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

import json
import random
import math
from typing import List
from copy import deepcopy
from unittest.mock import MagicMock
from arrow import Arrow
import pandas as pd
import numpy as np
from freqtrade import optimize
from freqtrade.optimize.backtesting import Backtesting, start, setup_configuration
from freqtrade.arguments import Arguments
from freqtrade.analyze import Analyze
from freqtrade.tests.conftest import default_conf, log_has


# Avoid to reinit the same object again and again
_BACKTESTING = Backtesting(default_conf())


def get_args(args) -> List[str]:
    return Arguments(args, '').get_parsed_arg()


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


def load_data_test(what):
    timerange = ((None, 'line'), None, -100)
    data = optimize.load_data(None, ticker_interval=1, pairs=['BTC_UNITEST'], timerange=timerange)
    pair = data['BTC_UNITEST']
    datalen = len(pair)
    # Depending on the what parameter we now adjust the
    # loaded data looks:
    # pair :: [{'O': 0.123, 'H': 0.123, 'L': 0.123,
    #           'C': 0.123, 'V': 123.123,
    #           'T': '2017-11-04T23:02:00', 'BV': 0.123}]
    base = 0.001
    if what == 'raise':
        return {'BTC_UNITEST':
                [{'T': pair[x]['T'],  # Keep old dates
                  'V': pair[x]['V'],  # Keep old volume
                  'BV': pair[x]['BV'],  # keep too
                  'O': x * base,        # But replace O,H,L,C
                  'H': x * base + 0.0001,
                  'L': x * base - 0.0001,
                  'C': x * base} for x in range(0, datalen)]}
    if what == 'lower':
        return {'BTC_UNITEST':
                [{'T': pair[x]['T'],  # Keep old dates
                  'V': pair[x]['V'],  # Keep old volume
                  'BV': pair[x]['BV'],  # keep too
                  'O': 1 - x * base,        # But replace O,H,L,C
                  'H': 1 - x * base + 0.0001,
                  'L': 1 - x * base - 0.0001,
                  'C': 1 - x * base} for x in range(0, datalen)]}
    if what == 'sine':
        hz = 0.1  # frequency
        return {'BTC_UNITEST':
                [{'T': pair[x]['T'],  # Keep old dates
                  'V': pair[x]['V'],  # Keep old volume
                  'BV': pair[x]['BV'],  # keep too
                  # But replace O,H,L,C
                  'O': math.sin(x * hz) / 1000 + base,
                  'H': math.sin(x * hz) / 1000 + base + 0.0001,
                  'L': math.sin(x * hz) / 1000 + base - 0.0001,
                  'C': math.sin(x * hz) / 1000 + base} for x in range(0, datalen)]}
    return data


def simple_backtest(config, contour, num_results) -> None:
    backtesting = _BACKTESTING

    data = load_data_test(contour)
    processed = backtesting.tickerdata_to_dataframe(data)
    assert isinstance(processed, dict)
    results = backtesting.backtest(
        {
            'stake_amount': config['stake_amount'],
            'processed': processed,
            'max_open_trades': 1,
            'realistic': True
        }
    )
    # results :: <class 'pandas.core.frame.DataFrame'>
    assert len(results) == num_results


def mocked_load_data(datadir, pairs=[], ticker_interval=0, refresh_pairs=False, timerange=None):
    tickerdata = optimize.load_tickerdata_file(datadir, 'BTC_UNITEST', 1, timerange=timerange)
    pairdata = {'BTC_UNITEST': tickerdata}
    return pairdata


# use for mock freqtrade.exchange.get_ticker_history'
def _load_pair_as_ticks(pair, tickfreq):
    ticks = optimize.load_data(None, ticker_interval=tickfreq, pairs=[pair])
    ticks = trim_dictlist(ticks, -200)
    return ticks[pair]


