def test_lagfit(self) :
def f(x) :
return x*(x - 1)*(x - 2)
# Test exceptions
assert_raises(ValueError, lag.lagfit, [1], [1], -1)
assert_raises(TypeError, lag.lagfit, [[1]], [1], 0)
assert_raises(TypeError, lag.lagfit, [], [1], 0)
assert_raises(TypeError, lag.lagfit, [1], [[[1]]], 0)
assert_raises(TypeError, lag.lagfit, [1, 2], [1], 0)
assert_raises(TypeError, lag.lagfit, [1], [1, 2], 0)
assert_raises(TypeError, lag.lagfit, [1], [1], 0, w=[[1]])
assert_raises(TypeError, lag.lagfit, [1], [1], 0, w=[1,1])
# Test fit
x = np.linspace(0,2)
y = f(x)
#
coef3 = lag.lagfit(x, y, 3)
assert_equal(len(coef3), 4)
assert_almost_equal(lag.lagval(x, coef3), y)
#
coef4 = lag.lagfit(x, y, 4)
assert_equal(len(coef4), 5)
assert_almost_equal(lag.lagval(x, coef4), y)
#
coef2d = lag.lagfit(x, np.array([y,y]).T, 3)
assert_almost_equal(coef2d, np.array([coef3,coef3]).T)
# test weighting
w = np.zeros_like(x)
yw = y.copy()
w[1::2] = 1
y[0::2] = 0
wcoef3 = lag.lagfit(x, yw, 3, w=w)
assert_almost_equal(wcoef3, coef3)
#
wcoef2d = lag.lagfit(x, np.array([yw,yw]).T, 3, w=w)
assert_almost_equal(wcoef2d, np.array([coef3,coef3]).T)
def test_lagfit(self) :
def f(x) :
return x*(x - 1)*(x - 2)
# Test exceptions
assert_raises(ValueError, lag.lagfit, [1], [1], -1)
assert_raises(TypeError, lag.lagfit, [[1]], [1], 0)
assert_raises(TypeError, lag.lagfit, [], [1], 0)
assert_raises(TypeError, lag.lagfit, [1], [[[1]]], 0)
assert_raises(TypeError, lag.lagfit, [1, 2], [1], 0)
assert_raises(TypeError, lag.lagfit, [1], [1, 2], 0)
assert_raises(TypeError, lag.lagfit, [1], [1], 0, w=[[1]])
assert_raises(TypeError, lag.lagfit, [1], [1], 0, w=[1,1])
# Test fit
x = np.linspace(0,2)
y = f(x)
#
coef3 = lag.lagfit(x, y, 3)
assert_equal(len(coef3), 4)
assert_almost_equal(lag.lagval(x, coef3), y)
#
coef4 = lag.lagfit(x, y, 4)
assert_equal(len(coef4), 5)
assert_almost_equal(lag.lagval(x, coef4), y)
#
coef2d = lag.lagfit(x, np.array([y,y]).T, 3)
assert_almost_equal(coef2d, np.array([coef3,coef3]).T)
# test weighting
w = np.zeros_like(x)
yw = y.copy()
w[1::2] = 1
y[0::2] = 0
wcoef3 = lag.lagfit(x, yw, 3, w=w)
assert_almost_equal(wcoef3, coef3)
#
wcoef2d = lag.lagfit(x, np.array([yw,yw]).T, 3, w=w)
assert_almost_equal(wcoef2d, np.array([coef3,coef3]).T)
def mainPricing(I, M, df):
S = GenS(I, M) # generate stock price paths
h = IV(S) # inner value matrix
