# pragma pylint: disable=missing-docstring, C0103
import logging
from shutil import copyfile

import numpy as np
import pandas as pd
import pytest
from pandas.testing import assert_frame_equal

from freqtrade.configuration.timerange import TimeRange
from freqtrade.data.converter import (convert_ohlcv_format, convert_trades_format,
                                      convert_trades_to_ohlcv, ohlcv_fill_up_missing_data,
                                      ohlcv_to_dataframe, reduce_dataframe_footprint,
                                      trades_df_remove_duplicates, trades_dict_to_list,
                                      trades_to_ohlcv, trim_dataframe)
from freqtrade.data.history import (get_timerange, load_data, load_pair_history,
                                    validate_backtest_data)
from freqtrade.data.history.idatahandler import IDataHandler
from freqtrade.enums import CandleType
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
from tests.conftest import generate_test_data, generate_trades_history, log_has, log_has_re
from tests.data.test_history import _clean_test_file


def test_dataframe_correct_columns(dataframe_1m):
    assert dataframe_1m.columns.tolist() == ['date', 'open', 'high', 'low', 'close', 'volume']


def test_ohlcv_to_dataframe(ohlcv_history_list, caplog):
    columns = ['date', 'open', 'high', 'low', 'close', 'volume']

    caplog.set_level(logging.DEBUG)
    # Test file with BV data
    dataframe = ohlcv_to_dataframe(ohlcv_history_list, '5m', pair="UNITTEST/BTC",
                                   fill_missing=True)
    assert dataframe.columns.tolist() == columns
    assert log_has('Converting candle (OHLCV) data to dataframe for pair UNITTEST/BTC.', caplog)


def test_trades_to_ohlcv(trades_history_df, caplog):

    caplog.set_level(logging.DEBUG)
    with pytest.raises(ValueError, match="Trade-list empty."):
        trades_to_ohlcv(pd.DataFrame(columns=trades_history_df.columns), '1m')

    df = trades_to_ohlcv(trades_history_df, '1m')
    assert not df.empty
    assert len(df) == 1
    assert 'open' in df.columns
    assert 'high' in df.columns
    assert 'low' in df.columns
    assert 'close' in df.columns
    assert df.iloc[0, :]['high'] == 0.019627
    assert df.iloc[0, :]['low'] == 0.019626
    assert df.iloc[0, :]['date'] == pd.Timestamp('2019-08-14 15:59:00+0000')

    df_1h = trades_to_ohlcv(trades_history_df, '1h')
    assert len(df_1h) == 1
    assert df_1h.iloc[0, :]['high'] == 0.019627
    assert df_1h.iloc[0, :]['low'] == 0.019626
    assert df_1h.iloc[0, :]['date'] == pd.Timestamp('2019-08-14 15:00:00+0000')

    df_1s = trades_to_ohlcv(trades_history_df, '1s')
    assert len(df_1s) == 2
    assert df_1s.iloc[0, :]['high'] == 0.019627
    assert df_1s.iloc[0, :]['low'] == 0.019627
    assert df_1s.iloc[0, :]['date'] == pd.Timestamp('2019-08-14 15:59:49+0000')
    assert df_1s.iloc[-1, :]['date'] == pd.Timestamp('2019-08-14 15:59:59+0000')


