Forecasting app: hybrid port to HNF stack

Python FastAPI ML backend kept intact; auth replaced with central hnf_session cookie verification. Frontend rebuilt on React 18 + TS + Vite with stack design system, Plotly charts retained. Shared Postgres via DATABASE_URL; schema applied on startup.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

backend/jobs/metrics_aggregation.py

@@ -0,0 +1,150 @@
+"""
+Metrics aggregation job for forecast_data database
+Populates daily_metrics from newbook_bookings_stats
+
+This is the data source for forecasting models (Prophet, XGBoost, CatBoost).
+"""
+import logging
+from datetime import date, timedelta
+from typing import List, Optional
+
+from sqlalchemy import text
+from database import SyncSessionLocal
+
+logger = logging.getLogger(__name__)
+
+
+async def run_metrics_aggregation(
+    from_date: Optional[date] = None,
+    to_date: Optional[date] = None
+):
+    """
+    Populate daily_metrics table from newbook_bookings_stats.
+
+    This provides the historical actuals needed for forecasting models.
+
+    Args:
+        from_date: Start date (defaults to 2 years ago)
+        to_date: End date (defaults to yesterday)
+    """
+    logger.info("Starting metrics aggregation job")
+
+    db = next(iter([SyncSessionLocal()]))
+
+    try:
+        # Default date range: 2 years of history
+        if from_date is None:
+            from_date = date.today() - timedelta(days=730)
+        if to_date is None:
+            to_date = date.today() - timedelta(days=1)
+
+        logger.info(f"Aggregating metrics from {from_date} to {to_date}")
+
+        # Get data from newbook_bookings_stats
+        result = db.execute(
+            text("""
+            SELECT
+                date,
+                booking_count,           -- room nights (occupied rooms)
+                total_occupancy_pct,     -- occupancy percentage
+                guests_count,            -- total guests
+                adults_count,
+                children_count,
+                rooms_count,             -- available rooms
+                bookable_count           -- bookable rooms (rooms - maintenance)
+            FROM newbook_bookings_stats
+            WHERE date BETWEEN :from_date AND :to_date
+            ORDER BY date
+            """),
+            {"from_date": from_date, "to_date": to_date}
+        )
+        stats_rows = result.fetchall()
+
+        if not stats_rows:
+            logger.warning("No data found in newbook_bookings_stats")
+            return
+
+        logger.info(f"Found {len(stats_rows)} days of data to aggregate")
+
+        # Metrics to populate
+        metrics_count = 0
+
+        for row in stats_rows:
+            d = row.date
+
+            # Define metrics from newbook_bookings_stats
+            metrics_to_insert = []
+
+            # Room nights (occupied rooms)
+            if row.booking_count is not None:
+                metrics_to_insert.append(("hotel_room_nights", row.booking_count))
+
+            # Occupancy percentage
+            if row.total_occupancy_pct is not None:
+                metrics_to_insert.append(("hotel_occupancy_pct", float(row.total_occupancy_pct)))
+
+            # Guest count
+            if row.guests_count is not None:
+                metrics_to_insert.append(("hotel_guests", row.guests_count))
+
+            # Insert/update all metrics
+            for metric_code, actual_value in metrics_to_insert:
+                db.execute(
+                    text("""
+                    INSERT INTO daily_metrics (date, metric_code, actual_value, source, updated_at)
+                    VALUES (:date, :metric_code, :actual_value, 'newbook', NOW())
+                    ON CONFLICT (date, metric_code) DO UPDATE SET
+                        actual_value = :actual_value,
+                        updated_at = NOW()
+                    """),
+                    {
+                        "date": d,
+                        "metric_code": metric_code,
+                        "actual_value": actual_value
+                    }
+                )
+                metrics_count += 1
+
+        db.commit()
+        logger.info(f"Aggregated {metrics_count} metric records from {len(stats_rows)} days")
+
+    except Exception as e:
+        logger.error(f"Metrics aggregation failed: {e}")
+        db.rollback()
+        raise
+    finally:
+        db.close()
+
+
+async def backfill_daily_metrics():
+    """
+    Backfill all available history from newbook_bookings_stats to daily_metrics.
+    Call this once when setting up forecasting on forecast_data database.
+    """
+    logger.info("Starting full backfill of daily_metrics")
+
+    db = next(iter([SyncSessionLocal()]))
+
+    try:
+        # Find the earliest date in newbook_bookings_stats
+        result = db.execute(text("SELECT MIN(date) as min_date FROM newbook_bookings_stats"))
+        row = result.fetchone()
+
+        if not row or not row.min_date:
+            logger.warning("No data in newbook_bookings_stats to backfill")
+            return
+
+        from_date = row.min_date
+        to_date = date.today() - timedelta(days=1)
+
+        logger.info(f"Backfilling from {from_date} to {to_date}")
+
+        await run_metrics_aggregation(from_date=from_date, to_date=to_date)
+
+        logger.info("Backfill completed successfully")
+
+    except Exception as e:
+        logger.error(f"Backfill failed: {e}")
+        raise
+    finally:
+        db.close()