ClucHAnix_BB_RPB_MOD

from datetime import datetime, timedelta, timezone
from functools import reduce
from typing import List

import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import pandas as pd
import pandas_ta as pta
import talib.abstract as ta
import technical.indicators as ftt
from freqtrade.persistence import Trade, PairLocks
from freqtrade.strategy import (BooleanParameter, DecimalParameter,
                                IntParameter, stoploss_from_open, merge_informative_pair)
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame, Series
from skopt.space import Dimension, Integer

def bollinger_bands(stock_price, window_size, num_of_std):
    rolling_mean = stock_price.rolling(window=window_size).mean()
    rolling_std = stock_price.rolling(window=window_size).std()
    lower_band = rolling_mean - (rolling_std * num_of_std)
    return np.nan_to_num(rolling_mean), np.nan_to_num(lower_band)

def ha_typical_price(bars):
    res = (bars['ha_high'] + bars['ha_low'] + bars['ha_close']) / 3.
    return Series(index=bars.index, data=res)

class ClucHAnix_BB_RPB_MOD(IStrategy):

    # Buy hyperspace params:
    buy_params = {
        "antipump_threshold": 0.133,
        "buy_btc_safe_1d": -0.311,
        "clucha_bbdelta_close": 0.04796,
        "clucha_bbdelta_tail": 0.93112,
        "clucha_close_bblower": 0.01645,
        "clucha_closedelta_close": 0.00931,
        "clucha_enabled": False,
        "clucha_rocr_1h": 0.41663,
        "cofi_adx": 8,
        "cofi_ema": 0.639,
        "cofi_enabled": False,
        "cofi_ewo_high": 5.6,
        "cofi_fastd": 40,
        "cofi_fastk": 13,
        "ewo_1_enabled": False,
        "ewo_1_rsi_14": 45,
        "ewo_1_rsi_4": 7,
        "ewo_candles_buy": 13,
        "ewo_candles_sell": 19,
        "ewo_high": 5.249,
        "ewo_high_offset": 1.04116,
        "ewo_low": -11.424,
        "ewo_low_enabled": True,
        "ewo_low_offset": 0.97463,
        "ewo_low_rsi_4": 35,
        "lambo1_ema_14_factor": 1.054,
        "lambo1_enabled": False,
        "lambo1_rsi_14_limit": 26,
        "lambo1_rsi_4_limit": 18,
        "lambo2_ema_14_factor": 0.981,
        "lambo2_enabled": True,
        "lambo2_rsi_14_limit": 39,
        "lambo2_rsi_4_limit": 44,
        "local_trend_bb_factor": 0.823,
        "local_trend_closedelta": 19.253,
        "local_trend_ema_diff": 0.125,
        "local_trend_enabled": True,
        "nfi32_cti_limit": -1.09639,
        "nfi32_enabled": True,
        "nfi32_rsi_14": 15,
        "nfi32_rsi_4": 49,
        "nfi32_sma_factor": 0.93391,
    }

    # Sell hyperspace params:
    sell_params = {
        # custom stoploss params, come from BB_RPB_TSL
        "pHSL": -0.32,
        "pPF_1": 0.02,
        "pPF_2": 0.047,
        "pSL_1": 0.02,
        "pSL_2": 0.046, 

        'sell-fisher': 0.38414, 
        'sell-bbmiddle-close': 1.07634
    }

    # ROI table:
    minimal_roi = {
        "0": 0.05,
        "15": 0.04,
        "51": 0.03,
        "81": 0.02,
        "112": 0.01,
        "154": 0.0001,
        "240": -10
    }

    # Stoploss:
    stoploss = -0.99   # use custom stoploss

    # Trailing stop:
    trailing_stop = False
    trailing_stop_positive = 0.001
    trailing_stop_positive_offset = 0.012
    trailing_only_offset_is_reached = False

    """
    END HYPEROPT
    """

    timeframe = '1m'

    # Make sure these match or are not overridden in config
    use_sell_signal = True
    sell_profit_only = False
    ignore_roi_if_buy_signal = False

    # Custom stoploss
    use_custom_stoploss = True

    process_only_new_candles = True
    startup_candle_count = 200

    order_types = {
        'buy': 'market',
        'sell': 'market',
        'emergencysell': 'market',
        'forcebuy': "market",
        'forcesell': 'market',
        'stoploss': 'market',
        'stoploss_on_exchange': False,

