MultiMA_TSL3b

import freqtrade.vendor.qtpylib.indicators as qtpylib
from typing import Dict, List, Optional
import numpy as np
import talib.abstract as ta
from freqtrade.strategy import IStrategy, informative
from freqtrade.strategy import (merge_informative_pair,
                                DecimalParameter, IntParameter, BooleanParameter, timeframe_to_minutes)
from pandas import DataFrame, Series
from functools import reduce
from freqtrade.persistence import Trade
from datetime import datetime, timedelta, timezone
from freqtrade.exchange import timeframe_to_prev_date
from technical.indicators import zema, VIDYA, vwma
import math
import pandas_ta as pta

###########################################################################################################
##    MultiMA_TSL, modded by stash86, based on SMAOffsetProtectOptV1 (modded by Perkmeister)             ##
##    Based on @Lamborghini Store's SMAOffsetProtect strat, heavily based on @tirail's original SMAOffset##
##                                                                                                       ##
##    Strategy for Freqtrade https://github.com/freqtrade/freqtrade                                      ##
##                                                                                                       ##
##    Thanks to                                                                                          ##
##    - Perkmeister, for their snippets for the sell signals and decaying EMA sell                       ##
##    - ChangeToTower, for the PMax idea                                                                 ##
##    - JimmyNixx, for their snippet to limit close value from the peak (that I modify into 5m tf check) ##
##    - froggleston, for the Heikinashi check snippet from Cryptofrog                                    ##
##    - Uzirox, for their pump detection code                                                            ##
##                                                                                                       ##
##                                                                                                       ##
###########################################################################################################

# I hope you do enough testing before proceeding, either backtesting and/or dry run.
# Any profits and losses are all your responsibility

class MultiMA_TSL3b(IStrategy):
    def version(self) -> str:
        return "v3b"

    INTERFACE_VERSION = 2

    buy_params = {
        "base_nb_candles_buy_trima": 17,
        "base_nb_candles_buy_trima2": 37,
        "low_offset_trima": 0.941,
        "low_offset_trima2": 0.914,

        "base_nb_candles_buy_ema": 80,
        "base_nb_candles_buy_ema2": 79,
        "low_offset_ema": 1.074,
        "low_offset_ema2": 0.942,

        "base_nb_candles_buy_zema": 62,
        "base_nb_candles_buy_zema2": 73,
        "low_offset_zema": 0.961,
        "low_offset_zema2": 0.98,

        "base_nb_candles_buy_hma": 80,
        "base_nb_candles_buy_hma2": 75,
        "low_offset_hma": 0.96,
        "low_offset_hma2": 0.965,

        "base_nb_candles_buy_vwma": 26,
        "base_nb_candles_buy_vwma2": 16,
        "low_offset_vwma": 0.949,
        "low_offset_vwma2": 0.951,

        "ewo_high": 5.8,
        "ewo_high2": 6.0,
        "ewo_low": -10.8,
        "ewo_low2": -15.3,
        "rsi_buy": 60,
        "rsi_buy2": 45,

    }

    sell_params = {
        "base_nb_candles_ema_sell": 6,
        "high_offset_sell_ema": 0.991,
    }

    # ROI table:
    minimal_roi = {
        "0": 100
    }

    stoploss = -0.25

    optimize_sell_ema = False
    base_nb_candles_ema_sell = IntParameter(5, 80, default=20, space='sell', optimize=False)
    high_offset_sell_ema = DecimalParameter(0.99, 1.1, default=1.012, space='sell', optimize=False)
    base_nb_candles_ema_sell2 = IntParameter(5, 80, default=20, space='sell', optimize=False)

    # Multi Offset
    optimize_buy_ema = False
    base_nb_candles_buy_ema = IntParameter(5, 80, default=buy_params['base_nb_candles_buy_ema'], space='buy', optimize=optimize_buy_ema)
    low_offset_ema = DecimalParameter(0.9, 1.1, default=buy_params['low_offset_ema'], space='buy', optimize=optimize_buy_ema)
    base_nb_candles_buy_ema2 = IntParameter(5, 80, default=buy_params['base_nb_candles_buy_ema2'], space='buy', optimize=optimize_buy_ema)
    low_offset_ema2 = DecimalParameter(0.9, 1.1, default=buy_params['low_offset_ema2'], space='buy', optimize=optimize_buy_ema)

