stable/tests/optimize/test_backtesting.py

# pragma pylint: disable=missing-docstring, W0212, line-too-long, C0103, unused-argument

import random
from copy import deepcopy
from datetime import datetime, timedelta, timezone
from pathlib import Path
from unittest.mock import MagicMock, PropertyMock

import numpy as np
import pandas as pd
import pytest
from arrow import Arrow

from freqtrade import constants
from freqtrade.commands.optimize_commands import setup_optimize_configuration, start_backtesting
from freqtrade.configuration import TimeRange
from freqtrade.data import history
from freqtrade.data.btanalysis import BT_DATA_COLUMNS, evaluate_result_multi
from freqtrade.data.converter import clean_ohlcv_dataframe
from freqtrade.data.dataprovider import DataProvider
from freqtrade.data.history import get_timerange
from freqtrade.enums import CandleType, ExitType, RunMode
from freqtrade.exceptions import DependencyException, OperationalException
from freqtrade.exchange.exchange import timeframe_to_next_date
from freqtrade.optimize.backtest_caching import get_strategy_run_id
from freqtrade.optimize.backtesting import Backtesting
from freqtrade.persistence import LocalTrade, Trade
from freqtrade.resolvers import StrategyResolver
from tests.conftest import (CURRENT_TEST_STRATEGY, EXMS, get_args, log_has, log_has_re,
                            patch_exchange, patched_configuration_load_config_file)


ORDER_TYPES = [
    {
        'entry': 'limit',
        'exit': 'limit',
        'stoploss': 'limit',
        'stoploss_on_exchange': False
    },
    {
        'entry': 'limit',
        'exit': 'limit',
        'stoploss': 'limit',
        'stoploss_on_exchange': True
    }]


def trim_dictlist(dict_list, num):
    new = {}
    for pair, pair_data in dict_list.items():
        new[pair] = pair_data[num:].reset_index()
    return new


def load_data_test(what, testdatadir):
    timerange = TimeRange.parse_timerange('1510694220-1510700340')
    data = history.load_pair_history(pair='UNITTEST/BTC', datadir=testdatadir,
                                     timeframe='1m', timerange=timerange,
                                     drop_incomplete=False,
                                     fill_up_missing=False)

    base = 0.001
    if what == 'raise':
        data.loc[:, 'open'] = data.index * base
        data.loc[:, 'high'] = data.index * base + 0.0001
        data.loc[:, 'low'] = data.index * base - 0.0001
        data.loc[:, 'close'] = data.index * base

    if what == 'lower':
        data.loc[:, 'open'] = 1 - data.index * base
        data.loc[:, 'high'] = 1 - data.index * base + 0.0001
        data.loc[:, 'low'] = 1 - data.index * base - 0.0001
        data.loc[:, 'close'] = 1 - data.index * base

    if what == 'sine':
        hz = 0.1  # frequency
        data.loc[:, 'open'] = np.sin(data.index * hz) / 1000 + base
        data.loc[:, 'high'] = np.sin(data.index * hz) / 1000 + base + 0.0001
        data.loc[:, 'low'] = np.sin(data.index * hz) / 1000 + base - 0.0001
        data.loc[:, 'close'] = np.sin(data.index * hz) / 1000 + base

    return {'UNITTEST/BTC': clean_ohlcv_dataframe(data, timeframe='1m', pair='UNITTEST/BTC',
                                                  fill_missing=True, drop_incomplete=True)}


# FIX: fixturize this?
def _make_backtest_conf(mocker, datadir, conf=None, pair='UNITTEST/BTC'):
    data = history.load_data(datadir=datadir, timeframe='1m', pairs=[pair])
    data = trim_dictlist(data, -201)
    patch_exchange(mocker)
    backtesting = Backtesting(conf)
    backtesting._set_strategy(backtesting.strategylist[0])
    processed = backtesting.strategy.advise_all_indicators(data)
    min_date, max_date = get_timerange(processed)
    return {
        'processed': processed,
        'start_date': min_date,
        'end_date': max_date,
    }


def _trend(signals, buy_value, sell_value):
    n = len(signals['low'])
    buy = np.zeros(n)
    sell = np.zeros(n)
    for i in range(0, len(signals['date'])):
        if random.random() > 0.5:  # Both buy and sell signals at same timeframe
            buy[i] = buy_value
            sell[i] = sell_value
    signals['enter_long'] = buy
    signals['exit_long'] = sell
    signals['enter_short'] = 0
    signals['exit_short'] = 0
    return signals


def _trend_alternate(dataframe=None, metadata=None):
    signals = dataframe
    low = signals['low']
    n = len(low)
    buy = np.zeros(n)
    sell = np.zeros(n)
    for i in range(0, len(buy)):
        if i % 2 == 0:
            buy[i] = 1
        else:
            sell[i] = 1
    signals['enter_long'] = buy
    signals['exit_long'] = sell
    signals['enter_short'] = 0
    signals['exit_short'] = 0
    return dataframe


# Unit tests
def test_setup_optimize_configuration_without_arguments(mocker, default_conf, caplog) -> None:
    patched_configuration_load_config_file(mocker, default_conf)

    args = [
        'backtesting',
        '--config', 'config.json',
        '--strategy', CURRENT_TEST_STRATEGY,
        '--export', 'none'
    ]

    config = setup_optimize_configuration(get_args(args), RunMode.BACKTEST)
    assert 'max_open_trades' in config
    assert 'stake_currency' in config
    assert 'stake_amount' in config
    assert 'exchange' in config
    assert 'pair_whitelist' in config['exchange']
    assert 'datadir' in config
    assert log_has('Using data directory: {} ...'.format(config['datadir']), caplog)
    assert 'timeframe' in config
    assert not log_has_re('Parameter -i/--ticker-interval detected .*', caplog)

    assert 'position_stacking' not in config
    assert not log_has('Parameter --enable-position-stacking detected ...', caplog)

    assert 'timerange' not in config
    assert 'export' in config
    assert config['export'] == 'none'
    assert 'runmode' in config
    assert config['runmode'] == RunMode.BACKTEST


def test_setup_bt_configuration_with_arguments(mocker, default_conf, caplog) -> None:
    patched_configuration_load_config_file(mocker, default_conf)
    mocker.patch(
        'freqtrade.configuration.configuration.create_datadir',
        lambda c, x: x
    )

    args = [
        'backtesting',
        '--config', 'config.json',
        '--strategy', CURRENT_TEST_STRATEGY,
        '--datadir', '/foo/bar',
        '--timeframe', '1m',
        '--enable-position-stacking',
        '--disable-max-market-positions',
        '--timerange', ':100',
        '--export-filename', 'foo_bar.json',
        '--fee', '0',
    ]

    config = setup_optimize_configuration(get_args(args), RunMode.BACKTEST)
    assert 'max_open_trades' in config
    assert 'stake_currency' in config
    assert 'stake_amount' in config
    assert 'exchange' in config
    assert 'pair_whitelist' in config['exchange']
    assert 'datadir' in config
    assert config['runmode'] == RunMode.BACKTEST

    assert log_has('Using data directory: {} ...'.format(config['datadir']), caplog)
    assert 'timeframe' in config
    assert log_has('Parameter -i/--timeframe detected ... Using timeframe: 1m ...',
                   caplog)

    assert 'position_stacking' in config
    assert log_has('Parameter --enable-position-stacking detected ...', caplog)

    assert 'use_max_market_positions' in config
    assert log_has('Parameter --disable-max-market-positions detected ...', caplog)
    assert log_has('max_open_trades set to unlimited ...', caplog)

    assert 'timerange' in config
    assert log_has('Parameter --timerange detected: {} ...'.format(config['timerange']), caplog)

    assert 'export' in config
    assert 'exportfilename' in config
    assert isinstance(config['exportfilename'], Path)
    assert log_has('Storing backtest results to {} ...'.format(config['exportfilename']), caplog)

    assert 'fee' in config
    assert log_has('Parameter --fee detected, setting fee to: {} ...'.format(config['fee']), caplog)


def test_setup_optimize_configuration_stake_amount(mocker, default_conf, caplog) -> None:

    patched_configuration_load_config_file(mocker, default_conf)

    args = [
        'backtesting',
        '--config', 'config.json',
        '--strategy', CURRENT_TEST_STRATEGY,
        '--stake-amount', '1',
        '--starting-balance', '2'
    ]

    conf = setup_optimize_configuration(get_args(args), RunMode.BACKTEST)
    assert isinstance(conf, dict)

    args = [
        'backtesting',
        '--config', 'config.json',
        '--strategy', CURRENT_TEST_STRATEGY,
        '--stake-amount', '1',
        '--starting-balance', '0.5'
    ]
    with pytest.raises(OperationalException, match=r"Starting balance .* smaller .*"):
        setup_optimize_configuration(get_args(args), RunMode.BACKTEST)


def test_start(mocker, fee, default_conf, caplog) -> None:
    start_mock = MagicMock()
    mocker.patch(f'{EXMS}.get_fee', fee)
    patch_exchange(mocker)
    mocker.patch('freqtrade.optimize.backtesting.Backtesting.start', start_mock)
    patched_configuration_load_config_file(mocker, default_conf)

    args = [
        'backtesting',
        '--config', 'config.json',
        '--strategy', CURRENT_TEST_STRATEGY,
    ]
    pargs = get_args(args)
    start_backtesting(pargs)
    assert log_has('Starting freqtrade in Backtesting mode', caplog)
    assert start_mock.call_count == 1