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

    # hard stoploss profit
    pHSL = DecimalParameter(-0.500, -0.040, default=-0.08, decimals=3, space='sell', load=True)
    # profit threshold 1, trigger point, SL_1 is used
    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)

    # profit threshold 2, SL_2 is used
    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)
    
    # buy param
    # ClucHA
    clucha_bbdelta_close = DecimalParameter(0.01,0.05, default=buy_params['clucha_bbdelta_close'], decimals=5, space='buy', optimize=True)
    clucha_bbdelta_tail = DecimalParameter(0.7, 1.2, default=buy_params['clucha_bbdelta_tail'], decimals=5, space='buy', optimize=True)
    clucha_close_bblower = DecimalParameter(0.001, 0.05, default=buy_params['clucha_close_bblower'], decimals=5, space='buy', optimize=True)
    clucha_closedelta_close = DecimalParameter(0.001, 0.05, default=buy_params['clucha_closedelta_close'], decimals=5, space='buy', optimize=True)
    clucha_rocr_1h = DecimalParameter(0.1, 1.0, default=buy_params['clucha_rocr_1h'], decimals=5, space='buy', optimize=True)

    # lambo1
    lambo1_ema_14_factor = DecimalParameter(0.8, 1.2, decimals=3,  default=buy_params['lambo1_ema_14_factor'], space='buy', optimize=True)
    lambo1_rsi_4_limit = IntParameter(5, 60, default=buy_params['lambo1_rsi_4_limit'], space='buy', optimize=True)
    lambo1_rsi_14_limit = IntParameter(5, 60, default=buy_params['lambo1_rsi_14_limit'], space='buy', optimize=True)

    # lambo2
    lambo2_ema_14_factor = DecimalParameter(0.8, 1.2, decimals=3,  default=buy_params['lambo2_ema_14_factor'], space='buy', optimize=True)
    lambo2_rsi_4_limit = IntParameter(5, 60, default=buy_params['lambo2_rsi_4_limit'], space='buy', optimize=True)
    lambo2_rsi_14_limit = IntParameter(5, 60, default=buy_params['lambo2_rsi_14_limit'], space='buy', optimize=True)

    # local_uptrend
    local_trend_ema_diff = DecimalParameter(0, 0.2, default=buy_params['local_trend_ema_diff'], space='buy', optimize=True)
    local_trend_bb_factor = DecimalParameter(0.8, 1.2, default=buy_params['local_trend_bb_factor'], space='buy', optimize=True)
    local_trend_closedelta = DecimalParameter(5.0, 30.0, default=buy_params['local_trend_closedelta'], space='buy', optimize=True)

    # ewo_1 and ewo_low
    ewo_candles_buy = IntParameter(2, 30, default=buy_params['ewo_candles_buy'], space='buy', optimize=True)
    ewo_candles_sell = IntParameter(2, 35, default=buy_params['ewo_candles_sell'], space='buy', optimize=True)
    ewo_low_offset = DecimalParameter(0.7, 1.2, default=buy_params['ewo_low_offset'], decimals=5, space='buy', optimize=True)
    ewo_high_offset = DecimalParameter(0.75, 1.5, default=buy_params['ewo_high_offset'], decimals=5, space='buy', optimize=True)
    ewo_high = DecimalParameter(2.0, 15.0, default=buy_params['ewo_high'], space='buy', optimize=True)
    ewo_1_rsi_14 = IntParameter(10, 100, default=buy_params['ewo_1_rsi_14'], space='buy', optimize=True)
    ewo_1_rsi_4 = IntParameter(1, 50, default=buy_params['ewo_1_rsi_4'], space='buy', optimize=True)
    ewo_low_rsi_4 = IntParameter(1, 50, default=buy_params['ewo_low_rsi_4'], space='buy', optimize=True)
    ewo_low = DecimalParameter(-20.0, -8.0, default=buy_params['ewo_low'], space='buy', optimize=True)

    # cofi
    cofi_ema = DecimalParameter(0.6, 1.4, default=buy_params['cofi_ema'] , space='buy', optimize=True)
    cofi_fastk = IntParameter(1, 100, default=buy_params['cofi_fastk'], space='buy', optimize=True)
    cofi_fastd = IntParameter(1, 100, default=buy_params['cofi_fastd'], space='buy', optimize=True)
    cofi_adx = IntParameter(1, 100, default=buy_params['cofi_adx'], space='buy', optimize=True)
    cofi_ewo_high = DecimalParameter(1.0, 15.0, default=buy_params['cofi_ewo_high'], space='buy', optimize=True)

