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.

src/dataset_builder.py

@@ -11,10 +11,10 @@ import pandas_ta as ta
 
 from src.ml_features import FEATURE_COLS
 
-LOOKAHEAD    = 90
+LOOKAHEAD    = 24   # 15분봉 × 24 = 6시간 뷰
 ATR_SL_MULT  = 1.5
 ATR_TP_MULT  = 2.0
-WARMUP       = 60   # 지표 안정화에 필요한 최소 행 수
+WARMUP       = 60   # 15분봉 기준 60캔들 = 15시간 (지표 안정화 충분)
 
 
 def _calc_indicators(df: pd.DataFrame) -> pd.DataFrame: