feat: enhance MLX model training with combined data handling

- Introduced a new function `_split_combined` to separate XRP, BTC, and ETH data from a combined DataFrame.
- Updated `train_mlx` to utilize the new function, improving data management and feature handling.
- Adjusted dataset generation to accommodate BTC and ETH features, with warnings for missing features.
- Changed default data path in `train_mlx` and `train_model` to point to the combined dataset for consistency.
- Increased `LOOKAHEAD` from 60 to 90 and adjusted `ATR_TP_MULT` for better model performance.

src/dataset_builder.py

@@ -11,9 +11,9 @@ import pandas_ta as ta
 
 from src.ml_features import FEATURE_COLS
 
-LOOKAHEAD    = 60
+LOOKAHEAD    = 90
 ATR_SL_MULT  = 1.5
-ATR_TP_MULT  = 3.0
+ATR_TP_MULT  = 2.0
 WARMUP       = 60   # 지표 안정화에 필요한 최소 행 수