scispace - formally typeset
Book ChapterDOI

Development of Information System for Monitoring the Rate of Digital Currency for Investing

E. V. Butsenko
- pp 176-185
Reads0
Chats0
TLDR
The PyCharm integrated development environment in Python is used in this paper to monitor and analyze the cryptocurrency exchange rate in real-time, which can be used to evaluate the growth or fall in the value of digital assets.
Abstract
AbstractThe changes currently taking place in the world economy stimulate the emergence of new software tools for evaluating and analyzing financial investments. While investments are aimed at obtaining profit by the investor, they are not a guaranteed way to receive it. Different ways of investing provide different guarantees of income, but in all cases there is a risk that instead of profit, the investor will receive a loss. Therefore, the development of investment software that allows you to analyze and evaluate the growth or fall in the value of digital assets is an urgent issue. The computer system under consideration is designed to monitor and analyze the cryptocurrency exchange rate in real time. The purpose of the work is to describe the functional purpose and structural elements of the system for tracking changes in the cryptocurrency exchange rate to make a decision on its purchase/sale. The information system is written in the PyCharm integrated development environment in Python. The article presents the development technology of the system under consideration and shows a practical example of its work on the process of monitoring the bitcoin cryptocurrency. In the future, this system can be improved with additional functionality and a more flexible interface #CSOC1120.KeywordsExchange rateCryptocurrencyDigital currencyInvestmentsMonitoringSystem developmentBitcoinPythonData parsing

read more

Content maybe subject to copyright    Report

References
More filters
Book ChapterDOI

Machine Learning Algorithm for Cryptocurrencies Price Prediction

TL;DR: In this article, the authors used machine learning to construct a model for the Stock and Cryptocurrency price prediction using technical indicators that are most important for market trend study, which outperformed other models in terms of Bitcoin, Ether and Litecoin cryptocurrencies.
Journal ArticleDOI

Forecasting mid-price movement of Bitcoin futures using machine learning.

TL;DR: In this paper, the performance of the various type of machine learning algorithms (MLAs) to predict mid-price movement for Bitcoin futures prices was explored, and the average classification accuracy for five out of the six MLAs was consistently above the 50% threshold, indicating that MLAs outperformed benchmark models such as ARIMA and random walk in forecasting Bitcoin futures price.
Journal ArticleDOI

Do cryptocurrencies really have (no) intrinsic value

TL;DR: In this paper, the authors briefly summarize existing standpoints and suggest three alternative propositions: (1) to avoid using the term "intrinsic value" for the valuation of cryptocurrencies, (2) to refer to the sum total of all properties that could potentially qualify them as money, and (3) to consider the amount of capital and energy that is needed to create them.
Book ChapterDOI

The New Money: The Utility of Cryptocurrencies and the Need for a New Monetary Policy

TL;DR: This paper sets out to explore the utility of cryptocurrencies and CBDC, their implications on the economy and the government’s ability to use monetary policy, and compares the approaches to CBDCs suggested by various governments.
Proceedings ArticleDOI

Network models for solving the problem of multicriterial adaptive optimization of investment projects control with several acceptable technologies

TL;DR: In this article, a new economic and mathematical model and a method for solving the problem of multicriterial adaptive optimization the control of investment projects in the presence of several technologies is presented.
Related Papers (5)