V = IV(S) # value matrix
for t in range(M - 1, -1, -1):
# rg = polyfit(S[t,:], V[t+1,:]*df, reg) # regression at time t
rg = a.lagfit(S[t, :], V[t + 1, :] * df, reg)
C = a.lagval(S[t, :], rg, True)
# C = polyval(rg, S[t, :])
##C = polyval(rg,S[t,:]) # continuation values
V[t, :] = where(h[t, :] > C, h[t, :],
V[t + 1, :] * df) # exercise decision
V0 = sum(V[0, :]) / I # LSM estimator
return V0
def lagfit(xs, ys, deg):
coeffs = lag.lagfit(xs, ys, deg)
p = lag.Laguerre(coeffs)
return mkseries(xs, ys, p)
def test_lagfit(self):
def f(x):
return x*(x - 1)*(x - 2)
# Test exceptions
assert_raises(ValueError, lag.lagfit, [1], [1], -1)
assert_raises(TypeError, lag.lagfit, [[1]], [1], 0)
assert_raises(TypeError, lag.lagfit, [], [1], 0)
assert_raises(TypeError, lag.lagfit, [1], [[[1]]], 0)
assert_raises(TypeError, lag.lagfit, [1, 2], [1], 0)
assert_raises(TypeError, lag.lagfit, [1], [1, 2], 0)
assert_raises(TypeError, lag.lagfit, [1], [1], 0, w=[[1]])
assert_raises(TypeError, lag.lagfit, [1], [1], 0, w=[1, 1])
assert_raises(ValueError, lag.lagfit, [1], [1], [-1,])
assert_raises(ValueError, lag.lagfit, [1], [1], [2, -1, 6])
assert_raises(TypeError, lag.lagfit, [1], [1], [])
# Test fit
x = np.linspace(0, 2)
y = f(x)
#
coef3 = lag.lagfit(x, y, 3)
assert_equal(len(coef3), 4)
assert_almost_equal(lag.lagval(x, coef3), y)
coef3 = lag.lagfit(x, y, [0, 1, 2, 3])
assert_equal(len(coef3), 4)
assert_almost_equal(lag.lagval(x, coef3), y)
#
coef4 = lag.lagfit(x, y, 4)
assert_equal(len(coef4), 5)
assert_almost_equal(lag.lagval(x, coef4), y)
coef4 = lag.lagfit(x, y, [0, 1, 2, 3, 4])
assert_equal(len(coef4), 5)
assert_almost_equal(lag.lagval(x, coef4), y)
#
coef2d = lag.lagfit(x, np.array([y, y]).T, 3)
assert_almost_equal(coef2d, np.array([coef3, coef3]).T)
coef2d = lag.lagfit(x, np.array([y, y]).T, [0, 1, 2, 3])
assert_almost_equal(coef2d, np.array([coef3, coef3]).T)
# test weighting
w = np.zeros_like(x)
yw = y.copy()
w[1::2] = 1
y[0::2] = 0
wcoef3 = lag.lagfit(x, yw, 3, w=w)
assert_almost_equal(wcoef3, coef3)
wcoef3 = lag.lagfit(x, yw, [0, 1, 2, 3], w=w)
assert_almost_equal(wcoef3, coef3)
#
wcoef2d = lag.lagfit(x, np.array([yw, yw]).T, 3, w=w)
assert_almost_equal(wcoef2d, np.array([coef3, coef3]).T)
wcoef2d = lag.lagfit(x, np.array([yw, yw]).T, [0, 1, 2, 3], w=w)
assert_almost_equal(wcoef2d, np.array([coef3, coef3]).T)
