from typing import Dict, List
from datetime import datetime
from functools import reduce

from skopt.space import Dimension, Integer
import numpy as np

from freqtrade.persistence import Trade
from freqtrade.strategy import IntParameter, DecimalParameter, CategoricalParameter, informative
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame, Series

import talib.abstract as ta
import numpy  # noqa


def RMI(dataframe, *, length=20, mom=5):
    """
    Source: https://github.com/freqtrade/technical/blob/master/technical/indicators/indicators.py#L912
    """
    df = dataframe.copy()

    df['maxup'] = (df['close'] - df['close'].shift(mom)).clip(lower=0)
    df['maxdown'] = (df['close'].shift(mom) - df['close']).clip(lower=0)

    df.fillna(0, inplace=True)

    df["emaInc"] = ta.EMA(df, price='maxup', timeperiod=length)
    df["emaDec"] = ta.EMA(df, price='maxdown', timeperiod=length)

    df['RMI'] = np.where(df['emaDec'] == 0, 0, 100 - 100 / (1 + df["emaInc"] / df["emaDec"]))

    return df["RMI"]


def zema(dataframe, period, field='close'):
    """
    Source: https://github.com/freqtrade/technical/blob/master/technical/indicators/overlap_studies.py#L79
    Modified slightly to use ta.EMA instead of technical ema
    """
    df = dataframe.copy()

    df['ema1'] = ta.EMA(df[field], timeperiod=period)
    df['ema2'] = ta.EMA(df['ema1'], timeperiod=period)
    df['d'] = df['ema1'] - df['ema2']
    df['zema'] = df['ema1'] + df['d']

    return df['zema']


def same_length(bigger, shorter):
    return np.concatenate((np.full((bigger.shape[0] - shorter.shape[0]), np.nan), shorter))


def mastreak(dataframe: DataFrame, period: int = 4, field='close') -> Series:
    """
    MA Streak
    Port of: https://www.tradingview.com/script/Yq1z7cIv-MA-Streak-Can-Show-When-a-Run-Is-Getting-Long-in-the-Tooth/
    """
    df = dataframe.copy()

    avgval = zema(df, period, field)

    arr = np.diff(avgval)
    pos = np.clip(arr, 0, 1).astype(bool).cumsum()
    neg = np.clip(arr, -1, 0).astype(bool).cumsum()
    streak = np.where(arr >= 0, pos - np.maximum.accumulate(np.where(arr <= 0, pos, 0)),
                      -neg + np.maximum.accumulate(np.where(arr >= 0, neg, 0)))

    res = same_length(df['close'], streak)

    return res


def linear_growth(start: float, end: float, start_time: int, end_time: int, trade_time: int) -> float:
    """
    Simple linear growth function. Grows from start to end after end_time minutes (starts after start_time minutes)
    """
    time = max(0, trade_time - start_time)
    rate = (end - start) / (end_time - start_time)

    return min(end, start + (rate * time))


def pcc(dataframe: DataFrame, period: int = 20, mult: int = 2):
    """
    Percent Change Channel
    PCC is like KC unless it uses percentage changes in price to set channel distance.
    https://www.tradingview.com/script/6wwAWXA1-MA-Streak-Change-Channel/
    """
    df = dataframe.copy()

    df['previous_close'] = df['close'].shift()

    df['close_change'] = (df['close'] - df['previous_close']) / df['previous_close'] * 100
    df['high_change'] = (df['high'] - df['close']) / df['close'] * 100
    df['low_change'] = (df['low'] - df['close']) / df['close'] * 100

    df['delta'] = df['high_change'] - df['low_change']

    mid = zema(df, period, 'close_change')
    rangema = zema(df, period, 'delta')

    upper = mid + rangema * mult
    lower = mid - rangema * mult

    return upper, rangema, lower


def SSLChannels_ATR(dataframe, length=7):
    """
    SSL Channels with ATR: https://www.tradingview.com/script/SKHqWzql-SSL-ATR-channel/
    Credit to @JimmyNixx for python
    """
    df = dataframe.copy()

    df['ATR'] = ta.ATR(df, timeperiod=14)
    df['smaHigh'] = df['high'].rolling(length).mean() + df['ATR']
    df['smaLow'] = df['low'].rolling(length).mean() - df['ATR']
    df['hlv'] = np.where(df['close'] > df['smaHigh'], 1, np.where(df['close'] < df['smaLow'], -1, np.NAN))
    df['hlv'] = df['hlv'].ffill()
    df['sslDown'] = np.where(df['hlv'] < 0, df['smaHigh'], df['smaLow'])
    df['sslUp'] = np.where(df['hlv'] < 0, df['smaLow'], df['smaHigh'])

