In [1]:

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)

/home/danwagnerco/.cache/pypoetry/virtualenvs/dans-magic-house-uvW-KuUb-py3.12/lib/python3.12/site-packages/lets_plot/plot/annotation.py:150: SyntaxWarning: invalid escape sequence '\^'
  """
/home/danwagnerco/.cache/pypoetry/virtualenvs/dans-magic-house-uvW-KuUb-py3.12/lib/python3.12/site-packages/lets_plot/plot/tooltip.py:226: SyntaxWarning: invalid escape sequence '\^'
  """

Out[1]:

polars.config.Config

Get recent Flow Alert-type trades for single-name equities

Data from option-trades/flow alerts endpoint: https://api.unusualwhales.com/docs#/operations/PublicApi.OptionTradeController.flow_alerts
We use a params dictionary to send some additional criteria to the endpoint:
issue_types[] = ['Common Stock', 'ADR'] to skip ETFs and indexes
min_dte = 1, which means we will always skip 0dte trades
min_premium = 200000, which means only trades involving $200K or more will be returned
min_volume_oi_ratio = 1.0, which helps us narrow down the universe to potentially opening trades
rule_name[] = ['RepeatedHits', 'RepeatedHitsAscendingFill', 'RepeatedHitsDescendingFill'] to focus specifically on "urgent" multi-exchange screen trades

We execute the API call in the next cell:

In [2]:

"""
API documentation:
https://api.unusualwhales.com/docs#/operations/PublicApi.OptionTradeController.flow_alerts
"""
url = 'https://api.unusualwhales.com/api/option-trades/flow-alerts'
params = {
    'issue_types[]': ['Common Stock', 'ADR'],
    'min_dte': 1,
    'min_premium': 200000,
    'min_volume_oi_ratio': 1.0,
    'rule_name[]': ['RepeatedHits', 'RepeatedHitsAscendingFill', 'RepeatedHitsDescendingFill']
}
r = requests.get(url, headers=headers, params=params)

Use a DataFrame package (like polars) to process the response

First step is to ingest the json response into a polars DataFrame
Then we will convert some columns that are expressed as strings into floats

Let's have at it:

In [3]:

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

Out[3]:

shape: (250, 28)

