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

/*

 Copyright (C) 2004, 2005, 2008 Klaus Spanderen

 Copyright (C) 2007 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

 <https://www.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 hestonmodel.hpp

  \brief analytic pricing engine for a heston option

  based on fourier transformation

*/

#include <ql/instruments/payoffs.hpp>

#include <ql/math/integrals/discreteintegrals.hpp>

#include <ql/math/integrals/exponentialintegrals.hpp>

#include <ql/math/integrals/gausslobattointegral.hpp>

#include <ql/math/integrals/kronrodintegral.hpp>

#include <ql/math/integrals/simpsonintegral.hpp>

#include <ql/math/integrals/trapezoidintegral.hpp>

#include <ql/math/integrals/expsinhintegral.hpp>

#include <ql/math/solvers1d/brent.hpp>

#include <ql/math/expm1.hpp>

#include <ql/math/functional.hpp>

#include <ql/pricingengines/blackcalculator.hpp>

#include <ql/pricingengines/vanilla/analytichestonengine.hpp>

#include <boost/math/tools/minima.hpp>

#include <boost/math/special_functions/sign.hpp>

#include <functional>

#include <cmath>

#include <limits>

#include <utility>

#if defined(QL_PATCH_MSVC)

#pragma warning(disable: 4180)

#endif

namespace QuantLib {

    namespace {

        class integrand1 {

          private:

            const Real c_inf_;

            const std::function<Real(Real)> f_;

          public:

            integrand1(Real c_inf, std::function<Real(Real)> f) : c_inf_(c_inf), f_(std::move(f)) {}

            Real operator()(Real x) const {

                if ((1.0-x)*c_inf_ > QL_EPSILON)

                    return f_(-std::log(0.5-0.5*x)/c_inf_)/((1.0-x)*c_inf_);

                else

                    return 0.0;

            }

        };

        class integrand2 {

          private:

            const Real c_inf_;

            const std::function<Real(Real)> f_;

          public:

            integrand2(Real c_inf, std::function<Real(Real)> f) : c_inf_(c_inf), f_(std::move(f)) {}

            Real operator()(Real x) const {

                if (x*c_inf_ > QL_EPSILON) {

                    return f_(-std::log(x)/c_inf_)/(x*c_inf_);

                } else {

                    return 0.0;

                }

            }

        };

        class integrand3 {

          private:

            const integrand2 int_;

          public:

            integrand3(Real c_inf, const std::function<Real(Real)>& f)

            : int_(c_inf, f) {}

            Real operator()(Real x) const { return int_(1.0-x); }

        };

        class u_Max {

          public:

            u_Max(Real c_inf, Real epsilon) : c_inf_(c_inf), logEpsilon_(std::log(epsilon)) {}

            Real operator()(Real u) const {

                ++evaluations_;

                return c_inf_*u + std::log(u) + logEpsilon_;

            }

            Size evaluations() const { return evaluations_; }

          private:

            const Real c_inf_, logEpsilon_;

            mutable Size evaluations_ = 0;

        };

        class uHat_Max {

          public:

            uHat_Max(Real v0T2, Real epsilon) : v0T2_(v0T2), logEpsilon_(std::log(epsilon)) {}

            Real operator()(Real u) const {

                ++evaluations_;

                return v0T2_*u*u + std::log(u) + logEpsilon_;

            }

            Size evaluations() const { return evaluations_; }

          private:

            const Real v0T2_, logEpsilon_;

            mutable Size evaluations_ = 0;

        };

    }

    // helper class for integration

    class AnalyticHestonEngine::Fj_Helper {

    public:

      Fj_Helper(Real kappa, Real theta, Real sigma, Real v0,

                Real s0, Real rho,

                const AnalyticHestonEngine* engine,

                ComplexLogFormula cpxLog,

                Time term, Real strike, Real ratio, Size j);

      Real operator()(Real phi) const;

    private:

        const Size j_;

        //     const VanillaOption::arguments& arg_;

        const Real kappa_, theta_, sigma_, v0_;

        const ComplexLogFormula cpxLog_;

        // helper variables

        const Time term_;

        const Real x_, sx_, dd_;

        const Real sigma2_, rsigma_;

        const Real t0_;

        // log branch counter

        mutable int  b_ = 0;     // log branch counter

        mutable Real g_km1_ = 0; // imag part of last log value

        const AnalyticHestonEngine* const engine_;

