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/bot.py

@@ -20,7 +20,7 @@ class TradingBot:
         self.current_trade_side: str | None = None  # "LONG" | "SHORT"
         self.stream = MultiSymbolStream(
             symbols=[config.symbol, "BTCUSDT", "ETHUSDT"],
-            interval="1m",
+            interval="15m",
             on_candle=self._on_candle_closed,
         )