diff options
| -rw-r--r-- | src/main.cpp | 29 | ||||
| -rw-r--r-- | src/opti.cpp | 20 | ||||
| -rw-r--r-- | src/option.cpp | 61 | ||||
| -rw-r--r-- | src/stratified_sampling.cpp | 2 | ||||
| -rw-r--r-- | src/stratified_sampling.hpp | 33 |
5 files changed, 66 insertions, 79 deletions
diff --git a/src/main.cpp b/src/main.cpp index f6e998a..561b8c2 100644 --- a/src/main.cpp +++ b/src/main.cpp @@ -6,6 +6,7 @@ #include "stratified_sampling.hpp" #include <cmath> #include <algorithm> +#include "opti.hpp" using namespace std; //--génération quantiles-- @@ -43,16 +44,30 @@ x = 1.64*S.estimator().second; }; + void exemple2 (){ + std::vector<double> mu(16); + mu = argmax(0.05, 1.0, 50, 0.1, 45, 16); + double norm_mu = 0; + std::vector<double> u(16); + for(int i=0; i<16; i++) { + norm_mu += mu[i]*mu[i]; + u[i] = mu[i]/norm_mu; + } + vector<double> q = quantile_norm(100, 1); + vector<double> p(100, 0.01); + asian_option A(0.05, 1.0, 50, 0.1, 45, 16); + f_mu G(mu,A); + multi_gaussian_truncated MG(q[50],q[51], u); + for(int i=0; i<10; i++){ + std::cout<<G(MG())<<std::endl; + } +} + int main() { - std::vector<double> u {sqrt(0.2), sqrt(0.2), sqrt(0.2), sqrt(0.2), sqrt(0.2)}; - multi_gaussian_truncated G(0, 2, u); - std::vector<double> r(5); - r = G(); - for (int i=0; i<5; i++){ - std::cout<<r[i]<<std::endl; - } + + exemple2(); return 0; } diff --git a/src/opti.cpp b/src/opti.cpp index 702e8c2..23ddff3 100644 --- a/src/opti.cpp +++ b/src/opti.cpp @@ -1,8 +1,4 @@ -#include <vector> -#include <nlopt.hpp> -#include <cmath> -#include <algorithm> -#include <iostream> +#include "opti.hpp" double pos (double x){ return x>0?x:0; @@ -30,17 +26,17 @@ double f (const std::vector<double> &X, std::vector<double> &grad, void *params) -int main() { +std::vector<double> argmax(double r, double T, double S0, double V, double K, int d){ - double params[5] = {0.05, 1, 50, 0.1, 45}; + double params[5] = {r, T, S0, V, K}; - nlopt::opt opt(nlopt::LN_COBYLA, 16); + nlopt::opt opt(nlopt::LN_COBYLA, d); opt.set_max_objective(f, ¶ms); opt.set_xtol_rel(1e-4); - std::vector<double> x(16,0); + std::vector<double> x(d,0); std::vector<double> g(0); @@ -48,12 +44,8 @@ int main() { double maxf; nlopt::result result = opt.optimize(x, maxf); - for(int i=0; i<16; i++){ - std::cout<<x[i]<<std::endl; - } - std::cout<<"valeur à la fin : "<<maxf<<std::endl; - return 0; + return x; } diff --git a/src/option.cpp b/src/option.cpp index 0e5a56c..b8c2799 100644 --- a/src/option.cpp +++ b/src/option.cpp @@ -1,62 +1,13 @@ -#include <algorithm> +#include "option.hpp" #include <iostream> -#include "rqmc.hpp" -#include "p_adic.o" - -double frac_part(double x){ - return x - floor(x); -} double pos (double x){ return x>0?x:0; } -struct asian_option : public std::unary_function<std::vector<double>, double> -{ - asian_option(double r, double T, double S0, double V, int d, double K) - : r(r), T(T), S0(S0), V(V), d(d), K(K) {}; - - double operator()(std::vector<double> X) const { - std::vector<double> S(d); - S[0]= S0*exp((r-V*V/2)*(T/d)+V*sqrt(T/d)*X[0]); - for(int i=1;i<d;i++){ - S[i]=S[i-1]*exp((r-V*V/2)*(T/d)+V*sqrt(T/d)*X[i]); - } - double temp = std::accumulate(S.begin(), S.end(), 0.)