Like many industries in the modern world, asset traders everywhere integrate statistical and mathematical techniques to help them make more informed trading decisions. Experienced investor Daniel Calugar explains that, as a result, the field of quantitative finance and the strategy of algorithmic trading are both exploding in popularity.
These two fields require extensive data and powerful technology to help traders quickly and easily identify prime investment opportunities. In many cases, the systems they set up can be programmed to execute trades automatically and at an extremely high rate.
Below, Dan Calugar explores the latest trends in these two fields, including the growing use of AI and machine learning, the emergence of new trading strategies, and the impact of big data and alternative data sources.
The Impact of Big Data
In many ways, "big data" and algorithmic trading are synonymous. Algorithmic trading, after all, relies on big data to work properly.
The basis of all algorithmic trading strategies is a huge data set that is inputted from various sources simultaneously, multiple times daily. Moreover, all of this data is analyzed in a matter of milliseconds, speeds that human traders simply could not keep up with.
Daniel Calugar says algo trading aims to help investors execute trades at the perfect time and price. They do this through mathematical algorithms that traders set up and code into the trading systems.
Without big data, quantitative finance simply wouldn't exist.
Benefits of Big Data and Algo Trading
Big data has four fundamentals, which are referred to as the 4 V's -- velocity, veracity, variety, and volume. Investors can leverage advanced technology to harness the power of big data to produce keen insights using these 4 V's.
Financial markets today produce an enormous amount of data, too much for the human brain to handle. But algo trading systems can process big data quickly and efficiently.
Investors and even large firms that don't utilize quantitative finance and algorithmic trading are forced to focus on smaller subsets of the data. This might include only a particular sector of the markets or even only a few specific companies.
Taking this narrow approach could result in huge missed opportunities and incorrect decisions. Algo trading helps traders process massive amounts of data on a minute-by-minute basis.
But it's not only about speed. Through the use of technological tools, investors can remove the element of human error from the process -- executing the proper trades at the most opportune times.
As long as the algorithms are programmed and coded correctly, the system will consistently execute the trades that it's supposed to, based on the various parameters set for it. Moreover, investors can back-test different strategies and then tweak their algorithms based on the results before they risk any actual dollars.
Drawbacks of Big Data and Algo Trading
Undoubtedly, algorithmic trading aims to remove a human element from the investment process. In fact, it's actually one of the system's main goals, as mentioned before. But while that's great for removing human error from the equation, it can also be a drawback since it doesn't integrate human instincts.
Experienced traders may know when certain pieces of information or data are telling a story that's not really true. But, unfortunately, algo trading systems may not, even though some of the more advanced ones that use artificial intelligence and machine learning could adjust over time -- as we'll soon discuss.
And finally, one of the significant concerns regarding big data is that it can sometimes be unstructured. This means that as these systems bring in information from various sources -- emails, health records, and social media, for instance -- it could ultimately include personal information that people would rather keep private.
Artificial Intelligence and Machine Learning
Arguably the most noteworthy trend in quantitative finance is the increasing use of artificial intelligence (AI) and its subset, machine learning (ML). And while both of these technological advancements have been around for a while, they've only recently been utilized in a way that harnesses their full power.
Generally speaking, AI and machine learning allow computers to process a massive amount of data at speeds that humans could only dream of, according to Daniel Calugar. But, the systems aren't just collecting the data; they are also analyzing it in a lightning-quick fashion so that accurate insights can be derived instantly.
By using AI and machine learning, traders can set up specific algorithms to identify different trends and conduct additional analyses that weren't possible by other means. Investors can set themselves apart from others in the market by using these tools to identify patterns and trends that more traditional analysis methods may not have uncovered.
Benefits of AI and ML
The obvious benefit of AI and ML in quantitative finance and algorithmic trading is that they can help identify trends that traders may have otherwise missed on their own. In addition, the amount of data that AI and ML can handle is truly mind-boggling, and they can evolve and adapt over time to identify opportunities, even as the market constantly changes.
What's more, these tools can help investors stay ahead of the curve by identifying outliers as markets begin to change. The most powerful aspect of AI and ML, Dan Calugar says, is the fact that they can point out these potential changes in the market before others can. This provides the potential to get in (or get out) of a particular asset at the most opportune time.
Drawbacks of AI and ML
Quantitative finance and algorithmic trading are rooted in data. Therefore, the systems will only perform as well as the data allows them to perform. In other words, if you input bad data into the systems, they will likely produce bad results.
One of the main goals of algorithmic trading is to remove human subjectivity from the investment analysis, which allows the system to produce objective results based on fact rather than opinion.
The challenge, though, is that opinionated humans are the ones responsible for setting up these systems in the first place. If their own emotions aren't removed from the setup process, it's possible that the data could be biased, leading to improper AI and ML training, in turn leading to very biased results.
Automation is great, but it can be extremely detrimental, too, if you're not careful about the data and design of the systems.
Current Use of AI and ML in Algo Trading
There are many different ways that AI and ML are being used in quantitative finance and algorithmic trading. Some systems rely on text, while others may look at particular satellite imagery, data from credit cards, or other sensor networks to cull insights from real-world happenings.
The most basic forms of AI and ML are text-based. These systems may analyze the various data points companies produce when they issue new filings reports, release updated news, create posts on social media channels, and hold calls to review their quarterly earnings.
