Time unification

inventory-server/scripts/forecast/forecast_engine.py

@@ -18,6 +18,18 @@ import json
 import time
 import logging
 from datetime import datetime, date, timedelta
+from zoneinfo import ZoneInfo
+
+BUSINESS_TZ = ZoneInfo('America/Chicago')
+
+
+def business_today() -> date:
+    """The business date (Chicago calendar date — see docs/TIME.md).
+
+    Never business_today(): the server runs on Europe/Berlin, which flips to the
+    next day at 5/6pm Central and would shift forecast_date anchors.
+    """
+    return datetime.now(BUSINESS_TZ).date()
 
 import numpy as np
 import pandas as pd
@@ -740,7 +752,7 @@ def batch_load_product_data(conn, products):
         GROUP BY pid
         """
         adf = execute_query(conn, arrival_sql, [preorder_pids])
-        today = date.today()
+        today = business_today()
         for _, row in adf.iterrows():
             pid = int(row['pid'])
             fa = row['future_arrival']
@@ -948,7 +960,7 @@ def forecast_mature(product, history_df):
     hist['snapshot_date'] = pd.to_datetime(hist['snapshot_date'])
     hist = hist.set_index('snapshot_date')['units_sold']
     full_index = pd.date_range(
-        end=pd.Timestamp(date.today() - timedelta(days=1)),
+        end=pd.Timestamp(business_today() - timedelta(days=1)),
         periods=EXP_SMOOTHING_WINDOW, freq='D')
     series = hist.reindex(full_index, fill_value=0.0).values.astype(float)
 
@@ -1070,7 +1082,7 @@ def generate_all_forecasts(conn, curves_df, dow_indices, monthly_indices=None,
     log.info("Batch loading product data...")
     batch_data = batch_load_product_data(conn, products)
 
-    today = date.today()
+    today = business_today()
     forecast_dates = [today + timedelta(days=i) for i in range(FORECAST_HORIZON_DAYS)]
 
     # Pre-compute DOW and seasonal multipliers for each forecast date.
@@ -1676,7 +1688,7 @@ def backfill_accuracy_data(conn, backfill_days=30):
     # Batch load product data for per-product scaling
     batch_data = batch_load_product_data(conn, active)
 
-    today = date.today()
+    today = business_today()
     backfill_start = today - timedelta(days=backfill_days)
 
     # Create a synthetic run entry