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finmath lib is a Mathematical Finance Library in Java. It provides algorithms and methodologies related to mathematical finance.
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package net.finmath.fouriermethod.calibration;

import java.util.ArrayList;
import java.util.Map;
import java.util.function.Function;

import org.apache.commons.lang3.ArrayUtils;

import net.finmath.exception.CalculationException;
import net.finmath.fouriermethod.calibration.models.CalibrableProcess;
import net.finmath.fouriermethod.models.CharacteristicFunctionModel;
import net.finmath.fouriermethod.products.smile.EuropeanOptionSmile;
import net.finmath.marketdata.model.volatilities.OptionSmileData;
import net.finmath.marketdata.model.volatilities.OptionSurfaceData;
import net.finmath.marketdata.model.volatilities.VolatilitySurface.QuotingConvention;
import net.finmath.optimizer.Optimizer;
import net.finmath.optimizer.OptimizerFactory;
import net.finmath.optimizer.SolverException;

/**
 * This class solves a calibration problem. The problem is defined in terms of:
 * 
 *  a generic container of market data OptionSurfaceData.
 * 
 a generic pricing model.
 * 
 a generic calibration algorithm.
 * 
 a generic pricer for claims.
 * 
 *
 * The class supports both calibration in terms of:
 * 
 *  Prices
 * 
 Log-normal implied volatilities.
 * 
 Normal implied volatilities.
 * 
 *
 * To change the calibration entity please change the convention in the option surface.
 * The calibration entity (i.e. price/vol/normal vol) is directly detected from market data.
 *
 * @author Alessandro Gnoatto
 *
 */
public class CalibratedModel {

	private final OptionSurfaceData surface; //target calibration instruments. They dictate the calibration entity: vol/price.
	private final CalibrableProcess model; //Pricing model
	private final OptimizerFactory optimizerFactory; //construct the instance of the optimization algorithm inside the class.
	private final EuropeanOptionSmile pricer; //How do we compute prices: Carr Madan, Cos, Conv, Lewis...

	//Optimizer parameters
	private final double[] initialParameters;
	private final double[] lowerBound;
	private final double[] upperBound;
	private final double[] parameterStep;

	public CalibratedModel(OptionSurfaceData surface, CalibrableProcess model,
			OptimizerFactory optimizerFactory, EuropeanOptionSmile pricer, double[] initialParameters,
			double[] parameterStep) {
		super();
		this.surface = surface;
		this.model = model;
		this.optimizerFactory = optimizerFactory;
		this.pricer = pricer;
		this.initialParameters = initialParameters;
		this.lowerBound = model.getParameterLowerBounds();
		this.upperBound = model.getParameterUpperBounds();
		this.parameterStep = parameterStep;
	}

	/**
	 * Solves the calibration problem thus providing a calibrated model.
	 * @return the calibrated model wrapped in an {@link OptimizationResult}.
	 * @throws SolverException Thrown if the calibration problem cannot be solved.
	 */
	public OptimizationResult getCalibration() throws SolverException {

		Optimizer.ObjectiveFunction objectiveFunction = new Optimizer.ObjectiveFunction() {
			@Override
			public void setValues(double[] parameters, double[] values) {

				//We change the parameters of the model
				CalibrableProcess newModel = model.getCloneForModifiedParameters(parameters);
				CharacteristicFunctionModel newModelFourier = newModel.getCharacteristiFunction();

				int numberOfMaturities = surface.getMaturities().length;
				double mats[] = surface.getMaturities();

				QuotingConvention targetConvention = surface.getQuotingConvention();

				for(int t = 0; t> currentModelPrices = newPricer.getValue(0.0, newModelFourier);

						for(int i = 0; i calibrationOutput = outputCalibrationResult(optimizer.getBestFitParameters());

		CalibrableProcess calibratedModel = model.getCloneForModifiedParameters(optimizer.getBestFitParameters());

		return new OptimizationResult(calibratedModel,optimizer.getBestFitParameters(),optimizer.getIterations(),optimizer.getRootMeanSquaredError(),calibrationOutput);
	}

	/**
	 * This is a service method that takes care of putting al the target values in a single array.
	 * @return
	 */
	private double[] formatTargetValuesForOptimizer() {
		//Put all values in an array for the optimizer.
		int numberOfMaturities = surface.getMaturities().length;
		double mats[] = surface.getMaturities();

		ArrayList vals = new ArrayList();

		for(int t = 0; t outputCalibrationResult(double[] parameters) {

		ArrayList calibrationOutput = new ArrayList();

		//We change the parameters of the model
		CalibrableProcess newModel = model.getCloneForModifiedParameters(parameters);
		CharacteristicFunctionModel newModelFourier = newModel.getCharacteristiFunction();

		int numberOfMaturities = surface.getMaturities().length;
		double mats[] = surface.getMaturities();

		QuotingConvention targetConvention = surface.getQuotingConvention();

		double value;
		double targetValue;
		double T;
		double K;

		calibrationOutput.add("Strike"+ "\t" + "Maturity"+ "\t" + "Market Value" + "\t" + "Model Value" + "\t" + "Squared Error");

		for(int t = 0; t> currentModelPrices = newPricer.getValue(0.0, newModelFourier);

				for(int i = 0; i calibrationOutput;

		public OptimizationResult(CalibrableProcess model, double[] bestFitParameters,
				int iterations, double rootMeanSquaredError, ArrayList calibrationOutput) {
			super();
			this.model = model;
			this.bestFitParameters = bestFitParameters;
			this.iterations = iterations;
			this.rootMeanSquaredError = rootMeanSquaredError;
			this.calibrationOutput = calibrationOutput;
		}

		public CalibrableProcess getModel() {
			return model;
		}

		public double[] getBestFitParameters() {
			return bestFitParameters;
		}

		public int getIterations() {
			return iterations;
		}

		public double getRootMeanSquaredError() {
			return rootMeanSquaredError;
		}

		public ArrayList getCalibrationOutput(){
			return this.calibrationOutput;
		}

	}

}