# test scaling with complex values x points whose square
# is zero when summed.
x = [1, 1j, -1, -1j]
assert_almost_equal(lag.lagfit(x, x, 1), [1, -1])
assert_almost_equal(lag.lagfit(x, x, [0, 1]), [1, -1])
def test_lagfit(self):
def f(x):
return x*(x - 1)*(x - 2)
# Test exceptions
assert_raises(ValueError, lag.lagfit, [1], [1], -1)
assert_raises(TypeError, lag.lagfit, [[1]], [1], 0)
assert_raises(TypeError, lag.lagfit, [], [1], 0)
assert_raises(TypeError, lag.lagfit, [1], [[[1]]], 0)
assert_raises(TypeError, lag.lagfit, [1, 2], [1], 0)
assert_raises(TypeError, lag.lagfit, [1], [1, 2], 0)
assert_raises(TypeError, lag.lagfit, [1], [1], 0, w=[[1]])
assert_raises(TypeError, lag.lagfit, [1], [1], 0, w=[1, 1])
assert_raises(ValueError, lag.lagfit, [1], [1], [-1,])
assert_raises(ValueError, lag.lagfit, [1], [1], [2, -1, 6])
assert_raises(TypeError, lag.lagfit, [1], [1], [])
# Test fit
x = np.linspace(0, 2)
y = f(x)
#
coef3 = lag.lagfit(x, y, 3)
assert_equal(len(coef3), 4)
assert_almost_equal(lag.lagval(x, coef3), y)
coef3 = lag.lagfit(x, y, [0, 1, 2, 3])
assert_equal(len(coef3), 4)
assert_almost_equal(lag.lagval(x, coef3), y)
#
coef4 = lag.lagfit(x, y, 4)
assert_equal(len(coef4), 5)
assert_almost_equal(lag.lagval(x, coef4), y)
coef4 = lag.lagfit(x, y, [0, 1, 2, 3, 4])
assert_equal(len(coef4), 5)
assert_almost_equal(lag.lagval(x, coef4), y)
#
coef2d = lag.lagfit(x, np.array([y, y]).T, 3)
assert_almost_equal(coef2d, np.array([coef3, coef3]).T)
coef2d = lag.lagfit(x, np.array([y, y]).T, [0, 1, 2, 3])
assert_almost_equal(coef2d, np.array([coef3, coef3]).T)
# test weighting
w = np.zeros_like(x)
yw = y.copy()
w[1::2] = 1
y[0::2] = 0
wcoef3 = lag.lagfit(x, yw, 3, w=w)
assert_almost_equal(wcoef3, coef3)
wcoef3 = lag.lagfit(x, yw, [0, 1, 2, 3], w=w)
assert_almost_equal(wcoef3, coef3)
#
wcoef2d = lag.lagfit(x, np.array([yw, yw]).T, 3, w=w)
assert_almost_equal(wcoef2d, np.array([coef3, coef3]).T)
wcoef2d = lag.lagfit(x, np.array([yw, yw]).T, [0, 1, 2, 3], w=w)
assert_almost_equal(wcoef2d, np.array([coef3, coef3]).T)
# test scaling with complex values x points whose square
# is zero when summed.
x = [1, 1j, -1, -1j]
assert_almost_equal(lag.lagfit(x, x, 1), [1, -1])
assert_almost_equal(lag.lagfit(x, x, [0, 1]), [1, -1])
for m in range(1, 10):
# Simulation Parameters
I = 25000
M = 50
dt=T/M
df = exp(-r * dt)
# Stock Price Paths
S = S0 * np.exp(np.cumsum((r - 0.5 * sigma ** 2) * dt
+ sigma * math.sqrt(dt) * np.random.standard_normal((M + 1, I)), axis=0))
S[0] = S0
# Inner Values
h = np.maximum(K - S, 0)
# Present Value Vector (Initialization)
V = h[-1]
# American Option Valuation by Backwards Induction
for t in xrange(M - 1, 0, -1):
rg= laguerre.lagfit(S[t], V * df, m)
C = laguerre.lagval(S[t], rg)# continuation values V = np.where(h[t] > C, h[t], V * df)
# exercise decision
V0 = df*np.sum(V)/I #LSMestimator
error.append(abs(V0 - Vbsm))
print error
plt.xlabel('Order of Polynomial for LS regression')
plt.ylabel('Errors')
plt.plot(range(1, 10), error)
plt.show()
def test_lagfit(self):
def f(x):
return x * (x - 1) * (x - 2)
# Test exceptions
assert_raises(ValueError, lag.lagfit, [1], [1], -1)
