# pragma pylint: disable=missing-docstring
from operator import itemgetter
import json
import logging
import os
from functools import reduce
from math import exp

import pytest
import arrow
from pandas import DataFrame
from qtpylib.indicators import crossed_above

from hyperopt import fmin, tpe, hp, Trials, STATUS_OK

from freqtrade.analyze import analyze_ticker
from freqtrade.main import should_sell
from freqtrade.persistence import Trade

from freqtrade.tests.test_backtesting import backtest, format_results

logging.disable(logging.DEBUG) # disable debug logs that slow backtesting a lot

# set TARGET_TRADES to suit your number concurrent trades so its realistic to 20days of data
TARGET_TRADES = 1200


@pytest.fixture
def pairs():
    return ['btc-neo', 'btc-eth', 'btc-omg', 'btc-edg', 'btc-pay',
            'btc-pivx', 'btc-qtum', 'btc-mtl', 'btc-etc', 'btc-ltc']

@pytest.fixture
def conf():
    return {
        "minimal_roi": {
            "40":  0.0,
            "30":  0.01,
            "20":  0.02,
            "0":  0.04
        },
        "stoploss": -0.05
    }

def buy_strategy_generator(params):
    print(params)
    def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
        conditions = []
        # GUARDS AND TRENDS
        if params['below_sma']['enabled']:
            conditions.append(dataframe['close'] < dataframe['sma'])
        if params['over_sma']['enabled']:
            conditions.append(dataframe['close'] > dataframe['sma'])
        if params['mfi']['enabled']:
            conditions.append(dataframe['mfi'] < params['mfi']['value'])
        if params['fastd']['enabled']:
            conditions.append(dataframe['fastd'] < params['fastd']['value'])
        if params['adx']['enabled']:
            conditions.append(dataframe['adx'] > params['adx']['value'])
        if params['cci']['enabled']:
            conditions.append(dataframe['cci'] < params['cci']['value'])
        if params['over_sar']['enabled']:
            conditions.append(dataframe['close'] > dataframe['sar'])
        if params['uptrend_sma']['enabled']:
            prevsma = dataframe['sma'].shift(1)
            conditions.append(dataframe['sma'] > prevsma)

        prev_fastd = dataframe['fastd'].shift(1)
        # TRIGGERS
        triggers = {
            'lower_bb': dataframe['tema'] <= dataframe['blower'],
            'faststoch10': (dataframe['fastd'] >= 10) & (prev_fastd < 10),
            'ao_cross_zero': (crossed_above(dataframe['ao'], 0.0)),
        }
        conditions.append(triggers.get(params['trigger']['type']))

        dataframe.loc[
            reduce(lambda x, y: x & y, conditions),
            'buy'] = 1
        dataframe.loc[dataframe['buy'] == 1, 'buy_price'] = dataframe['close']

        return dataframe
    return populate_buy_trend

@pytest.mark.skipif(not os.environ.get('BACKTEST', False), reason="BACKTEST not set")
def test_hyperopt(conf, pairs, mocker):
    def optimizer(params):
        buy_strategy = buy_strategy_generator(params)
        mocker.patch('freqtrade.analyze.populate_buy_trend', side_effect=buy_strategy)
        results = backtest(conf, pairs, mocker)

        result = format_results(results)
        print(result)

        total_profit = results.profit.sum() * 1000
        trade_count = len(results.index)
        
        trade_loss = 1 - 0.8 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5)
        profit_loss = exp(-total_profit**3 / 10**11)

        return {
            'loss': trade_loss + profit_loss,
            'status': STATUS_OK,
            'result': result
        }

    space = {
        'mfi': hp.choice('mfi', [
            {'enabled': False},
            {'enabled': True, 'value': hp.uniform('mfi-value', 2, 40)}
        ]),
        'fastd': hp.choice('fastd', [
            {'enabled': False},
            {'enabled': True, 'value': hp.uniform('fastd-value', 2, 40)}
        ]),
        'adx': hp.choice('adx', [
            {'enabled': False},
            {'enabled': True, 'value': hp.uniform('adx-value', 2, 40)}
        ]),
        'cci': hp.choice('cci', [
            {'enabled': False},
            {'enabled': True, 'value': hp.uniform('cci-value', -200, -100)}
        ]),
        'below_sma': hp.choice('below_sma', [
            {'enabled': False},
            {'enabled': True}
        ]),
        'over_sma': hp.choice('over_sma', [
            {'enabled': False},
            {'enabled': True}
        ]),
        'over_sar': hp.choice('over_sar', [
            {'enabled': False},
            {'enabled': True}
        ]),
        'uptrend_sma': hp.choice('uptrend_sma', [
            {'enabled': False},
            {'enabled': True}
        ]),
        'trigger': hp.choice('trigger', [
            {'type': 'lower_bb'},
            {'type': 'faststoch10'},
            {'type': 'ao_cross_zero'}
        ]),
    }
    trials = Trials()
    best = fmin(fn=optimizer, space=space, algo=tpe.suggest, max_evals=40, trials=trials)
    print('\n\n\n\n====================== HYPEROPT BACKTESTING REPORT ================================')
    print('Best parameters {}'.format(best))
    newlist = sorted(trials.results, key=itemgetter('loss'))
    print('Result: {}'.format(newlist[0]['result']))