I want to convert twin range filter in trading view indicator to python code but results does not same

trading view indicator pine script:
// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
//@version=5
indicator(title=’Twin Range Filter’, overlay=true, timeframe=”)
source = input(defval=close, title=’Source’)
showsignals = input(title=’Show Buy/Sell Signals ?’, defval=true)
per1 = input.int(defval=27, minval=1, title=’Fast period’)
mult1 = input.float(defval=1.6, minval=0.1, title=’Fast range’)
per2 = input.int(defval=55, minval=1, title=’Slow period’)
mult2 = input.float(defval=2, minval=0.1, title=’Slow range’)
smoothrng(x, t, m) =>
wper = t * 2 – 1
avrng = ta.ema(math.abs(x – x[1]), t)
smoothrng = ta.ema(avrng, wper) * m
smoothrng
smrng1 = smoothrng(source, per1, mult1)
smrng2 = smoothrng(source, per2, mult2)
smrng = (smrng1 + smrng2) / 2
rngfilt(x, r) =>
rngfilt = x
rngfilt := x > nz(rngfilt[1]) ? x – r < nz(rngfilt[1]) ? nz(rngfilt[1]) : x – r : x + r > nz(rngfilt[1]) ? nz(rngfilt[1]) : x + r
rngfilt
filt = rngfilt(source, smrng)
upward = 0.0
upward := filt > filt[1] ? nz(upward[1]) + 1 : filt < filt[1] ? 0 : nz(upward[1])
downward = 0.0
downward := filt < filt[1] ? nz(downward[1]) + 1 : filt > filt[1] ? 0 : nz(downward[1])
STR = filt + smrng
STS = filt – smrng
FUB = 0.0
FUB := STR < nz(FUB[1]) or close[1] > nz(FUB[1]) ? STR : nz(FUB[1])
FLB = 0.0
FLB := STS > nz(FLB[1]) or close[1] < nz(FLB[1]) ? STS : nz(FLB[1])
TRF = 0.0
TRF := nz(TRF[1]) == FUB[1] and close <= FUB ? FUB : nz(TRF[1]) == FUB[1] and close >= FUB ? FLB : nz(TRF[1]) == FLB[1] and close >= FLB ? FLB : nz(TRF[1]) == FLB[1] and close <= FLB ? FUB : FUB
long = ta.crossover(close, TRF)
short = ta.crossunder(close, TRF)
plotshape(showsignals and long, title=’Long’, text=’BUY’, style=shape.labelup, textcolor=color.white, size=size.tiny, location=location.belowbar, color=color.rgb(0, 19, 230))
plotshape(showsignals and short, title=’Short’, text=’SELL’, style=shape.labeldown, textcolor=color.white, size=size.tiny, location=location.abovebar, color=color.rgb(0, 19, 230))
alertcondition(long, title=’Long’, message=’Long’)
alertcondition(short, title=’Short’, message=’Short’)
Trfff = plot(TRF)
mPlot = plot(ohlc4, title=”, style=plot.style_circles, linewidth=0)
longFillColor = close > TRF ? color.green : na
shortFillColor = close < TRF ? color.red : na
fill(mPlot, Trfff, title=’UpTrend Highligter’, color=longFillColor, transp=90)
fill(mPlot, Trfff, title=’DownTrend Highligter’, color=shortFillColor, transp=90)

my python code:

    import time
    import pandas as pd
    import math

    import numpy as np
    import win32api
    from binance.client import Client
    from datetime import datetime#timedelta, 
    #------------------------

    binance_api_key9 =    ''    #Enter your own API-key here
    binance_api_secret9 = '' #Enter your own API-secret

    binance_client = Client(api_key=binance_api_key9, api_secret=binance_api_secret9)


    def ema(series, period):
        return series.ewm(span=period, adjust=False).mean()

    def smoothrng(source, t, m):
        wper = t * 2 - 1
        avrng = ema(abs(source - source.shift(1)), t)
        smoothrng = ema(avrng, wper) * m
        return smoothrng

    def rngfilt(source, smrng):
        filt = source.copy()
        for i in range(1, len(source)):
            if source[i] > filt[i - 1]:
                filt[i] = filt[i - 1] if source[i] - smrng[i] < filt[i - 1] else source[i] - smrng[i]
            elif source[i] + smrng[i] > filt[i - 1]:
                filt[i] = filt[i - 1]
            else:
                filt[i] = source[i] + smrng[i]
        return filt

    def twin_range_filter(df, per1=27, mult1=1.6, per2=55, mult2=2.0):
        # Source data (close prices)
        source = df['close']
        
        # Smooth Average Range Calculation
        smrng1 = smoothrng(source, per1, mult1)
        smrng2 = smoothrng(source, per2, mult2)
        smrng = (smrng1 + smrng2) / 2

        # Range Filter
        filt = rngfilt(source, smrng)
        
        # Trend Determination
        upward = (filt > filt.shift(1)).cumsum()
        downward = (filt < filt.shift(1)).cumsum()
        
        # Long and Short Conditions
        long_cond = (source > filt) & ((source > source.shift(1)) & (upward > 0) | (source < source.shift(1)) & (upward > 0))
        short_cond = (source < filt) & ((source < source.shift(1)) & (downward > 0) | (source > source.shift(1)) & (downward > 0))

        # Initial Condition Tracking
        cond_ini = pd.Series(np.where(long_cond, 1, np.where(short_cond, -1, 0)))
        cond_ini = cond_ini.shift(1).fillna(0)
        
        # Long and Short Signals
        long_signal = long_cond & (cond_ini == -1)
        short_signal = short_cond & (cond_ini == 1)
        
        # Add signals to DataFrame
        df['long'] = long_signal
        df['short'] = short_signal

        return df


    st=binance_client.get_server_time()['serverTime']

    gt = binance_client.get_server_time()
    tt=time.gmtime(int((gt["serverTime"])/1000))
    ww=win32api.SetSystemTime(tt[0],tt[1],0,tt[2],tt[3],tt[4],tt[5],0)
    startint=st-(5000)*240*60000
    print('start kline')
    kk1=[]
    data=[]
    sy='BTCUSDT'
    filename = 'C:\Users\Administrator\Desktop\Btwin\%s4h.csv'  % (sy)
    kk = binance_client.futures_klines(symbol=sy, interval='4h', startTime=startint)
    while kk[-1][6]<float(st):
        #print(kk[-1][6])
        kk2 = binance_client.futures_klines(symbol=sy, interval='4h', startTime=kk[-1][6])
        kk.extend(kk2)
    for line in kk:
        kk1.append([pd.to_datetime(int(line[0])+12600000, unit='ms'),float(line[1]),float(line[2]),float(line[3]),float(line[4]),0,0,0,0,0,0,0])
        data.append(float(line[4]))



    data1 = pd.DataFrame({'close': data })

    result = twin_range_filter(data1)
    for i in range(0,len(kk1)):
        kk1[i][9]=result['close'][i]
        kk1[i][10]=result['long'][i]
        kk1[i][11]=result['short'][i]
    print(kk1)
    kkn=pd.DataFrame(kk1 ,columns = ['timestamp', 'open', 'high', 'low', 'close', '0', '0','0', '0', '0','0','0'        ])
            
        #print(kkt[-1])
    kkn.to_csv(filename)