assert_raises(TypeError, lag.lagfit, [[1]], [1], 0)
assert_raises(TypeError, lag.lagfit, [], [1], 0)
assert_raises(TypeError, lag.lagfit, [1], [[[1]]], 0)
assert_raises(TypeError, lag.lagfit, [1, 2], [1], 0)
assert_raises(TypeError, lag.lagfit, [1], [1, 2], 0)
assert_raises(TypeError, lag.lagfit, [1], [1], 0, w=[[1]])
assert_raises(TypeError, lag.lagfit, [1], [1], 0, w=[1, 1])
# Test fit
x = np.linspace(0, 2)
y = f(x)
#
coef3 = lag.lagfit(x, y, 3)
assert_equal(len(coef3), 4)
assert_almost_equal(lag.lagval(x, coef3), y)
#
coef4 = lag.lagfit(x, y, 4)
assert_equal(len(coef4), 5)
assert_almost_equal(lag.lagval(x, coef4), y)
#
coef2d = lag.lagfit(x, np.array([y, y]).T, 3)
assert_almost_equal(coef2d, np.array([coef3, coef3]).T)
# test weighting
w = np.zeros_like(x)
yw = y.copy()
w[1::2] = 1
y[0::2] = 0
wcoef3 = lag.lagfit(x, yw, 3, w=w)
assert_almost_equal(wcoef3, coef3)
#
wcoef2d = lag.lagfit(x, np.array([yw, yw]).T, 3, w=w)
assert_almost_equal(wcoef2d, np.array([coef3, coef3]).T)
#test NA
y = f(x)
y[10] = 100
xm = x.view(maskna=1)
xm[10] = np.NA
res = lag.lagfit(xm, y, 3)
assert_almost_equal(res, coef3)
ym = y.view(maskna=1)
ym[10] = np.NA
res = lag.lagfit(x, ym, 3)
assert_almost_equal(res, coef3)
y2 = np.vstack((y, y)).T
y2[10, 0] = 100
y2[15, 1] = 100
y2m = y2.view(maskna=1)
y2m[10, 0] = np.NA
y2m[15, 1] = np.NA
res = lag.lagfit(x, y2m, 3).T
assert_almost_equal(res[0], coef3)
assert_almost_equal(res[1], coef3)
wm = np.ones_like(x, maskna=1)
wm[10] = np.NA
res = lag.lagfit(x, y, 3, w=wm)
assert_almost_equal(res, coef3)
def apply_fitting_fields(plotgui):
"""
Apply the set fitting parameters from the window.
This routine reads the fitting parameters from the window and then
applies the fitting. Each time it is called a new data set should
be generated, unless the fit order is too large for the number of
points. The fit order must be at most 1 less than the number
of points, otherwise the routine just shows an error message
pop-up and returns.
Parameters
----------
plotgui: by assumption a matplotlib_user_interface object
Returns
-------
None
"""
fit_type = plotgui.set_fitting_fields[0].get()
if 'Cubic Spline' in fit_type:
fit_order = float(plotgui.set_fitting_fields[1].get())
else:
try:
fit_order = int(plotgui.set_fitting_fields[1].get())
except ValueError:
str1 = 'Error: bad fit order (%s). Settng to 4.' % (
plotgui.set_fitting_fields[1].get())
plotgui.fit_text.insert(Tk.END, str1)
plotgui.fit_text.see(Tk.END)
fit_order = 4
set_number = plotgui.set_fitting_list_area.current()
fit_flag = plotgui.fit_option.get()
if fit_flag == 0:
xvalues = numpy.copy(plotgui.xdata[set_number]['values'])
yvalues = numpy.copy(plotgui.ydata[set_number]['values'])
else:
xvalues = numpy.copy(plotgui.ydata[set_number]['values'])
yvalues = numpy.copy(plotgui.xdata[set_number]['values'])
inds = numpy.argsort(xvalues)
xvalues = xvalues[inds]
yvalues = yvalues[inds]
npoints = len(xvalues)
if ('Spline' not in fit_type) and ('Internal' not in fit_type):
if npoints + 1 <= fit_order:
tkinter.messagebox.showinfo(
'Error', 'The number of points is too few for the fit order.' +
' Please check your inputs.')