# FIX: fixturize this?
def _make_backtest_conf(conf=None, pair='BTC_UNITEST', record=None):
    data = optimize.load_data(None, ticker_interval=8, pairs=[pair])
    data = trim_dictlist(data, -200)
    return {
        'stake_amount': conf['stake_amount'],
        'processed': _BACKTESTING.tickerdata_to_dataframe(data),
        'max_open_trades': 10,
        'realistic': True,
        'record': record
    }


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['buy'])):
        if random.random() > 0.5:  # Both buy and sell signals at same timeframe
            buy[i] = buy_value
            sell[i] = sell_value
    signals['buy'] = buy
    signals['sell'] = sell
    return signals


def _trend_alternate(dataframe=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['buy'] = buy
    signals['sell'] = sell
    return dataframe


def _run_backtest_1(fun, backtest_conf):
    # strategy is a global (hidden as a singleton), so we
    # emulate strategy being pure, by override/restore here
    # if we dont do this, the override in strategy will carry over
    # to other tests
    old_buy = _BACKTESTING.populate_buy_trend
    old_sell = _BACKTESTING.populate_sell_trend
    _BACKTESTING.populate_buy_trend = fun  # Override
    _BACKTESTING.populate_sell_trend = fun  # Override
    results = _BACKTESTING.backtest(backtest_conf)
    _BACKTESTING.populate_buy_trend = old_buy  # restore override
    _BACKTESTING.populate_sell_trend = old_sell  # restore override
    return results


# Unit tests
def test_setup_configuration_without_arguments(mocker, default_conf, caplog) -> None:
    """
    Test setup_configuration() function
    """
    mocker.patch('freqtrade.configuration.open', mocker.mock_open(
        read_data=json.dumps(default_conf)
    ))

    args = [
        '--config', 'config.json',
        '--strategy', 'default_strategy',
        'backtesting'
    ]

    config = setup_configuration(get_args(args))
    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(
        'Parameter --datadir detected: {} ...'.format(config['datadir']),
        caplog.record_tuples
    )
    assert 'ticker_interval' in config
    assert not log_has('Parameter -i/--ticker-interval detected ...', caplog.record_tuples)

    assert 'live' not in config
    assert not log_has('Parameter -l/--live detected ...', caplog.record_tuples)

    assert 'realistic_simulation' not in config
    assert not log_has('Parameter --realistic-simulation detected ...', caplog.record_tuples)

    assert 'refresh_pairs' not in config
    assert not log_has('Parameter -r/--refresh-pairs-cached detected ...', caplog.record_tuples)

    assert 'timerange' not in config
    assert 'export' not in config


def test_setup_configuration_with_arguments(mocker, default_conf, caplog) -> None:
    """
    Test setup_configuration() function
    """
    mocker.patch('freqtrade.configuration.open', mocker.mock_open(
        read_data=json.dumps(default_conf)
    ))

    args = [
        '--config', 'config.json',
        '--strategy', 'default_strategy',
        '--datadir', '/foo/bar',
        'backtesting',
        '--ticker-interval', '1',
        '--live',
        '--realistic-simulation',
        '--refresh-pairs-cached',
        '--timerange', ':100',
        '--export', '/bar/foo'
    ]

    config = setup_configuration(get_args(args))
    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(
        'Parameter --datadir detected: {} ...'.format(config['datadir']),
        caplog.record_tuples
    )
    assert 'ticker_interval' in config
    assert log_has('Parameter -i/--ticker-interval detected ...', caplog.record_tuples)
    assert log_has(
        'Using ticker_interval: 1 ...',
        caplog.record_tuples
    )

    assert 'live' in config
    assert log_has('Parameter -l/--live detected ...', caplog.record_tuples)

    assert 'realistic_simulation'in config
    assert log_has('Parameter --realistic-simulation detected ...', caplog.record_tuples)
    assert log_has('Using max_open_trades: 1 ...', caplog.record_tuples)

    assert 'refresh_pairs'in config
    assert log_has('Parameter -r/--refresh-pairs-cached detected ...', caplog.record_tuples)
    assert 'timerange' in config
    assert log_has(
        'Parameter --timerange detected: {} ...'.format(config['timerange']),
        caplog.record_tuples
    )

