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.

models/training_log.json

@@ -135,5 +135,32 @@
     "features": 21,
     "time_weight_decay": 3.0,
     "model_path": "models/lgbm_filter.pkl"
+  },
+  {
+    "date": "2026-03-01T22:12:06.299119",
+    "backend": "mlx",
+    "auc": 0.5746,
+    "samples": 533,
+    "train_sec": 0.2,
+    "time_weight_decay": 2.0,
+    "model_path": "models/mlx_filter.weights"
+  },
+  {
+    "date": "2026-03-01T22:13:20.434893",
+    "backend": "mlx",
+    "auc": 0.5663,
+    "samples": 533,
+    "train_sec": 0.2,
+    "time_weight_decay": 2.0,
+    "model_path": "models/mlx_filter.weights"
+  },
+  {
+    "date": "2026-03-01T22:15:43.163315",
+    "backend": "lgbm",
+    "auc": 0.5581,
+    "samples": 533,
+    "features": 21,
+    "time_weight_decay": 2.0,
+    "model_path": "models/lgbm_filter.pkl"
   }
 ]