return
xmin = numpy.min(xvalues)
xmax = numpy.max(xvalues)
delx = xmax - xmin
xstep = 1.2 * delx / 1201.
xout = numpy.arange(xmin - delx / 10., xmax + delx / 10., xstep)
if 'Internal' in fit_type:
if npoints < 2:
tkinter.messagebox.showinfo(
'Error', 'The number of points is too few for a linear fit.' +
' Please check your inputs.')
return
if fit_flag == 0:
yerrors = (plotgui.ydata[set_number]['lowerror'] +
plotgui.ydata[set_number]['higherror']) / 2.
else:
yerrors = (plotgui.xdata[set_number]['lowerror'] +
plotgui.xdata[set_number]['higherror']) / 2.
if (numpy.min(yerrors) == 0.) and (numpy.max(yerrors) == 0.):
yerrors = yerrors + 1.
slope, intercept, slope_error, intercept_error, covariance, \
correlation = general_utilities.slope_calculation(
xvalues, yvalues, yerrors)
if slope is None:
tkinter.messagebox.showinfo('Error',
'Error in the standard slope fit.\n')
return
yfit = intercept + xvalues * slope
yout = intercept + xout * slope
errorterm1 = xout * 0. + intercept_error
errorterm2 = xout * slope_error
youterror = numpy.sqrt(errorterm1 * errorterm1 +
errorterm2 * errorterm2)
labelstring = 'Standard linear fit'
rms = numpy.sqrt(numpy.mean((yvalues - yfit) * (yvalues - yfit)))
str1 = 'Regression calculation results:\n'
str1 = str1 + 'Slope: %g +/- %g\n' % (slope, slope_error)
str1 = str1 + 'Intercept: %g +/- %g\n' % (intercept, intercept_error)
str1 = str1 + 'Covariance: %f\n' % (covariance)
str1 = str1 + 'Correlation: %f\n' % (correlation)
str1 = str1 + 'RMS deviation: %f\n' % (rms)
tkinter.messagebox.showinfo('Information', str1)
outfile = open('fit_values.txt', 'a')
print(str1, file=outfile)
print(' ', file=outfile)
outfile.close()
if fit_type == 'Polynomial':
fitpars = polynomial.polyfit(xvalues, yvalues, fit_order)
yout = polynomial.polyval(xout, fitpars)
yfit = polynomial.polyval(xvalues, fitpars)
labelstring = 'Order %d polynomial fit' % (fit_order)
general_utilities.list_polynomial_fitpars(fit_type, fit_order, fitpars)
if fit_type == 'Legendre':
fitpars = legendre.legfit(xvalues, yvalues, fit_order)
yout = legendre.legval(xout, fitpars)
yfit = legendre.legval(xvalues, fitpars)
labelstring = 'Order %d Legendre polynomial fit' % (fit_order)
general_utilities.list_polynomial_fitpars(fit_type, fit_order, fitpars)
if fit_type == 'Laguerre':
fitpars = laguerre.lagfit(xvalues, yvalues, fit_order)
yout = laguerre.lagval(xout, fitpars)
yfit = laguerre.lagval(xvalues, fitpars)
labelstring = 'Order %d Laguerre polynomial fit' % (fit_order)
general_utilities.list_polynomial_fitpars(fit_type, fit_order, fitpars)
if fit_type == 'Chebyshev':
fitpars = chebyshev.chebfit(xvalues, yvalues, fit_order)
yout = chebyshev.chebval(xout, fitpars)
yfit = chebyshev.chebval(xvalues, fitpars)
labelstring = 'Order %d Chebyshev polynomial fit' % (fit_order)
general_utilities.list_polynomial_fitpars(fit_type, fit_order, fitpars)
if fit_type == 'Least-Squares Spline':
if fit_flag == 0:
yerrors = (plotgui.ydata[set_number]['lowerror'] +
plotgui.ydata[set_number]['higherror']) / 2.
else:
yerrors = (plotgui.xdata[set_number]['lowerror'] +
plotgui.xdata[set_number]['higherror']) / 2.
