Combine most hyperopt-loss tests to one
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@ -39,16 +39,17 @@ def hyperopt(hyperopt_conf, mocker):
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def hyperopt_results():
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return pd.DataFrame(
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{
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'pair': ['ETH/BTC', 'ETH/BTC', 'ETH/BTC'],
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'profit_ratio': [-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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'pair': ['ETH/BTC', 'ETH/BTC', 'ETH/BTC', 'ETH/BTC'],
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'profit_ratio': [-0.1, 0.2, -0.1, 0.3],
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'profit_abs': [-0.2, 0.4, -0.2, 0.6],
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'trade_duration': [10, 30, 10, 10],
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'sell_reason': [SellType.STOP_LOSS, SellType.ROI, SellType.STOP_LOSS, SellType.ROI],
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'close_date':
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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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datetime(2019, 3, 1, 9, 26, 3, 478039),
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datetime(2019, 4, 1, 9, 26, 3, 478039),
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]
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}
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)
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@ -35,6 +35,7 @@ def test_hyperoptlossresolver_wrongname(default_conf) -> None:
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def test_loss_calculation_prefer_correct_trade_count(hyperopt_conf, hyperopt_results) -> None:
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hyperopt_conf.update({'hyperopt_loss': "ShortTradeDurHyperOptLoss"})
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hl = HyperOptLossResolver.load_hyperoptloss(hyperopt_conf)
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correct = hl.hyperopt_loss_function(hyperopt_results, 600,
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datetime(2019, 1, 1), datetime(2019, 5, 1))
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@ -50,6 +51,7 @@ def test_loss_calculation_prefer_shorter_trades(hyperopt_conf, hyperopt_results)
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resultsb = hyperopt_results.copy()
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resultsb.loc[1, 'trade_duration'] = 20
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hyperopt_conf.update({'hyperopt_loss': "ShortTradeDurHyperOptLoss"})
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hl = HyperOptLossResolver.load_hyperoptloss(hyperopt_conf)
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longer = hl.hyperopt_loss_function(hyperopt_results, 100,
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datetime(2019, 1, 1), datetime(2019, 5, 1))
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@ -64,6 +66,7 @@ def test_loss_calculation_has_limited_profit(hyperopt_conf, hyperopt_results) ->
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results_under = hyperopt_results.copy()
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results_under['profit_ratio'] = hyperopt_results['profit_ratio'] / 2
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hyperopt_conf.update({'hyperopt_loss': "ShortTradeDurHyperOptLoss"})
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hl = HyperOptLossResolver.load_hyperoptloss(hyperopt_conf)
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correct = hl.hyperopt_loss_function(hyperopt_results, 600,
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datetime(2019, 1, 1), datetime(2019, 5, 1))
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@ -75,91 +78,28 @@ def test_loss_calculation_has_limited_profit(hyperopt_conf, hyperopt_results) ->
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assert under > correct
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def test_sharpe_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_ratio'] = hyperopt_results['profit_ratio'] * 2
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results_under = hyperopt_results.copy()
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results_under['profit_ratio'] = hyperopt_results['profit_ratio'] / 2
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default_conf.update({'hyperopt_loss': 'SharpeHyperOptLoss'})
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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_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_ratio'] = hyperopt_results['profit_ratio'] * 2
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results_under = hyperopt_results.copy()
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results_under['profit_ratio'] = hyperopt_results['profit_ratio'] / 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_sortino_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_ratio'] = hyperopt_results['profit_ratio'] * 2
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results_under = hyperopt_results.copy()
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results_under['profit_ratio'] = hyperopt_results['profit_ratio'] / 2
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default_conf.update({'hyperopt_loss': 'SortinoHyperOptLoss'})
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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_sortino_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_ratio'] = hyperopt_results['profit_ratio'] * 2
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results_under = hyperopt_results.copy()
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results_under['profit_ratio'] = hyperopt_results['profit_ratio'] / 2
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default_conf.update({'hyperopt_loss': 'SortinoHyperOptLossDaily'})
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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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@pytest.mark.parametrize('lossfunction', [
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"OnlyProfitHyperOptLoss",
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"SortinoHyperOptLoss",
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"SortinoHyperOptLossDaily",
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"SharpeHyperOptLoss",
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"SharpeHyperOptLossDaily",
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])
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def test_loss_functions_better_profits(default_conf, hyperopt_results, lossfunction) -> None:
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results_over = hyperopt_results.copy()
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results_over['profit_abs'] = hyperopt_results['profit_abs'] * 2
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results_over['profit_ratio'] = hyperopt_results['profit_ratio'] * 2
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results_under = hyperopt_results.copy()
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results_under['profit_abs'] = hyperopt_results['profit_abs'] / 2
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results_under['profit_ratio'] = hyperopt_results['profit_ratio'] / 2
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default_conf.update({'hyperopt_loss': 'OnlyProfitHyperOptLoss'})
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default_conf.update({'hyperopt_loss': lossfunction})
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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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over = hl.hyperopt_loss_function(results_over, len(results_over),
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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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under = hl.hyperopt_loss_function(results_under, len(results_under),
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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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