BB_RPB_TSL_RNG_3

import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import talib.abstract as ta
import pandas_ta as pta

from freqtrade.persistence import Trade
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame, Series, DatetimeIndex, merge
from datetime import datetime, timedelta
from freqtrade.strategy import merge_informative_pair, CategoricalParameter, DecimalParameter, IntParameter, stoploss_from_open
from functools import reduce
from technical.indicators import RMI, zema

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:
    """Williams %R, or just %R, is a technical analysis oscillator showing the current closing price in relation to the high and low
        of the past N days (for a given N). It was developed by a publisher and promoter of trading materials, Larry Williams.
        Its purpose is to tell whether a stock or commodity market is trading near the high or the low, or somewhere in between,
        of its recent trading range.
        The oscillator is on a negative scale, from −100 (lowest) up to 0 (highest).
    """

    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 BB_RPB_TSL_RNG_3(IStrategy):
    '''
        BB_RPB_TSL
        @author jilv220
        Simple bollinger brand strategy inspired by this blog  ( https://hacks-for-life.blogspot.com/2020/12/freqtrade-notes.html )
        RPB, which stands for Real Pull Back, taken from ( https://github.com/GeorgeMurAlkh/freqtrade-stuff/blob/main/user_data/strategies/TheRealPullbackV2.py )
        The trailing custom stoploss taken from BigZ04_TSL from Perkmeister ( modded by ilya )
        I modified it to better suit my taste and added Hyperopt for this strategy.
    '''



    buy_params = {

        "buy_btc_safe": -289,
        "buy_btc_safe_1d": -0.05,

        "buy_threshold": 0.003,
        "buy_bb_factor": 0.999,
        "buy_bb_delta": 0.025,
        "buy_bb_width": 0.095,

        "buy_cci": -116,
        "buy_cci_length": 25,
        "buy_rmi": 49,
        "buy_rmi_length": 17,
        "buy_srsi_fk": 32,

        "buy_closedelta": 12.148,
        "buy_ema_diff": 0.022,

        "buy_adx": 20,
        "buy_fastd": 20,
        "buy_fastk": 22,
        "buy_ema_cofi": 0.98,
        "buy_ewo_high": 4.179,

        "buy_ema_high_2": 1.087,
        "buy_ema_low_2": 0.970,

    }

    sell_params = {
        "pHSL": -0.178,
        "pPF_1": 0.019,
        "pPF_2": 0.065,
        "pSL_1": 0.019,
        "pSL_2": 0.062,
        "sell_btc_safe": -389,
        "base_nb_candles_sell": 24,
        "high_offset": 0.991,
        "high_offset_2": 0.997        
    }

    minimal_roi = {
        "0": 0.10,
    }

    timeframe = '5m'
    inf_1h = '1h'

    stoploss = -0.99

    use_custom_stoploss = True
    use_sell_signal = True
    
    process_only_new_candles = True



    is_optimize_dip = False
    buy_rmi = IntParameter(30, 50, default=35, optimize= is_optimize_dip)
    buy_cci = IntParameter(-135, -90, default=-133, optimize= is_optimize_dip)
    buy_srsi_fk = IntParameter(30, 50, default=25, optimize= is_optimize_dip)
    buy_cci_length = IntParameter(25, 45, default=25, optimize = is_optimize_dip)
    buy_rmi_length = IntParameter(8, 20, default=8, optimize = is_optimize_dip)

    is_optimize_break = False
    buy_bb_width = DecimalParameter(0.05, 0.2, default=0.15, optimize = is_optimize_break)
    buy_bb_delta = DecimalParameter(0.025, 0.08, default=0.04, optimize = is_optimize_break)

    is_optimize_local_dip = False
    buy_ema_diff = DecimalParameter(0.022, 0.027, default=0.025, optimize = is_optimize_local_dip)
    buy_bb_factor = DecimalParameter(0.990, 0.999, default=0.995, optimize = False)
    buy_closedelta = DecimalParameter(12.0, 18.0, default=15.0, optimize = is_optimize_local_dip)

    is_optimize_ewo = False
    buy_rsi_fast = IntParameter(35, 50, default=45, optimize = False)
    buy_rsi = IntParameter(15, 30, default=35, optimize = False)
    buy_ewo = DecimalParameter(-6.0, 5, default=-5.585, optimize = is_optimize_ewo)
    buy_ema_low = DecimalParameter(0.9, 0.99, default=0.942 , optimize = is_optimize_ewo)
    buy_ema_high = DecimalParameter(0.95, 1.2, default=1.084 , optimize = is_optimize_ewo)

    is_optimize_ewo_2 = False
    buy_ema_low_2 = DecimalParameter(0.96, 0.978, default=0.96 , optimize = is_optimize_ewo_2)
    buy_ema_high_2 = DecimalParameter(1.05, 1.2, default=1.09 , optimize = is_optimize_ewo_2)

