diff --git a/freqtrade/data/converter/orderflow.py b/freqtrade/data/converter/orderflow.py
index 5af63dfb8..6d2df7d3d 100644
--- a/freqtrade/data/converter/orderflow.py
+++ b/freqtrade/data/converter/orderflow.py
@@ -265,20 +265,23 @@ def stacked_imbalance(
     """
     imbalance = df[f"{label}_imbalance"]
     int_series = pd.Series(np.where(imbalance, 1, 0))
-    stacked = int_series * (
-        int_series.groupby((int_series != int_series.shift()).cumsum()).cumcount() + 1
-    )
-
-    stacked_imbalance_idx = stacked.index[stacked >= stacked_imbalance_range]
-    stacked_imbalance_prices = []
+    # Group consecutive True values and get their counts
+    groups = (int_series != int_series.shift()).cumsum()
+    counts = int_series.groupby(groups).cumsum()
     
-    if not stacked_imbalance_idx.empty:
-        indices = (
-            stacked_imbalance_idx
+    # Find indices where count meets or exceeds the range requirement
+    valid_indices = counts[counts >= stacked_imbalance_range].index
+    
+    stacked_imbalance_prices = []
+    if not valid_indices.empty:
+        # Get all prices from valid indices from beginning of the range
+        valid_prices = [imbalance.index.values[idx-(stacked_imbalance_range-1)] for idx in valid_indices]
+        # Sort prices according to direction
+        stacked_imbalance_prices = (
+            sorted(valid_prices)
             if not should_reverse
-            else np.flipud(stacked_imbalance_idx)
+            else sorted(valid_prices, reverse=True)
         )
-        stacked_imbalance_prices = [float(imbalance.index[idx]) for idx in indices]
     
     return stacked_imbalance_prices if stacked_imbalance_prices else [np.nan]
 
diff --git a/tests/data/test_converter_orderflow.py b/tests/data/test_converter_orderflow.py
index 50b47b7d5..792daea45 100644
--- a/tests/data/test_converter_orderflow.py
+++ b/tests/data/test_converter_orderflow.py
@@ -193,8 +193,8 @@ def test_public_trades_mock_populate_dataframe_with_trades__check_orderflow(
     assert pytest.approx(results["delta"]) == -20.862
     assert pytest.approx(results["min_delta"]) == -54.559999
     assert 82.842 == results["max_delta"]
-    assert results["stacked_imbalances_bid"] == [234.99]
-    assert results["stacked_imbalances_ask"] == [234.96]
+    assert results["stacked_imbalances_bid"] == [234.97]
+    assert results["stacked_imbalances_ask"] == [234.94]
 
     # Repeat assertions for the last row
     results = df.iloc[-1]
@@ -586,22 +586,22 @@ def test_stacked_imbalances_multiple_prices():
     # Create a sample DataFrame with known imbalances
     df = pd.DataFrame(
         {
-            'bid_imbalance': [True, True, True, False, False, True, True, False],
-            'ask_imbalance': [False, False, True, True, True, False, False, True]
+            'bid_imbalance': [True, True, True, False, False, True, True, False, True],
+            'ask_imbalance': [False, False, True, True, True, False, False, True, True]
         },
-        index=[234.95, 234.96, 234.97, 234.98, 234.99, 235.00, 235.01, 235.02]
+        index=[234.95, 234.96, 234.97, 234.98, 234.99, 235.00, 235.01, 235.02, 235.03]
     )
     # Test bid imbalances (should return prices in ascending order)
     bid_prices = stacked_imbalance(df, "bid", stacked_imbalance_range=2, should_reverse=False)
-    assert bid_prices == [234.95, 234.96, 234.97, 235.00, 235.01]
+    assert bid_prices == [234.95, 234.96, 235.00]
     
     # Test ask imbalances (should return prices in descending order)
     ask_prices = stacked_imbalance(df, "ask", stacked_imbalance_range=2, should_reverse=True)
-    assert ask_prices == [235.02, 234.99, 234.98, 234.97]
+    assert ask_prices == [235.02, 234.98, 234.97]
     
     # Test with higher stacked_imbalance_range
     bid_prices_higher = stacked_imbalance(df, "bid", stacked_imbalance_range=3, should_reverse=False)
-    assert bid_prices_higher == [234.95, 234.96, 234.97]
+    assert bid_prices_higher == [234.95]