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


def test_start(mocker, default_conf, caplog) -> None:
    """
    Test start() function
    """
    start_mock = MagicMock()
    mocker.patch('freqtrade.optimize.backtesting.Backtesting.start', start_mock)
    mocker.patch('freqtrade.configuration.open', mocker.mock_open(
        read_data=json.dumps(default_conf)
    ))
    args = [
        '--config', 'config.json',
        '--strategy', 'default_strategy',
        'backtesting'
    ]
    args = get_args(args)
    start(args)
    assert log_has(
        'Starting freqtrade in Backtesting mode',
        caplog.record_tuples
    )
    assert start_mock.call_count == 1


def test_backtesting__init__(mocker, default_conf) -> None:
    """
    Test Backtesting.__init__() method
    """
    init_mock = MagicMock()
    mocker.patch('freqtrade.optimize.backtesting.Backtesting._init', init_mock)

    backtesting = Backtesting(default_conf)
    assert backtesting.config == default_conf
    assert backtesting.analyze is None
    assert backtesting.ticker_interval is None
    assert backtesting.tickerdata_to_dataframe is None
    assert backtesting.populate_buy_trend is None
    assert backtesting.populate_sell_trend is None
    assert init_mock.call_count == 1


def test_backtesting_init(default_conf) -> None:
    """
    Test Backtesting._init() method
    """
    backtesting = Backtesting(default_conf)
    assert backtesting.config == default_conf
    assert isinstance(backtesting.analyze, Analyze)
    assert backtesting.ticker_interval == 5
    assert callable(backtesting.tickerdata_to_dataframe)
    assert callable(backtesting.populate_buy_trend)
    assert callable(backtesting.populate_sell_trend)


def test_tickerdata_to_dataframe(default_conf) -> None:
    """
    Test Backtesting.tickerdata_to_dataframe() method
    """

    timerange = ((None, 'line'), None, -100)
    tick = optimize.load_tickerdata_file(None, 'BTC_UNITEST', 1, timerange=timerange)
    tickerlist = {'BTC_UNITEST': tick}

    backtesting = _BACKTESTING
    data = backtesting.tickerdata_to_dataframe(tickerlist)
    assert len(data['BTC_UNITEST']) == 100

    # Load Analyze to compare the result between Backtesting function and Analyze are the same
    analyze = Analyze(default_conf)
    data2 = analyze.tickerdata_to_dataframe(tickerlist)
    assert data['BTC_UNITEST'].equals(data2['BTC_UNITEST'])


def test_get_timeframe() -> None:
    """
    Test Backtesting.get_timeframe() method
    """
    backtesting = _BACKTESTING

    data = backtesting.tickerdata_to_dataframe(
        optimize.load_data(
            None,
            ticker_interval=1,
            pairs=['BTC_UNITEST']
        )
    )
    min_date, max_date = backtesting.get_timeframe(data)
    assert min_date.isoformat() == '2017-11-04T23:02:00+00:00'
    assert max_date.isoformat() == '2017-11-14T22:59:00+00:00'


def test_generate_text_table():
    """
    Test Backtesting.generate_text_table() method
    """
    backtesting = _BACKTESTING

    results = pd.DataFrame(
        {
            'currency': ['BTC_ETH', 'BTC_ETH'],
            'profit_percent': [0.1, 0.2],
            'profit_BTC': [0.2, 0.4],
            'duration': [10, 30],
            'profit': [2, 0],
            'loss': [0, 0]
        }
    )

    result_str = (
        'pair       buy count    avg profit %    '
        'total profit BTC    avg duration    profit    loss\n'
        '-------  -----------  --------------  '
        '------------------  --------------  --------  ------\n'
        'BTC_ETH            2           15.00          '
        '0.60000000            20.0         2       0\n'
        'TOTAL              2           15.00          '
        '0.60000000            20.0         2       0'
    )

