1. Why Trees Work Well on Tabular Market Data

Tree-based models excel in financial applications because they:

• Capture nonlinearities and interactions (e.g., momentum only matters in low-vol regimes).
• Are robust to scaling — no need for careful normalization.
• Naturally handle mixed feature types (continuous, categorical, binary).

2. Random Forest vs Extra Trees

Random Forest:

• Uses bootstrap samples + random feature subsets at each split.
• More variance reduction through averaging.
• Generally more accurate but slower to train.

Extra Trees (Extremely Randomized Trees):

• More randomness at split thresholds; often faster.
• Slightly more bias, less variance.
• Good for quick benchmarks and feature importance exploration.

3. Use Cases in volarixs

Random forests are particularly useful for:

• Predicting 5d/21d returns with rich feature sets (technical + fundamental + regime).
• Quick benchmarking of nonlinear capacity vs linear models.
• Useful for feature importance exploration — which factors matter most?

4. Limitations

While powerful, tree-based models have trade-offs:

• Harder to interpret than linear regression (but feature importance helps).
• Can overfit without depth / min_samples tuning.
• Not as smooth in time as explicit TS models.