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start transition toward outsourcing the data pipeline with objective of improving pipeline flexibility

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robcaulk
2023-05-26 18:40:14 +02:00
parent c23a045de4
commit 31e19add27
8 changed files with 579 additions and 586 deletions
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96
tests/freqai/test_freqai_datakitchen.py
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@@ -9,9 +9,9 @@ from freqtrade.configuration import TimeRange
from freqtrade.data.dataprovider import DataProvider
from freqtrade.exceptions import OperationalException
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from tests.conftest import get_patched_exchange, log_has_re
from tests.conftest import get_patched_exchange # , log_has_re
from tests.freqai.conftest import (get_patched_data_kitchen, get_patched_freqai_strategy,
make_data_dictionary, make_unfiltered_dataframe)
make_unfiltered_dataframe) # make_data_dictionary,
from tests.freqai.test_freqai_interface import is_mac
@@ -72,66 +72,66 @@ def test_check_if_model_expired(mocker, freqai_conf):
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_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) == 1.98
# 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) == 1.98
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 7.83%",
caplog,
)
# 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 7.83%",
# 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_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_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_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_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 any('_max' in entry for entry in freqai.dk.data.keys())
assert any('_min' in entry for entry in freqai.dk.data.keys())
# 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 any('_max' in entry for entry in freqai.dk.data.keys())
# assert any('_min' in entry for entry in freqai.dk.data.keys())
def test_filter_features(mocker, freqai_conf):