diff --git a/ArbitrageTool/generate_signals_profile.py b/ArbitrageTool/generate_signals_profile.py
index 232e8693aa92666d8bd3e04667e787c4a87be4ec..3ca1424d7f2bad1335d5d64677e47b76c9bfbcee 100644
--- a/ArbitrageTool/generate_signals_profile.py
+++ b/ArbitrageTool/generate_signals_profile.py
@@ -42,17 +42,26 @@ def extract_elec_price_profile(dstart, dend, t_step):
         price_df = price_df.loc[dstart:dend]
         price_df = price_df.resample(str(t_step) + 'H').mean().interpolate('linear')
 
+    elif year == 2021:
+        pricedata_path = r'C:\Users\10947\sciebo\MA Mao\Preisdaten\Intraday_2021.csv'
+        raw_data = pd.read_csv(pricedata_path, index_col=0)
+        raw_data.index = pd.to_datetime(raw_data.index)
+        price_df = raw_data.loc[dstart:dend]
+        price_df = price_df.astype(float)
+        price_df = price_df.resample(str(t_step) + 'H').mean().interpolate('linear')
+
     else:
         if year == 2020:
             pricedata_path = r'C:\Users\10947\sciebo\MA Mao\Preisdaten\Stromproduktion_und_Boersenstrompreise_in_Deutschland_2020.csv'
-        elif year == 2021:
-            pricedata_path = r'C:\Users\10947\sciebo\MA Mao\Preisdaten\Stromproduktion_und_Boersenstrompreise_in_Deutschland_2021.csv'
+
         elif year == 2030:
             pricedata_path = r'C:\Users\10947\sciebo\MA Mao\Preisdaten\predicted_Strompreise_in_Deutschland_2030.csv'
 
         raw_data = pd.read_csv(pricedata_path, usecols=['Intraday Continuous Average Price']) # this data has resolution of 1h
-        price_year_data = raw_data.replace(to_replace='None', value=np.nan).dropna()
+        #price_year_data = raw_data.replace(to_replace='None', value=np.nan).dropna()
+        price_year_data = raw_data.replace(to_replace='None', value=np.nan)
         price_year_data.index = pd.date_range(datetime.datetime(year, 1, 1, 00, 00, 00), periods=len(price_year_data), freq='1h')
+        #price_year_data.round(2).to_csv(r'C:\Users\10947\sciebo\MA Mao\Preisdaten_bearbeitet\expex_2021.csv')
         price_df = price_year_data.loc[dstart:dend]
         price_df = price_df.astype(float)
         price_df = price_df.resample(str(t_step) + 'H').mean().interpolate('linear')