161 lines
5.8 KiB
Python
161 lines
5.8 KiB
Python
import shutil
|
|
from datetime import datetime, timedelta, timezone
|
|
from pathlib import Path
|
|
|
|
import pytest
|
|
|
|
from freqtrade.exceptions import OperationalException
|
|
from tests.conftest import log_has_re
|
|
from tests.freqai.conftest import (get_patched_data_kitchen, make_data_dictionary,
|
|
make_unfiltered_dataframe)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"timerange, train_period_days, expected_result",
|
|
[
|
|
("20220101-20220201", 30, "20211202-20220201"),
|
|
("20220301-20220401", 15, "20220214-20220401"),
|
|
],
|
|
)
|
|
def test_create_fulltimerange(
|
|
timerange, train_period_days, expected_result, freqai_conf, mocker, caplog
|
|
):
|
|
dk = get_patched_data_kitchen(mocker, freqai_conf)
|
|
assert dk.create_fulltimerange(timerange, train_period_days) == expected_result
|
|
shutil.rmtree(Path(dk.full_path))
|
|
|
|
|
|
def test_create_fulltimerange_incorrect_backtest_period(mocker, freqai_conf):
|
|
dk = get_patched_data_kitchen(mocker, freqai_conf)
|
|
with pytest.raises(OperationalException, match=r"backtest_period_days must be an integer"):
|
|
dk.create_fulltimerange("20220101-20220201", 0.5)
|
|
with pytest.raises(OperationalException, match=r"backtest_period_days must be positive"):
|
|
dk.create_fulltimerange("20220101-20220201", -1)
|
|
shutil.rmtree(Path(dk.full_path))
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"timerange, train_period_days, backtest_period_days, expected_result",
|
|
[
|
|
("20220101-20220201", 30, 7, 9),
|
|
("20220101-20220201", 30, 0.5, 120),
|
|
("20220101-20220201", 10, 1, 80),
|
|
],
|
|
)
|
|
def test_split_timerange(
|
|
mocker, freqai_conf, timerange, train_period_days, backtest_period_days, expected_result
|
|
):
|
|
freqai_conf.update({"timerange": "20220101-20220401"})
|
|
dk = get_patched_data_kitchen(mocker, freqai_conf)
|
|
tr_list, bt_list = dk.split_timerange(timerange, train_period_days, backtest_period_days)
|
|
assert len(tr_list) == len(bt_list) == expected_result
|
|
|
|
with pytest.raises(
|
|
OperationalException, match=r"train_period_days must be an integer greater than 0."
|
|
):
|
|
dk.split_timerange("20220101-20220201", -1, 0.5)
|
|
shutil.rmtree(Path(dk.full_path))
|
|
|
|
|
|
def test_check_if_model_expired(mocker, freqai_conf):
|
|
|
|
dk = get_patched_data_kitchen(mocker, freqai_conf)
|
|
now = datetime.now(tz=timezone.utc).timestamp()
|
|
assert dk.check_if_model_expired(now) is False
|
|
now = (datetime.now(tz=timezone.utc) - timedelta(hours=2)).timestamp()
|
|
assert dk.check_if_model_expired(now) is True
|
|
shutil.rmtree(Path(dk.full_path))
|
|
|
|
|
|
def test_use_DBSCAN_to_remove_outliers(mocker, freqai_conf, caplog):
|
|
freqai = make_data_dictionary(mocker, freqai_conf)
|
|
# freqai_conf['freqai']['feature_parameters'].update({"outlier_protection_percentage": 1})
|
|
freqai.dk.use_DBSCAN_to_remove_outliers(predict=False)
|
|
assert log_has_re(r"DBSCAN found eps of 1\.7\d\.", caplog)
|
|
|
|
|
|
def test_compute_distances(mocker, freqai_conf):
|
|
freqai = make_data_dictionary(mocker, freqai_conf)
|
|
freqai_conf['freqai']['feature_parameters'].update({"DI_threshold": 1})
|
|
avg_mean_dist = freqai.dk.compute_distances()
|
|
assert round(avg_mean_dist, 2) == 1.99
|
|
|
|
|
|
def test_use_SVM_to_remove_outliers_and_outlier_protection(mocker, freqai_conf, caplog):
|
|
freqai = make_data_dictionary(mocker, freqai_conf)
|
|
freqai_conf['freqai']['feature_parameters'].update({"outlier_protection_percentage": 0.1})
|
|
freqai.dk.use_SVM_to_remove_outliers(predict=False)
|
|
assert log_has_re(
|
|
"SVM detected 7.36%",
|
|
caplog,
|
|
)
|
|
|
|
|
|
def test_compute_inlier_metric(mocker, freqai_conf, caplog):
|
|
freqai = make_data_dictionary(mocker, freqai_conf)
|
|
freqai_conf['freqai']['feature_parameters'].update({"inlier_metric_window": 10})
|
|
freqai.dk.compute_inlier_metric(set_='train')
|
|
assert log_has_re(
|
|
"Inlier metric computed and added to features.",
|
|
caplog,
|
|
)
|
|
|
|
|
|
def test_add_noise_to_training_features(mocker, freqai_conf):
|
|
freqai = make_data_dictionary(mocker, freqai_conf)
|
|
freqai_conf['freqai']['feature_parameters'].update({"noise_standard_deviation": 0.1})
|
|
freqai.dk.add_noise_to_training_features()
|
|
|
|
|
|
def test_remove_beginning_points_from_data_dict(mocker, freqai_conf):
|
|
freqai = make_data_dictionary(mocker, freqai_conf)
|
|
freqai.dk.remove_beginning_points_from_data_dict(set_='train')
|
|
|
|
|
|
def test_principal_component_analysis(mocker, freqai_conf, caplog):
|
|
freqai = make_data_dictionary(mocker, freqai_conf)
|
|
freqai.dk.principal_component_analysis()
|
|
assert log_has_re(
|
|
"reduced feature dimension by",
|
|
caplog,
|
|
)
|
|
|
|
|
|
def test_normalize_data(mocker, freqai_conf):
|
|
freqai = make_data_dictionary(mocker, freqai_conf)
|
|
data_dict = freqai.dk.data_dictionary
|
|
freqai.dk.normalize_data(data_dict)
|
|
assert len(freqai.dk.data) == 32
|
|
|
|
|
|
def test_filter_features(mocker, freqai_conf):
|
|
freqai, unfiltered_dataframe = make_unfiltered_dataframe(mocker, freqai_conf)
|
|
freqai.dk.find_features(unfiltered_dataframe)
|
|
|
|
filtered_df, labels = freqai.dk.filter_features(
|
|
unfiltered_dataframe,
|
|
freqai.dk.training_features_list,
|
|
freqai.dk.label_list,
|
|
training_filter=True,
|
|
)
|
|
|
|
assert len(filtered_df.columns) == 14
|
|
|
|
|
|
def test_make_train_test_datasets(mocker, freqai_conf):
|
|
freqai, unfiltered_dataframe = make_unfiltered_dataframe(mocker, freqai_conf)
|
|
freqai.dk.find_features(unfiltered_dataframe)
|
|
|
|
features_filtered, labels_filtered = freqai.dk.filter_features(
|
|
unfiltered_dataframe,
|
|
freqai.dk.training_features_list,
|
|
freqai.dk.label_list,
|
|
training_filter=True,
|
|
)
|
|
|
|
data_dictionary = freqai.dk.make_train_test_datasets(features_filtered, labels_filtered)
|
|
|
|
assert data_dictionary
|
|
assert len(data_dictionary) == 7
|
|
assert len(data_dictionary['train_features'].index) == 1916
|