Can Algorithmic Or Automated Trading Beat Human Trading?

Hi guys, automated trading or algorithmic trading is a gift of modern science. algorithmic trading or automated trading which utilizes Artificial Intelligence (AI) technology can undoubtedly be very helpful in finding market signals.

Because of it traders are now becoming more profitable in less time. But the question is can automated trading or algorithmic trading  beat a human trader? So, today I'll be writing about this topic.

Therefore without wasting time, let's get started.

First of all, it is worth mentioning that Wall Street depends heavily on algorithmic trade, especially on the stock market. However, don't forget important aspects - the whole AI (Artificial Intelligence) -based trading process involving big money is eventually traced by humans, who act as supervisors during different stages and time frames.

While it is true that human factors control all markets, many institutional investors prefer to implement several automated trading tools to reduce the risk caused by emotions.

Last week, Bloomberg reported that Ashok Krishnan, head of automated trading at bank of America's global market division, had collaborated with Phil Allison of Morgan Stanley and Mark Goodman of UBS to promote and develop machines that focus on trading bonds and currency pairs rather than just stocks .

This shows that trends are becoming prevalent even in markets that traditionally reject algorithmic trading. Some believe that overall, humans win - if you don't believe, you can ask your algorithm to try to defeat Meir Barak, the tradenet founder in his direct trading room.

Experienced traders and bestselling author of "The Market Whisperer", he stressed that active trading is not for robots. Barak inspires day traders through your YouTube trading channel, calling for active involvement in the trading process.

Automated trading conquers the stock market in real terms, but this is the problem - this concept may apply to institutional investors who have long-term goals. Daily traders and even swing traders must be somewhat skeptical of the idea that one can arrange multiple applications and sleep because money will flow.

The concept of passive income sounds interesting, but active trading requires a lot of involvement and even pressure, especially in the case of beginners. Let's compare the daily trading process with problem solving - there is no systematic approach that has been determined for a problem.

If it's a math problem with lots of limitations, AI will be good at solving it based on a predetermined set. However, the weakness of the AI ​​is that it will not be able to build the entire context of the problem - it will not understand that restrictions are restrictions.

AI may be ideal for automating several processes, but it cannot realize the context in the same way as it cannot realize itself - we are far from the singularity, right? Humans are very good at understanding the context of different scenarios and how they can affect certain markets or assets.

This is because the context is not an intellectual problem but an emotional problem, and, as we know, all markets are ultimately driven by emotion. If you take the time and look around, you will easily note that we are constantly dealing with people in the world who are by definition spontaneous and unpredictable.

Thus, the context of various things always changes. Machine learning and AI are usually applied to large data sets, which involve an extraordinary volume of information. Although this might help traders identify market signals based on programmed sets and those set among a large number of financial instruments.

It is important to understand that the relation between these instruments changes, and AI is not always accurate even in finding good entry points. , not to mention active trading. Also, some AI-based models may work well during certain market cycles, but they will eventually fail when the cycle ends.

The stock market is known for its volatility, internal variety, and spontaneity, which is why setting the AI to apply a set of features for two or more different stocks is quite difficult. No one denies the fact that strong correlations exists, but things become tricky when the context is not understood.

The AI-based model would maybe explain and perfectly analyze the behavior of one stock while failing to do the same with the other stock.This is actually what happened to the Black–Scholes model.

The formula was implemented in the late 1960s by Fischer Black and Myron Scholes, who started an investment firm based on the model that they had developed.

It worked well until market conditions changed and they lost a huge portion of investors' funds. Note that this example related to the world of investing, but when it comes to day trading, things are even more difficult because of short-term volatility.

So, are you guys agree with my opinions regarding this topic? And let me know if you want me to cover any topic for you.

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