    # nfi32
    nfi32_rsi_4 = IntParameter(1, 100, default=buy_params['nfi32_rsi_4'], space='buy', optimize=True)
    nfi32_rsi_14 = IntParameter(1, 100, default=buy_params['nfi32_rsi_4'], space='buy', optimize=True)
    nfi32_sma_factor = DecimalParameter(0.7, 1.2, default=buy_params['nfi32_sma_factor'], decimals=5, space='buy', optimize=True)
    nfi32_cti_limit = DecimalParameter(-1.2, 0, default=buy_params['nfi32_cti_limit'], decimals=5, space='buy', optimize=True)

    buy_btc_safe_1d = DecimalParameter(-0.5, -0.015, default=buy_params['buy_btc_safe_1d'], optimize=True)
    antipump_threshold = DecimalParameter(0, 0.4, default=buy_params['antipump_threshold'], space='buy', optimize=True)

    ewo_1_enabled = BooleanParameter(default=buy_params['ewo_1_enabled'], space='buy', optimize=True)
    ewo_low_enabled = BooleanParameter(default=buy_params['ewo_low_enabled'], space='buy', optimize=True)
    cofi_enabled = BooleanParameter(default=buy_params['cofi_enabled'], space='buy', optimize=True)
    lambo1_enabled = BooleanParameter(default=buy_params['lambo1_enabled'], space='buy', optimize=True)
    lambo2_enabled = BooleanParameter(default=buy_params['lambo2_enabled'], space='buy', optimize=True)
    local_trend_enabled = BooleanParameter(default=buy_params['local_trend_enabled'], space='buy', optimize=True)
    nfi32_enabled = BooleanParameter(default=buy_params['nfi32_enabled'], space='buy', optimize=True)
    clucha_enabled = BooleanParameter(default=buy_params['clucha_enabled'], space='buy', optimize=True)


    def informative_pairs(self):
        pairs = self.dp.current_whitelist()
        informative_pairs = [(pair, '1h') for pair in pairs]
        informative_pairs += [("BTC/USDT", "1m")]
        informative_pairs += [("BTC/USDT", "1d")]

        return informative_pairs


    ############################################################################

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

        # For profits between PF_1 and PF_2 the stoploss (sl_profit) used is linearly interpolated
        # between the values of SL_1 and SL_2. For all profits above PL_2 the sl_profit value
        # rises linearly with current profit, for profits below PF_1 the hard stoploss profit is used.

        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

        # Only for hyperopt invalid return
        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:
        # Heikin Ashi Candles
        heikinashi = qtpylib.heikinashi(dataframe)
        dataframe['ha_open'] = heikinashi['open']
        dataframe['ha_close'] = heikinashi['close']
        dataframe['ha_high'] = heikinashi['high']
        dataframe['ha_low'] = heikinashi['low']

        dataframe['ema_8'] = ta.EMA(dataframe, timeperiod=8)
        dataframe['ema_14'] = ta.EMA(dataframe, timeperiod=14)
        dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26)
        dataframe['sma_15'] = ta.SMA(dataframe, timeperiod=15)
        dataframe['rsi_4'] = ta.RSI(dataframe, timeperiod=4)
        dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['rsi_20'] = ta.RSI(dataframe, timeperiod=20)

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

        # Cofi
        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)

        # Set Up Bollinger Bands
        mid, lower = bollinger_bands(ha_typical_price(dataframe), window_size=40, num_of_std=2)
        dataframe['lower'] = lower
        dataframe['mid'] = mid

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


        # # ClucHA
        dataframe['bbdelta'] = (mid - dataframe['lower']).abs()
        dataframe['ha_closedelta'] = (dataframe['ha_close'] - dataframe['ha_close'].shift()).abs()
        dataframe['tail'] = (dataframe['ha_close'] - dataframe['ha_low']).abs()
        dataframe['bb_lowerband'] = dataframe['lower']
        dataframe['bb_middleband'] = dataframe['mid']

        dataframe['ema_fast'] = ta.EMA(dataframe['ha_close'], timeperiod=3)
        dataframe['ema_slow'] = ta.EMA(dataframe['ha_close'], timeperiod=50)
        dataframe['rocr'] = ta.ROCR(dataframe['ha_close'], timeperiod=28)