Instead of having a human read through all the cumbersome text and data from these sources -- or sit through an entire earnings call listening for key points of information -- the quantitative finance systems can do this for you.
Daniel Calugar says this can be enormously valuable for traders, as it allows them to gather insights and investment opportunities from a wide range of company data all at the same time. There is no waiting period for the analysis to be done, so they can make instant trading decisions once the information is released.
New Trading Strategies
As quantitative finance and algorithmic trading continue to advance, new trading strategies are popping up all over the place. These strategies are completely novel and weren't even possible in many cases without the high-performing technology that powers them.
Some of these new strategies are outlined below.
At its base, algo trading uses algorithms to analyze a large amount of data to outline and highlight key indicators and insights for investors to use as trading opportunities. HFT takes this to the next level by automatically executing trades based on completed analyses.
But it's not just about making automated trades. It's about making a large number of automated trades at a rapid frequency based on various market conditions.
Without the proper algorithms, HFT wouldn't be possible. That's because HFT is focused on taking advantage of even the slightest price discrepancies, for instance, so that traders can profit.
In many cases, the executed trades may take place over an extremely short period of time. In other words, the traders aren't looking to hang onto these investments for the long term. Instead, they may execute a purchase one minute and then turn around and sell it as soon as they can the next.
This isn't a viable strategy for everyone, especially since the profit gained with many high-frequency trades may only be a few dollars or sometimes even less. That means that for this to be a strategy that pays off, the trader would have to execute rather large purchases and sales.
HFT allows investors to take advantage of these small discrepancies in price for a brief period of time. It will execute the initial trade automatically for a large purchase amount the moment the market experiences volatility. Then, the instant another endpoint is reached, it will perform the exact opposite trade of those shares to realize the profit.
There's considerable debate as to whether HFT is fair and ethical, with some people even arguing that it's downright illegal. But, for now, Dan Calugar says it's a new trading strategy that's here to stay thanks to quantitative finance and algorithmic trading.
Building on the point above, the reason algo trading can be so successful if it integrates HFT is that it can take advantage of volatile markets. Because it can execute trades automatically with perfect timing, investors don't have to worry about getting in or out at a bad price.
This is why quantitative finance concepts are now being used more than ever in the cryptocurrency market. While crypto still isn't nearly as mainstream as the major stock indices, it's becoming increasingly more mainstream by the day.
These digital currencies provide plenty of opportunities for profit if you know what you're looking for and can time the market right. One of the biggest benefits of crypto is that it's based on blockchain technology. This decentralized ledger system bypasses central authorities while increasing security and transparency at the same time.
These ledgers are much more difficult for people to manipulate or alter on a fraudulent basis, which could -- in theory -- increase people's trust in the overall financial system.
Quantitative finance and algorithmic trading concepts can be used to zero in on the best investment opportunities in this highly volatile asset class.
However, there are still some major pitfalls.
The major one is that it's a very volatile asset class. The value of cryptos can rise and fall 10% or more in a matter of minutes at times. It's also still a relatively new technology that is completely unregulated. With so much uncertainty, many investors tend to just stay away.
Alternative Data Sources
Another major trend in quantitative finance and algorithmic trading is using alternative data sources to make investment decisions. Instead of looking at traditional sources of data -- such as a company's quarterly earnings report or trends in stock prices -- this strategy utilizes other data points to predict the future values of certain assets.
So many different aspects of life today produce a wealth of data, which can be assembled into valuable information with algo trading. Dan Calugar points out some of the most popular alternative data sources below.
Satellite imagery can give a literal big-picture view of many different things on Earth. One investment example is that traders can use images of big box store parking lots to analyze how many people are actually visiting the store.
For instance, if satellite imagery shows a lot of traffic in Target parking lots throughout the country, that could indicate that the company is about to experience a boost in revenue and profit.
What's so valuable about this source of alternative data is it gives traders real-time insight into, in this case, Target's potential earnings. This would allow investors to make trading decisions based on the vehicle traffic in the parking lots, which could give them advanced notice of the store's pending success or failure.
Social media is such an integral part of life today for people. And, as they're having conversations and interacting online, they're leaving a trail of data that can be used to help investors make informed decisions.
One example would be analyzing all of the mentions on social media about a particular company. This would allow investors to see the public sentiment about that company. If people are raving about the company, investors might decide its current stock price is undervalued. If the opposite is happening, then it could be a great time to sell or short the stock.
The weather has an enormous effect on day-to-day life. Therefore, by studying weather patterns and forecasts, investors could deduce certain trading insights.
This could be especially true of specific industries that rely on the weather for their success. It can also be used to predict future demand for a particular entity.
For instance, if a freezing cold winter looks to be on the horizon, it could result in a huge boon for the energy industry, as demand for heating around the country could soar. Or an especially cold or dry growing season could dramatically affect agricultural commodities.
If traders are equipped with this information in advance, then they can potentially make smarter predictions about how an industry or company might perform.
Quantitative finance and algorithmic trading are here to stay in asset trading. The above trends are already here but are likely to continue to evolve and grow.
About Daniel Calugar
Daniel Calugar is a versatile and experienced investor with a background in computer science, business, and law. While working as a pension lawyer, he developed a passion for investing and leveraged his technical capabilities to write computer programs that helped him identify more profitable investment strategies.