@pytest.mark.parametrize('timeframe,rows,days,candles,start,end,weekday', [
    ('1s', 20_000, 5, 19522, '2020-01-01 00:00:05', '2020-01-05 23:59:27', None),
    ('1m', 20_000, 5, 6745, '2020-01-01 00:00:00', '2020-01-05 23:59:00', None),
    ('5m', 20_000, 5, 1440, '2020-01-01 00:00:00', '2020-01-05 23:55:00', None),
    ('15m', 20_000, 5, 480, '2020-01-01 00:00:00', '2020-01-05 23:45:00', None),
    ('1h', 20_000, 5, 120, '2020-01-01 00:00:00', '2020-01-05 23:00:00', None),
    ('2h', 20_000, 5, 60, '2020-01-01 00:00:00', '2020-01-05 22:00:00', None),
    ('4h', 20_000, 5, 30, '2020-01-01 00:00:00', '2020-01-05 20:00:00', None),
    ('8h', 20_000, 5, 15, '2020-01-01 00:00:00', '2020-01-05 16:00:00', None),
    ('12h', 20_000, 5, 10, '2020-01-01 00:00:00', '2020-01-05 12:00:00', None),
    ('1d', 20_000, 5, 5, '2020-01-01 00:00:00', '2020-01-05 00:00:00', 'Sunday'),
    ('7d', 20_000, 37, 6, '2020-01-06 00:00:00', '2020-02-10 00:00:00', 'Monday'),
    ('1w', 20_000, 37, 6, '2020-01-06 00:00:00', '2020-02-10 00:00:00', 'Monday'),
    ('1M', 20_000, 74, 3, '2020-01-01 00:00:00', '2020-03-01 00:00:00', None),
    ('3M', 20_000, 100, 2, '2020-01-01 00:00:00', '2020-04-01 00:00:00', None),
    ('1y', 20_000, 1000, 3, '2020-01-01 00:00:00', '2022-01-01 00:00:00', None),
])
def test_trades_to_ohlcv_multi(timeframe, rows, days, candles, start, end, weekday):
    trades_history = generate_trades_history(n_rows=rows, days=days)
    df = trades_to_ohlcv(trades_history, timeframe)
    assert not df.empty
    assert len(df) == candles
    assert df.iloc[0, :]['date'] == pd.Timestamp(f'{start}+0000')
    assert df.iloc[-1, :]['date'] == pd.Timestamp(f'{end}+0000')
    if weekday:
        # Weekday is only relevant for daily and weekly candles.
        assert df.iloc[-1, :]['date'].day_name() == weekday


def test_ohlcv_fill_up_missing_data(testdatadir, caplog):
    data = load_pair_history(datadir=testdatadir,
                             timeframe='1m',
                             pair='UNITTEST/BTC',
                             fill_up_missing=False)
    caplog.set_level(logging.DEBUG)
    data2 = ohlcv_fill_up_missing_data(data, '1m', 'UNITTEST/BTC')
    assert len(data2) > len(data)
    # Column names should not change
    assert (data.columns == data2.columns).all()

    assert log_has_re(f"Missing data fillup for UNITTEST/BTC, 1m: before: "
                      f"{len(data)} - after: {len(data2)}.*", caplog)

    # Test fillup actually fixes invalid backtest data
    min_date, max_date = get_timerange({'UNITTEST/BTC': data})
    assert validate_backtest_data(data, 'UNITTEST/BTC', min_date, max_date, 1)
    assert not validate_backtest_data(data2, 'UNITTEST/BTC', min_date, max_date, 1)


def test_ohlcv_fill_up_missing_data2(caplog):
    timeframe = '5m'
    ticks = [
        [
            1511686200000,  # 8:50:00
            8.794e-05,  # open
            8.948e-05,  # high
            8.794e-05,  # low
            8.88e-05,  # close
            2255,  # volume (in quote currency)
        ],
        [
            1511686500000,  # 8:55:00
            8.88e-05,
            8.942e-05,
            8.88e-05,
            8.893e-05,
            9911,
        ],
        [
            1511687100000,  # 9:05:00
            8.891e-05,
            8.893e-05,
            8.875e-05,
            8.877e-05,
            2251
        ],
        [
            1511687400000,  # 9:10:00
            8.877e-05,
            8.883e-05,
            8.895e-05,
            8.817e-05,
            123551
        ]
    ]

    # Generate test-data without filling missing
    data = ohlcv_to_dataframe(ticks, timeframe, pair="UNITTEST/BTC",
                              fill_missing=False)
    assert len(data) == 3
    caplog.set_level(logging.DEBUG)
    data2 = ohlcv_fill_up_missing_data(data, timeframe, "UNITTEST/BTC")
    assert len(data2) == 4
    # 3rd candle has been filled
    row = data2.loc[2, :]
    assert row['volume'] == 0
    # close should match close of previous candle
    assert row['close'] == data.loc[1, 'close']
    assert row['open'] == row['close']
    assert row['high'] == row['close']
    assert row['low'] == row['close']
    # Column names should not change
    assert (data.columns == data2.columns).all()

    assert log_has_re(f"Missing data fillup for UNITTEST/BTC, {timeframe}: before: "
                      f"{len(data)} - after: {len(data2)}.*", caplog)


