diff --git a/piker/data/_timeseries.py b/piker/data/_timeseries.py
index 2d73c263..7055854b 100644
--- a/piker/data/_timeseries.py
+++ b/piker/data/_timeseries.py
@@ -18,7 +18,8 @@
 Financial time series processing utilities usually
 pertaining to OHLCV style sampled data.
 
-Routines are generally implemented in either ``numpy`` or ``polars`` B)
+Routines are generally implemented in either ``numpy`` or
+``polars`` B)
 
 '''
 from __future__ import annotations
@@ -269,9 +270,14 @@ def detect_time_gaps(
     # gap_dt_unit: t_unit = 'minutes',
     # gap_thresh: int = 1,
 
-    # legacy stock mkts
+    # NOTE: legacy stock mkts have venue operating hours
+    # and thus gaps normally no more then 1-2 days at
+    # a time.
+    # XXX -> must be valid ``polars.Expr.dt.<name>``
+    # TODO: allow passing in a frame of operating hours
+    # durations/ranges for faster legit gap checks.
     gap_dt_unit: t_unit = 'days',
-    gap_thresh: int = 2,
+    gap_thresh: int = 1,
 
 ) -> pl.DataFrame:
     '''
@@ -281,18 +287,17 @@ def detect_time_gaps(
     actual missing data segments.
 
     '''
-    dt_gap_col: str = f'{gap_dt_unit}_diff'
-    return with_dts(
-        df
-    ).filter(
-        pl.col('s_diff').abs() > expect_period
-    ).with_columns(
-        getattr(
-            pl.col('dt_diff').dt,
-            gap_dt_unit,  # NOTE: must be valid ``Expr.dt.<name>``
-        )().alias(dt_gap_col)
-    ).filter(
-        pl.col(dt_gap_col).abs() > gap_thresh
+    return (
+        with_dts(df)
+        .filter(
+            pl.col('s_diff').abs() > expect_period
+        )
+        .filter(
+            getattr(
+                pl.col('dt_diff').dt,
+                gap_dt_unit,
+            )().abs() > gap_thresh
+        )
     )