@pytest.mark.parametrize("order_types", ORDER_TYPES)
def test_backtesting_init(mocker, default_conf, order_types) -> None:
    """
    Check that stoploss_on_exchange is set to False while backtesting
    since backtesting assumes a perfect stoploss anyway.
    """
    default_conf["order_types"] = order_types
    patch_exchange(mocker)
    get_fee = mocker.patch(f'{EXMS}.get_fee', MagicMock(return_value=0.5))
    backtesting = Backtesting(default_conf)
    backtesting._set_strategy(backtesting.strategylist[0])
    assert backtesting.config == default_conf
    assert backtesting.timeframe == '5m'
    assert callable(backtesting.strategy.advise_all_indicators)
    assert callable(backtesting.strategy.advise_entry)
    assert callable(backtesting.strategy.advise_exit)
    assert isinstance(backtesting.strategy.dp, DataProvider)
    get_fee.assert_called()
    assert backtesting.fee == 0.5
    assert not backtesting.strategy.order_types["stoploss_on_exchange"]
    assert backtesting.strategy.bot_started is True


def test_backtesting_init_no_timeframe(mocker, default_conf, caplog) -> None:
    patch_exchange(mocker)
    del default_conf['timeframe']
    default_conf['strategy_list'] = [CURRENT_TEST_STRATEGY,
                                     'HyperoptableStrategy']

    mocker.patch(f'{EXMS}.get_fee', MagicMock(return_value=0.5))
    with pytest.raises(OperationalException,
                       match=r"Timeframe needs to be set in either configuration"):
        Backtesting(default_conf)


def test_data_with_fee(default_conf, mocker) -> None:
    patch_exchange(mocker)
    default_conf['fee'] = 0.1234

    fee_mock = mocker.patch(f'{EXMS}.get_fee', MagicMock(return_value=0.5))
    backtesting = Backtesting(default_conf)
    backtesting._set_strategy(backtesting.strategylist[0])
    assert backtesting.fee == 0.1234
    assert fee_mock.call_count == 0

    default_conf['fee'] = 0.0
    backtesting = Backtesting(default_conf)
    backtesting._set_strategy(backtesting.strategylist[0])
    assert backtesting.fee == 0.0
    assert fee_mock.call_count == 0


def test_data_to_dataframe_bt(default_conf, mocker, testdatadir) -> None:
    patch_exchange(mocker)
    timerange = TimeRange.parse_timerange('1510694220-1510700340')
    data = history.load_data(testdatadir, '1m', ['UNITTEST/BTC'], timerange=timerange,
                             fill_up_missing=True)
    backtesting = Backtesting(default_conf)
    backtesting._set_strategy(backtesting.strategylist[0])
    processed = backtesting.strategy.advise_all_indicators(data)
    assert len(processed['UNITTEST/BTC']) == 103

    # Load strategy to compare the result between Backtesting function and strategy are the same
    strategy = StrategyResolver.load_strategy(default_conf)

    processed2 = strategy.advise_all_indicators(data)
    assert processed['UNITTEST/BTC'].equals(processed2['UNITTEST/BTC'])


def test_backtest_abort(default_conf, mocker, testdatadir) -> None:
    patch_exchange(mocker)
    backtesting = Backtesting(default_conf)
    backtesting.check_abort()

    backtesting.abort = True

    with pytest.raises(DependencyException, match="Stop requested"):
        backtesting.check_abort()
    # abort flag resets
    assert backtesting.abort is False
    assert backtesting.progress.progress == 0


def test_backtesting_start(default_conf, mocker, caplog) -> None:
    def get_timerange(input1):
        return Arrow(2017, 11, 14, 21, 17), Arrow(2017, 11, 14, 22, 59)

    mocker.patch('freqtrade.data.history.get_timerange', get_timerange)
    patch_exchange(mocker)
    mocker.patch('freqtrade.optimize.backtesting.Backtesting.backtest')
    mocker.patch('freqtrade.optimize.backtesting.generate_backtest_stats')
    mocker.patch('freqtrade.optimize.backtesting.show_backtest_results')
    sbs = mocker.patch('freqtrade.optimize.backtesting.store_backtest_stats')
    sbc = mocker.patch('freqtrade.optimize.backtesting.store_backtest_signal_candles')
    mocker.patch('freqtrade.plugins.pairlistmanager.PairListManager.whitelist',
                 PropertyMock(return_value=['UNITTEST/BTC']))

    default_conf['timeframe'] = '1m'
    default_conf['export'] = 'signals'
    default_conf['exportfilename'] = 'export.txt'
    default_conf['timerange'] = '-1510694220'
    default_conf['runmode'] = RunMode.BACKTEST

    backtesting = Backtesting(default_conf)
    backtesting._set_strategy(backtesting.strategylist[0])
    backtesting.strategy.bot_loop_start = MagicMock()
    backtesting.strategy.bot_start = MagicMock()
    backtesting.start()
    # check the logs, that will contain the backtest result
    exists = [
        'Backtesting with data from 2017-11-14 21:17:00 '
        'up to 2017-11-14 22:59:00 (0 days).'
    ]
    for line in exists:
        assert log_has(line, caplog)
    assert backtesting.strategy.dp._pairlists is not None
    assert backtesting.strategy.bot_start.call_count == 1
    assert backtesting.strategy.bot_loop_start.call_count == 0
    assert sbs.call_count == 1
    assert sbc.call_count == 1


def test_backtesting_start_no_data(default_conf, mocker, caplog, testdatadir) -> None:
    def get_timerange(input1):
        return Arrow(2017, 11, 14, 21, 17), Arrow(2017, 11, 14, 22, 59)

    mocker.patch('freqtrade.data.history.history_utils.load_pair_history',
                 MagicMock(return_value=pd.DataFrame()))
    mocker.patch('freqtrade.data.history.get_timerange', get_timerange)
    patch_exchange(mocker)
    mocker.patch('freqtrade.optimize.backtesting.Backtesting.backtest')
    mocker.patch('freqtrade.plugins.pairlistmanager.PairListManager.whitelist',
                 PropertyMock(return_value=['UNITTEST/BTC']))

    default_conf['timeframe'] = "1m"
    default_conf['export'] = 'none'
    default_conf['timerange'] = '20180101-20180102'

    backtesting = Backtesting(default_conf)
    backtesting._set_strategy(backtesting.strategylist[0])
    with pytest.raises(OperationalException, match='No data found. Terminating.'):
        backtesting.start()


def test_backtesting_no_pair_left(default_conf, mocker, caplog, testdatadir) -> None:
    mocker.patch(f'{EXMS}.exchange_has', MagicMock(return_value=True))
    mocker.patch('freqtrade.data.history.history_utils.load_pair_history',
                 MagicMock(return_value=pd.DataFrame()))
    mocker.patch('freqtrade.data.history.get_timerange', get_timerange)
    patch_exchange(mocker)
    mocker.patch('freqtrade.optimize.backtesting.Backtesting.backtest')
    mocker.patch('freqtrade.plugins.pairlistmanager.PairListManager.whitelist',
                 PropertyMock(return_value=[]))

    default_conf['timeframe'] = "1m"
    default_conf['export'] = 'none'
    default_conf['timerange'] = '20180101-20180102'

    with pytest.raises(OperationalException, match='No pair in whitelist.'):
        Backtesting(default_conf)

    default_conf['pairlists'] = [{"method": "VolumePairList", "number_assets": 5}]
    with pytest.raises(OperationalException,
                       match=r'VolumePairList not allowed for backtesting\..*StaticPairList.*'):
        Backtesting(default_conf)

    default_conf.update({
        'pairlists': [{"method": "StaticPairList"}],
        'timeframe_detail': '1d',
    })

    with pytest.raises(OperationalException,
                       match='Detail timeframe must be smaller than strategy timeframe.'):
        Backtesting(default_conf)


def test_backtesting_pairlist_list(default_conf, mocker, caplog, testdatadir, tickers) -> None:
    mocker.patch(f'{EXMS}.exchange_has', MagicMock(return_value=True))
    mocker.patch(f'{EXMS}.get_tickers', tickers)
    mocker.patch(f'{EXMS}.price_to_precision', lambda s, x, y: y)
    mocker.patch('freqtrade.data.history.get_timerange', get_timerange)
    patch_exchange(mocker)
    mocker.patch('freqtrade.optimize.backtesting.Backtesting.backtest')
    mocker.patch('freqtrade.plugins.pairlistmanager.PairListManager.whitelist',
                 PropertyMock(return_value=['XRP/BTC']))
    mocker.patch('freqtrade.plugins.pairlistmanager.PairListManager.refresh_pairlist')

    default_conf['ticker_interval'] = "1m"
    default_conf['export'] = 'none'
    # Use stoploss from strategy
    del default_conf['stoploss']
    default_conf['timerange'] = '20180101-20180102'

    default_conf['pairlists'] = [{"method": "VolumePairList", "number_assets": 5}]
    with pytest.raises(OperationalException,
                       match=r'VolumePairList not allowed for backtesting\..*StaticPairList.*'):
        Backtesting(default_conf)

    default_conf['pairlists'] = [{"method": "StaticPairList"}, {"method": "PerformanceFilter"}]
    with pytest.raises(OperationalException,
                       match='PerformanceFilter not allowed for backtesting.'):
        Backtesting(default_conf)

    default_conf['pairlists'] = [{"method": "StaticPairList"}, {"method": "PrecisionFilter"}, ]
    Backtesting(default_conf)

    # Multiple strategies
    default_conf['strategy_list'] = [CURRENT_TEST_STRATEGY, 'StrategyTestV2']
    with pytest.raises(OperationalException,
                       match='PrecisionFilter not allowed for backtesting multiple strategies.'):
        Backtesting(default_conf)


def test_backtest__enter_trade(default_conf, fee, mocker) -> None:
    default_conf['use_exit_signal'] = False
    mocker.patch(f'{EXMS}.get_fee', fee)
    mocker.patch(f'{EXMS}.get_min_pair_stake_amount', return_value=0.00001)
    mocker.patch(f'{EXMS}.get_max_pair_stake_amount', return_value=float('inf'))
    patch_exchange(mocker)
    default_conf['stake_amount'] = 'unlimited'
    default_conf['max_open_trades'] = 2
    backtesting = Backtesting(default_conf)
    backtesting._set_strategy(backtesting.strategylist[0])
    pair = 'UNITTEST/BTC'
    row = [
        pd.Timestamp(year=2020, month=1, day=1, hour=5, minute=0),
        1,  # Buy
        0.001,  # Open
        0.0011,  # Close
        0,  # Sell
        0.00099,  # Low
        0.0012,  # High
        '',  # Buy Signal Name
    ]
    trade = backtesting._enter_trade(pair, row=row, direction='long')
    assert isinstance(trade, LocalTrade)
    assert trade.stake_amount == 495

    # Fake 2 trades, so there's not enough amount for the next trade left.
    LocalTrade.trades_open.append(trade)
    LocalTrade.trades_open.append(trade)
    backtesting.wallets.update()
    trade = backtesting._enter_trade(pair, row=row, direction='long')
    assert trade is None
    LocalTrade.trades_open.pop()
    trade = backtesting._enter_trade(pair, row=row, direction='long')
    assert trade is not None

    backtesting.strategy.custom_stake_amount = lambda **kwargs: 123.5
    backtesting.wallets.update()
    trade = backtesting._enter_trade(pair, row=row, direction='long')
    assert trade
    assert trade.stake_amount == 123.5

