Is GPT4 a Good Trader?

TL;DR

This study evaluates GPT-4's understanding and application of classic trading theories, like Elliott Wave, in analyzing real-world stock data, to assess its reliability and potential to aid human traders.

Contribution

It provides a systematic assessment of GPT-4's comprehension of trading theories and its effectiveness in practical data analysis, highlighting its strengths and limitations.

Findings

GPT-4 demonstrates a notable understanding of technical trading theories.

The analysis reveals areas where GPT-4's interpretations align or diverge from established theories.

Insights from GPT-4's analysis can inform better integration of AI in trading strategies.

Abstract

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.