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/environment.py

@@ -0,0 +1,87 @@
+from typing import Dict, Tuple
+import numpy as np
+
+
+class BatteryEnvironment:
+    def __init__(
+        self,
+        capacity: float = 100.0,
+        charge_rate: float = 50.0,
+        discharge_rate: float = 50.0,
+        efficiency: float = 0.9,
+        min_reserve: float = 0.1,
+        max_charge: float = 0.9,
+    ):
+        self.capacity = capacity
+        self.charge_rate = charge_rate
+        self.discharge_rate = discharge_rate
+        self.efficiency = efficiency
+        self.min_reserve = min_reserve
+        self.max_charge = max_charge
+
+        self.charge_level = capacity * 0.5
+        self.current_price = 50.0
+        self.time_step = 0
+
+    def reset(self) -> np.ndarray:
+        self.charge_level = self.capacity * 0.5
+        self.current_price = 50.0
+        self.time_step = 0
+        return self._get_state()
+
+    def _get_state(self) -> np.ndarray:
+        charge_pct = self.charge_level / self.capacity
+        price_norm = np.clip(self.current_price / 200.0, 0, 1)
+        time_norm = (self.time_step % 1440) / 1440.0
+
+        return np.array([charge_pct, price_norm, time_norm])
+
+    def step(self, action: int) -> Tuple[np.ndarray, float, bool, Dict]:
+        old_price = self.current_price
+
+        if action == 0:
+            charge_amount = min(self.charge_rate, self.capacity * self.max_charge - self.charge_level)
+            self.charge_level += charge_amount * self.efficiency
+            reward = -charge_amount * old_price / 1000.0
+        elif action == 1:
+            reward = 0.0
+        elif action == 2:
+            discharge_amount = min(
+                self.discharge_rate,
+                self.charge_level - self.capacity * self.min_reserve
+            )
+            revenue = discharge_amount * old_price
+            self.charge_level -= discharge_amount / self.efficiency
+            reward = revenue / 1000.0
+        else:
+            reward = 0.0
+
+        self.charge_level = np.clip(self.charge_level, self.capacity * self.min_reserve, self.capacity * self.max_charge)
+
+        self.current_price = old_price + np.random.randn() * 5
+        self.current_price = np.clip(self.current_price, 0, 300)
+
+        self.time_step += 1
+
+        state = self._get_state()
+
+        info = {
+            "charge_level": self.charge_level,
+            "price": self.current_price,
+            "action": action,
+        }
+
+        done = self.time_step >= 1440
+
+        return state, reward, done, info
+
+    @property
+    def action_space(self):
+        return 3
+
+    @property
+    def observation_space(self):
+        return 3
+
+
+__all__ = ["BatteryEnvironment"]