CMVReconciler4.java

package org.drip.sample.assetallocationexcel;

import org.drip.function.rdtor1descent.LineStepEvolutionControl;
import org.drip.function.rdtor1solver.InteriorPointBarrierControl;
import org.drip.measure.statistics.MultivariateMoments;
import org.drip.numerical.common.FormatUtil;
import org.drip.portfolioconstruction.allocator.*;
import org.drip.portfolioconstruction.asset.*;
import org.drip.portfolioconstruction.params.AssetUniverseStatisticalProperties;
import org.drip.service.env.EnvManager;

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

/*!
 * Copyright (C) 2020 Lakshmi Krishnamurthy
 * Copyright (C) 2019 Lakshmi Krishnamurthy
 * Copyright (C) 2018 Lakshmi Krishnamurthy
 * Copyright (C) 2017 Lakshmi Krishnamurthy
 * Copyright (C) 2016 Lakshmi Krishnamurthy
 * 
 *  This file is part of DROP, an open-source library targeting analytics/risk, transaction cost analytics,
 *      asset liability management analytics, capital, exposure, and margin analytics, valuation adjustment
 *      analytics, and portfolio construction analytics within and across fixed income, credit, commodity,
 *      equity, FX, and structured products. It also includes auxiliary libraries for algorithm support,
 *      numerical analysis, numerical optimization, spline builder, model validation, statistical learning,
 *      and computational support.
 *  
 *      https://lakshmidrip.github.io/DROP/
 *  
 *  DROP is composed of three modules:
 *  
 *  - DROP Product Core - https://lakshmidrip.github.io/DROP-Product-Core/
 *  - DROP Portfolio Core - https://lakshmidrip.github.io/DROP-Portfolio-Core/
 *  - DROP Computational Core - https://lakshmidrip.github.io/DROP-Computational-Core/
 * 
 *  DROP Product Core implements libraries for the following:
 *  - Fixed Income Analytics
 *  - Loan Analytics
 *  - Transaction Cost Analytics
 * 
 *  DROP Portfolio Core implements libraries for the following:
 *  - Asset Allocation Analytics
 *  - Asset Liability Management Analytics
 *  - Capital Estimation Analytics
 *  - Exposure Analytics
 *  - Margin Analytics
 *  - XVA Analytics
 * 
 *  DROP Computational Core implements libraries for the following:
 *  - Algorithm Support
 *  - Computation Support
 *  - Function Analysis
 *  - Model Validation
 *  - Numerical Analysis
 *  - Numerical Optimizer
 *  - Spline Builder
 *  - Statistical Learning
 * 
 *  Documentation for DROP is Spread Over:
 * 
 *  - Main                     => https://lakshmidrip.github.io/DROP/
 *  - Wiki                     => https://github.com/lakshmiDRIP/DROP/wiki
 *  - GitHub                   => https://github.com/lakshmiDRIP/DROP
 *  - Repo Layout Taxonomy     => https://github.com/lakshmiDRIP/DROP/blob/master/Taxonomy.md
 *  - Javadoc                  => https://lakshmidrip.github.io/DROP/Javadoc/index.html
 *  - Technical Specifications => https://github.com/lakshmiDRIP/DROP/tree/master/Docs/Internal
 *  - Release Versions         => https://lakshmidrip.github.io/DROP/version.html
 *  - Community Credits        => https://lakshmidrip.github.io/DROP/credits.html
 *  - Issues Catalog           => https://github.com/lakshmiDRIP/DROP/issues
 *  - JUnit                    => https://lakshmidrip.github.io/DROP/junit/index.html
 *  - Jacoco                   => https://lakshmidrip.github.io/DROP/jacoco/index.html
 * 
 *  Licensed under the Apache License, Version 2.0 (the "License");
 *      you may not use this file except in compliance with the License.
 *   
 *  You may obtain a copy of the License at
 *      http://www.apache.org/licenses/LICENSE-2.0
 *  
 *  Unless required by applicable law or agreed to in writing, software
 *      distributed under the License is distributed on an "AS IS" BASIS,
 *      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 *  
 *  See the License for the specific language governing permissions and
 *      limitations under the License.
 */

/**
 * <i>CMVReconciler4</i> demonstrates the Execution and Reconciliation of the Dual Constrained Mean Variance
 * against an XL-based Implementation for Portfolio Design Returns #4.
 *  
 * <br><br>
 *  <ul>
 *      <li><b>Module </b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/PortfolioCore.md">Portfolio Core Module</a></li>
 *      <li><b>Library</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/AssetAllocationAnalyticsLibrary.md">Asset Allocation Analytics</a></li>
 *      <li><b>Project</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/src/main/java/org/drip/sample/README.md">DROP API Construction and Usage</a></li>
 *      <li><b>Package</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/src/main/java/org/drip/sample/assetallocationexcel/README.md">Asset-Bound Allocator Excel Reconciliation</a></li>
 *  </ul>
 * <br><br>
 *
 * @author Lakshmi Krishnamurthy
 */

public class CMVReconciler4
{

    public static final void main (
        final String[] argumentArray)
        throws Exception
    {
        EnvManager.InitEnv (
            "",
            true
        );

