In [1]:

import os
import requests
import polars as pl
from rich import print

We will tackle this data collect in three steps. First, we will collect all the expiration dates for a given ticker using the "Expiry Breakdown" endpoint:

https://api.unusualwhales.com/docs#/operations/PublicApi.OptionContractController.expiry_breakdown

Once we have our list of expiration dates, our second step will use the "Greeks" endpoint to collect the greeks for each strike contained in each expiry:

https://api.unusualwhales.com/docs#/operations/PublicApi.TickerController.greeks

Our final step will use the "Option Contracts" endpoint to collect the open interest for each strike contained in each expiry:

https://api.unusualwhales.com/docs#/operations/PublicApi.OptionContractController.option_contracts

Let's get started using AAPL as an example:

In [2]:

uw_token = os.environ['UW_TOKEN']
headers = {'Accept': 'application/json, text/plain', 'Authorization': uw_token}
ticker = 'AAPL'
expiry_url = f'https://api.unusualwhales.com/api/stock/{ticker}/expiry-breakdown'
expiry_rsp = requests.get(expiry_url, headers=headers)
expiry_rsp.status_code

Out[2]:

Cool, expiry_rsp.json() gives us a collection of dictionaries like this:

{'data': [{'chains': 94,
   'expires': '2025-04-17',
   'open_interest': 126905,
   'volume': '14231'},
  {'chains': 90,
   'expires': '2025-01-24',
   'open_interest': 33185,
   'volume': '6566'},
   ...
   {'chains': 130,
   'expires': '2025-06-20',
   'open_interest': 509670,
   'volume': '11906'}]}

So let's grab the expires values for now and use those to collect data from the "Greeks" endpoint shortly after, which will give us (by expiry) a collection of directionaries like this:

{'data': [{'call_charm': '0.0000000000000000137155316518',
   'call_delta': '1',
   'call_gamma': '0',
   'call_rho': '0.000411816279807205',
   'call_theta': '0',
   'call_vanna': '-0.0000000000000000019712257766',
   'call_vega': '0',
   'call_volatility': '1.394776602024026',
   'date': '2024-12-17',
   'expiry': '2024-12-20',
   'put_charm': '0',
   'put_delta': '0',
   'put_gamma': '0',
   'put_rho': '0',
   'put_theta': '0',
   'put_vanna': '0',
   'put_vega': '0',
   'put_volatility': '1.022728627372261',
   'strike': '5'},
   ...

To make this data easier to slice and dice, I will compile polars DataFrames for each expiry then combine them into one:

In [3]:

expiration_dates = [d['expires'] for d in expiry_rsp.json()['data']]
greeks_url = f'https://api.unusualwhales.com/api/stock/{ticker}/greeks'
dfs = []
for exp_date in expiration_dates:
    params = {'expiry': exp_date}
    greeks_rsp = requests.get(greeks_url, headers=headers, params=params)
    desired_column_order = [
        'date', 'expiry', 'strike',
        'call_charm', 'call_delta', 'call_gamma', 'call_rho', 'call_theta', 'call_vanna', 'call_vega', 'call_volatility',
        'put_charm', 'put_delta', 'put_gamma', 'put_rho', 'put_theta', 'put_vanna', 'put_vega', 'put_volatility'
    ]
    raw_df = pl.DataFrame(greeks_rsp.json()['data'])
    dfs.append(raw_df.select(desired_column_order))

dfs[0].head(5)

Out[3]:

shape: (5, 19)

date	expiry	strike	call_charm	call_delta	call_gamma	call_rho	call_theta	call_vanna	call_vega	call_volatility	put_charm	put_delta	put_gamma	put_rho	put_theta	put_vanna	put_vega	put_volatility
str	str	str	str	str	str	str	str	str	str	str	str	str	str	str	str	str	str	str
"2024-12-17"	"2025-04-17"	"110"	"-0.00819380435…	"0.970405797093…	"0.000517543704…	"0.333672095211…	"-0.03612717187…	"0.048662473371…	"0.098247520241…	"0.890166551468…	"-0.00106144588…	"-0.00422319160…	"0.000144888947…	"-0.00396126044…	"-0.00440997170…	"-0.06451123277…	"0.018145090485…	"0.588342992809…
"2024-12-17"	"2025-04-17"	"115"	"-0.00834647235…	"0.967749147436…	"0.000577890942…	"0.347721900129…	"-0.03730091581…	"0.048462855114…	"0.105397115497…	"0.856775652353…	"-0.00106289194…	"-0.00485460674…	"0.000170031148…	"-0.00454538895…	"-0.00481942603…	"-0.07339708574…	"0.020568044513…	"0.567232648069…
"2024-12-17"	"2025-04-17"	"120"	"-0.00846467288…	"0.965058197901…	"0.000641560236…	"0.361727043208…	"-0.03833737241…	"0.047654788174…	"0.112583805691…	"0.824371113404…	"-0.00112892459…	"-0.00570207501…	"0.000203489880…	"-0.00532168150…	"-0.00536173935…	"-0.08230634255…	"0.023710890533…	"0.547408773611…
"2024-12-17"	"2025-04-17"	"125"	"-0.00861218502…	"0.961955038038…	"0.000712745973…	"0.375278820655…	"-0.03966551493…	"0.046852778125…	"0.120752452733…	"0.795208511890…	"-0.00120641724…	"-0.00664354329…	"0.000241397127…	"-0.00618635060…	"-0.00592517117…	"-0.09185955390…	"0.027148141686…	"0.528341487933…
"2024-12-17"	"2025-04-17"	"130"	"-0.00872444722…	"0.958726756848…	"0.000791962464…	"0.388809977744…	"-0.04075300924…	"0.045219628894…	"0.128966874453…	"0.764993693819…	"-0.00127317635…	"-0.00764818236…	"0.000283684277…	"-0.00710522739…	"-0.00646390179…	"-0.10290160210…	"0.030743440474…	"0.508989296257…

