The creation of an autonomous, agentic system that can pursue goals, define and solve tasks, and do all sorts of other great things is right around the corner, according to computer science geeks. However, we are still waiting for the arrival of a defining AI trading bot that can change the paradigm and prove that machines can operate in complex environments, such as financial markets, without direct supervision from humans.
Using artificial intelligence in trading
Since the biggest advantage of machine learning and neural networks is pattern recognition, some fields seem to be more suitable for the implementation of the technology than others. Researching markets and finding the right valuation for certain assets are complex tasks that often require superficial qualities to be accomplished. We are all aware of how intuition can be quite important for any investment activity.
Fundamental analysis is often considered the best form of research in any market. By taking various factors into consideration, a smart investor can make educated decisions. Machines are still quite bad at understanding social sentiment or assessing factors like brand value.
On the other hand, technical analysis is based on identifying patterns and trying to predict market behavior based on the analysis of market history. It is exactly how AI analyzes market trends and tries to forecast price action fluctuations. In essence, an automated trading system (ATS) powered by a smart machine is a very complex, dynamic algorithm that can use various tricks from the arsenal of technical analysis better than most humans.
Some companies are trying to implement the idea in a wide range of other products, adjacent or directly tied to the world of finance. However, these often have niche applications that cannot be extended to direct asset trading.
The difference between AI bots and traditional trading bots
A typical signal bot operates as an algorithm that can be initiated manually or by an alert from an external source. A retail trader creates a sequence of actions that a centralized exchange or a brokerage service provider must execute when certain conditions are met. This sequence is executed automatically by a platform or piece of software.
This is an elegant solution that works for the overwhelming majority of investors who are interested in building wealth through proactive market activities. Since you can use a variety of risk management features to reduce potential losses, it is a good solution for many capital holders who prefer sticking to the safer, more conservative approach to investing.
According to research, close to 65% of all individuals and over 99% of institutional traders employ various forms of automation to at least some degree. This prevalence of ATS setups in the crypto market is particularly reasonable as many digital assets display traits characteristic of highly volatile, speculative, and dangerous instruments.
For instance, the average monthly volatility of Bitcoin over the last 4 years has been close to 72%, which is incomparable to the S&P 500 with its 19.5%. In such choppy waters, using reliable tools and focusing on avoiding potential dangers are two main priorities for an investor looking from the perspective of long-term capital allocation in the crypto market.
Traditional algorithms can be extremely powerful when used correctly by people with experience and the necessary skill sets to create a consistent ATS. Unfortunately, the vast majority of newcomers to the crypto ecosystem lack these crucial skills and knowledge. They are rarely successful at using robots.
Algorithmic trading with AI
One of the most obvious solutions to this particular issue is the introduction of products that can remove some of the pressure from the shoulders of individual investors entering the market for the first time. Using agentic software solutions tasked with optimizing profitability seems like a logical application that can elevate the experience of investors to the next level.
By using the ability of such systems to analyze massive swaths of data and quickly identify patterns that occur regularly, it is possible to create extremely efficient tools capable of delivering outstanding performances under various conditions, including high volatility.
During the last five years, several service providers in the category of automation vendors managed to successfully implement agentic AI signal bots and create compelling products that attract investors interested in advanced fintech solutions.
WunderTrading is one such company focused on using complex neural networks in trading. Others are trying to roll out unique solutions too. The diversity in this sector of the fintech industry is incredibly important as it allows for massive contributions on all fronts to ensure the rapid evolution and improvement of technology.
Successful cases of machine learning in financial markets
We will cover some recent developments in this sector to give you a better picture of the fintech ecosystem from the perspective of artificial intelligence implementation. As mentioned previously, multiple companies are working diligently to deliver new AI-powered signal bots and other unique products that implement smart, autonomous agents.
Here are some of the examples:
● WunderTrading is an automation vendor offering a rich catalog of tools designed to improve investment outcomes for proactive retail traders who want to achieve consistent performance from their portfolios. Automated trading with AI can be very profitable for people who can select the right digital assets to work with. The statistical arbitrage system at WunderTrading can manage a massive portfolio by adjusting positions according to changing market conditions. It is a great choice for investors concerned with long-term returns and the risk profile of their asset compositions.
