Web scraper is used in most data science projects, to help gather more and more data on topics. Mostly data scientists will deal with the algorithm development and data engineers will deal with the infrastructure requirements, and thus someone with web scrapping experience has also become important. This screen scraping software can extract text from applications that are under execution. It can automate the process of scraping and provides quick as well as reliable data. It comes with a reliable library for screen scraping and wizards to generate screen scraping code quickly. It can work on web browsers, SAP, Siebel, etc. Web Scraper allows you to build Site Maps from different types of selectors. This system makes it possible to tailor data extraction to different site structures. Export data in CSV, XLSX and JSON formats Build scrapers, scrape sites and export data in CSV format directly from your browser.
Sometimes the data you need is available online, but not through a dedicated REST API. Luckily for JavaScript developers, there are a variety of tools available in Node.js for scraping and parsing data directly from websites to use in your projects and applications.
Web Scraper Api
Let's walk through 4 of these libraries to see how they work and how they compare to each other.
Make sure you have up to date versions of Node.js (at least 12.0.0) and npm installed on your machine. Run the terminal command in the directory where you want your code to live:
For some of these applications, we'll be using the Got library for making HTTP requests, so install that with this command in the same directory:
Let's try finding all of the links to unique MIDI files on this web page from the Video Game Music Archive with a bunch of Nintendo music as the example problem we want to solve for each of these libraries.
Tips and tricks for web scraping
Before moving onto specific tools, there are some common themes that are going to be useful no matter which method you decide to use.
Before writing code to parse the content you want, you typically will need to take a look at the HTML that’s rendered by the browser. Every web page is different, and sometimes getting the right data out of them requires a bit of creativity, pattern recognition, and experimentation.
There are helpful developer tools available to you in most modern browsers. If you right-click on the element you're interested in, you can inspect the HTML behind that element to get more insight.
You will also frequently need to filter for specific content. This is often done using CSS selectors, which you will see throughout the code examples in this tutorial, to gather HTML elements that fit a specific criteria. Regular expressions are also very useful in many web scraping situations. On top of that, if you need a little more granularity, you can write functions to filter through the content of elements, such as this one for determining whether a hyperlink tag refers to a MIDI file:
It is also good to keep in mind that many websites prevent web scraping in their Terms of Service, so always remember to double check this beforehand. With that, let's dive into the specifics!
jsdom
jsdom is a pure-JavaScript implementation of many web standards for Node.js, and is a great tool for testing and scraping web applications. Install it in your terminal using the following command:
The following code is all you need to gather all of the links to MIDI files on the Video Game Music Archive page referenced earlier:
Web Scraper App
This uses a very simple query selector, a, to access all hyperlinks on the page, along with a few functions to filter through this content to make sure we're only getting the MIDI files we want. The noParens() filter function uses a regular expression to leave out all of the MIDI files that contain parentheses, which means they are just alternate versions of the same song.
Save that code to a file named index.js, and run it with the command node index.js in your terminal.
If you want a more in-depth walkthrough on this library, check out this other tutorial I wrote on using jsdom.
Cheerio
Cheerio is a library that is similar to jsdom but was designed to be more lightweight, making it much faster. It implements a subset of core jQuery, providing an API that many JavaScript developers are familiar with.
Install it with the following command:
The code we need to accomplish this same task is very similar:
Here you can see that using functions to filter through content is built into Cheerio’s API, so we don't need any extra code for converting the collection of elements to an array. Replace the code in index.js with this new code, and run it again. The execution should be noticeably quicker because Cheerio is a less bulky library.
If you want a more in-depth walkthrough, check out this other tutorial I wrote on using Cheerio.
Puppeteer
Puppeteer is much different than the previous two in that it is primarily a library for headless browser scripting. Puppeteer provides a high-level API to control Chrome or Chromium over the DevTools protocol. It’s much more versatile because you can write code to interact with and manipulate web applications rather than just reading static data.
