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/model.py

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+import pickle
+from typing import Optional
+import xgboost as xgb
+import numpy as np
+
+
+class PricePredictionModel:
+    def __init__(self, horizon: int, model_id: Optional[str] = None):
+        self.horizon = horizon
+        self.model_id = model_id or f"price_prediction_{horizon}m"
+        self.model: Optional[xgb.XGBRegressor] = None
+        self.feature_names = []
+
+    def fit(self, X, y):
+        self.model = xgb.XGBRegressor(
+            n_estimators=200,
+            max_depth=6,
+            learning_rate=0.1,
+            subsample=0.8,
+            colsample_bytree=0.8,
+            random_state=42,
+        )
+
+        if isinstance(X, np.ndarray):
+            self.feature_names = [f"feature_{i}" for i in range(X.shape[1])]
+        else:
+            self.feature_names = list(X.columns)
+
+        self.model.fit(X, y)
+
+    def predict(self, X):
+        if self.model is None:
+            raise ValueError("Model not trained")
+        return self.model.predict(X)
+
+    def save(self, filepath: str):
+        with open(filepath, "wb") as f:
+            pickle.dump(self, f)
+
+    @classmethod
+    def load(cls, filepath: str):
+        with open(filepath, "rb") as f:
+            return pickle.load(f)
+
+    @property
+    def feature_importances_(self):
+        if self.model is None:
+            raise ValueError("Model not trained")
+        return self.model.feature_importances_
+
+
+__all__ = ["PricePredictionModel"]