        String[] assetIDArray = new String[]
        {
            "TOK",
            "EWJ",
            "HYG",
            "LQD",
            "EMD",
            "GSG",
            "BWX"
        };
        double[] assetHoldingsLowerBoundArray = new double[]
        {
            0.05,
            0.05,
            0.05,
            0.10,
            0.05,
            0.05,
            0.15
        };
        double[] assetHoldingsUpperBoundArray = new double[]
        {
            0.40,
            0.40,
            0.30,
            0.30,
            0.35,
            0.15,
            0.50
        };
        double[] expectedAssetReturnsArray = new double[]
        {
            0.1100,
            0.1000,
            0.0800,
            0.0500,
            0.0600,
            0.0700,
            0.0200
        };
        double portfolioDesignReturn = 0.08000;
        double[][] assetReturnsCovarianceMatrix = new double[][]
        {
            {0.002733 * 12, 0.002083 * 12, 0.001593 * 12, 0.000488 * 12, 0.001172 * 12, 0.002312 * 12, 0.000710 * 12},
            {0.002083 * 12, 0.002768 * 12, 0.001302 * 12, 0.000457 * 12, 0.001105 * 12, 0.001647 * 12, 0.000563 * 12},
            {0.001593 * 12, 0.001302 * 12, 0.001463 * 12, 0.000639 * 12, 0.001050 * 12, 0.001110 * 12, 0.000519 * 12},
            {0.000488 * 12, 0.000457 * 12, 0.000639 * 12, 0.000608 * 12, 0.000663 * 12, 0.000042 * 12, 0.000370 * 12},
            {0.001172 * 12, 0.001105 * 12, 0.001050 * 12, 0.000663 * 12, 0.001389 * 12, 0.000825 * 12, 0.000661 * 12},
            {0.002312 * 12, 0.001647 * 12, 0.001110 * 12, 0.000042 * 12, 0.000825 * 12, 0.005211 * 12, 0.000749 * 12},
            {0.000710 * 12, 0.000563 * 12, 0.000519 * 12, 0.000370 * 12, 0.000661 * 12, 0.000749 * 12, 0.000703 * 12}
        };
        double[] reconcilerAssetWeightsArray = new double[]
        {
            0.3976,
            0.1625,
            0.0500,
            0.1399,
            0.0500,
            0.0500,
            0.1500
        };

        AssetComponent[] reconcilerAssetComponentArray =
            new AssetComponent[reconcilerAssetWeightsArray.length];

        for (int assetIndex = 0;
            assetIndex < reconcilerAssetWeightsArray.length;
            ++assetIndex)
        {
            reconcilerAssetComponentArray[assetIndex] = new AssetComponent (
                assetIDArray[assetIndex],
                reconcilerAssetWeightsArray[assetIndex]
            );
        }

        AssetUniverseStatisticalProperties assetUniverseStatisticalProperties =
            AssetUniverseStatisticalProperties.FromMultivariateMetrics (
                MultivariateMoments.Standard (
                    assetIDArray,
                    expectedAssetReturnsArray,
                    assetReturnsCovarianceMatrix
                )
            );

        double[][] covarianceMatrix = assetUniverseStatisticalProperties.covariance (
            assetIDArray
        );

        System.out.println ("\n\n\t|------------------------------------------------------------------------------------------------||");

        System.out.println ("\t|                                  CROSS ASSET COVARIANCE MATRIX                                 ||");

        System.out.println ("\t|------------------------------------------------------------------------------------------------||");

        String header = "\t|     |";

        for (int assetIndex = 0;
            assetIndex < assetIDArray.length;
            ++assetIndex)
        {
            header += "    " + assetIDArray[assetIndex] + "     |";
        }

        System.out.println (header + "|");

        System.out.println ("\t|------------------------------------------------------------------------------------------------||");

        for (int assetIndexI = 0;
            assetIndexI < assetIDArray.length;
            ++assetIndexI)
        {
            String dump = "\t| " + assetIDArray[assetIndexI] + " ";

            for (int assetIndexJ = 0;
                assetIndexJ < assetIDArray.length;
                ++assetIndexJ)
            {
                dump += "|" + FormatUtil.FormatDouble (
                    covarianceMatrix[assetIndexI][assetIndexJ], 1, 8, 1.
                ) + " ";
            }

            System.out.println (dump + "||");
        }

        System.out.println ("\t|------------------------------------------------------------------------------------------------||\n\n");

        System.out.println ("\t|-------------------||");

        System.out.println ("\t|   ASSET BOUNDS    ||");

