Machine learning and crypto trading
Many predict a great future for machine learning and artificial intelligence. However, the best developments in this direction belong either to the academic community or to big business, mostly in advertising. Therefore, there are not many working projects that would allow cryptocurrency traders to use artificial intelligence in their service. Let’s understand how machine learning works in cryptocurrency trading, as well as look at one of the automated trading options. And in the next article we will create and train our own bot, which in theory is able to show positive results, however, its use is highly not recommended.
Table of Contents
Many predict a great future for machine learning and artificial intelligence. However, the best developments in this direction belong either to the academic community or to big business, mostly in advertising. Therefore, there are not many working projects that would allow cryptocurrency traders to use artificial intelligence in their service. Let’s understand how machine learning works in cryptocurrency trading, as well as look at one of the automated trading options. And in the next article we will create and train our own bot, which in theory is able to show positive results, however, its use is highly not recommended.
What is machine learning in cryptocurrency trading
More often than not, machine learning in cryptocurrency trading refers to the use of artificial intelligence in buying or selling decisions. And in most cases this is true. However, this approach has many more negatives than positives for the average trader.
There are two approaches used in machine learning for cryptocurrency trading – reinforcement learning and teacher learning. To consider the specific advantages and disadvantages of each model, we will have to delve into algorithms and data, which is beyond the scope of this article, but we will talk about the general principles.
So, the main thing that stops most traders from creating and using artificial intelligence in cryptocurrency trading is the cost of developing a machine learning model. A robust example can cost several million dollars, and the training itself will take a long time or require leasing expensive facilities.
That said, most of the existing working algorithms that are reliably known to be generally profitable spend most of their time waiting for the right moment, only occasionally making transactions in the cryptocurrency market in automatic mode. From this we can conclude that in order to recoup the development, launch and maintenance of artificial intelligence, one will have to risk very large sums, and even if there is enough money in the trading account, other drawbacks will not allow using this strategy effectively.
Why machine learning doesn’t work well in cryptocurrency trading
The easiest way to look at the disadvantages of machine learning in cryptocurrency trading is with a concrete example.
The first thing worth mentioning is that in artificial intelligence when trading digital assets, as in the case of arbitrage trading, there will always be a time lag between the appearance of a situation and the execution of a trade due to network delays, and in some cases, the order will not be executed at all due to a strong change in the market situation.
Also, we should not forget that liquidity in the cryptocurrency market is still relatively low. As we said earlier, for machine learning to work effectively in cryptocurrency trading, it is necessary to make rather large transactions, and even if computer intelligence correctly determines further market movement, entry and exit points, lack of demand or supply will not allow to extract maximum profit, and in some cases it will even lead to losses.
A simple example of applying machine learning to the cryptocurrency market
Consider the example of artificial intelligence in the cryptocurrency market, trained with a teacher. Here we ourselves prompt the algorithm in which direction the quotes will go, and it makes independent decisions in the future based on the experience gained.
For example, a bitcoin is worth $20,000, and in the next moment we determine with certainty that the price will start moving upward, reaching $20,100, in the next minute. But in order to make a deal, we need to have a sufficient supply in the market, or, to put it simply, a sell order in the stack, so that the volume of the potential transaction will allow us to make a sufficient profit. And, if by acting independently, the trader minimizes the time of making a decision, the computer will spend an additional few fractions of a second for analysis, during which the situation can change.
Let the artificial intelligence managed to buy the necessary amount of bitcoin coins at $20,000 and the price actually went up. Having reached the $20,100 mark, our machine learning algorithm places a sell order, only to be left with a scenario where there is no buyer for that amount of cryptocurrency on a given exchange. And if the price turns in the opposite direction, then there can be a situation where a live person could minimize losses, if not profit, but the computer can make losses, and sometimes significant ones.
Considering this example, we do not take into account exchange commissions and other account maintenance costs, which can make the use of machine learning in the cryptocurrency market absolutely unprofitable.
Conclusion
Machine learning in cryptocurrency trading is an interesting phenomenon in itself. Although we found a lot of disadvantages, artificial intelligence finds its application here as well, but more often as an additional indicator or in such strategies where the risks of large losses are minimal, for example, in scalping. But it should be understood that this technology is quite expensive, and in order to pay it off, you need to have a solid trading capital.