● Gunbot is another platform that develops ATS solutions for centralized and decentralized exchanges. This company has chosen a different route for the implementation of this technology. Instead of making their robots autonomous, they allow users to receive assistance from an LLM (large language model). It can understand inquiries written in natural language and use them to build fully automated strategies that can be immediately deployed.
● Axyon AI is an Italian startup that works with a variety of large companies, including Microsoft, IBM, and NVidia. The platform is all about risk management in AI trading. It offers a wide range of different portfolio optimization options to hedge funds, commodity traders, and managers of large portfolios focused on diversification. The investment platform uses its smart algorithms to provide assistance to users who are unsure of what to do with their capital.
● AiDA is a Singaporean startup that focuses on insurance and better customer service. While its area of expertise is far-removed from any form of algorithmic trading and does not focus on financial instruments, it is an excellent example of how contemporary companies can use a wide range of different techniques to enhance processes that require efficient computation. Big data processing for trading, insurance, and other forms of financial activities can be the next frontier for the fintech industry.
Note that many companies that focus on developing advanced solutions for capital holders are very careful with the implementation of autonomous solutions. For instance, WunderTrading has one of the most successful AI-driven trading bots in the whole sector, yet it is still limited to a position where it can be directly managed by users. It is hugely important to keep our eyes on various risks of AI in trading while developing new, exciting tools.
What does the future hold?
When assessing the prospects of this particular sector of the fintech industry, it is extremely important to keep in mind the main advantage of using agentic software solutions — its ability to identify patterns and analyze enormous sets of data faster than any team of humans.
All efficient strategies used by AI-powered bots rely heavily on technical analysis. The problem is that this approach does not always produce actionable insights. In fact, it can be misleading when applied to the highly volatile and unpredictable cryptocurrency market.
The inefficiencies of such analytical methods make it hard for developers to sculpt appropriate software and deliver something that can be trusted with life-changing money. It is true that technical analysis with AI-powered tools is already more productive and reliable than anything that human traders can come up with, but the degree of uncertainty is still too high to trust these ATS setups without caring about potential losses.
The future of the technology depends on how developers solve current issues. Some of them can be summed up as follows:
● Technological inefficiencies. Autonomous software agents rely on the robustness of the infrastructure and the reliability of hardware. While cloud technologies allow for many redundancies, the hardware problem is overlaying other issues such as bugs in the code, API connectivity issues, and many others.
● The quality of training data is another problem. Historical market data can be an aggregation of information received from different sources. There is also “noise” recorded by some liquidity providers during periods of high volatility, leading to slippages and corruption of information. The quality of data presented to neural networks strongly affects the potential level of performance.
● Adoption is quite slow despite the quick rollout of new features. Investors who have large investable capital do not like experimenting with their strategies. It means that people who are taking risks are the least prepared to suffer the consequences of any failures associated with using untested technology for financial activities. Onboarding can be an issue, too.
● Automated trading with AI is the next step in the evolution of the automation sector. It has the biggest potential in the crypto market where participants are more open to experimenting with new tools and taking chances. At the same time, the growth will be slow without rapid adoption by large capital holders who often define the fate of new fintech products.
If you are interested in building a crypto trading bot that can perform under all sorts of circumstances and deliver consistent returns, it is a good idea to work with reliable companies that approach AI implementation carefully while doing their due diligence. WunderTrading is an excellent choice for tech enthusiasts who want to use these new, exciting tools safely and as a diversification option for their portfolios.
The main takeaway
The proliferation of AI technology through the fintech ecosystem is only natural. Some experts believe that the hype surrounding the tech is proof that it is nothing but a massive bubble destined to pop in the near future. A good counter-argument is that the .com bubble wiped out the majority of the market, but it also gave us the best tech companies and unprecedented economic growth. The same will probably happen to this technology.
We strongly believe that retail traders should experiment with these instruments. However, it is important to approach them with caution.
