from pyFi.methods import binomial as bi from pyFi.methods import mc from pyFi.methods import fd from pyFi.chain import chain as ch # This section gets our constants exercise = 5. print 'Exercise: $'+str(exercise) spot = 4. print 'Price: $'+ str(spot) sigma = 0.3 print 'Sigma: '+ str(float(sigma)*100.0)+'%' rfint = 0.04 print 'Rate: '+str(float(rfint)*100.0)+'%' T = 1. print 'Time: '+str(T) print '\n--------------------------' #This section tests our solvers solver = bi.binomial_euro(S=spot,E=exercise,r=rfint,M=60000,sigma=sigma,method='higham',T=T,opt='put') print 'Binomial Method: $'+str(solver.solve()) solver2 = mc.mcfast_euro(S=spot,E=exercise,r=rfint,M=100000000,sigma=sigma,T=T,opt='put') print 'Monte Carlo Method: $'+str(solver2.solve()) solver3 = fd.BS_fd_explicit(S=spot,E=exercise,r=rfint,M=120,k=9000,L=exercise,sigma=sigma,T=T,opt='put') print 'Finite Difference: $'+str(float(solver3.solve()))
print '\n--------------------------'
#This section tests our solvers
solver = bi.binomial_euro(S=spot,
E=exercise,
r=rfint,
M=60000,
sigma=sigma,
method='higham',
T=T,
opt='put')
print 'Binomial Method: $' + str(solver.solve())
solver2 = mc.mcfast_euro(S=spot,
E=exercise,
r=rfint,
M=100000000,
sigma=sigma,
T=T,
opt='put')
print 'Monte Carlo Method: $' + str(solver2.solve())
solver3 = fd.BS_fd_explicit(S=spot,
E=exercise,
r=rfint,
M=120,
k=9000,
L=exercise,
sigma=sigma,
T=T,
opt='put')
print 'Finite Difference: $' + str(float(solver3.solve()))
# This section gets our constants
symbol = 'AAPL'
exercise = 590
print 'Exercise: $'+str(exercise)
spot = api.get_quote_google(symbol)
print symbol+ ' Price: $'+ str(spot)
sigma = volatility.hist_vol(symbol,30)
print symbol+' Sigma: '+ str(float(sigma)*100.0)+'%'
rfint = api.get_risk_free()
print 'Risk Free rate: '+str(float(rfint)*100.0)+'%'
print '\n--------------------------'
#This section tests our solvers
solver = bi.binomial_euro(S=spot,E=exercise,r=rfint,M=40000,sigma=sigma,method='higham',T=2.0/365.0,opt='put')
print 'Binomial Method: $'+str(solver.solve())
solver2 = mc.mcfast_euro(S=spot,E=exercise,r=rfint,M=4000000,sigma=sigma,T=2.0/365.0,opt='put')
print 'Monte Carlo Method: $'+str(solver2.solve())
solver3 = fd.BS_fd_explicit(S=spot,E=exercise,r=rfint,M=1201,k=1700,L=2*exercise,sigma=sigma,T=2.0/365.0,opt='put')
print 'Finite Difference: $'+str(float(solver3.solve()))
#This section produces an option chain
chain = ch.option_chain(spot,rfint,sigma,T=2.0/365,range=(520,5,640),method='bi')
chain.generate()
chain.export_csv('test.csv')
print '\n--------------------------\nOpen test.csv for option chain.\n'
