flow alerts multiple tickers

import os
import csv
import requests
import polars as pl
from pathlib import Path
from datetime import datetime
from lets_plot import *
LetsPlot.setup_html()

uw_token = os.environ['UW_TOKEN']
headers = {'Accept': 'application/json, text/plain', 'Authorization': uw_token}
pl.Config.set_tbl_cols(40)
pl.Config.set_fmt_str_lengths(40)

raw_df = pl.DataFrame(r.json()['data'])
clean_df = (
    raw_df
    .with_columns(
        [
            pl.col('price').cast(pl.Float64),
            pl.col('total_ask_side_prem').cast(pl.Float64),
            pl.col('total_bid_side_prem').cast(pl.Float64),
            pl.col('total_premium').cast(pl.Float64),
            pl.col('underlying_price').cast(pl.Float64),
            pl.col('volume_oi_ratio').cast(pl.Float64),
        ]
    )
)
clean_df

opener_df = (
    clean_df
    .filter(
        (
            pl.col('total_size') > pl.col('open_interest')
        )
    )
)
opener_df

display_df = (
    opener_df
    .with_columns(
        pl.col('created_at').str.strptime(pl.Datetime, '%Y-%m-%dT%H:%M:%S%.fZ').alias('created_at_datetime')
    )
    .with_columns(
        pl.col('created_at_datetime').dt.convert_time_zone('America/New_York').alias('created_at_datetime_eastern')
    )
    .with_columns(
        pl.col('created_at_datetime_eastern').dt.strftime('%Y-%m-%d %I:%M:%S %p').alias('created_at_east_str')
    )
    .with_columns(
        [
            (pl.col('total_ask_side_prem') / pl.col('total_premium')).alias('pct_ask'),
            (pl.col('total_bid_side_prem') / pl.col('total_premium')).alias('pct_bid')
        ]
    )
    .with_columns(
        pl.when(pl.col('pct_ask') > 0.80).then(pl.lit('ask'))
        .when(pl.col('pct_bid') > 0.80).then(pl.lit('bid'))
        .otherwise(pl.lit('mixed')).alias('side')
    )
    .with_columns(
        (
            ((pl.col('pct_ask').round(2) * 100).cast(pl.Utf8)) +
            '% Ask / ' +
            ((pl.col('pct_bid').round(2) * 100).cast(pl.Utf8)) +
            '% Bid'
        ).str.replace_all('.0%', '%').alias('pct_ask_bid_str')
    )
    .with_columns(
        pl.when((pl.col('side') == 'ask') & (pl.col('type') == 'call')).then(pl.lit('bullish'))
        .when((pl.col('side') == 'ask') & (pl.col('type') == 'put')).then(pl.lit('bearish'))
        .when((pl.col('side') == 'bid') & (pl.col('type') == 'call')).then(pl.lit('bearish'))
        .when((pl.col('side') == 'bid') & (pl.col('type') == 'put')).then(pl.lit('bullish'))
        .otherwise(pl.lit('mixed')).alias('direction')
    )
    .with_columns(
        pl.col('expiry').str.strptime(pl.Date, '%Y-%m-%d').alias('expiry_date')
    )
    .with_columns(
        (pl.col('expiry_date') - pl.lit(datetime.today().date())).alias('dte')
    )
    .with_columns(
        (pl.col('dte').dt.total_milliseconds() / 86_400_000).alias('dte_days')
    )
    .with_columns(
        pl.col('dte_days').cast(pl.Int64).alias('dte_days_int')
    )
    .with_columns(
        (
            pl.when(pl.col('type') == 'call')
            .then(pl.col('strike') + 'C')
            .otherwise(pl.col('strike') + 'P')
        ).alias('strike_type')
    )
    .with_columns(
        (
            pl.col('ticker') + ' ' +
            pl.col('strike_type') + ' ' +
            (pl.col('expiry_date').dt.strftime('%m/%d/%Y')).alias('expiry_date_str')
        ).alias('contract_look_up_format')
    )
)
display_df

final_df = (
    display_df
    .select(
        [
            'created_at_east_str',
            'contract_look_up_format',
            'total_size',
            'price',
            'total_premium',
            'pct_ask_bid_str',
            'direction',
            'underlying_price',
            'dte_days_int'
        ]
    )
)
final_df