alert_rule	all_opening_trades	created_at	end_time	expiry	expiry_count	has_floor	has_multileg	has_singleleg	has_sweep	id	open_interest	option_chain	price	rule_id	sector	start_time	strike	ticker	total_ask_side_prem	total_bid_side_prem	total_premium	total_size	trade_count	type	underlying_price	volume	volume_oi_ratio
str	bool	str	i64	str	i64	bool	bool	bool	bool	str	i64	str	f64	str	str	i64	str	str	f64	f64	f64	i64	i64	str	f64	i64	f64
"RepeatedHitsAscendingFill"	false	"2024-11-06T21:00:24.359921Z"	1730926798701	"2024-11-15"	1	false	false	true	false	"96d5b321-c096-4897-84f4-e6243481d2e5"	10513	"TSM241115C00200000"	2.59	"a0979b52-28e4-4585-8f22-881faad2dd8e"	"Technology"	1730926798301	"200"	"TSM"	264870.0	271996.0	536866.0	2100	37	"call"	193.3962	18920	1.799677
"RepeatedHitsDescendingFill"	false	"2024-11-06T21:00:24.359921Z"	1730926798163	"2024-11-15"	1	false	false	true	false	"f06b0f92-d20e-4a1e-a3b3-0b755fcbea75"	10513	"TSM241115C00200000"	2.52	"5ce5ec11-087c-4c00-b164-08106b015856"	"Technology"	1730926797890	"200"	"TSM"	358977.0	17262.0	385215.0	1531	109	"call"	193.41	16820	1.599924
"RepeatedHitsAscendingFill"	false	"2024-11-06T20:59:52.879808Z"	1730926786160	"2024-11-15"	1	false	false	true	false	"fc2a519b-81d9-4d3d-b42c-7a1c788749f8"	10513	"TSM241115C00200000"	2.5	"a0979b52-28e4-4585-8f22-881faad2dd8e"	"Technology"	1730926786128	"200"	"TSM"	425025.0	7641.0	434402.0	1746	81	"call"	193.61	15209	1.446685
"RepeatedHitsAscendingFill"	false	"2024-11-06T20:58:40.820468Z"	1730926712832	"2025-03-21"	1	false	false	true	false	"7f310b51-df89-4838-9371-840a6f5d4ae4"	424	"DELL250321C00140000"	16.8	"a0979b52-28e4-4585-8f22-881faad2dd8e"	"Technology"	1730926712826	"140"	"DELL"	614876.0	0.0	614876.0	366	54	"call"	138.28	430	1.014151
"RepeatedHits"	false	"2024-11-06T20:58:35.586376Z"	1730926708124	"2024-11-08"	1	false	false	true	false	"ec27caaf-6f09-491e-a142-1ca5484bbd59"	474	"ARM241108C00146000"	7.7	"e6b9f0b6-fcd9-44fe-9d1c-f53521c152c3"	"Technology"	1730926708117	"146"	"ARM"	46200.0	192500.0	238700.0	310	12	"call"	144.9	1026	2.164557
…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…
"RepeatedHits"	false	"2024-11-06T16:11:17.563654Z"	1730909470325	"2024-11-15"	1	false	false	true	true	"6bb43063-bbae-4c0e-ba80-86d0634125ae"	29596	"TSLA241115C00300000"	5.3	"e6b9f0b6-fcd9-44fe-9d1c-f53521c152c3"	"Consumer Cyclical"	1730909470314	"300"	"TSLA"	0.0	530000.0	530000.0	1000	7	"call"	284.98	54470	1.840451
"RepeatedHits"	false	"2024-11-06T16:11:01.937594Z"	1730909455013	"2024-11-15"	1	false	false	true	true	"8ee9b95b-e293-428d-81b8-00c12e2ce300"	29596	"TSLA241115C00300000"	5.25	"e6b9f0b6-fcd9-44fe-9d1c-f53521c152c3"	"Consumer Cyclical"	1730909455009	"300"	"TSLA"	0.0	262500.0	262500.0	500	15	"call"	284.88	53395	1.804129
"RepeatedHitsDescendingFill"	false	"2024-11-06T16:10:27.666171Z"	1730909420942	"2024-11-15"	1	false	false	true	true	"c27e0b31-4f97-4f64-9673-64819569b51e"	29596	"TSLA241115C00300000"	5.15	"5ce5ec11-087c-4c00-b164-08106b015856"	"Consumer Cyclical"	1730909420917	"300"	"TSLA"	228138.0	0.0	228138.0	443	42	"call"	284.68	52464	1.772672
"RepeatedHitsAscendingFill"	false	"2024-11-06T16:05:53.721312Z"	1730909144715	"2024-11-15"	1	false	false	true	false	"844f3d6d-c31d-4a27-8e68-332c1f7d3421"	29596	"TSLA241115C00300000"	4.85	"a0979b52-28e4-4585-8f22-881faad2dd8e"	"Consumer Cyclical"	1730909144381	"300"	"TSLA"	722123.0	485.0	723088.0	1501	122	"call"	283.87	51040	1.724557
"RepeatedHits"	false	"2024-11-06T16:02:46.942791Z"	1730908961380	"2024-11-15"	1	false	false	true	false	"43ca87f4-5b60-46bc-bda1-ba1f578ef205"	492	"TSLA241115P00285000"	11.2	"e6b9f0b6-fcd9-44fe-9d1c-f53521c152c3"	"Consumer Cyclical"	1730908961351	"285"	"TSLA"	269920.0	0.0	269920.0	241	5	"put"	283.0	7687	15.623984

Use our knowledge of total_size vs. open_interest for more filtering

We can be a LOT more certain that a trade was an opener if the size of the trade itself is greater than the open interest on that contract
So with that in mind, let's filter out trades where the size of the trade is less than the open interest

Filtering a DataFrame in polars is pretty easy so let's knock that out next:

In [4]:

