I have an OHLC stock data in a Polars dataframe for which for each day i want to calculate the maximum expanding close between two times say ,

start = '09:15'
end = '10:15'
start_time = datetime.time.fromisoformat(start)
end_time = datetime.time.fromisoformat(end)


 # Filter and calculate the expanding max within the time range for each day

max_values = und_df2.filter((und_df2['timestamp'].dt.time() >= start_time) & (und_df2['timestamp'].dt.time() <= end_time)) 
        .group_by_dynamic('timestamp', every='1d', closed='left') 
        .agg(pl.col('Close').cum_max().alias('fbarh3')) 


# df.with_columns(pl.col(date_col).dt.truncate("1d").alias("date")) 
#              .join(
#                  df.group_by_dynamic("timestamp", every="1d").agg(agg_func.alias(alias))
#                    .with_columns(pl.col(alias).shift(shift_value))
#                    ,
#                  left_on="date", right_on="timestamp", how='left'
#              ) 
#              .drop("date")


#Join the maximum values back to the original dataframe

und_df2 = und_df2.with_columns(pl.col('timestamp').dt.truncate('1d').alias('date')).join(max_values, left_on='date', right_on='timestamp' ,  how='left' ).drop('date')

max_values is although correctly calculated which is expanding highest of close between start and end time, but since I filtered the dataframe to these specific times I lost the time component after aggregation.
How do I make it back to the original frame? (und_df2)

#== this is max_values generated 

shape: (824, 2)
timestamp   fbarh3
datetime[μs]    list[f64]
2020-03-02 00:00:00 [11356.8, 11358.45, … 11388.65]
2020-03-03 00:00:00 [11293.65, 11293.95, … 11310.7]
2020-03-04 00:00:00 [11296.95, 11296.95, … 11325.25]
2020-03-05 00:00:00 [11312.65, 11312.65, … 11312.65]
2020-03-06 00:00:00 [10894.8, 10914.85, … 10965.3]

Output should be expanding highest(cum_max) from start to end for each day. I can’t seem to join back the max_values back to und_df2.