    optimize_buy_trima = False
    base_nb_candles_buy_trima = IntParameter(5, 80, default=buy_params['base_nb_candles_buy_trima'], space='buy', optimize=optimize_buy_trima)
    low_offset_trima = DecimalParameter(0.9, 0.99, default=buy_params['low_offset_trima'], space='buy', optimize=optimize_buy_trima)
    base_nb_candles_buy_trima2 = IntParameter(5, 80, default=buy_params['base_nb_candles_buy_trima2'], space='buy', optimize=optimize_buy_trima)
    low_offset_trima2 = DecimalParameter(0.9, 0.99, default=buy_params['low_offset_trima2'], space='buy', optimize=optimize_buy_trima)
    
    optimize_buy_zema = False
    base_nb_candles_buy_zema = IntParameter(5, 80, default=buy_params['base_nb_candles_buy_zema'], space='buy', optimize=optimize_buy_zema)
    low_offset_zema = DecimalParameter(0.9, 0.99, default=buy_params['low_offset_zema'], space='buy', optimize=optimize_buy_zema)
    base_nb_candles_buy_zema2 = IntParameter(5, 80, default=buy_params['base_nb_candles_buy_zema2'], space='buy', optimize=optimize_buy_zema)
    low_offset_zema2 = DecimalParameter(0.9, 0.99, default=buy_params['low_offset_zema2'], space='buy', optimize=optimize_buy_zema)

    optimize_buy_hma = False
    base_nb_candles_buy_hma = IntParameter(5, 80, default=buy_params['base_nb_candles_buy_hma'], space='buy', optimize=optimize_buy_hma)
    low_offset_hma = DecimalParameter(0.9, 0.99, default=buy_params['low_offset_hma'], space='buy', optimize=optimize_buy_hma)
    base_nb_candles_buy_hma2 = IntParameter(5, 80, default=buy_params['base_nb_candles_buy_hma2'], space='buy', optimize=optimize_buy_hma)
    low_offset_hma2 = DecimalParameter(0.9, 0.99, default=buy_params['low_offset_hma2'], space='buy', optimize=optimize_buy_hma)

    optimize_buy_vwma = False
    base_nb_candles_buy_vwma = IntParameter(5, 80, default=buy_params['base_nb_candles_buy_vwma'], space='buy', optimize=optimize_buy_vwma)
    low_offset_vwma = DecimalParameter(0.9, 0.99, default=buy_params['low_offset_vwma'], space='buy', optimize=optimize_buy_vwma)
    base_nb_candles_buy_vwma2 = IntParameter(5, 80, default=buy_params['base_nb_candles_buy_vwma2'], space='buy', optimize=optimize_buy_vwma)
    low_offset_vwma2 = DecimalParameter(0.9, 0.99, default=buy_params['low_offset_vwma2'], space='buy', optimize=optimize_buy_vwma)

    buy_condition_enable_optimize = False
    buy_condition_trima_enable = BooleanParameter(default=True, space='buy', optimize=buy_condition_enable_optimize)
    buy_condition_zema_enable = BooleanParameter(default=False, space='buy', optimize=buy_condition_enable_optimize)
    buy_condition_hma_enable = BooleanParameter(default=True, space='buy', optimize=buy_condition_enable_optimize)
    buy_condition_vwma_enable = BooleanParameter(default=True, space='buy', optimize=buy_condition_enable_optimize)


    # Protection
    ewo_check_optimize = False
    ewo_low = DecimalParameter(-20.0, -8.0, default=-20.0, decimals = 1, space='buy', optimize=ewo_check_optimize)
    ewo_high = DecimalParameter(2.0, 12.0, default=6.0, decimals = 1, space='buy', optimize=ewo_check_optimize)
    ewo_low2 = DecimalParameter(-20.0, -8.0, default=-20.0, decimals = 1, space='buy', optimize=ewo_check_optimize)
    ewo_high2 = DecimalParameter(2.0, 12.0, default=6.0, decimals = 1, space='buy', optimize=ewo_check_optimize)

    rsi_buy_optimize = False
    rsi_buy = IntParameter(30, 70, default=50, space='buy', optimize=rsi_buy_optimize)
    rsi_buy2 = IntParameter(30, 70, default=50, space='buy', optimize=rsi_buy_optimize)
    buy_rsi_fast = IntParameter(0, 50, default=35, space='buy', optimize=False)

    fast_ewo = IntParameter(10, 50, default=50, space='buy', optimize=False)
    slow_ewo = IntParameter(100, 200, default=200, space='buy', optimize=False)

    min_rsi_sell = IntParameter(30, 100, default=50, space='sell', optimize=False)
    