    return df['sslDown'], df['sslUp']


def linear_decay(start: float, end: float, start_time: int, end_time: int, trade_time: int) -> float:
    """
    Simple linear decay function. Decays from start to end after end_time minutes (starts after start_time minutes)
    """
    time = max(0, trade_time - start_time)
    rate = (start - end) / (end_time - start_time)

    return max(end, start - (rate * time))


class BinHV27_short_10(IStrategy):
    """

        strategy sponsored by user BinH from slack

    """

    minimal_roi = {
        "0": 100
    }

    stoploss = -0.1
    timeframe = '5m'

    process_only_new_candles = False
    startup_candle_count = 240

    use_custom_stoploss = False

    custom_trade_info = {}

    can_short = True

    order_types = {
        'entry': 'market',
        'exit': 'market',
        'emergency_exit': 'market',
        'force_entry': 'market',
        'force_exit': "market",
        'stoploss': 'market',
        'stoploss_on_exchange': False,

        'stoploss_on_exchange_interval': 60,
        'stoploss_on_exchange_limit_ratio': 0.99
    }

    buy_optimize = False
    buy_adx1 = IntParameter(low=10, high=100, default=25, space='buy', optimize=buy_optimize)
    buy_emarsi1 = IntParameter(low=10, high=100, default=20, space='buy', optimize=buy_optimize)
    buy_adx2 = IntParameter(low=20, high=100, default=30, space='buy', optimize=buy_optimize)
    buy_emarsi2 = IntParameter(low=20, high=100, default=20, space='buy', optimize=buy_optimize)
    buy_adx3 = IntParameter(low=10, high=100, default=35, space='buy', optimize=buy_optimize)
    buy_emarsi3 = IntParameter(low=10, high=100, default=20, space='buy', optimize=buy_optimize)
    buy_adx4 = IntParameter(low=20, high=100, default=30, space='buy', optimize=buy_optimize)
    buy_emarsi4 = IntParameter(low=20, high=100, default=25, space='buy', optimize=buy_optimize)

    leverage_optimize = True
    leverage_num = IntParameter(low=1, high=10, default=1, space='buy', optimize=leverage_optimize)

    ce_op = True
    csell_pullback_amount = DecimalParameter(0.005, 0.15, default=0.01, space='sell', load=True, optimize=ce_op)
    csell_roi_type = CategoricalParameter(['static', 'decay', 'step'], default='step', space='sell', load=True,
                                          optimize=ce_op)
    csell_roi_start = DecimalParameter(0.01, 0.15, default=0.01, space='sell', load=True, optimize=ce_op)
    csell_roi_end = DecimalParameter(0.0, 0.01, default=0, space='sell', load=True, optimize=ce_op)
    csell_roi_time = IntParameter(720, 1440, default=720, space='sell', load=True, optimize=ce_op)
    csell_trend_type = CategoricalParameter(['rmi', 'ssl', 'candle', 'any', 'none'], default='any', space='sell',
                                            load=True, optimize=ce_op)
    csell_pullback = CategoricalParameter([True, False], default=True, space='sell', load=True, optimize=ce_op)
    csell_pullback_respect_roi = CategoricalParameter([True, False], default=False, space='sell', load=True,
                                                      optimize=ce_op)
    csell_endtrend_respect_roi = CategoricalParameter([True, False], default=False, space='sell', load=True,
                                                      optimize=ce_op)

    class HyperOpt:
        @staticmethod
        def generate_roi_table(params: Dict) -> Dict[int, float]:
            roi_table = {params['roi_t1']: 0}
            return roi_table

        @staticmethod
        def roi_space() -> List[Dimension]:
            roi_min_time = 10
            roi_max_time = 720

            roi_limits = {
                'roi_t1_min': int(roi_min_time),
                'roi_t1_max': int(roi_max_time)
            }

            return [
                Integer(roi_limits['roi_t1_min'], roi_limits['roi_t1_max'], name='roi_t1')
            ]

    @informative('1d', 'BTC/USDT')
    def populate_indicators_btc_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['ema90'] = ta.EMA(dataframe, timeperiod=90)
        return dataframe