    };

    AnalyticHestonEngine::Fj_Helper::Fj_Helper(Real kappa, Real theta,

        Real sigma, Real v0, Real s0, Real rho,

        const AnalyticHestonEngine* const engine,

        ComplexLogFormula cpxLog,

        Time term, Real strike, Real ratio, Size j)

        :

        j_(j),

        kappa_(kappa),

        theta_(theta),

        sigma_(sigma),

        v0_(v0),

        cpxLog_(cpxLog),

        term_(term),

        x_(std::log(s0)),

        sx_(std::log(strike)),

        dd_(x_-std::log(ratio)),

        sigma2_(sigma_*sigma_),

        rsigma_(rho*sigma_),

        t0_(kappa - ((j== 1)? rho*sigma : Real(0))),

        engine_(engine)

    {

    }

    Real AnalyticHestonEngine::Fj_Helper::operator()(Real phi) const

    {

        const Real rpsig(rsigma_*phi);

        const std::complex<Real> t1 = t0_+std::complex<Real>(0, -rpsig);

        const std::complex<Real> d =

            std::sqrt(t1*t1 - sigma2_*phi

                      *std::complex<Real>(-phi, (j_== 1)? 1 : -1));

        const std::complex<Real> ex = std::exp(-d*term_);

        const std::complex<Real> addOnTerm =

            engine_ != nullptr ? engine_->addOnTerm(phi, term_, j_) : Real(0.0);

        if (cpxLog_ == Gatheral) {

            if (phi != 0.0) {

                if (sigma_ > 1e-5) {

                    const std::complex<Real> p = (t1-d)/(t1+d);

                    const std::complex<Real> g

                                            = std::log((1.0 - p*ex)/(1.0 - p));

                    return

                        std::exp(v0_*(t1-d)*(1.0-ex)/(sigma2_*(1.0-ex*p))

                                 + (kappa_*theta_)/sigma2_*((t1-d)*term_-2.0*g)

                                 + std::complex<Real>(0.0, phi*(dd_-sx_))

                                 + addOnTerm

                                 ).imag()/phi;

                }

                else {

                    const std::complex<Real> td = phi/(2.0*t1)

                                   *std::complex<Real>(-phi, (j_== 1)? 1 : -1);

                    const std::complex<Real> p = td*sigma2_/(t1+d);

                    const std::complex<Real> g = p*(1.0-ex);

                    return

                        std::exp(v0_*td*(1.0-ex)/(1.0-p*ex)

                                 + (kappa_*theta_)*(td*term_-2.0*g/sigma2_)

                                 + std::complex<Real>(0.0, phi*(dd_-sx_))

                                 + addOnTerm

                                 ).imag()/phi;

                }

            }

            else {

                // use l'Hospital's rule to get lim_{phi->0}

                if (j_ == 1) {

                    const Real kmr = rsigma_-kappa_;

                    if (std::fabs(kmr) > 1e-7) {

                        return dd_-sx_

                            + (std::exp(kmr*term_)*kappa_*theta_

                               -kappa_*theta_*(kmr*term_+1.0) ) / (2*kmr*kmr)

                            - v0_*(1.0-std::exp(kmr*term_)) / (2.0*kmr);

                    }

                    else

                        // \kappa = \rho * \sigma

                        return dd_-sx_ + 0.25*kappa_*theta_*term_*term_

                                       + 0.5*v0_*term_;

                }

                else {

                    return dd_-sx_

                        - (std::exp(-kappa_*term_)*kappa_*theta_

                           +kappa_*theta_*(kappa_*term_-1.0))/(2*kappa_*kappa_)

                        - v0_*(1.0-std::exp(-kappa_*term_))/(2*kappa_);

                }

            }

        }

        else if (cpxLog_ == BranchCorrection) {

            const std::complex<Real> p = (t1+d)/(t1-d);

            // next term: g = std::log((1.0 - p*std::exp(d*term_))/(1.0 - p))

            std::complex<Real> g;

            // the exp of the following expression is needed.

            const std::complex<Real> e = std::log(p)+d*term_;

            // does it fit to the machine precision?

            if (std::exp(-e.real()) > QL_EPSILON) {

                g = std::log((1.0 - p/ex)/(1.0 - p));

            } else {

                // use a "big phi" approximation

                g = d*term_ + std::log(p/(p - 1.0));

                if (g.imag() > M_PI || g.imag() <= -M_PI) {

                    // get back to principal branch of the complex logarithm

                    Real im = std::fmod(g.imag(), 2*M_PI);

                    if (im > M_PI)

                        im -= 2*M_PI;

                    else if (im <= -M_PI)

                        im += 2*M_PI;

                    g = std::complex<Real>(g.real(), im);

                }

            }

            // be careful here as we have to use a log branch correction

            // to deal with the discontinuities of the complex logarithm.