        # Elliot
        dataframe['EWO'] = EWO(dataframe, 50, 200)

        rsi = ta.RSI(dataframe)
        dataframe["rsi"] = rsi
        rsi = 0.1 * (rsi - 50)
        dataframe["fisher"] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)

        inf_tf = '1h'

        informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf)

        inf_heikinashi = qtpylib.heikinashi(informative)

        informative['ha_close'] = inf_heikinashi['close']
        informative['rocr'] = ta.ROCR(informative['ha_close'], timeperiod=168)


        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True)

        ### BTC protection
        dataframe['btc_1m']= self.dp.get_pair_dataframe('BTC/USDT', timeframe='1m')['close']
        btc_1d = self.dp.get_pair_dataframe('BTC/USDT', timeframe='1d')[['date', 'close']].rename(columns={"close": "btc"}).shift(1)
        dataframe = merge_informative_pair(dataframe, btc_1d, '1m', '1d', ffill=True)

        # Pump strength
        dataframe['zema_30'] = ftt.zema(dataframe, period=30)
        dataframe['zema_200'] = ftt.zema(dataframe, period=200)
        dataframe['pump_strength'] = (dataframe['zema_30'] - dataframe['zema_200']) / dataframe['zema_30']

        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        dataframe.loc[:, 'buy_tag'] = ''

        dataframe[f'ma_buy_{self.ewo_candles_buy.value}'] = ta.EMA(dataframe, timeperiod=int(self.ewo_candles_buy.value))
        dataframe[f'ma_sell_{self.ewo_candles_sell.value}'] = ta.EMA(dataframe, timeperiod=int(self.ewo_candles_sell.value))

        is_btc_safe = (
            (pct_change(dataframe['btc_1d'], dataframe['btc_1m']).fillna(0) > self.buy_btc_safe_1d.value) &
            (dataframe['volume'] > 0)           # Make sure Volume is not 0
        )

        is_pump_safe = (
            (dataframe['pump_strength'] < self.antipump_threshold.value)
        )

        lambo1 = (
            bool(self.lambo1_enabled.value) &
            (dataframe['close'] < (dataframe['ema_14'] * self.lambo1_ema_14_factor.value)) &
            (dataframe['rsi_4'] < int(self.lambo1_rsi_4_limit.value)) &
            (dataframe['rsi_14'] < int(self.lambo1_rsi_14_limit.value))
        )
        dataframe.loc[lambo1, 'buy_tag'] += 'lambo1_'
        conditions.append(lambo1)

        lambo2 = (
            bool(self.lambo2_enabled.value) &
            (dataframe['close'] < (dataframe['ema_14'] * self.lambo2_ema_14_factor.value)) &
            (dataframe['rsi_4'] < int(self.lambo2_rsi_4_limit.value)) &
            (dataframe['rsi_14'] < int(self.lambo2_rsi_14_limit.value))
        )
        dataframe.loc[lambo2, 'buy_tag'] += 'lambo2_'
        conditions.append(lambo2)

        local_uptrend = (
            bool(self.local_trend_enabled.value) &
            (dataframe['ema_26'] > dataframe['ema_14']) &
            (dataframe['ema_26'] - dataframe['ema_14'] > dataframe['open'] * self.local_trend_ema_diff.value) &
            (dataframe['ema_26'].shift() - dataframe['ema_14'].shift() > dataframe['open'] / 100) &
            (dataframe['close'] < dataframe['bb_lowerband2'] * self.local_trend_bb_factor.value) &
            (dataframe['closedelta'] > dataframe['close'] * self.local_trend_closedelta.value / 1000 )
        )
        dataframe.loc[local_uptrend, 'buy_tag'] += 'local_uptrend_'
        conditions.append(local_uptrend)

        nfi_32 = (
            bool(self.nfi32_enabled.value) &
            (dataframe['rsi_20'] < dataframe['rsi_20'].shift(1)) &
            (dataframe['rsi_4'] < self.nfi32_rsi_4.value) &
            (dataframe['rsi_14'] > self.nfi32_rsi_14.value) &
            (dataframe['close'] < dataframe['sma_15'] * self.nfi32_sma_factor.value) &
            (dataframe['cti'] < self.nfi32_cti_limit.value)
        )
        dataframe.loc[nfi_32, 'buy_tag'] += 'nfi_32_'
        conditions.append(nfi_32)

