diff --git a/freqtrade/optimize/hyperopt_loss_sortino_daily.py b/freqtrade/optimize/hyperopt_loss_sortino_daily.py
index 0f81ffca5..16dc26142 100644
--- a/freqtrade/optimize/hyperopt_loss_sortino_daily.py
+++ b/freqtrade/optimize/hyperopt_loss_sortino_daily.py
@@ -5,7 +5,6 @@ This module defines the alternative HyperOptLoss class which can be used for
 Hyperoptimization.
 """
 import math
-import statistics
 from datetime import datetime
 
 from pandas import DataFrame, date_range
@@ -56,7 +55,9 @@ class SortinoHyperOptLossDaily(IHyperOptLoss):
         sum_daily['downside_returns'] = 0
         sum_daily.loc[total_profit < 0, 'downside_returns'] = total_profit
         total_downside = sum_daily['downside_returns']
-        down_stdev = statistics.pstdev(total_downside, 0)
+        # Here total_downside contains min(0, P - MAR) values,
+        # where P = sum_daily["profit_percent_after_slippage"]
+        down_stdev = math.sqrt((total_downside**2).sum() / len(total_downside))
 
         if (down_stdev != 0.):
             sortino_ratio = expected_returns_mean / down_stdev * math.sqrt(days_in_year)