def _act_360_fraction(self, date):
'''Implement daycount method Act/360
'''
date = BankDate(date)
if date and date > self._valuation_date:
return self._valuation_date.nbr_of_days(date) / to_decimal(360)
else:
return to_decimal(0)
def _360_360_fraction(self, date):
'''Implement daycount method 360/360
'''
day1 = BankDate(date)
day2 = self._valuation_date
if day1 and day1 > day2:
number_of_days = 360 * (day1.year - day2.year) \
+ 30 * (day1.month - day2.month) \
+ (day1.day - day2.day)
number_of_days = number_of_days % 360
return to_decimal(number_of_days) / to_decimal(360)
else:
return to_decimal(0)
def modified_convexity(self, spread):
'''
:param spread: A rate based generel spread. Default is 0.0 ie no spread
:type spread: A float between 0 and 1
:return: modified_convexity for the DateFlow and time valuation
parameters given at instantiation
*Formula for macauley_convexity:*
.. math::
macauley \\textunderscore convexity &= \\frac {1}
{NPV \\left( spread \\right)} \\cdot \\frac
{\\partial ^{2} NPV \\left( spread \\right)}
{\\partial spread ^{2}}\\\\
&= \\frac {1}{NPV \\left( spread \\right)}\\cdot npv\\_d2
.. note::
Actually when no discountcurve is used the spread can be used to
implement the discountcurve with constant discountfactor for each
future period of fixed length.
'''
if self.__nonzero__():
spread = to_decimal(spread)
npv = self.npv_value(spread)
if spread and npv:
return self.npv_d2(spread) / npv
def _act_act_isda_fraction(self, date):
'''Implement daycount method Act/Act ISDA
'''
date = BankDate(date)
if date and date > self._valuation_date:
date_year = date.year
days_in_year = to_decimal(366 if isleap(date.year) else 365)
days_in_valuation_year = to_decimal(
366 if isleap(self._valuation_date.year) else 365)
if self._valuation_date.year == date.year:
return self._valuation_date.nbr_of_days(date) \
/ days_in_valuation_year
else:
mid_day = BankDate('%s-01-01' % date_year)
return self._valuation_date.nbr_of_days(mid_day) \
/ days_in_valuation_year \
+ mid_day.nbr_of_days(date) / days_in_year
else:
return to_decimal(0)
def npv_spread(self, npv_0, precision=1e-16, max_iteration=30):
'''
:param npv_0: The present value of the cashflow
:type npv_0: Float or decimal
:param max_iteration: The precision/tolerance for the result.
Default is 1e-16
:type max_iteration: Float or decimal
:param max_iter: The maximal number of iterations. Default is 30
:type max_iter: A positive integer
:return: The par rate of the discounted cashflow
'''
npv_0 = to_decimal(npv_0)
if npv_0: # TODO: Valuate for a solution
spread_func = Solver(self.npv_value, self.npv_d1, precision,
max_iteration)
return spread_func(npv_0)
def npv_d2(self, spread):
'''
:param spread: A rate based generel spread.
:type spread: A float between 0 and 1
:return: Second order derivative for the DateFlow and time valuation
parameters given at instantiation
*Formula for npv_d2:*
.. math::
npv \\textunderscore d2 = \\frac{\\partial ^{2} NPV
\\left( spread \\right)}{\\partial spread ^{2}}
'''
spread = to_decimal(spread)
discounts = -self.future_times() * (-self.future_times() - 1) \
* (1 + spread) ** (-self.future_times() - 2)
# discounted_values is called as a function to get vector
# multiplication
return self.discounted_values()(discounts)
def npv_value(self, spread):
'''
:param spread: A rate based generel spread.
:type spread: A float between 0 and 1
:param used_discountcurve: Used discountcurve. Default is None
ie. no discounting
:type used_discountcurve: A discountcurve or None
:return: npv_value for the DateFlow and time valuation parameters given
at instantiation
*Short hand notation for npv_value in the documentation:*
.. math::
NPV ( spread ) = npv \\textunderscore value ( spread )
.. note::
Actually when no discountcurve is used the spread can be used to
implement the discountcurve with constant discountfactor for each
future period of fixed length.
'''
# discounted_values is called as a function to get vector
# multiplication
spread = to_decimal(spread)
return self.discounted_values()((1 + spread)**-self.future_times())
class ClassicRiskMeasures:
__metaclass__ = ABCMeta
basispoint = to_decimal('0.0001')
@abstractmethod
def npv_value(self, spread):
'''npv_value has to be defined before using ClassicRiskMeasures
'''
raise NotImplementedError
@abstractmethod
def npv_d1(self, spread):
'''npv_d1 has to be defined before using ClassicRiskMeasures
'''
raise NotImplementedError
@abstractmethod
def npv_d2(self, spread):
'''npv_d2 has to be defined before using ClassicRiskMeasures
'''
raise NotImplementedError
@vector_function(1, True)
def modified_duration(self, spread):
'''
:param spread: A rate based generel spread.
