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As -sharp_ratio is returned the value should be nagative.

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This leads in a high positive result of the loss function, as it is a minimal optimizer
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Cedric Schmeits 2019-08-08 22:10:51 +02:00 committed by GitHub
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freqtrade/optimize/hyperopt_loss_sharpe.py
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@ -39,7 +39,7 @@ class SharpeHyperOptLoss(IHyperOptLoss):
sharp_ratio = expected_yearly_return / np.std(total_profit) * np.sqrt(365) sharp_ratio = expected_yearly_return / np.std(total_profit) * np.sqrt(365)
else: else:
# Define high (negative) sharpe ratio to be clear that this is NOT optimal. # Define high (negative) sharpe ratio to be clear that this is NOT optimal.
sharp_ratio = 20. sharp_ratio = -20.
# print(expected_yearly_return, np.std(total_profit), sharp_ratio) # print(expected_yearly_return, np.std(total_profit), sharp_ratio)
return -sharp_ratio return -sharp_ratio