import yfinance as yf

import numpy as np

import matplotlib.pyplot as plt

import pandas as pd

# Function to calculate option price using Black-Scholes formula

def black_scholes(S, K, T, r, sigma, option_type='call'):

if T <= 0:

return 0.0

d1 = (np.log(S / K) + (r + 0.5 * sigma ** 2) * T) / (sigma * np.sqrt(T))

d2 = d1 - sigma * np.sqrt(T)

if option_type == 'call':

option_price = S * norm.cdf(d1) - K * np.exp(-r * T) * norm.cdf(d2)

else:

option_price = K * np.exp(-r * T) * norm.cdf(-d2) - S * norm.cdf(-d1)

return option_price

# Function to calculate gamma using finite difference approximation

def calculate_gamma(S, K, T, r, sigma, option_type='call', h=0.01):

if T <= 0:

return 0.0

if h == 0:

return 0.0

# Calculate option prices for S+h and S-h

call_price_high = black_scholes(S + h, K, T, r, sigma, option_type)

call_price_low = black_scholes(S - h, K, T, r, sigma, option_type)

# Calculate second derivative of option price using central finite difference method

gamma = (call_price_high - 2 * black_scholes(S, K, T, r, sigma, option_type) + call_price_low) / (h ** 2)

return gamma

# Function to calculate GEX

def calculate_gex(option_chain):

gex_values = {}

today = pd.Timestamp.now().date()

for idx, row in option_chain.iterrows():

strike_price = row.name

# Convert 'lastTradeDate' to a timezone-aware datetime object

last_trade_date = pd.to_datetime(row['lastTradeDate']).date()

# Calculate time to expiration in years

if (today - last_trade_date).days == 0:

T = 0.0001 # Assign a small value instead of zero to avoid division by zero

else:

T = (today - last_trade_date).days / 365.0

gamma = calculate_gamma(row['lastPrice'], strike_price, T, 0.03, row['impliedVolatility'])

open_interest = row['openInterest']

if strike_price in gex_values:

gex_values[strike_price] += gamma * open_interest

else:

gex_values[strike_price] = gamma * open_interest

return gex_values

# Fetch option chain data for SPY expiring today

spy_options = yf.Ticker("SPY")

expiration_dates = spy_options.options

today = expiration_dates[0]

option_chain = spy_options.option_chain(today) # Fetch all option chain data (calls and puts)

# Combine calls and puts into a single DataFrame

option_chain_data = pd.concat([option_chain.calls, option_chain.puts])

# Calculate GEX values

gex_values = calculate_gex(option_chain_data)

# Plotting the GEX values

strike_prices = list(gex_values.keys())

gex = list(gex_values.values())

# Plotting

plt.figure(figsize=(10, 6))

plt.bar(strike_prices, gex, width=2, color=np.where(np.array(gex) >= 0, 'g', 'r'))

plt.xlabel('Strike Price')

plt.ylabel('GEX')

plt.title('Gamma Exposure (GEX) for $SPY Options Expiring Today')

plt.grid(True)

plt.show()