    # In case of error - use proposed stake
    backtesting.strategy.custom_stake_amount = lambda **kwargs: 20 / 0
    trade = backtesting._enter_trade(pair, row=row, direction='long')
    assert trade
    assert trade.stake_amount == 495
    assert trade.is_short is False

    trade = backtesting._enter_trade(pair, row=row, direction='short')
    assert trade
    assert trade.stake_amount == 495
    assert trade.is_short is True

    mocker.patch(f"{EXMS}.get_max_pair_stake_amount", return_value=300.0)
    trade = backtesting._enter_trade(pair, row=row, direction='long')
    assert trade
    assert trade.stake_amount == 300.0


def test_backtest__enter_trade_futures(default_conf_usdt, fee, mocker) -> None:
    default_conf_usdt['use_exit_signal'] = False
    mocker.patch(f'{EXMS}.get_fee', fee)
    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=0.00001)
    mocker.patch(f"{EXMS}.get_max_pair_stake_amount", return_value=float('inf'))
    mocker.patch(f"{EXMS}.get_max_leverage", return_value=100)
    mocker.patch("freqtrade.optimize.backtesting.price_to_precision", lambda p, *args: p)
    patch_exchange(mocker)
    default_conf_usdt['stake_amount'] = 300
    default_conf_usdt['max_open_trades'] = 2
    default_conf_usdt['trading_mode'] = 'futures'
    default_conf_usdt['margin_mode'] = 'isolated'
    default_conf_usdt['stake_currency'] = 'USDT'
    default_conf_usdt['exchange']['pair_whitelist'] = ['.*']
    backtesting = Backtesting(default_conf_usdt)
    backtesting._set_strategy(backtesting.strategylist[0])
    pair = 'ETH/USDT:USDT'
    row = [
        pd.Timestamp(year=2020, month=1, day=1, hour=5, minute=0),
        0.1,  # Open
        0.12,  # High
        0.099,  # Low
        0.11,  # Close
        1,  # enter_long
        0,  # exit_long
        1,  # enter_short
        0,  # exit_hsort
        '',  # Long Signal Name
        '',  # Short Signal Name
        '',  # Exit Signal Name
    ]

    backtesting.strategy.leverage = MagicMock(return_value=5.0)
    mocker.patch(f"{EXMS}.get_maintenance_ratio_and_amt",
                 return_value=(0.01, 0.01))

    # leverage = 5
    # ep1(trade.open_rate) = 0.1
    # position(trade.amount) = 15000
    # stake_amount = 300 -> wb = 300 / 5 = 60
    # mmr = 0.01
    # cum_b = 0.01
    # side_1: -1 if is_short else 1
    # liq_buffer = 0.05
    #
    # Binance, Long
    # liquidation_price
    #   = ((wb + cum_b) - (side_1 * position * ep1)) / ((position * mmr_b) - (side_1 * position))
    #   = ((300 + 0.01) - (1 * 15000 * 0.1)) / ((15000 * 0.01) - (1 * 15000))
    #   = 0.0008080740740740741
    # freqtrade_liquidation_price = liq + (abs(open_rate - liq) * liq_buffer * side_1)
    #   = 0.08080740740740741 + ((0.1 - 0.08080740740740741) * 0.05 * 1)
    #   = 0.08176703703703704

    trade = backtesting._enter_trade(pair, row=row, direction='long')
    assert pytest.approx(trade.liquidation_price) == 0.081767037

    # Binance, Short
    # liquidation_price
    #   = ((wb + cum_b) - (side_1 * position * ep1)) / ((position * mmr_b) - (side_1 * position))
    #   = ((300 + 0.01) - ((-1) * 15000 * 0.1)) / ((15000 * 0.01) - ((-1) * 15000))
    #   = 0.0011881254125412541
    # freqtrade_liquidation_price = liq + (abs(open_rate - liq) * liq_buffer * side_1)
    #   = 0.11881254125412541 + (abs(0.1 - 0.11881254125412541) * 0.05 * -1)
    #   = 0.11787191419141915

    trade = backtesting._enter_trade(pair, row=row, direction='short')
    assert pytest.approx(trade.liquidation_price) == 0.11787191

    # Stake-amount too high!
    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=600.0)

    trade = backtesting._enter_trade(pair, row=row, direction='long')
    assert trade is None

    # Stake-amount throwing error
    mocker.patch("freqtrade.wallets.Wallets.get_trade_stake_amount",
                 side_effect=DependencyException)

    trade = backtesting._enter_trade(pair, row=row, direction='long')
    assert trade is None


def test_backtest__check_trade_exit(default_conf, fee, mocker) -> None:
    default_conf['use_exit_signal'] = False
    mocker.patch(f'{EXMS}.get_fee', fee)
    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=0.00001)
    mocker.patch(f"{EXMS}.get_max_pair_stake_amount", return_value=float('inf'))
    patch_exchange(mocker)
    default_conf['timeframe_detail'] = '1m'
    default_conf['max_open_trades'] = 2
    backtesting = Backtesting(default_conf)
    backtesting._set_strategy(backtesting.strategylist[0])
    pair = 'UNITTEST/BTC'
    row = [
        pd.Timestamp(year=2020, month=1, day=1, hour=4, minute=55, tzinfo=timezone.utc),
        200,  # Open
        201.5,  # High
        195,  # Low
        201,  # Close
        1,  # enter_long
        0,  # exit_long
        0,  # enter_short
        0,  # exit_hsort
        '',  # Long Signal Name
        '',  # Short Signal Name
        '',  # Exit Signal Name
    ]

    trade = backtesting._enter_trade(pair, row=row, direction='long')
    assert isinstance(trade, LocalTrade)

    row_sell = [
        pd.Timestamp(year=2020, month=1, day=1, hour=5, minute=0, tzinfo=timezone.utc),
        200,  # Open
        210.5,  # High
        195,  # Low
        201,  # Close
        0,  # enter_long
        0,  # exit_long
        0,  # enter_short
        0,  # exit_short
        '',  # long Signal Name
        '',  # Short Signal Name
        '',  # Exit Signal Name

    ]

    # No data available.
    res = backtesting._check_trade_exit(trade, row_sell)
    assert res is not None
    assert res.exit_reason == ExitType.ROI.value
    assert res.close_date_utc == datetime(2020, 1, 1, 5, 0, tzinfo=timezone.utc)

    # Enter new trade
    trade = backtesting._enter_trade(pair, row=row, direction='long')
    assert isinstance(trade, LocalTrade)
    # Assign empty ... no result.
    backtesting.detail_data[pair] = pd.DataFrame(
        [], columns=['date', 'open', 'high', 'low', 'close', 'enter_long', 'exit_long',
                     'enter_short', 'exit_short', 'long_tag', 'short_tag', 'exit_tag'])

    res = backtesting._check_trade_exit(trade, row)
    assert res is None


def test_backtest_one(default_conf, fee, mocker, testdatadir) -> None:
    default_conf['use_exit_signal'] = False
    default_conf['max_open_trades'] = 10

    mocker.patch(f'{EXMS}.get_fee', fee)
    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=0.00001)
    mocker.patch(f"{EXMS}.get_max_pair_stake_amount", return_value=float('inf'))
    patch_exchange(mocker)
    backtesting = Backtesting(default_conf)
    backtesting._set_strategy(backtesting.strategylist[0])
    pair = 'UNITTEST/BTC'
    timerange = TimeRange('date', None, 1517227800, 0)
    data = history.load_data(datadir=testdatadir, timeframe='5m', pairs=['UNITTEST/BTC'],
                             timerange=timerange)
    processed = backtesting.strategy.advise_all_indicators(data)
    min_date, max_date = get_timerange(processed)

    result = backtesting.backtest(
        processed=deepcopy(processed),
        start_date=min_date,
        end_date=max_date,
    )
    results = result['results']
    assert not results.empty
    assert len(results) == 2

    expected = pd.DataFrame(
        {'pair': [pair, pair],
         'stake_amount': [0.001, 0.001],
         'max_stake_amount': [0.001, 0.001],
         'amount': [0.00957442, 0.0097064],
         'open_date': pd.to_datetime([Arrow(2018, 1, 29, 18, 40, 0).datetime,
                                      Arrow(2018, 1, 30, 3, 30, 0).datetime], utc=True
                                     ),
         'close_date': pd.to_datetime([Arrow(2018, 1, 29, 22, 35, 0).datetime,
                                       Arrow(2018, 1, 30, 4, 10, 0).datetime], utc=True),
         'open_rate': [0.104445, 0.10302485],
         'close_rate': [0.104969, 0.103541],
         'fee_open': [0.0025, 0.0025],
         'fee_close': [0.0025, 0.0025],
         'trade_duration': [235, 40],
         'profit_ratio': [0.0, 0.0],
         'profit_abs': [0.0, 0.0],
         'exit_reason': [ExitType.ROI.value, ExitType.ROI.value],
         'initial_stop_loss_abs': [0.0940005, 0.09272236],
         'initial_stop_loss_ratio': [-0.1, -0.1],
         'stop_loss_abs': [0.0940005, 0.09272236],
         'stop_loss_ratio': [-0.1, -0.1],
         'min_rate': [0.10370188, 0.10300000000000001],
         'max_rate': [0.10501, 0.1038888],
         'is_open': [False, False],
         'enter_tag': [None, None],
         "leverage": [1.0, 1.0],
         "is_short": [False, False],
         'open_timestamp': [1517251200000, 1517283000000],
         'close_timestamp': [1517265300000, 1517285400000],
         'orders': [
            [
                {'amount': 0.00957442, 'safe_price': 0.104445, 'ft_order_side': 'buy',
                 'order_filled_timestamp': 1517251200000, 'ft_is_entry': True},
                {'amount': 0.00957442, 'safe_price': 0.10496853383458644, 'ft_order_side': 'sell',
                 'order_filled_timestamp': 1517265300000, 'ft_is_entry': False}
            ], [
                {'amount': 0.0097064, 'safe_price': 0.10302485, 'ft_order_side': 'buy',
                 'order_filled_timestamp': 1517283000000, 'ft_is_entry': True},
                {'amount': 0.0097064, 'safe_price': 0.10354126528822055, 'ft_order_side': 'sell',
                 'order_filled_timestamp': 1517285400000, 'ft_is_entry': False}
            ]
         ]
         })
    pd.testing.assert_frame_equal(results, expected)
    assert 'orders' in results.columns
    data_pair = processed[pair]
    for _, t in results.iterrows():
        assert len(t['orders']) == 2
        ln = data_pair.loc[data_pair["date"] == t["open_date"]]
        # Check open trade rate aligns to open rate
        assert not ln.empty
        assert round(ln.iloc[0]["open"], 6) == round(t["open_rate"], 6)
        # check close trade rate aligns to close rate or is between high and low
        ln1 = data_pair.loc[data_pair["date"] == t["close_date"]]
        assert (round(ln1.iloc[0]["open"], 6) == round(t["close_rate"], 6) or
                round(ln1.iloc[0]["low"], 6) < round(
                t["close_rate"], 6) < round(ln1.iloc[0]["high"], 6))