    is_optimize_cofi = False
    buy_ema_cofi = DecimalParameter(0.96, 0.98, default=0.97 , optimize = is_optimize_cofi)
    buy_fastk = IntParameter(20, 30, default=20, optimize = is_optimize_cofi)
    buy_fastd = IntParameter(20, 30, default=20, optimize = is_optimize_cofi)
    buy_adx = IntParameter(20, 30, default=30, optimize = is_optimize_cofi)
    buy_ewo_high = DecimalParameter(2, 12, default=3.553, optimize = is_optimize_cofi)

    is_optimize_btc_safe = False
    buy_btc_safe = IntParameter(-300, 50, default=-200, optimize = is_optimize_btc_safe)
    buy_btc_safe_1d = DecimalParameter(-0.075, -0.025, default=-0.05, optimize = is_optimize_btc_safe)
    buy_threshold = DecimalParameter(0.003, 0.012, default=0.008, optimize = is_optimize_btc_safe)

    buy_is_dip_enabled = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_is_break_enabled = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)

    sell_btc_safe = IntParameter(-400, -300, default=-365, optimize = True)
    base_nb_candles_sell = IntParameter(5, 80, default=sell_params['base_nb_candles_sell'], space='sell', optimize=True)
    high_offset          = DecimalParameter(0.95, 1.1, default=sell_params['high_offset'], space='sell', optimize=True)
    high_offset_2        = DecimalParameter(0.99, 1.5, default=sell_params['high_offset_2'], space='sell', optimize=True)      


    pHSL = DecimalParameter(-0.200, -0.040, default=-0.08, decimals=3, space='sell', load=True)

    pPF_1 = DecimalParameter(0.008, 0.020, default=0.016, decimals=3, space='sell', load=True)
    pSL_1 = DecimalParameter(0.008, 0.020, default=0.011, decimals=3, space='sell', load=True)

    pPF_2 = DecimalParameter(0.040, 0.100, default=0.080, decimals=3, space='sell', load=True)
    pSL_2 = DecimalParameter(0.020, 0.070, default=0.040, decimals=3, space='sell', load=True)


    def informative_pairs(self):

        informative_pairs = [("BTC/USDT", "5m")]

        return informative_pairs


    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 (sl_profit >= current_profit):
            return -0.99

        return stoploss_from_open(sl_profit, current_profit)


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

        assert self.dp, "DataProvider is required for multiple timeframes."

        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']

        bollinger3 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=3)
        dataframe['bb_lowerband3'] = bollinger3['lower']
        dataframe['bb_middleband3'] = bollinger3['mid']
        dataframe['bb_upperband3'] = bollinger3['upper']


        inf_tf = '5m'
        informative = self.dp.get_pair_dataframe('BTC/USDT', timeframe=inf_tf)
        informative_past = informative.copy().shift(1)                                                                                                   # Get recent BTC info

        informative_past_source = (informative_past['open'] + informative_past['close'] + informative_past['high'] + informative_past['low']) / 4        # Get BTC price
        informative_threshold = informative_past_source * self.buy_threshold.value                                                                       # BTC dump n% in 5 min
        informative_past_delta = informative_past['close'].shift(1) - informative_past['close']                                                          # should be positive if dump
        informative_diff = informative_threshold - informative_past_delta                                                                                # Need be larger than 0
        dataframe['btc_threshold'] = informative_threshold
        dataframe['btc_diff'] = informative_diff

        informative_past_1d = informative.copy().shift(288)
        informative_past_source_1d = (informative_past_1d['open'] + informative_past_1d['close'] + informative_past_1d['high'] + informative_past_1d['low']) / 4
        dataframe['btc_5m'] = informative_past_source
        dataframe['btc_1d'] = informative_past_source_1d


        dataframe['bb_width'] = ((dataframe['bb_upperband2'] - dataframe['bb_lowerband2']) / dataframe['bb_middleband2'])
        dataframe['bb_delta'] = ((dataframe['bb_lowerband2'] - dataframe['bb_lowerband3']) / dataframe['bb_lowerband2'])
        dataframe['bb_bottom_cross'] = qtpylib.crossed_below(dataframe['close'], dataframe['bb_lowerband3']).astype('int')

        for val in self.buy_cci_length.range:
            dataframe[f'cci_length_{val}'] = ta.CCI(dataframe, val)

        dataframe['cci'] = ta.CCI(dataframe, 26)
        dataframe['cci_long'] = ta.CCI(dataframe, 170)

        for val in self.buy_rmi_length.range:
            dataframe[f'rmi_length_{val}'] = RMI(dataframe, length=val, mom=4)


        stoch = ta.STOCHRSI(dataframe, 15, 20, 2, 2)
        dataframe['srsi_fk'] = stoch['fastk']
        dataframe['srsi_fd'] = stoch['fastd']

        dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs()

        dataframe['sma_15'] = ta.SMA(dataframe, timeperiod=15)
        dataframe['sma_30'] = ta.SMA(dataframe, timeperiod=30)