@pytest.mark.parametrize('timeframe', [
    '1s', '1m', '5m', '15m', '1h', '2h', '4h', '8h', '12h', '1d', '7d', '1w', '1M', '3M', '1y'
])
def test_ohlcv_to_dataframe_multi(timeframe):
    data = generate_test_data(timeframe, 180)
    assert len(data) == 180
    df = ohlcv_to_dataframe(data, timeframe, 'UNITTEST/USDT')
    assert len(df) == len(data) - 1
    df1 = ohlcv_to_dataframe(data, timeframe, 'UNITTEST/USDT', drop_incomplete=False)
    assert len(df1) == len(data)
    assert data.equals(df1)

    data1 = data.copy()
    if timeframe in ('1M', '3M', '1y'):
        data1.loc[:, 'date'] = data1.loc[:, 'date'] + pd.to_timedelta('1w')
    else:
        # Shift by half a timeframe
        data1.loc[:, 'date'] = data1.loc[:, 'date'] + (pd.to_timedelta(timeframe) / 2)
    df2 = ohlcv_to_dataframe(data1, timeframe, 'UNITTEST/USDT')

    assert len(df2) == len(data) - 1
    tfs = timeframe_to_seconds(timeframe)
    tfm = timeframe_to_minutes(timeframe)
    if 1 <= tfm < 10000:
        # minute based resampling does not work on timeframes >= 1 week
        ohlcv_dict = {
            'open': 'first',
            'high': 'max',
            'low': 'min',
            'close': 'last',
            'volume': 'sum'
        }
        dfs = data1.resample(f"{tfs}s", on='date').agg(ohlcv_dict).reset_index(drop=False)
        dfm = data1.resample(f"{tfm}min", on='date').agg(ohlcv_dict).reset_index(drop=False)

        assert dfs.equals(dfm)
        assert dfs.equals(df1)


def test_ohlcv_to_dataframe_1M():

    # Monthly ticks from 2019-09-01 to 2023-07-01
    ticks = [
        [1567296000000, 8042.08, 10475.54, 7700.67, 8041.96, 608742.1109999999],
        [1569888000000, 8285.31, 10408.48, 7172.76, 9150.0, 2439561.887],
        [1572566400000, 9149.88, 9550.0, 6510.19, 7542.93, 4042674.725],
        [1575158400000, 7541.08, 7800.0, 6427.0, 7189.0, 4063882.296],
        [1577836800000, 7189.43, 9599.0, 6863.44, 9364.51, 5165281.358],
        [1580515200000, 9364.5, 10540.0, 8450.0, 8531.98, 4581788.124],
        [1583020800000, 8532.5, 9204.0, 3621.81, 6407.1, 10859497.479],
        [1585699200000, 6407.1, 9479.77, 6140.0, 8624.76, 11276526.968],
        [1588291200000, 8623.61, 10080.0, 7940.0, 9446.43, 12469561.02],
        [1590969600000, 9446.49, 10497.25, 8816.4, 9138.87, 6684044.201],
        [1593561600000, 9138.88, 11488.0, 8900.0, 11343.68, 5709327.926],
        [1596240000000, 11343.67, 12499.42, 10490.0, 11658.11, 6746487.129],
        [1598918400000, 11658.11, 12061.07, 9808.58, 10773.0, 6442697.051],
        [1601510400000, 10773.0, 14140.0, 10371.03, 13783.73, 7404103.004],
        [1604188800000, 13783.73, 19944.0, 13195.0, 19720.0, 12328272.549],
        [1606780800000, 19722.09, 29376.7, 17555.0, 28951.68, 10067314.24],
        [1609459200000, 28948.19, 42125.51, 27800.0, 33126.21, 12408873.079],
        [1612137600000, 33125.11, 58472.14, 32322.47, 45163.36, 8784474.482],
        [1614556800000, 45162.64, 61950.0, 44972.49, 58807.24, 9459821.267],
        [1617235200000, 58810.99, 64986.11, 46930.43, 57684.16, 7895051.389],
        [1619827200000, 57688.29, 59654.0, 28688.0, 37243.38, 16790964.443],
        [1622505600000, 37244.36, 41413.0, 28780.01, 35031.39, 23474519.886],
        [1625097600000, 35031.39, 48168.6, 29242.24, 41448.11, 16932491.175],
        [1627776000000, 41448.1, 50600.0, 37291.0, 47150.32, 13645800.254],
        [1630454400000, 47150.32, 52950.0, 39503.58, 43796.57, 10734742.869],
        [1633046400000, 43799.49, 67150.0, 43260.01, 61348.61, 9111112.847],
        [1635724800000, 61347.14, 69198.7, 53245.0, 56975.0, 7111424.463],
        [1638316800000, 56978.06, 59100.0, 40888.89, 46210.56, 8404449.024],
        [1640995200000, 46210.57, 48000.0, 32853.83, 38439.04, 11047479.277],
        [1643673600000, 38439.04, 45847.5, 34303.7, 43155.0, 10910339.91],
        [1646092800000, 43155.0, 48200.0, 37134.0, 45506.0, 10459721.586],
        [1648771200000, 45505.9, 47448.0, 37550.0, 37614.5, 8463568.862],
        [1651363200000, 37614.4, 40071.7, 26631.0, 31797.8, 14463715.774],
        [1654041600000, 31797.9, 31986.1, 17593.2, 19923.5, 20710810.306],
        [1656633600000, 19923.3, 24700.0, 18780.1, 23290.1, 20582518.513],
        [1659312000000, 23290.1, 25200.0, 19508.0, 20041.5, 17221921.557],
        [1661990400000, 20041.4, 22850.0, 18084.3, 19411.7, 21935261.414],
        [1664582400000, 19411.6, 21088.0, 17917.8, 20482.0, 16625843.584],
        [1667260800000, 20482.1, 21473.7, 15443.2, 17153.3, 18460614.013],
        [1669852800000, 17153.4, 18400.0, 16210.0, 16537.6, 9702408.711],
        [1672531200000, 16537.5, 23962.7, 16488.0, 23119.4, 14732180.645],
        [1675209600000, 23119.5, 25347.6, 21338.0, 23129.6, 15025197.415],
        [1677628800000, 23129.7, 29184.8, 19521.6, 28454.9, 23317458.541],
        [1680307200000, 28454.8, 31059.0, 26919.3, 29223.0, 14654208.219],
        [1682899200000, 29223.0, 29840.0, 25751.0, 27201.1, 13328157.284],
        [1685577600000, 27201.1, 31500.0, 24777.0, 30460.2, 14099299.273],
        [1688169600000, 30460.2, 31850.0, 28830.0, 29338.8, 8760361.377]
    ]

