Since the 1990s the Artificial Intelligence (AI) has been a part of the nerdiest of dreams. With movies like Terminator that are about AIs taking over the world and becoming the overlords of the enslaved human race and what not dominating the fantasy market.
However, when the reality sets in everything becomes much more understandable. The idea that there is going to be a true artificial intelligence running rampant is still far from the truth. The markets that are interested in such technology are not only limited
to the military. With the introduction of High-Frequency Trading, the AI has already been utilized to work on exchanges to some extent.
High-Frequency Trading, or HFT, is an automated trading platform that huge investment companies like banks, hedge funds, and high-net-worth investors are utilizing to make an extremely large number of orders in the tiniest of time frames. This is a way of
utilizing high-level complex algorithms to analyze and make decisions on multiple markets at the same time at the speed of light. However, HFT is still far from true AI as the computer is not reimagining any algorithms but is operating on the already set in
Funny thing is that a lot of military generals and CEOs that are working on creating a business strategy are operating on the same bases albeit with different ideas. Both have to identify the strengths and weaknesses of their opponent as well as their own
and start developing a strategy to counter and win the engagement. While the military sees huge applications for predictive technology the business sector does as well. WIth different scholarly articles appearing as far as in 1991 when
MIT Sloan Management Review published an article stating well in advance that computerized predictive analysis will be used to manage foreign exchange markets. This was followed up with the University of Cambridge with a more
elaborate idea that adaptive reinforcement learning would provide not only assistance but in time could even take over the whole FX trading systems.
AI development has come a long way during the last decades. When MIT published their research they could not even fathom the level of progress AI would undergo in just 20 years. Nowadays the technology has evolved enough that the current algorithms can predict
different financial patterns for the next weeks or months. Lots of the
top American Forex brokers are relying on such algorithmic programs to determine trends as closely as possible and make educated guesses based on the information already provided by such AIs. It is worth noting that the trends that AIs are setting can also
be simulated in demo accounts. However, this is not directly how AI is helping the traders. The absolute power of computers comes into play when there is big data to analyze. The time it takes for a human to go through all of the charts and details is much
more considerable than that of an AI that, if given enough processing power, can scan through all of the trends in a matter of seconds. What the AI also offers is the emotionless, pure logic-based analysis.
It is not a secret that a lot of traders can become very emotional during the trade and end up making preemptive decisions resulting in less than fortunate results. The market panic is quite real and is very visible in current times as the novel coronavirus
has been hitting the world economics hard day by day for months now. This has resulted in lots of traders buying up dollars due to the fact that it was
predicted and to an extent turned out to be the most reliable in times of crisis.
It is predicted that the AIs will make markets much
more stable and less volatile due to the fact that the decisions are based purely on data points that predict future price changes. AI has possibly been the biggest addition to the already existent market with its precise data analysis abilities. However,
not everything is so good as there are a multitude of challenges in the way of proper AI development.
One of the biggest challenges is figuring out exactly what kind of data needs to be fed to the AI when making foreign market exchange trading artificial intelligence models. The acquisition of such data is also incredibly difficult due to the limitation
of each market as there is no globalized way of data collection.
The second issue is set to be the algorithms themselves. There are many different ones that are all working for machine learning (ML) framework but all of them have different purposes and fill niches of their own. All of them provide a different outlook
on the market and thus make a bit different predictions that are more or less correct in their own manner but which one suits which situation is still up to the US to decide and the stakes are high.
The platforms that are already in existence also need to be updated to suit the new datasets that will be utilized in the machine learning process. The provider company also needs to have enough hardware to satisfy the computing power required to handle
such huge chunks of data on the fly.
Last but not least, the AI cannot just take over what humans are doing and operate on its own. Any machine requires human supervision due to a simple fact that business objectives are set by humans and not a predictive machine. This market insight is extremely
important for the machine to operate in a full capacity and data management to be driven in the correct direction. This sets a requirement for capable data scientists who will basically be bridges between the knowledgeable market analyst and the piece of technology
at hand as well as the business that shows its interest in the development.