barrier6.py

def get_barrier_price(strike_num):
    for i in range(1,num_steps+1):
        # fill out the first value with our initial stock price
        term_val[i-1][0] = np.full((num_simulations*i), S_0)

        for j in range (1,num_of_months):
            # update current month to reflect the monthly simulation we are currently in
            current_month = (j-1)/12
            norm_array = norm.rvs(size = num_simulations*i)
            term_val[i-1][j] = terminal_shareprice(term_val[i-1][j-1],risk_free,sigma,norm_array,dT)

        # Compute discounted barrier Price of the option 
        mbarrier_val[i-1] = discounted_call_payoff(term_val[i-1][12],strike,risk_free,T-current_time)

        # use the above formula to calculate the values of the barrier option
        ## get array of booleans for when stock is knocked out or not
        knock_out_array = (np.max(term_val[i-1],axis = 0) < barrier)
        ## times it by the value of the previously calculated barrier option
        mbarrier_val[i-1] = mbarrier_val[i-1] * knock_out_array

        # compute mean and standard deviation of entire path
        mbarrier_estimates[i-1] = np.mean(mbarrier_val[i-1])
        mbarrier_std[i-1] = np.std(mbarrier_val[i-1]/np.sqrt(i*num_simulations))

    return np.mean(mbarrier_estimates)