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

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

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

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 RunMode, SellType
from freqtrade.exceptions import DependencyException, OperationalException
from freqtrade.optimize.backtesting import Backtesting
from freqtrade.persistence import LocalTrade
from freqtrade.resolvers import StrategyResolver
from tests.conftest import (get_args, log_has, log_has_re, patch_exchange,
                            patched_configuration_load_config_file)

def test_backtest_position_adjustment(default_conf, fee, mocker, testdatadir) -> None:
    default_conf['use_sell_signal'] = False
    mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
    mocker.patch("freqtrade.exchange.Exchange.get_min_pair_stake_amount", return_value=0.00001)
    patch_exchange(mocker)
    default_conf.update({
        "position_adjustment_enable": True,
        "stake_amount": 100.0,
        "dry_run_wallet": 1000.0,
        "strategy": "StrategyTestV2"
    })
    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=processed,
        start_date=min_date,
        end_date=max_date,
        max_open_trades=10,
        position_stacking=False,
    )
    results = result['results']
    assert not results.empty
    assert len(results) == 2

    expected = pd.DataFrame(
        {'pair': [pair, pair],
         'stake_amount': [500.0, 100.0],
         'amount': [4806.87657523, 970.63960782],
         '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, 00, 0).datetime,
                                       Arrow(2018, 1, 30, 4, 10, 0).datetime], utc=True),
         'open_rate': [0.10401764894444211, 0.10302485],
         'close_rate': [0.10453904066847439, 0.103541],
         'fee_open': [0.0025, 0.0025],
         'fee_close': [0.0025, 0.0025],
         'trade_duration': [200, 40],
         'profit_ratio': [0.0, 0.0],
         'profit_abs': [0.0, 0.0],
         'sell_reason': [SellType.ROI.value, SellType.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': [0.10481985, 0.1038888],
         'is_open': [False, False],
         'buy_tag': [None, None],
         })
    pd.testing.assert_frame_equal(results, expected)
    data_pair = processed[pair]
    for _, t in results.iterrows():
        ln = data_pair.loc[data_pair["date"] == t["open_date"]]
        # Check open trade rate alignes to open rate
        assert ln is not None
        # check close trade rate alignes to close rate or is between high and low
        ln = data_pair.loc[data_pair["date"] == t["close_date"]]
        assert (round(ln.iloc[0]["open"], 6) == round(t["close_rate"], 6) or
                round(ln.iloc[0]["low"], 6) < round(
                t["close_rate"], 6) < round(ln.iloc[0]["high"], 6))