diff --git a/freqtrade/optimize/hyperopt.py b/freqtrade/optimize/hyperopt.py
index 06c7ae495..252b76252 100644
--- a/freqtrade/optimize/hyperopt.py
+++ b/freqtrade/optimize/hyperopt.py
@@ -63,9 +63,11 @@ class Hyperopt(Backtesting):
         # if eval ends with higher value, we consider it a failed eval
         self.max_accepted_trade_duration = 300
 
-        # this is expexted avg profit * expected trade count
-        # for example 3.5%, 1100 trades, self.expected_max_profit = 3.85
-        # check that the reported Σ% values do not exceed this!
+        # This is assumed to be expected avg profit * expected trade count.
+        # For example, for 0.35% avg per trade (or 0.0035 as ratio) and 1100 trades,
+        # self.expected_max_profit = 3.85
+        # Check that the reported Σ% values do not exceed this!
+        # Note, this is ratio. 3.85 stated above means 385Σ%.
         self.expected_max_profit = 3.0
 
         # Previous evaluations
@@ -211,8 +213,8 @@ class Hyperopt(Backtesting):
         trade_count = len(results.index)
         trade_duration = results.trade_duration.mean()
 
-        # If this evaluation contains too short small amount of trades
-        # to be interesting -- consider it as 'bad' (assign max. loss value)
+        # If this evaluation contains too short amount of trades to be
+        # interesting -- consider it as 'bad' (assigned max. loss value)
         # in order to cast this hyperspace point away from optimization
         # path. We do not want to optimize 'hodl' strategies.
         if trade_count < self.config['hyperopt_min_trades']:
@@ -235,15 +237,15 @@ class Hyperopt(Backtesting):
         Return the format result in a string
         """
         trades = len(results.index)
-        avg_profit = results.profit_percent.mean()
+        avg_profit = results.profit_percent.mean() * 100.0
         total_profit = results.profit_abs.sum()
         stake_cur = self.config['stake_currency']
-        profit = results.profit_percent.sum()
+        profit = results.profit_percent.sum() * 100.0
         duration = results.trade_duration.mean()
 
-        return (f'{trades:6d} trades. Avg profit {avg_profit: 9.6f}%. '
+        return (f'{trades:6d} trades. Avg profit {avg_profit: 5.2f}%. '
                 f'Total profit {total_profit: 11.8f} {stake_cur} '
-                f'({profit:.4f}Σ%). Avg duration {duration:5.1f} mins.')
+                f'({profit: 7.2f}Σ%). Avg duration {duration:5.1f} mins.')
 
     def get_optimizer(self, cpu_count) -> Optimizer:
         return Optimizer(
@@ -318,7 +320,7 @@ class Hyperopt(Backtesting):
                         })
                         logger.debug(f"Optimizer params: {f_val[j]['params']}")
                     for j in range(jobs):
-                        logger.debug(f"Opimizer state: Xi: {opt.Xi[-j-1]}, yi: {opt.yi[-j-1]}")
+                        logger.debug(f"Optimizer state: Xi: {opt.Xi[-j-1]}, yi: {opt.yi[-j-1]}")
         except KeyboardInterrupt:
             print('User interrupted..')