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QuantLibAddin and Monte Carlo simulation

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Lapin

QuantLibAddin and Monte Carlo simulation

Hi all,

I am trying to expose MCBarrierEngine into quantLibXL but I am really struggling to pass the PseudoRandomNumber generator...

Let appart the relevant includes, in my view you would need to add the following code in pricingengine.cpp

template<typename T>
class MCBarrierEngine : public PricingEngine {
public:
MCBarrierEngine(
const boost::shared_ptr<ObjectHandler::ValueObject>& properties ,
const boost::shared_ptr<QuantLib::GeneralizedBlackScholesProcess>& process ,
const QuantLib::Size maxTimeStepsPerYear ,
bool brownianBridge ,
bool antitheticVariate ,
bool controlVariate ,
const QuantLib::Size requiredSamples ,
const QuantLib::Real requiredTolerance ,
const QuantLib::Size maxSamples ,
bool isBiased ,
const T& RSQ ,
const QuantLib::BigNatural seed ,
bool permanent );

And this in pricingengine.cpp

template<typename T>
MCBarrierEngine<T>::MCBarrierEngine(
const boost::shared_ptr<ObjectHandler::ValueObject>& properties ,
const boost::shared_ptr<QuantLib::GeneralizedBlackScholesProcess>& process ,
const QuantLib::Size maxTimeStepsPerYear ,
const bool brownianBridge ,
const bool antitheticVariate ,
const bool controlVariate ,
const QuantLib::Size requiredSamples ,
const QuantLib::Real requiredTolerance ,
const QuantLib::Size maxSamples ,
const bool isBiased ,
const T& RSQ ,
const QuantLib::BigNatural seed ,
bool permanent )
: PricingEngine(properties, permanent)
{
libraryObject_ = boost::shared_ptr<QuantLib::PricingEngine>(new
QuantLib::MCBarrierEngine<RSQ>(process,
maxTimeStepsPerYear, brownianBridge,
antitheticVariate, controlVariate,
requiredSamples, requiredTolerance,
maxSamples, isBiased, seed)) ;
}

To finish, you need to add the following constructor in pricingengine.xml

<Constructor name='qlMCBarrierEnginer'>
<libraryFunction>MCBarrierEngine</libraryFunction>
<SupportedPlatforms>
<SupportedPlatform name='Excel'/>
</SupportedPlatforms>
<ParameterList>
<Parameters>
<Parameter name='ProcessID' >
<type>QuantLib::GeneralizedBlackScholesProcess</type>
<superType>libraryClass</superType>
<tensorRank>scalar</tensorRank>
<description>GeneralizedBlackScholesProcess object ID.</description>
</Parameter>
<Parameter name='TimeStepPerYear'>
<type>long</type>
<tensorRank>scalar</tensorRank>
<description>Time Steps Per Year</description>
</Parameter>
<Parameter name='BrownianBridge' default='FALSE'>
<type>bool</type>
<tensorRank>scalar</tensorRank>
<description>Using a Brownian Bridge?</description>
</Parameter>
<Parameter name='AntitheticVariate' default='FALSE'>
<type>bool</type>
<tensorRank>scalar</tensorRank>
<description>Using a Antithetic Variate?</description>
</Parameter>
<Parameter name='ControlVariate' default='FALSE'>
<type>bool</type>
<tensorRank>scalar</tensorRank>
<description>Using a Control Variate?</description>
</Parameter>
<Parameter name='RequiredSamples'>
<type>long</type>
<tensorRank>scalar</tensorRank>
<description>Minimum number of pathes</description>
</Parameter>
<Parameter name='RequiredTolerance'>
<type>long</type>
<tensorRank>scalar</tensorRank>
<description>Tolerance on the error</description>
</Parameter>
<Parameter name='MaxSamples'>
<type>long</type>
<tensorRank>scalar</tensorRank>
<description>Maximum number of pathes</description>
</Parameter>
<Parameter name='RandomNumberGenerator'>
<type>QuantLib::PseudoRandomSequenceGenerator</type>
<superType>underlyingClass</superType>
<tensorRank>scalar</tensorRank>
<description>Random Number Generator</description>
</Parameter>
<Parameter name='Seed' default='123'>
<type>long</type>
<tensorRank>scalar</tensorRank>
<description>Seed used for the random numbers</description>
</Parameter>
</Parameters>
</ParameterList>
</Constructor>

I get an error C3203: 'PseudoRandomSequenceGenerator' : unspecialized class template can't be used as a template argument for template parameter 'T', expected a real type ... since I use a non unspecialized template class for the generator.

Is there a way to do 1 XL function for all the Pseudo Random Generators?

Cheers

Andrew Downes

Re: QuantLibAddin and Monte Carlo simulation

Hi,

You can't directly expose template classes to Excel. To expose this
class the template type will need to be passed as a string eventually,
although there's a reasonable amount of extra code you need to write
first. Try having a look at the PiecewiseYieldCurve class which has
already been done. For this example, in particular have a look at the
files termstructuresfactory.hpp (in the project
QuantLibObjects/enumerations/factories), register_pais.cpp (in the
project QuantLibObjects/enumerations/register), enumeratedpairs.*pp (in
the project QuantLibObjects/enumerations/constructors) and
enumeratedpais.xml (in the project qlgensrc/enumerations).