@pytest.mark.parametrize('use_detail', [True, False])
def test_backtest_one_detail(default_conf_usdt, fee, mocker, testdatadir, use_detail) -> None:
    default_conf_usdt['use_exit_signal'] = False
    mocker.patch(f'{EXMS}.get_fee', fee)
    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=0.00001)
    mocker.patch(f"{EXMS}.get_max_pair_stake_amount", return_value=float('inf'))
    if use_detail:
        default_conf_usdt['timeframe_detail'] = '1m'
    patch_exchange(mocker)

    def advise_entry(df, *args, **kwargs):
        # Mock function to force several entries
        df.loc[(df['rsi'] < 40), 'enter_long'] = 1
        return df

    def custom_entry_price(proposed_rate, **kwargs):
        return proposed_rate * 0.997

    default_conf_usdt['max_open_trades'] = 10

    backtesting = Backtesting(default_conf_usdt)
    backtesting._set_strategy(backtesting.strategylist[0])
    backtesting.strategy.populate_entry_trend = advise_entry
    backtesting.strategy.custom_entry_price = custom_entry_price
    pair = 'XRP/ETH'
    # Pick a timerange adapted to the pair we use to test
    timerange = TimeRange.parse_timerange('20191010-20191013')
    data = history.load_data(datadir=testdatadir, timeframe='5m', pairs=[pair],
                             timerange=timerange)
    if use_detail:
        data_1m = history.load_data(datadir=testdatadir, timeframe='1m', pairs=[pair],
                                    timerange=timerange)
        backtesting.detail_data = data_1m
    processed = backtesting.strategy.advise_all_indicators(data)
    min_date, max_date = get_timerange(processed)

    result = backtesting.backtest(
        processed=deepcopy(processed),
        start_date=min_date,
        end_date=max_date,
    )
    results = result['results']
    assert not results.empty
    # Timeout settings from default_conf = entry: 10, exit: 30
    assert len(results) == (2 if use_detail else 3)

    assert 'orders' in results.columns
    data_pair = processed[pair]

    data_1m_pair = data_1m[pair] if use_detail else pd.DataFrame()
    late_entry = 0
    for _, t in results.iterrows():
        assert len(t['orders']) == 2

        entryo = t['orders'][0]
        entry_ts = datetime.fromtimestamp(entryo['order_filled_timestamp'] // 1000, tz=timezone.utc)
        if entry_ts > t['open_date']:
            late_entry += 1

        # Get "entry fill" candle
        ln = (data_1m_pair.loc[data_1m_pair["date"] == entry_ts]
              if use_detail else data_pair.loc[data_pair["date"] == entry_ts])
        # Check open trade rate aligns to open rate
        assert not ln.empty

        # assert round(ln.iloc[0]["open"], 6) == round(t["open_rate"], 6)
        assert round(ln.iloc[0]["low"], 6) <= round(
                t["open_rate"], 6) <= round(ln.iloc[0]["high"], 6)
        # check close trade rate aligns to close rate or is between high and low
        ln1 = data_pair.loc[data_pair["date"] == t["close_date"]]
        if use_detail:
            ln1_1m = data_1m_pair.loc[data_1m_pair["date"] == t["close_date"]]
            assert not ln1.empty or not ln1_1m.empty
        else:
            assert not ln1.empty
        ln2 = ln1_1m if ln1.empty else ln1

        assert (round(ln2.iloc[0]["low"], 6) <= round(
                t["close_rate"], 6) <= round(ln2.iloc[0]["high"], 6))

    assert late_entry > 0


@pytest.mark.parametrize('use_detail', [True, False])
def test_backtest_one_detail_futures(
        default_conf_usdt, fee, mocker, testdatadir, use_detail) -> None:
    default_conf_usdt['use_exit_signal'] = False
    default_conf_usdt['trading_mode'] = 'futures'
    default_conf_usdt['margin_mode'] = 'isolated'
    default_conf_usdt['candle_type_def'] = CandleType.FUTURES

    mocker.patch(f'{EXMS}.get_fee', fee)
    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=0.00001)
    mocker.patch(f"{EXMS}.get_max_pair_stake_amount", return_value=float('inf'))
    mocker.patch('freqtrade.plugins.pairlistmanager.PairListManager.whitelist',
                 PropertyMock(return_value=['XRP/USDT:USDT']))
    mocker.patch(f"{EXMS}.get_maintenance_ratio_and_amt",
                 return_value=(0.01, 0.01))
    default_conf_usdt['timeframe'] = '1h'
    if use_detail:
        default_conf_usdt['timeframe_detail'] = '5m'
    patch_exchange(mocker)

    def advise_entry(df, *args, **kwargs):
        # Mock function to force several entries
        df.loc[(df['rsi'] < 40), 'enter_long'] = 1
        return df

    def custom_entry_price(proposed_rate, **kwargs):
        return proposed_rate * 0.997

    default_conf_usdt['max_open_trades'] = 10

    backtesting = Backtesting(default_conf_usdt)
    backtesting._set_strategy(backtesting.strategylist[0])
    backtesting.strategy.populate_entry_trend = advise_entry
    backtesting.strategy.custom_entry_price = custom_entry_price
    pair = 'XRP/USDT:USDT'
    # Pick a timerange adapted to the pair we use to test
    timerange = TimeRange.parse_timerange('20211117-20211119')
    data = history.load_data(datadir=Path(testdatadir), timeframe='1h', pairs=[pair],
                             timerange=timerange, candle_type=CandleType.FUTURES)
    backtesting.load_bt_data_detail()
    processed = backtesting.strategy.advise_all_indicators(data)
    min_date, max_date = get_timerange(processed)

    result = backtesting.backtest(
        processed=deepcopy(processed),
        start_date=min_date,
        end_date=max_date,
    )
    results = result['results']
    assert not results.empty
    # Timeout settings from default_conf = entry: 10, exit: 30
    assert len(results) == (5 if use_detail else 2)

    assert 'orders' in results.columns
    data_pair = processed[pair]

    data_1m_pair = backtesting.detail_data[pair] if use_detail else pd.DataFrame()
    late_entry = 0
    for _, t in results.iterrows():
        assert len(t['orders']) == 2

        entryo = t['orders'][0]
        entry_ts = datetime.fromtimestamp(entryo['order_filled_timestamp'] // 1000, tz=timezone.utc)
        if entry_ts > t['open_date']:
            late_entry += 1

        # Get "entry fill" candle
        ln = (data_1m_pair.loc[data_1m_pair["date"] == entry_ts]
              if use_detail else data_pair.loc[data_pair["date"] == entry_ts])
        # Check open trade rate aligns to open rate
        assert not ln.empty

        assert round(ln.iloc[0]["low"], 6) <= round(
                t["open_rate"], 6) <= round(ln.iloc[0]["high"], 6)
        # check close trade rate aligns to close rate or is between high and low
        ln1 = data_pair.loc[data_pair["date"] == t["close_date"]]
        if use_detail:
            ln1_1m = data_1m_pair.loc[data_1m_pair["date"] == t["close_date"]]
            assert not ln1.empty or not ln1_1m.empty
        else:
            assert not ln1.empty
        ln2 = ln1_1m if ln1.empty else ln1

        assert (round(ln2.iloc[0]["low"], 6) <= round(
                t["close_rate"], 6) <= round(ln2.iloc[0]["high"], 6))
    assert -0.0181 < Trade.trades[1].funding_fees < -0.01
    # assert late_entry > 0


@pytest.mark.parametrize('use_detail', [True, False])
def test_backtest_one_detail_futures_funding_fees(
        default_conf_usdt, fee, mocker, testdatadir, use_detail) -> None:
    default_conf_usdt['use_exit_signal'] = False
    default_conf_usdt['trading_mode'] = 'futures'
    default_conf_usdt['margin_mode'] = 'isolated'
    default_conf_usdt['candle_type_def'] = CandleType.FUTURES
    default_conf_usdt['minimal_roi'] = {'0': 1}
    default_conf_usdt['dry_run_wallet'] = 100000

    mocker.patch(f'{EXMS}.get_fee', fee)
    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=0.00001)
    mocker.patch(f"{EXMS}.get_max_pair_stake_amount", return_value=float('inf'))
    mocker.patch('freqtrade.plugins.pairlistmanager.PairListManager.whitelist',
                 PropertyMock(return_value=['XRP/USDT:USDT']))
    mocker.patch(f"{EXMS}.get_maintenance_ratio_and_amt",
                 return_value=(0.01, 0.01))
    default_conf_usdt['timeframe'] = '1h'
    if use_detail:
        default_conf_usdt['timeframe_detail'] = '5m'
    patch_exchange(mocker)

    def advise_entry(df, *args, **kwargs):
        # Mock function to force several entries
        df.loc[:, 'enter_long'] = 1
        return df

    def adjust_trade_position(trade, current_time, **kwargs):
        if current_time > datetime(2021, 11, 18, 2, 0, 0, tzinfo=timezone.utc):
            return None
        return default_conf_usdt['stake_amount']

    default_conf_usdt['max_open_trades'] = 1

    backtesting = Backtesting(default_conf_usdt)
    backtesting._set_strategy(backtesting.strategylist[0])
    backtesting.strategy.populate_entry_trend = advise_entry
    backtesting.strategy.adjust_trade_position = adjust_trade_position
    backtesting.strategy.leverage = lambda **kwargs: 1
    backtesting.strategy.position_adjustment_enable = True
    pair = 'XRP/USDT:USDT'
    # Pick a timerange adapted to the pair we use to test
    timerange = TimeRange.parse_timerange('20211117-20211119')
    data = history.load_data(datadir=Path(testdatadir), timeframe='1h', pairs=[pair],
                             timerange=timerange, candle_type=CandleType.FUTURES)
    backtesting.load_bt_data_detail()
    processed = backtesting.strategy.advise_all_indicators(data)
    min_date, max_date = get_timerange(processed)

    result = backtesting.backtest(
        processed=deepcopy(processed),
        start_date=min_date,
        end_date=max_date,
    )
    results = result['results']
    assert not results.empty
    # Only one result - as we're not selling.
    assert len(results) == 1

