diff --git a/freqtrade/optimize/hyperopt/hyperopt.py b/freqtrade/optimize/hyperopt/hyperopt.py
index 131fca753..3db5ec9fe 100644
--- a/freqtrade/optimize/hyperopt/hyperopt.py
+++ b/freqtrade/optimize/hyperopt/hyperopt.py
@@ -330,7 +330,6 @@ class Hyperopt:
                 self.config,
                 self.hyperopter.get_strategy_name(),
                 self.current_best_epoch,
-                self.hyperopter.o_dimensions,
             )
 
             HyperoptTools.show_epoch_details(
diff --git a/freqtrade/optimize/hyperopt_tools.py b/freqtrade/optimize/hyperopt_tools.py
index f2178d63e..aaea6c016 100644
--- a/freqtrade/optimize/hyperopt_tools.py
+++ b/freqtrade/optimize/hyperopt_tools.py
@@ -7,7 +7,6 @@ from typing import Any
 
 import numpy as np
 import rapidjson
-from optuna.distributions import BaseDistribution
 from pandas import isna, json_normalize
 
 from freqtrade.constants import FTHYPT_FILEVERSION, Config
@@ -15,7 +14,6 @@ from freqtrade.enums import HyperoptState
 from freqtrade.exceptions import OperationalException
 from freqtrade.misc import deep_merge_dicts, round_dict, safe_value_fallback2
 from freqtrade.optimize.hyperopt_epoch_filters import hyperopt_filter_epochs
-from freqtrade.optimize.space import SKDecimal, _adjust_discrete_uniform
 
 
 logger = logging.getLogger(__name__)
@@ -67,22 +65,11 @@ class HyperoptTools:
         params,
         strategy_name: str,
         filename: Path,
-        o_dimensions: dict[str, BaseDistribution] | None = None,
     ):
         """
         Generate files
         """
         final_params = deepcopy(params["params_not_optimized"])
-        if o_dimensions:
-            for key, val in params["params_details"].items():
-                if isinstance(val, dict):
-                    for key1, val1 in val.items():
-                        if isinstance(o_dimensions.get(key1), SKDecimal):
-                            step = getattr(o_dimensions.get(key1), "step", None)
-                            if step:
-                                params["params_details"][key][key1] = _adjust_discrete_uniform(
-                                    val1, step
-                                )
         final_params = deep_merge_dicts(params["params_details"], final_params)
         final_params = {
             "strategy_name": strategy_name,
@@ -115,15 +102,12 @@ class HyperoptTools:
         config: Config,
         strategy_name: str,
         params: dict,
-        o_dimensions: dict[str, BaseDistribution] | None = None,
     ):
         if params.get(FTHYPT_FILEVERSION, 1) >= 2 and not config.get("disableparamexport", False):
             # Export parameters ...
             fn = HyperoptTools.get_strategy_filename(config, strategy_name)
             if fn:
-                HyperoptTools.export_params(
-                    params, strategy_name, fn.with_suffix(".json"), o_dimensions
-                )
+                HyperoptTools.export_params(params, strategy_name, fn.with_suffix(".json"))
             else:
                 logger.warning("Strategy not found, not exporting parameter file.")
 
diff --git a/freqtrade/optimize/space/__init__.py b/freqtrade/optimize/space/__init__.py
index a32937864..4a9855234 100644
--- a/freqtrade/optimize/space/__init__.py
+++ b/freqtrade/optimize/space/__init__.py
@@ -1,4 +1,4 @@
-from .decimalspace import SKDecimal, _adjust_discrete_uniform
+from .decimalspace import SKDecimal
 from .optunaspaces import (
     DimensionProtocol,
     ft_CategoricalDistribution,
@@ -13,4 +13,4 @@ Categorical = ft_CategoricalDistribution
 Integer = ft_IntDistribution
 Real = ft_FloatDistribution
 
-__all__ = ["Categorical", "Dimension", "Integer", "Real", "SKDecimal", "_adjust_discrete_uniform"]
+__all__ = ["Categorical", "Dimension", "Integer", "Real", "SKDecimal"]
diff --git a/freqtrade/optimize/space/decimalspace.py b/freqtrade/optimize/space/decimalspace.py
index adb90eaf7..ef6945e24 100644
--- a/freqtrade/optimize/space/decimalspace.py
+++ b/freqtrade/optimize/space/decimalspace.py
@@ -1,4 +1,4 @@
-import decimal
+from math import log10
 
 from optuna.distributions import FloatDistribution
 
@@ -22,32 +22,7 @@ class SKDecimal(FloatDistribution):
         self.name = name
 
         super().__init__(
-            low=_adjust_discrete_uniform(low, self.step),
-            high=_adjust_discrete_uniform_high(low, high, self.step),
+            low=round(low, int(log10(1 / self.step))) if self.step < 1 else low,
+            high=round(high, int(log10(1 / self.step))) if self.step < 1 else high,
             step=self.step,
         )
-
-
-def _adjust_discrete_uniform_high(low: float, high: float, step: float | None) -> float:
-    if step:
-        d_high = decimal.Decimal(str(high))
-        d_low = decimal.Decimal(str(low))
-        d_step = decimal.Decimal(str(step))
-
-        d_r = d_high - d_low
-
-        if d_r % d_step != decimal.Decimal("0"):
-            high = float((d_r // d_step) * d_step + d_low)
-
-    return high
-
-
-def _adjust_discrete_uniform(val: float, step: float | None) -> float:
-    if step:
-        d_val = decimal.Decimal(str(val))
-        d_step = decimal.Decimal(str(step))
-
-        if d_val % d_step != decimal.Decimal("0"):
-            val = float((d_val // d_step) * d_step)
-
-    return val