    # Trailing stoploss (not used)
    trailing_stop = False
    trailing_only_offset_is_reached = True
    trailing_stop_positive = 0.01
    trailing_stop_positive_offset = 0.018

    use_custom_stoploss = False

    # Protection hyperspace params:
    protection_params = {
        "low_profit_lookback": 48,
        "low_profit_min_req": 0.04,
        "low_profit_stop_duration": 14,

        "cooldown_lookback": 2,  # value loaded from strategy
        "stoploss_lookback": 72,  # value loaded from strategy
        "stoploss_stop_duration": 20,  # value loaded from strategy
    }

    cooldown_lookback = IntParameter(2, 48, default=2, space="protection", optimize=False)

    low_profit_optimize = False
    low_profit_lookback = IntParameter(2, 60, default=20, space="protection", optimize=low_profit_optimize)
    low_profit_stop_duration = IntParameter(12, 200, default=20, space="protection", optimize=low_profit_optimize)
    low_profit_min_req = DecimalParameter(-0.05, 0.05, default=-0.05, space="protection", decimals=2, optimize=low_profit_optimize)

    @property
    def protections(self):
        prot = []

        prot.append({
            "method": "CooldownPeriod",
            "stop_duration_candles": self.cooldown_lookback.value
        })
        prot.append({
            "method": "LowProfitPairs",
            "lookback_period_candles": self.low_profit_lookback.value,
            "trade_limit": 1,
            "stop_duration": int(self.low_profit_stop_duration.value),
            "required_profit": self.low_profit_min_req.value
        })

        return prot

    # Optimal timeframe for the strategy.
    timeframe = '5m'

    # Run "populate_indicators()" only for new candle.
    process_only_new_candles = True

    # These values can be overridden in the "ask_strategy" section in the config.
    use_sell_signal = True
    sell_profit_only = False
    ignore_roi_if_buy_signal = False

    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 200

    age_filter = 30

    @informative('1d')
    def populate_indicators_1d(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['age_filter_ok'] = (dataframe['volume'].rolling(window=self.age_filter, min_periods=self.age_filter).min() > 0)
        return dataframe

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # EWO
        dataframe['ewo'] = EWO(dataframe, self.fast_ewo.value, self.slow_ewo.value)

        # RSI
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4)
        dataframe['rsi_84'] = ta.RSI(dataframe, timeperiod=84)
        dataframe['rsi_112'] = ta.RSI(dataframe, timeperiod=112)

        # Heiken Ashi
        heikinashi = qtpylib.heikinashi(dataframe)
        heikinashi["volume"] = dataframe["volume"]

        # Profit Maximizer - PMAX
        dataframe['pm'], dataframe['pmx'] = pmax(heikinashi, MAtype=1, length=9, multiplier=27, period=10, src=3)
        dataframe['source'] = (dataframe['high'] + dataframe['low'] + dataframe['open'] + dataframe['close'])/4
        dataframe['pmax_thresh'] = ta.EMA(dataframe['source'], timeperiod=9)

        dataframe = HA(dataframe, 4)

        if self.config['runmode'].value in ('live', 'dry_run'):
            # Exchange downtime protection
            dataframe['live_data_ok'] = (dataframe['volume'].rolling(window=72, min_periods=72).min() > 0)
        else:
            dataframe['live_data_ok'] = True

        if (self.dp.runmode.value in ('live', 'dry_run')):
            dataframe['ema_offset_buy'] = ta.EMA(dataframe, int(self.base_nb_candles_buy_ema.value)) *self.low_offset_ema.value
            dataframe['ema_offset_buy2'] = ta.EMA(dataframe, int(self.base_nb_candles_buy_ema2.value)) *self.low_offset_ema2.value
            dataframe['ema_offset_sell'] = ta.EMA(dataframe, int(self.base_nb_candles_ema_sell.value)) * self.high_offset_sell_ema.value

        return dataframe

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

        if not (self.dp.runmode.value in ('live', 'dry_run')):
            dataframe['ema_offset_buy'] = ta.EMA(dataframe, int(self.base_nb_candles_buy_ema.value)) *self.low_offset_ema.value
            dataframe['ema_offset_buy2'] = ta.EMA(dataframe, int(self.base_nb_candles_buy_ema2.value)) *self.low_offset_ema2.value
            dataframe['ema_offset_sell'] = ta.EMA(dataframe, int(self.base_nb_candles_ema_sell.value)) * self.high_offset_sell_ema.value
        
