Filter good and affordable calls

call-shortlist.py

# Execute this in a Jupyter notebook
import os
import json
import base64
import pandas as pd
from pprint import pprint


# Import USD 10B+, 1MM vol+, 25+ P/E, Buy/Strong Buy rated
buy_rated_tradingview = pd.read_csv('~/Downloads/america_2021-04-11.csv')
buy_rated_tradingview.head()

# Rate limit is 120 queries per min
tickers = buy_rated_tradingview.Ticker.values
len(tickers)

# Get API key at - https://developer(dot)tdameritrade(dot)com
with open(os.path.expanduser('~/Documents/myapp.key'), "rt") as f:
    pwd64 = f.readline().strip()
API_KEY=base64.b64decode(pwd64).decode('utf-8').strip()

for TICKER in tickers:
    
    calls = !curl -sS -X GET --header "Authorization: " "https://api.tdameritrade.com/v1/marketdata/chains?apikey=$API_KEY&symbol=$TICKER&contractType=CALL&strategy=ANALYTICAL"
    calls = calls[0]
    calls = json.loads(calls)

    for chain, options in calls['callExpDateMap'].items():
        date_of_expiry, days_left = chain.split(':')
        days_left = int(days_left)

        to_print = {}

        MAX_SPEND_PER_OPTION = 6     # USD, Since contracts are in 100x, you'll pay this amountx100 max
        OPEN_INTEREST_THRESH = 5000
        MIN_DAYS_TO_EXP      = 50
        DELTA_THRESH         = 0     # 0.4

        for price, data in options.items():
            data = data[0]

            price = float(price)

            # → Refer: https://www.optionsplaybook.com/options-introduction/option-greeks/

            # That’s the amount an option value will change in theory based on a 
            # one percentage-point change in interest rates.
            rho   = float(data['rho'])

            # sensitivity of the price of an option to changes in volatility
            vega  = float(data['vega'])

            # the dollar amount an option will lose each day due to the passage of time
            theta = float(data['theta'])

            # [-1, 1] indicates how much the value of an option should change 
            # when the price of the underlying stock rises by one dollar.
            delta = float(data['delta'])  

            # Gamma measures the rate of change in the delta
            gamma = float(data['gamma'])

            bid   = float(data['bid'])
            ask   = float(data['ask'])

            tval  = float(data['theoreticalOptionValue'])
            tvol  = float(data['theoreticalVolatility'])
            act   = (bid+ask)/2.

            time  = float(data['timeValue'])
            volat = float(data['volatility'])
            vol   = int(data['totalVolume'])

            is_itm = 'I' if bool(data['inTheMoney']) else 'O'
            openint = int(data['openInterest'])

            # (r){rho:.3f}
            greeks = f'(v){vega:.3f}\t(t){theta:.3f}\t(g){gamma:.3f}\t(d){delta:.3f}'

            if (openint > OPEN_INTEREST_THRESH) \
                    and (tval > act) \
                    and (act < MAX_SPEND_PER_OPTION) \
                    and (days_left > MIN_DAYS_TO_EXP) \
                    and (delta > DELTA_THRESH):
                to_print[price] = f"{price:.2f}({tval:.2f}, {act:.2f}, {is_itm})\t{greeks}\t{volat}\t{openint}({vol})"

        if to_print:
            print(TICKER)
            print(date_of_expiry, days_left)
            print('-'*len(chain))
            for k, v in to_print.items():
                print(v)
            print()
    print('-x-\n')