def main():
if(a00acc() != 1):
print "Cannot find a valid NAG license"
return
try:
QuoteData = 'QuoteData.dat'
# except IndexError:
# sys.stderr.write("Usage: imp_vol.py QuotaData.dat\n")
# sys.exit(1)
# if os.path.isfile(QuoteData):
qd = open(QuoteData, 'r')
qd_head = []
qd_head.append(qd.readline())
qd_head.append(qd.readline())
qd.close()
except:
sys.stderr.write("Couldn't read %s" % QuoteData)
print "Implied Volatility for %s %s" % (qd_head[0].strip(), qd_head[1])
# Parse the header information in QuotaData
first = qd_head[0].split(',')
second = qd_head[1].split()
qd_date = qd_head[1].split(',')[0]
company = first[0]
underlyingprice = float(first[1])
month, day = second[:2]
today = cumulative_month[month] + int(day) - 30
data = pandas.io.parsers.read_csv(QuoteData, sep=',', header=2, na_values=' ')
# Need to fill the NA values in dataframe
data = data.fillna(0.0)
# Let's look at data where there was a recent sale
data = data[data.Calls > 0]
data = data[(data['Last Sale'] > 0) | (data['Last Sale.1'] > 0)]
# Get the Options Expiration Date
exp = data.Calls.apply(getexpiration)
exp.name = 'Expiration'
# Get the Strike Prices
strike = data.Calls.apply(getstrike)
strike.name = 'Strike'
data = data.join(exp)
data = data.join(strike)
print 'Calculating Implied Vol of Calls...'
impvolcall = pandas.Series(pandas.np.zeros(len(data.index)),
index=data.index, name='impvolCall')
for i in data.index:
impvolcall[i] = (calcvol(data.Expiration[i],
data.Strike[i],
today,
underlyingprice,
(data.Bid[i] + data.Ask[i]) / 2, Nag_Call))
print 'Calculated Implied Vol for %d Calls' % len(data.index)
data = data.join(impvolcall)
print 'Calculating Implied Vol of Puts...'
impvolput = pandas.Series(numpy.zeros(len(data.index)),
index=data.index, name='impvolPut')
for i in data.index:
impvolput[i] = (calcvol(data.Expiration[i],
data.Strike[i],
today,
underlyingprice,
(data['Bid.1'][i] + data['Ask.1'][i]) / 2.0, Nag_Put))
print 'Calculated Implied Vol for %i Puts' % len(data.index)
data = data.join(impvolput)
fig = plt.figure(1)
fig.subplots_adjust(hspace=.4, wspace=.3)
# Plot the Volatility Curves
# Encode graph layout: 3 rows, 3 columns, 1 is first graph.
num = 331
max_xticks = 4
for date in dates:
# add each subplot to the figure
plot_year, plot_month = date.split()
plot_date = (int(plot_year) - 13) * 365 + cumulative_month[plot_month]
plot_call = data[(data.impvolCall > .01) &
(data.impvolCall < 1) &
(data.Expiration == plot_date) &
(data['Last Sale'] > 0)]
plot_put = data[(data.impvolPut > .01) &
(data.impvolPut < 1) &
(data.Expiration == plot_date) &
(data['Last Sale.1'] > 0)]
myfig = fig.add_subplot(num)
xloc = plt.MaxNLocator(max_xticks)
myfig.xaxis.set_major_locator(xloc)
myfig.set_title('Expiry: %s 20%s' % (plot_month, plot_year))
myfig.plot(plot_call.Strike, plot_call.impvolCall, 'pr', label='call')
myfig.plot(plot_put.Strike, plot_put.impvolPut, 'p', label='put')
myfig.legend(loc=1, numpoints=1, prop={'size': 10})
myfig.set_ylim([0,1])
myfig.set_xlabel('Strike Price')
myfig.set_ylabel('Implied Volatility')
num += 1
plt.suptitle('Implied Volatility for %s Current Price: %s Date: %s' %
(company, underlyingprice, qd_date))
print "\nPlotting Volatility Curves/Surface"
"""
The code below will plot the Volatility Surface
It uses e02ca to fit with a polynomial and e02cb to evalute at
intermediate points
"""
m = numpy.empty(len(dates), dtype=get_nag_int_type())
