How to Scrape Yahoo Finance and Extract Stock Market Data Using Python?

For technology companies, the stock market is an enormous database having millions of records, which get updated each second! As there are a lot of companies, which do offer finance data of the companies, normally it gets through the API and APIs are always have paid versions. A reliable resource for stock market information is Yahoo Finance. As Yahoo is also having an API, it is a paid version. Instead, you can have the stock information of any business on the website absolutely free.

Although it is hugely popular with the people that keep track of the stocks, it has survived in the market where a lot of big competitors including Google Finance have failed. For people who want to observe the share market, Yahoo shows the newest news associated with the stock market and companies.

Why Do Data Scraping from Yahoo Finance?

In case, you are dealing with the stock market data, as well as need a clean, free, and reliable resource, Yahoo Finance might be the finest option. Different company-profile pages are created in an identical structure and in case, you write the script for scraping data from Microsoft financial page, a similar script might also be utilized for scraping data from Apple financial pages.

Which Data Points You Can Scrape from the Yahoo Finance?

You can scrape various data points with Yahoo Finance Data Scraping. The current value and contemporary growth or fallen percentage are of the highest importance. Additional data points, while viewed together, provide a clear picture as well as helps one in deciding whether making investments in the company stock might be a better thought or not. Considering the snapshots of data could not be very effective nevertheless. Scraping Yahoo Finance data at consistent intervals as well as using the big datasets to forecast future stock prices might prove to be more helpful in a long time.

Here are some other significant data points, which you can capture:

  • BID & ASK: Both these prices are the quotes on the single share. The bid means the prices, which purchasers are ready to pay whereas the question is what the sellers are ready to sell for. A multiplier means total shares awaiting trade at the respective prices.
  • OPEN: The starting price is referred to as Open, which is the primary price of the share on the trading day. For instance, the opening pricing for the stock markets on NYSE (New York Stock Exchange) would be the price at Eastern time 9:30 a.m.
  • PE_RATIO: Perhaps the most significant single factor, which investors are looking into, the ratio is intended by separating the existing market prices of the company stock by the company’s earnings for every share. In simple terms, it is an amount of money a person is willing to pay every rupee value of the company’s earnings.
  • PREV_CLOSE: It is the closing pricing of the stock on previous day trading. The previous day’s value of the stock raises only to the most recent closing price on the day whenever the stock market is open.
  • TD_VOLUME: This is just the number of shares, which changed hands throughout the day.

Which Other Data You Can Scrape?

Different data can be scraped from the summary pages of the company website of Yahoo Finance. Every company comes with chart pages, whereas you can observe stock data of around five years. Whereas the data isn’t well-structured, Yahoo Finance Data Extraction could provide you a good insight into the past performances of companies’ stocks.

A statistics page provides you over 30 data points. They are in the well-structured format as well as could be scraped through a code related to the given code.

Any historical data consists of data from a chart page, however, in the format just like CSV — you may easily scrape the data as well as store that in the CSV. Pages like financials, analysis, profile, holders, options, and sustainability could provide you a very good estimation of how a company’s shares will do compared to the competitors.

Yahoo Finance Use Cases

You can utilize different historical data for forecasting stock prices or you might make an application, which uses frequently reorganized data from the scraping engine for offering user updates like while they need to sell stocks or while they need to buy more.


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