    assert backtesting._generate_text_table(data={'BTC_ETH': {}}, results=results) == result_str


def test_backtesting_start(default_conf, mocker, caplog) -> None:
    """
    Test Backtesting.start() method
    """
    def get_timeframe(input1, input2):
        return Arrow(2017, 11, 14, 21, 17), Arrow(2017, 11, 14, 22, 59)

    mocker.patch('freqtrade.freqtradebot.Analyze', MagicMock())
    mocker.patch('freqtrade.optimize.load_data', mocked_load_data)
    mocker.patch('freqtrade.exchange.get_ticker_history')
    mocker.patch.multiple(
        'freqtrade.optimize.backtesting.Backtesting',
        backtest=MagicMock(),
        _generate_text_table=MagicMock(return_value='1'),
        get_timeframe=get_timeframe,
    )

    conf = deepcopy(default_conf)
    conf['exchange']['pair_whitelist'] = ['BTC_UNITEST']
    conf['ticker_interval'] = 1
    conf['live'] = False
    conf['datadir'] = None
    conf['export'] = None
    conf['timerange'] = '-100'

    backtesting = Backtesting(conf)
    backtesting.start()
    # check the logs, that will contain the backtest result
    exists = [
        'Using local backtesting data (using whitelist in given config) ...',
        'Using stake_currency: BTC ...',
        'Using stake_amount: 0.001 ...',
        'Measuring data from 2017-11-14T21:17:00+00:00 '
        'up to 2017-11-14T22:59:00+00:00 (0 days)..'
    ]
    for line in exists:
        assert log_has(line, caplog.record_tuples)


def test_backtest(default_conf) -> None:
    """
    Test Backtesting.backtest() method
    """
    backtesting = _BACKTESTING

    data = optimize.load_data(None, ticker_interval=5, pairs=['BTC_ETH'])
    data = trim_dictlist(data, -200)
    results = backtesting.backtest(
        {
            'stake_amount': default_conf['stake_amount'],
            'processed': backtesting.tickerdata_to_dataframe(data),
            'max_open_trades': 10,
            'realistic': True
        }
    )
    assert not results.empty


def test_backtest_1min_ticker_interval(default_conf) -> None:
    """
    Test Backtesting.backtest() method with 1 min ticker
    """
    backtesting = _BACKTESTING

    # Run a backtesting for an exiting 5min ticker_interval
    data = optimize.load_data(None, ticker_interval=1, pairs=['BTC_UNITEST'])
    data = trim_dictlist(data, -200)
    results = backtesting.backtest(
        {
            'stake_amount': default_conf['stake_amount'],
            'processed': backtesting.tickerdata_to_dataframe(data),
            'max_open_trades': 1,
            'realistic': True
        }
    )
    assert not results.empty


def test_processed() -> None:
    """
    Test Backtesting.backtest() method with offline data
    """
    backtesting = _BACKTESTING

    dict_of_tickerrows = load_data_test('raise')
    dataframes = backtesting.tickerdata_to_dataframe(dict_of_tickerrows)
    dataframe = dataframes['BTC_UNITEST']
    cols = dataframe.columns
    # assert the dataframe got some of the indicator columns
    for col in ['close', 'high', 'low', 'open', 'date',
                'ema50', 'ao', 'macd', 'plus_dm']:
        assert col in cols


def test_backtest_pricecontours(default_conf) -> None:
    tests = [['raise', 17], ['lower', 0], ['sine', 17]]
    for [contour, numres] in tests:
        simple_backtest(default_conf, contour, numres)


