/* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */

/*
 Copyright (C) 2003, 2004 Neil Firth
 Copyright (C) 2003, 2004 Ferdinando Ametrano
 Copyright (C) 2003, 2004, 2005 StatPro Italia srl

 This file is part of QuantLib, a free-software/open-source library
 for financial quantitative analysts and developers - http://quantlib.org/

 QuantLib is free software: you can redistribute it and/or modify it
 under the terms of the QuantLib license.  You should have received a
 copy of the license along with this program; if not, please email
 <quantlib-dev@lists.sf.net>. The license is also available online at
 <http://quantlib.org/license.shtml>.

 This program is distributed in the hope that it will be useful, but WITHOUT
 ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
 FOR A PARTICULAR PURPOSE.  See the license for more details.
*/

/*! \file mcbarrierengine.hpp
    \brief Monte Carlo barrier option engines
*/

#ifndef quantlib_mc_barrier_engines_hpp
#define quantlib_mc_barrier_engines_hpp

#include <ql/instruments/barrieroption.hpp>
#include <ql/pricingengines/mcsimulation.hpp>
#include <ql/processes/blackscholesprocess.hpp>

namespace QuantLib {

    //! Pricing engine for barrier options using Monte Carlo simulation
    /*! Uses the Brownian-bridge correction for the barrier found in
        <i>
        Going to Extremes: Correcting Simulation Bias in Exotic
        Option Valuation - D.R. Beaglehole, P.H. Dybvig and G. Zhou
        Financial Analysts Journal; Jan/Feb 1997; 53, 1. pg. 62-68
        </i>
        and
        <i>
        Simulating path-dependent options: A new approach -
        M. El Babsiri and G. Noel
        Journal of Derivatives; Winter 1998; 6, 2; pg. 65-83
        </i>

        \ingroup barrierengines

        \test the correctness of the returned value is tested by
              reproducing results available in literature.
    */
    template <class RNG = PseudoRandom, class S = Statistics>
    class MCBarrierEngine : public BarrierOption::engine,
                            public McSimulation<SingleVariate,RNG,S> {
      public:
        typedef
        typename McSimulation<SingleVariate,RNG,S>::path_generator_type
            path_generator_type;
        typedef typename McSimulation<SingleVariate,RNG,S>::path_pricer_type
            path_pricer_type;
        typedef typename McSimulation<SingleVariate,RNG,S>::stats_type
            stats_type;
        // constructor
        MCBarrierEngine(Size maxTimeStepsPerYear,
                        bool brownianBridge,
                        bool antitheticVariate,
                        bool controlVariate,
                        Size requiredSamples,
                        Real requiredTolerance,
                        Size maxSamples,
                        bool isBiased,
                        BigNatural seed);
        void calculate() const {
            McSimulation<SingleVariate,RNG,S>::calculate(requiredTolerance_,
                                                         requiredSamples_,
                                                         maxSamples_);
            results_.value = this->mcModel_->sampleAccumulator().mean();
            if (RNG::allowsErrorEstimate)
            results_.errorEstimate =
                this->mcModel_->sampleAccumulator().errorEstimate();
        }
      protected:
        // McSimulation implementation
        TimeGrid timeGrid() const;
        boost::shared_ptr<path_generator_type> pathGenerator() const {
            boost::shared_ptr<GeneralizedBlackScholesProcess> process =
                boost::dynamic_pointer_cast<GeneralizedBlackScholesProcess>(
                                                arguments_.stochasticProcess);
            QL_REQUIRE(process, "Black-Scholes process required");
            TimeGrid grid = timeGrid();
            typename RNG::rsg_type gen =
                RNG::make_sequence_generator(grid.size()-1,seed_);
            return boost::shared_ptr<path_generator_type>(
                         new path_generator_type(process,
                                                 grid, gen, brownianBridge_));
        }
        boost::shared_ptr<path_pricer_type> pathPricer() const;
        // Real controlVariateValue() const;
        // data members
        Size maxTimeStepsPerYear_;
        Size requiredSamples_, maxSamples_;
        Real requiredTolerance_;
        bool isBiased_;
        bool brownianBridge_;
        BigNatural seed_;
    };


    class BarrierPathPricer : public PathPricer<Path> {
      public:
        BarrierPathPricer(
                    Barrier::Type barrierType,
                    Real barrier,
                    Real rebate,
                    Option::Type type,
                    Real strike,
                    const std::vector<DiscountFactor>& discounts,
                    const boost::shared_ptr<StochasticProcess1D>& diffProcess,
                    const PseudoRandom::ursg_type& sequenceGen);
        Real operator()(const Path& path) const;
      private:
        Barrier::Type barrierType_;
        Real barrier_;
        Real rebate_;
        boost::shared_ptr<StochasticProcess1D> diffProcess_;
        PseudoRandom::ursg_type sequenceGen_;
        PlainVanillaPayoff payoff_;
        std::vector<DiscountFactor> discounts_;
    };