if (numpy.min(yerrors) == 0.) and (numpy.max(yerrors) == 0.):
yerrors = yerrors + 1.
xmin1 = numpy.min(xvalues)
xmax1 = numpy.max(xvalues)
xrange = xmax1 - xmin1
nknots = int(fit_order)
if (nknots < 3) or (nknots > int(len(xvalues) / 2)):
nknots = 3
xstep = xrange / (nknots - 2)
xknots = numpy.arange(
numpy.min(xvalues) + xstep,
numpy.max(xvalues) * 0.999999999, xstep)
k = 3
# Use cubic splines
knotedges = numpy.r_[(xmin, ) * (k + 1), xknots, (xmax, ) * (k + 1)]
weights = 1. / yerrors
weights[yerrors == 0.] = 0.
fitobject = make_lsq_spline(xvalues, yvalues, knotedges, k, w=weights)
yout = fitobject(xout)
yfit = fitobject(xvalues)
labelstring = 'Least squares spline fit, sections = %d' % (nknots)
if fit_type == 'Spline':
fitpars = UnivariateSpline(xvalues,
yvalues,
k=1,
s=None,
bbox=[xmin - delx, xmax + delx])
labelstring = 'Default spline fit'
yout = fitpars(xout)
yfit = fitpars(xvalues)
if fit_type == 'Cubic Spline':
if fit_order < 0.:
str1 = 'Error: smoothing value %f (< 0) is not allowed.'\
+ ' Settng to 0.0' % (fit_order)
plotgui.fit_text.insert(Tk.END, str1)
plotgui.fit_text.see(Tk.END)
fit_order = 0.0
fitpars = UnivariateSpline(xvalues,
yvalues,
k=3,
bbox=[xmin - delx, xmax + delx],
s=fit_order)
yout = fitpars(xout)
yfit = fitpars(xvalues)
labelstring = 'Cubic spline fit, smoothing = %f' % (fit_order)
rms = fit_statistics(yvalues, yfit)
if 'Internal' in fit_type:
if fit_flag == 0:
xlowerror = youterror * 0.
xhigherror = youterror * 0.
ylowerror = youterror
yhigherror = youterror
else:
xlowerror = youterror
xhigherror = youterror
ylowerror = youterror * 0.
yhigherror = youterror * 0.
else:
xlowerror = xout * 0.
xhigherror = xout * 0.
ylowerror = yout * 0.
yhigherror = yout * 0.
xmin = numpy.min(xout)
xmax = numpy.max(xout)
ymin = numpy.min(yfit)
ymax = numpy.max(yfit)
if rms is not None:
str1 = 'Fit: RMS = %g for %d points\n' % (rms, len(yfit))
plotgui.fit_text.insert(Tk.END, str1)
plotgui.fit_text.see(Tk.END)
if fit_flag == 0:
plotgui.xdata[plotgui.nsets] = {
'values': xout,
'lowerror': xlowerror,
'higherror': xhigherror,
'minimum': xmin,
'maximum': xmax,
'errors': False,
'legend': True
}
plotgui.ydata[plotgui.nsets] = {
'values': yout,
'lowerror': ylowerror,
'higherror': yhigherror,
'minimum': ymin,
'maximum': ymax,
'errors': True,
'legend': True
}
else:
plotgui.xdata[plotgui.nsets] = {
'values': yout,
'lowerror': ylowerror,
'higherror': yhigherror,
'minimum': ymin,
'maximum': ymax,
'errors': False,
'legend': True
}
plotgui.ydata[plotgui.nsets] = {
'values': xout,
'lowerror': xlowerror,
'higherror': xhigherror,
'minimum': xmin,
'maximum': xmax,
'errors': False,
'legend': True
}
m = plotgui.nsets % 10
n = int(math.floor(plotgui.nsets / 10))
plotgui.set_properties[plotgui.nsets]['symbol'] = None
plotgui.set_properties[plotgui.nsets]['linestyle'] = '-'
plotgui.set_properties[plotgui.nsets]['colour'] = plotgui.colourset[m]
plotgui.set_properties[plotgui.nsets]['symbolsize'] = 4.0 + 0.3 * n