y = numpy.empty(len(dates), dtype=numpy.double)
xmin = numpy.empty(len(dates), dtype=numpy.double)
xmax = numpy.empty(len(dates), dtype=numpy.double)
data = data.sort('Strike') # Need to sort for NAG Algorithm
k = 3 # this is the degree of polynomial for x-axis (Strike Price)
l = 3 # this is the degree of polynomial for y-axis (Expiration Date)
i = 0
for date in dates:
plot_year, plot_month = date.split()
plot_date = (int(plot_year) - 13) * 365 + cumulative_month[plot_month]
call_data = data[(data.Expiration == plot_date) &
(data.impvolPut > .01) &
(data.impvolPut < 1) &
(data['Last Sale.1'] > 0)]
exp_sizes = call_data.Expiration.size
if(exp_sizes > 0):
m[i] = exp_sizes
n = len(dates)
if(i == 0):
x = call_data.Strike
call = call_data.impvolPut
xmin[0] = x.min()
xmax[0] = x.max()
else:
x2 = call_data.Strike
x = x.append(x2)
call2 = call_data.impvolPut
call = call.append(call2)
xmin[i] = x2.min()
xmax[i] = x2.max()
y[i] = plot_date-today
i+=1
nux = numpy.zeros(1,dtype=numpy.double)
nuy = numpy.zeros(1,dtype=numpy.double)
inux = 1
inuy = 1
if(len(dates) != i):
print "Error with data: the CBOE may not be open for trading or one expiration date has null data"
return 0
weight = numpy.ones(call.size, dtype=numpy.double)
output_coef = (c_double * ((k + 1) * (l + 1)))(0.0)
# To input data into NAG function we convert variables to ctypes
mx = m.ctypes.data_as(POINTER(get_ctype()))
xx = x.ctypes.data_as(POINTER(c_double))
yx = y.ctypes.data_as(POINTER(c_double))
callx = call.ctypes.data_as(POINTER(c_double))
weightx = weight.ctypes.data_as(POINTER(c_double))
xminx = xmin.ctypes.data_as(POINTER(c_double))
xmaxx = xmax.ctypes.data_as(POINTER(c_double))
nuxx = nux.ctypes.data_as(POINTER(c_double))
nuyx = nuy.ctypes.data_as(POINTER(c_double))
fail = NagError()
#Call the NAG Chebyshev fitting function
e02cac(mx,n,k,l,xx,yx,callx,weightx,output_coef,xminx,xmaxx,nuxx,inux,nuyx,inuy,fail)
if(fail.code != 0):
print fail.message
return 0
"""
Now that we have fit the function,
we use e02cb to evaluate at different strikes/expirations
"""
nStrikes = 100 # number of Strikes to evaluate
spacing = 20 # number of Expirations to evaluate
for i in range(spacing):
mfirst = 1
mlast = nStrikes
xmin = data.Strike.min()
xmax = data.Strike.max()
x = numpy.linspace(xmin, xmax, nStrikes)
ymin = data.Expiration.min() - today
ymax = data.Expiration.max() - today
y = (ymin) + i * numpy.floor((ymax - ymin) / spacing)
xx = x.ctypes.data_as(POINTER(c_double))
fx=(c_double * nStrikes)(0.0)
fail=NagError()
e02cbc(mfirst,mlast,k,l,xx,xmin,xmax,y,ymin,ymax,fx,output_coef,fail)
if(fail.code != 0):
print fail.message
if 'xaxis' in locals():
xaxis = numpy.append(xaxis, x)
temp = numpy.empty(len(x))
temp.fill(y)
yaxis = numpy.append(yaxis, temp)
for j in range(len(x)):
zaxis.append(fx[j])
else:
xaxis = x
yaxis = numpy.empty(len(x), dtype=numpy.double)
yaxis.fill(y)
zaxis = []
for j in range(len(x)):
zaxis.append(fx[j])
fig = plt.figure(2)
ax = fig.add_subplot(111, projection='3d')
ax.plot_trisurf(xaxis, yaxis, zaxis, cmap=cm.jet)
ax.set_xlabel('Strike Price')
ax.set_ylabel('Days to Expiration')
ax.set_zlabel('Implied Volatility for Put Options')
plt.suptitle('Implied Volatility Surface for %s Current Price: %s Date: %s' %
(company, underlyingprice, qd_date))
plt.show()
def main():
if(a00acc() != 1):
print("Cannot find a valid NAG license")
sys.exit(1)
try:
if(len(sys.argv)>1):
