diff --git a/freqtrade/data/btanalysis/__init__.py b/freqtrade/data/btanalysis/__init__.py
index 48fdcd9b5..520568558 100644
--- a/freqtrade/data/btanalysis/__init__.py
+++ b/freqtrade/data/btanalysis/__init__.py
@@ -25,6 +25,7 @@ from .bt_fileutils import (
     trade_list_to_dataframe,
     update_backtest_metadata,
 )
+from .historic_precision import get_significant_digits_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
new file mode 100644
index 000000000..96d12ba68
--- /dev/null
+++ b/freqtrade/data/btanalysis/historic_precision.py
@@ -0,0 +1,27 @@
+from pandas import DataFrame, Series
+
+
+def get_significant_digits_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.
+    :param candles: DataFrame with OHLCV data
+    :return: Series with the average number of significant digits for each month
+    """
+    # count the number of significant digits for the open and close prices
+    for col in ["open", "high", "low", "close"]:
+        candles[f"{col}_count"] = (
+            candles[col].round(14).astype(str).str.extract(r"\.(\d*[1-9])")[0].str.len()
+        )
+    candles["max_count"] = candles[["open_count", "close_count", "high_count", "low_count"]].max(
+        axis=1
+    )
+
+    candles1 = candles.set_index("date", drop=True)
+    # Group by month and calculate the average number of significant digits
+    monthly_count_avg1 = candles1["max_count"].resample("ME").max()
+    # monthly_open_count_avg
+    # convert monthly_open_count_avg from 5.0 to 0.00001, 4.0 to 0.0001, ...
+    monthly_open_count_avg = 1 / 10**monthly_count_avg1
+
+    return monthly_open_count_avg