Scrapping Bybit Kline data with Pybit

import pandas as pd
from datetime import date, datetime, timedelta

from pybit.unified_trading import HTTP
import numpy as np
import dateparser
import time
import math

session = HTTP(testnet=False, api_key=API_KEY, api_secret=SECRET_KEY)

def GetHistoricalData(currency, start_date, end_date, interval):
    start_time = dateparser.parse(start_date)
    end_time = dateparser.parse(end_date)
    start_ts = int(start_time.timestamp()*1000) #ms
    end_ts = int(end_time.timestamp()*1000) #ms

    page = 0
    # we will query for 2 pages per day, with limit of 720 bar returned every call, we then get 1440min for every 2 pages
    # note that API call below will use 720 as limit because that's easier for pagination
    # hence total pages = num of days * 2
    totalPage = math.ceil(start_ts - end_ts / (1000 * 60 * 60 * 24)) * 2 
    df = pd.DataFrame(columns=['startTime', 'openPrice', 'highPrice', 'lowPrice', 'closePrice', 'volume'])

    while True:
        # Kline API call, 1min interval is the smallest bar we can get, limit max at 1000 but I use 720 here
        bars = session.get_kline(symbol=currency, interval=str(interval), start=start_ts, category="linear", limit=720)
        # counter starts from end of list then goes backward since oldest data appear last
        idx = len(bars['result']['list']) - 1

        while True:
            bar = bars['result']['list'][idx]
            new_row = {'startTime': pd.to_datetime(int(bar[0]), unit= 'ms'), 'openPrice': bar[1], 'highPrice': bar[2], 'lowPrice': bar[3], 'closePrice': bar[4], 'volume': bar[5]}
            # if the kline bar exceed end_time, means reaches target date, break out of loop
            if int(bar[0]) > end_ts:
                start_ts = end_ts
                break
            # append the new row into dataframe
            df.loc[len(df)] = new_row
            # check if we reaches first item in array
            if idx == 0:
                start_ts = start_ts + 43200*1000 # increment by num of ms for a day to get start time for tmr
                break
            idx -= 1
        page += 1
        # check if the end_date is reached
        if start_ts >= end_ts:
            # exit the while loop
            break
        if page > totalPage:
            break
        # time delay to prevent api rate limit
        time.sleep(0.02)

    return df

data = GetHistoricalData("BTCUSDT", "May 01, 2023 00:00 UTC", "July 31, 2023 23:59 UTC", 1)
data.to_csv("./{}_May-01-2023-July-31-2023.csv".format("BTCUSDT"), index=False)