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/rl_battery/policy.py

@@ -0,0 +1,65 @@
+from typing import Dict
+from app.ml.rl_battery.agent import QLearningAgent
+from app.ml.rl_battery.environment import BatteryEnvironment
+from app.utils.logger import get_logger
+
+logger = get_logger(__name__)
+
+
+class BatteryPolicy:
+    def __init__(self, policy_path: str = "models/rl_battery"):
+        self.policy_path = policy_path
+        self.agent: QLearningAgent = None
+        self.env: BatteryEnvironment = None
+        self._load_policy()
+
+    def _load_policy(self):
+        from pathlib import Path
+
+        filepath = Path(self.policy_path) / "battery_policy.pkl"
+        if filepath.exists():
+            self.agent = QLearningAgent.load(filepath)
+            self.env = BatteryEnvironment()
+            logger.info(f"Loaded policy from {filepath}")
+
+    def get_action(
+        self,
+        charge_level: float,
+        current_price: float,
+        price_forecast_1m: float = 0,
+        price_forecast_5m: float = 0,
+        price_forecast_15m: float = 0,
+        hour: int = 0,
+    ) -> Dict:
+        if self.agent is None:
+            return {
+                "action": "hold",
+                "q_values": [0.0, 0.0, 0.0],
+                "confidence": 0.0,
+            }
+
+        self.env.charge_level = charge_level
+        self.env.current_price = current_price
+        self.env.time_step = hour * 60
+
+        state = self.env._get_state()
+
+        action_idx = self.agent.get_action(state, training=False)
+
+        actions = ["charge", "hold", "discharge"]
+        action_name = actions[action_idx]
+
+        state_idx = self.agent._discretize_state(state)
+        q_values = self.agent.q_table[state_idx].tolist() if self.agent.q_table is not None else [0.0, 0.0, 0.0]
+
+        max_q = max(q_values) if q_values else 0.0
+        confidence = (max_q - min(q_values)) / (max_q + 0.001) if q_values else 0.0
+
+        return {
+            "action": action_name,
+            "q_values": q_values,
+            "confidence": min(confidence, 1.0),
+        }
+
+
+__all__ = ["BatteryPolicy"]