import shutil
from datetime import datetime, timedelta, timezone
from pathlib import Path
from unittest.mock import MagicMock

import pytest

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.freqai.conftest import (get_patched_data_kitchen, get_patched_freqai_strategy,
                                   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) == 1.98


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_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 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):
    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


@pytest.mark.parametrize('model', [
    'LightGBMRegressor'
    ])
def test_get_full_model_path(mocker, freqai_conf, model):
    freqai_conf.update({"freqaimodel": model})
    freqai_conf.update({"timerange": "20180110-20180130"})
    freqai_conf.update({"strategy": "freqai_test_strat"})

    strategy = get_patched_freqai_strategy(mocker, freqai_conf)
    exchange = get_patched_exchange(mocker, freqai_conf)
    strategy.dp = DataProvider(freqai_conf, exchange)
    strategy.freqai_info = freqai_conf.get("freqai", {})
    freqai = strategy.freqai
    freqai.live = True
    freqai.dk = FreqaiDataKitchen(freqai_conf)
    timerange = TimeRange.parse_timerange("20180110-20180130")
    freqai.dd.load_all_pair_histories(timerange, freqai.dk)

    freqai.dd.pair_dict = MagicMock()

    data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
    new_timerange = TimeRange.parse_timerange("20180120-20180130")

    freqai.extract_data_and_train_model(
        new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)

    model_path = freqai.dk.get_full_models_path(freqai_conf)
    assert model_path.is_dir() is True