Real-world problems cannot be solved by applying simple, traditional algorithms and methods, so software creators must use new technologies. Machine learning is one of these solutions.
While the foundations of machine learning in the traditional sense can be traced back to the late 1940s, the technology itself has only recently begun to emerge, thanks to the rapid growth in the computing power available to train systems.
When it comes to smart market analysis systems, using machine learning tools can eliminate the disadvantages of many traditional methods. The CrypTIcs platform actively uses machine learning methods to create systems for analyzing password markets and algorithmic transactions. This allows it to improve the reliability of data generated by the system, thereby reducing risk and saving investors' funds.
Below we try to explain to readers the nature of the most interesting machine learning methods in simple language, as well as examples of applying these solutions in practice.
1. Statistical analysis of time series using neural networksWhen analyzing transaction information for cryptocurrencies, there are two types of data that must be processed by the analysis system. The first type is raw data that is directly obtained through the API of the transaction. These data are usually composed of numerical values ​​that can be analyzed mathematically and statistically, and they usually have an ordered structure.
However, there are some information, and the principle criteria for its selection are not clearly defined. For example, information from different sources of information, such as information from rating agencies, social networks, information about the level of interest of investors in a particular product.
In general, in order to obtain the desired result, it is necessary to analyze the entire set of data and must be regularly identified. In order to achieve this goal, the CrypTIcs system is implemented using time series statistical analysis techniques in conjunction with machine learning algorithms.
In extremely simple words, the algorithm assigns specific objects to each type of data, and they can be represented by a set of parameters that describe their state. The set of connections for all objects is analyzed by the neural network using the Kohonen mapping method. This allows the algorithm to solve the problem of finding similar objects and grouping them.
2. Capital asset pricing model and risk assessmentThe Capital Asset Pricing Model (CAPM) is a model used to assess the profitability of financial assets. The essence of this model is to assume that there is a highly liquid asset market, for example, a cryptocurrency, which concludes that the amount of profit required is not entirely determined by the specific risk profile of the current asset. Just as the amount of profit in the cryptocurrency as a whole is determined by the general risk characteristics.
Using this model, combined with a machine learning approach, CrypTIcs is able to analyze the profitability and risk of a particular encryption behavior in real time with sufficient accuracy.
3. Integrated learnersThe foundation for using integrated learners is the idea of ​​learning a few basic objects in the same data sample and using the union of the results of the different objects to predict the encryption mechanism for subsequent changes. The mathematical basis of this method is the jury trial theorem formulated in the early eighteenth century.
According to this theorem, most participants' decisions after analysis are most likely to be correct. This allows the network to analyze market indicators that have little impact on exchange rate changes, and to develop a solution based on these indicators so that the errors in the total data sample will be less than the errors generated by applying each indicator separately.
4. Q-learningQ-learning, or reinforcement learning, can improve the performance of neural networks with feedback. Based on the results of the algorithm, a utility function is formed. As a result of this feature, the algorithm receives data about past experience, which can rule out some details of the deliberate loss of event development.
Of course, this is just the tip of the machine learning iceberg. The entire CrypTIcs subsystem and the technical methods used in the framework cannot be summarized in this article. Every topic and formula, calculation of a wide range of topics and descriptions require a lot of space to explain.
The use of machine learning tools allows our products to significantly improve the performance of their algorithms, which has a positive impact on the efficiency of the entire system.
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