The rapid advancements in deep learning have enabled the development of models capable of addressing a wide range of tasks across domains such as natural language processing, computer vision, and time series forecasting (Vaswani et al., 2017; Devlin et al., 2018). However, predicting financial market behavior, especially identifying price surges in cryptocurrency markets, remains a challenging problem due to the stochastic nature of financial data and the influence of external factors (Benth et al., 2003; Cont, 2001). In recent years, Transformer-based models have demonstrated exceptional performance in time series forecasting by capturing long-range dependencies and temporal interactions(Vaswani et al., 2017; Lim and Zohren, 2021; Zhou et al., 2021). Simultaneously, the emergence of large language models (LLMs) has paved the way for transfer learning applications in financial time series data, including cryptocurrency markets (Raffel et al., 2020; Liu et al., 2019). This study introduces BreakGPT, an architecture that combines the strengths of LLMs and Transformer-based models for predicting cryptocurrency price surges. We evaluate multiple architectures, including a modified TimeLLM (Doe and Lee, 2023) and TimeGPT (Smith and Johnson, 2023), assessing their effectiveness in detecting price surges in assets like Bitcoin and Solana(Nakamoto, 2008; Zhang and McGovern, 2019). Key contributions of this study include: Development of a modified TimeLLM architecture that adapts GPT-2 for time series prediction using domain-specific prompts and embeddings (Doe and Lee, 2023; Radford et al., 2019). Implementation and comparison of various Transformer-based models that utilize attention mechanisms and convolutional layers to process financial time series data.

Trading range breakout (TRB) is a key method in the technical analysis of financial trading, widely employed by traders in financial markets such as stocks, futures, and foreign exchange. However, distinguishing between true and false breakout and providing the correct rationale cause significant challenges to investors. Recently, large language models have achieved success in various downstream applications, but their effectiveness in the domain of financial breakout detection has been subpar. The reason is that the unique data and specific knowledge are required in breakout detection. To address these issues, we introduce BreakGPT, the first large language model for financial breakout detection. Furthermore, we have developed a novel framework for large language models, namely multi-stage structure, effectively reducing mistakes in downstream applications. Experimental results indicate that compared to GPT-3.5, BreakGPT improves the accuracy of answers and rational by 44%, with the multi-stage structure contributing 17.6% to the improvement. Additionally, it outperforms ChatGPT-4 by 42.07%. Our Code is publicly available: https://github.com/Neviim96/BreakGPT