AI Trading: Evaluating Large Language Models for Technical Market Analysis
A systematic evaluation of five major LLMs for technical market analysis finds GPT-4 Turbo achieves highest annualized return and Sharpe ratio, while FinGPT shows competitive risk-adjusted performance through domain fine-tuning. The study also identifies failure modes including numerical hallucination and context window limitations.
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[Submitted on 16 Jul 2026]
Title:AI Trading: Evaluating Large Language Models for Technical Market Analysis
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Abstract:Large Language Models (LLMs) have emerged as powerful tools for processing the heterogeneous information environments of modern financial markets. This paper presents a systematic, comparative evaluation of five prominent LLMs: GPT-4 Turbo, Claude 3 Opus, Gemini 1.5 Pro, Llama 3 70B, and the domain-specialized FinGPT, with respect to their capacity for technical market analysis. The evaluation spans four structured tasks: candlestick pattern recognition from OHLCV data, directional signal generation (BUY/SELL/HOLD), backtesting of signal quality through a simulated execution pipeline, and financial report comprehension. Our experimental framework employs rigorous quantitative metrics, including Sharpe ratio, maximum drawdown, Sortino ratio, information coefficient, F1-score, and BLEU score. Findings from simulated backtesting indicate that GPT-4 Turbo achieves the highest annualized return and Sharpe ratio among general-purpose models, while FinGPT demonstrates competitive risk-adjusted performance due to domain-specific fine-tuning. Both models outperform a passive S&P 500 benchmark under the tested conditions. The study identifies persistent failure modes across all evaluated models, including numerical hallucination, context-window limitations, and inconsistent performance in sideways market regimes. We conclude that while LLMs hold genuine promise within AI trading systems, robust deployment requires careful task decomposition, rigorous backtesting protocols, and domain-aware fine-tuning strategies.
Comments: Master's research paper, Georgia Institute of Technology. 31 pages, 4 figures
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computational Finance (q-fin.CP)
Report number: GT-SMARTech-81871
Cite as: arXiv:2607.15414 [cs.LG]
(or arXiv:2607.15414v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.15414
arXiv-issued DOI via DataCite (pending registration)
Journal reference: Georgia Institute of Technology, 2026
Submission history
From: Geofrey Ntale [view email] [v1] Thu, 16 Jul 2026 19:38:23 UTC (674 KB)
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