diff --git a/etl/src/data/etl_region_movAverage.py b/etl/src/data/etl_region_movAverage.py
index c3bd6c9..8818251 100644
--- a/etl/src/data/etl_region_movAverage.py
+++ b/etl/src/data/etl_region_movAverage.py
@@ -6,6 +6,9 @@ import polars as pl
 import data
 from data import etl_cache
 
+
+import matplotlib.pyplot as plt
+
 d = data.load()
 
 def region_movingAverage(id: int, scrape_date_start_min: str):
@@ -78,7 +81,7 @@ def region_movingAverage(id: int, scrape_date_start_min: str):
                 calDF = calDF.with_columns((pl.col("dates").str.to_date()))
 
                 # Filter out all Data that's in the calculation frame
-                calDF = calDF.filter((pl.col("dates") >= scrape_date_end_min))
+                calDF = calDF.filter((pl.col("dates") >= (scrape_date_start_min + timedelta(days=1))))
                 calDF = calDF.filter((pl.col("dates") < final_end_date))
 
                 # Join all information into one Dataframe
@@ -93,6 +96,7 @@ def region_movingAverage(id: int, scrape_date_start_min: str):
         outDF = outDF.sort('dates')
         outDFList.append(outDF)
 
+
     # Calculate the horizontal Sum for all Dates
     arrayCunter = 0
     tempDFList = []
@@ -108,14 +112,13 @@ def region_movingAverage(id: int, scrape_date_start_min: str):
         tempDFList.append(newDF)
 
     # Join actual and predict Values
-    outDF = tempDFList[1].join(tempDFList[0], on='dates', how='outer')
+    outDF = tempDFList[0].join(tempDFList[1], on='dates', how='outer')
 
     # Rename Columns for clarity
-    outDF = outDF.drop_nulls()
     outDF = outDF.drop('dates_right')
 
     # sum_hor_predict is the data from the earlier ScrapeDate
-    outDF = outDF.rename({'sum_hor': 'sum_hor_actual', 'sum_hor_right': 'sum_hor_predict'})
+    outDF = outDF.rename({'sum_hor_right': 'sum_hor_actual', 'sum_hor': 'sum_hor_predict'})
 
     # Calculate Moving average from Start
     baseValues = outDF.get_column('sum_hor_predict').to_list()
@@ -133,6 +136,7 @@ def region_movingAverage(id: int, scrape_date_start_min: str):
 
     # Add moving_averages to df
     outDF = outDF.with_columns(moving_averages=pl.Series(moving_averages))
+
     result = {'dates':outDF.get_column('dates').to_list(), 'cap_earlierTimeframe':outDF.get_column('sum_hor_predict').to_list(), 'cap_laterTimeframe':outDF.get_column('sum_hor_actual').to_list(), 'movAvg':outDF.get_column('moving_averages').to_list(),}
     etl_cache.saveObj(file, result)
     return result
\ No newline at end of file