Install it with the following command:
Web scraping with Puppeteer is much different than the previous two tools because rather than writing code to grab raw HTML from a URL and then feeding it to an object, you're writing code that is going to run in the context of a browser processing the HTML of a given URL and building a real document object model out of it.
The following code snippet instructs Puppeteer's browser to go to the URL we want and access all of the same hyperlink elements that we parsed for previously:
Notice that we are still writing some logic to filter through the links on the page, but instead of declaring more filter functions, we're just doing it inline. There is some boilerplate code involved for telling the browser what to do, but we don't have to use another Node module for making a request to the website we're trying to scrape. Overall it's a lot slower if you're doing simple things like this, but Puppeteer is very useful if you are dealing with pages that aren't static.
For a more thorough guide on how to use more of Puppeteer's features to interact with dynamic web applications, I wrote another tutorial that goes deeper into working with Puppeteer.
Playwright
Playwright is another library for headless browser scripting, written by the same team that built Puppeteer. It's API and functionality are nearly identical to Puppeteer's, but it was designed to be cross-browser and works with FireFox and Webkit as well as Chrome/Chromium.
Install it with the following command:
The code for doing this task using Playwright is largely the same, with the exception that we need to explicitly declare which browser we're using:
This code should do the same thing as the code in the Puppeteer section and should behave similarly. The advantage to using Playwright is that it is more versatile as it works with more than just one type of browser. Try running this code using the other browsers and seeing how it affects the behavior of your script.
Like the other libraries, I also wrote another tutorial that goes deeper into working with Playwright if you want a longer walkthrough.
The vast expanse of the World Wide Web
Now that you can programmatically grab things from web pages, you have access to a huge source of data for whatever your projects need. One thing to keep in mind is that changes to a web page’s HTML might break your code, so make sure to keep everything up to date if you're building applications that rely on scraping.
I’m looking forward to seeing what you build. Feel free to reach out and share your experiences or ask any questions.
- Email: sagnew@twilio.com
- Twitter: @Sagnewshreds
- Github: Sagnew
- Twitch (streaming live code): Sagnewshreds
Nasdaq, the second largest stock exchange market in the globe has invested in technology and web scraping by acquisition of Quandal, one of the largest alternate data platforms.
The need to hold data insights have always been a norm in the financial industry, primarily to drive insights and make well-evaluated investment decisions. This is why financial institutions – hedge funds, banks, asset managers all hoard data to keep their big-buck bearing investment decisions data-backed. Though the sector well understands the need for information, be it for equity research analysis, venture capital investment, hedge funds management, asset management etc. they do not have the tools to extract the data, get them in a structured format to draw insights.
Why consider scraping in finance?
There are so many sources and forms in which data is available. Turns out, every bit of this is as important and can really contribute to making better decisions. For instance, look how hints of mergers and acquisition data can be identified by tracking CEO’s travel patterns as Kamel, CEO of Quandel rightly states the data significance.
“What we’re interested in doing is tracking corporate, private jets, most companies hide the identity of their corporate jets, but it’s possible to unmask them, researchers carefully watching websites like FlightAware.com could theoretically piece together flight records to figure out individual planes’ tail numbers”.
Tracking of volumes of information such as news, social media, satellite data, app data etc. through an automated process such as scraping can help financial companies gain a lot of valuable insights.
Another interesting example is the one where Goldman Sachs asset management was able to identify an increase in visitors to the HomeDepot.com website by scraping website traffic from alexa.com. This helped asset manager to buy the stock well in advance of the company raising its outlook and its stock eventually appreciating.
Web scraping in hedge funds
Hedge funds are an investment that carries some risk in the ROI and hence the need to rely on data to accommodate the nature of volatility in the hedge fund market. Web scraping, however, will provide the investor’s information covering all angles – market forces, consumer behavior, competitive intelligence etc. that makes strategic decisions an easier process.
Going past the traditional methods like market data (earnings and macroeconomic data), a majority of the hedge fund managers are beginning to see the potential in alternate data such as information available in satellite imagery, geolocation, web scraping etc. The prowess of the web data is being increasingly recognized by the procurer of such data to unbox tremendous insights to have an informational advantage over the peers.