    assert 'orders' in results.columns

    for t in Trade.trades:
        # At least 4 adjustment orders
        assert t.nr_of_successful_entries >= 6
        # Funding fees will vary depending on the number of adjustment orders
        # That number is a lot higher with detail data.
        assert -20 < t.funding_fees < -0.1


def test_backtest_timedout_entry_orders(default_conf, fee, mocker, testdatadir) -> None:
    # This strategy intentionally places unfillable orders.
    default_conf['strategy'] = 'StrategyTestV3CustomEntryPrice'
    default_conf['startup_candle_count'] = 0
    # Cancel unfilled order after 4 minutes on 5m timeframe.
    default_conf["unfilledtimeout"] = {"entry": 4}
    mocker.patch(f'{EXMS}.get_fee', fee)
    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=0.00001)
    mocker.patch(f"{EXMS}.get_max_pair_stake_amount", return_value=float('inf'))
    patch_exchange(mocker)
    default_conf['max_open_trades'] = 1
    backtesting = Backtesting(default_conf)
    backtesting._set_strategy(backtesting.strategylist[0])
    # Testing dataframe contains 11 candles. Expecting 10 timed out orders.
    timerange = TimeRange('date', 'date', 1517227800, 1517231100)
    data = history.load_data(datadir=testdatadir, timeframe='5m', pairs=['UNITTEST/BTC'],
                             timerange=timerange)
    min_date, max_date = get_timerange(data)

    result = backtesting.backtest(
        processed=deepcopy(data),
        start_date=min_date,
        end_date=max_date,
    )

    assert result['timedout_entry_orders'] == 10


def test_backtest_1min_timeframe(default_conf, fee, mocker, testdatadir) -> None:
    default_conf['use_exit_signal'] = False
    default_conf['max_open_trades'] = 1
    mocker.patch(f'{EXMS}.get_fee', fee)
    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=0.00001)
    mocker.patch(f"{EXMS}.get_max_pair_stake_amount", return_value=float('inf'))
    patch_exchange(mocker)
    backtesting = Backtesting(default_conf)
    backtesting._set_strategy(backtesting.strategylist[0])

    # Run a backtesting for an exiting 1min timeframe
    timerange = TimeRange.parse_timerange('1510688220-1510700340')
    data = history.load_data(datadir=testdatadir, timeframe='1m', pairs=['UNITTEST/BTC'],
                             timerange=timerange)
    processed = backtesting.strategy.advise_all_indicators(data)
    min_date, max_date = get_timerange(processed)
    results = backtesting.backtest(
        processed=processed,
        start_date=min_date,
        end_date=max_date,
    )
    assert not results['results'].empty
    assert len(results['results']) == 1


def test_backtest_trim_no_data_left(default_conf, fee, mocker, testdatadir) -> None:
    default_conf['use_exit_signal'] = False
    default_conf['max_open_trades'] = 10

    mocker.patch(f'{EXMS}.get_fee', fee)
    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=0.00001)
    mocker.patch(f"{EXMS}.get_max_pair_stake_amount", return_value=float('inf'))
    patch_exchange(mocker)
    backtesting = Backtesting(default_conf)
    backtesting._set_strategy(backtesting.strategylist[0])
    timerange = TimeRange('date', None, 1517227800, 0)
    backtesting.required_startup = 100
    backtesting.timerange = timerange
    data = history.load_data(datadir=testdatadir, timeframe='5m', pairs=['UNITTEST/BTC'],
                             timerange=timerange)
    df = data['UNITTEST/BTC']
    df['date'] = df.loc[:, 'date'] - timedelta(days=1)
    # Trimming 100 candles, so after 2nd trimming, no candle is left.
    df = df.iloc[:100]
    data['XRP/USDT'] = df
    processed = backtesting.strategy.advise_all_indicators(data)
    min_date, max_date = get_timerange(processed)

    backtesting.backtest(
        processed=deepcopy(processed),
        start_date=min_date,
        end_date=max_date,
    )


def test_processed(default_conf, mocker, testdatadir) -> None:
    patch_exchange(mocker)
    backtesting = Backtesting(default_conf)
    backtesting._set_strategy(backtesting.strategylist[0])

    dict_of_tickerrows = load_data_test('raise', testdatadir)
    dataframes = backtesting.strategy.advise_all_indicators(dict_of_tickerrows)
    dataframe = dataframes['UNITTEST/BTC']
    cols = dataframe.columns
    # assert the dataframe got some of the indicator columns
    for col in ['close', 'high', 'low', 'open', 'date',
                'ema10', 'rsi', 'fastd', 'plus_di']:
        assert col in cols


def test_backtest_dataprovider_analyzed_df(default_conf, fee, mocker, testdatadir) -> None:
    default_conf['use_exit_signal'] = False
    default_conf['max_open_trades'] = 10
    mocker.patch(f'{EXMS}.get_fee', fee)
    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=0.00001)
    mocker.patch(f"{EXMS}.get_max_pair_stake_amount", return_value=100000)
    patch_exchange(mocker)
    backtesting = Backtesting(default_conf)
    backtesting._set_strategy(backtesting.strategylist[0])
    timerange = TimeRange('date', None, 1517227800, 0)
    data = history.load_data(datadir=testdatadir, timeframe='5m', pairs=['UNITTEST/BTC'],
                             timerange=timerange)
    processed = backtesting.strategy.advise_all_indicators(data)
    min_date, max_date = get_timerange(processed)

    global count
    count = 0

    def tmp_confirm_entry(pair, current_time, **kwargs):
        dp = backtesting.strategy.dp
        df, _ = dp.get_analyzed_dataframe(pair, backtesting.strategy.timeframe)
        current_candle = df.iloc[-1].squeeze()
        assert current_candle['enter_long'] == 1

        candle_date = timeframe_to_next_date(backtesting.strategy.timeframe, current_candle['date'])
        assert candle_date == current_time
        # These asserts don't properly raise as they are nested,
        # therefore we increment count and assert for that.
        global count
        count = count + 1

    backtesting.strategy.confirm_trade_entry = tmp_confirm_entry
    backtesting.backtest(
        processed=deepcopy(processed),
        start_date=min_date,
        end_date=max_date,
    )
    assert count == 5


def test_backtest_pricecontours_protections(default_conf, fee, mocker, testdatadir) -> None:
    # While this test IS a copy of test_backtest_pricecontours, it's needed to ensure
    # results do not carry-over to the next run, which is not given by using parametrize.
    patch_exchange(mocker)
    default_conf['protections'] = [
        {
            "method": "CooldownPeriod",
            "stop_duration": 3,
        }]

    default_conf['enable_protections'] = True
    default_conf['timeframe'] = '1m'
    default_conf['max_open_trades'] = 1
    mocker.patch(f'{EXMS}.get_fee', fee)
    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=0.00001)
    mocker.patch(f"{EXMS}.get_max_pair_stake_amount", return_value=float('inf'))
    tests = [
        ['sine', 9],
        ['raise', 10],
        ['lower', 0],
        ['sine', 9],
        ['raise', 10],
    ]
    backtesting = Backtesting(default_conf)
    backtesting._set_strategy(backtesting.strategylist[0])

    # While entry-signals are unrealistic, running backtesting
    # over and over again should not cause different results
    for [contour, numres] in tests:
        # Debug output for random test failure
        print(f"{contour}, {numres}")
        data = load_data_test(contour, testdatadir)
        processed = backtesting.strategy.advise_all_indicators(data)
        min_date, max_date = get_timerange(processed)
        assert isinstance(processed, dict)
        results = backtesting.backtest(
            processed=processed,
            start_date=min_date,
            end_date=max_date,
        )
        assert len(results['results']) == numres


@pytest.mark.parametrize('protections,contour,expected', [
    (None, 'sine', 35),
    (None, 'raise', 19),
    (None, 'lower', 0),
    (None, 'sine', 35),
    (None, 'raise', 19),
    ([{"method": "CooldownPeriod", "stop_duration": 3}], 'sine', 9),
    ([{"method": "CooldownPeriod", "stop_duration": 3}], 'raise', 10),
    ([{"method": "CooldownPeriod", "stop_duration": 3}], 'lower', 0),
    ([{"method": "CooldownPeriod", "stop_duration": 3}], 'sine', 9),
    ([{"method": "CooldownPeriod", "stop_duration": 3}], 'raise', 10),
])
def test_backtest_pricecontours(default_conf, fee, mocker, testdatadir,
                                protections, contour, expected) -> None:
    if protections:
        default_conf['protections'] = protections
        default_conf['enable_protections'] = True

    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=0.00001)
    mocker.patch(f"{EXMS}.get_max_pair_stake_amount", return_value=float('inf'))
    mocker.patch(f'{EXMS}.get_fee', fee)
    # While entry-signals are unrealistic, running backtesting
    # over and over again should not cause different results

    patch_exchange(mocker)
    default_conf['timeframe'] = '1m'
    backtesting = Backtesting(default_conf)
    backtesting._set_strategy(backtesting.strategylist[0])

    data = load_data_test(contour, testdatadir)
    processed = backtesting.strategy.advise_all_indicators(data)
    min_date, max_date = get_timerange(processed)
    assert isinstance(processed, dict)
    backtesting.strategy.max_open_trades = 1
    backtesting.config.update({'max_open_trades': 1})
    results = backtesting.backtest(
        processed=processed,
        start_date=min_date,
        end_date=max_date,
    )
    assert len(results['results']) == expected


def test_backtest_clash_buy_sell(mocker, default_conf, testdatadir):
    # Override the default buy trend function in our StrategyTest
    def fun(dataframe=None, pair=None):
        buy_value = 1
        sell_value = 1
        return _trend(dataframe, buy_value, sell_value)
    default_conf['max_open_trades'] = 10
    backtest_conf = _make_backtest_conf(mocker, conf=default_conf, datadir=testdatadir)
    backtesting = Backtesting(default_conf)
    backtesting._set_strategy(backtesting.strategylist[0])
    backtesting.strategy.advise_entry = fun  # Override
    backtesting.strategy.advise_exit = fun  # Override
    result = backtesting.backtest(**backtest_conf)
    assert result['results'].empty


def test_backtest_only_sell(mocker, default_conf, testdatadir):
    # Override the default buy trend function in our StrategyTest
    def fun(dataframe=None, pair=None):
        buy_value = 0
        sell_value = 1
        return _trend(dataframe, buy_value, sell_value)