    data = ohlcv_to_dataframe(ticks, '1M', pair="UNITTEST/USDT",
                              fill_missing=False, drop_incomplete=False)
    assert len(data) == len(ticks)
    assert data.iloc[0]['date'].strftime('%Y-%m-%d') == '2019-09-01'
    assert data.iloc[-1]['date'].strftime('%Y-%m-%d') == '2023-07-01'

    # Test with filling missing data
    data = ohlcv_to_dataframe(ticks, '1M', pair="UNITTEST/USDT",
                              fill_missing=True, drop_incomplete=False)
    assert len(data) == len(ticks)
    assert data.iloc[0]['date'].strftime('%Y-%m-%d') == '2019-09-01'
    assert data.iloc[-1]['date'].strftime('%Y-%m-%d') == '2023-07-01'


def test_ohlcv_drop_incomplete(caplog):
    timeframe = '1d'
    ticks = [
        [
            1559750400000,  # 2019-06-04
            8.794e-05,  # open
            8.948e-05,  # high
            8.794e-05,  # low
            8.88e-05,  # close
            2255,  # volume (in quote currency)
        ],
        [
            1559836800000,  # 2019-06-05
            8.88e-05,
            8.942e-05,
            8.88e-05,
            8.893e-05,
            9911,
        ],
        [
            1559923200000,  # 2019-06-06
            8.891e-05,
            8.893e-05,
            8.875e-05,
            8.877e-05,
            2251
        ],
        [
            1560009600000,  # 2019-06-07
            8.877e-05,
            8.883e-05,
            8.895e-05,
            8.817e-05,
            123551
        ]
    ]
    caplog.set_level(logging.DEBUG)
    data = ohlcv_to_dataframe(ticks, timeframe, pair="UNITTEST/BTC",
                              fill_missing=False, drop_incomplete=False)
    assert len(data) == 4
    assert not log_has("Dropping last candle", caplog)

    # Drop last candle
    data = ohlcv_to_dataframe(ticks, timeframe, pair="UNITTEST/BTC",
                              fill_missing=False, drop_incomplete=True)
    assert len(data) == 3

    assert log_has("Dropping last candle", caplog)


def test_trim_dataframe(testdatadir) -> None:
    data = load_data(
        datadir=testdatadir,
        timeframe='1m',
        pairs=['UNITTEST/BTC']
    )['UNITTEST/BTC']
    min_date = int(data.iloc[0]['date'].timestamp())
    max_date = int(data.iloc[-1]['date'].timestamp())
    data_modify = data.copy()

