As -sharp_ratio is returned the value should be nagative.

This leads in a high positive result of the loss function, as it is a minimal optimizer
2019-08-08 22:10:51 +02:00 · 2019-08-08 22:10:51 +02:00 · 8ad5afd3a1
parent 02b2de5c73
commit 8ad5afd3a1
1 changed files with 1 additions and 1 deletions
--- a/freqtrade/optimize/hyperopt_loss_sharpe.py
+++ b/freqtrade/optimize/hyperopt_loss_sharpe.py
@ -39,7 +39,7 @@ class SharpeHyperOptLoss(IHyperOptLoss):
            sharp_ratio = expected_yearly_return / np.std(total_profit) * np.sqrt(365)
        else:
            # 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)
        return -sharp_ratio