added daily sharpe ratio test and modified hyperopt_loss_sharpe_daily
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@ -1,5 +1,5 @@
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"""
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SharpeHyperOptLoss
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SharpeHyperOptLossDaily
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This module defines the alternative HyperOptLoss class which can be used for
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Hyperoptimization.
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@ -43,7 +43,7 @@ class SharpeHyperOptLossDaily(IHyperOptLoss):
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* 100.0
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)
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if np.std(results.profit_percent) != 0.0:
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if (np.std(sum_daily.profit_percent) != 0.):
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sharp_ratio = (
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sum_daily["profit_percent"].mean()
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/ sum_daily["profit_percent"].std()
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@ -42,7 +42,13 @@ def hyperopt_results():
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'profit_percent': [-0.1, 0.2, 0.3],
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'profit_abs': [-0.2, 0.4, 0.6],
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'trade_duration': [10, 30, 10],
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'sell_reason': [SellType.STOP_LOSS, SellType.ROI, SellType.ROI]
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'sell_reason': [SellType.STOP_LOSS, SellType.ROI, SellType.ROI],
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'close_time':
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[
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datetime(2019, 1, 1, 9, 26, 3, 478039),
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datetime(2019, 2, 1, 9, 26, 3, 478039),
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datetime(2019, 3, 1, 9, 26, 3, 478039)
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]
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}
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)
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@ -336,6 +342,24 @@ def test_sharpe_loss_prefers_higher_profits(default_conf, hyperopt_results) -> N
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assert under > correct
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def test_sharpe_loss_daily_prefers_higher_profits(default_conf, hyperopt_results) -> None:
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results_over = hyperopt_results.copy()
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results_over['profit_percent'] = hyperopt_results['profit_percent'] * 2
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results_under = hyperopt_results.copy()
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results_under['profit_percent'] = hyperopt_results['profit_percent'] / 2
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default_conf.update({'hyperopt_loss': 'SharpeHyperOptLossDaily'})
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hl = HyperOptLossResolver.load_hyperoptloss(default_conf)
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correct = hl.hyperopt_loss_function(hyperopt_results, len(hyperopt_results),
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datetime(2019, 1, 1), datetime(2019, 5, 1))
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over = hl.hyperopt_loss_function(results_over, len(hyperopt_results),
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datetime(2019, 1, 1), datetime(2019, 5, 1))
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under = hl.hyperopt_loss_function(results_under, len(hyperopt_results),
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datetime(2019, 1, 1), datetime(2019, 5, 1))
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assert over < correct
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assert under > correct
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def test_onlyprofit_loss_prefers_higher_profits(default_conf, hyperopt_results) -> None:
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results_over = hyperopt_results.copy()
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results_over['profit_percent'] = hyperopt_results['profit_percent'] * 2
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