A hedge fund manager requires the assistance of web extraction to obtain these data sets from a third-party scraping service provider. Such data can be put to scrutiny by the data scientists partnering with portfolio managers to draw insights.
A huge part of web scraping for information that helps in efficient decision-making is dependent on the effectiveness in the financial structure and identification of the right data sets by the data scientists and the portfolio managers. Identification of alpha opportunities (a metric that represents the active returns on investment).
According to Greenwich / Thomson Reuters research the average investment firm is spending about $900,000 yearly on alternative data, and of this alternative data, clearly, the most popular form being used investment professionals is web-scraped data. Of all the methods in alternate data for hedge funds, web scraping is identified as the most effective methods.
What are the use cases of scraping in finance?
Equity research analysis
A huge investment decision requires an assessment of the financial position of the company in which you are intending to invest. Viper for mac free download. Generally, the information needs to be gathered from the profit and loss statement, balance sheet and cash flow statements for numerous years. These numbers can be obtained through ratio analysis ( solvency and profitability ratios).
Now, these data are available on the websites in the investor relation sections ( most of the public limited companies have a dedicated page) and in the quarterly or annual reports. The information available on these sites and PDFs can be scraped to gain insights into the financial strength.
You can take a look at the investor relation page of Walt disney .
This type of data is also available in the EDGAR databases that hold annual reports and the filings are available for download or can be viewed for free. How to download winzip for mac.
Let’s quickly get to an example of sample code for scraping annual reports (PDFs) from the Walt Disney website. These annual reports have tons of financial data points and extracting these data from annual reports or quarterly reports for several years will help in identifying a pattern and a thorough analysis of the same will help in making better-informed decisions.
Best Free Web Scraper
Here’ a sample code to scrape out a critical piece – the balance sheet from the PDF document from Walt Disney.
This code is developed as a sample to scrape specific pages with financial data points from a PDF document with high volumes.
The output would look like this:
Financial data and credit ratings
To assess the financial strength of borrowing entities for qualifying their ability to meet principal and interest payments. This information is particularly useful for the clients if such rating agencies like the institutional investors, banks, and insurance companies) to evaluate using near real-time updates. This type of data can be scraped from websites, Google Finance Pages, and Bloomberg Research.
Venture capital
Small businesses or start-ups require funding/investment form big businesses and hence the need to research the companies before investing. This kind of data is usually available in some websites that have information on profiling of new business and products like techcrunch and venturebeat.
Also, there are a ton of trends, technology and portfolio companies that are required to be monitored before making an investment decision. A solution like scraping will help in extracting and aggregating this data in a structured format to make a strategic venture capital decision.
Risk mitigation and compliance
Compliance with regulation is very important in the financial industry and these are put into great scrutiny leading to millions of dollars as a penalty and successive reformation cost as a consequence of a breach. Through automated monitoring, of sources that post regular updates – government regulations, court records, sanction lists etc you can effectively improve your compliance and risk management position. Utorrent free download for mac os x 10.8 5.
Even if these sites are complex or difficult to access scraping helps in extracting regulatory updates to stay abreast of the happenings and identifying frauds.
Ditch internet surfing and use scraping instead.
Finance industry needs tons of crucial information to make strategic business decisions. Scraping has been the ultimate solution for various use cases including venture capital, hedge funds, equity research analysis etc. The potential of scraping is immense and the volume and variety of data that scraping can give within a quick TAT is something every financial service provider should leverage upon.
Scrapeworks is architectured to scour the web data in the most fashionable and structured manner that can give information which can forever redefine the value of information the Internet has got.
You can set your parameters for the scraping requirements and we can deliver the data that you want.
Read through our customer stories to understand how we extracted crucial data points from company reports and financial statements for a leading news agency and extensive crawling and extraction of financial information for a leading financial services firm.
If you have a similar need, do get in touch with us.