fix: don't sort stacked imbalances, return empty list if no found...

... also removes helper functions `stacked_imbalance_bid` & `stacked_imbalance_ask`

freqtrade/data/converter/orderflow.py

@@ -164,12 +164,12 @@ def populate_dataframe_with_trades(
                 dataframe.at[index, "imbalances"] = imbalances.to_dict(orient="index")
 
                 stacked_imbalance_range = config_orderflow["stacked_imbalance_range"]
-                dataframe.at[index, "stacked_imbalances_bid"] = stacked_imbalance_bid(
-                    imbalances, stacked_imbalance_range=stacked_imbalance_range
+                dataframe.at[index, "stacked_imbalances_bid"] = stacked_imbalance(
+                    imbalances, label="bid", stacked_imbalance_range=stacked_imbalance_range
                 )
 
-                dataframe.at[index, "stacked_imbalances_ask"] = stacked_imbalance_ask(
-                    imbalances, stacked_imbalance_range=stacked_imbalance_range
+                dataframe.at[index, "stacked_imbalances_ask"] = stacked_imbalance(
+                    imbalances, label="ask", stacked_imbalance_range=stacked_imbalance_range
                 )
 
                 bid = np.where(
@@ -256,9 +256,7 @@ def trades_orderflow_to_imbalances(df: pd.DataFrame, imbalance_ratio: int, imbal
     return dataframe
 
 
-def stacked_imbalance(
-    df: pd.DataFrame, label: str, stacked_imbalance_range: int, should_reverse: bool
-):
+def stacked_imbalance(df: pd.DataFrame, label: str, stacked_imbalance_range: int):
     """
     y * (y.groupby((y != y.shift()).cumsum()).cumcount() + 1)
     https://stackoverflow.com/questions/27626542/counting-consecutive-positive-values-in-python-pandas-array
@@ -268,27 +266,14 @@ def stacked_imbalance(
     # Group consecutive True values and get their counts
     groups = (int_series != int_series.shift()).cumsum()
     counts = int_series.groupby(groups).cumsum()
-    
+
     # 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 sorted(valid_prices, reverse=True)
-        )
-    
-    return stacked_imbalance_prices if stacked_imbalance_prices else [np.nan]
-
-
-def stacked_imbalance_ask(df: pd.DataFrame, stacked_imbalance_range: int):
-    return stacked_imbalance(df, "ask", stacked_imbalance_range, should_reverse=True)
-
-
-def stacked_imbalance_bid(df: pd.DataFrame, stacked_imbalance_range: int):
-    return stacked_imbalance(df, "bid", stacked_imbalance_range, should_reverse=False)
+        stacked_imbalance_prices = [
+            imbalance.index.values[idx - (stacked_imbalance_range - 1)] for idx in valid_indices
+        ]
+    return stacked_imbalance_prices if stacked_imbalance_prices else []

tests/data/test_converter_orderflow.py

@@ -5,9 +5,9 @@ from freqtrade.constants import DEFAULT_TRADES_COLUMNS
 from freqtrade.data.converter import populate_dataframe_with_trades
 from freqtrade.data.converter.orderflow import (
     ORDERFLOW_ADDED_COLUMNS,
+    stacked_imbalance,
     timeframe_to_DateOffset,
     trades_to_volumeprofile_with_total_delta_bid_ask,
-    stacked_imbalance,
 )
 from freqtrade.data.converter.trade_converter import trades_list_to_df
 from freqtrade.data.dataprovider import DataProvider
@@ -185,8 +185,8 @@ def test_public_trades_mock_populate_dataframe_with_trades__check_orderflow(
     assert results["max_delta"] == 17.298
 
     # Assert that stacked imbalances are NaN (not applicable in this test)
-    assert results["stacked_imbalances_bid"] == [np.nan]
-    assert results["stacked_imbalances_ask"] == [np.nan]
+    assert results["stacked_imbalances_bid"] == []
+    assert results["stacked_imbalances_ask"] == []
 
     # Repeat assertions for the third from last row
     results = df.iloc[-2]
@@ -201,8 +201,8 @@ def test_public_trades_mock_populate_dataframe_with_trades__check_orderflow(
     assert pytest.approx(results["delta"]) == -49.302
     assert results["min_delta"] == -70.222
     assert pytest.approx(results["max_delta"]) == 11.213
-    assert results["stacked_imbalances_bid"] == [np.nan]
-    assert results["stacked_imbalances_ask"] == [np.nan]
+    assert results["stacked_imbalances_bid"] == []
+    assert results["stacked_imbalances_ask"] == []
 
 
 def test_public_trades_trades_mock_populate_dataframe_with_trades__check_trades(
@@ -575,34 +575,33 @@ def test_stacked_imbalances_multiple_prices():
     # Test with empty result
     df_no_stacks = pd.DataFrame(
         {
-            'bid_imbalance': [False, False, True, False],
-            'ask_imbalance': [False, True, False, False]
+            "bid_imbalance": [False, False, True, False],
+            "ask_imbalance": [False, True, False, False],
         },
-        index=[234.95, 234.96, 234.97, 234.98]
+        index=[234.95, 234.96, 234.97, 234.98],
     )
-    no_stacks = stacked_imbalance(df_no_stacks, "bid", stacked_imbalance_range=2, should_reverse=False)
-    assert no_stacks == [np.nan]
-    
+    no_stacks = stacked_imbalance(df_no_stacks, "bid", stacked_imbalance_range=2)
+    assert no_stacks == []
+
     # Create a sample DataFrame with known imbalances
     df = pd.DataFrame(
         {
-            'bid_imbalance': [True, True, True, False, False, True, True, False, True],
-            'ask_imbalance': [False, False, True, True, True, False, False, True, 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, 235.03]
+        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)
+    bid_prices = stacked_imbalance(df, "bid", stacked_imbalance_range=2)
     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, 234.98, 234.97]
-    
+    ask_prices = stacked_imbalance(df, "ask", stacked_imbalance_range=2)
+    assert ask_prices == [234.97, 234.98, 235.02]
+
     # Test with higher stacked_imbalance_range
-    bid_prices_higher = stacked_imbalance(df, "bid", stacked_imbalance_range=3, should_reverse=False)
+    bid_prices_higher = stacked_imbalance(df, "bid", stacked_imbalance_range=3)
     assert bid_prices_higher == [234.95]
-    
 
 
 def test_timeframe_to_DateOffset():