The 9-dataset Toolkit to Unlock Stock Markets

At DataHub.io, we’ve never made a secret of our passion for stock market data. And now, after (x) days of featuring it among our top 5 collections to fall in love with, we’re here to reaffirm it: our bundle of 9 specialized datasets is the ideal toolkit to power your investments, safeguard your portfolio, and elevate your trading strategies on multiple fronts.

As we’ll explore, the world of finance is becoming increasingly data-driven. For better or worse, the era of Wall Street wolves making bold moves based purely on instinct is fading. Of course, this shift demands that anyone in finance be comfortable navigating complex and ever-evolving data. But it also opens the door for anyone to tap into national and international markets thanks to the right tools.

Fully aware of how daunting and disorienting the flood of daily financial information can be, we’ve developed not only a comprehensive open-source Stock Market kit, but also this short guide to help you make the most of it, no matter where your interests lie within the financial world.

Finance and Data: An Inseparable Relationship

Until a few years ago, managing big data wasn’t automatically associated with the financial sector. And we’re not the first to say this. Back in 2017, Jennifer Q. Trelewicz, Risk Officer and CTO at the Deutsche Bank Technology Centre, wrote an entire article for InfoQ highlighting how the link between data management and stock markets is often underestimated. She brought back the famous “3Vs” framework from 2001 - velocity, variety, and volume - as a foundation to rethink the relationship.

Despite the fact that, as we’ll soon see, big data is now central to modern financial management, the popular imagination is still anchored to the stereotype of the sharp-suited businessman making magical investments based on instinct alone. Just think of The Wolf of Wall Street: an iconic film, but still rooted in a very 20th-century view of markets and their players.

Today, the game has changed. Like it or not, numbers often speak louder than instinct. Or better yet, they complement it.

The numbers of financial data

As always, we like to support our claims with concrete examples.

When it comes to volume, financial markets generate several terabytes of data daily. 

• The NYSE alone was already producing over one terabyte a day back in 2013 (source: InfoQ quoting PwC FS Viewpoint);
• By February 2025, NASDAQ reported more than 46 million trades (source: Nasdaq Trader).

In terms of data velocity, on August 5th, 2024, the CBOE Volatility Index (VIX) reached its highest intraday spike ever - likely due to a rise in bid-ask spreads (source: BIS Bulletin) - illustrating how quickly market variables can shift.

This ever-growing volume of data can’t be handled efficiently by humans alone. The scale of today’s financial markets seems to push independent investors aside, while demanding massive resources from the bigger players.

But fortunately, a growing ecosystem of tools is making it easier for everyone to manage this tidal wave of information and focus on strategy rather than just data wrangling. One example is Agentic AI, a cutting-edge IT innovation already used by Big Four firms for administrative and financial data tasks (source: Business Insider).

Still, even the smartest robot needs good data to work with. That’s where we come in.

A One-Stop Data Hub for Financial Markets

Our collection gathers all the datasets you need to monitor the health of financial markets, combining historical series with frequently updated data designed to meet the needs of professionals across a wide spectrum.

Let’s break it down. Here’s our Stock Market Data Collection ID card.

S&P 500: Understanding the American Stock Market

The first group of datasets focuses on the S&P 500, the most prominent stock index in the United States and a solid indicator of the market’s overall health. For the more finance-savvy, it’s a free-float, capitalization-weighted index tracking the 500 largest publicly traded US companies.

We offer three datasets related to the S&P 500:

• S&P 500 Companies: 8 variables describing each company included in the index;
• S&P 500 Companies with Financial Information: 2 datasets with 20+ in-depth
  variables, including labelling information, prices, highs and lows etc;
• S&P 500 Index Data Including Dividend, Earnings and P/E Ratio: 10 variables including real earnings, dividend, PE10 and Long Interest Rate.

Our collection includes three datasets.

Market Listings

To dive into specific exchanges, we’ve included two datasets: one focused on NASDAQ listings, and another covering NYSE and other major exchanges. These are comprehensive listings of traded companies, featuring fields such as:

• Market Category;
• Test Issue;
• Financial Status;
• Round Lot Size;
• ETF;
• Next Shares.

Each database includes both a simple list of companies with their symbols, and a dataset with more detailed characteristics. The official source? NASDAQ’s own webpage, using the “Listed Securities” file under the Public Domain Dedication and License.

You can process the info with our Python script; both datasets are regularly updated on our dedicated repository.

Understanding Market Sentiment

Two of our favorite datasets are essential for understanding market sentiment and volatility, two factors that shape not only the decisions of finance professionals but also the strategies of any business impacted by global market trends.

This kind of data isn't just helpful for identifying new investment opportunities, it’s equally powerful when building robust defensive strategies to protect your portfolio:

• The first dataset tracks gold prices, long considered a “safe haven” during times of geopolitical or economic uncertainty;
• The second focuses on the CBOE Volatility Index (VIX), widely regarded as the most reliable indicator of market volatility today.

When I wrote an article on this just a month ago, I witnessed firsthand how sentiment indicators align with historical events and how they help traders navigate between growth assets and safer investments. Some even take it a step further, speculating directly on volatility itself through VIX-linked instruments like futures and options.

Soil Resources Data

The third and fourth dataset groups offer concrete insights into energy markets, tracking Brent and WTI spot prices and natural gas prices, vital for forecasting costs and monitoring long-term trends. They can be seen as both investment tools and hedging instruments on multiple global arenas:

• Natural gas prices include the US Henry Hub benchmark;
• Brent and WTI prices are available daily, weekly, monthly, and annually, with Brent reflecting European trends, and WTI tied to American patterns.

Extremely simple, presenting only date and price variables in each dataset, this information is ideal to follow the trends, eliminating rumor. In other words, they are the best fit for anyone aiming at straightforward and actionable insights for real-world decision-making. 

What Makes Our Stock Market Collection Irresistible

While even the Big Four are investing in AI agents to manage their financial data, we at DataHub.io decided to go a different route - creating a unique, comprehensive repository to help independent traders, investors, and companies stop chasing data and focus on strategy.

In line with our mission of data democratization, we provide open-source knowledge to our community, sparing them from costly alternative data subscriptions. Newcomers may overlook these costs, but insiders know: by 2025, the investment management industry will spend $15.4 billion** on alternative data. In just five years, that number could soar to **$40 billion, according to Neudata.

That’s where our Stock Market Collection steps in - not just as a dataset hub, but as a strategic ally. 

Whether you're building your first portfolio, fine-tuning a hedge fund model, or simply exploring financial trends, our collection offers both breadth and depth. Accessible, reliable, and constantly updated, it empowers users of all levels to make data-driven decisions without the barriers of high cost or complexity. 

Because at DataHub.io, we believe that smart investing starts with open data.