    default_conf['max_open_trades'] = 10
    backtest_conf = _make_backtest_conf(mocker, conf=default_conf, datadir=testdatadir)
    backtesting = Backtesting(default_conf)
    backtesting._set_strategy(backtesting.strategylist[0])
    backtesting.strategy.advise_entry = fun  # Override
    backtesting.strategy.advise_exit = fun  # Override
    result = backtesting.backtest(**backtest_conf)
    assert result['results'].empty


def test_backtest_alternate_buy_sell(default_conf, fee, mocker, testdatadir):
    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=0.00001)
    mocker.patch(f"{EXMS}.get_max_pair_stake_amount", return_value=float('inf'))
    mocker.patch(f'{EXMS}.get_fee', fee)
    default_conf['max_open_trades'] = 10
    backtest_conf = _make_backtest_conf(mocker, conf=default_conf,
                                        pair='UNITTEST/BTC', datadir=testdatadir)
    default_conf['timeframe'] = '1m'
    backtesting = Backtesting(default_conf)
    backtesting.required_startup = 0
    backtesting._set_strategy(backtesting.strategylist[0])
    backtesting.strategy.advise_entry = _trend_alternate  # Override
    backtesting.strategy.advise_exit = _trend_alternate  # Override
    result = backtesting.backtest(**backtest_conf)
    # 200 candles in backtest data
    # won't buy on first (shifted by 1)
    # 100 buys signals
    results = result['results']
    assert len(results) == 100
    # Cached data should be 200
    analyzed_df = backtesting.dataprovider.get_analyzed_dataframe('UNITTEST/BTC', '1m')[0]
    assert len(analyzed_df) == 200
    # Expect last candle to be 1 below end date (as the last candle is assumed as "incomplete"
    # during backtesting)
    expected_last_candle_date = backtest_conf['end_date'] - timedelta(minutes=1)
    assert analyzed_df.iloc[-1]['date'].to_pydatetime() == expected_last_candle_date

    # One trade was force-closed at the end
    assert len(results.loc[results['is_open']]) == 0


@pytest.mark.parametrize("pair", ['ADA/BTC', 'LTC/BTC'])
@pytest.mark.parametrize("tres", [0, 20, 30])
def test_backtest_multi_pair(default_conf, fee, mocker, tres, pair, testdatadir):

    def _trend_alternate_hold(dataframe=None, metadata=None):
        """
        Buy every xth candle - sell every other xth -2 (hold on to pairs a bit)
        """
        if metadata['pair'] in ('ETH/BTC', 'LTC/BTC'):
            multi = 20
        else:
            multi = 18
        dataframe['enter_long'] = np.where(dataframe.index % multi == 0, 1, 0)
        dataframe['exit_long'] = np.where((dataframe.index + multi - 2) % multi == 0, 1, 0)
        dataframe['enter_short'] = 0
        dataframe['exit_short'] = 0
        return dataframe

    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=0.00001)
    mocker.patch(f"{EXMS}.get_max_pair_stake_amount", return_value=float('inf'))
    mocker.patch(f'{EXMS}.get_fee', fee)
    patch_exchange(mocker)

    pairs = ['ADA/BTC', 'DASH/BTC', 'ETH/BTC', 'LTC/BTC', 'NXT/BTC']
    data = history.load_data(datadir=testdatadir, timeframe='5m', pairs=pairs)
    # Only use 500 lines to increase performance
    data = trim_dictlist(data, -500)

    # Remove data for one pair from the beginning of the data
    if tres > 0:
        data[pair] = data[pair][tres:].reset_index()
    default_conf['timeframe'] = '5m'
    default_conf['max_open_trades'] = 3

    backtesting = Backtesting(default_conf)
    backtesting._set_strategy(backtesting.strategylist[0])
    backtesting.strategy.advise_entry = _trend_alternate_hold  # Override
    backtesting.strategy.advise_exit = _trend_alternate_hold  # Override

    processed = backtesting.strategy.advise_all_indicators(data)
    min_date, max_date = get_timerange(processed)

    backtest_conf = {
        'processed': deepcopy(processed),
        'start_date': min_date,
        'end_date': max_date,
    }

    results = backtesting.backtest(**backtest_conf)

    # Make sure we have parallel trades
    assert len(evaluate_result_multi(results['results'], '5m', 2)) > 0
    # make sure we don't have trades with more than configured max_open_trades
    assert len(evaluate_result_multi(results['results'], '5m', 3)) == 0

    # Cached data correctly removed amounts
    offset = 1 if tres == 0 else 0
    removed_candles = len(data[pair]) - offset - backtesting.strategy.startup_candle_count
    assert len(backtesting.dataprovider.get_analyzed_dataframe(pair, '5m')[0]) == removed_candles
    assert len(
        backtesting.dataprovider.get_analyzed_dataframe('NXT/BTC', '5m')[0]
    ) == len(data['NXT/BTC']) - 1 - backtesting.strategy.startup_candle_count

    backtesting.strategy.max_open_trades = 1
    backtesting.config.update({'max_open_trades': 1})
    backtest_conf = {
        'processed': deepcopy(processed),
        'start_date': min_date,
        'end_date': max_date,
    }
    results = backtesting.backtest(**backtest_conf)
    assert len(evaluate_result_multi(results['results'], '5m', 1)) == 0


def test_backtest_start_timerange(default_conf, mocker, caplog, testdatadir):

    patch_exchange(mocker)
    mocker.patch('freqtrade.optimize.backtesting.Backtesting.backtest')
    mocker.patch('freqtrade.optimize.backtesting.generate_backtest_stats')
    mocker.patch('freqtrade.optimize.backtesting.show_backtest_results')
    mocker.patch('freqtrade.plugins.pairlistmanager.PairListManager.whitelist',
                 PropertyMock(return_value=['UNITTEST/BTC']))
    patched_configuration_load_config_file(mocker, default_conf)

    args = [
        'backtesting',
        '--config', 'config.json',
        '--strategy', CURRENT_TEST_STRATEGY,
        '--datadir', str(testdatadir),
        '--timeframe', '1m',
        '--timerange', '1510694220-1510700340',
        '--enable-position-stacking',
        '--disable-max-market-positions'
    ]
    args = get_args(args)
    start_backtesting(args)
    # check the logs, that will contain the backtest result
    exists = [
        'Parameter -i/--timeframe detected ... Using timeframe: 1m ...',
        'Ignoring max_open_trades (--disable-max-market-positions was used) ...',
        'Parameter --timerange detected: 1510694220-1510700340 ...',
        f'Using data directory: {testdatadir} ...',
        'Loading data from 2017-11-14 20:57:00 '
        'up to 2017-11-14 22:59:00 (0 days).',
        'Backtesting with data from 2017-11-14 21:17:00 '
        'up to 2017-11-14 22:59:00 (0 days).',
        'Parameter --enable-position-stacking detected ...'
    ]

    for line in exists:
        assert log_has(line, caplog)


@pytest.mark.filterwarnings("ignore:deprecated")
def test_backtest_start_multi_strat(default_conf, mocker, caplog, testdatadir):

    default_conf.update({
        "use_exit_signal": True,
        "exit_profit_only": False,
        "exit_profit_offset": 0.0,
        "ignore_roi_if_entry_signal": False,
    })
    patch_exchange(mocker)
    backtestmock = MagicMock(return_value={
        'results': pd.DataFrame(columns=BT_DATA_COLUMNS),
        'config': default_conf,
        'locks': [],
        'rejected_signals': 20,
        'timedout_entry_orders': 0,
        'timedout_exit_orders': 0,
        'canceled_trade_entries': 0,
        'canceled_entry_orders': 0,
        'replaced_entry_orders': 0,
        'final_balance': 1000,
    })
    mocker.patch('freqtrade.plugins.pairlistmanager.PairListManager.whitelist',
                 PropertyMock(return_value=['UNITTEST/BTC']))
    mocker.patch('freqtrade.optimize.backtesting.Backtesting.backtest', backtestmock)
    text_table_mock = MagicMock()
    sell_reason_mock = MagicMock()
    strattable_mock = MagicMock()
    strat_summary = MagicMock()

    mocker.patch.multiple('freqtrade.optimize.optimize_reports',
                          text_table_bt_results=text_table_mock,
                          text_table_strategy=strattable_mock,
                          generate_pair_metrics=MagicMock(),
                          generate_exit_reason_stats=sell_reason_mock,
                          generate_strategy_comparison=strat_summary,
                          generate_daily_stats=MagicMock(),
                          )
    patched_configuration_load_config_file(mocker, default_conf)

    args = [
        'backtesting',
        '--config', 'config.json',
        '--datadir', str(testdatadir),
        '--strategy-path', str(Path(__file__).parents[1] / 'strategy/strats'),
        '--timeframe', '1m',
        '--timerange', '1510694220-1510700340',
        '--enable-position-stacking',
        '--disable-max-market-positions',
        '--strategy-list',
        CURRENT_TEST_STRATEGY,
        'StrategyTestV2',
    ]
    args = get_args(args)
    start_backtesting(args)
    # 2 backtests, 4 tables
    assert backtestmock.call_count == 2
    assert text_table_mock.call_count == 4
    assert strattable_mock.call_count == 1
    assert sell_reason_mock.call_count == 2
    assert strat_summary.call_count == 1

    # check the logs, that will contain the backtest result
    exists = [
        'Parameter -i/--timeframe detected ... Using timeframe: 1m ...',
        'Ignoring max_open_trades (--disable-max-market-positions was used) ...',
        'Parameter --timerange detected: 1510694220-1510700340 ...',
        f'Using data directory: {testdatadir} ...',
        'Loading data from 2017-11-14 20:57:00 '
        'up to 2017-11-14 22:59:00 (0 days).',
        'Backtesting with data from 2017-11-14 21:17:00 '
        'up to 2017-11-14 22:59:00 (0 days).',
        'Parameter --enable-position-stacking detected ...',
        f'Running backtesting for Strategy {CURRENT_TEST_STRATEGY}',
        'Running backtesting for Strategy StrategyTestV2',
    ]

    for line in exists:
        assert log_has(line, caplog)


def test_backtest_start_multi_strat_nomock(default_conf, mocker, caplog, testdatadir, capsys):
    default_conf.update({
        "use_exit_signal": True,
        "exit_profit_only": False,
        "exit_profit_offset": 0.0,
        "ignore_roi_if_entry_signal": False,
    })
    patch_exchange(mocker)
    result1 = pd.DataFrame({'pair': ['XRP/BTC', 'LTC/BTC'],
                            'profit_ratio': [0.0, 0.0],
                            'profit_abs': [0.0, 0.0],
                            'open_date': pd.to_datetime(['2018-01-29 18:40:00',
                                                         '2018-01-30 03:30:00', ], utc=True
                                                        ),
                            'close_date': pd.to_datetime(['2018-01-29 20:45:00',
                                                          '2018-01-30 05:35:00', ], utc=True),
                            'trade_duration': [235, 40],
                            'is_open': [False, False],
                            'stake_amount': [0.01, 0.01],
                            'open_rate': [0.104445, 0.10302485],
                            'close_rate': [0.104969, 0.103541],
                            "is_short": [False, False],

