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test_hyperoptloss.py
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from datetime import datetime
from unittest.mock import MagicMock
import pytest
from freqtrade.exceptions import OperationalException
from freqtrade.optimize.hyperopt_loss.hyperopt_loss_short_trade_dur import ShortTradeDurHyperOptLoss
from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver
def test_hyperoptlossresolver_noname(default_conf):
with pytest.raises(OperationalException,
match="No Hyperopt loss set. Please use `--hyperopt-loss` to specify "
"the Hyperopt-Loss class to use."):
HyperOptLossResolver.load_hyperoptloss(default_conf)
def test_hyperoptlossresolver(mocker, default_conf) -> None:
hl = ShortTradeDurHyperOptLoss
mocker.patch(
'freqtrade.resolvers.hyperopt_resolver.HyperOptLossResolver.load_object',
MagicMock(return_value=hl())
)
default_conf.update({'hyperopt_loss': 'SharpeHyperOptLossDaily'})
x = HyperOptLossResolver.load_hyperoptloss(default_conf)
assert hasattr(x, "hyperopt_loss_function")
def test_hyperoptlossresolver_wrongname(default_conf) -> None:
default_conf.update({'hyperopt_loss': "NonExistingLossClass"})
with pytest.raises(OperationalException, match=r'Impossible to load HyperoptLoss.*'):
HyperOptLossResolver.load_hyperoptloss(default_conf)
def test_loss_calculation_prefer_correct_trade_count(hyperopt_conf, hyperopt_results) -> None:
hyperopt_conf.update({'hyperopt_loss': "ShortTradeDurHyperOptLoss"})
hl = HyperOptLossResolver.load_hyperoptloss(hyperopt_conf)
correct = hl.hyperopt_loss_function(hyperopt_results, 600,
datetime(2019, 1, 1), datetime(2019, 5, 1))
over = hl.hyperopt_loss_function(hyperopt_results, 600 + 100,
datetime(2019, 1, 1), datetime(2019, 5, 1))
under = hl.hyperopt_loss_function(hyperopt_results, 600 - 100,
datetime(2019, 1, 1), datetime(2019, 5, 1))
assert over > correct
assert under > correct
def test_loss_calculation_prefer_shorter_trades(hyperopt_conf, hyperopt_results) -> None:
resultsb = hyperopt_results.copy()
resultsb.loc[1, 'trade_duration'] = 20
hyperopt_conf.update({'hyperopt_loss': "ShortTradeDurHyperOptLoss"})
hl = HyperOptLossResolver.load_hyperoptloss(hyperopt_conf)
longer = hl.hyperopt_loss_function(hyperopt_results, 100,
datetime(2019, 1, 1), datetime(2019, 5, 1))
shorter = hl.hyperopt_loss_function(resultsb, 100,
datetime(2019, 1, 1), datetime(2019, 5, 1))
assert shorter < longer
def test_loss_calculation_has_limited_profit(hyperopt_conf, hyperopt_results) -> None:
results_over = hyperopt_results.copy()
results_over['profit_ratio'] = hyperopt_results['profit_ratio'] * 2
results_under = hyperopt_results.copy()
results_under['profit_ratio'] = hyperopt_results['profit_ratio'] / 2
hyperopt_conf.update({'hyperopt_loss': "ShortTradeDurHyperOptLoss"})
hl = HyperOptLossResolver.load_hyperoptloss(hyperopt_conf)
correct = hl.hyperopt_loss_function(hyperopt_results, 600,
datetime(2019, 1, 1), datetime(2019, 5, 1))
over = hl.hyperopt_loss_function(results_over, 600,
datetime(2019, 1, 1), datetime(2019, 5, 1))
under = hl.hyperopt_loss_function(results_under, 600,
datetime(2019, 1, 1), datetime(2019, 5, 1))
assert over < correct
assert under > correct
@pytest.mark.parametrize('lossfunction', [
"OnlyProfitHyperOptLoss",
"SortinoHyperOptLoss",
"SortinoHyperOptLossDaily",
"SharpeHyperOptLoss",
"SharpeHyperOptLossDaily",
"MaxDrawDownHyperOptLoss",
"MaxDrawDownRelativeHyperOptLoss",
"CalmarHyperOptLoss",
"ProfitDrawDownHyperOptLoss",
])
def test_loss_functions_better_profits(default_conf, hyperopt_results, lossfunction) -> None:
results_over = hyperopt_results.copy()
results_over['profit_abs'] = hyperopt_results['profit_abs'] * 2 + 0.2
results_over['profit_ratio'] = hyperopt_results['profit_ratio'] * 2
results_under = hyperopt_results.copy()
results_under['profit_abs'] = hyperopt_results['profit_abs'] / 2 - 0.2
results_under['profit_ratio'] = hyperopt_results['profit_ratio'] / 2
default_conf.update({'hyperopt_loss': lossfunction})
hl = HyperOptLossResolver.load_hyperoptloss(default_conf)
correct = hl.hyperopt_loss_function(
hyperopt_results,
trade_count=len(hyperopt_results),
min_date=datetime(2019, 1, 1),
max_date=datetime(2019, 5, 1),
config=default_conf,
processed=None,
backtest_stats={'profit_total': hyperopt_results['profit_abs'].sum()}
)
over = hl.hyperopt_loss_function(
results_over,
trade_count=len(results_over),
min_date=datetime(2019, 1, 1),
max_date=datetime(2019, 5, 1),
config=default_conf,
processed=None,
backtest_stats={'profit_total': results_over['profit_abs'].sum()}
)
under = hl.hyperopt_loss_function(
results_under,
trade_count=len(results_under),
min_date=datetime(2019, 1, 1),
max_date=datetime(2019, 5, 1),
config=default_conf,
processed=None,
backtest_stats={'profit_total': results_under['profit_abs'].sum()}
)
assert over < correct
assert under > correct