    # Remove first 30 minutes (1800 s)
    tr = TimeRange('date', None, min_date + 1800, 0)
    data_modify = trim_dataframe(data_modify, tr)
    assert not data_modify.equals(data)
    assert len(data_modify) < len(data)
    assert len(data_modify) == len(data) - 30
    assert all(data_modify.iloc[-1] == data.iloc[-1])
    assert all(data_modify.iloc[0] == data.iloc[30])

    data_modify = data.copy()
    tr = TimeRange('date', None, min_date + 1800, 0)
    # Remove first 20 candles - ignores min date
    data_modify = trim_dataframe(data_modify, tr, startup_candles=20)
    assert not data_modify.equals(data)
    assert len(data_modify) < len(data)
    assert len(data_modify) == len(data) - 20
    assert all(data_modify.iloc[-1] == data.iloc[-1])
    assert all(data_modify.iloc[0] == data.iloc[20])

    data_modify = data.copy()
    # Remove last 30 minutes (1800 s)
    tr = TimeRange(None, 'date', 0, max_date - 1800)
    data_modify = trim_dataframe(data_modify, tr)
    assert not data_modify.equals(data)
    assert len(data_modify) < len(data)
    assert len(data_modify) == len(data) - 30
    assert all(data_modify.iloc[0] == data.iloc[0])
    assert all(data_modify.iloc[-1] == data.iloc[-31])

    data_modify = data.copy()
    # Remove first 25 and last 30 minutes (1800 s)
    tr = TimeRange('date', 'date', min_date + 1500, max_date - 1800)
    data_modify = trim_dataframe(data_modify, tr)
    assert not data_modify.equals(data)
    assert len(data_modify) < len(data)
    assert len(data_modify) == len(data) - 55
    # first row matches 25th original row
    assert all(data_modify.iloc[0] == data.iloc[25])


def test_trades_df_remove_duplicates(trades_history_df):
    trades_history1 = pd.concat([trades_history_df, trades_history_df, trades_history_df]
                                ).reset_index(drop=True)
    assert len(trades_history1) == len(trades_history_df) * 3
    res = trades_df_remove_duplicates(trades_history1)
    assert len(res) == len(trades_history_df)
    assert res.equals(trades_history_df)


def test_trades_dict_to_list(fetch_trades_result):
    res = trades_dict_to_list(fetch_trades_result)
    assert isinstance(res, list)
    assert isinstance(res[0], list)
    for i, t in enumerate(res):
        assert t[0] == fetch_trades_result[i]['timestamp']
        assert t[1] == fetch_trades_result[i]['id']
        assert t[2] == fetch_trades_result[i]['type']
        assert t[3] == fetch_trades_result[i]['side']
        assert t[4] == fetch_trades_result[i]['price']
        assert t[5] == fetch_trades_result[i]['amount']
        assert t[6] == fetch_trades_result[i]['cost']


def test_convert_trades_format(default_conf, testdatadir, tmp_path):
    files = [{'old': tmp_path / "XRP_ETH-trades.json.gz",
              'new': tmp_path / "XRP_ETH-trades.json"},
             {'old': tmp_path / "XRP_OLD-trades.json.gz",
              'new': tmp_path / "XRP_OLD-trades.json"},
             ]
    for file in files:
        copyfile(testdatadir / file['old'].name, file['old'])
        assert not file['new'].exists()

    default_conf['datadir'] = tmp_path

    convert_trades_format(default_conf, convert_from='jsongz',
                          convert_to='json', erase=False)

    for file in files:
        assert file['new'].exists()
        assert file['old'].exists()

        # Remove original file
        file['old'].unlink()
    # Convert back
    convert_trades_format(default_conf, convert_from='json',
                          convert_to='jsongz', erase=True)
    for file in files:
        assert file['old'].exists()
        assert not file['new'].exists()

        _clean_test_file(file['old'])
        if file['new'].exists():
            file['new'].unlink()


