environment/share/doc/ipython/examples/parallel/options/mckernel.py
def price_options(S=100.0, K=100.0, sigma=0.25, r=0.05, days=260, paths=10000):
    """
    Price European and Asian options using a Monte Carlo method.

    Parameters
    ----------
    S : float
        The initial price of the stock.
    K : float
        The strike price of the option.
    sigma : float
        The volatility of the stock.
    r : float
        The risk free interest rate.
    days : int
        The number of days until the option expires.
    paths : int
        The number of Monte Carlo paths used to price the option.

    Returns
    -------
    A tuple of (E. call, E. put, A. call, A. put) option prices.
    """
    import numpy as np
    from math import exp,sqrt
    
    h = 1.0/days
    const1 = exp((r-0.5*sigma**2)*h)
    const2 = sigma*sqrt(h)
    stock_price = S*np.ones(paths, dtype='float64')
    stock_price_sum = np.zeros(paths, dtype='float64')
    for j in range(days):
        growth_factor = const1*np.exp(const2*np.random.standard_normal(paths))
        stock_price = stock_price*growth_factor
        stock_price_sum = stock_price_sum + stock_price
    stock_price_avg = stock_price_sum/days
    zeros = np.zeros(paths, dtype='float64')
    r_factor = exp(-r*h*days)
    euro_put = r_factor*np.mean(np.maximum(zeros, K-stock_price))
    asian_put = r_factor*np.mean(np.maximum(zeros, K-stock_price_avg))
    euro_call = r_factor*np.mean(np.maximum(zeros, stock_price-K))
    asian_call = r_factor*np.mean(np.maximum(zeros, stock_price_avg-K))
    return (euro_call, euro_put, asian_call, asian_put)
def price_options(S=100.0, K=100.0, sigma=0.25, r=0.05, days=260, paths=10000):
    """
    Price European and Asian options using a Monte Carlo method.

    Parameters
    ----------
    S : float
        The initial price of the stock.
    K : float
        The strike price of the option.
    sigma : float
        The volatility of the stock.
    r : float
        The risk free interest rate.
    days : int
        The number of days until the option expires.
    paths : int
        The number of Monte Carlo paths used to price the option.

    Returns
    -------
    A tuple of (E. call, E. put, A. call, A. put) option prices.
    """
    import numpy as np
    from math import exp,sqrt
    
    h = 1.0/days
    const1 = exp((r-0.5*sigma**2)*h)
    const2 = sigma*sqrt(h)
    stock_price = S*np.ones(paths, dtype='float64')
    stock_price_sum = np.zeros(paths, dtype='float64')
    for j in range(days):
        growth_factor = const1*np.exp(const2*np.random.standard_normal(paths))
        stock_price = stock_price*growth_factor
        stock_price_sum = stock_price_sum + stock_price
    stock_price_avg = stock_price_sum/days
    zeros = np.zeros(paths, dtype='float64')
    r_factor = exp(-r*h*days)
    euro_put = r_factor*np.mean(np.maximum(zeros, K-stock_price))
    asian_put = r_factor*np.mean(np.maximum(zeros, K-stock_price_avg))
    euro_call = r_factor*np.mean(np.maximum(zeros, stock_price-K))
    asian_call = r_factor*np.mean(np.maximum(zeros, stock_price_avg-K))
    return (euro_call, euro_put, asian_call, asian_put)
def price_options ( S = 100.0 , K = 100.0 , sigma = 0.25 , r = 0.05 , days = 260 , paths = 10000 ) : import numpy as np from math import exp , sqrt h = 1.0 / days const1 = exp ( ( r - 0.5 * sigma ** 2 ) * h ) const2 = sigma * sqrt ( h ) stock_price = S * np . ones ( paths , dtype = 'float64' ) stock_price_sum = np . zeros ( paths , dtype = 'float64' ) for j in range ( days ) : growth_factor = const1 * np . exp ( const2 * np . random . standard_normal ( paths ) ) stock_price = stock_price * growth_factor stock_price_sum = stock_price_sum + stock_price stock_price_avg = stock_price_sum / days zeros = np . zeros ( paths , dtype = 'float64' ) r_factor = exp ( - r * h * days ) euro_put = r_factor * np . mean ( np . maximum ( zeros , K - stock_price ) ) asian_put = r_factor * np . mean ( np . maximum ( zeros , K - stock_price_avg ) ) euro_call = r_factor * np . mean ( np . maximum ( zeros , stock_price - K ) ) asian_call = r_factor * np . mean ( np . maximum ( zeros , stock_price_avg - K ) ) return ( euro_call , euro_put , asian_call , asian_put )
Price European and Asian options using a Monte Carlo method.

    Parameters
    ----------
    S : float
        The initial price of the stock.
    K : float
        The strike price of the option.
    sigma : float
        The volatility of the stock.
    r : float
        The risk free interest rate.
    days : int
        The number of days until the option expires.
    paths : int
        The number of Monte Carlo paths used to price the option.

    Returns
    -------
    A tuple of (E. call, E. put, A. call, A. put) option prices.
Price European and Asian options using a Monte Carlo method .