def error(x, market_datas):
kappa, theta, sigma, rho, v0 = x
print kappa, theta, sigma, rho, v0
result = 0.0
for market_data in market_datas:
s0, k, market_price, r, T = market_data
#print s0, k, market_price, r, T
heston_price = heston.call_price(kappa, theta, sigma, rho, v0, r, T, s0, k)
result += (heston_price - market_price)**2
return result
for market_data in market_datas:
s0, k, market_price, r, T = market_data
#print s0, k, market_price, r, T
heston_price = heston.call_price(kappa, theta, sigma, rho, v0, r, T, s0, k)
result += (heston_price - market_price)**2
return result
opt = fmin(error, init_val, args = (market_datas,), maxiter = 20)
return opt
if __name__ == '__main__':
#load market data
header, market_datas = sample_data()
#Initialize kappa, theta, sigma, rho, v0
init_val = [1.1, 0.1, 0.4, -0.0, 0.1]
#calibration of parameters
kappa, theta, sigma, rho, v0 = calibrate(init_val, market_datas)
#
market_prices = np.array([])
heston_prices = np.array([])
K = np.array([])
for market_data in market_datas:
s0, k, market_price, r, T = market_data
heston_prices = np.append(heston_prices, heston.call_price(kappa, theta, sigma, rho, v0, r, T, s0, k))
market_prices = np.append(market_prices, market_price)
K = np.append(K,k)
#plot result
plt.plot(K, market_prices, 'g*',K, heston_prices, 'b')
plt.xlabel('Strike (K)')
plt.ylabel('Price')
plt.show()
