clear; clc; 

% load data
load problem_cs_hedging_portfolio_of_options_with_XVaR

% or import problem from TXT format
%[problem_statement, toolboxstruc_arr] = tbpsg_problem_importfromtext('.', 'problem_cs_hedging_portfolio_of_options_with_XVaR');


% generate a set of parameter Q values and corresponding header for Cycle
q_value=[]; ki=0;
Cycle_header='for {Q#}={';
for iq=500:25:999 
  ki=ki+1; q_value(ki)=iq/1000.; 
  Cycle_header=[Cycle_header, int2str(iq), '},{' ];
end
ki=ki+1; q_value(ki)=0.9875; Cycle_header=[Cycle_header, '9875},{' ];
ki=ki+1; q_value(ki)=0.99375; Cycle_header=[Cycle_header, '99375},{' ];
ki=ki+1; q_value(ki)=0.999999; Cycle_header=[Cycle_header, '999999}' ];

% write problem statement with Cycle
problem_statement=[Cycle_header,char(10),...
              ' ',char(10),...
              'problem_xvar_risk_Q#, minimize', char(10),...
              '  evar(0.Q#, matrix_options_scenarios_45)', char(10),...
              'Constraint: <= 100', char(10),...
              '  linear(matrix_options_prices)', char(10),...
              'Box: >= 0, <= point_upperbounds' ];

% solve a set of problems with different parameter values
[solution_str,outargstruc_arr] = tbpsg_run(problem_statement, toolboxstruc_arr);


% get corresponding set of optimal objective values
[obj_value] = tbpsg_objective(solution_str, outargstruc_arr);


%------- Make figures  ----------
fig1 = figure;
fig1.Name='                    Optimal Expectile value depending on parameter q';
fig1.NumberTitle='off'; 
%fig1.ToolBar='none'; fig1.MenuBar='none';
scatter(q_value,obj_value,'filled')
%plot(q_value,obj_value)