132 lines
5.4 KiB
Python
132 lines
5.4 KiB
Python
from datetime import datetime
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from unittest.mock import MagicMock
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import pytest
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from freqtrade.exceptions import OperationalException
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from freqtrade.optimize.hyperopt_loss.hyperopt_loss_short_trade_dur import ShortTradeDurHyperOptLoss
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from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver
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def test_hyperoptlossresolver_noname(default_conf):
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with pytest.raises(OperationalException,
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match="No Hyperopt loss set. Please use `--hyperopt-loss` to specify "
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"the Hyperopt-Loss class to use."):
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HyperOptLossResolver.load_hyperoptloss(default_conf)
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def test_hyperoptlossresolver(mocker, default_conf) -> None:
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hl = ShortTradeDurHyperOptLoss
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mocker.patch(
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'freqtrade.resolvers.hyperopt_resolver.HyperOptLossResolver.load_object',
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MagicMock(return_value=hl())
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)
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default_conf.update({'hyperopt_loss': 'SharpeHyperOptLossDaily'})
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x = HyperOptLossResolver.load_hyperoptloss(default_conf)
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assert hasattr(x, "hyperopt_loss_function")
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def test_hyperoptlossresolver_wrongname(default_conf) -> None:
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default_conf.update({'hyperopt_loss': "NonExistingLossClass"})
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with pytest.raises(OperationalException, match=r'Impossible to load HyperoptLoss.*'):
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HyperOptLossResolver.load_hyperoptloss(default_conf)
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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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over = hl.hyperopt_loss_function(hyperopt_results, 600 + 100,
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datetime(2019, 1, 1), datetime(2019, 5, 1))
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under = hl.hyperopt_loss_function(hyperopt_results, 600 - 100,
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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_loss_calculation_prefer_shorter_trades(hyperopt_conf, hyperopt_results) -> None:
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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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shorter = hl.hyperopt_loss_function(resultsb, 100,
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datetime(2019, 1, 1), datetime(2019, 5, 1))
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assert shorter < longer
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def test_loss_calculation_has_limited_profit(hyperopt_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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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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over = hl.hyperopt_loss_function(results_over, 600,
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datetime(2019, 1, 1), datetime(2019, 5, 1))
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under = hl.hyperopt_loss_function(results_under, 600,
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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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@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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"MaxDrawDownHyperOptLoss",
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"MaxDrawDownRelativeHyperOptLoss",
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"CalmarHyperOptLoss",
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"ProfitDrawDownHyperOptLoss",
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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 + 0.2
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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 - 0.2
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results_under['profit_ratio'] = hyperopt_results['profit_ratio'] / 2
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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(
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hyperopt_results,
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trade_count=len(hyperopt_results),
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min_date=datetime(2019, 1, 1),
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max_date=datetime(2019, 5, 1),
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config=default_conf,
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processed=None,
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backtest_stats={'profit_total': hyperopt_results['profit_abs'].sum()}
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)
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over = hl.hyperopt_loss_function(
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results_over,
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trade_count=len(results_over),
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min_date=datetime(2019, 1, 1),
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max_date=datetime(2019, 5, 1),
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config=default_conf,
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processed=None,
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backtest_stats={'profit_total': results_over['profit_abs'].sum()}
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)
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under = hl.hyperopt_loss_function(
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results_under,
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trade_count=len(results_under),
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min_date=datetime(2019, 1, 1),
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max_date=datetime(2019, 5, 1),
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config=default_conf,
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processed=None,
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backtest_stats={'profit_total': results_under['profit_abs'].sum()}
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)
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assert over < correct
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assert under > correct
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