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/fetch_history.py

@@ -113,9 +113,9 @@ def main():
     )
     parser.add_argument("--symbols", nargs="+", default=["XRPUSDT"])
     parser.add_argument("--symbol",   default=None, help="단일 심볼 (--symbols 미사용 시)")
-    parser.add_argument("--interval", default="1m")
-    parser.add_argument("--days",     type=int, default=90)
-    parser.add_argument("--output",   default="data/xrpusdt_1m.parquet")
+    parser.add_argument("--interval", default="15m")
+    parser.add_argument("--days",     type=int, default=365)
+    parser.add_argument("--output",   default="data/combined_15m.parquet")
     args = parser.parse_args()
 
     # 하위 호환: --symbol 단독 사용 시 symbols로 통합