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/time_features.py

@@ -0,0 +1,35 @@
+import pandas as pd
+
+
+def add_time_features(df: pd.DataFrame, timestamp_col: str = "timestamp") -> pd.DataFrame:
+    result = df.copy()
+
+    if timestamp_col not in result.columns:
+        return result
+
+    result[timestamp_col] = pd.to_datetime(result[timestamp_col])
+
+    result["hour"] = result[timestamp_col].dt.hour
+    result["day_of_week"] = result[timestamp_col].dt.dayofweek
+    result["day_of_month"] = result[timestamp_col].dt.day
+    result["month"] = result[timestamp_col].dt.month
+
+    result["hour_sin"] = _sin_encode(result["hour"], 24)
+    result["hour_cos"] = _cos_encode(result["hour"], 24)
+    result["day_sin"] = _sin_encode(result["day_of_week"], 7)
+    result["day_cos"] = _cos_encode(result["day_of_week"], 7)
+
+    return result
+
+
+def _sin_encode(x, period):
+    import numpy as np
+    return np.sin(2 * np.pi * x / period)
+
+
+def _cos_encode(x, period):
+    import numpy as np
+    return np.cos(2 * np.pi * x / period)
+
+
+__all__ = ["add_time_features"]