        dataframe.loc[:, 'buy_tag'] = ''
        dataframe.loc[:, 'buy_copy'] = 0
        dataframe.loc[:, 'buy'] = 0

        if (self.buy_condition_trima_enable.value):
            dataframe['trima_offset_buy'] = ta.TRIMA(dataframe, int(self.base_nb_candles_buy_trima.value)) *self.low_offset_trima.value
            dataframe['trima_offset_buy2'] = ta.TRIMA(dataframe, int(self.base_nb_candles_buy_trima2.value)) *self.low_offset_trima2.value

            buy_offset_trima = (
                (
                    (dataframe['close'] < dataframe['trima_offset_buy'])
                    &
                    (dataframe['pm'] <= dataframe['pmax_thresh'])
                )
                |
                (
                    (dataframe['close'] < dataframe['trima_offset_buy2'])
                    &
                    (dataframe['pm'] > dataframe['pmax_thresh'])
                )
            )
            dataframe.loc[buy_offset_trima, 'buy_tag'] += 'trima '
            conditions.append(buy_offset_trima)

        if (self.buy_condition_zema_enable.value):
            dataframe['zema_offset_buy'] = zema(dataframe, int(self.base_nb_candles_buy_zema.value)) *self.low_offset_zema.value
            dataframe['zema_offset_buy2'] = zema(dataframe, int(self.base_nb_candles_buy_zema2.value)) *self.low_offset_zema2.value
            buy_offset_zema = (
                (
                    (dataframe['close'] < dataframe['zema_offset_buy'])
                    &
                    (dataframe['pm'] <= dataframe['pmax_thresh'])
                )
                |
                (
                    (dataframe['close'] < dataframe['zema_offset_buy2'])
                    &
                    (dataframe['pm'] > dataframe['pmax_thresh'])
                )
            )
            dataframe.loc[buy_offset_zema, 'buy_tag'] += 'zema '
            conditions.append(buy_offset_zema)

        if (self.buy_condition_hma_enable.value):
            dataframe['hma_offset_buy'] = tv_hma(dataframe, int(self.base_nb_candles_buy_hma.value)) *self.low_offset_hma.value
            dataframe['hma_offset_buy2'] = tv_hma(dataframe, int(self.base_nb_candles_buy_hma2.value)) *self.low_offset_hma2.value
            buy_offset_hma = (
                (
                    (
                        (dataframe['close'] < dataframe['hma_offset_buy'])
                        &
                        (dataframe['pm'] <= dataframe['pmax_thresh'])
                        &
                        (dataframe['rsi'] < 35)
    
                    )
                    |
                    (
                        (dataframe['close'] < dataframe['hma_offset_buy2'])
                        &
                        (dataframe['pm'] > dataframe['pmax_thresh'])
                        &
                        (dataframe['rsi'] < 30)
                    )
                )
                &
                (dataframe['rsi_fast'] < 30)
                
            )
            dataframe.loc[buy_offset_hma, 'buy_tag'] += 'hma '
            conditions.append(buy_offset_hma)

        if (self.buy_condition_vwma_enable.value):
            dataframe['vwma_offset_buy'] = pta.vwma(dataframe["close"], dataframe["volume"], int(self.base_nb_candles_buy_vwma.value)) *self.low_offset_vwma.value
            dataframe['vwma_offset_buy2'] = pta.vwma(dataframe["close"], dataframe["volume"], int(self.base_nb_candles_buy_vwma2.value)) *self.low_offset_vwma2.value
            buy_offset_vwma = (
                (
                    (
                        (dataframe['close'] < dataframe['vwma_offset_buy'])
                        &
                        (dataframe['pm'] <= dataframe['pmax_thresh'])
    
