diff --git a/freqtrade/optimize/SortinoLossBalance.py b/freqtrade/optimize/SortinoLossBalance.py
index effd34412..6a1cc8fa5 100644
--- a/freqtrade/optimize/SortinoLossBalance.py
+++ b/freqtrade/optimize/SortinoLossBalance.py
@@ -4,8 +4,8 @@ This module defines the alternative HyperOptLoss class which can be used for
 Hyperoptimization.
 """
 import logging
-import os
 from datetime import datetime
+from typing import Dict
 
 import numpy as np
 from pandas import DataFrame, Timedelta
@@ -16,11 +16,7 @@ from freqtrade.optimize.hyperopt import IHyperOptLoss
 
 logger = logging.getLogger(__name__)
 
-interval = os.getenv("FQT_TIMEFRAME") or "5m"
-slippage = 0.0005
 target = 0
-annualize = np.sqrt(365 * (Timedelta("1D") / Timedelta(interval)))
-
 logger.info(f"SortinoLossBalance target is set to: {target}")
 
 
@@ -31,28 +27,24 @@ class SortinoLossBalance(IHyperOptLoss):
     """
 
     @staticmethod
-    def hyperopt_loss_function(
-        results: DataFrame,
-        trade_count: int,
-        min_date: datetime,
-        max_date: datetime,
-        *args,
-        **kwargs,
-    ) -> float:
+    def hyperopt_loss_function(results: DataFrame, trade_count: int,
+                               min_date: datetime, max_date: datetime,
+                               config: Dict, processed: Dict[str, DataFrame],
+                               *args, **kwargs) -> float:
         """
         Objective function, returns smaller number for more optimal results.
         Uses Sortino Ratio calculation.
         """
-        hloc = kwargs["processed"]
         timeframe = SortinoLossBalance.timeframe
+        annualize = np.sqrt(365 * (Timedelta("1D") / Timedelta(timeframe)))
 
-        balance_total = calculate_outstanding_balance(results, timeframe, hloc)
+        balance_total = calculate_outstanding_balance(results, timeframe, processed)
 
         returns = balance_total.mean()
         # returns = balance_total.values.mean()
 
         downside_returns = np.where(balance_total < 0, balance_total, 0)
-        downside_risk = np.sqrt((downside_returns ** 2).sum() / len(hloc))
+        downside_risk = np.sqrt((downside_returns ** 2).sum() / len(processed))
 
         if downside_risk != 0.0:
             sortino_ratio = (returns - target) / downside_risk * annualize