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.

scripts/train_model.py

@@ -261,7 +261,7 @@ def train(data_path: str, time_weight_decay: float = 2.0):
 
 def main():
     parser = argparse.ArgumentParser()
-    parser.add_argument("--data", default="data/xrpusdt_1m.parquet")
+    parser.add_argument("--data", default="data/combined_1m.parquet")
     parser.add_argument(
         "--decay", type=float, default=2.0,
         help="시간 가중치 감쇠 강도 (0=균등, 2.0=최신이 ~7.4배 높음)",