diff --git a/freqtrade/data/btanalysis/__init__.py b/freqtrade/data/btanalysis/__init__.py
index 520568558..1889429f6 100644
--- a/freqtrade/data/btanalysis/__init__.py
+++ b/freqtrade/data/btanalysis/__init__.py
@@ -25,7 +25,7 @@ from .bt_fileutils import (
     trade_list_to_dataframe,
     update_backtest_metadata,
 )
-from .historic_precision import get_significant_digits_over_time
+from .historic_precision import get_tick_size_over_time
 from .trade_parallelism import (
     analyze_trade_parallelism,
     evaluate_result_multi,
diff --git a/freqtrade/data/btanalysis/historic_precision.py b/freqtrade/data/btanalysis/historic_precision.py
index c4d186924..c8aa4fbee 100644
--- a/freqtrade/data/btanalysis/historic_precision.py
+++ b/freqtrade/data/btanalysis/historic_precision.py
@@ -1,7 +1,7 @@
 from pandas import DataFrame, Series
 
 
-def get_significant_digits_over_time(candles: DataFrame) -> Series:
+def get_tick_size_over_time(candles: DataFrame) -> Series:
     """
     Calculate the number of significant digits for candles over time.
     It's using the Monthly maximum of the number of significant digits for each month.
diff --git a/freqtrade/optimize/backtesting.py b/freqtrade/optimize/backtesting.py
index 3fc533e29..6229a9c96 100644
--- a/freqtrade/optimize/backtesting.py
+++ b/freqtrade/optimize/backtesting.py
@@ -18,7 +18,7 @@ from freqtrade.constants import DATETIME_PRINT_FORMAT, Config, IntOrInf, LongSho
 from freqtrade.data import history
 from freqtrade.data.btanalysis import (
     find_existing_backtest_stats,
-    get_significant_digits_over_time,
+    get_tick_size_over_time,
     trade_list_to_dataframe,
 )
 from freqtrade.data.converter import trim_dataframe, trim_dataframes
@@ -323,7 +323,8 @@ class Backtesting:
         self.price_pair_prec = {}
         for pair in self.pairlists.whitelist:
             if pair in data:
-                self.price_pair_prec[pair] = get_significant_digits_over_time(data[pair])
+                # Load price precision logic
+                self.price_pair_prec[pair] = get_tick_size_over_time(data[pair])
         return data, self.timerange
 
     def _load_bt_data_detail(self) -> None:
diff --git a/tests/data/test_historic_precision.py b/tests/data/test_historic_precision.py
index a79be0f49..472218b8b 100644
--- a/tests/data/test_historic_precision.py
+++ b/tests/data/test_historic_precision.py
@@ -6,12 +6,12 @@ import pandas as pd
 from numpy import nan
 from pandas import DataFrame, Timestamp
 
-from freqtrade.data.btanalysis.historic_precision import get_significant_digits_over_time
+from freqtrade.data.btanalysis.historic_precision import get_tick_size_over_time
 
 
-def test_get_significant_digits_over_time():
+def test_get_tick_size_over_time():
     """
-    Test the get_significant_digits_over_time function with predefined data
+    Test the get_tick_size_over_time function with predefined data
     """
     # Create test dataframe with different levels of precision
     data = {
@@ -36,7 +36,7 @@ def test_get_significant_digits_over_time():
     candles = DataFrame(data)
 
     # Calculate significant digits
-    result = get_significant_digits_over_time(candles)
+    result = get_tick_size_over_time(candles)
 
     # Check that the result is a pandas Series
     assert isinstance(result, pd.Series)
@@ -60,9 +60,9 @@ def test_get_significant_digits_over_time():
     assert result.iloc[0] == 0.00001
 
 
-def test_get_significant_digits_over_time_real_data(testdatadir):
+def test_get_tick_size_over_time_real_data(testdatadir):
     """
-    Test the get_significant_digits_over_time function with real data from the testdatadir
+    Test the get_tick_size_over_time function with real data from the testdatadir
     """
     from freqtrade.data.history import load_pair_history
 
@@ -80,7 +80,7 @@ def test_get_significant_digits_over_time_real_data(testdatadir):
     assert not candles.empty, "No test data found, cannot run test"
 
     # Calculate significant digits
-    result = get_significant_digits_over_time(candles)
+    result = get_tick_size_over_time(candles)
 
     assert isinstance(result, pd.Series)