adelorenzo/stable
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Merge branch 'develop' into align_userdata

Browse Source
This commit is contained in:
Matthias
2019-08-01 19:33:45 +02:00
parent 2a141af42e 4c005e7086
commit bcccdda7c0
13 changed files with 160 additions and 116 deletions
Download Patch File Download Diff File Expand all files Collapse all files

42
freqtrade/tests/optimize/test_hyperopt.py
View File

@@ -9,7 +9,7 @@ from arrow import Arrow
from filelock import Timeout
from pathlib import Path
from freqtrade import DependencyException
from freqtrade import DependencyException, OperationalException
from freqtrade.data.converter import parse_ticker_dataframe
from freqtrade.data.history import load_tickerdata_file
from freqtrade.optimize import setup_configuration, start_hyperopt
@@ -189,6 +189,13 @@ def test_hyperoptresolver(mocker, default_conf, caplog) -> None:
assert hasattr(x, "ticker_interval")
def test_hyperoptresolver_wrongname(mocker, default_conf, caplog) -> None:
default_conf.update({'hyperopt': "NonExistingHyperoptClass"})
with pytest.raises(OperationalException, match=r'Impossible to load Hyperopt.*'):
HyperOptResolver(default_conf, ).hyperopt
def test_hyperoptlossresolver(mocker, default_conf, caplog) -> None:
hl = DefaultHyperOptLoss
@@ -196,9 +203,15 @@ def test_hyperoptlossresolver(mocker, default_conf, caplog) -> None:
'freqtrade.resolvers.hyperopt_resolver.HyperOptLossResolver._load_hyperoptloss',
MagicMock(return_value=hl)
)
x = HyperOptResolver(default_conf, ).hyperopt
assert hasattr(x, "populate_indicators")
assert hasattr(x, "ticker_interval")
x = HyperOptLossResolver(default_conf, ).hyperoptloss
assert hasattr(x, "hyperopt_loss_function")
def test_hyperoptlossresolver_wrongname(mocker, default_conf, caplog) -> None:
default_conf.update({'hyperopt_loss': "NonExistingLossClass"})
with pytest.raises(OperationalException, match=r'Impossible to load HyperoptLoss.*'):
HyperOptLossResolver(default_conf, ).hyperopt
def test_start(mocker, default_conf, caplog) -> None:
@@ -360,13 +373,13 @@ def test_onlyprofit_loss_prefers_higher_profits(default_conf, hyperopt_results)
def test_log_results_if_loss_improves(hyperopt, capsys) -> None:
hyperopt.current_best_loss = 2
hyperopt.total_epochs = 2
hyperopt.log_results(
{
'loss': 1,
'current_tries': 1,
'total_tries': 2,
'result': 'foo.',
'initial_point': False
'current_epoch': 1,
'results_explanation': 'foo.',
'is_initial_point': False
}
)
out, err = capsys.readouterr()
@@ -423,7 +436,7 @@ def test_roi_table_generation(hyperopt) -> None:
assert hyperopt.custom_hyperopt.generate_roi_table(params) == {0: 6, 15: 3, 25: 1, 30: 0}
def test_start_calls_optimizer(mocker, default_conf, caplog) -> None:
def test_start_calls_optimizer(mocker, default_conf, caplog, capsys) -> None:
dumper = mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock())
mocker.patch('freqtrade.optimize.hyperopt.load_data', MagicMock())
mocker.patch(
@@ -433,7 +446,7 @@ def test_start_calls_optimizer(mocker, default_conf, caplog) -> None:
parallel = mocker.patch(
'freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel',
MagicMock(return_value=[{'loss': 1, 'result': 'foo result', 'params': {}}])
MagicMock(return_value=[{'loss': 1, 'results_explanation': 'foo result', 'params': {}}])
)
patch_exchange(mocker)
@@ -447,8 +460,11 @@ def test_start_calls_optimizer(mocker, default_conf, caplog) -> None:
hyperopt.strategy.tickerdata_to_dataframe = MagicMock()
hyperopt.start()
parallel.assert_called_once()
assert log_has('Best result:\nfoo result\nwith values:\n', caplog.record_tuples)
out, err = capsys.readouterr()
assert 'Best result:\n* 1/1: foo result Objective: 1.00000\nwith values:\n' in out
assert dumper.called
# Should be called twice, once for tickerdata, once to save evaluations
assert dumper.call_count == 2
@@ -588,8 +604,8 @@ def test_generate_optimizer(mocker, default_conf) -> None:
}
response_expected = {
'loss': 1.9840569076926293,
'result': ' 1 trades. Avg profit 2.31%. Total profit 0.00023300 BTC '
'( 2.31Σ%). Avg duration 100.0 mins.',
'results_explanation': ' 1 trades. Avg profit 2.31%. Total profit 0.00023300 BTC '
'( 2.31Σ%). Avg duration 100.0 mins.',
'params': optimizer_param
}