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

Out[4]:

shape: (92, 28)

alert_rule	all_opening_trades	created_at	end_time	expiry	expiry_count	has_floor	has_multileg	has_singleleg	has_sweep	id	open_interest	option_chain	price	rule_id	sector	start_time	strike	ticker	total_ask_side_prem	total_bid_side_prem	total_premium	total_size	trade_count	type	underlying_price	volume	volume_oi_ratio
str	bool	str	i64	str	i64	bool	bool	bool	bool	str	i64	str	f64	str	str	i64	str	str	f64	f64	f64	i64	i64	str	f64	i64	f64
"RepeatedHits"	false	"2024-11-06T20:56:09.194335Z"	1730926563559	"2025-01-17"	1	false	false	true	true	"d60bc445-8a2f-4ffd-a0cc-5afe0dfd2e92"	873	"NVO250117P00095000"	2.42	"e6b9f0b6-fcd9-44fe-9d1c-f53521c152c3"	"Healthcare"	1730926563555	"95"	"NVO"	241516.0	0.0	241516.0	998	40	"put"	105.3199	1137	1.302405
"RepeatedHits"	false	"2024-11-06T20:53:11.204172Z"	1730926384223	"2025-03-21"	1	false	false	true	true	"76468e20-6c83-4c47-8325-fd517beb2741"	62	"GS250321C00660000"	15.95	"e6b9f0b6-fcd9-44fe-9d1c-f53521c152c3"	"Financial Services"	1730926384212	"660"	"GS"	234462.0	0.0	234462.0	147	41	"call"	597.37	1032	16.645161
"RepeatedHitsAscendingFill"	false	"2024-11-06T20:47:52.731549Z"	1730926066898	"2024-11-08"	1	false	false	true	true	"a6ffdd74-6baa-4a5a-958b-7168adcbec1c"	2245	"COIN241108C00300000"	0.74	"a0979b52-28e4-4585-8f22-881faad2dd8e"	"Technology"	1730926066169	"300"	"COIN"	73080.0	128711.0	219341.0	3008	161	"call"	252.3775	8760	3.902004
"RepeatedHitsAscendingFill"	false	"2024-11-06T20:45:06.893254Z"	1730925901345	"2025-08-15"	1	false	false	true	false	"0e712a35-1243-4611-887b-e791cd34c0e2"	316	"TSLA250815P00220000"	19.0	"a0979b52-28e4-4585-8f22-881faad2dd8e"	"Consumer Cyclical"	1730925901263	"220"	"TSLA"	146300.0	659346.0	805646.0	424	18	"put"	288.79	611	1.933544
"RepeatedHitsDescendingFill"	false	"2024-11-06T20:40:36.898760Z"	1730925631939	"2027-01-15"	1	false	false	true	false	"f16f636d-a714-4e09-a66b-6b4bd4e30692"	1	"COIN270115P00260000"	93.55	"5ce5ec11-087c-4c00-b164-08106b015856"	"Technology"	1730925631905	"260"	"COIN"	467742.0	0.0	467742.0	50	13	"put"	253.36	59	59.0
…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…
"RepeatedHitsDescendingFill"	false	"2024-11-06T16:23:39.243295Z"	1730910212644	"2027-01-15"	1	false	false	true	false	"89f90634-b5be-47be-8ec9-28a4756f6822"	1	"AXP270115C00370000"	27.55	"5ce5ec11-087c-4c00-b164-08106b015856"	"Financial Services"	1730910212643	"370"	"AXP"	217643.0	2750.0	220393.0	80	21	"call"	291.06	97	97.0
"RepeatedHits"	false	"2024-11-06T16:22:12.891265Z"	1730910126927	"2024-11-08"	1	false	false	true	false	"a529583c-99ca-4261-8e7f-b177312b471b"	2799	"LYFT241108P00013500"	0.77	"e6b9f0b6-fcd9-44fe-9d1c-f53521c152c3"	"Technology"	1730910126926	"13.5"	"LYFT"	0.0	326634.0	335258.0	4354	12	"put"	14.115	14935	5.335834
"RepeatedHits"	false	"2024-11-06T16:14:34.117465Z"	1730909660739	"2024-11-29"	1	false	false	true	false	"6487a13a-8b11-427f-bac9-4af77f8da0c4"	10	"HIMS241129P00022500"	1.5	"e6b9f0b6-fcd9-44fe-9d1c-f53521c152c3"	"Consumer Defensive"	1730909660656	"22.5"	"HIMS"	235200.0	2250.0	244650.0	1631	15	"put"	23.52	8158	815.8
"RepeatedHits"	false	"2024-11-06T16:13:32.029822Z"	1730909604489	"2025-02-21"	1	false	false	true	false	"64c56012-8832-4828-8d0d-2c4f02c1ceaa"	58	"VKTX250221P00062500"	9.5	"e6b9f0b6-fcd9-44fe-9d1c-f53521c152c3"	"Healthcare"	1730909604487	"62.5"	"VKTX"	0.0	240350.0	240350.0	253	12	"put"	64.535	266	4.586207
"RepeatedHits"	false	"2024-11-06T16:11:20.569713Z"	1730909474810	"2024-11-29"	1	false	false	true	false	"9be1d040-84a3-444f-9c3e-0868e052a67b"	10	"HIMS241129P00022500"	1.5	"e6b9f0b6-fcd9-44fe-9d1c-f53521c152c3"	"Consumer Defensive"	1730909474664	"22.5"	"HIMS"	220950.0	1050.0	228150.0	1521	41	"put"	23.44	6525	652.5