@pytest.mark.parametrize('file_base,candletype', [
    (['XRP_ETH-5m', 'XRP_ETH-1m'], CandleType.SPOT),
    (['UNITTEST_USDT_USDT-1h-mark', 'XRP_USDT_USDT-1h-mark'], CandleType.MARK),
    (['XRP_USDT_USDT-1h-futures'], CandleType.FUTURES),
])
def test_convert_ohlcv_format(default_conf, testdatadir, tmp_path, file_base, candletype):
    prependix = '' if candletype == CandleType.SPOT else 'futures/'
    files_orig = []
    files_temp = []
    files_new = []
    for file in file_base:
        file_orig = testdatadir / f"{prependix}{file}.feather"
        file_temp = tmp_path / f"{prependix}{file}.feather"
        file_new = tmp_path / f"{prependix}{file}.json.gz"
        IDataHandler.create_dir_if_needed(file_temp)
        copyfile(file_orig, file_temp)

        files_orig.append(file_orig)
        files_temp.append(file_temp)
        files_new.append(file_new)

    default_conf['datadir'] = tmp_path
    default_conf['candle_types'] = [candletype]

    if candletype == CandleType.SPOT:
        default_conf['pairs'] = ['XRP/ETH', 'XRP/USDT', 'UNITTEST/USDT']
    else:
        default_conf['pairs'] = ['XRP/ETH:ETH', 'XRP/USDT:USDT', 'UNITTEST/USDT:USDT']
    default_conf['timeframes'] = ['1m', '5m', '1h']

    assert not file_new.exists()

    convert_ohlcv_format(
        default_conf,
        convert_from='feather',
        convert_to='jsongz',
        erase=False,
    )
    for file in (files_temp + files_new):
        assert file.exists()

    # Remove original files
    for file in (files_temp):
        file.unlink()
    # Convert back
    convert_ohlcv_format(
        default_conf,
        convert_from='jsongz',
        convert_to='feather',
        erase=True,
    )
    for file in (files_temp):
        assert file.exists()
    for file in (files_new):
        assert not file.exists()


def test_reduce_dataframe_footprint():
    data = generate_test_data('15m', 40)

    data['open_copy'] = data['open']
    data['close_copy'] = data['close']
    data['close_copy'] = data['close']

    assert data['open'].dtype == np.float64
    assert data['open_copy'].dtype == np.float64
    assert data['close_copy'].dtype == np.float64

    df2 = reduce_dataframe_footprint(data)

    # Does not modify original dataframe
    assert data['open'].dtype == np.float64
    assert data['open_copy'].dtype == np.float64
    assert data['close_copy'].dtype == np.float64

    # skips ohlcv columns
    assert df2['open'].dtype == np.float64
    assert df2['high'].dtype == np.float64
    assert df2['low'].dtype == np.float64
    assert df2['close'].dtype == np.float64
    assert df2['volume'].dtype == np.float64

    # Changes dtype of returned dataframe
    assert df2['open_copy'].dtype == np.float32
    assert df2['close_copy'].dtype == np.float32


def test_convert_trades_to_ohlcv(testdatadir, tmp_path, caplog):
    pair = 'XRP/ETH'
    file1 = tmp_path / 'XRP_ETH-1m.feather'
    file5 = tmp_path / 'XRP_ETH-5m.feather'
    filetrades = tmp_path / 'XRP_ETH-trades.json.gz'
    copyfile(testdatadir / file1.name, file1)
    copyfile(testdatadir / file5.name, file5)
    copyfile(testdatadir / filetrades.name, filetrades)

    # Compare downloaded dataset with converted dataset
    dfbak_1m = load_pair_history(datadir=tmp_path, timeframe="1m", pair=pair)
    dfbak_5m = load_pair_history(datadir=tmp_path, timeframe="5m", pair=pair)

    tr = TimeRange.parse_timerange('20191011-20191012')

    convert_trades_to_ohlcv([pair], timeframes=['1m', '5m'],
                            data_format_trades='jsongz',
                            datadir=tmp_path, timerange=tr, erase=True)

    assert log_has("Deleting existing data for pair XRP/ETH, interval 1m.", caplog)
    # Load new data
    df_1m = load_pair_history(datadir=tmp_path, timeframe="1m", pair=pair)
    df_5m = load_pair_history(datadir=tmp_path, timeframe="5m", pair=pair)

    assert_frame_equal(dfbak_1m, df_1m, check_exact=True)
    assert_frame_equal(dfbak_5m, df_5m, check_exact=True)
    msg = 'Could not convert NoDatapair to OHLCV.'
    assert not log_has(msg, caplog)

    convert_trades_to_ohlcv(['NoDatapair'], timeframes=['1m', '5m'],
                            data_format_trades='jsongz',
                            datadir=tmp_path, timerange=tr, erase=True)
    assert log_has(msg, caplog)