                    )
                    |
                    (
                        (dataframe['close'] < dataframe['vwma_offset_buy2'])
                        &
                        (dataframe['pm'] > dataframe['pmax_thresh'])
                    )
                )                
            )
            dataframe.loc[buy_offset_vwma, 'buy_tag'] += 'vwma '
            conditions.append(buy_offset_vwma)

        add_check = (
            (dataframe['live_data_ok'])
            &
            (dataframe['close'] < dataframe['Smooth_HA_L'])
            &
            (dataframe['close'] < dataframe['ema_offset_sell'])
            &
            (dataframe['close'].rolling(288).max() >= (dataframe['close'] * 1.10 ))
            &
            (dataframe['Smooth_HA_O'].shift(1) < dataframe['Smooth_HA_H'].shift(1))
            &
            (dataframe['rsi_fast'] < self.buy_rsi_fast.value)
            &
            (dataframe['rsi_84'] < 60)
            &
            (dataframe['rsi_112'] < 60)
            &
            (
                (
                    (dataframe['close'] < dataframe['ema_offset_buy'])
                    &
                    (dataframe['pm'] <= dataframe['pmax_thresh'])
                    &
                    (
                        (dataframe['ewo'] < self.ewo_low.value)
                        |
                        (
                            (dataframe['ewo'] > self.ewo_high.value)
                            &
                            (dataframe['rsi'] < self.rsi_buy.value)
                        )
                    )
                )
                |
                (
                    (dataframe['close'] < dataframe['ema_offset_buy2'])
                    &
                    (dataframe['pm'] > dataframe['pmax_thresh'])
                    &
                    (
                        (dataframe['ewo'] < self.ewo_low2.value)
                        |
                        (
                            (dataframe['ewo'] > self.ewo_high2.value)
                            &
                            (dataframe['rsi'] < self.rsi_buy2.value)
                        )
                    )
                )
            )
            &
            (dataframe['volume'] > 0)
        )
        
        if conditions:
            dataframe.loc[
                (add_check & reduce(lambda x, y: x | y, conditions)),
                'buy'
            ]=1

        return dataframe

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

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

        sell_cond_1 = (
            (dataframe['close'] > dataframe['ema_offset_sell'])
            &
            (dataframe['volume'] > 0)
            &
            (dataframe['rsi'] > self.min_rsi_sell.value)
        )

        conditions.append(sell_cond_1)
        dataframe.loc[sell_cond_1, 'exit_tag'] += 'EMA '

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

        return dataframe


# Elliot Wave Oscillator
def EWO(dataframe, sma1_length=5, sma2_length=35):
    df = dataframe.copy()
    sma1 = ta.SMA(df, timeperiod=sma1_length)
    sma2 = ta.SMA(df, timeperiod=sma2_length)
    smadif = (sma1 - sma2) / df['close'] * 100
    return smadif

# PMAX
def pmax(df, period, multiplier, length, MAtype, src):

    period = int(period)
    multiplier = int(multiplier)
    length = int(length)
    MAtype = int(MAtype)
    src = int(src)

    mavalue = f'MA_{MAtype}_{length}'
    atr = f'ATR_{period}'
    pm = f'pm_{period}_{multiplier}_{length}_{MAtype}'
    pmx = f'pmX_{period}_{multiplier}_{length}_{MAtype}'

    # MAtype==1 --> EMA
    # MAtype==2 --> DEMA
    # MAtype==3 --> T3
    # MAtype==4 --> SMA
    # MAtype==5 --> VIDYA
    # MAtype==6 --> TEMA
    # MAtype==7 --> WMA
    # MAtype==8 --> VWMA
    # MAtype==9 --> zema
    if src == 1:
        masrc = df["close"]
    elif src == 2:
        masrc = (df["high"] + df["low"]) / 2
    elif src == 3:
        masrc = (df["high"] + df["low"] + df["close"] + df["open"]) / 4

    if MAtype == 1:
        mavalue = ta.EMA(masrc, timeperiod=length)
    elif MAtype == 2:
        mavalue = ta.DEMA(masrc, timeperiod=length)
    elif MAtype == 3:
        mavalue = ta.T3(masrc, timeperiod=length)
    elif MAtype == 4:
        mavalue = ta.SMA(masrc, timeperiod=length)
    elif MAtype == 5:
        mavalue = VIDYA(df, length=length)
    elif MAtype == 6:
        mavalue = ta.TEMA(masrc, timeperiod=length)
    elif MAtype == 7:
        mavalue = ta.WMA(df, timeperiod=length)
    elif MAtype == 8:
        mavalue = vwma(df, length)
    elif MAtype == 9:
        mavalue = zema(df, period=length)

    df[atr] = ta.ATR(df, timeperiod=period)
    df['basic_ub'] = mavalue + ((multiplier/10) * df[atr])
    df['basic_lb'] = mavalue - ((multiplier/10) * df[atr])


