The cryptocurrency market is expanding aggressively, and it will reach $4.94 billion by 2030. Bitcoin, Ethereum, Ripple, Cardano, and Tether are some of the popular cryptocurrencies. The cutting-edge machine learning technologies can be applicable in several use cases in the cryptocurrency market.
Predictions and forecasts can be easier with the effective use of machine learning techniques. Cryptocurrencies use Blockchain technology for transaction authentication and security. However, there are security concerns and other issues that may emerge with cryptocurrencies. Machine learning techniques and models can be helpful in the handling and resolution of such circumstances.
Ripple is a type of digital currency that can be transferred over the internet. XRP, the native cryptocurrency, is used as a bridge currency for other currencies such as XRP to USD.
Machine Learning Techniques and Models for Cryptocurrency Market
Graph neural networks, GNN is an ML technique that operates using graph data structures. The method can be helpful in the cryptocurrency market to analyze the exchanges and identify the impacts on the price. It can also be applicable in the development of new quant methods for price predictions.
A generative model is a form of the deep learning method, and it can generate synthetic data matching with the distribution of the training dataset. In the world of cryptocurrency, the technique can help combine real and synthetic datasets. It can then be applicable in training a sophisticated deep learning model. The method works well with generative adversarial neural networks.
Labeled datasets are not readily available in the cryptocurrency market. Semi-supervised learning is the ML technique that works adequately with small labeled datasets. It includes massive volumes of unlabeled data to train a model. The method can be useful in using the labeled data available, such as trade frequency of trade size and the unlabeled data, to expand the training.
Representation learning is one of the forms of machine learning that focuses on the automation of the learning of solid features. The process makes it easier to develop effective models. For example, it can identify the features relevant for the prediction of Bitcoin prices. Feature extraction in this technique comes from the unlabeled dataset. It is not possible to gain such insights with the traditional methods and manual feature engineering.
Neural architecture search is the technique that automates the creation models. The method evaluates a large number of neural network architectures to come up with the most suitable for a specific problem scenario. For instance, the technique can be useful in processing the datasets comprising of the trades in decentralized exchanges. It can then provide the relevant models and features to make predictions on the Ethereum or Bitcoin prices.
Machine Learning Applications and Use Cases in Cryptocurrency Market
Bad Actor Detection
It is essential to determine the possible fraudulent behavior to detect and avoid financial crimes timely. Most of the financial authorities are currently looking to identify malevolent entities. Traditional methods are not sufficient enough to deal with such issues. Machine learning technologies can constantly analyze the factors to keep track of the existing and possible fraudulent activities. ML techniques, such as decision trees, clustering, and classification, can be extremely significant in the bad actor detection in the cryptocurrency market.
Cryptojacking – Deep Learning
Cryptojacking is a form of malware attack in the cryptocurrency space, and it involves mining the cryptocurrency without the user’s knowledge. The frequency of such occurrences is increasing by the day.
Deep learning techniques can be very effective in the detection of malware that may carry out hijacking and mining activities. Reinforcement learning with deep learning techniques can optimize the entire process and functions in crypto trading.
Exchange Classification
Behavior prediction can become easier in crypto assets with the proper classification of the addresses. The categorization of the address as an exchange or individual wallet can be a complex task to carry out. Machine Learning can be applied in the process by determining the behavior of the exchanges while identifying the new ones. Classification and clustering techniques can be useful.
Wallet behavior analysis is an important aspect from the investors’ point of view. Adequate analysis can enable the investors to buy or sell the crypto assets to attain maximum profits. Machine Learning makes it possible to classify the investors and groups of investors as per their unique features and characteristics. Unsupervised machine learning models can easily determine the patterns in a specific group of token holders. Individual and group behavior and predictions become easier with all of such information in place. Recurrent and convolutional neural networks are some of the techniques useful in conducting behavior analytics and making predictions accordingly.
Crypto Trading – Reinforcement Learning
Bitcoin and Ethereum are the two widely popular trading cryptocurrencies across the globe. Trading bots are now used in the stock market, and machine learning holds an important role in the design and usage of these bots. ML algorithms are useful in the cryptocurrency market for trading.
Reinforcement ML techniques can be applied in determining the trading strategies to assure better profits and returns.
On-Chain Power Factors
Several factors can predict the behavior of the investor and the crypto-assets. These factors may change from time to time, and the traditional methods may not keep track of the existing and new factors. Some of the power factors that can have an important role in the prediction of the behavior can be hash rate, mining rewards distribution, or others.
Linear regression and recurrent neural networks are machine learning techniques that can identify the patterns as per the power factors.
Machine Learning is an emerging technology with a wide scope of application and implementation. Many of the ML algorithms and technologies are already in use in banks and financial institutions. The cryptocurrency market can also benefit from the application of ML techniques. The potential use and application of ML in the cryptocurrency space are not limited to price and behavior predictions. It can be applied in the resolution of security and privacy concerns. Fraudulent activities, for instance, can reduce with the effective usage and application of the ML techniques. Cryptojacking and other forms of cybersecurity attacks can also reduce with ML models and technologies.
