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, focusing on 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;
- Financial report comprehension.
Our experimental framework employs rigorous quantitative metrics, including Sharpe ratio, maximum drawdown, Sortino ratio, information ratio, 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.
Blogger's Review: This study showcases the potential of large language models in financial market analysis, particularly in signal generation and backtesting. However, technical challenges like numerical hallucination and performance inconsistency remain, and future research should focus on addressing these issues for more reliable AI trading systems.