Nástroj pro nastavení parametrů a automatizované obchodování na kryptoměnové burze

Abstract

The aim of this thesis was to develop a system that generates recommendations for buying and selling cryptocurrencies based on historical and continuously updated data. As part of the thesis, an analysis of existing automated trading tools was first conducted, based on which specific strategies were selected for testing. The data was obtained from the Binance cryptocurrency exchange via its API, which was used to build the infrastructure for processing the data, testing various approaches, and subsequently executing trade orders on the exchange. Trading strategies based on technical indicators and machine learning methods were then implemented and evaluated. The result is the design and implementation of a system that enables the testing and optimization of trading strategies using real-world data and generates recommendations for automated trading.