                            'exit_reason': [ExitType.ROI, ExitType.ROI]
                            })
    result2 = pd.DataFrame({'pair': ['XRP/BTC', 'LTC/BTC', 'ETH/BTC'],
                            'profit_ratio': [0.03, 0.01, 0.1],
                            'profit_abs': [0.01, 0.02, 0.2],
                            'open_date': pd.to_datetime(['2018-01-29 18:40:00',
                                                         '2018-01-30 03:30:00',
                                                         '2018-01-30 05:30:00'], utc=True
                                                        ),
                            'close_date': pd.to_datetime(['2018-01-29 20:45:00',
                                                          '2018-01-30 05:35:00',
                                                          '2018-01-30 08:30:00'], utc=True),
                            'trade_duration': [47, 40, 20],
                            'is_open': [False, False, False],
                            'stake_amount': [0.01, 0.01, 0.01],
                            'open_rate': [0.104445, 0.10302485, 0.122541],
                            'close_rate': [0.104969, 0.103541, 0.123541],
                            "is_short": [False, False, False],
                            'exit_reason': [ExitType.ROI, ExitType.ROI, ExitType.STOP_LOSS]
                            })
    backtestmock = MagicMock(side_effect=[
        {
            'results': result1,
            'config': default_conf,
            'locks': [],
            'rejected_signals': 20,
            'timedout_entry_orders': 0,
            'timedout_exit_orders': 0,
            'canceled_trade_entries': 0,
            'canceled_entry_orders': 0,
            'replaced_entry_orders': 0,
            'final_balance': 1000,
        },
        {
            'results': result2,
            'config': default_conf,
            'locks': [],
            'rejected_signals': 20,
            'timedout_entry_orders': 0,
            'timedout_exit_orders': 0,
            'canceled_trade_entries': 0,
            'canceled_entry_orders': 0,
            'replaced_entry_orders': 0,
            'final_balance': 1000,
        }
    ])
    mocker.patch('freqtrade.plugins.pairlistmanager.PairListManager.whitelist',
                 PropertyMock(return_value=['UNITTEST/BTC']))
    mocker.patch('freqtrade.optimize.backtesting.Backtesting.backtest', backtestmock)

    patched_configuration_load_config_file(mocker, default_conf)

    args = [
        'backtesting',
        '--config', 'config.json',
        '--datadir', str(testdatadir),
        '--strategy-path', str(Path(__file__).parents[1] / 'strategy/strats'),
        '--timeframe', '1m',
        '--timerange', '1510694220-1510700340',
        '--enable-position-stacking',
        '--disable-max-market-positions',
        '--breakdown', 'day',
        '--strategy-list',
        CURRENT_TEST_STRATEGY,
        'StrategyTestV2',
    ]
    args = get_args(args)
    start_backtesting(args)

    # check the logs, that will contain the backtest result
    exists = [
        'Parameter -i/--timeframe detected ... Using timeframe: 1m ...',
        'Ignoring max_open_trades (--disable-max-market-positions was used) ...',
        'Parameter --timerange detected: 1510694220-1510700340 ...',
        f'Using data directory: {testdatadir} ...',
        'Loading data from 2017-11-14 20:57:00 '
        'up to 2017-11-14 22:59:00 (0 days).',
        'Backtesting with data from 2017-11-14 21:17:00 '
        'up to 2017-11-14 22:59:00 (0 days).',
        'Parameter --enable-position-stacking detected ...',
        f'Running backtesting for Strategy {CURRENT_TEST_STRATEGY}',
        'Running backtesting for Strategy StrategyTestV2',
    ]

    for line in exists:
        assert log_has(line, caplog)

    captured = capsys.readouterr()
    assert 'BACKTESTING REPORT' in captured.out
    assert 'EXIT REASON STATS' in captured.out
    assert 'DAY BREAKDOWN' in captured.out
    assert 'LEFT OPEN TRADES REPORT' in captured.out
    assert '2017-11-14 21:17:00 -> 2017-11-14 22:59:00 | Max open trades : 1' in captured.out
    assert 'STRATEGY SUMMARY' in captured.out


@pytest.mark.filterwarnings("ignore:deprecated")
def test_backtest_start_futures_noliq(default_conf_usdt, mocker,
                                      caplog, testdatadir, capsys):
    # Tests detail-data loading
    default_conf_usdt.update({
        "trading_mode": "futures",
        "margin_mode": "isolated",
        "use_exit_signal": True,
        "exit_profit_only": False,
        "exit_profit_offset": 0.0,
        "ignore_roi_if_entry_signal": False,
        "strategy": CURRENT_TEST_STRATEGY,
    })
    patch_exchange(mocker)

    mocker.patch('freqtrade.plugins.pairlistmanager.PairListManager.whitelist',
                 PropertyMock(return_value=['HULUMULU/USDT', 'XRP/USDT:USDT']))
    # mocker.patch('freqtrade.optimize.backtesting.Backtesting.backtest', backtestmock)

    patched_configuration_load_config_file(mocker, default_conf_usdt)

    args = [
        'backtesting',
        '--config', 'config.json',
        '--datadir', str(testdatadir),
        '--strategy-path', str(Path(__file__).parents[1] / 'strategy/strats'),
        '--timeframe', '1h',
    ]
    args = get_args(args)
    with pytest.raises(OperationalException, match=r"Pairs .* got no leverage tiers available\."):
        start_backtesting(args)


@pytest.mark.filterwarnings("ignore:deprecated")
def test_backtest_start_nomock_futures(default_conf_usdt, mocker,
                                       caplog, testdatadir, capsys):
    # Tests detail-data loading
    default_conf_usdt.update({
        "trading_mode": "futures",
        "margin_mode": "isolated",
        "use_exit_signal": True,
        "exit_profit_only": False,
        "exit_profit_offset": 0.0,
        "ignore_roi_if_entry_signal": False,
        "strategy": CURRENT_TEST_STRATEGY,
    })
    patch_exchange(mocker)
    result1 = pd.DataFrame({'pair': ['XRP/USDT:USDT', 'XRP/USDT:USDT'],
                            'profit_ratio': [0.0, 0.0],
                            'profit_abs': [0.0, 0.0],
                            'open_date': pd.to_datetime(['2021-11-18 18:00:00',
                                                         '2021-11-18 03:00:00', ], utc=True
                                                        ),
                            'close_date': pd.to_datetime(['2021-11-18 20:00:00',
                                                          '2021-11-18 05:00:00', ], utc=True),
                            'trade_duration': [235, 40],
                            'is_open': [False, False],
                            'is_short': [False, False],
                            'stake_amount': [0.01, 0.01],
                            'open_rate': [0.104445, 0.10302485],
                            'close_rate': [0.104969, 0.103541],
                            'exit_reason': [ExitType.ROI, ExitType.ROI]
                            })
    result2 = pd.DataFrame({'pair': ['XRP/USDT:USDT', 'XRP/USDT:USDT', 'XRP/USDT:USDT'],
                            'profit_ratio': [0.03, 0.01, 0.1],
                            'profit_abs': [0.01, 0.02, 0.2],
                            'open_date': pd.to_datetime(['2021-11-19 18:00:00',
                                                         '2021-11-19 03:00:00',
                                                         '2021-11-19 05:00:00'], utc=True
                                                        ),
                            'close_date': pd.to_datetime(['2021-11-19 20:00:00',
                                                          '2021-11-19 05:00:00',
                                                          '2021-11-19 08:00:00'], utc=True),
                            'trade_duration': [47, 40, 20],
                            'is_open': [False, False, False],
                            'is_short': [False, False, False],
                            'stake_amount': [0.01, 0.01, 0.01],
                            'open_rate': [0.104445, 0.10302485, 0.122541],
                            'close_rate': [0.104969, 0.103541, 0.123541],
                            'exit_reason': [ExitType.ROI, ExitType.ROI, ExitType.STOP_LOSS]
                            })
    backtestmock = MagicMock(side_effect=[
        {
            'results': result1,
            'config': default_conf_usdt,
            'locks': [],
            'rejected_signals': 20,
            'timedout_entry_orders': 0,
            'timedout_exit_orders': 0,
            'canceled_trade_entries': 0,
            'canceled_entry_orders': 0,
            'replaced_entry_orders': 0,
            'final_balance': 1000,
        },
        {
            'results': result2,
            'config': default_conf_usdt,
            'locks': [],
            'rejected_signals': 20,
            'timedout_entry_orders': 0,
            'timedout_exit_orders': 0,
            'canceled_trade_entries': 0,
            'canceled_entry_orders': 0,
            'replaced_entry_orders': 0,
            'final_balance': 1000,
        }
    ])
    mocker.patch('freqtrade.plugins.pairlistmanager.PairListManager.whitelist',
                 PropertyMock(return_value=['XRP/USDT:USDT']))
    mocker.patch('freqtrade.optimize.backtesting.Backtesting.backtest', backtestmock)

    patched_configuration_load_config_file(mocker, default_conf_usdt)

    args = [
        'backtesting',
        '--config', 'config.json',
        '--datadir', str(testdatadir),
        '--strategy-path', str(Path(__file__).parents[1] / 'strategy/strats'),
        '--timeframe', '1h',
    ]
    args = get_args(args)
    start_backtesting(args)

    # check the logs, that will contain the backtest result
    exists = [
        'Parameter -i/--timeframe detected ... Using timeframe: 1h ...',
        f'Using data directory: {testdatadir} ...',
        'Loading data from 2021-11-17 01:00:00 '
        'up to 2021-11-21 04:00:00 (4 days).',
        'Backtesting with data from 2021-11-17 21:00:00 '
        'up to 2021-11-21 04:00:00 (3 days).',
        'XRP/USDT:USDT, funding_rate, 8h, data starts at 2021-11-18 00:00:00',
        'XRP/USDT:USDT, mark, 8h, data starts at 2021-11-18 00:00:00',
        f'Running backtesting for Strategy {CURRENT_TEST_STRATEGY}',
    ]

    for line in exists:
        assert log_has(line, caplog)

    captured = capsys.readouterr()
    assert 'BACKTESTING REPORT' in captured.out
    assert 'EXIT REASON STATS' in captured.out
    assert 'LEFT OPEN TRADES REPORT' in captured.out


