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OptionPricing.py
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OptionPricing.py
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import numpy as np
from scipy.stats import norm
import pandas as pd
from datetime import datetime
import os
from config import FILE_NAME
#Black-Scholes Way
"""
C = price of the call option
P = price of the put option
S = current price of the underlying asset
X = strike price of the option
r = risk-free interest rate
q = dividend yield = 0 for Index
T = time to maturity(in years)
N() = norm.cdf() = cumulative distribution function of the standard normal distribution
sigma = volatility of the underlying asset
d1 = (ln(S_0/X)+(r+sigma^2/2)*T)/(sigma*sqrt(T))
d2 = d1 - sigma*sqrt(T)
"""
class BlackScholesOptions:
def __init__(self, S, X, r, T, sigma):
self.S = S
self.X = X
self.r = r
self.T = T
self.sigma = sigma
def _calculate_d1_d2(self):
d1 = (np.log(self.S/self.X)+(self.r + self.sigma**2 * 0.5)* self.T)/(self.sigma * np.sqrt(self.T))
d2 = d1 - self.sigma * np.sqrt(self.T)
return d1, d2
def black_scholes_call(self):
d1, d2 = self._calculate_d1_d2()
call_price = self.S * norm.cdf(d1) - self.X * np.exp(-self.r * self.T) * norm.cdf(d2)
return np.round(call_price,2)
def black_scholes_put(self):
d1, d2 = self._calculate_d1_d2()
put_price = self.X * np.exp(-self.r * self.T) * norm.cdf(-d2) - self.S * norm.cdf(-d1)
return np.round(put_price, 2)
#Greeks
def delta_call(self):
d1, _ = self._calculate_d1_d2()
delta_c = norm.cdf(d1)
return np.round(delta_c, 2)
def delta_put(self):
d1, _ = self._calculate_d1_d2()
delta_p = -norm.cdf(-d1)
return np.round(delta_p, 2)
def gamma(self):
d1, _ = self._calculate_d1_d2()
gamma_option = 1/(self.S * self.sigma * np.sqrt(self.T)) * norm.pdf(d1)
return np.round(gamma_option, 2)
def theta_call(self):
d1, d2 = self._calculate_d1_d2()
theta_c = (-((self.S * self.sigma*norm.pdf(d1))/(2*np.sqrt(self.T)))) - self.r * self.X * np.exp(-self.r * self.T) * norm.cdf(d2)
return np.round(theta_c, 2)
def theta_put(self):
d1, d2 = self._calculate_d1_d2()
theta_p = (-((self.S * self.sigma * norm.pdf(d1))/(2 * np.sqrt(self.T)))) + self.r * self.X * np.exp(-self.r * self.T) * norm.cdf(-d2)
return np.round(theta_p, 2)
def vega(self):
d1, _ = self._calculate_d1_d2()
vega_option = self.S * np.sqrt(self.T) * norm.pdf(d1)
return np.round(vega_option, 2)
def rho_call(self):
d1, d2 = self._calculate_d1_d2()
rho_c = self.X * self.T * np.exp(-self.r*self.T) * norm.cdf(d2)
return np.round(rho_c, 2)
def rho_put(self):
d1, d2 = self._calculate_d1_d2()
rho_p = -self.X * self.T * np.exp(-self.r * self.T) * norm.cdf(-d2)
return np.round(rho_p, 2)
def vanna(self):
d1, _ = self._calculate_d1_d2()
vanna = self.S * d1 * self.T / self.sigma
return np.round(vanna, 2)
def volga(self):
d1, d2 = self._calculate_d1_d2()
volga = self.S * np.sqrt(self.T) * d1 * d2
return np.round(volga, 2)
def parameters(self):
return {'S': self.S, 'r': self.r, 'T': self.T, 'sigma': self.sigma}
columns = ['Date', 'Call Price', 'Put Price', 'Delta Call', 'Delta Put', 'Gamma', 'Theta Call', 'Theta Put', 'Vega', 'Rho Call', 'Rho Put', 'Vanna', 'Volga', 'Parameters']
file_name = FILE_NAME
def load_existing_df(file_name):
if os.path.exists(file_name):
try:
df = pd.read_csv(file_name)
print("Df loaded successfully")
except Exception as e:
print(f"Error loading CSV file: {e}")
df = pd.DataFrame(columns=columns)
else:
print("Df not found")
df = pd.DataFrame(columns=columns)
return df
def update_dataframe(S, X, r, T, sigma):
df = load_existing_df(file_name)
model = BlackScholesOptions(S, X, r, T, sigma)
today = datetime.now().strftime('%Y-%m-%d')
if today not in df['Date'].values:
call_price = model.black_scholes_call()
put_price = model.black_scholes_put()
delta_call = model.delta_call()
delta_put = model.delta_put()
gamma = model.gamma()
theta_call = model.theta_call()
theta_put = model.theta_put()
rho_call = model.rho_call()
rho_put = model.rho_put()
vega = model.vega()
vanna = model.vanna()
volga = model.volga()
parameters = model.parameters()
new_row = {
'Date': today,
'Call Price': call_price,
'Put Price': put_price,
'Delta Call': delta_call,
'Delta Put': delta_put,
'Gamma': gamma,
'Theta Call': theta_call,
'Theta Put': theta_put,
'Rho Call': rho_call,
'Rho Put': rho_put,
'Vega': vega,
'Vanna': vanna,
'Volga': volga,
'Parameters': parameters
}
new_df = pd.DataFrame([new_row])
updated_df = pd.concat([df, new_df], ignore_index=True)
else:
print(f"Data for {today} already exists. Skipping update.")
return updated_df
#Save DF to csv
def save_to_csv(df, file_name=FILE_NAME):
df.to_csv(file_name, index=False)
df_update = update_dataframe(S = 5718.26, X = 5475, r = 0.0491, T = 0.06575, sigma = 0.173)
save_to_csv(df_update)