diff --git a/freqtrade/optimize/hyperopt_loss_sharpe_daily.py b/freqtrade/optimize/hyperopt_loss_sharpe_daily.py
index f9394d78a..2c73b8e61 100644
--- a/freqtrade/optimize/hyperopt_loss_sharpe_daily.py
+++ b/freqtrade/optimize/hyperopt_loss_sharpe_daily.py
@@ -4,10 +4,10 @@ SharpeHyperOptLossDaily
 This module defines the alternative HyperOptLoss class which can be used for
 Hyperoptimization.
 """
+import math
 from datetime import datetime
 
 from pandas import DataFrame, date_range
-import numpy as np
 
 from freqtrade.optimize.hyperopt import IHyperOptLoss
 
@@ -35,7 +35,8 @@ class SharpeHyperOptLossDaily(IHyperOptLoss):
         risk_free_rate = annual_risk_free_rate / days_in_year
 
         # apply slippage per trade to profit_percent
-        results.loc[:, 'profit_percent_after_slippage'] = results['profit_percent'] - slippage_per_trade_ratio
+        results.loc[:, 'profit_percent_after_slippage'] = \
+            results['profit_percent'] - slippage_per_trade_ratio
 
         # create the index within the min_date and end max_date
         t_index = date_range(start=min_date, end=max_date, freq=resample_freq)
@@ -50,11 +51,11 @@ class SharpeHyperOptLossDaily(IHyperOptLoss):
         up_stdev = total_profit.std()
 
         if (up_stdev != 0.):
-            sharp_ratio = expected_returns_mean / up_stdev * np.sqrt(days_in_year)
+            sharp_ratio = expected_returns_mean / up_stdev * math.sqrt(days_in_year)
         else:
             # Define high (negative) sharpe ratio to be clear that this is NOT optimal.
             sharp_ratio = -20.
 
-        #print(t_index, sum_daily, total_profit)
-        #print(risk_free_rate, expected_returns_mean, up_stdev, sharp_ratio)
+        # print(t_index, sum_daily, total_profit)
+        # print(risk_free_rate, expected_returns_mean, up_stdev, sharp_ratio)
         return -sharp_ratio