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/features/battery_features.py

@@ -0,0 +1,35 @@
+import pandas as pd
+
+
+def add_battery_features(
+    df: pd.DataFrame,
+    price_df: pd.DataFrame,
+    battery_col: str = "charge_level_mwh",
+    capacity_col: str = "capacity_mwh",
+    timestamp_col: str = "timestamp",
+    battery_id_col: str = "battery_id",
+) -> pd.DataFrame:
+    result = df.copy()
+
+    if battery_col in result.columns and capacity_col in result.columns:
+        result["charge_level_pct"] = result[battery_col] / result[capacity_col]
+        result["discharge_potential_mwh"] = result[battery_col] * result.get("efficiency", 0.9)
+        result["charge_capacity_mwh"] = result[capacity_col] - result[battery_col]
+
+    if price_df is not None and "real_time_price" in price_df.columns and timestamp_col in result.columns:
+        merged = result.merge(
+            price_df[[timestamp_col, "real_time_price"]],
+            on=timestamp_col,
+            how="left",
+            suffixes=("", "_market")
+        )
+
+        if "real_time_price_market" in merged.columns:
+            result["market_price"] = merged["real_time_price_market"]
+            result["charge_cost_potential"] = result["charge_capacity_mwh"] * result["market_price"]
+            result["discharge_revenue_potential"] = result["discharge_potential_mwh"] * result["market_price"]
+
+    return result
+
+
+__all__ = ["add_battery_features"]