The only applications of generative AI I can envisage for trading, systematically or otherwise are the following:

  - data extraction: It's possible to get pretty good levels of accuracy on unstructured data, eg financial reports with relatively little effort compared to before decent llm's
   - sentiment analysis: Why bother with complicated sentiment analysis when you can just feed an article into an LLM for scoring?
   - reports: You could use it to generate reports on your financial performance, current positions etc
   - code: It can generate some code that might sometimes be useful in the development of a system

The issue is that these models don't really reason and they trade in what might as well be a random way. For example, a stock might have just dropped 5%. One LLM might say that we should buy the stock now and follow a mean reversion strategy. Another may say we should short the stock and follow the trend. The same LLM may give the same output on a different call. A miniscule difference in price, time or other data will potentially change the output when really a signal should be relatively robust.

And if you're going to tell the model say, 'we want to look for mean reversion opportunities' - then why bother with an LLM?

Another angle: LLM's are trained on the vast swathe of scammy internet content and rubbish in relation to the stock market. 90%+ of active retail traders lose money. If an llm is fed on losing / scammy rubbish, how could it possibly produce a return?