plotgui.set_properties[plotgui.nsets]['label'] = labelstring
plotgui.nsets = plotgui.nsets + 1
make_plot.make_plot(plotgui)
import matplotlib.pyplot as plt import numpy as np import pandas as pd from numpy.polynomial.laguerre import lagfit, lagval import random random.seed(1) x = np.linspace(10, 100, 30) y = random.sample(range(10, 100), 30) y = np.ma.masked_less_equal(y, 50) p = lagfit(x, y, 3) print(p) c = lagval(x, p) plt.scatter(x, y, label="actual") plt.plot(x, c, label="fit") plt.legend() plt.show()
from numpy.polynomial.laguerre import lagfit, lagval from numpy.polynomial.legendre import legval, legfit import numpy as np import matplotlib.pyplot as plt N = 100 X = np.linspace(0, N, N) Y = np.sin(0.3 * X) Y = np.sin(0.1 * (X + np.random.normal(0, 0.1, size=100))) regression = lagfit(X, Y, 4) regression1 = legfit(X, Y, 4) fit = lagval(X, regression) fit1 = legval(X, regression1) print(fit - fit1) plt.plot(X, Y, linewidth=1, alpha=1, label="Actual") plt.plot(X, fit, linewidth=0.5, alpha=1, label="Laguerre") plt.plot(X, fit1, linewidth=0.5, alpha=1, label="Legendre") plt.legend() plt.show()
df = exp(-r * dt)
# Stock Price Paths
S = S0 * np.exp(
np.cumsum((r - 0.5 * sigma**2) * dt +
sigma * math.sqrt(dt) * np.random.standard_normal(
(M + 1, I)),
axis=0))
S[0] = S0
# Inner Values
h = np.maximum(K - S, 0)
# Present Value Vector (Initialization)
V = h[-1]
# American Option Valuation by Backwards Induction
for t in xrange(M - 1, 0, -1):
rg = laguerre.lagfit(S[t], V * df, m)
C = laguerre.lagval(
S[t],
rg) # continuation values V = np.where(h[t] > C, h[t], V * df)
# exercise decision
V0 = df * np.sum(V) / I #LSMestimator
error.append(abs(V0 - Vbsm))
print error
plt.xlabel('Order of Polynomial for LS regression')
plt.ylabel('Errors')
plt.plot(range(1, 10), error)
plt.show()
def test_lagfit(self) :
def f(x) :
return x*(x - 1)*(x - 2)
# Test exceptions
assert_raises(ValueError, lag.lagfit, [1], [1], -1)
assert_raises(TypeError, lag.lagfit, [[1]], [1], 0)
assert_raises(TypeError, lag.lagfit, [], [1], 0)
assert_raises(TypeError, lag.lagfit, [1], [[[1]]], 0)
assert_raises(TypeError, lag.lagfit, [1, 2], [1], 0)
assert_raises(TypeError, lag.lagfit, [1], [1, 2], 0)
assert_raises(TypeError, lag.lagfit, [1], [1], 0, w=[[1]])
assert_raises(TypeError, lag.lagfit, [1], [1], 0, w=[1,1])
# Test fit
x = np.linspace(0,2)
y = f(x)
#
coef3 = lag.lagfit(x, y, 3)
assert_equal(len(coef3), 4)
assert_almost_equal(lag.lagval(x, coef3), y)
#
coef4 = lag.lagfit(x, y, 4)
assert_equal(len(coef4), 5)
assert_almost_equal(lag.lagval(x, coef4), y)
#
coef2d = lag.lagfit(x, np.array([y,y]).T, 3)
assert_almost_equal(coef2d, np.array([coef3,coef3]).T)
# test weighting
w = np.zeros_like(x)
yw = y.copy()
w[1::2] = 1
y[0::2] = 0
wcoef3 = lag.lagfit(x, yw, 3, w=w)
assert_almost_equal(wcoef3, coef3)
#
wcoef2d = lag.lagfit(x, np.array([yw,yw]).T, 3, w=w)
assert_almost_equal(wcoef2d, np.array([coef3,coef3]).T)
#test NA
y = f(x)
y[10] = 100