            // the principal branch is not always the correct one.

            // (s. A. Sepp, chapter 4)

            // remark: there is still the change that we miss a branch

            // if the order of the integration is not high enough.

            const Real tmp = g.imag() - g_km1_;

            if (tmp <= -M_PI)

                ++b_;

            else if (tmp > M_PI)

                --b_;

            g_km1_ = g.imag();

            g += std::complex<Real>(0, 2*b_*M_PI);

            return std::exp(v0_*(t1+d)*(ex-1.0)/(sigma2_*(ex-p))

                            + (kappa_*theta_)/sigma2_*((t1+d)*term_-2.0*g)

                            + std::complex<Real>(0,phi*(dd_-sx_))

                            + addOnTerm

                            ).imag()/phi;

        }

        else {

            QL_FAIL("unknown complex logarithm formula");

        }

    }

    AnalyticHestonEngine::OptimalAlpha::OptimalAlpha(

        const Time t,

        const AnalyticHestonEngine* const enginePtr)

    : t_(t),

      fwd_(enginePtr->model_->process()->s0()->value()

              * enginePtr->model_->process()->dividendYield()->discount(t)

              / enginePtr->model_->process()->riskFreeRate()->discount(t)),

      kappa_(enginePtr->model_->kappa()),

      theta_(enginePtr->model_->theta()),

      sigma_(enginePtr->model_->sigma()),

      rho_(enginePtr->model_->rho()),

      eps_(std::pow(2, -int(0.5*std::numeric_limits<Real>::digits))),

      enginePtr_(enginePtr)

      {

        km_ = k(0.0, -1);

        kp_ = k(0.0,  1);

    }

    Real AnalyticHestonEngine::OptimalAlpha::alphaMax(Real strike) const {

        const Real eps = 1e-8;

        const auto cm = [this](Real k) -> Real { return M(k) - t_; };

        Real alpha_max;

        const Real adx = kappa_ - sigma_*rho_;

        if (adx > 0.0) {

            const Real kp_2pi = k(2*M_PI, 1);

            alpha_max = Brent().solve(

                cm, eps_, 0.5*(kp_ + kp_2pi), (1+eps)*kp_, (1-eps)*kp_2pi

            ) - 1.0;

        }

        else if (adx < 0.0) {

            const Time tCut = -2/(kappa_ - sigma_*rho_*kp_);

            if (t_ < tCut) {

                const Real kp_pi = k(M_PI, 1);

                alpha_max = Brent().solve(

                    cm, eps_, 0.5*(kp_ + kp_pi), (1+eps)*kp_, (1-eps)*kp_pi

                ) - 1.0;

            }

            else {

                alpha_max = Brent().solve(

                    cm, eps_, 0.5*(1.0 + kp_),

                    1 + eps, (1-eps)*kp_

                ) - 1.0;

            }

        }

        else { // adx == 0.0

            const Real kp_pi = k(M_PI, 1);

            alpha_max = Brent().solve(

                cm, eps_, 0.5*(kp_ + kp_pi), (1+eps)*kp_, (1-eps)*kp_pi

            ) - 1.0;

        }

        QL_REQUIRE(alpha_max >= 0.0, "alpha max must be larger than zero");

        return alpha_max;

    }

    std::pair<Real, Real>

    AnalyticHestonEngine::OptimalAlpha::alphaGreaterZero(Real strike) const {

        const Real alpha_max = alphaMax(strike);

        return findMinima(eps_, std::max(2*eps_, (1.0-1e-6)*alpha_max), strike);

    }

    Real AnalyticHestonEngine::OptimalAlpha::alphaMin(Real strike) const {

        const auto cm = [this](Real k) -> Real { return M(k) - t_; };

        const Real km_2pi = k(2*M_PI, -1);

        const Real alpha_min = Brent().solve(

            cm, eps_, 0.5*(km_2pi + km_), (1-1e-8)*km_2pi, (1+1e-8)*km_

        ) - 1.0;

