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.

翻译：近期，大型语言模型（LLMs），特别是GPT-4，在各类规划与推理任务中展现出显著能力\cite{cheng2023gpt4,bubeck2023sparks}。受此进展驱动，研究者们愈发倾向于利用GPT-4能力自动设计不重复现有因子库的量化因子，以期实现阿尔法收益\cite{webpagequant}。与上述研究不同，本研究旨在检验GPT-4对经典交易理论的理解忠实度，及其运用代码解释能力分析真实交易数据的熟练程度。这一探索有助于辨明GPT-4用于交易的底层逻辑是否本质可靠。此外，鉴于多数交易理论固有的阐释弹性，我们试图从GPT-4的分析过程中提炼出更精确的理论部署方法，这或能为人类交易者提供宝贵洞见。为实现该目标，我们选取了特定资产（如上证指数）在特定区间的日线蜡烛图（K线）数据。通过精细的提示工程，引导GPT-4基于艾略特波浪理论等特定理论分析数据中嵌入的技术结构，随后对其分析结果进行人工评估，从多维度衡量其对这些交易理论的解读深度与准确性。本研究的结果与发现可为交易领域中人类专业知识与人工智能洞见的协同融合奠定基础。