Python - how can identify ranges of numbers in a OHLC dataset?

I'm working on a side project where i try to identify support zones in a OHLCV dataset of Bitcoin historical prices, here is what my data looks like in a pandas dataframe:

                                    startTime          time     open     high      low    close        volume                date direction
date
2022-04-08 21:00:00  2022-04-08T21:00:00+00:00  1.649452e+09  42802.0  42856.0  42439.0  42560.0  6.051324e+07 2022-04-08 21:00:00      down
2022-04-08 22:00:00  2022-04-08T22:00:00+00:00  1.649455e+09  42560.0  42664.0  42228.0  42297.0  1.318070e+08 2022-04-08 22:00:00      down
2022-04-08 23:00:00  2022-04-08T23:00:00+00:00  1.649459e+09  42297.0  42359.0  42101.0  42258.0  1.252274e+08 2022-04-08 23:00:00      down
2022-04-09 00:00:00  2022-04-09T00:00:00+00:00  1.649462e+09  42258.0  42457.0  42144.0  42435.0  8.506876e+07 2022-04-09 00:00:00        up
2022-04-09 01:00:00  2022-04-09T01:00:00+00:00  1.649466e+09  42435.0  42461.0  42284.0  42428.0  4.037635e+07 2022-04-09 01:00:00      down
...                                        ...           ...      ...      ...      ...      ...           ...                 ...       ...
2022-06-10 04:00:00  2022-06-10T04:00:00+00:00  1.654834e+09  30094.0  30126.0  30030.0  30069.0  3.618291e+07 2022-06-10 04:00:00      down
2022-06-10 05:00:00  2022-06-10T05:00:00+00:00  1.654837e+09  30069.0  30170.0  30061.0  30078.0  4.745932e+07 2022-06-10 05:00:00        up
2022-06-10 06:00:00  2022-06-10T06:00:00+00:00  1.654841e+09  30078.0  30212.0  30045.0  30076.0  6.220629e+07 2022-06-10 06:00:00      down
2022-06-10 07:00:00  2022-06-10T07:00:00+00:00  1.654844e+09  30076.0  30114.0  30030.0  30030.0  2.871507e+07 2022-06-10 07:00:00      down
2022-06-10 08:00:00  2022-06-10T08:00:00+00:00  1.654848e+09  30030.0  30092.0  29965.0  30005.0  5.683842e+07 2022-06-10 08:00:00      down

My goal is simple: for every 'low' in my data, calculate a range 0.5% above and 0.5% below that range and find how many other lows there are in that area.

What i tried:

1. group all low prices in a list called lows
2. loop through lows: for every low in lows calculate a range that's 0.5% above and below the value of low
3. inside of the first loop, loop again through lows and for each element, if that element's value is inside of that range, append it to a list.

Here is my whole code:

import httpx
import json
import pandas as pd

def get_ohlc(market):
    req = httpx.get(f'https://ftx.com/api/markets/{market}/candles?resolution=3600')
    data = req.json()['result']
    ohlcv = pd.DataFrame(data)
    ohlcv['time'] = ohlcv['time'].astype('int64')
    ohlcv['time'] = ohlcv['time']/1000
    ohlcv['date'] = pd.to_datetime(ohlcv['time'], unit='s')
    ohlcv = ohlcv.set_index(pd.DatetimeIndex(ohlcv['date']))
    return ohlcv

ohlcv = get_ohlc('BTC-PERP')
supports = {}
lows = ohlcv['low'].tolist()

for low in lows:
    low_up = low + (low/100)*0.5
    low_down = low - (low/100)*0.5
    supports.update({low: []})

    for sub_low in lows:
        if sub_low < low_up and sub_low > low_down:
            supports[low].append(sub_low)

print(supports)

What's wrong with my current code: i feel it's not very efficient since i'm looping multiple times through the same list. Is there a more efficient way to do this? Are there other algorithms that allow me to cluster values in a similar way, maybe with scipy or other packages? I hope my question was clear enough, thanks in advance!

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