        QL_REQUIRE(alpha_min <= -1.0, "alpha min must be smaller than minus one");

        return alpha_min;

    }

    std::pair<Real, Real>

    AnalyticHestonEngine::OptimalAlpha::alphaSmallerMinusOne(Real strike) const {

        const Real alpha_min = alphaMin(strike);

        return findMinima(

            std::min(-1.0-1e-6, -1.0 + (1.0-1e-6)*(alpha_min + 1.0)),

            -1.0 - eps_, strike

        );

    }

    Real AnalyticHestonEngine::OptimalAlpha::operator()(Real strike) const {

        try {

            const std::pair<Real, Real> minusOne = alphaSmallerMinusOne(strike);

            const std::pair<Real, Real> greaterZero = alphaGreaterZero(strike);

            if (minusOne.second < greaterZero.second) {

                return minusOne.first;

            }

            else {

                return greaterZero.first;

            }

        }

        catch (const Error&) {

            return -0.5;

        }

    }

    Size AnalyticHestonEngine::OptimalAlpha::numberOfEvaluations() const {

        return evaluations_;

    }

    std::pair<Real, Real> AnalyticHestonEngine::OptimalAlpha::findMinima(

        Real lower, Real upper, Real strike) const {

        const Real freq = std::log(fwd_/strike);

        return boost::math::tools::brent_find_minima(

            [freq, this](Real alpha) -> Real {

                ++evaluations_;

                const std::complex<Real> z(0, -(alpha+1));

                return enginePtr_->lnChF(z, t_).real()

                    - std::log(alpha*(alpha+1)) + alpha*freq;

            },

            lower, upper, int(0.5*std::numeric_limits<Real>::digits)

        );

    }

    Real AnalyticHestonEngine::OptimalAlpha::M(Real k) const {

        const Real beta = kappa_ - sigma_*rho_*k;

        if (k >= km_ && k <= kp_) {

            const Real D = std::sqrt(beta*beta - sigma_*sigma_*k*(k-1));

            return std::log((beta-D)/(beta+D)) / D;

        }

        else {

            const Real D_imag =

                std::sqrt(-(beta*beta - sigma_*sigma_*k*(k-1)));

            return 2/D_imag

                * ( ((beta>0.0)? M_PI : 0.0) - std::atan(D_imag/beta) );

        }

    }

    Real AnalyticHestonEngine::OptimalAlpha::k(Real x, Integer sgn) const {

        return ( (sigma_ - 2*rho_*kappa_)

                + sgn*std::sqrt(

                      squared(sigma_-2*rho_*kappa_)

                    + 4*(kappa_*kappa_ + x*x/(t_*t_))*(1-rho_*rho_)))

               /(2*sigma_*(1-rho_*rho_));

    }

    AnalyticHestonEngine::AP_Helper::AP_Helper(

        Time term, Real fwd, Real strike, ComplexLogFormula cpxLog,

        const AnalyticHestonEngine* const enginePtr,

        const Real alpha)

    : term_(term),

      fwd_(fwd),

      strike_(strike),

      freq_(std::log(fwd/strike)),

      cpxLog_(cpxLog),

      enginePtr_(enginePtr),

      alpha_(alpha),

      s_alpha_(std::exp(alpha*freq_))

      {

        QL_REQUIRE(enginePtr != nullptr, "pricing engine required");

        const Real v0    = enginePtr->model_->v0();

        const Real kappa = enginePtr->model_->kappa();

        const Real theta = enginePtr->model_->theta();

        const Real sigma = enginePtr->model_->sigma();

        const Real rho   = enginePtr->model_->rho();

        switch(cpxLog_) {

          case AndersenPiterbarg:

              vAvg_ = (1-std::exp(-kappa*term))*(v0 - theta)

                        /(kappa*term) + theta;

            break;

          case AndersenPiterbargOptCV:

              vAvg_ = -8.0*std::log(enginePtr->chF(

                         std::complex<Real>(0, alpha_), term).real())/term;

            break;

          case AsymptoticChF:

            phi_ = -(v0+term*kappa*theta)/sigma

                * std::complex<Real>(std::sqrt(1-rho*rho), rho);

            psi_ = std::complex<Real>(

                (kappa- 0.5*rho*sigma)*(v0 + term*kappa*theta)

                + kappa*theta*std::log(4*(1-rho*rho)),

                - ((0.5*rho*rho*sigma - kappa*rho)/std::sqrt(1-rho*rho)

