In any trading strategy, having access to reliable data is crucial. As noted by seasoned investor Daniel Calugar, data serves as a vital tool for informing traders and aiding them in making the most effective investment decisions. This is especially important for algorithmic trading systems, which heavily rely on vast amounts of data to perform complex analyses within a fraction of a second. Without accurate and sufficient data, these sophisticated algorithms would not be able to operate effectively.
Traditionally, these algorithmic trading systems have relied on data from traditional financial sources. This includes performance data on individual assets or stock exchanges in general and information from news organizations – all of which can help identify market opportunities.
Today, there has been a rise in the use of alternative data sources – everything from social media to satellite imagery to weather patterns. These sources are helping investors change the game by taking a novel approach to trading.
Alternative Data Explained
Before delving into the sources, examples, and applications of alternative data, let’s first dive into what alternative data is. Generally speaking, alternative data refers to any data set that’s gathered from a source that wouldn’t be considered “traditional.”
For investing, this means pulling data from sources other than corporate earnings reports, Labor Department jobs reports, or historical market performance. Anything that doesn’t fit into that general category of traditional information could be considered alternative data for investing purposes.
How ‘Traditional Data’ Has Changed
When investors integrate alternative data as a part of their research and analysis, they aren’t doing so just to be different. Instead, they are doing so to find new angles and glean new information in what’s becoming an overcrowded market of information.
There used to be a time when only top traders could access the key data points that helped them make informed decisions. Professional traders and investment firms were the only ones who could access this information and have the technological setup to analyze it in a timely manner.
That began to change with the advent of the internet and “day trading” in the late 1990s and early 2000s. Today, anyone can easily access basic and even complex market data with the click of a few buttons on a computer or the tap of their fingers on a smartphone.
According to Daniel Calugar, this has made these data sets more commonplace and less specialized. If a large number of people are turning toward the same data to make investment decisions, it becomes more difficult to glean any ground-breaking insight before everyone else makes the same conclusions.
The Power of Alternative Data
With traditional data becoming more readily available, seasoned investors are seeking out alternative sources of information to gain an edge in the market. These investors meticulously comb through a wide variety of data to uncover valuable insights that may be hidden within even the smallest nuggets of information.
Alternative data wouldn’t be more than an interesting concept if it weren’t already proving to help investors succeed.
Part of the reason alternative data is so powerful for traders is that the information isn’t as easily accessible as traditional data. Anyone can Google search a public company’s earnings statements and get results in a matter of seconds. It’s not as simple to pour over data from satellite imagery to determine whether a big box store is seeing an influx of customers or a decline in them.
Alternative data is powerful for that simple reason: It can provide crucial information but isn’t displayed on a billboard, as it seems traditional data today is.
Alternative Data in Algorithmic Trading
There is seemingly no limit to what sources could be considered alternative data. This is great for traders, as it means they can gain advantages in the market by being creative and discovering new ways to conduct analyses that the rest of the market doesn’t know about.
At the same time, this provides a significant challenge. With so much information and data out there, how do you determine which sources are relevant and which aren’t? And, even when you make those determinations, how do you begin to pour through all the raw data to make a definitive investment conclusion?
This is the intersection where alternative data and algorithmic trading meet.
Dan Calugar says algorithmic trading systems were made for alternative data sources. For example, high-powered computers can be set up to receive an enormous amount of raw data from a variety of sources automatically. Then, based on the parameters you set, the systems can analyze the data, looking for potential discrepancies or bits to highlight.
When all this analysis is complete – which will be done in milliseconds – investors can be alerted of prime trading opportunities, or automatic trades can be executed on their behalf.
It can be challenging to use alternative data successfully on a consistent basis for investment purposes without using algorithms. One of the main reasons for this is that, unlike traditional data, you won’t always be able to glean usable insights from the same sources of alternative data.
For example, while basic company earnings data such as revenue, profit, and projections will always tell you a certain story, the same cannot be said for satellite imagery. In other words, you won’t always be able to determine the performance of a big box store by satellite imagery of cars in its parking lot.
As such, you need something powerful, like an algorithmic trading system to pour through the loads of data sets. These computer systems have the power to do what humans simply cannot.
Alternative Data Sources
Alternative data can be taken from many different sources. The possibilities are endless, according to Daniel Calugar. That being said, there are some common sources for alternative data, and each of them is used slightly differently by algorithmic trading strategies.
Here are some of those most common sources and how they are used.
Billions of people around the world use social media to meet and connect. People connect not just with people they know but with people who have common interests.
As such, a treasure trove of data is available on social media platforms, and some of that information can be useful in trading decisions. There are many different examples of this.
The now-infamous “meme stock” explosion in early January 2021 is a prime example. Members of a forum on Reddit called r/WallStreet bets decided to get together and pump money into GameStop, a fledgling retail store that sold video games and accessories. The members bound together to do this, they said, to fight against corporate short sellers.
The results were staggering. GameStop’s stock price soared nearly 3,000% in less than a month – from about $17.25 to more than $500 per share.
Other times, social media messages sent by well-known people can affect short-term stock prices. Elon Musk – the CEO of Tesla, SpaceX, and Twitter – has done this with his tweets a number of different times.
In early 2021, Musk tweeted that he loves Etsy. Immediately after, the e-commerce site’s stock price jumped 8%.
He’s also been known to tweet information about his own companies. In 2018, he sent a message on the social media platform that said he was considering taking Tesla private. A 6% increase in the company’s stock value followed.
And in 2020, when he tweeted that he believed Tesla’s stock price was too high, the electric vehicle company dropped 10% in one day.
