Add FastAPI backend for energy trading system

Implements FastAPI backend with ML model support for energy trading,
including price prediction models and RL-based battery trading policy.
Features dashboard, trading, backtest, and settings API routes with
WebSocket support for real-time updates.

backend/app/ml/price_prediction/predictor.py

@@ -0,0 +1,86 @@
+from typing import Dict, Optional
+import pandas as pd
+import numpy as np
+from app.ml.price_prediction.model import PricePredictionModel
+from app.ml.price_prediction.trainer import PricePredictionTrainer
+from app.utils.logger import get_logger
+
+logger = get_logger(__name__)
+
+
+class PricePredictor:
+    def __init__(self, models_dir: str = "models/price_prediction"):
+        self.models_dir = models_dir
+        self.models: Dict[int, PricePredictionModel] = {}
+        self._load_models()
+
+    def _load_models(self):
+        self.models = PricePredictionTrainer.load_models(self.models_dir)
+        logger.info(f"Loaded {len(self.models)} prediction models")
+
+    def predict(
+        self, current_data: pd.DataFrame, horizon: int = 15, region: Optional[str] = None
+    ) -> float:
+        if horizon not in self.models:
+            raise ValueError(f"No model available for horizon {horizon}")
+
+        model = self.models[horizon]
+
+        from app.ml.features import build_price_features
+
+        df_features = build_price_features(current_data)
+
+        feature_cols = [col for col in df_features.columns if col not in ["timestamp", "region", "day_ahead_price", "real_time_price"]]
+
+        if region and "region" in df_features.columns:
+            df_features = df_features[df_features["region"] == region]
+
+        latest_row = df_features.iloc[-1:][feature_cols]
+
+        prediction = model.predict(latest_row.values)
+
+        return float(prediction[0])
+
+    def predict_all_horizons(self, current_data: pd.DataFrame, region: Optional[str] = None) -> Dict[int, float]:
+        predictions = {}
+
+        for horizon in sorted(self.models.keys()):
+            try:
+                pred = self.predict(current_data, horizon, region)
+                predictions[horizon] = pred
+            except Exception as e:
+                logger.error(f"Failed to predict for horizon {horizon}: {e}")
+                predictions[horizon] = None
+
+        return predictions
+
+    def predict_with_confidence(
+        self, current_data: pd.DataFrame, horizon: int = 15, region: Optional[str] = None
+    ) -> Dict:
+        prediction = self.predict(current_data, horizon, region)
+
+        return {
+            "prediction": prediction,
+            "confidence_lower": prediction * 0.95,
+            "confidence_upper": prediction * 1.05,
+            "horizon": horizon,
+        }
+
+    def get_feature_importance(self, horizon: int) -> pd.DataFrame:
+        if horizon not in self.models:
+            raise ValueError(f"No model available for horizon {horizon}")
+
+        model = self.models[horizon]
+
+        importances = model.feature_importances_
+        feature_names = model.feature_names
+
+        df = pd.DataFrame({
+            "feature": feature_names,
+            "importance": importances,
+        }).sort_values("importance", ascending=False)
+
+        return df
+
+
+__all__ = ["PricePredictor"]