                        *(v0 + kappa*theta*term)

                  - 2*kappa*theta*std::atan(rho/std::sqrt(1-rho*rho))))

                          /(sigma*sigma);

            [[fallthrough]];

          case AngledContour:

            vAvg_ = (1-std::exp(-kappa*term))*(v0 - theta)

                      /(kappa*term) + theta;

            [[fallthrough]];

          case AngledContourNoCV:

            {

              const Real r = rho - sigma*freq_ / (v0 + kappa*theta*term);

              tanPhi_ = std::tan(

                     (r*freq_ < 0.0)? M_PI/12*boost::math::sign(freq_) : 0.0

                );

            }

            break;

          default:

            QL_FAIL("unknown control variate");

        }

    }

    Real AnalyticHestonEngine::AP_Helper::operator()(Real u) const {

        QL_REQUIRE(   enginePtr_->addOnTerm(u, term_, 1)

                        == std::complex<Real>(0.0)

                   && enginePtr_->addOnTerm(u, term_, 2)

                        == std::complex<Real>(0.0),

                   "only Heston model is supported");

        constexpr std::complex<double> i(0, 1);

        if (cpxLog_ == AngledContour || cpxLog_ == AngledContourNoCV || cpxLog_ == AsymptoticChF) {

            const std::complex<Real> h_u(u, u*tanPhi_ - alpha_);

            const std::complex<Real> hPrime(h_u-i);

            std::complex<Real> phiBS(0.0);

            if (cpxLog_ == AngledContour)

                phiBS = std::exp(

                    -0.5*vAvg_*term_*(hPrime*hPrime +

                            std::complex<Real>(-hPrime.imag(), hPrime.real())));

            else if (cpxLog_ == AsymptoticChF)

                phiBS = std::exp(u*std::complex<Real>(1, tanPhi_)*phi_ + psi_);

            return std::exp(-u*tanPhi_*freq_)

                    *(std::exp(std::complex<Real>(0.0, u*freq_))

                      *std::complex<Real>(1, tanPhi_)

                      *(phiBS - enginePtr_->chF(hPrime, term_))/(h_u*hPrime)

                      ).real()*s_alpha_;

        }

        else if (cpxLog_ == AndersenPiterbarg || cpxLog_ == AndersenPiterbargOptCV) {

            const std::complex<Real> z(u, -alpha_);

            const std::complex<Real> zPrime(u, -alpha_-1);

            const std::complex<Real> phiBS = std::exp(

                -0.5*vAvg_*term_*(zPrime*zPrime +

                        std::complex<Real>(-zPrime.imag(), zPrime.real()))

            );

            return (std::exp(std::complex<Real> (0.0, u*freq_))

                * (phiBS - enginePtr_->chF(zPrime, term_)) / (z*zPrime)

                ).real()*s_alpha_;

        }

        else

            QL_FAIL("unknown control variate");

    }

    Real AnalyticHestonEngine::AP_Helper::controlVariateValue() const {

        if (   cpxLog_ == AngledContour

            || cpxLog_ == AndersenPiterbarg || cpxLog_ == AndersenPiterbargOptCV) {

              return BlackCalculator(

                  Option::Call, strike_, fwd_, std::sqrt(vAvg_*term_))

                      .value();

        }

        else if (cpxLog_ == AsymptoticChF) {

            QL_REQUIRE(alpha_ == -0.5, "alpha must be equal to -0.5");

            const std::complex<Real> phiFreq(phi_.real(), phi_.imag() + freq_);

            using namespace ExponentialIntegral;

            return fwd_ - std::sqrt(strike_*fwd_)/M_PI*

                (std::exp(psi_)*(

                      -2.0*Ci(-0.5*phiFreq)*std::sin(0.5*phiFreq)

                       +std::cos(0.5*phiFreq)*(M_PI+2.0*Si(0.5*phiFreq)))).real();

        }

        else if (cpxLog_ == AngledContourNoCV) {

            return     ((alpha_ <=  0.0)? fwd_ : 0.0)

                  -    ((alpha_ <= -1.0)? strike_ : 0.0)

                  -0.5*((alpha_ ==  0.0)? fwd_ :0.0)

                  +0.5*((alpha_ == -1.0)? strike_: 0.0);

        }

        else

            QL_FAIL("unknown control variate");

