using ampl;
using ampl.Entities;
using System;
using System.Linq;

namespace Examples
{
    public class EfficientFrontier
    {
        public static int Main(string[] args)
        {
            string modelDirectory = ((args != null) && (args.Length > 0)) ? args[0]
                  : "../../models";
            modelDirectory += "/qpmv";
            string solver = ((args != null) && (args.Length > 1)) ? args[1] : null;

            /*
            // If the AMPL installation directory is not in the system search path:
            ampl.Environment env = new ampl.Environment(
              "full path to the AMPL installation directory");
            // Create an AMPL instance
            using (AMPL a = new AMPL(env)) {}
            */

            // Create an AMPL instance
            using (AMPL ampl = new AMPL())
            {
                // Number of steps of the efficient frontier
                const int steps = 10;
                if (solver != null) ampl.SetOption("solver", solver);
                ampl.SetOption("reset_initial_guesses", true);
                ampl.SetOption("send_statuses", false);
                ampl.SetOption("Solver", "cplex");

                // Load the AMPL model from file
                ampl.Read(modelDirectory + "/qpmv.mod");
                ampl.Read(modelDirectory + "/qpmvbit.run");

                // set tables directory (parameter used in the script above)
                ampl.GetParameter("data_dir").Set(modelDirectory);
                // Read tables
                ampl.ReadTable("assetstable");
                ampl.ReadTable("astrets");

                Variable portfolioReturn = ampl.GetVariable("portret");
                Parameter averageReturn = ampl.GetParameter("averret");
                Parameter targetReturn = ampl.GetParameter("targetret");
                Objective variance = ampl.GetObjective("cst");

                // Relax the integrality
                ampl.SetOption("relax_integrality", true);
                // Solve the problem
                ampl.Solve();
                // Calibrate the efficient frontier range
                double minret = portfolioReturn.Value;
                DataFrame values = averageReturn.GetValues();
                DataFrame.Column col = values.GetColumn("averret");

                double maxret = col.Max().Dbl;
                double stepsize = (maxret - minret) / steps;
                double[] returns = new double[steps];
                double[] variances = new double[steps];

                for (int i = 0; i < steps; i++)
                {
                    Console.WriteLine(string.Format("Solving for return = {0}",
                      maxret - (i - 1) * stepsize));
                    // set target return to the desired point
                    targetReturn.Set(maxret - (i - 1) * stepsize);
                    ampl.Eval("let stockopall:={};let stockrun:=stockall;");
                    // Relax integrality
                    ampl.SetOption("relax_integrality", true);
                    ampl.Solve();
                    Console.WriteLine(string.Format("QP result = {0}",
                      variance.Value));
                    // Adjust included stocks
                    ampl.Eval("let stockrun:={i in stockrun:weights[i]>0};");
                    ampl.Eval("let stockopall:={i in stockrun:weights[i]>0.5};");
                    // set integrality back
                    ampl.SetOption("relax_integrality", false);
                    ampl.Solve();
                    Console.WriteLine(string.Format("QMIP result = {0}",
                      variance.Value));
                    // Store data of corrent frontier point
                    returns[i] = maxret - (i - 1) * stepsize;
                    variances[i] = variance.Value;
                }

                // Display efficient frontier points
                Console.WriteLine("    RETURN    VARIANCE");
                for (int i = 0; i < steps; i++)
                    Console.WriteLine(string.Format("{0,10:0.00000}  {1,10:0.00000}", returns[i], variances[i]));
            }
            return 0;
        }
    }
}