Can we add some informative and convenient calculations for display?

Execution time (eastern time zone)
% Bid and % Ask
Bullish, Bearish, or Mixed (we'll use 80% as our side threshold)
Days To Expiration
Strike and Type Combined

Lots of column manipulation in the polars DataFrame in the next cell, but hopefully the "clustering" of these tasks makes it relatively easy to see what is happening each step of the way:

In [5]:

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

Out[5]:

shape: (92, 42)

alert_rule	all_opening_trades	created_at	end_time	expiry	expiry_count	has_floor	has_multileg	has_singleleg	has_sweep	id	open_interest	option_chain	price	rule_id	sector	start_time	strike	ticker	total_ask_side_prem	…	total_size	trade_count	type	underlying_price	volume	volume_oi_ratio	created_at_datetime	created_at_datetime_eastern	created_at_east_str	pct_ask	pct_bid	side	pct_ask_bid_str	direction	expiry_date	dte	dte_days	dte_days_int	strike_type	contract_look_up_format
str	bool	str	i64	str	i64	bool	bool	bool	bool	str	i64	str	f64	str	str	i64	str	str	f64	…	i64	i64	str	f64	i64	f64	datetime[ns]	datetime[ns, America/New_York]	str	f64	f64	str	str	str	date	duration[ms]	f64	i64	str	str
"RepeatedHits"	false	"2024-11-06T20:56:09.194335Z"	1730926563559	"2025-01-17"	1	false	false	true	true	"d60bc445-8a2f-4ffd-a0cc-5afe0dfd2e92"	873	"NVO250117P00095000"	2.42	"e6b9f0b6-fcd9-44fe-9d1c-f53521c152c3"	"Healthcare"	1730926563555	"95"	"NVO"	241516.0	…	998	40	"put"	105.3199	1137	1.302405	2024-11-06 20:56:09.194335	2024-11-06 15:56:09.194335 EST	"2024-11-06 03:56:09 PM"	1.0	0.0	"ask"	"100% Ask / 0% Bid"	"bearish"	2025-01-17	72d	72.0	72	"95P"	"NVO 95P 01/17/2025"
"RepeatedHits"	false	"2024-11-06T20:53:11.204172Z"	1730926384223	"2025-03-21"	1	false	false	true	true	"76468e20-6c83-4c47-8325-fd517beb2741"	62	"GS250321C00660000"	15.95	"e6b9f0b6-fcd9-44fe-9d1c-f53521c152c3"	"Financial Services"	1730926384212	"660"	"GS"	234462.0	…	147	41	"call"	597.37	1032	16.645161	2024-11-06 20:53:11.204172	2024-11-06 15:53:11.204172 EST	"2024-11-06 03:53:11 PM"	1.0	0.0	"ask"	"100% Ask / 0% Bid"	"bullish"	2025-03-21	135d	135.0	135	"660C"	"GS 660C 03/21/2025"
"RepeatedHitsAscendingFill"	false	"2024-11-06T20:47:52.731549Z"	1730926066898	"2024-11-08"	1	false	false	true	true	"a6ffdd74-6baa-4a5a-958b-7168adcbec1c"	2245	"COIN241108C00300000"	0.74	"a0979b52-28e4-4585-8f22-881faad2dd8e"	"Technology"	1730926066169	"300"	"COIN"	73080.0	…	3008	161	"call"	252.3775	8760	3.902004	2024-11-06 20:47:52.731549	2024-11-06 15:47:52.731549 EST	"2024-11-06 03:47:52 PM"	0.33318	0.586808	"mixed"	"33% Ask / 59% Bid"	"mixed"	2024-11-08	2d	2.0	2	"300C"	"COIN 300C 11/08/2024"
"RepeatedHitsAscendingFill"	false	"2024-11-06T20:45:06.893254Z"	1730925901345	"2025-08-15"	1	false	false	true	false	"0e712a35-1243-4611-887b-e791cd34c0e2"	316	"TSLA250815P00220000"	19.0	"a0979b52-28e4-4585-8f22-881faad2dd8e"	"Consumer Cyclical"	1730925901263	"220"	"TSLA"	146300.0	…	424	18	"put"	288.79	611	1.933544	2024-11-06 20:45:06.893254	2024-11-06 15:45:06.893254 EST	"2024-11-06 03:45:06 PM"	0.181593	0.818407	"bid"	"18% Ask / 82% Bid"	"bullish"	2025-08-15	282d	282.0	282	"220P"	"TSLA 220P 08/15/2025"