Now we have compiled each expiration's Call and Put greeks by strike into individual DataFrames, so next I want to combine them all together, convert strings into floats for delta and gamma, drop the unnecessary greek values, and create an "option symbol"-format column, like AAPL241217C00110000 for the Dec24 110C:

In [4]:

raw_df = pl.concat(dfs)
desired_columns = [
    'date', 'expiry', 'strike',
    'call_delta', 'call_gamma', 'put_delta', 'put_gamma'
]
desired_column_order = [
    'date', 'expiry', 'strike', 'call_option_symbol', 'put_option_symbol',
    'call_delta', 'call_gamma', 'put_delta', 'put_gamma'
]
greeks_df = (
    raw_df
    .select(desired_columns)
    .with_columns(
        pl.lit(ticker).alias('ticker')
    )
    .with_columns(
        pl.col('call_delta').cast(pl.Float64),
        pl.col('call_gamma').cast(pl.Float64),
        pl.col('put_delta').cast(pl.Float64),
        pl.col('put_gamma').cast(pl.Float64)
    )
    .with_columns(
        pl.col('strike').str.replace_all(r'\.', '')
        .cast(pl.Int64)
        .mul(1000)
        .map_elements(lambda x: f'{x:08}')
        .alias('contract_strike')
    )
    .with_columns(
        pl.col('expiry').str.strptime(pl.Date, '%Y-%m-%d')
        .dt.strftime('%y%m%d')
        .alias('contract_expiry')
    )
    .with_columns(
        pl.concat_str(
            [
                pl.col('ticker'),
                pl.col('contract_expiry'),
                pl.lit('C'),
                pl.col('contract_strike')
            ]
        ).alias('call_option_symbol'),
        pl.concat_str(
            [
                pl.col('ticker'),
                pl.col('contract_expiry'),
                pl.lit('P'),
                pl.col('contract_strike')
            ]
        ).alias('put_option_symbol')
    )
    .select(desired_column_order)
    .sort('expiry')
)
greeks_df

Out[4]:

shape: (1_117, 9)

date	expiry	strike	call_option_symbol	put_option_symbol	call_delta	call_gamma	put_delta	put_gamma
str	str	str	str	str	f64	f64	f64	f64
"2024-12-17"	"2024-12-20"	"5"	"AAPL241220C000…	"AAPL241220P000…	1.0	0.0	0.0	0.0
"2024-12-17"	"2024-12-20"	"10"	"AAPL241220C000…	"AAPL241220P000…	1.0	0.0	0.0	0.0
"2024-12-17"	"2024-12-20"	"15"	"AAPL241220C000…	"AAPL241220P000…	1.0	0.0	0.0	0.0
"2024-12-17"	"2024-12-20"	"20"	"AAPL241220C000…	"AAPL241220P000…	1.0	0.0	0.0	0.0
"2024-12-17"	"2024-12-20"	"25"	"AAPL241220C000…	"AAPL241220P000…	1.0	0.0	0.0	0.0
…	…	…	…	…	…	…	…	…
"2024-12-17"	"2027-01-15"	"410"	"AAPL270115C004…	"AAPL270115P004…	0.124266	0.002244	-0.987318	0.000629
"2024-12-17"	"2027-01-15"	"420"	"AAPL270115C004…	"AAPL270115P004…	0.11151	0.002075	-0.98784	0.000582
"2024-12-17"	"2027-01-15"	"430"	"AAPL270115C004…	"AAPL270115P004…	0.100097	0.001916	-0.988231	0.000545
"2024-12-17"	"2027-01-15"	"440"	"AAPL270115C004…	"AAPL270115P004…	0.088896	0.001759	-0.988506	0.000514
"2024-12-17"	"2027-01-15"	"450"	"AAPL270115C004…	"AAPL270115P004…	0.078587	0.001608	-0.988238	0.000503

