feat: implement 15-minute timeframe upgrade for model training and data processing

- Introduced a new markdown document detailing the plan to transition the entire pipeline from a 1-minute to a 15-minute timeframe, aiming to improve model AUC from 0.49-0.50 to over 0.53.
- Updated key parameters across multiple scripts, including `LOOKAHEAD` adjustments and default data paths to reflect the new 15-minute interval.
- Modified data fetching and training scripts to ensure compatibility with the new timeframe, including changes in `fetch_history.py`, `train_model.py`, and `train_and_deploy.sh`.
- Enhanced the bot's data stream configuration to operate on a 15-minute interval, ensuring real-time data processing aligns with the new model training strategy.
- Updated training logs to capture new model performance metrics under the revised timeframe.

scripts/train_model.py

@@ -53,7 +53,7 @@ def _cgroup_cpu_count() -> int:
     return cpu_count()
 
 
-LOOKAHEAD = 60
+LOOKAHEAD = 24  # 15분봉 × 24 = 6시간 (dataset_builder.py와 동기화)
 ATR_SL_MULT = 1.5
 ATR_TP_MULT = 3.0
 MODEL_PATH = Path("models/lgbm_filter.pkl")
@@ -357,7 +357,7 @@ def walk_forward_auc(
 
 def main():
     parser = argparse.ArgumentParser()
-    parser.add_argument("--data", default="data/combined_1m.parquet")
+    parser.add_argument("--data", default="data/combined_15m.parquet")
     parser.add_argument(
         "--decay", type=float, default=2.0,
         help="시간 가중치 감쇠 강도 (0=균등, 2.0=최신이 ~7.4배 높음)",