    basic_ub = df['basic_ub'].values
    final_ub = np.full(len(df), 0.00)
    basic_lb = df['basic_lb'].values
    final_lb = np.full(len(df), 0.00)

    for i in range(period, len(df)):
        final_ub[i] = basic_ub[i] if (
            basic_ub[i] < final_ub[i - 1]
            or mavalue[i - 1] > final_ub[i - 1]) else final_ub[i - 1]
        final_lb[i] = basic_lb[i] if (
            basic_lb[i] > final_lb[i - 1]
            or mavalue[i - 1] < final_lb[i - 1]) else final_lb[i - 1]

    df['final_ub'] = final_ub
    df['final_lb'] = final_lb

    pm_arr = np.full(len(df), 0.00)
    for i in range(period, len(df)):
        pm_arr[i] = (
            final_ub[i] if (pm_arr[i - 1] == final_ub[i - 1]
                                    and mavalue[i] <= final_ub[i])
        else final_lb[i] if (
            pm_arr[i - 1] == final_ub[i - 1]
            and mavalue[i] > final_ub[i]) else final_lb[i]
        if (pm_arr[i - 1] == final_lb[i - 1]
            and mavalue[i] >= final_lb[i]) else final_ub[i]
        if (pm_arr[i - 1] == final_lb[i - 1]
            and mavalue[i] < final_lb[i]) else 0.00)

    pm = Series(pm_arr)

    # Mark the trend direction up/down
    pmx = np.where((pm_arr > 0.00), np.where((mavalue < pm_arr), 'down',  'up'), np.NaN)

    return pm, pmx

# smoothed Heiken Ashi
def HA(dataframe, smoothing=None):
    df = dataframe.copy()

    df['HA_Close']=(df['open'] + df['high'] + df['low'] + df['close'])/4

    df.reset_index(inplace=True)

    ha_open = [ (df['open'][0] + df['close'][0]) / 2 ]
    [ ha_open.append((ha_open[i] + df['HA_Close'].values[i]) / 2) for i in range(0, len(df)-1) ]
    df['HA_Open'] = ha_open

    df.set_index('index', inplace=True)

    df['HA_High']=df[['HA_Open','HA_Close','high']].max(axis=1)
    df['HA_Low']=df[['HA_Open','HA_Close','low']].min(axis=1)

    if smoothing is not None:
        sml = abs(int(smoothing))
        if sml > 0:
            df['Smooth_HA_O']=ta.EMA(df['HA_Open'], sml)
            df['Smooth_HA_C']=ta.EMA(df['HA_Close'], sml)
            df['Smooth_HA_H']=ta.EMA(df['HA_High'], sml)
            df['Smooth_HA_L']=ta.EMA(df['HA_Low'], sml)
            
    return df

def pump_warning(dataframe, perc=15):
    df = dataframe.copy()    
    df["change"] = df["high"] - df["low"]
    df["test1"] = (df["close"] > df["open"])
    df["test2"] = ((df["change"]/df["low"]) > (perc/100))
    df["result"] = (df["test1"] & df["test2"]).astype('int')
    return df['result']

def tv_wma(dataframe, length = 9, field="close") -> DataFrame:
    """
    Source: Tradingview "Moving Average Weighted"
    Pinescript Author: Unknown
    Args :
        dataframe : Pandas Dataframe
        length : WMA length
        field : Field to use for the calculation
    Returns :
        dataframe : Pandas DataFrame with new columns 'tv_wma'
    """

    norm = 0
    sum = 0

    for i in range(1, length - 1):
        weight = (length - i) * length
        norm = norm + weight
        sum = sum + dataframe[field].shift(i) * weight

    dataframe["tv_wma"] = (sum / norm) if norm > 0 else 0
    return dataframe["tv_wma"]

def tv_hma(dataframe, length = 9, field="close") -> DataFrame:
    """
    Source: Tradingview "Hull Moving Average"
    Pinescript Author: Unknown
    Args :
        dataframe : Pandas Dataframe
        length : HMA length
        field : Field to use for the calculation
    Returns :
        dataframe : Pandas DataFrame with new columns 'tv_hma'
    """

    dataframe["h"] = 2 * tv_wma(dataframe, math.floor(length / 2), field) - tv_wma(dataframe, length, field)

    dataframe["tv_hma"] = tv_wma(dataframe, math.floor(math.sqrt(length)), "h")
    # dataframe.drop("h", inplace=True, axis=1)

    return dataframe["tv_hma"]