Cheers,

Andrew

-----Original Message-----
From: Yomi [mailto:[hidden email]]
Sent: Saturday, 26 September 2009 12:21 AM
To: [hidden email]
Subject: [Quantlib-users] QuantLibAddin and Monte Carlo simulation

Hi all,

I am trying to expose MCBarrierEngine into quantLibXL but I am really
struggling to pass the PseudoRandomNumber generator...

Let appart the relevant includes, in my view you would need to add the
following code in pricingengine.cpp

template<typename T>
class MCBarrierEngine : public PricingEngine {
public:
MCBarrierEngine(
const boost::shared_ptr<ObjectHandler::ValueObject>&

properties ,
const
boost::shared_ptr<QuantLib::GeneralizedBlackScholesProcess>&
process ,
const QuantLib::Size
maxTimeStepsPerYear ,
bool
brownianBridge ,
bool
antitheticVariate ,
bool
controlVariate ,
const QuantLib::Size
requiredSamples ,
const QuantLib::Real
requiredTolerance ,
const QuantLib::Size
maxSamples ,
bool
isBiased ,
const T&
RSQ ,
const QuantLib::BigNatural
seed ,
bool
permanent );

And this in pricingengine.cpp

template<typename T>
MCBarrierEngine<T>::MCBarrierEngine(
const boost::shared_ptr<ObjectHandler::ValueObject>&

properties ,
const
boost::shared_ptr<QuantLib::GeneralizedBlackScholesProcess>&
process ,
const QuantLib::Size
maxTimeStepsPerYear ,
const bool
brownianBridge ,
const bool
antitheticVariate ,
const bool
controlVariate ,
const QuantLib::Size
requiredSamples ,
const QuantLib::Real
requiredTolerance ,
const QuantLib::Size
maxSamples ,
const bool
isBiased ,
const T&
RSQ ,
const QuantLib::BigNatural
seed ,
bool
permanent )
: PricingEngine(properties, permanent)
{
libraryObject_ = boost::shared_ptr<QuantLib::PricingEngine>(new
QuantLib::MCBarrierEngine<RSQ>(process,
maxTimeStepsPerYear,
brownianBridge,
antitheticVariate,
controlVariate,
requiredSamples,
requiredTolerance,
maxSamples, isBiased, seed))

;
}

To finish, you need to add the following constructor in
pricingengine.xml

<Constructor name='qlMCBarrierEnginer'>
<libraryFunction>MCBarrierEngine</libraryFunction>
<SupportedPlatforms>
<SupportedPlatform name='Excel'/>
</SupportedPlatforms>
<ParameterList>
<Parameters>
<Parameter name='ProcessID' >

<type>QuantLib::GeneralizedBlackScholesProcess</type>

<superType>libraryClass</superType>

<tensorRank>scalar</tensorRank>

<description>GeneralizedBlackScholesProcess object ID.</description>
</Parameter>
<Parameter name='TimeStepPerYear'>
<type>long</type>

<tensorRank>scalar</tensorRank>
<description>Time Steps Per
Year</description>
</Parameter>
<Parameter name='BrownianBridge'
default='FALSE'>
<type>bool</type>

<tensorRank>scalar</tensorRank>
<description>Using a Brownian
Bridge?</description>
</Parameter>
<Parameter name='AntitheticVariate'
default='FALSE'>
<type>bool</type>

<tensorRank>scalar</tensorRank>
<description>Using a
Antithetic Variate?</description>
</Parameter>
<Parameter name='ControlVariate'
default='FALSE'>
<type>bool</type>

<tensorRank>scalar</tensorRank>
<description>Using a Control
Variate?</description>
</Parameter>
<Parameter name='RequiredSamples'>
<type>long</type>

<tensorRank>scalar</tensorRank>
<description>Minimum number of
pathes</description>
</Parameter>
<Parameter name='RequiredTolerance'>
<type>long</type>

<tensorRank>scalar</tensorRank>
<description>Tolerance on the
error</description>
</Parameter>
<Parameter name='MaxSamples'>
<type>long</type>

<tensorRank>scalar</tensorRank>
<description>Maximum number of
pathes</description>
</Parameter>
<Parameter
name='RandomNumberGenerator'>

<type>QuantLib::PseudoRandomSequenceGenerator</type>

<superType>underlyingClass</superType>

<tensorRank>scalar</tensorRank>
<description>Random Number
Generator</description>
</Parameter>
<Parameter name='Seed' default='123'>
<type>long</type>

<tensorRank>scalar</tensorRank>
<description>Seed used for the
random numbers</description>
</Parameter>
</Parameters>
</ParameterList>
</Constructor>

I get an error C3203: 'PseudoRandomSequenceGenerator' : unspecialized
class template can't be used as a template argument for template
parameter 'T', expected a real type ... since I use a non unspecialized
template class for the generator.

Is there a way to do 1 XL function for all the Pseudo Random Generators?

Cheers

--
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