Calculate (European option) gamma pr dollar spent on premium

gamma_per_dollar.py

import yfinance as yf
import pandas as pd
import numpy as np

from scipy.stats import norm

'''
Calculate realised volatility of stock
:ticker: yfinance ticker
:window: trading days in rolling window
:dpy: trading days per year
'''
def realised_vol(ticker, window, dpy):
    ann_factor = dpy / window

    df = ticker.history(period='100d', interval='1d')
    df['log_rtn'] = np.log(df['Close']).diff()
    df['real_var'] = df['log_rtn'].rolling(window).var() * ann_factor
    df['real_vol'] = np.sqrt(df['real_var'])
    return df['real_vol'][-1]


'''
Calculate European Option gamma
:s: price of underlying
:x: strike
:t: time to expiry in (trading) years
:r: risk free rate
:vol: realised vol
'''
def gamma(s, x, t, r, vol):
    if t > 0.6:
        return 0
    sqrt_t = np.sqrt(t)
    d1 = (np.log(s / x) + (r + np.square(vol) / 2) * t) / (vol * sqrt_t)
    return norm.pdf(d1) / (s * vol * sqrt_t)


ticker = yf.Ticker("RKT")

dates = ticker.options

todays_price_df = ticker.history(period='10m', interval='1m')

timestamp = todays_price_df.index[-1]
underlying = todays_price_df['Close'][-1]

_r_fixed = 0.05

_dpy = 252  # trading days per year

real_vol = realised_vol(ticker, 21, _dpy)
print("Real vol: " + str(real_vol))

was_merged = False
merged_df = None

for date in dates:

    chain = ticker.option_chain(date)

    expiration_date = pd.Timestamp(date + 'T17', tz='America/New_York')
    t_years = (expiration_date - timestamp).total_seconds() / 60 / 60 / 24 / _dpy

    def gamma_applied(x):
        return gamma(underlying, x, t_years, _r_fixed, real_vol) * 100

    for (df, kind) in ((chain.puts, 'put'), (chain.calls, 'call')):

        df['gamma'] = df.apply(lambda x: gamma_applied(x['strike']), axis=1)

        selection = df[['strike', 'gamma']][df.gamma.gt(0.00001)]

        selection['kind'] = kind
        selection['expiration_date'] = expiration_date

        selection['gammaPerDollarBid'] = df['gamma'] / df['bid']
        selection['gammaPerDollarAsk'] = df['gamma'] / df['ask']
        selection['gammaPerDollarLastPrice'] = df['gamma'] / df['lastPrice']

        if was_merged:
            merged_df = pd.concat([merged_df, selection], ignore_index=True)
        else:
            merged_df = selection
            was_merged = True

sorted = merged_df.sort_values(by='gammaPerDollarLastPrice', ascending=False)
print(sorted.head(20))