Introduction of the article
Ever since trade has existed, market participants have sought to profit from exchange rate movements. We’ve come a long way from trading tulips to cryptocurrency exchanges, but with one statement, everyone agrees: It doesn’t work for everyone.
First, the advent of computers and then the internet opened a new chapter in investment, with the emergence of online brokers, cryptocurrencies, trading robots. Currently, human traders and robot traders coexist. In this article, we will compare the latter two actors. We examine how people make decisions and how the machine has become able to learn which team is in the majority and who is more successful!
The Human Trader
How do traders make decisions? What can be the outcome of the decision? How many times do they have to decide?
The accepted conclusion in the trader world is that no one should trade without a carefully put together strategy. The strategy should cover selling and buying decisions, when not to do anything when to get out. The first phase of the strategy is always market entry. What do I need to see to get on either the buy or sell side? This signal can be one or more indicators, a chart pattern, a support/resistance level, a news event. Whichever one you choose, one thing is for sure: none of it will deliver a 100% result, so you have to enter the market over and over again for every signal. The strategy has been carefully tested and I know that if I follow the signals I will be 60% right and will be in net profit.
All of this assumes that I am perfectly capable of implementing my strategy every time. If it’s that simple, why are there so many articles on the importance of trader psychology? Why do success traders see influencing their emotions as the key to their success? And why Trading in the Zone by Mark Douglas became so successful?
The strategy is easy to implement in the beginning, but much more difficult once some time has passed, especially if we have suffered a few losses. If we logically rethink what the strategy says in this situation, it is clear what we need to do, but for some reason, we do not make that decision. Why does a trader behave irrationally when he has decided not to do so?
We have to look for the answer in the brain.
Studies show that the decision is made in the brain much sooner than we could consciously grasp. Furthermore, external influences can also obscure our logical brain and make a decision we would not have made under other circumstances.
What affects?
Emotional state
Neurons in the brain respond to a state of self-confidence and make decisions more easily without prior information gathering. Remember the trade that, although it did not follow the strategy, for some reason we felt it would work. Following the strategy correctly is only possible if we can make an unbiased, emotion-free decision every time and it is difficult in the long run. Not only confidence but a negative emotional state also affects. “I don’t dare get into this trade, I didn’t make it last time, I don’t feel confident.”
The Prospect theory developed by Daniel Kahneman and Amos Tversky in 1979 states that it would hurt people to lose $ 1,000 more than it would feel good to win $ 1,000. Because of this effect, one makes decisions that will help him avoid losing and the gain will be less desirable for him. Prospect Theory was awarded the Nobel Memorial Prize in Economics in 2002 for the work of Kahneman. It is the foundation of behavioral economics and behavioral finance.
Intuition
Intuition is a process in which information is not acquired through processes of thought or inference. That’s the famous Gut feeling. We don’t know how we know what we know, but we know. Can intuition be used in trading? Intuition can be very useful as well as very harmful. A trader who trades with such a feeling does not think about why it would be worthwhile to enter the market, simply based on his experience so far, he thinks that what he is doing is a good decision. And he makes the decision himself very quickly. Such day-traders don’t even stay in position for long. Daniel Kahneman mentions in his interview with TheStreet that over the years, such traders develop within themselves a skill that they use in the way of an athlete in their trading. If someone’s intuition is so advanced, it can be very helpful, but a lot of time until it develops.
A human is influenced by emotions, prone to irrational decisions, have difficulty following his strategy, having biological needs, and can easily fall behind in a position during sleep that is a mistake to miss. At the same time, he can learn, try new theories, make intuitive decisions, and develop skills. It takes a lot of time for your abilities to develop and for you to be able to master your emotions At least 95% of human traders are loss-making in trading and only a lucky 5% can make a profit in the long run.
The AI Trader
Traditional trading algorithm
Trader algorithms have been around since the 20th century, but have not been widely used until now. According to a 2016 study, 80% of trades in the Forex market are made by automated trading systems instead of people. They are not affected by emotions and can execute orders at a speed and in a quantity that one will never be able to. The trader algorithm works according to pre-written rules, it does not make an independent decision, it is always just what it is written for. Most often, it trades based on some method of technical analysis, such as an indicator. The stick is an easy backtest to see if our strategy would have worked in the past. Unfortunately, such backtests are no guarantee of future performance.
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Furthermore, they may work particularly poorly when market conditions change. A strategy that uses moving averages can give good results in a trending market, but perform poorly in a volatile market. Mathematically describing it how to distinguish the state of the market is extremely difficult. At the same time, it frees one from the psychological and biological burden, which gives a significant advantage to the trader but can never be more than what was originally written for. They are even more vulnerable to unforeseen, significant events. When market conditions suddenly change, the algorithm can still do nothing but implement the strategy as before, if the new conditions are no longer appropriate, a loss is guaranteed.
Machine learning algorithm
With the advent of artificial intelligence, a new era began. In front of machine learning algorithms, there is always a goal and a data set. Based on the data, they build mathematical models for predicting the future and making decisions. They do not follow a predetermined command line but develop a solution to the problem based on their own mistakes and experience. The science of machine learning is used in many areas, from facial recognition to self-driving cars to finance. Artificial intelligence has already been proven in areas where previous algorithms have failed. The DeepMind program can play games like Go or Chess after a few days. Such a game cannot be played successfully by executing a rigid command line. The strategy must change and respond to the opponent’s actions. The same is true for cryptocurrency trading. The market does not give the luxury of being predictable. Artificial intelligence, like a human, can learn from its mistakes and develop itself, but much faster and more precisely than a human.
B-cube.ai is a fintech startup specializing in machine learning algorithms. The first trading bot made with this technology trades in Bitcoin and Ethereum cryptocurrencies. During the first six months of its life, the bot also experienced major events such as the March 2020 collapse as a result of the COVID-19 pandemic. Many human traders and traditional algorithms have gone bankrupt as a result of the unprecedented event. However, the B-cube’s bot closed all 6 months in profit, and its best month was just the crash-weighted March with a profit of 230%. Its success is due on the one hand to the combined methods of analysis and on the other hand to the advanced machine learning models.
Overall, both groups have good and bad qualities. Humans can develop skills, but it takes a very long time and they are prisoners of their own emotions. On the other hand, traditional algorithms have no emotions, but they cannot work profitably in the long run.
All in all, the AI trader who uses machine learning has the most potential. It is not a prisoner of its emotions, it is not a prisoner of biology and most importantly it is not a prisoner of its own mistakes.
Credit: BecomingHuman By: B-cube.ai