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

        dataframe['ema_8'] = ta.EMA(dataframe, timeperiod=8)
        dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12)
        dataframe['ema_13'] = ta.EMA(dataframe, timeperiod=13)
        dataframe['ema_16'] = ta.EMA(dataframe, timeperiod=16)
        dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26)
        dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50)
        dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100)   
        dataframe['sma_9'] = ta.SMA(dataframe, timeperiod=9)        

        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)

        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)

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

        dataframe['volume_mean_4'] = dataframe['volume'].rolling(4).mean().shift(1)

        for val in self.base_nb_candles_sell.range:
            dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val)        

        return dataframe

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


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

        if self.buy_is_dip_enabled.value:

            is_dip = (
                (dataframe[f'rmi_length_{self.buy_rmi_length.value}']  <  self.buy_rmi.value) &
                (dataframe[f'cci_length_{self.buy_cci_length.value}']  <= self.buy_cci.value) &
                (dataframe['srsi_fk']  <  self.buy_srsi_fk.value)
            )


        if self.buy_is_break_enabled.value:

            is_break = (

                (   (dataframe['bb_delta'] > self.buy_bb_delta.value)                                   #"buy_bb_delta": 0.025 0.036
                    &                                                                                   #"buy_bb_width": 0.095 0.133
                    (dataframe['bb_width'] > self.buy_bb_width.value)
                )
                &
                (dataframe['closedelta'] > dataframe['close'] * self.buy_closedelta.value / 1000 ) &    # from BinH
                (dataframe['close']  <  dataframe['bb_lowerband3'] * self.buy_bb_factor.value)
            )


        is_local_uptrend = (                                                                            # from NFI next gen

                (dataframe['ema_26'] > dataframe['ema_12']) &
                (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.buy_ema_diff.value) &
                (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) &
                (dataframe['close']  <  dataframe['bb_lowerband2'] * self.buy_bb_factor.value) &
                (dataframe['closedelta'] > dataframe['close'] * self.buy_closedelta.value / 1000 )
            )

        is_ewo = (                                                                                      # from SMA offset
                (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_ewo_2 = (
                (dataframe['rsi_fast']  <  self.buy_rsi_fast.value) &
                (dataframe['close']  <  dataframe['ema_8'] * self.buy_ema_low_2.value) &
                (dataframe['EWO'] > self.buy_ewo_high.value) &
                (dataframe['close']  <  dataframe['ema_16'] * self.buy_ema_high_2.value) &
                (dataframe['rsi']  <  self.buy_rsi.value)
            )

        is_cofi = (
                (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)
            )


        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)
            )

        is_nfi_33 = (
                (dataframe['close']  <  (dataframe['ema_13'] * 0.978)) &
                (dataframe['EWO'] > 8) &
                (dataframe['cti']  <  -0.88) &
                (dataframe['rsi']  <  32) &
                (dataframe['r_14']  <  -98.0) &
                (dataframe['volume']  <  (dataframe['volume_mean_4'] * 2.5))
            )






        is_BB_checked = is_dip & is_break






        conditions.append(is_BB_checked)          # ~1.7 89%
        dataframe.loc[is_BB_checked, 'buy_tag'] += 'bb '

        conditions.append(is_local_uptrend)       # ~3.84 90.2%
        dataframe.loc[is_local_uptrend, 'buy_tag'] += 'local uptrend '

        conditions.append(is_ewo)                 # ~2.26 93.5%
        dataframe.loc[is_ewo, 'buy_tag'] += 'ewo '

        conditions.append(is_ewo_2)               # ~3.68 90.3%
        dataframe.loc[is_ewo_2, 'buy_tag'] += 'ewo2 '

        conditions.append(is_cofi)                # ~3.21 90.8%
        dataframe.loc[is_cofi, 'buy_tag'] += 'cofi '

        conditions.append(is_nfi_32)              # ~2.43 91.3%
        dataframe.loc[is_nfi_32, 'buy_tag'] += 'nfi 32 '

        conditions.append(is_nfi_33)              # ~0.11 100%
        dataframe.loc[is_nfi_33, 'buy_tag'] += 'nfi 33 '

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

        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []

        conditions.append(
            (                   
                (dataframe['close'] > dataframe['sma_9'])&
                (dataframe['close'] > (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset_2.value)) &
                (dataframe['rsi']>50)&
                (dataframe['volume'] > 0)&                
                (dataframe['rsi_fast'] > dataframe['rsi_slow'])

            )
            |
            (
                (dataframe['sma_9'] > (dataframe['sma_9'].shift(1) + dataframe['sma_9'].shift(1)*0.005 )) &
                (dataframe['close']  <  dataframe['hma_50'])&
                (dataframe['close'] > (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) &
                (dataframe['volume'] > 0)&
                (dataframe['rsi_fast']>dataframe['rsi_slow'])
            )    
            
        )

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

        return dataframe