diff --git a/tests/data/test_historic_precision.py b/tests/data/test_historic_precision.py
new file mode 100644
index 000000000..a79be0f49
--- /dev/null
+++ b/tests/data/test_historic_precision.py
@@ -0,0 +1,92 @@
+# pragma pylint: disable=missing-docstring, C0103
+
+from datetime import timezone
+
+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
+
+
+def test_get_significant_digits_over_time():
+    """
+    Test the get_significant_digits_over_time function with predefined data
+    """
+    # Create test dataframe with different levels of precision
+    data = {
+        "date": [
+            Timestamp("2020-01-01 00:00:00", tz=timezone.utc),
+            Timestamp("2020-01-02 00:00:00", tz=timezone.utc),
+            Timestamp("2020-01-03 00:00:00", tz=timezone.utc),
+            Timestamp("2020-01-15 00:00:00", tz=timezone.utc),
+            Timestamp("2020-01-16 00:00:00", tz=timezone.utc),
+            Timestamp("2020-01-31 00:00:00", tz=timezone.utc),
+            Timestamp("2020-02-01 00:00:00", tz=timezone.utc),
+            Timestamp("2020-02-15 00:00:00", tz=timezone.utc),
+            Timestamp("2020-03-15 00:00:00", tz=timezone.utc),
+        ],
+        "open": [1.23456, 1.234, 1.23, 1.2, 1.23456, 1.234, 2.3456, 2.34, 2.34],
+        "high": [1.23457, 1.235, 1.24, 1.3, 1.23456, 1.235, 2.3457, 2.34, 2.34],
+        "low": [1.23455, 1.233, 1.22, 1.1, 1.23456, 1.233, 2.3455, 2.34, 2.34],
+        "close": [1.23456, 1.234, 1.23, 1.2, 1.23456, 1.234, 2.3456, 2.34, 2.34],
+        "volume": [100, 200, 300, 400, 500, 600, 700, 800, 900],
+    }
+
+    candles = DataFrame(data)
+
+    # Calculate significant digits
+    result = get_significant_digits_over_time(candles)
+
+    # Check that the result is a pandas Series
+    assert isinstance(result, pd.Series)
+
+    # Check that we have three months of data (Jan, Feb and March 2020 )
+    assert len(result) == 3
+
+    # Before
+    assert result.asof("2019-01-01 00:00:00+00:00") is nan
+    # January should have 5 significant digits (based on 1.23456789 being the most precise value)
+    # which should be converted to 0.00001
+
+    assert result.asof("2020-01-01 00:00:00+00:00") == 0.00001
+    assert result.asof("2020-01-01 00:00:00+00:00") == 0.00001
+    assert result.asof("2020-02-25 00:00:00+00:00") == 0.0001
+    assert result.asof("2020-03-25 00:00:00+00:00") == 0.01
+    assert result.asof("2020-04-01 00:00:00+00:00") == 0.01
+    # Value far past the last date should be the last value
+    assert result.asof("2025-04-01 00:00:00+00:00") == 0.01
+
+    assert result.iloc[0] == 0.00001
+
+
+def test_get_significant_digits_over_time_real_data(testdatadir):
+    """
+    Test the get_significant_digits_over_time function with real data from the testdatadir
+    """
+    from freqtrade.data.history import load_pair_history
+
+    # Load some test data from the testdata directory
+    pair = "UNITTEST/BTC"
+    timeframe = "1m"
+
+    candles = load_pair_history(
+        datadir=testdatadir,
+        pair=pair,
+        timeframe=timeframe,
+    )
+
+    # Make sure we have test data
+    assert not candles.empty, "No test data found, cannot run test"
+
+    # Calculate significant digits
+    result = get_significant_digits_over_time(candles)
+
+    assert isinstance(result, pd.Series)
+
+    # Verify that all values are between 0 and 1 (valid precision values)
+    assert all(result > 0)
+    assert all(result < 1)
+
+    assert all(result <= 0.0001)
+    assert all(result >= 0.00000001)