diff --git a/freqtrade/optimize/hyperopt_tools.py b/freqtrade/optimize/hyperopt_tools.py
index ed9583bdc..b51db4db2 100755
--- a/freqtrade/optimize/hyperopt_tools.py
+++ b/freqtrade/optimize/hyperopt_tools.py
@@ -459,21 +459,14 @@ class HyperoptTools():
         trials['Best'] = ''
         trials['Stake currency'] = config['stake_currency']
 
-        if 'results_metrics.total_trades' in trials:
-            base_metrics = ['Best', 'current_epoch', 'results_metrics.total_trades',
-                            'results_metrics.profit_mean', 'results_metrics.profit_median',
-                            'results_metrics.profit_total',
-                            'Stake currency',
-                            'results_metrics.profit_total_abs', 'results_metrics.holding_avg',
-                            'loss', 'is_initial_point', 'is_best']
-            perc_multi = 100
-        else:
-            perc_multi = 1
-            base_metrics = ['Best', 'current_epoch', 'results_metrics.trade_count',
-                            'results_metrics.avg_profit', 'results_metrics.median_profit',
-                            'results_metrics.total_profit',
-                            'Stake currency', 'results_metrics.profit', 'results_metrics.duration',
-                            'loss', 'is_initial_point', 'is_best']
+        base_metrics = ['Best', 'current_epoch', 'results_metrics.total_trades',
+                        'results_metrics.profit_mean', 'results_metrics.profit_median',
+                        'results_metrics.profit_total',
+                        'Stake currency',
+                        'results_metrics.profit_total_abs', 'results_metrics.holding_avg',
+                        'loss', 'is_initial_point', 'is_best']
+        perc_multi = 100
+
         param_metrics = [("params_dict."+param) for param in results[0]['params_dict'].keys()]
         trials = trials[base_metrics + param_metrics]
 
@@ -501,11 +494,6 @@ class HyperoptTools():
         trials['Avg profit'] = trials['Avg profit'].apply(
             lambda x: f'{x * perc_multi:,.2f}%' if not isna(x) else ""
         )
-        if perc_multi == 1:
-            trials['Avg duration'] = trials['Avg duration'].apply(
-                lambda x: f'{x:,.1f} m' if isinstance(
-                    x, float) else f"{x.total_seconds() // 60:,.1f} m" if not isna(x) else ""
-            )
         trials['Objective'] = trials['Objective'].apply(
             lambda x: f'{x:,.5f}' if x != 100000 else ""
         )