161 lines
5.8 KiB
Python
161 lines
5.8 KiB
Python
import shutil
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from datetime import datetime, timedelta, timezone
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from pathlib import Path
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import pytest
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from freqtrade.exceptions import OperationalException
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from tests.conftest import log_has_re
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from tests.freqai.conftest import (get_patched_data_kitchen, make_data_dictionary,
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make_unfiltered_dataframe)
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@pytest.mark.parametrize(
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"timerange, train_period_days, expected_result",
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[
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("20220101-20220201", 30, "20211202-20220201"),
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("20220301-20220401", 15, "20220214-20220401"),
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],
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)
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def test_create_fulltimerange(
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timerange, train_period_days, expected_result, freqai_conf, mocker, caplog
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):
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dk = get_patched_data_kitchen(mocker, freqai_conf)
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assert dk.create_fulltimerange(timerange, train_period_days) == expected_result
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shutil.rmtree(Path(dk.full_path))
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def test_create_fulltimerange_incorrect_backtest_period(mocker, freqai_conf):
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dk = get_patched_data_kitchen(mocker, freqai_conf)
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with pytest.raises(OperationalException, match=r"backtest_period_days must be an integer"):
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dk.create_fulltimerange("20220101-20220201", 0.5)
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with pytest.raises(OperationalException, match=r"backtest_period_days must be positive"):
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dk.create_fulltimerange("20220101-20220201", -1)
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shutil.rmtree(Path(dk.full_path))
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@pytest.mark.parametrize(
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"timerange, train_period_days, backtest_period_days, expected_result",
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[
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("20220101-20220201", 30, 7, 9),
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("20220101-20220201", 30, 0.5, 120),
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("20220101-20220201", 10, 1, 80),
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],
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)
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def test_split_timerange(
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mocker, freqai_conf, timerange, train_period_days, backtest_period_days, expected_result
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):
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freqai_conf.update({"timerange": "20220101-20220401"})
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dk = get_patched_data_kitchen(mocker, freqai_conf)
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tr_list, bt_list = dk.split_timerange(timerange, train_period_days, backtest_period_days)
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assert len(tr_list) == len(bt_list) == expected_result
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with pytest.raises(
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OperationalException, match=r"train_period_days must be an integer greater than 0."
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):
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dk.split_timerange("20220101-20220201", -1, 0.5)
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shutil.rmtree(Path(dk.full_path))
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def test_check_if_model_expired(mocker, freqai_conf):
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dk = get_patched_data_kitchen(mocker, freqai_conf)
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now = datetime.now(tz=timezone.utc).timestamp()
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assert dk.check_if_model_expired(now) is False
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now = (datetime.now(tz=timezone.utc) - timedelta(hours=2)).timestamp()
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assert dk.check_if_model_expired(now) is True
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shutil.rmtree(Path(dk.full_path))
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def test_use_DBSCAN_to_remove_outliers(mocker, freqai_conf, caplog):
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freqai = make_data_dictionary(mocker, freqai_conf)
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# freqai_conf['freqai']['feature_parameters'].update({"outlier_protection_percentage": 1})
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freqai.dk.use_DBSCAN_to_remove_outliers(predict=False)
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assert log_has_re(r"DBSCAN found eps of 2\.3\d\.", caplog)
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def test_compute_distances(mocker, freqai_conf):
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freqai = make_data_dictionary(mocker, freqai_conf)
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freqai_conf['freqai']['feature_parameters'].update({"DI_threshold": 1})
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avg_mean_dist = freqai.dk.compute_distances()
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assert round(avg_mean_dist, 2) == 1.99
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def test_use_SVM_to_remove_outliers_and_outlier_protection(mocker, freqai_conf, caplog):
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freqai = make_data_dictionary(mocker, freqai_conf)
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freqai_conf['freqai']['feature_parameters'].update({"outlier_protection_percentage": 0.1})
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freqai.dk.use_SVM_to_remove_outliers(predict=False)
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assert log_has_re(
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"SVM detected 8.66%",
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caplog,
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)
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def test_compute_inlier_metric(mocker, freqai_conf, caplog):
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freqai = make_data_dictionary(mocker, freqai_conf)
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freqai_conf['freqai']['feature_parameters'].update({"inlier_metric_window": 10})
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freqai.dk.compute_inlier_metric(set_='train')
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assert log_has_re(
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"Inlier metric computed and added to features.",
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caplog,
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)
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def test_add_noise_to_training_features(mocker, freqai_conf):
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freqai = make_data_dictionary(mocker, freqai_conf)
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freqai_conf['freqai']['feature_parameters'].update({"noise_standard_deviation": 0.1})
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freqai.dk.add_noise_to_training_features()
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def test_remove_beginning_points_from_data_dict(mocker, freqai_conf):
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freqai = make_data_dictionary(mocker, freqai_conf)
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freqai.dk.remove_beginning_points_from_data_dict(set_='train')
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def test_principal_component_analysis(mocker, freqai_conf, caplog):
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freqai = make_data_dictionary(mocker, freqai_conf)
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freqai.dk.principal_component_analysis()
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assert log_has_re(
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"reduced feature dimension by",
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caplog,
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)
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def test_normalize_data(mocker, freqai_conf):
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freqai = make_data_dictionary(mocker, freqai_conf)
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data_dict = freqai.dk.data_dictionary
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freqai.dk.normalize_data(data_dict)
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assert len(freqai.dk.data) == 32
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def test_filter_features(mocker, freqai_conf):
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freqai, unfiltered_dataframe = make_unfiltered_dataframe(mocker, freqai_conf)
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freqai.dk.find_features(unfiltered_dataframe)
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filtered_df, labels = freqai.dk.filter_features(
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unfiltered_dataframe,
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freqai.dk.training_features_list,
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freqai.dk.label_list,
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training_filter=True,
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)
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assert len(filtered_df.columns) == 14
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def test_make_train_test_datasets(mocker, freqai_conf):
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freqai, unfiltered_dataframe = make_unfiltered_dataframe(mocker, freqai_conf)
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freqai.dk.find_features(unfiltered_dataframe)
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features_filtered, labels_filtered = freqai.dk.filter_features(
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unfiltered_dataframe,
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freqai.dk.training_features_list,
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freqai.dk.label_list,
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training_filter=True,
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)
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data_dictionary = freqai.dk.make_train_test_datasets(features_filtered, labels_filtered)
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assert data_dictionary
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assert len(data_dictionary) == 7
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assert len(data_dictionary['train_features'].index) == 1916
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