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# https://ep2013.europython.eu/media/conference/slides/fast-data-mining-with-pytables-and-pandas.pdf

#1. Data Gathering
from pylab import *
from pandas.io.data import *
AAPL  = DataReader('AAPL', 'yahoo', start='01/01/2010')


    

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# 2. Data Analysis
from pandas import *
AAPL['Ret'] = log(AAPL['Close']/AAPL['Close'].shift(1))


    

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# 3. Plotting 
%matplotlib inline
from matplotlib import *

subplot(211)
AAPL['Close'].plot()
ylabel('Index Level')

subplot(212)
AAPL['Ret'].plot()
ylabel('Log Returns')


    

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#4. Monte Carlo Simulation
S0 = AAPL['Close'][-1]
vol = std(AAPL['Ret'])*sqrt(262)
r = 0.026; K=S0*1.1; T=1.0; M=50; dt=T/M; I=10000
S=zeros((M+1,I)); S[0,:]=S0
for t in range(1,M+1):
    ran = standard_normal(I)
    S[t,:]=S[t-1,:]*exp((r-vol**2/2)*dt*vol*sqrt(dt)*ran)


    

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#5. Option Valuation
V0=exp(-r*T)*sum(maximum(S[-1]-K,0))/I
print ("Call Value %8.3f" % V0)


    

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