stable/tests/freqai/test_freqai_datakitchen.py

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(
        "DBSCAN found eps of 1.75.",
        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