The thing about social media, according to Dan Calugar, is that the information posted on it is not always this obvious – or in your face. Sometimes, messages being sent, or what’s being talked about, is hidden in more remote forums or groups that would take forever to go through. And sometimes, the pertinent messages aren’t so directly related to investing.
Integrating social media data into algorithmic trading systems can help wade through all this information.
Other Web Data
Online platforms provide traders with a considerable amount of valuable information, including various data points and metrics. This wealth of alternative information can greatly benefit traders in making informed decisions.
For instance, recent posts from current and former employees about a company on a job review site could forecast the success or failure of that company. Similar information can be gleaned through product reviews on major e-commerce sites or company reviews on business review sites.
Updated pricing on products, including flash sales, may foreshadow a bump in sales for a retail store. A spike in website traffic or app downloads – or a drop in such – could also be very informative to traders.
While visits and views don’t always translate directly into purchases, they often signal a spike in interest. This interest could be enough to predict the future price of a company’s stock price.
All of this information can be pumped into an algorithmic trading platform and then analyzed to see if any valuable information can be used.
Credit Card Transactions
Such a large percentage of payments today are made via digital forms. This makes it much simpler to track data and identify potential trends than when most purchases were made using cash or checks.
Credit card transaction data can provide a real-time look into trends that could be emerging. For example, it can tell you where consumers spend their money as they’re spending their money. This could help you identify which companies are performing well and which aren’t before they release their quarterly performance results.
This can be analyzed from a broader perspective, too. For example, you can track what categories of products may be experiencing a spike in activity and then use that information to determine companies that may benefit from changing consumer behavior.
Remember that credit card transactions don’t just apply to consumer behavior. For example, most companies use credit cards to pay their bills.
You can track payment patterns from companies, including how much they paid and whether they’re paying on time. If there is a sudden spike in late payments, for instance, it could signal that the company is having issues with cash flow.
Again, the powerful part about alternative data is that investors can see it in real time rather than waiting until the rest of the world is privy to the information when companies release their quarterly earnings.
Satellite imagery is used for all different purposes nowadays, and traders are even using it as a source of alternative data for algorithmic trading purposes. The pictures of the Earth taken from above can reveal a lot of information.
These images of the parking lot of big box stores, for instance, could show how many people are going in and out of that store at different periods of time – or over a prolonged period of time. This could help traders predict a spike, or drop, in sales for that particular store long before they release that information to the public.
Satellite images can be used for a number of different predictive applications, too.
It could show the number of vehicles entering and exiting a manufacturing facility, which could predict increased productivity and output. It could be used to show the progress of major construction projects, which could predict early launches and new sources of revenue. It could be used to see whether oil fields are proving prosperous, which could predict high earnings for the particular company running it, or for the oil industry as a whole.
The weather is something that affects everyone’s lives on a daily basis, but it’s something we all seem to take for granted. Most people check the weather to determine what clothing they should wear or whether it’s a good or bad day to do certain activities.
But the weather has a profound impact on business and, as a result, investments.
Weather can dramatically impact the travel and tourism industries, for good or bad. Severe weather events can also dramatically impact the supply chain, disrupt industries or even help fledgling industries.
Weather data can be tracked in a variety of ways, including through satellite imagery, news reports, forecasts and more. Pumping this raw information into an algorithmic trading system and letting it conduct complex analyses could help traders glean investment insights.
Internet of Things (IoT)
The Internet of Things, better known as IoT, collects a wealth of data points through the use of multiple sensors that are attached to physical assets. Almost an unlimited amount of data and information is being collected nowadays through IoT, yet the power of much of this data isn’t being harnessed properly.
IoT data can reveal the efficiency of innovation, production, and distribution. It can reveal now how many people are shopping in a store, but exactly what paths they take when traveling around a store.
Most people look at IoT data as information that companies will use internally to inform them of how things are going and what opportunities they might have to change and capitalize on.
However, Daniel Calugar says this information can prove invaluable to investors, too. Red flags can be raised with this IoT data, not just for the company in question but for traders who are looking to make investment decisions about that company.
The challenge, of course, is that there is so much IoT data – and so many sources of that data – that it can be quite overwhelming. But this is where algorithms can do their magic. They can process the seemingly never-ending data sets and spit out easy-to-digest bits of information that traders can use as part of their investment strategy.
Insurance data tells many stories. This is because all people and companies need insurance of some form. Therefore, all insurance policies produce a significant amount of data, which can be used to make investment decisions.
For instance, the number of new auto insurance policies directly correlates to the volume of new car sales. By tracking new auto insurance policies, you can get real-time insights into the new car sales broken down by manufacturer. Again, this could help you buy or sell that manufacturer’s stock before it releases its earnings report.
That information can also be used to make other predictions about related items. Dan Calugar points out that new auto insurance policies could reveal a spike in new car sales, which could predict a spike in fuel demand. Or, it could predict a drop in fuel demand if, say, a majority of those new car sales were electric vehicles.
Similar analyses could be conducted for any type of insurance policy, such as home, life, workers’ compensation, etc.
What This All Means
Those are only a few examples of the types of alternative data and how they can be used to inform trading decisions. Daniel Calugar emphasizes that all of this information can only be useful, though, if you’re able to break down the raw data to draw predictive conclusions.
That’s very difficult, if not impossible, for humans to do on their own. This is why more and more investors are turning to algorithmic trading to integrate alternative data into their everyday analysis. By doing so, they are realizing that the alternative data sets can truly be game-changing.
About Daniel Calugar
Daniel Calugar is a versatile and experienced investor with a background in computer science, business, and law. He developed a passion for investing while working as a pension lawyer and leveraged his technical capabilities to write computer programs that helped him identify more profitable investment strategies. When Dan Calugar is not working, he enjoys spending time working out and being with friends and family and volunteering with Angel Flight.