    }

    std::complex<Real> AnalyticHestonEngine::chF(

        const std::complex<Real>& z, Time t) const {

        if (model_->sigma() > 1e-6 || model_->kappa() < 1e-8) {

            return std::exp(lnChF(z, t));

        }

        else {

            const Real kappa = model_->kappa();

            const Real sigma = model_->sigma();

            const Real theta = model_->theta();

            const Real rho   = model_->rho();

            const Real v0    = model_->v0();

            const Real sigma2 = sigma*sigma;

            const Real kt = kappa*t;

            const Real ekt = std::exp(kt);

            const Real e2kt = std::exp(2*kt);

            const Real rho2 = rho*rho;

            const std::complex<Real> zpi = z + std::complex<Real>(0.0, 1.0);

            return std::exp(-(((theta - v0 + ekt*((-1 + kt)*theta + v0))

                    *z*zpi)/ekt)/(2.*kappa))

                + (std::exp(-(kt) - ((theta - v0 + ekt

                    *((-1 + kt)*theta + v0))*z*zpi)

                /(2.*ekt*kappa))*rho*(2*theta + kt*theta -

                    v0 - kt*v0 + ekt*((-2 + kt)*theta + v0))

                *(1.0 - std::complex<Real>(-z.imag(),z.real()))*z*z)

                    /(2.*kappa*kappa)*sigma

                   + (std::exp(-2*kt - ((theta - v0 + ekt

                *((-1 + kt)*theta + v0))*z*zpi)/(2.*ekt*kappa))*z*z*zpi

                *(-2*rho2*squared(2*theta + kt*theta - v0 -

                    kt*v0 + ekt*((-2 + kt)*theta + v0))

                  *z*z*zpi + 2*kappa*v0*(-zpi

                    + e2kt*(zpi + 4*rho2*z) - 2*ekt*(2*rho2*z

                    + kt*(zpi + rho2*(2 + kt)*z))) + kappa*theta*(zpi + e2kt

                *(-5.0*zpi - 24*rho2*z+ 2*kt*(zpi + 4*rho2*z)) +

                4*ekt*(zpi + 6*rho2*z + kt*(zpi + rho2*(4 + kt)*z)))))

                /(16.*squared(squared(kappa)))*sigma2;

        }

    }

    std::complex<Real> AnalyticHestonEngine::lnChF(

        const std::complex<Real>& z, Time t) const {

        const Real kappa = model_->kappa();

        const Real sigma = model_->sigma();

        const Real theta = model_->theta();

        const Real rho   = model_->rho();

        const Real v0    = model_->v0();

        const Real sigma2 = sigma*sigma;

        const std::complex<Real> g

            = kappa + rho*sigma*std::complex<Real>(z.imag(), -z.real());

        const std::complex<Real> D = std::sqrt(

            g*g + (z*z + std::complex<Real>(-z.imag(), z.real()))*sigma2);

        // reduce cancelation errors, see. L. Andersen and M. Lake

        std::complex<Real> r(g-D);

        if (g.real()*D.real() + g.imag()*D.imag() > 0.0) {

            r = -sigma2*z*std::complex<Real>(z.real(), z.imag()+1)/(g+D);

        }

        std::complex<Real> y;

        if (D.real() != 0.0 || D.imag() != 0.0) {

            y = expm1(-D*t)/(2.0*D);

        }

        else

            y = -0.5*t;

        const std::complex<Real> A

            = kappa*theta/sigma2*(r*t - 2.0*log1p(-r*y));

        const std::complex<Real> B

            = z*std::complex<Real>(z.real(), z.imag()+1)*y/(1.0-r*y);

        return A+v0*B;

    }

    AnalyticHestonEngine::AnalyticHestonEngine(

                              const ext::shared_ptr<HestonModel>& model,

                              Size integrationOrder)

    : GenericModelEngine<HestonModel,

                         VanillaOption::arguments,

                         VanillaOption::results>(model),

      evaluations_(0),

      cpxLog_     (OptimalCV),

      integration_(new Integration(

                          Integration::gaussLaguerre(integrationOrder))),

      andersenPiterbargEpsilon_(Null<Real>()),

      alpha_(-0.5) {

    }

    AnalyticHestonEngine::AnalyticHestonEngine(

                              const ext::shared_ptr<HestonModel>& model,

                              Real relTolerance, Size maxEvaluations)

