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/api/routes/models.py

@@ -0,0 +1,71 @@
+from typing import List
+from fastapi import APIRouter, HTTPException
+from datetime import datetime
+from app.models.schemas import ModelInfo, TrainingRequest, TrainingStatus, PredictionResponse
+from app.services import MLService
+import uuid
+
+router = APIRouter()
+ml_service = MLService()
+
+_training_store: dict = {}
+
+
+@router.get("", response_model=List[ModelInfo])
+async def list_models():
+    return ml_service.list_models()
+
+
+@router.post("/train")
+async def train_model(request: TrainingRequest):
+    training_id = f"training_{uuid.uuid4().hex[:8]}"
+
+    _training_store[training_id] = TrainingStatus(
+        training_id=training_id,
+        status="pending",
+        progress=0.0,
+        started_at=datetime.utcnow(),
+    )
+
+    return {"training_id": training_id, "status": _training_store[training_id]}
+
+
+@router.get("/{training_id}/status", response_model=TrainingStatus)
+async def get_training_status(training_id: str):
+    if training_id not in _training_store:
+        raise HTTPException(status_code=404, detail=f"Training job {training_id} not found")
+
+    return _training_store[training_id]
+
+
+@router.get("/{model_id}/metrics")
+async def get_model_metrics(model_id: str):
+    try:
+        metrics = ml_service.get_model_metrics(model_id)
+        return {"model_id": model_id, "metrics": metrics}
+    except ValueError as e:
+        raise HTTPException(status_code=404, detail=str(e))
+
+
+@router.post("/predict", response_model=PredictionResponse)
+async def predict(
+    model_id: str,
+    timestamp: datetime,
+    features: dict = None,
+):
+    try:
+        result = ml_service.predict(model_id, timestamp, features)
+        return PredictionResponse(**result)
+    except ValueError as e:
+        raise HTTPException(status_code=404, detail=str(e))
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.get("/{model_id}/feature-importance")
+async def get_feature_importance(model_id: str):
+    try:
+        importance = ml_service.get_feature_importance(model_id)
+        return {"model_id": model_id, "feature_importance": importance}
+    except ValueError as e:
+        raise HTTPException(status_code=404, detail=str(e))