  Recently, large language models (LLMs), particularly GPT-4, have demonstrated
significant capabilities in various planning and reasoning tasks
\cite{cheng2023gpt4,bubeck2023sparks}. Motivated by these advancements, there
has been a surge of interest among researchers to harness the capabilities of
GPT-4 for the automated design of quantitative factors that do not overlap with
existing factor libraries, with an aspiration to achieve alpha returns
\cite{webpagequant}. In contrast to these work, this study aims to examine the
fidelity of GPT-4's comprehension of classic trading theories and its
proficiency in applying its code interpreter abilities to real-world trading
data analysis. Such an exploration is instrumental in discerning whether the
underlying logic GPT-4 employs for trading is intrinsically reliable.
Furthermore, given the acknowledged interpretative latitude inherent in most
trading theories, we seek to distill more precise methodologies of deploying
these theories from GPT-4's analytical process, potentially offering invaluable
insights to human traders.
  To achieve this objective, we selected daily candlestick (K-line) data from
specific periods for certain assets, such as the Shanghai Stock Index. Through
meticulous prompt engineering, we guided GPT-4 to analyze the technical
structures embedded within this data, based on specific theories like the
Elliott Wave Theory. We then subjected its analytical output to manual
evaluation, assessing its interpretative depth and accuracy vis-\`a-vis these
trading theories from multiple dimensions. The results and findings from this
study could pave the way for a synergistic amalgamation of human expertise and
AI-driven insights in the realm of trading.