    : GenericModelEngine<HestonModel,

                         VanillaOption::arguments,

                         VanillaOption::results>(model),

      evaluations_(0),

      cpxLog_(OptimalCV),

      integration_(new Integration(Integration::gaussLobatto(

                              relTolerance, Null<Real>(), maxEvaluations))),

      andersenPiterbargEpsilon_(1e-40),

      alpha_(-0.5) {

    }

    AnalyticHestonEngine::AnalyticHestonEngine(

                              const ext::shared_ptr<HestonModel>& model,

                              ComplexLogFormula cpxLog,

                              const Integration& integration,

                              Real andersenPiterbargEpsilon,

                              Real alpha)

    : GenericModelEngine<HestonModel,

                         VanillaOption::arguments,

                         VanillaOption::results>(model),

      evaluations_(0),

      cpxLog_(cpxLog),

      integration_(new Integration(integration)),

      andersenPiterbargEpsilon_(andersenPiterbargEpsilon),

      alpha_(alpha) {

        QL_REQUIRE(   cpxLog_ != BranchCorrection

                   || !integration.isAdaptiveIntegration(),

                   "Branch correction does not work in conjunction "

                   "with adaptive integration methods");

    }

    AnalyticHestonEngine::ComplexLogFormula

        AnalyticHestonEngine::optimalControlVariate(

        Time t, Real v0, Real kappa, Real theta, Real sigma, Real rho) {

        if (t > 0.15 && (v0+t*kappa*theta)/sigma*std::sqrt(1-rho*rho) < 0.15

                && ((kappa- 0.5*rho*sigma)*(v0 + t*kappa*theta)

                    + kappa*theta*std::log(4*(1-rho*rho)))/(sigma*sigma) < 0.1) {

            return AsymptoticChF;

        }

        else {

            return AngledContour;

        }

    }

    Size AnalyticHestonEngine::numberOfEvaluations() const {

        return evaluations_;

    }

    Real AnalyticHestonEngine::priceVanillaPayoff(

        const ext::shared_ptr<PlainVanillaPayoff>& payoff,

        const Date& maturity) const {

        const ext::shared_ptr<HestonProcess>& process = model_->process();

        const Real fwd = process->s0()->value()

             * process->dividendYield()->discount(maturity)

             / process->riskFreeRate()->discount(maturity);

        return priceVanillaPayoff(payoff, process->time(maturity), fwd);

    }

    Real AnalyticHestonEngine::priceVanillaPayoff(

        const ext::shared_ptr<PlainVanillaPayoff>& payoff, Time maturity) const {

        const ext::shared_ptr<HestonProcess>& process = model_->process();

        const Real fwd = process->s0()->value()

             * process->dividendYield()->discount(maturity)

             / process->riskFreeRate()->discount(maturity);

        return priceVanillaPayoff(payoff, maturity, fwd);

    }

    Real AnalyticHestonEngine::priceVanillaPayoff(

        const ext::shared_ptr<PlainVanillaPayoff>& payoff,

        Time maturity, Real fwd) const {

        Real value;

        const ext::shared_ptr<HestonProcess>& process = model_->process();

        const DiscountFactor dr = process->riskFreeRate()->discount(maturity);

        const Real strike = payoff->strike();

        const Real spot = process->s0()->value();

        QL_REQUIRE(spot > 0.0, "negative or null underlying given");

        const DiscountFactor df = spot/fwd;

        const DiscountFactor dd = dr/df;

        const Real kappa = model_->kappa();

        const Real sigma = model_->sigma();

        const Real theta = model_->theta();

        const Real rho   = model_->rho();

        const Real v0    = model_->v0();

        evaluations_ = 0;

        switch(cpxLog_) {

          case Gatheral:

          case BranchCorrection: {

            const Real c_inf = std::min(0.2, std::max(0.0001,

                std::sqrt(1.0-rho*rho)/sigma))*(v0 + kappa*theta*maturity);

            const Real p1 = integration_->calculate(c_inf,

                Fj_Helper(kappa, theta, sigma, v0, spot, rho, this,

                          cpxLog_, maturity, strike, df, 1))/M_PI;

            evaluations_ += integration_->numberOfEvaluations();

            const Real p2 = integration_->calculate(c_inf,

                Fj_Helper(kappa, theta, sigma, v0, spot, rho, this,

                          cpxLog_, maturity, strike, df, 2))/M_PI;

            evaluations_ += integration_->numberOfEvaluations();

            switch (payoff->optionType())

