diff --git a/freqtrade/optimize/hyperopt/hyperopt.py b/freqtrade/optimize/hyperopt/hyperopt.py
index a1cc0b808..f00cdde78 100644
--- a/freqtrade/optimize/hyperopt/hyperopt.py
+++ b/freqtrade/optimize/hyperopt/hyperopt.py
@@ -15,7 +15,7 @@ from typing import Any
 
 import rapidjson
 from joblib import Parallel, cpu_count
-from optuna.trial import Trial, TrialState
+from optuna.trial import FrozenTrial, Trial, TrialState
 
 from freqtrade.constants import FTHYPT_FILEVERSION, LAST_BT_RESULT_FN, Config
 from freqtrade.enums import HyperoptState
@@ -171,15 +171,19 @@ class Hyperopt:
             asked.append(self.opt.ask(dimensions))
         return asked
 
-    def duplicate_optuna_asked_points(self, trial: Trial) -> bool:
+    def duplicate_optuna_asked_points(self, trial: Trial, asked_trials: list[FrozenTrial]) -> bool:
+        asked_trials_no_dups: list[FrozenTrial] = []
         trials_to_consider = trial.study.get_trials(deepcopy=False, states=[TrialState.COMPLETE])
         # Check whether we already evaluated the sampled `params`.
         for t in reversed(trials_to_consider):
             if trial.params == t.params:
-                # logger.warning(
-                #     f"duplicate trial: Trial {trial.number} has same params as {t.number}"
-                # )
                 return True
+        # Check whether same`params` in one batch (asked_trials). Autosampler is doing this.
+        for t in asked_trials:
+            if t.params not in asked_trials_no_dups:
+                asked_trials_no_dups.append(t)
+        if len(asked_trials_no_dups) != len(asked_trials):
+            return True
         return False
 
     def get_asked_points(self, n_points: int, dimensions: dict) -> tuple[list[Any], list[bool]]:
@@ -189,26 +193,26 @@ class Hyperopt:
         Steps:
         1. Try to get points using `self.opt.ask` first
         2. Discard the points that have already been evaluated
-        3. Retry using `self.opt.ask` up to 5 times
+        3. Retry using `self.opt.ask` up to `n_points` times
         """
-        asked_non_tried: list[list[Any]] = []
-        asked_duplicates: list[Trial] = []
+        asked_non_tried: list[FrozenTrial] = []
         optuna_asked_trials = self.get_optuna_asked_points(n_points=n_points, dimensions=dimensions)
         asked_non_tried += [
-            x for x in optuna_asked_trials if not self.duplicate_optuna_asked_points(x)
+            x
+            for x in optuna_asked_trials
+            if not self.duplicate_optuna_asked_points(x, optuna_asked_trials)
         ]
         i = 0
-        while i < 5 and len(asked_non_tried) < n_points:
+        while i < 2 * n_points and len(asked_non_tried) < n_points:
             asked_new = self.get_optuna_asked_points(n_points=1, dimensions=dimensions)[0]
-            if not self.duplicate_optuna_asked_points(asked_new):
+            if not self.duplicate_optuna_asked_points(asked_new, asked_non_tried):
                 asked_non_tried.append(asked_new)
-            else:
-                asked_duplicates.append(asked_new)
             i += 1
-        if len(asked_duplicates) > 0 and len(asked_non_tried) < n_points:
-            for asked_duplicate in asked_duplicates:
-                logger.warning(f"duplicate params for Epoch {asked_duplicate.number}")
-            self.count_skipped_epochs += len(asked_duplicates)
+        if len(asked_non_tried) < n_points:
+            logger.warning(
+                "duplicate params detected. Please check if search space is not too small!"
+            )
+            self.count_skipped_epochs += n_points - len(asked_non_tried)
 
         return asked_non_tried, [False for _ in range(len(asked_non_tried))]