diff --git a/docs/hyperopt.md b/docs/hyperopt.md
index 4108946c2..74d03b8ab 100644
--- a/docs/hyperopt.md
+++ b/docs/hyperopt.md
@@ -186,6 +186,7 @@ class SuperDuperHyperOptLoss(IHyperOptLoss):
         Weights are distributed as follows:
         * 0.4 to trade duration
         * 0.25: Avoiding trade loss
+        * 1.0 to total profit, compared to the expected value (`EXPECTED_MAX_PROFIT`) defined above
         """
         total_profit = results.profit_percent.sum()
         trade_duration = results.trade_duration.mean()
diff --git a/freqtrade/optimize/default_hyperopt_loss.py b/freqtrade/optimize/default_hyperopt_loss.py
index 60faa9f61..58be44ab9 100644
--- a/freqtrade/optimize/default_hyperopt_loss.py
+++ b/freqtrade/optimize/default_hyperopt_loss.py
@@ -37,10 +37,11 @@ class DefaultHyperOptLoss(IHyperOptLoss):
                                *args, **kwargs) -> float:
         """
         Objective function, returns smaller number for better results
-        This is the legacy algorithm (used until now in freqtrade).
+        This is the Default algorithm
         Weights are distributed as follows:
         * 0.4 to trade duration
         * 0.25: Avoiding trade loss
+        * 1.0 to total profit, compared to the expected value (`EXPECTED_MAX_PROFIT`) defined above
         """
         total_profit = results.profit_percent.sum()
         trade_duration = results.trade_duration.mean()
diff --git a/user_data/hyperopts/sample_hyperopt.py b/user_data/hyperopts/sample_hyperopt.py
index 6428a1843..8650d0a98 100644
--- a/user_data/hyperopts/sample_hyperopt.py
+++ b/user_data/hyperopts/sample_hyperopt.py
@@ -42,21 +42,6 @@ class SampleHyperOpts(IHyperOpt):
     roi_space, generate_roi_table, stoploss_space
     """
 
-    @staticmethod
-    def hyperopt_loss_custom(results: DataFrame, trade_count: int,
-                             min_date: datetime, max_date: datetime, *args, **kwargs) -> float:
-        """
-        Objective function, returns smaller number for more optimal results
-        """
-        total_profit = results.profit_percent.sum()
-        trade_duration = results.trade_duration.mean()
-
-        trade_loss = 1 - 0.25 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.8)
-        profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT)
-        duration_loss = 0.4 * min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1)
-        result = trade_loss + profit_loss + duration_loss
-        return result
-
     @staticmethod
     def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
         dataframe['adx'] = ta.ADX(dataframe)