/d; - return exp(-r*T)*pos(temp-K); - }; - - private: - double r; - double T; - double S0; - double V; - int d; - double K; - }; - - - - -template <typename Fct, typename LDS> -struct quasi_option : public generator<typename Fct::result_type> -{ - quasi_option(int n, int d, Fct payoff) : n(n), d(d), payoff(payoff), G(d) {}; - - typename Fct::result_type operator()() { - double sum =0; - for(int i=0; i<n; i++){ - sum += payoff(G()); - - } - return sum/n; - }; - - private: - int n, d; - Fct payoff; - quasi_gaussian<LDS> G; - }; +double frac_part(double x){ + return x - floor(x); +} int main(){ init_alea(1); @@ -73,9 +24,9 @@ int main(){ } std::vector<double> result2(3); - result = monte_carlo(100, quasi_option<asian_option, halton> (N, d, A)); + result2 = monte_carlo(100, quasi_option<asian_option, halton> (N, d, A)); for(int i =0; i<3; i++){ - std::cout<<result[i]<<std::endl; + std::cout<<result2[i]<<std::endl; } diff --git a/src/stratified_sampling.cpp b/src/stratified_sampling.cpp index 156751f..1c8e670 100644 --- a/src/stratified_sampling.cpp +++ b/src/stratified_sampling.cpp @@ -1,5 +1,6 @@ #include "stratified_sampling.hpp" + std::pair<double, double> mean_var( std::vector<double> r){ std::pair<double, double> p; for(auto &x: r){ @@ -12,3 +13,4 @@ std::pair<double, double> mean_var( std::vector<double> r){ return p; } + diff --git a/src/stratified_sampling.hpp b/src/stratified_sampling.hpp index cebee8d..7573ef2 100644 --- a/src/stratified_sampling.hpp +++ b/src/stratified_sampling.hpp @@ -5,6 +5,7 @@ #include "rtnorm.hpp" #include "var_alea.hpp" #include <gsl/gsl_cdf.h> +#include "option.hpp" using namespace std; @@ -45,14 +46,14 @@ struct multi_gaussian_truncated : public var_alea<std::vector<double> > double v = V(); double Z = gsl_cdf_gaussian_Pinv(v,1); std::vector<double> Y(d); - double scal = 0; for(int i=0; i<d; i++){ Y[i] = G(); } - std::vector<double> X(d); + double scal = 0; for(int j=0; j<d; j++){ scal += Y[j]*u[j]; } + std::vector<double> X(d); for(int i=0; i<d; i++){ X[i] = u[i]*Z + Y[i] - u[i]*scal; } @@ -78,7 +79,7 @@ struct stratified_sampling { void print_sigma() const; pair<double,double> estimator() const; private: - void update(int N); + void update(int N); vector<double> p; vector<L> X; vector<int> M; @@ -187,3 +188,29 @@ pair<double,double> stratified_sampling<L>::estimator() const { } return {est_mean, est_std}; } + +struct f_mu : public std::unary_function<std::vector<double>, double> +{ + f_mu(std::vector<double> mu, asian_option A) : mu(mu), A(A){ + double norm_mu = 0; + for(int i=0; i<16; i++) { + norm_mu += mu[i]*mu[i]; + } + }; + + double operator()(std::vector<double> X) { + std::vector<double> Y(X.size()); + double scal = 0; + for (unsigned int i=0; i<X.size(); i++){ + Y[i] = X[i] + mu[i]; + scal+=X[i]*mu[i]; + } + return A(Y)*exp(-scal-0.5*norm_mu); + }; + + private : + std::vector<double> mu; + asian_option A; + double norm_mu; + }; + |