QuoteData = sys.argv[1]
else:
QuoteData = 'QuoteData.dat'
qd = open(QuoteData, 'r')
qd_head = []
qd_head.append(qd.readline())
qd_head.append(qd.readline())
qd.close()
except:
sys.stderr.write("Usage: implied_volatility.py QuoteData.dat\n")
sys.stderr.write("Couldn't read QuoteData")
sys.exit(1)
print("Implied Volatility for %s %s" % (qd_head[0].strip(), qd_head[1]))
# Parse the header information in QuotaData
first = qd_head[0].split(',')
second = qd_head[1].split()
qd_date = qd_head[1].split(',')[0]
company = first[0]
underlyingprice = float(first[1])
month, day = second[:2]
today = cumulative_month[month] + int(day) - 30
current_year = int(second[2])
def getExpiration(x):
monthday = x.split()
adate = monthday[0] + ' ' + monthday[1]
if adate not in dates:
dates.append(adate)
return (int(monthday[0]) - (current_year % 2000)) * 365 + cumulative_month[monthday[1]]
def getStrike(x):
monthday = x.split()
return float(monthday[2])
data = pandas.io.parsers.read_csv(QuoteData, sep=',', header=2, na_values=' ')
# Need to fill the NA values in dataframe
data = data.fillna(0.0)
# Let's look at data where there was a recent sale
# data = data[data.Calls > 0]
data = data[(data['Last Sale'] > 0) | (data['Last Sale.1'] > 0)]
# Get the Options Expiration Date
exp = data.Calls.apply(getExpiration)
exp.name = 'Expiration'
# Get the Strike Prices
strike = data.Calls.apply(getStrike)
strike.name = 'Strike'
data = data.join(exp).join(strike)
print('Calculating Implied Vol of Calls...')
impvolcall = pandas.Series(pandas.np.zeros(len(data.index)),
index=data.index, name='impvolCall')
np.save(i
for i in data.index:
impvolcall[i] = (calcvol(data.Expiration[i],
data.Strike[i],
today,
underlyingprice,
(data.Bid[i] + data.Ask[i]) / 2, Nag_Call))
print('Calculated Implied Vol for %d Calls' % len(data.index))
data = data.join(impvolcall)
print('Calculating Implied Vol of Puts...')
impvolput = pandas.Series(numpy.zeros(len(data.index)),
index=data.index, name='impvolPut')
for i in data.index:
impvolput[i] = (calcvol(data.Expiration[i],
data.Strike[i],
today,
underlyingprice,
(data['Bid.1'][i] + data['Ask.1'][i]) / 2.0, Nag_Put))
print('Calculated Implied Vol for %i Puts' % len(data.index))
data = data.join(impvolput)
fig = plt.figure(1)
fig.subplots_adjust(hspace=.4, wspace=.3)
# Plot the Volatility Curves
# Encode graph layout: 3 rows, 3 columns, 1 is first graph.
num = 331
max_xticks = 4
for date in dates:
# add each subplot to the figure
plot_year, plot_month = date.split()
plot_date = (int(plot_year) - (current_year % 2000)) * 365 + cumulative_month[plot_month]
plot_call = data[(data.impvolCall > .01) &
(data.impvolCall < 1) &
(data.Expiration == plot_date) &
(data['Last Sale'] > 0)]
plot_put = data[(data.impvolPut > .01) &
(data.impvolPut < 1) &
(data.Expiration == plot_date) &
(data['Last Sale.1'] > 0)]
myfig = fig.add_subplot(num)
xloc = plt.MaxNLocator(max_xticks)
myfig.xaxis.set_major_locator(xloc)
myfig.set_title('Expiry: %s 20%s' % (plot_month, plot_year))
myfig.plot(plot_call.Strike, plot_call.impvolCall, 'pr', label='call')
myfig.plot(plot_put.Strike, plot_put.impvolPut, 'p', label='put')
myfig.legend(loc=1, numpoints=1, prop={'size': 10})
myfig.set_ylim([0,1])
myfig.set_xlabel('Strike Price')
myfig.set_ylabel('Implied Volatility')
num += 1
plt.suptitle('Implied Volatility for %s Current Price: %s Date: %s' %
(company, underlyingprice, qd_date))