Removing Two Stale Macro Features

 

Removing Two Stale Macro Features

The model was trained on 11 features, two of which were macroeconomic sentiment indicators sourced from FRED. On inspection, both turned out to be monthly series — meaning they only update once a month and carry a publication lag on top of that. Despite this, the model had assigned them significant feature importance, essentially learning to lean on data that wasn't meaningfully changing day to day and wasn't even fully available in real time when historical training data was constructed.

Removing them dropped the feature set from 11 to 9. With those features gone, the model redistributed weight toward momentum and the remaining daily macro indicators in a more sensible way. Validation rank correlations improved on two of the three prediction horizons after the change. The two daily macro features that remained — VIX and treasury spread — are genuinely responsive to market conditions and carry the macro signal adequately on their own.

Both changes were low risk given that model predictions are used for monitoring purposes in this system rather than directly driving trading decisions.

Latest Changes to XGBoost Quant Financial Model

 

Latest Changes:

Added new Macros to my model  - CPI, PPI, VIX (last change I made was to add a beats/meets/misses surprise score a few weeks ago)

I added some interactive features based on these (5 in total). I have learned that these interactives move the model predictability like nothing else - which is why I added more. 

    R-squared score came back up to approaching .3 now with these.

    My correlation is still inverted from 1 yr fwd return - even more so. So I flip the score.

One major change was that I added a "graph_score" which was a nightmare to produce. LLMs can NOT seem to handle this task AT ALL. So I finally had to do it mostly myself, and got something working fairly well - it recognizes good graphs vs bad graphs and downscores bad graph patterns.

I forked the stockdex github project, and am making some changes that I will re-submit back with a git pull. The macrotrends datasource could not handle quarterly data. Once I realized it could be done, I decided to enhance the code to do this so I could run the model and get more quarterly statements alongside the pack of annual statements.  

Once I get the model run with annual+quarterly, I will probably retire this project and move onto more recent and LLM-based stuff.