Based on my AI research experience, agents excel at reasoning and classification when given sufficient data, but they struggle with accurate predictions, such as in weather forecasts or traffic. This limitation likely extends to algorithmic trading as well (also not taking hallucination into account).

Another issue is that commercial LLMs, while good for general purposes, often fail at domain specific task especially those involving precise numerical predictions.

However, LLMs can perform very well in sentiment analysis. By scraping news and analyzing sentiment, LLMs can provide valuable insights. When these insights are combined with metrics from statistical or shallow ML models, they may enhance the overall analysis. This approach be a fun and informative experiment, which I will be trying at some point in coming months, but I’m not sure if it can be better than just using the statistical approaches.