Nice! We are in the home stretch, all that's left to obtain is the is the open interest by contract:

https://api.unusualwhales.com/docs#/operations/PublicApi.OptionContractController.option_contracts

Which will give us a response that looks like this:

{'data': [{'ask_volume': 51140,
   'avg_price': '0.9626352941176470588235294113',
   'bid_volume': 33953,
   'floor_volume': 0,
   'high_price': '1.30',
   'implied_volatility': '0.2002082604759955',
   'last_price': '1.17',
   'low_price': '0.41',
   'mid_volume': 9682,
   'multi_leg_volume': 3553,
   'nbbo_ask': '1.20',
   'nbbo_bid': '1.14',
   'no_side_volume': 0,
   'open_interest': 35917,
   'option_symbol': 'AAPL241220C00255000',
   'prev_oi': 29454,
   'stock_multi_leg_volume': 83,
   'sweep_volume': 4010,
   'total_premium': '9123376.00',
   'volume': 94775},

The endpoint is capped at 500 records per response though, so we will iterate through expiry-by-expiry to make sure we collect all the open interest that we need for the final gamma exposure and delta exposure calculations.

And while some of this information will likely be valuable to you and your client, for now I'm going to chop it all the way down and just keep the option_symbol (to join with our earlier data compilation) and the open_interest (since that is what the gamma exposure and delta exposure calculations will be based on):

In [5]:

option_contracts_url = f'https://api.unusualwhales.com/api/stock/{ticker}/option-contracts'
option_contracts_dfs = []
for exp_date in expiration_dates:
    params = {'expiry': exp_date}
    option_contracts_rsp = requests.get(option_contracts_url, headers=headers, params=params)
    option_contracts_dfs.append(pl.DataFrame(option_contracts_rsp.json()['data']))

option_contracts_dfs[0].head(5)

Out[5]:

shape: (5, 20)

ask_volume	avg_price	bid_volume	floor_volume	high_price	implied_volatility	last_price	low_price	mid_volume	multi_leg_volume	nbbo_ask	nbbo_bid	no_side_volume	open_interest	option_symbol	prev_oi	stock_multi_leg_volume	sweep_volume	total_premium	volume
i64	str	i64	i64	str	str	str	str	i64	i64	str	str	i64	i64	str	i64	i64	i64	str	i64
429	"13.46424751243…	2804	0	"14.02"	"0.252700758209…	"13.89"	"11.95"	1591	4568	"14.30"	"13.80"	0	5649	"AAPL250417C002…	5730	0	2	"6495153.00"	4824
743	"7.331100386100…	601	0	"7.57"	"0.236887892937…	"7.55"	"6.00"	210	238	"7.70"	"7.30"	0	7658	"AAPL250417C002…	7788	0	17	"1139253.00"	1554
1232	"0.459975669099…	0	0	"0.46"	"0.383749786739…	"0.46"	"0.43"	1	1	"0.46"	"0.43"	0	420	"AAPL250417P001…	420	0	0	"56715.00"	1233
292	"6.515502717391…	83	0	"7.09"	"0.210339259626…	"6.40"	"6.30"	361	71	"6.45"	"6.30"	0	2561	"AAPL250417P002…	2494	0	0	"479541.00"	736
530	"0.648126126126…	25	0	"0.65"	"0.336462362621…	"0.61"	"0.61"	0	8	"0.66"	"0.62"	0	1276	"AAPL250417P001…	1274	0	6	"35971.00"	555

In [6]:

raw_option_contracts_df = pl.concat(option_contracts_dfs)
option_contracts_df = (
    raw_option_contracts_df
    .select(['option_symbol', 'open_interest'])
)
option_contracts_df

Out[6]:

shape: (2_234, 2)

option_symbol	open_interest
str	i64
"AAPL250417C002…	5649
"AAPL250417C002…	7658
"AAPL250417P001…	420
"AAPL250417P002…	2561
"AAPL250417P001…	1276
…	…
"AAPL250620P000…	325
"AAPL250620C001…	1315
"AAPL250620P001…	2099
"AAPL250620C001…	3048
"AAPL250620P000…	1023

And that's it! I will leave the matching of the option_symbol column inside the option_contracts_df to the corresponding greeks_df columns call_option_symbol or put_option_symbol (could even further simplify with a melt() on the greeks_df) in your capable hands as you calculate the gamma and delta exposure for the symbols of interest.