These tools seem too complex to me and may increase the risk of overfitting to the data.
If you prefer to focus on factor selection and economic intuition rather than factor weighting, you might consider a custom discrete optimizer.
Such an optimizer can:
- Set factor weights on a fixed grid (e.g., min = 0.025, max = 0.10, step = 0.025).
- Accept a dictionary of fixed weights for core factors you always want in the model.
The advantage is avoiding wasted computation on meaningless precision — in practice, a weight of 0.0487566486845644 will not perform differently from 0.05 out-of-sample.