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        if not metadata['pair'] in self.custom_trade_info:
            self.custom_trade_info[metadata['pair']] = {}
            if 'had-trend' not in self.custom_trade_info[metadata["pair"]]:
                self.custom_trade_info[metadata['pair']]['had-trend'] = False

        dataframe['rsi'] = numpy.nan_to_num(ta.RSI(dataframe, timeperiod=5))
        rsiframe = DataFrame(dataframe['rsi']).rename(columns={'rsi': 'close'})
        dataframe['emarsi'] = numpy.nan_to_num(ta.EMA(rsiframe, timeperiod=5))
        dataframe['adx'] = numpy.nan_to_num(ta.ADX(dataframe))
        dataframe['minusdi'] = numpy.nan_to_num(ta.MINUS_DI(dataframe))
        minusdiframe = DataFrame(dataframe['minusdi']).rename(columns={'minusdi': 'close'})
        dataframe['minusdiema'] = numpy.nan_to_num(ta.EMA(minusdiframe, timeperiod=25))
        dataframe['plusdi'] = numpy.nan_to_num(ta.PLUS_DI(dataframe))
        plusdiframe = DataFrame(dataframe['plusdi']).rename(columns={'plusdi': 'close'})
        dataframe['plusdiema'] = numpy.nan_to_num(ta.EMA(plusdiframe, timeperiod=5))
        dataframe['lowsma'] = numpy.nan_to_num(ta.EMA(dataframe, timeperiod=60))
        dataframe['highsma'] = numpy.nan_to_num(ta.EMA(dataframe, timeperiod=120))
        dataframe['fastsma'] = numpy.nan_to_num(ta.SMA(dataframe, timeperiod=120))
        dataframe['slowsma'] = numpy.nan_to_num(ta.SMA(dataframe, timeperiod=240))
        dataframe['bigup'] = dataframe['fastsma'].gt(dataframe['slowsma']) & (
                (dataframe['fastsma'] - dataframe['slowsma']) > dataframe['close'] / 300)
        dataframe['bigdown'] = ~dataframe['bigup']
        dataframe['trend'] = dataframe['fastsma'] - dataframe['slowsma']
        dataframe['preparechangetrend'] = dataframe['trend'].gt(dataframe['trend'].shift())
        dataframe['preparechangetrendconfirm'] = dataframe['preparechangetrend'] & dataframe['trend'].shift().gt(
            dataframe['trend'].shift(2))
        dataframe['continueup'] = dataframe['slowsma'].gt(dataframe['slowsma'].shift()) & dataframe[
            'slowsma'].shift().gt(dataframe['slowsma'].shift(2))
        dataframe['delta'] = dataframe['fastsma'] - dataframe['fastsma'].shift()
        dataframe['slowingdown'] = dataframe['delta'].lt(dataframe['delta'].shift())

        dataframe['rmi'] = RMI(dataframe, length=24, mom=5)
        dataframe['rmi-up'] = np.where(dataframe['rmi'] >= dataframe['rmi'].shift(), 1, 0)
        dataframe['rmi-down-trend'] = np.where(dataframe['rmi-up'].rolling(5).sum() <= 2, 1, 0)

        ssldown, sslup = SSLChannels_ATR(dataframe, length=21)
        dataframe['ssl-dir'] = np.where(sslup > ssldown, 'up', 'down')

        dataframe['candle-up'] = np.where(dataframe['close'] >= dataframe['open'], 1, 0)
        dataframe['candle-down-trend'] = np.where(dataframe['candle-up'].rolling(5).sum() <= 2, 1, 0)

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        conditions = []
        dataframe.loc[:, 'enter_tag'] = ''

        buy_1 = (
                dataframe['btc_usdt_close_1d'].lt(dataframe['btc_usdt_ema90_1d']) &
                dataframe['slowsma'].gt(0) &
                dataframe['close'].lt(dataframe['highsma']) &
                dataframe['close'].lt(dataframe['lowsma']) &
                dataframe['minusdi'].gt(dataframe['minusdiema']) &
                dataframe['rsi'].ge(dataframe['rsi'].shift()) &
                ~dataframe['preparechangetrend'] &
                ~dataframe['continueup'] &
                dataframe['adx'].gt(self.buy_adx1.value) &
                dataframe['bigdown'] &
                dataframe['emarsi'].le(self.buy_emarsi1.value)
        )

        buy_2 = (
                dataframe['btc_usdt_close_1d'].lt(dataframe['btc_usdt_ema90_1d']) &
                dataframe['slowsma'].gt(0) &
                dataframe['close'].lt(dataframe['highsma']) &
                dataframe['close'].lt(dataframe['lowsma']) &
                dataframe['minusdi'].gt(dataframe['minusdiema']) &
                dataframe['rsi'].ge(dataframe['rsi'].shift()) &
                ~dataframe['preparechangetrend'] &
                dataframe['continueup'] &
                dataframe['adx'].gt(self.buy_adx2.value) &
                dataframe['bigdown'] &
                dataframe['emarsi'].le(self.buy_emarsi2.value)
        )

