When scraping data from the web, most of the type is typically spent on waiting for server responses. In order to issue a large amount of queries and to get responses as fast as possible, issuing multiple queries in parallel is the right approach. To be as efficient as possible, you have to find the right balance between a large amount of parallel requests and being throttled (or blacklisted) by the remote service.
In your code, you are creating as many threads as there are requests. In general, you would want to limit the number of threads and reuse the threads once they have performed a request in order to save resources. This is called a thread pool.
Since you are using Python, another lighter alternative to multiple threads is to run parallel "I/O" tasks using asyncio in Python. Sample implementations of parallel requests using either a thread pool or asyncio are shown in this Stack Overflow answer .

Edit: here is an adapted example from your code using asyncio :

import asyncio
from aiohttp import ClientSession

stocks = ['ACC','ADANIENT','ADANIGREEN','ADANIPORTS','ADANITRANS','AMBUJACEM','ASIANPAINT','ATGL','BAJAJ-AUTO','BAJAJHLDNG']

async def fetch_price(session, stock, start_unix, end_unix, interval):
    url = f'https://priceapi.moneycontrol.com/techCharts/indianMarket/stock/history?symbol={stock}&resolution={interval}&from={start_unix}&to={end_unix}'
    async with session.get(url) as resp:
        data = await resp.json()
        return stock, data['c']

async def main():
    start_unix = 1669794745
    end_unix = start_unix + 1800
    interval = 1
    async with ClientSession() as session:
        tasks = []
        for stock in stocks:
            tasks.append(loop.create_task(
                fetch_price(session, stock, start_unix, end_unix, interval)
            ))
        prices = await asyncio.gather(*tasks)
    print(prices)

loop = asyncio.get_event_loop()
loop.run_until_complete(main())