BreakGPT: A Large Language Model with Multi-stage Structure for
  Financial Breakout Detection
Kang Zhang Osamu Yoshie Weiran Huang
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