"RepeatedHitsDescendingFill"	false	"2024-11-06T20:40:36.898760Z"	1730925631939	"2027-01-15"	1	false	false	true	false	"f16f636d-a714-4e09-a66b-6b4bd4e30692"	1	"COIN270115P00260000"	93.55	"5ce5ec11-087c-4c00-b164-08106b015856"	"Technology"	1730925631905	"260"	"COIN"	467742.0	…	50	13	"put"	253.36	59	59.0	2024-11-06 20:40:36.898760	2024-11-06 15:40:36.898760 EST	"2024-11-06 03:40:36 PM"	1.0	0.0	"ask"	"100% Ask / 0% Bid"	"bearish"	2027-01-15	800d	800.0	800	"260P"	"COIN 260P 01/15/2027"
…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…
"RepeatedHitsDescendingFill"	false	"2024-11-06T16:23:39.243295Z"	1730910212644	"2027-01-15"	1	false	false	true	false	"89f90634-b5be-47be-8ec9-28a4756f6822"	1	"AXP270115C00370000"	27.55	"5ce5ec11-087c-4c00-b164-08106b015856"	"Financial Services"	1730910212643	"370"	"AXP"	217643.0	…	80	21	"call"	291.06	97	97.0	2024-11-06 16:23:39.243295	2024-11-06 11:23:39.243295 EST	"2024-11-06 11:23:39 AM"	0.987522	0.012478	"ask"	"99% Ask / 1% Bid"	"bullish"	2027-01-15	800d	800.0	800	"370C"	"AXP 370C 01/15/2027"
"RepeatedHits"	false	"2024-11-06T16:22:12.891265Z"	1730910126927	"2024-11-08"	1	false	false	true	false	"a529583c-99ca-4261-8e7f-b177312b471b"	2799	"LYFT241108P00013500"	0.77	"e6b9f0b6-fcd9-44fe-9d1c-f53521c152c3"	"Technology"	1730910126926	"13.5"	"LYFT"	0.0	…	4354	12	"put"	14.115	14935	5.335834	2024-11-06 16:22:12.891265	2024-11-06 11:22:12.891265 EST	"2024-11-06 11:22:12 AM"	0.0	0.974277	"bid"	"0% Ask / 97% Bid"	"bullish"	2024-11-08	2d	2.0	2	"13.5P"	"LYFT 13.5P 11/08/2024"
"RepeatedHits"	false	"2024-11-06T16:14:34.117465Z"	1730909660739	"2024-11-29"	1	false	false	true	false	"6487a13a-8b11-427f-bac9-4af77f8da0c4"	10	"HIMS241129P00022500"	1.5	"e6b9f0b6-fcd9-44fe-9d1c-f53521c152c3"	"Consumer Defensive"	1730909660656	"22.5"	"HIMS"	235200.0	…	1631	15	"put"	23.52	8158	815.8	2024-11-06 16:14:34.117465	2024-11-06 11:14:34.117465 EST	"2024-11-06 11:14:34 AM"	0.961373	0.009197	"ask"	"96% Ask / 1% Bid"	"bearish"	2024-11-29	23d	23.0	23	"22.5P"	"HIMS 22.5P 11/29/2024"
"RepeatedHits"	false	"2024-11-06T16:13:32.029822Z"	1730909604489	"2025-02-21"	1	false	false	true	false	"64c56012-8832-4828-8d0d-2c4f02c1ceaa"	58	"VKTX250221P00062500"	9.5	"e6b9f0b6-fcd9-44fe-9d1c-f53521c152c3"	"Healthcare"	1730909604487	"62.5"	"VKTX"	0.0	…	253	12	"put"	64.535	266	4.586207	2024-11-06 16:13:32.029822	2024-11-06 11:13:32.029822 EST	"2024-11-06 11:13:32 AM"	0.0	1.0	"bid"	"0% Ask / 100% Bid"	"bullish"	2025-02-21	107d	107.0	107	"62.5P"	"VKTX 62.5P 02/21/2025"
"RepeatedHits"	false	"2024-11-06T16:11:20.569713Z"	1730909474810	"2024-11-29"	1	false	false	true	false	"9be1d040-84a3-444f-9c3e-0868e052a67b"	10	"HIMS241129P00022500"	1.5	"e6b9f0b6-fcd9-44fe-9d1c-f53521c152c3"	"Consumer Defensive"	1730909474664	"22.5"	"HIMS"	220950.0	…	1521	41	"put"	23.44	6525	652.5	2024-11-06 16:11:20.569713	2024-11-06 11:11:20.569713 EST	"2024-11-06 11:11:20 AM"	0.968442	0.004602	"ask"	"97% Ask / 0% Bid"	"bearish"	2024-11-29	23d	23.0	23	"22.5P"	"HIMS 22.5P 11/29/2024"