# Test backtest using offline data (testdata directory)
def test_backtest_ticks(default_conf):
    ticks = [1, 5]
    fun = _BACKTESTING.populate_buy_trend
    for tick in ticks:
        backtest_conf = _make_backtest_conf(conf=default_conf)
        results = _run_backtest_1(fun, backtest_conf)
        assert not results.empty


def test_backtest_clash_buy_sell(default_conf):
    # Override the default buy trend function in our default_strategy
    def fun(dataframe=None):
        buy_value = 1
        sell_value = 1
        return _trend(dataframe, buy_value, sell_value)

    backtest_conf = _make_backtest_conf(conf=default_conf)
    results = _run_backtest_1(fun, backtest_conf)
    assert results.empty


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

    backtest_conf = _make_backtest_conf(conf=default_conf)
    results = _run_backtest_1(fun, backtest_conf)
    assert results.empty


def test_backtest_alternate_buy_sell(default_conf):
    backtest_conf = _make_backtest_conf(conf=default_conf, pair='BTC_UNITEST')
    results = _run_backtest_1(_trend_alternate, backtest_conf)
    assert len(results) == 3


def test_backtest_record(default_conf, mocker):
    names = []
    records = []
    mocker.patch(
        'freqtrade.optimize.backtesting.file_dump_json',
        new=lambda n, r: (names.append(n), records.append(r))
    )
    backtest_conf = _make_backtest_conf(
        conf=default_conf,
        pair='BTC_UNITEST',
        record="trades"
    )
    results = _run_backtest_1(_trend_alternate, backtest_conf)
    assert len(results) == 3
    # Assert file_dump_json was only called once
    assert names == ['backtest-result.json']
    records = records[0]
    # Ensure records are of correct type
    assert len(records) == 3
    # ('BTC_UNITEST', 0.00331158, '1510684320', '1510691700', 0, 117)
    # Below follows just a typecheck of the schema/type of trade-records
    oix = None
    for (pair, profit, date_buy, date_sell, buy_index, dur) in records:
        assert pair == 'BTC_UNITEST'
        isinstance(profit, float)
        # FIX: buy/sell should be converted to ints
        isinstance(date_buy, str)
        isinstance(date_sell, str)
        isinstance(buy_index, pd._libs.tslib.Timestamp)
        if oix:
            assert buy_index > oix
        oix = buy_index
        assert dur > 0


def test_backtest_start_live(default_conf, mocker, caplog):
    default_conf['exchange']['pair_whitelist'] = ['BTC_UNITEST']
    mocker.patch('freqtrade.exchange.get_ticker_history',
                 new=lambda n, i: _load_pair_as_ticks(n, i))
    mocker.patch('freqtrade.optimize.backtesting.Backtesting.backtest', MagicMock())
    mocker.patch('freqtrade.optimize.backtesting.Backtesting._generate_text_table', MagicMock())
    mocker.patch('freqtrade.configuration.open', mocker.mock_open(
        read_data=json.dumps(default_conf)
    ))

    args = MagicMock()
    args.ticker_interval = 1
    args.level = 10
    args.live = True
    args.datadir = None
    args.export = None
    args.strategy = 'default_strategy'
    args.timerange = '-100'  # needed due to MagicMock malleability

    args = [
        '--config', 'config.json',
        '--strategy', 'default_strategy',
        'backtesting',
        '--ticker-interval', '1',
        '--live',
        '--timerange', '-100'
    ]
    args = get_args(args)
    start(args)
    # check the logs, that will contain the backtest result
    exists = [
        'Parameter -i/--ticker-interval detected ...',
        'Using ticker_interval: 1 ...',
        'Parameter -l/--live detected ...',
        'Using max_open_trades: 1 ...',
        'Parameter --timerange detected: -100 ..',
        'Parameter --datadir detected: freqtrade/tests/testdata ...',
        'Using stake_currency: BTC ...',
        'Using stake_amount: 0.001 ...',
        'Downloading data for all pairs in whitelist ...',
        'Measuring data from 2017-11-14T19:32:00+00:00 up to 2017-11-14T22:59:00+00:00 (0 days)..'
    ]

    for line in exists:
        log_has(line, caplog.record_tuples)