ClucHAnix_BB_RPB_HO2

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, merge_informative_pair)
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame, Series
from skopt.space import Dimension, Integer
# from py3cw.request import Py3CW


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_HO2(IStrategy):

    class HyperOpt:
        @staticmethod
        def generate_roi_table(params: dict):
            """
            Generate the ROI table that will be used by Hyperopt
            This implementation generates the default legacy Freqtrade ROI tables.
            Change it if you need different number of steps in the generated
            ROI tables or other structure of the ROI tables.
            Please keep it aligned with parameters in the 'roi' optimization
            hyperspace defined by the roi_space method.
            """
            roi_table = {}
            roi_table[0] = 0.05
            roi_table[params['roi_t6']] = 0.04
            roi_table[params['roi_t5']] = 0.03
            roi_table[params['roi_t4']] = 0.02
            roi_table[params['roi_t3']] = 0.01
            roi_table[params['roi_t2']] = 0.0001
            roi_table[params['roi_t1']] = -10

            return roi_table

        @staticmethod
        def roi_space() -> List[Dimension]:
            """
            Values to search for each ROI steps
            Override it if you need some different ranges for the parameters in the
            'roi' optimization hyperspace.
            Please keep it aligned with the implementation of the
            generate_roi_table method.
            """
            return [
                Integer(240, 720, name='roi_t1'),
                Integer(120, 240, name='roi_t2'),
                Integer(90, 120, name='roi_t3'),
                Integer(60, 90, name='roi_t4'),
                Integer(30, 60, name='roi_t5'),
                Integer(1, 30, name='roi_t6'),
            ]

    # 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,
    }

    # ROI table:
    minimal_roi = {
        "0": 0.05,
        "15": 0.04,
        "51": 0.03,
        "81": 0.02,
        "112": 0.01,
        "154": 0.0001,
        "200": -10
    }

    # Stoploss:
    stoploss = -0.99   # use custom stoploss

    # Trailing stop:
    trailing_stop = False
    trailing_stop_positive = 0.3207
    trailing_stop_positive_offset = 0.3849
    trailing_only_offset_is_reached = False

    """
    END HYPEROPT
    """

    timeframe = '1m'

    # Make sure these match or are not overridden in config
    use_sell_signal = False
    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
    }

    # 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:
        sl_new = 1

        if (current_profit > 0.2):
            sl_new = 0.05
        elif (current_profit > 0.1):
            sl_new = 0.03
        elif (current_profit > 0.06):
            sl_new = 0.02
        elif (current_profit > 0.03):
            sl_new = 0.015
        elif (current_profit > 0.015):
            sl_new = 0.0075

        return sl_new

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

    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['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)


        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:

        # dataframe.loc[
        #     (dataframe['fisher'] > self.sell_fisher.value) &
        #     (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'] * self.sell_bbmiddle_close.value) > 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 confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
    #                         time_in_force: str, current_time: datetime, **kwargs) -> bool:
    #     """
    #     Called right before placing a buy order.
    #     Timing for this function is critical, so avoid doing heavy computations or
    #     network requests in this method.

    #     For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/

    #     When not implemented by a strategy, returns True (always confirming).

    #     :param pair: Pair that's about to be bought.
    #     :param order_type: Order type (as configured in order_types). usually limit or market.
    #     :param amount: Amount in target (quote) currency that's going to be traded.
    #     :param rate: Rate that's going to be used when using limit orders
    #     :param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
    #     :param current_time: datetime object, containing the current datetime
    #     :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
    #     :return bool: When True is returned, then the buy-order is placed on the exchange.
    #         False aborts the process
    #     """
    #     coin, currency = pair.split('/')

    #     p3cw = Py3CW(
    #         key='.....',
    #         secret='......',
    #     )

    #     p3cw.request(
    #         entity='bots',
    #         action='start_new_deal',
    #         action_id='123123',
    #         payload={
    #             "bot_id": 123123,
    #             "pair": f"{currency}_{coin}",
    #         },
    #     )

    #     PairLocks.lock_pair(
    #         pair=pair,
    #         until=datetime.now(timezone.utc) + timedelta(minutes=5),
    #         reason="Send 3c buy order"
    #     )

    #     return False

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