Select specific columns for display to the end user

We've got all the data a trader might want to see at-a-glance, let's cut down this polars DataFrame to fewer columns so it's easier to read
Then let's export the DataFrame to a list of lists for easy display

Final manipulation before we are finished here! Let's go:

In [6]:

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

Out[6]:

shape: (92, 9)

created_at_east_str	contract_look_up_format	total_size	price	total_premium	pct_ask_bid_str	direction	underlying_price	dte_days_int
str	str	i64	f64	f64	str	str	f64	i64
"2024-11-06 03:56:09 PM"	"NVO 95P 01/17/2025"	998	2.42	241516.0	"100% Ask / 0% Bid"	"bearish"	105.3199	72
"2024-11-06 03:53:11 PM"	"GS 660C 03/21/2025"	147	15.95	234462.0	"100% Ask / 0% Bid"	"bullish"	597.37	135
"2024-11-06 03:47:52 PM"	"COIN 300C 11/08/2024"	3008	0.74	219341.0	"33% Ask / 59% Bid"	"mixed"	252.3775	2
"2024-11-06 03:45:06 PM"	"TSLA 220P 08/15/2025"	424	19.0	805646.0	"18% Ask / 82% Bid"	"bullish"	288.79	282
"2024-11-06 03:40:36 PM"	"COIN 260P 01/15/2027"	50	93.55	467742.0	"100% Ask / 0% Bid"	"bearish"	253.36	800
…	…	…	…	…	…	…	…	…
"2024-11-06 11:23:39 AM"	"AXP 370C 01/15/2027"	80	27.55	220393.0	"99% Ask / 1% Bid"	"bullish"	291.06	800
"2024-11-06 11:22:12 AM"	"LYFT 13.5P 11/08/2024"	4354	0.77	335258.0	"0% Ask / 97% Bid"	"bullish"	14.115	2
"2024-11-06 11:14:34 AM"	"HIMS 22.5P 11/29/2024"	1631	1.5	244650.0	"96% Ask / 1% Bid"	"bearish"	23.52	23
"2024-11-06 11:13:32 AM"	"VKTX 62.5P 02/21/2025"	253	9.5	240350.0	"0% Ask / 100% Bid"	"bullish"	64.535	107
"2024-11-06 11:11:20 AM"	"HIMS 22.5P 11/29/2024"	1521	1.5	228150.0	"97% Ask / 0% Bid"	"bearish"	23.44	23

In [7]:

# Export the results to CSV (for human review if interested lol)
filename = datetime.today().date().strftime('%Y-%m-%d') + '-flow-alert-openers.csv'
output_path = Path('raw_data') / filename
final_df.write_csv(output_path)