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/tasks/training_tasks.py

@@ -0,0 +1,50 @@
+from typing import Dict
+from datetime import datetime
+from app.utils.logger import get_logger
+from app.models.schemas import TrainingRequest, TrainingStatusEnum
+import uuid
+
+logger = get_logger(__name__)
+
+
+async def train_model_task(training_id: str, request: TrainingRequest):
+    logger.info(f"Training model: {request.model_type.value}, horizon: {request.horizon}")
+
+    try:
+        if request.model_type.value == "price_prediction":
+            from app.ml.price_prediction.trainer import PricePredictionTrainer
+
+            trainer = PricePredictionTrainer()
+            results = trainer.train_all(horizons=[request.horizon] if request.horizon else None)
+            trainer.save_models()
+
+            return {
+                "training_id": training_id,
+                "status": TrainingStatusEnum.COMPLETED,
+                "results": results,
+                "completed_at": datetime.utcnow().isoformat(),
+            }
+
+        elif request.model_type.value == "rl_battery":
+            from app.ml.rl_battery.trainer import BatteryRLTrainer
+
+            trainer = BatteryRLTrainer()
+            results = trainer.train(n_episodes=500)
+            trainer.save()
+
+            return {
+                "training_id": training_id,
+                "status": TrainingStatusEnum.COMPLETED,
+                "results": results,
+                "completed_at": datetime.utcnow().isoformat(),
+            }
+
+        else:
+            raise ValueError(f"Unknown model type: {request.model_type}")
+
+    except Exception as e:
+        logger.error(f"Training failed: {e}")
+        raise
+
+
+__all__ = ["train_model_task"]