            {

              case Option::Call:

                value = spot*dd*(p1+0.5)

                               - strike*dr*(p2+0.5);

                break;

              case Option::Put:

                value = spot*dd*(p1-0.5)

                               - strike*dr*(p2-0.5);

                break;

              default:

                QL_FAIL("unknown option type");

            }

          }

          break;

          case AndersenPiterbarg:

          case AndersenPiterbargOptCV:

          case AsymptoticChF:

          case AngledContour:

          case AngledContourNoCV:

          case OptimalCV: {

            const Real c_inf =

                std::sqrt(1.0-rho*rho)*(v0 + kappa*theta*maturity)/sigma;

            const Real epsilon = andersenPiterbargEpsilon_

                *M_PI/(std::sqrt(strike*fwd)*dr);

            const std::function<Real()> uM = [&](){

                return Integration::andersenPiterbargIntegrationLimit(

                    c_inf, epsilon, v0, maturity);

            };

            const ComplexLogFormula finalLog = (cpxLog_ == OptimalCV)

                ? optimalControlVariate(maturity, v0, kappa, theta, sigma, rho)

                : cpxLog_;

            const AP_Helper cvHelper(

                 maturity, fwd, strike, finalLog, this, alpha_

            );

            const Real cvValue = cvHelper.controlVariateValue();

            const Real vAvg = (1-std::exp(-kappa*maturity))*(v0-theta)/(kappa*maturity) + theta;

            const Real scalingFactor = (cpxLog_ != OptimalCV && cpxLog_ != AsymptoticChF)

                ? std::max(0.25, std::min(1000.0, 0.25/std::sqrt(0.5*vAvg*maturity)))

                : Real(1.0);

            const Real h_cv =

                fwd/M_PI*integration_->calculate(c_inf, cvHelper, uM, scalingFactor);

            evaluations_ += integration_->numberOfEvaluations();

            switch (payoff->optionType())

            {

              case Option::Call:

                value = (cvValue + h_cv)*dr;

                break;

              case Option::Put:

                value = (cvValue + h_cv - (fwd - strike))*dr;

                break;

              default:

                QL_FAIL("unknown option type");

            }

          }

          break;

          default:

            QL_FAIL("unknown complex log formula");

        }

        return value;

    }

    void AnalyticHestonEngine::calculate() const

    {

        // this is a european option pricer

        QL_REQUIRE(arguments_.exercise->type() == Exercise::European,

                   "not an European option");

        // plain vanilla

        ext::shared_ptr<PlainVanillaPayoff> payoff =

            ext::dynamic_pointer_cast<PlainVanillaPayoff>(arguments_.payoff);

        QL_REQUIRE(payoff, "non plain vanilla payoff given");

        const Date exerciseDate = arguments_.exercise->lastDate();

        results_.value = priceVanillaPayoff(payoff, exerciseDate);

    }

    AnalyticHestonEngine::Integration::Integration(Algorithm intAlgo,

                                                   ext::shared_ptr<Integrator> integrator)

    : intAlgo_(intAlgo), integrator_(std::move(integrator)) {}

    AnalyticHestonEngine::Integration::Integration(

        Algorithm intAlgo, ext::shared_ptr<GaussianQuadrature> gaussianQuadrature)

    : intAlgo_(intAlgo), gaussianQuadrature_(std::move(gaussianQuadrature)) {}

    AnalyticHestonEngine::Integration

    AnalyticHestonEngine::Integration::gaussLobatto(

       Real relTolerance, Real absTolerance, Size maxEvaluations, bool useConvergenceEstimate) {

       return Integration(GaussLobatto,

                           ext::shared_ptr<Integrator>(

                               new GaussLobattoIntegral(maxEvaluations,

                                                        absTolerance,

                                                        relTolerance,

                                                        useConvergenceEstimate)));

    }

    AnalyticHestonEngine::Integration

    AnalyticHestonEngine::Integration::gaussKronrod(Real absTolerance,

                                                   Size maxEvaluations) {

        return Integration(GaussKronrod,

                           ext::shared_ptr<Integrator>(

                               new GaussKronrodAdaptive(absTolerance,

                                                        maxEvaluations)));

    }

    AnalyticHestonEngine::Integration

    AnalyticHestonEngine::Integration::simpson(Real absTolerance,

                                               Size maxEvaluations) {