120 lines
5.5 KiB
Python
120 lines
5.5 KiB
Python
from datetime import datetime
|
|
from unittest.mock import MagicMock
|
|
|
|
import pytest
|
|
|
|
from freqtrade.exceptions import OperationalException
|
|
from freqtrade.optimize.hyperopt_loss_short_trade_dur import ShortTradeDurHyperOptLoss
|
|
from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver
|
|
from tests.strategy.test_strategy_helpers import generate_test_data
|
|
|
|
|
|
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, needsdata', [
|
|
("OnlyProfitHyperOptLoss", False),
|
|
("SortinoHyperOptLoss", False),
|
|
("SortinoHyperOptLossDaily", False),
|
|
("SharpeHyperOptLoss", False),
|
|
("SharpeHyperOptLossDaily", False),
|
|
("SortinoLossBalance", True),
|
|
])
|
|
def test_loss_functions_better_profits(default_conf, hyperopt_results,
|
|
lossfunction, needsdata) -> None:
|
|
results_over = hyperopt_results.copy()
|
|
results_over['profit_abs'] = hyperopt_results['profit_abs'] * 2
|
|
results_over['profit_ratio'] = hyperopt_results['profit_ratio'] * 2
|
|
results_under = hyperopt_results.copy()
|
|
results_under['profit_abs'] = hyperopt_results['profit_abs'] / 2
|
|
results_under['profit_ratio'] = hyperopt_results['profit_ratio'] / 2
|
|
|
|
if needsdata:
|
|
data = {'ETH/USDT': generate_test_data('5m', 1200, start='2019-01-01')}
|
|
else:
|
|
data = {}
|
|
|
|
default_conf.update({'hyperopt_loss': lossfunction})
|
|
hl = HyperOptLossResolver.load_hyperoptloss(default_conf)
|
|
correct = hl.hyperopt_loss_function(hyperopt_results, len(hyperopt_results),
|
|
datetime(2019, 1, 1), datetime(2019, 5, 1),
|
|
config=default_conf, processed=data,
|
|
)
|
|
over = hl.hyperopt_loss_function(results_over, len(results_over),
|
|
datetime(2019, 1, 1), datetime(2019, 5, 1),
|
|
config=default_conf, processed=data,
|
|
)
|
|
under = hl.hyperopt_loss_function(results_under, len(results_under),
|
|
datetime(2019, 1, 1), datetime(2019, 5, 1),
|
|
config=default_conf, processed=data,
|
|
)
|
|
assert over < correct
|
|
assert under > correct
|