers save routine and common tasks by incorporating the libraries into their projects.

 

3. Open-source software

Third, Python is open source and, hence, freely available for use, whereas other languages are patented and relatively expensive. Lastly, Python is compatible with many data applications, thereby making it appropriate for stock data scraping.

 

Stock Data Scrapers

Data scraping is the procedure carried out by scrapers to obtain the required data from multiple locations on the internet. Data scrapers are, therefore, scripts or algorithms set to extract specific types of information from the internet for use in data analysis.

The procedure followed by data scrapers includes downloading information from the target, extracting and storing the data, and finally, analyzing the data. The procedure for scraping stock data is similar to the procedure followed when scraping other types of data online.

The first step when scraping stock data is to download the target content from the database where the data is stored. Second, use the data scraper to extract data from its unstructured form into a structured format.

The third step involves storing the structured data into the preferred format, such as the CSV format or an Excel spreadsheet. The final step is analyzing the data obtained to generate important information about the stock market or specific stocks.

 

Steps in Scraping Data With Python

The first step when scraping stock data is to specify the URL(s) where the scraper will obtain data from the execution code. The URL then returns the requested information by displaying the HTML or XML page showing the data requested by the scraper.

Once the information is obtained, the scraper will inspect the data displayed in the target URL, identify the data required for extraction, and then run the code for execution. Once the data is scraped, the extracted data is converted and stored in the desired format.

Learn more about how to scrape data with Python through CFI’s Machine Learning for Finance – Python Fundamentals course!

 

Data Scraping Libraries

Python is a diverse programming language with many applications in the programming space. Each of the activities that are carried out using Python includes different libraries associated with them. Data scraping with Python uses many libraries, including Selenium, Beautiful Soup, and Pandas.

Selenium library is the best option for web testing and is widely used in the automation of browser activities. The Beautiful Soup library consists of a package that parses HTML and XML documents. The package works by creating parse trees that help in extracting data from the target. The Pandas library, on the other hand, is instrumental in the extraction, analysis, manipulation, and storage of data in the required format.

 

Practical Example

Below is a sample data scraping for Google stock on the Yahoo! Finance website.

The procedure begins by visiting the Yahoo Finance website and entering the trading symbol for the Google stock, “GOOG,”  in the search box. In response, the URL changes to include the search term, i.e., the symbol “GOOG.” The search results display the stock page, which shows specific information on the stock, such as the stock price, opening price, index for price per earnings, and the year’s trading range.

Next, inspect the stock data by right-clicking on the page and choosing “View page source” or “Inspect element”, depending on your browser. You can also use the shortcut provided on the GOOG stock page by highlighting the data you need, such as the current stock price.

Then, right-click on the highlighted area and choose “Inspect element” from the options provided. The output gives you the stock price and all other relevant details of the GOOG stock.

 

More Resources

CFI offers the Certified Banking & Credit Analyst (CBCA)™ certification program for those looking to take their careers to the next level. To keep learning and developing your knowledge base, please explore the additional relevant CFI resources below:

Financial Analyst Certification

Become a certified Financial Modeling and Valuation Analyst (FMVA)® by completing CFI’s online financial modeling classes and training program!