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No need for np; make flake happy

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hroff-1902 2020-02-05 18:05:41 +03:00
parent 8e6ab0eaaf
commit f3e94969b3
1 changed files with 6 additions and 5 deletions
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11
freqtrade/optimize/hyperopt_loss_sharpe_daily.py
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@ -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