        ewo_1 = (
            bool(self.ewo_1_enabled.value) &
            (dataframe['rsi_4'] < self.ewo_1_rsi_4.value) &
            (dataframe['close'] < (dataframe[f'ma_buy_{self.ewo_candles_buy.value}'] * self.ewo_low_offset.value)) &
            (dataframe['EWO'] > self.ewo_high.value) &
            (dataframe['rsi_14'] < self.ewo_1_rsi_14.value) &
            (dataframe['close'] < (dataframe[f'ma_sell_{self.ewo_candles_sell.value}'] * self.ewo_high_offset.value))
        )
        dataframe.loc[ewo_1, 'buy_tag'] += 'ewo1_'
        conditions.append(ewo_1)

        ewo_low = (
            bool(self.ewo_low_enabled.value) &
            (dataframe['rsi_4'] <  self.ewo_low_rsi_4.value) &
            (dataframe['close'] < (dataframe[f'ma_buy_{self.ewo_candles_buy.value}'] * self.ewo_low_offset.value)) &
            (dataframe['EWO'] < self.ewo_low.value) &
            (dataframe['close'] < (dataframe[f'ma_sell_{self.ewo_candles_sell.value}'] * self.ewo_high_offset.value))
        )
        dataframe.loc[ewo_low, 'buy_tag'] += 'ewo_low_'
        conditions.append(ewo_low)

        cofi = (
            bool(self.cofi_enabled.value) &
            (dataframe['open'] < dataframe['ema_8'] * self.cofi_ema.value) &
            (qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd'])) &
            (dataframe['fastk'] < self.cofi_fastk.value) &
            (dataframe['fastd'] < self.cofi_fastd.value) &
            (dataframe['adx'] > self.cofi_adx.value) &
            (dataframe['EWO'] > self.cofi_ewo_high.value)
        )
        dataframe.loc[cofi, 'buy_tag'] += 'cofi_'
        conditions.append(cofi)

        clucHA = (
            bool(self.clucha_enabled.value) &
            (dataframe['rocr_1h'].gt(self.clucha_rocr_1h.value)) &
            ((
                (dataframe['lower'].shift().gt(0)) &
                (dataframe['bbdelta'].gt(dataframe['ha_close'] * self.clucha_bbdelta_close.value)) &
                (dataframe['ha_closedelta'].gt(dataframe['ha_close'] * self.clucha_closedelta_close.value)) &
                (dataframe['tail'].lt(dataframe['bbdelta'] * self.clucha_bbdelta_tail.value)) &
                (dataframe['ha_close'].lt(dataframe['lower'].shift())) &
                (dataframe['ha_close'].le(dataframe['ha_close'].shift()))
            ) |
            (
                (dataframe['ha_close'] < dataframe['ema_slow']) &
                (dataframe['ha_close'] < self.clucha_close_bblower.value * dataframe['bb_lowerband'])
            ))
        )
        dataframe.loc[clucHA, 'buy_tag'] += 'clucHA_'
        conditions.append(clucHA)

        dataframe.loc[
            # is_btc_safe &  # broken?
            # is_pump_safe &
            reduce(lambda x, y: x | y, conditions),
            'buy'
        ] = 1

        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        params = self.sell_params
        
        dataframe.loc[
            (dataframe['fisher'] > params['sell-fisher']) &
            (dataframe['ha_high'].le(dataframe['ha_high'].shift(1))) &
            (dataframe['ha_high'].shift(1).le(dataframe['ha_high'].shift(2))) &
            (dataframe['ha_close'].le(dataframe['ha_close'].shift(1))) &
            (dataframe['ema_fast'] > dataframe['ha_close']) &
            ((dataframe['ha_close'] * params['sell-bbmiddle-close']) > dataframe['bb_middleband']) &
            (dataframe['volume'] > 0)
            ,
            'sell'
        ] = 1

        return dataframe

    def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
                           rate: float, time_in_force: str, sell_reason: str,
                           current_time: datetime, **kwargs) -> bool:

        trade.sell_reason = sell_reason + "_" + trade.buy_tag

        return True

def pct_change(a, b):
    return (b - a) / a

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