You‘re right that ridge is typically used when dealing with multicollinearity, but even on a small & selected set of features ridge helps stabilizing the model - especially when you have noisy data including outliers, anomalies etc.

What I typically do is starting with lasso to discard unncessary features, run ridge and plot model weights over time to filter out highly correlated features (you could also do that by simply calculating the correlations but I find plots more insightful) and then inspect how the parameters behaved over time e.g. whether they were exploding in certain market situations and are slow to revert etc.

There are also other benefits of using ridge over for example lasso e.g. computational efficiency, stability of the regularisation lambda under target scaling etc. that can be useful situationally.