It’s no secret that trading in financial markets widely employs advanced technologies, one of which is artificial intelligence (AI). While many traders debate how soon AI will be able to provide high-quality analytics and predict price movements with sufficient accuracy, others are already reaping unprecedented benefits from its use in trading algorithms. This is especially evident in applications where trading decisions need to be made at a speed beyond human capability, such as High-Frequency Trading (HFT). How is AI revolutionizing high-frequency trading?
What AI Cannot Do Yet
Many traders and investors believe that with the help of AI, they can create automated trading systems that will make flawless trades and help them earn quickly and significantly. They are absolutely convinced that if historical data is fed into a neural network, it will be able to predict the behavior of an asset in the current situation.
This is far from the truth. A neural network is not a predictor or oracle; its core function is approximation. This means it can approximate (calculate with some error) subsequent data based on the analysis of previous data. However, this only works if these results are generally subject to approximation.
In theory, any system can be modeled, as long as it is self-sufficient. This means that its description (or data for analysis) fully represents all possible information about the system. As a result, it does not require other external data, except for the initial state. In this case, a neural network or AI can provide accurate predictions by approximating previous states.
In the case of analyzing and predicting financial markets, the system is far from self-sufficient. Quotes, regardless of the historical period they cover, are merely the result of numerous events that are not accounted for in this statistic. Even if the history of quotes is supplemented with numerous data from economic, financial, political, and social spheres, the system still does not become self-sufficient.
The main component that must be introduced into the model in this system is the human factor. The market’s behavior is influenced by human reactions (both cause and effect), alongside external influences (such as news) that cause changes in the system’s behavior. However, modeling individual humans is not enough; it is necessary to model the entire market participants’ collective behavior and beyond (e.g., considering a company’s management reaction to changes in its stock price).
Thus, AI will be able to provide predictive analytics only if it can approximate the behavior of market participants and other people. While this task doesn’t seem unsolvable, it currently requires a significant increase in the flow of processed data and computational power. In practice, this means that neural networks cannot yet directly predict markets.
Do Neural Networks Have Investment Potential Today?
For AI enthusiasts, it’s important to note that the situation is not as bleak as it may seem. While neural networks cannot yet predict market behavior, they are quite capable of solving other tasks in the field of quantitative finance.
For instance, it is possible to train a neural network to identify market trends and flat segments with a high degree of probability. Solving this task alone would allow the use of AI in trading systems with a sufficiently high level of profitability.
Experts in big data analytics and the use of neural networks in trading and investing say that teaching artificial intelligence to trade is much easier than predicting the market. Indeed, in most cases, making trading decisions (entering the market, taking profit, limiting losses) can be formalized into a limited set of rules. Even if a human cannot formulate these rules, this task is ideal for artificial intelligence. By using historical data (possibly supplemented with external factors such as news) and precisely formulating conditions for profit extraction and loss limitation, quite decent trading bots can be created.
Many such bots, though not widely publicized, are already successfully working for the benefit of investors in large hedge funds and other market participants of the same scale. This also includes the use of artificial intelligence in HFT.
The Premises for Effective Use of AI in HFT
Today, high-speed robots work in High-Frequency Trading. They use traditional computational approaches of algorithmic trading, such as those based on calculating technical analysis indicators.
This allows for the exploitation of minor market inefficiencies to generate profit. Trades last for milliseconds, with each yielding only a few cents (or even fractions of a cent). However, due to the large number of market entries and position closures, this results in significant profits.
Thus, the speed of decision-making and order submission is one of the main principles of HFT. The advantage goes to those whose algorithms are faster than others. However, winning this race is not so simple. Practically all participants in this field possess high-performance equipment and can afford high-speed information exchange lines with trading platforms.
Under these conditions, the ability to process large volumes of data and find market inefficiencies that are not addressed by other participants comes to the fore. This is where AI algorithms find their application. They allow overcoming the limitations of classical computations, enabling traders to process and analyze data on an unprecedented scale and conduct real-time trading with lags not in milliseconds, but in units or tens of microseconds. Additionally, a neural network can identify market entry points that regular HFT algorithms miss.
Moreover, artificial intelligence has unique capabilities for adaptation without human intervention. In fact, modern AI algorithms can respond to changes in market conditions without losing efficiency. Traditional computational algorithms fall far behind in this regard: a new market situation requires them to be adjusted according to the changed conditions and parameters optimized. Neural networks handle this autonomously.
Furthermore, even current implementations of “trading” artificial intelligence can easily perform calculations of parallel scenarios simultaneously. By obtaining results from multiple experiments at once, the task of choosing the optimal actions is simplified, the accuracy of determining critical trade parameters is increased, and decision-making time is significantly reduced.
Advantages of Artificial Intelligence in High-Frequency Trading
Thus, AI in HFT can improve three main indicators compared to the currently used traditional computational algorithms:
- Speed.
- Accuracy.
- Adaptability.
These improvements define the main advantages of AI systems:
- Faster decision-making for market entry and position closure, leading to more trades. In HFT, where a trading robot can perform thousands of operations per day, even a slight increase in the number of trades can result in significant profit growth.
- Reduction in errors when entering the market and increased accuracy in determining optimal moments for opening and closing positions. This reduces losses and increases the percentage of realized profits from trade execution, improving the overall financial outcome in HFT.
- Changing decision-making rules in response to market conditions without trader intervention. Automatic adaptation reduces the number of failed trades in changing market conditions and minimizes trader losses compared to manual adjustment of computational algorithms.
Overall, the future of AI in HFT looks quite optimistic. It is already replacing standard computational algorithms, although only truly large investors can afford such systems. The development of hardware, such as the use of quantum computers, and the implementation of new machine learning algorithms will further enhance its advantages and, more importantly, its effectiveness. It is possible that soon, no other market participants will remain in High-Frequency Trading. However, it is important to remember that neural networks have their own drawbacks, and ML algorithms are created by people. Therefore, this approach has its risks, which must be mitigated by risk management systems and trading rules at the exchange level.
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