main.py

# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
import black_sholes
import heston
from scipy.optimize import fmin
#sample market data
def sample_data():
    x = [x.split() for x in open('marketdata.txt')]
    header = x[0]
    market_datas = []
    for market_data in x[1:]:
        market_datas += [map(lambda z:float(z), market_data)]
    return (header, market_datas)
#parameter calibration(kappa, theta, sigma, rho, v0)
def calibrate(init_val, market_datas):
    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
    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()