source: arxiv machine learning: ai trading: evaluating large language models for technical market analysis
level: research
researchers evaluated five large language models for technical market analysis: gpt-4 turbo, claude 3 opus, gemini 1.5 pro, llama 3 70b, and the finance-focused fingpt. the models were tested on four tasks: spotting candlestick patterns from price data, generating buy, sell, or hold signals, backtesting those signals in a simulated trading setup, and understanding financial reports. the study used standard metrics like sharpe ratio, maximum drawdown, sortino ratio, information coefficient, f1-score, and bleu score to measure performance.
in simulated backtesting, gpt-4 turbo led general-purpose models with the highest annualized return and sharpe ratio. the paper does not detail the exact performance of other models on all tasks, but it provides a structured comparison across the board. the evaluation framework is designed to be rigorous, using quantitative metrics that traders and analysts commonly rely on to assess strategy quality and risk-adjusted returns.
the work highlights how large language models can handle the mixed data types found in financial markets, from numerical price series to text-based reports. by testing both general and domain-specific models, the study offers a baseline for how these tools might assist in technical analysis. the results suggest that while general models can perform well, specialized models like fingpt may have advantages in certain financial tasks, though the abstract only notes gpt-4 turbo's top backtesting results.
why it matters: this comparison helps data scientists and traders understand which language models might be useful for automating parts of technical analysis, potentially saving time and improving signal quality.
source: arxiv machine learning: ai trading: evaluating large language models for technical market analysis