BBModCE

import freqtrade.vendor.qtpylib.indicators as qtpylib
import talib.abstract as ta
import pandas_ta as pta
import pandas as pd

from freqtrade.persistence import Trade
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame, Series
from datetime import datetime
from freqtrade.strategy import DecimalParameter, IntParameter, informative, stoploss_from_open
from functools import reduce




def ewo(dataframe, ema_length=5, ema2_length=35):
    df = dataframe.copy()
    ema1 = ta.EMA(df, timeperiod=ema_length)
    ema2 = ta.EMA(df, timeperiod=ema2_length)
    emadif = (ema1 - ema2) / df['low'] * 100
    return emadif

def williams_r(dataframe: DataFrame, period: int = 14) -> Series:

    highest_high = dataframe["high"].rolling(center=False, window=period).max()
    lowest_low = dataframe["low"].rolling(center=False, window=period).min()

    wr = Series(
        (highest_high - dataframe["close"]) / (highest_high - lowest_low),
        name=f"{period} Williams %R",
    )

    return wr * -100



class BBModCE(IStrategy):

    minimal_roi = {
        "0": 100
    }

    timeframe = '5m'

    process_only_new_candles = True
    startup_candle_count = 20

    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_market_ratio': 0.99
    }

    stoploss = -0.99

    use_custom_stoploss = True

    leverage_optimize = False
    leverage_num = IntParameter(low=1, high=3, default=3, space='buy', optimize=leverage_optimize)

    is_optimize_ewo = True
    buy_rsi_fast = IntParameter(35, 50, default=45, space='buy', optimize=is_optimize_ewo)
    buy_rsi = IntParameter(15, 35, default=35, space='buy', optimize=is_optimize_ewo)
    buy_ewo = DecimalParameter(-6.0, 5, default=-5.585, space='buy', optimize=is_optimize_ewo)
    buy_ema_low = DecimalParameter(0.9, 0.99, default=0.942, space='buy', optimize=is_optimize_ewo)
    buy_ema_high = DecimalParameter(0.95, 1.2, default=1.084, space='buy', optimize=is_optimize_ewo)

    is_optimize_cofi = True
    buy_roc_1h = IntParameter(-25, 200, default=10, space='buy', optimize=is_optimize_cofi)
    buy_bb_width_1h = DecimalParameter(0.3, 2.0, default=0.3, space='buy', optimize=is_optimize_cofi)
    buy_ema_cofi = DecimalParameter(0.94, 1.2, default=0.97, space='buy', optimize=is_optimize_cofi)
    buy_fastk = IntParameter(0, 40, default=20, space='buy', optimize=is_optimize_cofi)
    buy_fastd = IntParameter(0, 40, default=20, space='buy', optimize=is_optimize_cofi)
    buy_adx = IntParameter(0, 30, default=30, space='buy', optimize=is_optimize_cofi)
    buy_ewo_high = DecimalParameter(2, 12, default=3.553, space='buy', optimize=is_optimize_cofi)
    buy_cofi_cti = DecimalParameter(-0.9, -0.0, default=-0.5, space='buy', optimize=is_optimize_cofi)
    buy_cofi_r14 = DecimalParameter(-100, -44, default=-60, space='buy', optimize=is_optimize_cofi)

    trailing_optimize = True
    pHSL = DecimalParameter(-0.990, -0.040, default=-0.15, decimals=3, space='sell', optimize=False)
    pPF_1 = DecimalParameter(0.008, 0.100, default=0.03, decimals=3, space='sell', optimize=False)
    pSL_1 = DecimalParameter(0.01, 0.030, default=0.025, decimals=3, space='sell', optimize=trailing_optimize)
    pPF_2 = DecimalParameter(0.040, 0.200, default=0.080, decimals=3, space='sell', optimize=False)
    pSL_2 = DecimalParameter(0.050, 0.080, default=0.075, decimals=3, space='sell', optimize=trailing_optimize)

    def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime,
                        current_rate: float, current_profit: float, **kwargs) -> float:

        HSL = self.pHSL.value
        PF_1 = self.pPF_1.value
        SL_1 = self.pSL_1.value
        PF_2 = self.pPF_2.value
        SL_2 = self.pSL_2.value




        if current_profit > PF_2:
            sl_profit = SL_2 + (current_profit - PF_2)
        elif current_profit > PF_1:
            sl_profit = SL_1 + ((current_profit - PF_1) * (SL_2 - SL_1) / (PF_2 - PF_1))
        else:
            sl_profit = HSL

        if self.can_short:
            if (-1 + ((1 - sl_profit) / (1 - current_profit))) <= 0:
                return 1
        else:
            if (1 - ((1 + sl_profit) / (1 + current_profit))) <= 0:
                return 1

        return stoploss_from_open(sl_profit, current_profit, is_short=trade.is_short)

    @informative('1h')
    def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        dataframe['roc'] = ta.ROC(dataframe, timeperiod=9)

        bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bb_lowerband2'] = bollinger2['lower']
        dataframe['bb_middleband2'] = bollinger2['mid']
        dataframe['bb_upperband2'] = bollinger2['upper']
        dataframe['bb_width'] = ((dataframe['bb_upperband2'] - dataframe['bb_lowerband2']) / dataframe['bb_middleband2'])

        return dataframe

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

        dataframe['sma_15'] = ta.SMA(dataframe, timeperiod=15)

        dataframe['cti'] = pta.cti(dataframe["close"], length=20)

        dataframe['ema_8'] = ta.EMA(dataframe, timeperiod=8)
        dataframe['ema_16'] = ta.EMA(dataframe, timeperiod=16)

        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4)
        dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20)

        dataframe['EWO'] = ewo(dataframe, 50, 200)

        dataframe['r_14'] = williams_r(dataframe, period=14)

        stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0)
        dataframe['fastd'] = stoch_fast['fastd']
        dataframe['fastk'] = stoch_fast['fastk']
        dataframe['adx'] = ta.ADX(dataframe)

        return dataframe

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

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

        is_cofi = (
                (dataframe['roc_1h'] < self.buy_roc_1h.value) &
                (dataframe['bb_width_1h'] < self.buy_bb_width_1h.value) &
                (dataframe['open'] < dataframe['ema_8'] * self.buy_ema_cofi.value) &
                (qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd'])) &
                (dataframe['fastk'] < self.buy_fastk.value) &
                (dataframe['fastd'] < self.buy_fastd.value) &
                (dataframe['adx'] > self.buy_adx.value) &
                (dataframe['EWO'] > self.buy_ewo_high.value) &
                (dataframe['cti'] < self.buy_cofi_cti.value) &
                (dataframe['r_14'] < self.buy_cofi_r14.value)
        )

        is_ewo = (
                (dataframe['rsi_fast'] < self.buy_rsi_fast.value) &
                (dataframe['close'] < dataframe['ema_8'] * self.buy_ema_low.value) &
                (dataframe['EWO'] > self.buy_ewo.value) &
                (dataframe['close'] < dataframe['ema_16'] * self.buy_ema_high.value) &
                (dataframe['rsi'] < self.buy_rsi.value)
        )

        is_nfi_32 = (
                (dataframe['rsi_slow'] < dataframe['rsi_slow'].shift(1)) &
                (dataframe['rsi_fast'] < 46) &
                (dataframe['rsi'] > 19) &
                (dataframe['close'] < dataframe['sma_15'] * 0.942) &
                (dataframe['cti'] < -0.86)
        )

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

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

        conditions.append(is_nfi_32)
        dataframe.loc[is_nfi_32, 'enter_tag'] += 'nfi_32 '

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

        return dataframe

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