        buy_3 = (
                dataframe['btc_usdt_close_1d'].lt(dataframe['btc_usdt_ema90_1d']) &
                dataframe['slowsma'].gt(0) &
                dataframe['close'].lt(dataframe['highsma']) &
                dataframe['close'].lt(dataframe['lowsma']) &
                dataframe['minusdi'].gt(dataframe['minusdiema']) &
                dataframe['rsi'].ge(dataframe['rsi'].shift()) &
                ~dataframe['continueup'] &
                dataframe['adx'].gt(self.buy_adx3.value) &
                dataframe['bigup'] &
                dataframe['emarsi'].le(self.buy_emarsi3.value)
        )

        buy_4 = (
                dataframe['btc_usdt_close_1d'].lt(dataframe['btc_usdt_ema90_1d']) &
                dataframe['slowsma'].gt(0) &
                dataframe['close'].lt(dataframe['highsma']) &
                dataframe['close'].lt(dataframe['lowsma']) &
                dataframe['minusdi'].gt(dataframe['minusdiema']) &
                dataframe['rsi'].ge(dataframe['rsi'].shift()) &
                dataframe['continueup'] &
                dataframe['adx'].gt(self.buy_adx4.value) &
                dataframe['bigup'] &
                dataframe['emarsi'].le(self.buy_emarsi4.value)
        )

        conditions.append(buy_1)
        dataframe.loc[buy_1, 'enter_tag'] += 'buy_1'

        conditions.append(buy_2)
        dataframe.loc[buy_2, 'enter_tag'] += 'buy_2'

        conditions.append(buy_3)
        dataframe.loc[buy_3, 'enter_tag'] += 'buy_3'

        conditions.append(buy_4)
        dataframe.loc[buy_4, 'enter_tag'] += 'buy_4'

        if conditions:
            dataframe.loc[
                reduce(lambda x, y: x | y, conditions),
                'enter_short'] = 1

        dataframe.loc[(), ['enter_long', 'enter_tag']] = (0, 'long_in')

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[(), ['exit_short', 'exit_tag']] = (0, 'short_out')
        dataframe.loc[(), ['exit_long', 'exit_tag']] = (0, 'long_out')
        return dataframe

    def leverage(self, pair: str, current_time: datetime, current_rate: float,
                 proposed_leverage: float, max_leverage: float, side: str,
                 **kwargs) -> float:

        return self.leverage_num.value

    def custom_exit(self, pair: str, trade: Trade, current_time: 'datetime', current_rate: float,
                    current_profit: float, **kwargs):

        dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
        last_candle = dataframe.iloc[-1].squeeze()

        trade_dur = int((current_time.timestamp() - trade.open_date_utc.timestamp()) // 60)
        max_profit = max(0, trade.calc_profit_ratio(trade.max_rate))
        pullback_value = max(0, (max_profit - self.csell_pullback_amount.value))
        in_trend = False

        if self.csell_roi_type.value == 'static':
            min_roi = self.csell_roi_start.value
        elif self.csell_roi_type.value == 'decay':
            min_roi = linear_decay(self.csell_roi_start.value, self.csell_roi_end.value, 0, self.csell_roi_time.value,
                                   trade_dur)
        elif self.csell_roi_type.value == 'step':
            if trade_dur < self.csell_roi_time.value:
                min_roi = self.csell_roi_start.value
            else:
                min_roi = self.csell_roi_end.value

        if self.csell_trend_type.value == 'rmi' or self.csell_trend_type.value == 'any':
            if last_candle['rmi-down-trend'] == 1:
                in_trend = True
        if self.csell_trend_type.value == 'ssl' or self.csell_trend_type.value == 'any':
            if last_candle['ssl-dir'] == 'down':
                in_trend = True
        if self.csell_trend_type.value == 'candle' or self.csell_trend_type.value == 'any':
            if last_candle['candle-down-trend'] == 1:
                in_trend = True

        if in_trend and current_profit > 0:

            self.custom_trade_info[trade.pair]['had-trend'] = True

            if self.csell_pullback.value and (current_profit <= pullback_value):
                if self.csell_pullback_respect_roi.value and current_profit > min_roi:
                    return 'intrend_pullback_roi'
                elif not self.csell_pullback_respect_roi.value:
                    if current_profit > min_roi:
                        return 'intrend_pullback_roi'
                    else:
                        return 'intrend_pullback_noroi'

            return None

        elif not in_trend:
            if self.custom_trade_info[trade.pair]['had-trend']:
                if current_profit > min_roi:
                    self.custom_trade_info[trade.pair]['had-trend'] = False
                    return 'trend_roi'
                elif not self.csell_endtrend_respect_roi.value:
                    self.custom_trade_info[trade.pair]['had-trend'] = False
                    return 'trend_noroi'
            elif current_profit > min_roi:
                return 'notrend_roi'
        else:
            return None