@pytest.mark.filterwarnings("ignore:deprecated")
def test_backtest_start_multi_strat_nomock_detail(default_conf, mocker,
                                                  caplog, testdatadir, capsys):
    # Tests detail-data loading
    default_conf.update({
        "use_exit_signal": True,
        "exit_profit_only": False,
        "exit_profit_offset": 0.0,
        "ignore_roi_if_entry_signal": False,
    })
    patch_exchange(mocker)
    result1 = pd.DataFrame({'pair': ['XRP/BTC', 'LTC/BTC'],
                            'profit_ratio': [0.0, 0.0],
                            'profit_abs': [0.0, 0.0],
                            'open_date': pd.to_datetime(['2018-01-29 18:40:00',
                                                         '2018-01-30 03:30:00', ], utc=True
                                                        ),
                            'close_date': pd.to_datetime(['2018-01-29 20:45:00',
                                                          '2018-01-30 05:35:00', ], utc=True),
                            'trade_duration': [235, 40],
                            'is_open': [False, False],
                            'is_short': [False, False],
                            'stake_amount': [0.01, 0.01],
                            'open_rate': [0.104445, 0.10302485],
                            'close_rate': [0.104969, 0.103541],
                            'exit_reason': [ExitType.ROI, ExitType.ROI]
                            })
    result2 = pd.DataFrame({'pair': ['XRP/BTC', 'LTC/BTC', 'ETH/BTC'],
                            'profit_ratio': [0.03, 0.01, 0.1],
                            'profit_abs': [0.01, 0.02, 0.2],
                            'open_date': pd.to_datetime(['2018-01-29 18:40:00',
                                                         '2018-01-30 03:30:00',
                                                         '2018-01-30 05:30:00'], utc=True
                                                        ),
                            'close_date': pd.to_datetime(['2018-01-29 20:45:00',
                                                          '2018-01-30 05:35:00',
                                                          '2018-01-30 08:30:00'], utc=True),
                            'trade_duration': [47, 40, 20],
                            'is_open': [False, False, False],
                            'is_short': [False, False, False],
                            'stake_amount': [0.01, 0.01, 0.01],
                            'open_rate': [0.104445, 0.10302485, 0.122541],
                            'close_rate': [0.104969, 0.103541, 0.123541],
                            'exit_reason': [ExitType.ROI, ExitType.ROI, ExitType.STOP_LOSS]
                            })
    backtestmock = MagicMock(side_effect=[
        {
            'results': result1,
            'config': default_conf,
            'locks': [],
            'rejected_signals': 20,
            'timedout_entry_orders': 0,
            'timedout_exit_orders': 0,
            'canceled_trade_entries': 0,
            'canceled_entry_orders': 0,
            'replaced_entry_orders': 0,
            'final_balance': 1000,
        },
        {
            'results': result2,
            'config': default_conf,
            'locks': [],
            'rejected_signals': 20,
            'timedout_entry_orders': 0,
            'timedout_exit_orders': 0,
            'canceled_trade_entries': 0,
            'canceled_entry_orders': 0,
            'replaced_entry_orders': 0,
            'final_balance': 1000,
        }
    ])
    mocker.patch('freqtrade.plugins.pairlistmanager.PairListManager.whitelist',
                 PropertyMock(return_value=['XRP/ETH']))
    mocker.patch('freqtrade.optimize.backtesting.Backtesting.backtest', backtestmock)

    patched_configuration_load_config_file(mocker, default_conf)

    args = [
        'backtesting',
        '--config', 'config.json',
        '--datadir', str(testdatadir),
        '--strategy-path', str(Path(__file__).parents[1] / 'strategy/strats'),
        '--timeframe', '5m',
        '--timeframe-detail', '1m',
        '--strategy-list',
        CURRENT_TEST_STRATEGY
    ]
    args = get_args(args)
    start_backtesting(args)

    # check the logs, that will contain the backtest result
    exists = [
        'Parameter -i/--timeframe detected ... Using timeframe: 5m ...',
        'Parameter --timeframe-detail detected, using 1m for intra-candle backtesting ...',
        f'Using data directory: {testdatadir} ...',
        'Loading data from 2019-10-11 00:00:00 '
        'up to 2019-10-13 11:15:00 (2 days).',
        'Backtesting with data from 2019-10-11 01:40:00 '
        'up to 2019-10-13 11:15:00 (2 days).',
        f'Running backtesting for Strategy {CURRENT_TEST_STRATEGY}',
    ]

    for line in exists:
        assert log_has(line, caplog)

    captured = capsys.readouterr()
    assert 'BACKTESTING REPORT' in captured.out
    assert 'EXIT REASON STATS' in captured.out
    assert 'LEFT OPEN TRADES REPORT' in captured.out


@pytest.mark.filterwarnings("ignore:deprecated")
@pytest.mark.parametrize('run_id', ['2', 'changed'])
@pytest.mark.parametrize('start_delta', [{'days': 0}, {'days': 1}, {'weeks': 1}, {'weeks': 4}])
@pytest.mark.parametrize('cache', constants.BACKTEST_CACHE_AGE)
def test_backtest_start_multi_strat_caching(default_conf, mocker, caplog, testdatadir, run_id,
                                            start_delta, cache):
    default_conf.update({
        "use_exit_signal": True,
        "exit_profit_only": False,
        "exit_profit_offset": 0.0,
        "ignore_roi_if_entry_signal": False,
    })
    patch_exchange(mocker)
    backtestmock = MagicMock(return_value={
        'results': pd.DataFrame(columns=BT_DATA_COLUMNS),
        'config': default_conf,
        'locks': [],
        'rejected_signals': 20,
        'timedout_entry_orders': 0,
        'timedout_exit_orders': 0,
        'canceled_trade_entries': 0,
        'canceled_entry_orders': 0,
        'replaced_entry_orders': 0,
        'final_balance': 1000,
    })
    mocker.patch('freqtrade.plugins.pairlistmanager.PairListManager.whitelist',
                 PropertyMock(return_value=['UNITTEST/BTC']))
    mocker.patch('freqtrade.optimize.backtesting.Backtesting.backtest', backtestmock)
    mocker.patch('freqtrade.optimize.backtesting.show_backtest_results', MagicMock())

    now = min_backtest_date = datetime.now(tz=timezone.utc)
    start_time = now - timedelta(**start_delta) + timedelta(hours=1)
    if cache == 'none':
        min_backtest_date = now + timedelta(days=1)
    elif cache == 'day':
        min_backtest_date = now - timedelta(days=1)
    elif cache == 'week':
        min_backtest_date = now - timedelta(weeks=1)
    elif cache == 'month':
        min_backtest_date = now - timedelta(weeks=4)
    load_backtest_metadata = MagicMock(return_value={
        'StrategyTestV2': {'run_id': '1', 'backtest_start_time': now.timestamp()},
        'StrategyTestV3': {'run_id': run_id, 'backtest_start_time': start_time.timestamp()}
    })
    load_backtest_stats = MagicMock(side_effect=[
        {
            'metadata': {'StrategyTestV2': {'run_id': '1'}},
            'strategy': {'StrategyTestV2': {}},
            'strategy_comparison': [{'key': 'StrategyTestV2'}]
        },
        {
            'metadata': {'StrategyTestV3': {'run_id': '2'}},
            'strategy': {'StrategyTestV3': {}},
            'strategy_comparison': [{'key': 'StrategyTestV3'}]
        }
    ])
    mocker.patch('pathlib.Path.glob', return_value=[
        Path(datetime.strftime(datetime.now(), 'backtest-result-%Y-%m-%d_%H-%M-%S.json'))])
    mocker.patch.multiple('freqtrade.data.btanalysis',
                          load_backtest_metadata=load_backtest_metadata,
                          load_backtest_stats=load_backtest_stats)
    mocker.patch('freqtrade.optimize.backtesting.get_strategy_run_id', side_effect=['1', '2', '2'])

    patched_configuration_load_config_file(mocker, default_conf)

    args = [
        'backtesting',
        '--config', 'config.json',
        '--datadir', str(testdatadir),
        '--strategy-path', str(Path(__file__).parents[1] / 'strategy/strats'),
        '--timeframe', '1m',
        '--timerange', '1510694220-1510700340',
        '--enable-position-stacking',
        '--disable-max-market-positions',
        '--cache', cache,
        '--strategy-list',
        'StrategyTestV2',
        'StrategyTestV3',
    ]
    args = get_args(args)
    start_backtesting(args)

    # check the logs, that will contain the backtest result
    exists = [
        'Parameter -i/--timeframe detected ... Using timeframe: 1m ...',
        'Parameter --timerange detected: 1510694220-1510700340 ...',
        f'Using data directory: {testdatadir} ...',
        'Loading data from 2017-11-14 20:57:00 '
        'up to 2017-11-14 22:59:00 (0 days).',
        'Parameter --enable-position-stacking detected ...',
    ]

    for line in exists:
        assert log_has(line, caplog)

    if cache == 'none':
        assert backtestmock.call_count == 2
        exists = [
            'Running backtesting for Strategy StrategyTestV2',
            'Running backtesting for Strategy StrategyTestV3',
            'Ignoring max_open_trades (--disable-max-market-positions was used) ...',
            'Backtesting with data from 2017-11-14 21:17:00 up to 2017-11-14 22:59:00 (0 days).',
        ]
    elif run_id == '2' and min_backtest_date < start_time:
        assert backtestmock.call_count == 0
        exists = [
            'Reusing result of previous backtest for StrategyTestV2',
            'Reusing result of previous backtest for StrategyTestV3',
        ]
    else:
        exists = [
            'Reusing result of previous backtest for StrategyTestV2',
            'Running backtesting for Strategy StrategyTestV3',
            'Ignoring max_open_trades (--disable-max-market-positions was used) ...',
            'Backtesting with data from 2017-11-14 21:17:00 up to 2017-11-14 22:59:00 (0 days).',
        ]
        assert backtestmock.call_count == 1

    for line in exists:
        assert log_has(line, caplog)


def test_get_strategy_run_id(default_conf_usdt):
    default_conf_usdt.update({
        'strategy': 'StrategyTestV2',
        'max_open_trades': float('inf')
    })
    strategy = StrategyResolver.load_strategy(default_conf_usdt)
    x = get_strategy_run_id(strategy)
    assert isinstance(x, str)