How to utilize this Framework for live ticks about pandas-ta HOT 10 CLOSED

import pandas as pd
import pandas_ta as ta

momo_bands_sma_ta = [
    {"kind":"sma", "length": 50},
    {"kind":"sma", "length": 200},
    {"kind":"bbands", "length": 20},
    {"kind":"macd"},
    {"kind":"rsi"},
    {"kind":"log_return", "cumulative": True}, # Creates new column: "CUMLOGRET_1"
    {"kind":"sma", "close": "CUMLOGRET_1", "length": 5, "suffix": "CUMLOGRET"},
]
momo_bands_sma_strategy = ta.Strategy(
    "Momo, Bands and SMAs and Cumulative Log Returns", # name
    momo_bands_sma_ta, # ta
    "MACD and RSI Momo with BBANDS and SMAs 50 & 200 and Cumulative Log Returns" # description
)

# Since SMA requires 200 periods above, we will set last to 200
last = 200
# Indicator length buffer
last_buffer = 10
rows = last + last_buffer

histdf = YourHistoricalSource()
has_new_tick = True

while has_new_tick:
    lastdf = histdf.tail(rows).copy() # Get the last 210 rows from histdf
    lastdf.ta.strategy(momo_bands_sma_strategy) # Process the Strategy with lastdf
    histdf.append(lastdf.iloc[-1], ignore_index=True) # Append new TA to histdf
    histdf.to_<Storage>() # Save to your Storage if you require it

    new_tick = YourTickSource() # Request new tick for your Broker
    if new_tick has "ohlcv": # Or whatever satisfies your requirements
        has_new_tick = True
    else:
        has_new_tick = False

import pandas as pd
import pandas_ta as ta
from alphaVantageAPI.alphavantage import AlphaVantage

#AlphaVantage
def farm(ticker = 'AAPL', drop=['dividend', 'split_coefficient']):
    AV = AlphaVantage(api_key="YOUR API KEY", premium=False, clean=True, output_size='full')
    df = AV.data(symbol=ticker, function='D')
    df.set_index(pd.DatetimeIndex(df['date']), inplace=True) if not df.ta.datetime_ordered else None
    df.drop(['dividend', 'split_coefficient'], axis=1, inplace=True) if 'dividend' in df.columns and 'split_coefficient' in df.columns else None
    df.name = ticker
    return df

#Historical Data
ticker = 'GOOGL'
df = farm(ticker)

#Live Tick Iterator
def live_df_simulation(df, start, n, end):
    while True:
        if start+n < end:
            yield df.iloc[start: start+n]
            start += 1
        else:
            yield pd.DataFrame()

#Strategy Definition
strategy = [
    {"kind":"sma"},
    {"kind":"bbands"},
    {"kind":"macd"},
    {"kind":"rsi"},
    {"kind":"supertrend"},
    {"kind":"mfi"}
]

strategy = ta.Strategy('live_indicators', strategy, 'technical indicators live calculation')
df_hist = df.copy(deep=True)

#Calclating Indicators
df_hist.ta.strategy(strategy)

#Live Tick Simulation
start = 300 #Starts from index 300
end = len(df)-105
n = 30 

#df_slice iterates through 300-399, 301-400, 302-401 .... 3918-4017
df_slice = live_df_simulation(df, start, n, end)

#Live DataFrame
df_live = pd.DataFrame()
tmp = next(df_slice)

#Live Calculation
while tmp.empty != True:
    tmp.ta.strategy(strategy)
    df_live = df_live.append(tmp.iloc[-1])
    tmp = next(df_slice)
    

df_hist = df_hist[['open', 'high', 'low', 'close']+['SUPERT_7_3.0', 'RSI_14', 'SMA_10', 'MFI_14', 'MACD_12_26_9']]
df_live = df_live[['SUPERT_7_3.0', 'RSI_14', 'SMA_10', 'MFI_14', 'MACD_12_26_9']]

#Comparison b/w single_run calculation and live_tick calculation
comparision = df_hist.merge(df_live, how='inner', left_index=True, right_index=True)

#if we increase the number of rows n=100 or 200, there is a change in values only after the decimal point