diff --git a/freqtrade/optimize/hyperopt_loss_onlyprofit.py b/freqtrade/optimize/hyperopt_loss_onlyprofit.py
index 33f3f5bc6..4a3cf1b3b 100644
--- a/freqtrade/optimize/hyperopt_loss_onlyprofit.py
+++ b/freqtrade/optimize/hyperopt_loss_onlyprofit.py
@@ -9,23 +9,11 @@ from pandas import DataFrame
 from freqtrade.optimize.hyperopt import IHyperOptLoss
 
 
-# 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,
-# expected max profit = 3.85
-#
-# Note, this is ratio. 3.85 stated above means 385Σ%, 3.0 means 300Σ%.
-#
-# In this implementation it's only used in calculation of the resulting value
-# of the objective function as a normalization coefficient and does not
-# represent any limit for profits as in the Freqtrade legacy default loss function.
-EXPECTED_MAX_PROFIT = 3.0
-
-
 class OnlyProfitHyperOptLoss(IHyperOptLoss):
     """
     Defines the loss function for hyperopt.
 
-    This implementation takes only profit into account.
+    This implementation takes only absolute profit into account, not looking at any other indicator.
     """
 
     @staticmethod
@@ -34,5 +22,5 @@ class OnlyProfitHyperOptLoss(IHyperOptLoss):
         """
         Objective function, returns smaller number for better results.
         """
-        total_profit = results['profit_ratio'].sum()
-        return 1 - total_profit / EXPECTED_MAX_PROFIT
+        total_profit = results['profit_abs'].sum()
+        return -1 * total_profit
diff --git a/tests/optimize/test_hyperoptloss.py b/tests/optimize/test_hyperoptloss.py
index 73feeb007..ea0caac04 100644
--- a/tests/optimize/test_hyperoptloss.py
+++ b/tests/optimize/test_hyperoptloss.py
@@ -149,9 +149,9 @@ def test_sortino_loss_daily_prefers_higher_profits(default_conf, hyperopt_result
 
 def test_onlyprofit_loss_prefers_higher_profits(default_conf, hyperopt_results) -> None:
     results_over = hyperopt_results.copy()
-    results_over['profit_ratio'] = hyperopt_results['profit_ratio'] * 2
+    results_over['profit_abs'] = hyperopt_results['profit_abs'] * 2
     results_under = hyperopt_results.copy()
-    results_under['profit_ratio'] = hyperopt_results['profit_ratio'] / 2
+    results_under['profit_abs'] = hyperopt_results['profit_abs'] / 2
 
     default_conf.update({'hyperopt_loss': 'OnlyProfitHyperOptLoss'})
     hl = HyperOptLossResolver.load_hyperoptloss(default_conf)