Add daily model training scripts and terminal UI for live trading

- Introduced `train_daily.sh` for automating daily model retraining, including data download and model training steps.
- Added `install_cron.sh` for setting up a cron job to run the daily training script.
- Created `setup_schedule.sh` for configuring Systemd timers for daily training tasks.
- Implemented a terminal UI using Rich for real-time monitoring of trading performance, including metrics display and log handling.
- Updated `pyproject.toml` to include the `rich` dependency for UI functionality.
- Enhanced `.gitignore` to exclude model and log files.
- Added database support for trade persistence and metrics calculation.
- Updated README with installation and usage instructions for the new features.

live_trading/live_regime_strategy.py

@@ -39,18 +39,40 @@ class LiveRegimeStrategy:
         self.paths = path_config
         self.model: Optional[RandomForestClassifier] = None
         self.feature_cols: Optional[list] = None
+        self.horizon: int = 102  # Default horizon
+        self._last_model_load_time: float = 0.0
         self._load_or_train_model()
     
+    def reload_model_if_changed(self) -> None:
+        """Check if model file has changed and reload if necessary."""
+        if not self.paths.model_path.exists():
+            return
+            
+        try:
+            mtime = self.paths.model_path.stat().st_mtime
+            if mtime > self._last_model_load_time:
+                logger.info(f"Model file changed, reloading... (last: {self._last_model_load_time}, new: {mtime})")
+                self._load_or_train_model()
+        except Exception as e:
+            logger.warning(f"Error checking model file: {e}")
+
     def _load_or_train_model(self) -> None:
         """Load pre-trained model or train a new one."""
         if self.paths.model_path.exists():
             try:
+                self._last_model_load_time = self.paths.model_path.stat().st_mtime
                 with open(self.paths.model_path, 'rb') as f:
                     saved = pickle.load(f)
                     self.model = saved['model']
                     self.feature_cols = saved['feature_cols']
-                logger.info(f"Loaded model from {self.paths.model_path}")
-                return
+                    
+                    # Load horizon from metrics if available
+                    if 'metrics' in saved and 'horizon' in saved['metrics']:
+                        self.horizon = saved['metrics']['horizon']
+                        logger.info(f"Loaded model from {self.paths.model_path} (horizon={self.horizon})")
+                    else:
+                        logger.info(f"Loaded model from {self.paths.model_path} (default horizon={self.horizon})")
+                    return
             except Exception as e:
                 logger.warning(f"Could not load model: {e}")
         
@@ -66,6 +88,7 @@ class LiveRegimeStrategy:
                 pickle.dump({
                     'model': self.model,
                     'feature_cols': self.feature_cols,
+                    'metrics': {'horizon': self.horizon}  # Save horizon
                 }, f)
             logger.info(f"Saved model to {self.paths.model_path}")
         except Exception as e:
@@ -81,7 +104,7 @@ class LiveRegimeStrategy:
         logger.info(f"Training model on {len(features)} samples...")
         
         z_thresh = self.config.z_entry_threshold
-        horizon = 102  # Optimal horizon from research
+        horizon = self.horizon
         profit_target = 0.005  # 0.5% profit threshold
         
         # Define targets