diff --git a/docs/hyperopt.md b/docs/hyperopt.md
index ad812a5ad..30a22a969 100644
--- a/docs/hyperopt.md
+++ b/docs/hyperopt.md
@@ -6,9 +6,7 @@ algorithms included in the `scikit-optimize` package to accomplish this. The
 search will burn all your CPU cores, make your laptop sound like a fighter jet
 and still take a long time.
 
-In general, the search for best parameters starts with a few random combinations and then uses Bayesian search with a
-ML regressor algorithm (currently ExtraTreesRegressor) to quickly find a combination of parameters in the search hyperspace
-that minimizes the value of the [loss function](#loss-functions).
+In general, the search for best parameters starts with a few random combinations (see [below](#reproducible-results) for more details) and then uses Bayesian search with a ML regressor algorithm (currently ExtraTreesRegressor) to quickly find a combination of parameters in the search hyperspace that minimizes the value of the [loss function](#loss-functions).
 
 Hyperopt requires historic data to be available, just as backtesting does.
 To learn how to get data for the pairs and exchange you're interested in, head over to the [Data Downloading](data-download.md) section of the documentation.
@@ -311,7 +309,7 @@ You can also enable position stacking in the configuration file by explicitly se
 
 ### Reproducible results
 
-The search for optimal parameters starts with a few (currently 30) random combinations in the hyperspace of parameters, random Hyperopt epochs. These random epochs are marked with a leading asterisk sign at the Hyperopt output.
+The search for optimal parameters starts with a few (currently 30) random combinations in the hyperspace of parameters, random Hyperopt epochs. These random epochs are marked with an asterisk character (`*`) in the first column in the Hyperopt output.
 
 The initial state for generation of these random values (random state) is controlled by the value of the `--random-state` command line option. You can set it to some arbitrary value of your choice to obtain reproducible results.
 
diff --git a/freqtrade/optimize/hyperopt.py b/freqtrade/optimize/hyperopt.py
index fcf50af6a..79b6b8cb0 100644
--- a/freqtrade/optimize/hyperopt.py
+++ b/freqtrade/optimize/hyperopt.py
@@ -304,8 +304,9 @@ class Hyperopt:
         trials.columns = ['Best', 'Epoch', 'Trades', 'Avg profit', 'Total profit',
                           'Profit', 'Avg duration', 'Objective', 'is_initial_point', 'is_best']
         trials['is_profit'] = False
-        trials.loc[trials['is_initial_point'], 'Best'] = '*'
+        trials.loc[trials['is_initial_point'], 'Best'] = '*     '
         trials.loc[trials['is_best'], 'Best'] = 'Best'
+        trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best'
         trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
         trials['Trades'] = trials['Trades'].astype(str)
 
@@ -396,6 +397,7 @@ class Hyperopt:
         trials['is_profit'] = False
         trials.loc[trials['is_initial_point'], 'Best'] = '*'
         trials.loc[trials['is_best'], 'Best'] = 'Best'
+        trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best'
         trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
         trials['Epoch'] = trials['Epoch'].astype(str)
         trials['Trades'] = trials['Trades'].astype(str)