Information currency converter based on Telegram messenger

2022;
: pp. 106 - 121
1
Lviv Polytechnic National University, Computer Engineering Department
2
Lviv Polytechnic National University, Computer Aided Design Systems Department

The work is dedicated to the development of a mobile chatbot containing an information currency converter, designed for use by a wide range of people. A chatbot is a subject-oriented text-based dialog interface that allows a user to perform a limited set of tasks: getting information about the current rate of currencies (USD or EUR) relative to the national currency and finding out the current rate of cryptocurrencies (Bitcoin, Ethereum, Litecoin) in dollars or euros.

To achieve this goal, the selected subject area was analyzed and appropriate conclusions were made. A corresponding study of analogs who perform tasks similar in complexity was carried out, only a few chatbots were identified, as a certain number of bots posted in Telegram no longer provide their services or work incorrectly.

The algorithm of the service for currency conversion based on the Telegram messenger is described. The chatbot is implemented in the Python programming language and uses the Pycharm development environment, as it is best suited for programming the intended project and is easy to use. There are two options available to the user: the cryptocurrency rate from the CoinGecko site or the exchange rate from PrivatBank.

The article examines the development and improvement of chatbots. Similar Telegram bots, which function similarly to the created one, are reviewed. The author’s bot has been developed, and the architecture and algorithm of the CurrencyBot currency conversion service are presented.

  1. Hashemi A., Zare Chahooki M. A. Telegram group quality measurement by user behavior analysis. Soc. Netw. Anal. Min. 9, 33 (2019). https://doi.org/10.1007/s13278-019-0575-9 (accessed: 26 April 2022).
  2. Evolyutsiya chat-botiv. [Electronic resource] // TJournal. URL: https://tjournal.ru/tech/49880-telegram- facts (accessed: 28 October 2022).
  3. Oliphant T. E. "Python for Scientific Computing," in Computing in Science & Engineering. Vol. 9. No. 3. Pp. 10-20, May-June 2007. DOI: 10.1109/MCSE.2007.58 (accessed: 28 October 2022).
  4. M. M. T, S. K, R. G and C. G, "An Assessment on Classification in Python Using Data Science," 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA), 2021. Pp. 551–555. DOI: 10.1109/ICIRCA51532.2021.9544704 (accessed: 26 April 2022).
  5. Hu Q., Ma L. and Zhao J. "DeepGraph: A PyCharm Tool for Visualizing and Understanding Deep Learning Models," 2018 25th Asia-Pacific Software Engineering Conference (APSEC), 2018.Pp. 628–632. DOI: 10.1109/APSEC.2018.00079 (accessed: 28 October 2022).