How Can The Rise Of AI Help In Addressing HFT Challenges

High-frequency trading (HFT) involves an approach where the algorithm executes thousands of orders that occur in a short period of time with one instrument. In 2022, the global HFT market was estimated at around $6.5 billion. Moreover, the demand for HFT is expanding and is projected to grow at a CAGR of 12% annually by 2028. 

Each transaction of HFT brings minimal profit, occasionally falling below the broker's commissions and, more importantly, exchange fee. Nevertheless, a large volume of orders can lead to a substantial financial outcome. Furthermore, HFT serves as a risk-minimization strategy, given that effective algorithms typically produce a smooth and steady equity curve. 

It may seem that HFT is a perfect solution for trading. However, there are numerous shortcomings that cannot be noticed at first glance. Let’s delve into them and discuss whether AI can address this issue. 

Classifying The Limitations Of HFT 

The initial and foremost challenge lies in the entry threshold. Engaging in high-frequency algorithmic trading requires cutting-edge and high-speed hardware which is co-located within the exchange’s data center. This entails having a server, the fastest network controllers, and potentially specialized Field-Programmable Gate Array (FPGA) modules. While it may not reach the scale of millions of dollars, it still implies significant costs on a monthly basis.

The second drawback of HFT is high competition. The idea of buying on one market and selling on another is so apparent that numerous teams, individual IT specialists, and traders have ventured into this domain with differing levels of success. The crucial problem is that no one guarantees that your solution will at least secure a position among the top ten in the market.

Another barrier directly related to competition is liquidity. HFT captures a relatively restricted number of the most apparent market inefficiencies within brief time intervals. Regrettably, these inefficiencies do not occur beyond a certain frequency per unit of time. That is why the maximum possible profitability of HFT practices are, in fact, surprisingly modest.

Additionally, one of the particularities of HFT is the constant pursuit of technology. This leads to such a phenomenon that can be called “mort subite” – sudden death. Typically, there are several HFT players in a particular market having a similar level of technological competence. Then, one of the participants implements a fundamentally new solution, gaining a competitive edge, and for a while, breaks ahead, collecting all the profits. Temporarily, the profitability of other trading systems drops instantly and almost to zero. 

Can AI Address The Limitations Of HFT?

As we know, the elimination of shortcomings is the engine of progress. Neither stock exchanges nor brokers receive additional income from technological improvements of HFT players. Besides, traders even request lower fees for generating a large turnover for them, so on the side of the brokers, there is a growing desire to limit the above-mentioned race. It can manifest itself in introducing protective tariffs, and sometimes in limiting orders administratively. 

There are also changes on the part of traders. They are implementing technologies to make their algorithms not only “fast” but also “smart” in order to avoid the problem of sudden death. Why then invest in chasing nanoseconds when we can focus on anticipating market trends? 

Predicting the future and calculating tomorrow's closing prices may be beyond our capabilities. However, AI systems have demonstrated a remarkable ability to statistically forecast asset behavior and comprehend the microstructure of the market within short time periods, sometimes in milliseconds or even seconds. 

As an example of AI’s accuracy and efficiency, AI-driven order processing exhibits a notable 20% surge in fill rates, coupled with an 11% decline in mark-outs, as Nasdaq's research shows. By operating within the realm of a vast number of transactions, each of which statistically yields a marginal profit, it becomes possible to cut down on the hardware costs in pursuit of gaining an extra nanosecond.

Nevertheless, the use of AI in trading will not fundamentally solve the problem of liquidity for HFT and other constraints – this market will remain limited. Despite this, AI will completely shift the competition from players who have the fastest hardware to the ones who have the smartest algorithms. 

Unlocking Opportunities In The Field Of HFT With AI

Today, with the help of AI, a new window for entering the HFT industry has been opened, and the entry barrier has been significantly lowered. Regardless, the mentioned window is less widely open now than twenty years ago, and the cycle of complicated market entry has reset, providing a chance for new entrants. Although AI may not serve as a primary source of initial accumulation, as experienced by many IT teams in recent decades, those already involved or specializing in AI are likely to be at the forefront.

Still, aspiring market participants need not lose hope, as restrictions have weakened and the entry threshold has diminished for a certain period. For a small professional team or a talented newcomer, opportunities have emerged once again. If you were too young before, faced previous setbacks, or lagged in the technology race, it seems like your time has arrived. 

This article is from an external contributor. It does not represent Benzinga's reporting and has not been edited for content or accuracy.