From 3499f1b85c883a4ff0298d8d83de158a43d158ec Mon Sep 17 00:00:00 2001 From: Yazeed Al Oyoun Date: Sun, 2 Feb 2020 08:47:33 +0100 Subject: [PATCH] better readability and more consistent with daily sharpe loss method --- freqtrade/optimize/hyperopt_loss_sharpe.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/freqtrade/optimize/hyperopt_loss_sharpe.py b/freqtrade/optimize/hyperopt_loss_sharpe.py index 5631a75de..a4ec6f90a 100644 --- a/freqtrade/optimize/hyperopt_loss_sharpe.py +++ b/freqtrade/optimize/hyperopt_loss_sharpe.py @@ -28,18 +28,19 @@ class SharpeHyperOptLoss(IHyperOptLoss): Uses Sharpe Ratio calculation. """ - total_profit = results.profit_percent + total_profit = results["profit_percent"] days_period = (max_date - min_date).days # adding slippage of 0.1% per trade total_profit = total_profit - 0.0005 - expected_yearly_return = total_profit.sum() / days_period + expected_returns_mean = total_profit.sum() / days_period + up_stdev = np.std(total_profit) if (np.std(total_profit) != 0.): - sharp_ratio = expected_yearly_return / np.std(total_profit) * np.sqrt(365) + sharp_ratio = expected_returns_mean / up_stdev * np.sqrt(365) else: # Define high (negative) sharpe ratio to be clear that this is NOT optimal. sharp_ratio = -20. - # print(expected_yearly_return, np.std(total_profit), sharp_ratio) + # print(expected_returns_mean, up_stdev, sharp_ratio) return -sharp_ratio