diff --git a/freqtrade/arguments.py b/freqtrade/arguments.py
index 930342e5a..327915b61 100644
--- a/freqtrade/arguments.py
+++ b/freqtrade/arguments.py
@@ -325,6 +325,15 @@ class Arguments(object):
             type=Arguments.check_int_positive,
             metavar='INT',
         )
+        parser.add_argument(
+            '--min-trades',
+            help="Set minimal desired number of trades for evaluations in the hyperopt "
+                 "optimization path (default: 1).",
+            dest='hyperopt_min_trades',
+            default=1,
+            type=Arguments.check_int_positive,
+            metavar='INT',
+        )
 
     def _build_subcommands(self) -> None:
         """
diff --git a/freqtrade/configuration.py b/freqtrade/configuration.py
index 54862ff70..c19580c36 100644
--- a/freqtrade/configuration.py
+++ b/freqtrade/configuration.py
@@ -312,6 +312,10 @@ class Configuration(object):
 
         self._args_to_config(config, argname='hyperopt_random_state',
                              logstring='Parameter --random-state detected: {}')
+
+        self._args_to_config(config, argname='hyperopt_min_trades',
+                             logstring='Parameter --min-trades detected: {}')
+
         return config
 
     def _validate_config_schema(self, conf: Dict[str, Any]) -> Dict[str, Any]:
diff --git a/freqtrade/optimize/hyperopt.py b/freqtrade/optimize/hyperopt.py
index 52821d93b..235a20156 100644
--- a/freqtrade/optimize/hyperopt.py
+++ b/freqtrade/optimize/hyperopt.py
@@ -204,7 +204,11 @@ class Hyperopt(Backtesting):
         trade_count = len(results.index)
         trade_duration = results.trade_duration.mean()
 
-        if trade_count == 0:
+        # If this evaluation contains too short small amount of trades
+        # to be interesting -- consider it as 'bad' (assign 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']:
             return {
                 'loss': MAX_LOSS,
                 'params': params,