Application of Artificial Intelligence Algorithms in Volatility Trading of the Global Tether Market: Trend Analysis, Forecasting Modeling, Proposed Framework, and Performance Evaluation

Abstract

In recent years, the cryptocurrency market has transformed into one of the primary platforms for high-risk, high-return trading, characterized by unprecedented growth, extreme volatility, and the development of complex trading instruments. Among these, Tether (USDT), known as a stablecoin backed by the U.S. dollar, despite its goal of maintaining a stable value, experiences noticeable periodic fluctuations across different trading platforms, creating opportunities for short-term volatility trading. This study aims to design a hybrid predictive model for volatility trading in the global Tether market using artificial intelligence algorithms. In this research, a set of technical indicators (including RSI, MACD, EMA, Bollinger Bands, etc.) was extracted and used as input features for machine learning models (Random Forest, XGBoost) and deep learning models (LSTM, CNN, BiLSTM). Then, by implementing an intelligent hybrid framework, the short-term price volatility trends of Tether over a multi-year period were modeled, and the performance of the proposed model was compared with baseline models. The results obtained from the analysis of real trading data show that the proposed model achieved higher prediction accuracy in identifying tradable volatility and demonstrated a significant advantage in profitability compared to baseline algorithms. This research, focusing on a stablecoin that has previously received little attention in scientific studies, offers a novel framework for precise analysis and automated opportunity detection in quasi-stable financial markets.