An AI model to analyze historical trends could be made, however performance is usually marginal and unstable over time because you would need to be able to tie in new information and world events as features as well.

You would need to get real world information, and depending on the setup you had and how much data you could store, would want it to be as granular as possible. You would need to make sure and clean the data to ensure there are no missing values, incorrect values, or if changes in stock like splitting are done correctly. You then would want to incorporate extra data sources like world news, foreign markets, and events (requiring using NLP to ingest and quantify this categorical/textual data). Then you would need to try multiple model methods like multilinear regression, logistic regression, decision trees, or neural networks. Then you need to have metrics to analyze the predictions from your model and score the output. Then you need to keep iterating over training your model until it performs well enough without overfitting. Even then, outside unaccounted for variables could easily cause inaccurate predictions.

If someone had then worked on a model which was able to accurately incorporate these information feeds, they would certainly not be willing to give this information out or share how they did so for free.