mcpricer.ipynb
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Parallel Monto-Carlo options pricing¶
Problem setup¶
In [1]:
import sys
import time
from IPython.parallel import Client
import numpy as np
from mckernel import price_options
from matplotlib import pyplot as plt
In [2]:
cluster_profile = "default"
price = 100.0 # Initial price
rate = 0.05 # Interest rate
days = 260 # Days to expiration
paths = 10000 # Number of MC paths
n_strikes = 6 # Number of strike values
min_strike = 90.0 # Min strike price
max_strike = 110.0 # Max strike price
n_sigmas = 5 # Number of volatility values
min_sigma = 0.1 # Min volatility
max_sigma = 0.4 # Max volatility
In [3]:
strike_vals = np.linspace(min_strike, max_strike, n_strikes)
sigma_vals = np.linspace(min_sigma, max_sigma, n_sigmas)
Parallel computation across strike prices and volatilities¶
The Client is used to setup the calculation and works with all engines.
In [4]:
c = Client(profile=cluster_profile)
A LoadBalancedView is an interface to the engines that provides dynamic load balancing at the expense of not knowing which engine will execute the code.
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view = c.load_balanced_view()
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print "Strike prices: ", strike_vals
print "Volatilities: ", sigma_vals
Submit tasks for each (strike, sigma) pair.
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t1 = time.time()
async_results = []
for strike in strike_vals:
for sigma in sigma_vals:
ar = view.apply_async(price_options, price, strike, sigma, rate, days, paths)
async_results.append(ar)
In [8]:
print "Submitted tasks: ", len(async_results)
Block until all tasks are completed.
In [9]:
c.wait(async_results)
t2 = time.time()
t = t2-t1
print "Parallel calculation completed, time = %s s" % t
Process and visualize results¶
Get the results using the get method:
In [10]:
results = [ar.get() for ar in async_results]
Assemble the result into a structured NumPy array.
In [11]:
prices = np.empty(n_strikes*n_sigmas,
dtype=[('ecall',float),('eput',float),('acall',float),('aput',float)]
)
for i, price in enumerate(results):
prices[i] = tuple(price)
prices.shape = (n_strikes, n_sigmas)
Plot the value of the European call in (volatility, strike) space.
In [12]:
plt.figure()
plt.contourf(sigma_vals, strike_vals, prices['ecall'])
plt.axis('tight')
plt.colorbar()
plt.title('European Call')
plt.xlabel("Volatility")
plt.ylabel("Strike Price")
Out[12]:
Plot the value of the Asian call in (volatility, strike) space.
In [13]:
plt.figure()
plt.contourf(sigma_vals, strike_vals, prices['acall'])
plt.axis('tight')
plt.colorbar()
plt.title("Asian Call")
plt.xlabel("Volatility")
plt.ylabel("Strike Price")
Out[13]:
Plot the value of the European put in (volatility, strike) space.
In [14]:
plt.figure()
plt.contourf(sigma_vals, strike_vals, prices['eput'])
plt.axis('tight')
plt.colorbar()
plt.title("European Put")
plt.xlabel("Volatility")
plt.ylabel("Strike Price")
Out[14]:
Plot the value of the Asian put in (volatility, strike) space.
In [15]:
plt.figure()
plt.contourf(sigma_vals, strike_vals, prices['aput'])
plt.axis('tight')
plt.colorbar()
plt.title("Asian Put")
plt.xlabel("Volatility")
plt.ylabel("Strike Price")
In [16]:
plt.show()