    class BiasedBarrierPathPricer : public PathPricer<Path> {
      public:
        BiasedBarrierPathPricer(Barrier::Type barrierType,
                                Real barrier,
                                Real rebate,
                                Option::Type type,
                                Real strike,
                                const std::vector<DiscountFactor>& discounts);
        Real operator()(const Path& path) const;
      private:
        Barrier::Type barrierType_;
        Real barrier_;
        Real rebate_;
        PlainVanillaPayoff payoff_;
        std::vector<DiscountFactor> discounts_;
    };


    // template definitions

    template <class RNG, class S>
    inline MCBarrierEngine<RNG,S>::MCBarrierEngine(Size maxTimeStepsPerYear,
                                                   bool brownianBridge,
                                                   bool antitheticVariate,
                                                   bool controlVariate,
                                                   Size requiredSamples,
                                                   Real requiredTolerance,
                                                   Size maxSamples,
                                                   bool isBiased,
                                                   BigNatural seed)
    : McSimulation<SingleVariate,RNG,S>(antitheticVariate, controlVariate),
      maxTimeStepsPerYear_(maxTimeStepsPerYear),
      requiredSamples_(requiredSamples), maxSamples_(maxSamples),
      requiredTolerance_(requiredTolerance),
      isBiased_(isBiased),
      brownianBridge_(brownianBridge), seed_(seed) {}

    template <class RNG, class S>
    inline TimeGrid MCBarrierEngine<RNG,S>::timeGrid() const {

        Time residualTime = arguments_.stochasticProcess->time(
                                             arguments_.exercise->lastDate());
        return TimeGrid(residualTime,
                        Size(std::max<Real>(residualTime*maxTimeStepsPerYear_,
                                            1.0)));
    }


    template <class RNG, class S>
    inline
    boost::shared_ptr<QL_TYPENAME MCBarrierEngine<RNG,S>::path_pricer_type>
    MCBarrierEngine<RNG,S>::pathPricer() const {
        boost::shared_ptr<PlainVanillaPayoff> payoff =
            boost::dynamic_pointer_cast<PlainVanillaPayoff>(arguments_.payoff);
        QL_REQUIRE(payoff, "non-plain payoff given");

        boost::shared_ptr<GeneralizedBlackScholesProcess> process =
            boost::dynamic_pointer_cast<GeneralizedBlackScholesProcess>(
                                                arguments_.stochasticProcess);
        QL_REQUIRE(process, "Black-Scholes process required");

        TimeGrid grid = timeGrid();
        std::vector<DiscountFactor> discounts(grid.size());
        for (Size i=0; i<grid.size(); i++)
            discounts[i] = process->riskFreeRate()->discount(grid[i]);

        // do this with template parameters?
        if (isBiased_) {
            return boost::shared_ptr<
                        QL_TYPENAME MCBarrierEngine<RNG,S>::path_pricer_type>(
                new BiasedBarrierPathPricer(
                       arguments_.barrierType,
                       arguments_.barrier,
                       arguments_.rebate,
                       payoff->optionType(),
                       payoff->strike(),
                       discounts));
        } else {
            PseudoRandom::ursg_type sequenceGen(grid.size()-1,
                                                PseudoRandom::urng_type(5));
            return boost::shared_ptr<
                        QL_TYPENAME MCBarrierEngine<RNG,S>::path_pricer_type>(
                new BarrierPathPricer(
                    arguments_.barrierType,
                    arguments_.barrier,
                    arguments_.rebate,
                    payoff->optionType(),
                    payoff->strike(),
                    discounts,
                    process,
                    sequenceGen));
        }
    }

    /*
    template<class RNG, class S>
    inline
    Real MCBarrierEngine<RNG,S>::controlVariateValue() const {

        boost::shared_ptr<PricingEngine> controlPE =
                this->controlPricingEngine();
            QL_REQUIRE(controlPE,
                       "engine does not provide "
                       "control-variation pricing engine");

            BarrierOption::arguments* controlArguments =
                dynamic_cast<BarrierOption::arguments*>(
                    controlPE->arguments());
            *controlArguments = arguments_;
            controlPE->calculate();

            const BarrierOption::results* controlResults =
                dynamic_cast<const BarrierOption::results*>(
                    controlPE->results());

            return controlResults->value;
    }
    */

}


#endif