xm = x.view(maskna=1)
xm[10] = np.NA
res = lag.lagfit(xm, y, 3)
assert_almost_equal(res, coef3)
ym = y.view(maskna=1)
ym[10] = np.NA
res = lag.lagfit(x, ym, 3)
assert_almost_equal(res, coef3)
y2 = np.vstack((y,y)).T
y2[10,0] = 100
y2[15,1] = 100
y2m = y2.view(maskna=1)
y2m[10,0] = np.NA
y2m[15,1] = np.NA
res = lag.lagfit(x, y2m, 3).T
assert_almost_equal(res[0], coef3)
assert_almost_equal(res[1], coef3)
wm = np.ones_like(x, maskna=1)
wm[10] = np.NA
res = lag.lagfit(x, y, 3, w=wm)
assert_almost_equal(res, coef3)
def LSMC_Laguerre(price_matrix, K, r, paths, T, dt, type, polydegree):
from numpy.polynomial.laguerre import lagfit, lagval
# start timer
tic = time.time()
# total number of steps
N = T * dt
N = int(N)
# adjust yearly discount factor
r = (1 + r)**(1 / dt) - 1
# cash flow matrix
cf_matrix = np.zeros((N + 1, paths * 2))
# calculated cf when executed in time T (cfs European option)
cf_matrix[N] = payoff_executing(K, price_matrix[N], type)
# 1 if in the money, otherwise 0
execute = np.where(payoff_executing(K, price_matrix, type) > 0, 1, 0)
# execute = np.ones_like(execute) # use to convert to consider all paths
for t in range(1, N):
# discounted cf 1 time period
discounted_cf = cf_matrix[N - t + 1] * np.exp(-r)
# slice matrix and make all out of the money paths = 0 by multiplying with matrix "execute"
X = price_matrix[N - t, :] * execute[N - t, :]
# +1 here because otherwise will loose an in the money path at T-t,
# that is out of the money in T-t+1(and thus has payoff=0)
Y = (discounted_cf + 1) * execute[N - t, :]
# mask all zero values(out of the money paths) and run regression
#X1 = np.ma.masked_less_equal(X, 0)
#Y1 = np.ma.masked_less_equal(Y, 0) - 1
X1 = X[np.where(X > 0)]
Y1 = Y[np.where(X > 0)]
if np.count_nonzero(
X1
) > 0: # meaning all paths are out of the money, thus never optimal to exercise
regression = lagfit(X1, Y1, polydegree)
# warnings.simplefilter('ignore', np.RankWarning)
# calculate continuation value
cont_value = np.zeros_like(X)
cont_value[np.where(X > 0)] = lagval(X1, regression)
# update cash flow matrix
imm_ex = payoff_executing(K, X, type)
# works because for ootm --> imm_ex=0 + cont value =0(not true obviously) but condition works
cf_matrix[N - t] = np.where(imm_ex > cont_value, imm_ex,
cf_matrix[N - t + 1] * np.exp(-r))
cf_matrix[N - t + 1:] = np.where(imm_ex > cont_value, 0,
cf_matrix[N - t + 1:])
else:
cf_matrix[N - t] = cf_matrix[N - t + 1] * np.exp(-r)
# obtain option value
cf_matrix[0] = cf_matrix[1] * np.exp(-r)
option_value = np.sum(cf_matrix[0]) / (paths * 2)
# st dev
st_dev = np.std(cf_matrix[0]) / np.sqrt(paths)
# Time and print the elapsed time
toc = time.time()
elapsed_time = toc - tic
print('Total running time of LSMC: {:.2f} seconds'.format(elapsed_time))
print("Ran this with T: ", T, " and dt: ", dt, "\n")
print("Value of this", type, "option is:", option_value)
print("St dev of this", type, "option is:", st_dev, "\n")
return option_value