:type spread: A float between 0 and 1
:return: modified_duration for the DateFlow and time valuation
parameters given at instantiation
*Formula for modified_duration:*
.. math::
modified \\textunderscore duration &= \\frac {1}
{NPV \\left( spread \\right)} \\cdot
\\frac {\\partial NPV \\left( spread \\right)}{\\partial spread}\\\\
&= \\frac{1}{NPV \\left( spread \\right)} \\cdot
npv \\textunderscore d1
.. note::
Actually when no discountcurve is used the spread can be used to
implement the discountcurve with constant discountfactor for each
future period of fixed length.
'''
if self.__nonzero__():
npv = self.npv_value(spread)
if spread and npv:
return -self.npv_d1(spread) / npv
@vector_function(1, True)
def macauley_duration(self, spread):
'''
:param spread: A rate based generel spread. Default is 0.0 ie no spread
:type spread: A float between 0 and 1
:return: macauley_duration for the DateFlow and time valuation
parameters given at instantiation
*Formula for macauley_duration:*
.. math::
macauley \\textunderscore duration &= \\frac{1}
{NPV \\left( spread \\right) \\cdot \\left( 1 + spread \\right)}
\\cdot \\frac {\\partial NPV \\left( spread \\right)}
{\\partial spread}\\\\
&= \\frac {1}{NPV \\left( spread \\right) \\cdot
\\left( 1 + spread \\right)} \\cdot npv \\textunderscore d1
.. note::
Actually when no discountcurve is used the spread can be used to
implement the discountcurve with constant discountfactor for each
future period of fixed length.
'''
if self.__nonzero__():
npv = self.npv_value(spread)
if spread and npv:
return -self.npv_d1(spread) / npv * (1 + spread)
@vector_function(1, True)
def modified_convexity(self, spread):
'''
:param spread: A rate based generel spread. Default is 0.0 ie no spread
:type spread: A float between 0 and 1
:return: modified_convexity for the DateFlow and time valuation
parameters given at instantiation
*Formula for macauley_convexity:*
.. math::
macauley \\textunderscore convexity &= \\frac {1}
{NPV \\left( spread \\right)} \\cdot \\frac
{\\partial ^{2} NPV \\left( spread \\right)}
{\\partial spread ^{2}}\\\\
&= \\frac {1}{NPV \\left( spread \\right)}\\cdot npv\\_d2
.. note::
Actually when no discountcurve is used the spread can be used to
implement the discountcurve with constant discountfactor for each
future period of fixed length.
'''
if self.__nonzero__():
spread = to_decimal(spread)
npv = self.npv_value(spread)
if spread and npv:
return self.npv_d2(spread) / npv
@vector_function(1, True)
def macauley_convexity(self, spread):
'''
:param spread: A rate based generel spread.
:type spread: A float between 0 and 1
:return: macauley_convexity for the DateFlow and time valuation
parameters given at instantiation
*Formula for macauley_convexity:*
.. math::
macauley \\textunderscore convexity &= \\frac {1}
{NPV \\left( spread \\right) \\cdot \\left( 1 + spread \\right)^{2}}
\\cdot \\frac {\\partial ^{2} NPV \\left( spread \\right)}
{\\partial spread ^{2}}\\\\
&= \\frac {1}{NPV \\left( spread \\right) \\cdot
\\left( 1 + spread \\right) ^{2}} \\cdot npv \\textunderscore d2
.. note::
Actually when no discountcurve is used the spread can be used to
implement the discountcurve with constant discountfactor for each
future period of fixed length.
'''
if self.__nonzero__():
npv = self.npv_value(spread)
if spread and npv:
return self.npv_d2(spread) / npv * (1 + spread)**2
@vector_function(1, True)
def pv01(self, spread):
'''
:param spread: A rate based generel spread.
:type spread: A float between 0 and 1
:return: pv01 for the DateFlow and time valuation parameters given at
instantiation
*Formula for pv01:*
.. math::
pv01 &= \\frac {\\partial NPV \\left( spread \\right)}
{\\partial spread}\\cdot0.0001\\\\
&= npv \\textunderscore d1 \\left( spread \\right) \\cdot 0.0001
This definition has the advantage that it can handle DateFlows with NPV
near zero.
.. note::
Actually when no discountcurve is used the spread can be used to
implement the discountcurve with constant discountfactor for each
future period of fixed length.
'''
if self.__nonzero__():
if spread:
return -ClassicRiskMeasures.basispoint * self.npv_d1(spread)
@vector_function(1, True)
def pvbp(self, spread):
'''
:param spread: A rate based generel spread.
:type spread: A float between 0 and 1
:return: pvbp for the DateFlow and time valuation parameters given at
instantiation
*Formula for pvbp:*
.. math::
pvbp = NPV \\left( spread \\right)
- NPV \\left( spread + 0.0001 \\right)
This definition has the advantage that it can handle DateFlows with NPV
near zero.
.. note::
Actually when no discountcurve is used the spread can be used to
implement the discountcurve with constant discountfactor for each
future period of fixed length.
'''
if self.__nonzero__():
if spread:
return self.npv_value(spread) \
- self.npv_value(spread + ClassicRiskMeasures.basispoint)
