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

/*

 Copyright (C) 2024 Klaus Spanderen

 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.

*/

#include <ql/pricingengines/basket/bjerksundstenslandspreadengine.hpp>

#include <ql/math/distributions/normaldistribution.hpp>

#include <utility>

namespace QuantLib {

    BjerksundStenslandSpreadEngine::BjerksundStenslandSpreadEngine(

        ext::shared_ptr<GeneralizedBlackScholesProcess> process1,

        ext::shared_ptr<GeneralizedBlackScholesProcess> process2,

        Real correlation)

    : SpreadBlackScholesVanillaEngine(std::move(process1), std::move(process2), correlation) {

    }

    Real BjerksundStenslandSpreadEngine::calculate(

        Real f1, Real f2, Real k, Option::Type optionType,

        Real variance1, Real variance2, DiscountFactor df) const {

        const Real cp = (optionType == Option::Call) ? 1 : -1;

        const Real a = f2 + k;

        const Real b = f2/a;

        const Real sigma1 = std::sqrt(variance1);

        const Real sigma2 = std::sqrt(variance2);

        const Real stdev = std::sqrt(

            variance1 + b*b*variance2 - 2*rho_*b*sigma1*sigma2);

        const Real lfa = std::log(f1/a);

        const Real d1 =

            (lfa + (0.5*variance1 + 0.5*b*b*variance2 - b*rho_*sigma1*sigma2))/stdev;

        const Real d2 =

            (lfa + (-0.5*variance1 + variance2*b*(0.5*b - 1) + rho_*sigma1*sigma2))/stdev;

        const Real d3 = (lfa + (-0.5*variance1 + 0.5*b*b*variance2))/stdev;

        const CumulativeNormalDistribution phi;

        return df*cp*(f1*phi(cp*d1) - f2*phi(cp*d2) - k*phi(cp*d3));

    }

}