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I have built a model similar in vein to this. And I am not at all happy with the results. It is a sophisticated model that started out as a learning exercise, but I have the programming chops and some solid financial education as well, so once I got started on it I got hooked and kept pushing it.
Let me take you through where I went on this:
Step 1: I used FMP to download data as a trial kicker. I quickly realized I would have to pay, and I didn't want to at that time, so I abandoned FMP because their free tier didn't give enough data (although the data they do give is great).
Step 2. I used Yahoo, but quickly realized that they didn't give you enough historical data to run models.
Step 3. I got together with some folks and we built a neural bot that does screen scraping of fundamentals from various data sources. NOW I GOT ENOUGH DATA. Annuals and Quarterlies since as early as 2005. Thousands of rows of data. You cannot split the data and train, validate and test without enough data.
Step 4. I had a "Morningstar-like" stock rating app (Deterministic). I cloned it, and changed the code so that I could run Random Forest on it and do "score prioritization" based on features that had higher SHAP values. Cool idea when I started it, and I got it working, but in the end, the scores I generated had very low (and in fact shifting) correlations with fwd return.
Step 5. I changed the model to XGBoost after doing a bake-off between it and Random Forest (friend of mine is using XGBoost for a swing trading model he runs, and suggested this to me). The r-squareds on Annual were pretty darn high - until I realized I had some data issues and when I fixed those issues, the R-squared dropped. The annual model does have a considerably higher r-squared than the quarterly model does, but the models do overfit because the train r-squared is much higher than the final r-squared.
Step 6. I started to do an ensemble between Annual and Quarterly. Annual is producing about .25 r-squared, Quarterly is producing about .11 r-squared and the Ensemble is producing about .4. One thing that IS encouraging, is the correlation between predicted and actual fwd return, on the backtest portion (.44).

Step 7. I added LSTM to the model this week - only on Quarterly because there are a lot more rows of Quarterly data. I thought I would stack (combined) the XGBoost model, with the LSTM model.
The LSTM initially came out nicely when I ran it standalone as a prototype. But when I fully incorporated it into the larger code base, the LSTM model sucked - it did not improve the XGBoost, it dragged it down. I changed the feature engineering a bit (less imputing, more drops of columns with missing values), and it did not move the needle or help anywhere near enough.
The ANNUAL model does perform considerably better. Which makes sense because fundamentals like these start to take hold when you look at stocks over a longer time horizon. For quarterly, fundamentals are only one needle in a haystack when it comes to predicting fwd return. It is all about sentiment, Fed Announcements, Earnings Calls, News, and "events".
The \*only\* value in this quarterly model, I have decided, is if you ensemble it stacked with the annual model, and several more real-time models. And, while I initially predicted price, and switched it to predict fwd return, I agree with another poster on here that maybe going with an up/down price movement prediction or something might be a better adjustment.
So while this has been fun to do, I didn't come out of it with anything useful. Frankly, my Deterministic model is a lot more valuable for "assessing stocks". I will probably shelve this, and think about whether there is any kind of "next phase" I might consider. Doing the real-time stuff is a LOT more work.

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