Cryptoasset networks: Flows and regular players in Bitcoin and XRP

Cryptoassets flow among players as recorded in the ledger of blockchain for all the transactions, comprising a network of players as nodes and flows as edges. The last decade, on the other hand, has witnessed repeating bubbles and crashes of the price of cryptoassets in exchange markets with fiat currencies and other cryptos. We study the relationship between these two important aspects of dynamics, one in the bubble/crash of price and the other in the daily network of crypto, by investigating Bitcoin and XRP. We focus on “regular players” who frequently appear on a weekly basis during a period of time including bubble/crash, and quantify each player’s role with respect to outgoing and incoming flows by defining flow-weighted frequency. During the most significant period of one-year starting from the winter of 2017, we discovered the structure of three groups of players in the diagram of flow-weighted frequency, which is common to Bitcoin and XRP in spite of the different nature of the two cryptos. By examining the identity and business activity of some regular players in the case of Bitcoin, we can observe different roles of them, namely the players balancing surplus and deficit of cryptoassets (Bal-branch), those accumulating the cryptoassets (In-branch), and those reducing it (Out-branch). Using this information, we found that the regime switching among Bal-, In-, Out-branches was presumably brought about by the regular players who are not necessarily dominant and stable in the case of Bitcoin, while such players are simply absent in the case of XRP. We further discuss how one can understand the temporal transitions among the three branches

Создание и использование chainlet для прогнозирования цен криптовалют

Объектом исследования является проблема прогнозирования стоимости криптовалют. Целью работы является разработка и реализация методов для построения chainlet и модели прогнозирования с использованием chainlet извлеченных из транзакций блокчейна. Область применения: прогнозирование временных рядов. В представленной работе разработаны и реализованы методы по извлечению, построению и анализу chainlet. Так же были разработаны и реализованы модели прогнозирования ARIMA, ARIMA-GARCH, LSTM, позволяющие прогнозировать стоимость криптовалют, с использованием цепочек сетевого графа блокчейна.The object of the study is the problem of predicting the value of cryptocurrencies. The aim of the paper is to design and implement methods for chainlet construction and forecasting model using chainlet extracted from blockchain transactions. Scope: time series forecasting. In the presented work, methods for chainlet extraction, construction and analysis have been developed and implemented. Also, ARIMA, ARIMA-GARCH, LSTM forecasting models have been developed and implemented to predict the value of cryptocurrencies, using blockchain network graph chains

Simulation and Assessment of Bitcoin Prediction Using Machine Learning Methodology

The market for digital currencies is rapidly growing, attracting traders, investors, and businesspeople on a worldwide scale that hasn't been witnessed in this century. By providing comparison studies and insights from the price data of crypto currency marketplaces, it will help in recording the behaviour and habits of such a lucratively demanding and rapidly expanding business. The bitcoin market is reaching one of its peak levels ever in 2021. The emergence of new exchanges has made cryptocurrencies more approachable to the general public, hence boosting their attractiveness. This has increased the number of users and interest in cryptocurrencies, along with a number of reliable crypto ventures started by some of the founders. Virtual currencies are growing more and more well-liked, and businesses like Tesla, Dell, and Microsoft are now embracing them. Decentralized digital currencies are becoming more and more popular, thus it's more crucial than ever to properly inform the public about the new currencies as they proliferate so that people are aware of what they possess and how their money is being invested. Analysis shows that soft computing and machine learning techniques can anticipate more accurately than any other technique now available to researchers. Finally, it is claimed that ANN, SVMs, and other similar machine learning techniques are useful for predicting global stock market fluctuations.