        System.out.println ("\t|-------------------||");

        for (int assetIndex = 0;
            assetIndex < assetIDArray.length;
            ++assetIndex)
        {
            System.out.println (
                "\t| " + assetIDArray[assetIndex] + " | " +
                FormatUtil.FormatDouble (assetHoldingsLowerBoundArray[assetIndex], 2, 0, 100.) + "% | " +
                FormatUtil.FormatDouble (assetHoldingsUpperBoundArray[assetIndex], 2, 0, 100.) + "% ||"
            );
        }

        System.out.println ("\t|-------------------||\n\n");

        InteriorPointBarrierControl interiorPointBarrierControl = InteriorPointBarrierControl.Standard();

        System.out.println ("\t|--------------------------------------------||");

        System.out.println ("\t|  INTERIOR POINT METHOD BARRIER PARAMETERS  ||");

        System.out.println ("\t|--------------------------------------------||");

        System.out.println (
            "\t|    Barrier Decay Velocity        : " + 1. / interiorPointBarrierControl.decayVelocity()
        );

        System.out.println (
            "\t|    Barrier Decay Steps           : " + interiorPointBarrierControl.decayStepCount()
        );

        System.out.println (
            "\t|    Initial Barrier Strength      : " + interiorPointBarrierControl.initialStrength()
        );

        System.out.println (
            "\t|    Barrier Convergence Tolerance : " + interiorPointBarrierControl.relativeTolerance()
        );

        System.out.println ("\t|--------------------------------------------||\n\n");

        BoundedHoldingsAllocationControl boundedPortfolioConstructionParameters =
            new BoundedHoldingsAllocationControl (
                assetIDArray,
                CustomRiskUtilitySettings.VarianceMinimizer(),
                new EqualityConstraintSettings (
                    EqualityConstraintSettings.FULLY_INVESTED_CONSTRAINT |
                        EqualityConstraintSettings.RETURNS_CONSTRAINT,
                    portfolioDesignReturn
                )
            );

        for (int assetIndex = 0;
            assetIndex < assetIDArray.length;
            ++assetIndex)
        {
            boundedPortfolioConstructionParameters.addBound (
                assetIDArray[assetIndex],
                assetHoldingsLowerBoundArray[assetIndex],
                assetHoldingsUpperBoundArray[assetIndex]
            );
        }

        HoldingsAllocation optimizationOutput = new ConstrainedMeanVarianceOptimizer (
            interiorPointBarrierControl,
            LineStepEvolutionControl.NocedalWrightStrongWolfe (
                false
            )
        ).allocate (
            boundedPortfolioConstructionParameters,
            assetUniverseStatisticalProperties
        );

        AssetComponent[] optimalAssetComponentArray =
            optimizationOutput.optimalPortfolio().assetComponentArray();

        System.out.println ("\t|--------------------------||");

        System.out.println ("\t|   OPTIMAL ASSET WEIGHTS  ||");

        System.out.println ("\t|--------------------------||");

        System.out.println ("\t| ASSET |  DROP  |  EXCEL  ||");

        System.out.println ("\t|--------------------------||");

        for (int assetIndex = 0;
            assetIndex < optimalAssetComponentArray.length;
            ++assetIndex)
        {
            System.out.println (
                "\t|  " + optimalAssetComponentArray[assetIndex].id() + "  |" +
                FormatUtil.FormatDouble (
                    optimalAssetComponentArray[assetIndex].amount(), 2, 2, 100.
                ) + "% | " +
                FormatUtil.FormatDouble (
                    reconcilerAssetComponentArray[assetIndex].amount(), 2, 2, 100.
                ) + "% ||"
            );
        }

        System.out.println ("\t|--------------------------||\n\n");

        System.out.println ("\t|------------------------------------------------||");

        System.out.println (
            "\t| Optimal Portfolio Normalize          : " + FormatUtil.FormatDouble (
                optimizationOutput.optimalPortfolio().notional(), 1, 2, 1.
            ) + "   ||"
        );

        System.out.println (
            "\t| Optimal Portfolio Input Return       : " + FormatUtil.FormatDouble (
                portfolioDesignReturn, 1, 2, 100.
            ) + "%  ||"
        );

        System.out.println (
            "\t| Optimal Portfolio Expected Return    : " + FormatUtil.FormatDouble (
                optimizationOutput.optimalMetrics().excessReturnsMean(), 1, 2, 100.
            ) + "%  ||"
        );

        System.out.println (
            "\t| Optimal Portfolio Standard Deviation : " + FormatUtil.FormatDouble (
                optimizationOutput.optimalMetrics().excessReturnsStandardDeviation(), 2, 2, 100.
            ) + "% ||"
        );

        System.out.println (
            "\t| Excel Portfolio Standard Deviation   : " + FormatUtil.FormatDouble (
                Math.sqrt (
                    new Portfolio (
                        reconcilerAssetComponentArray
                    ).variance (
                        assetUniverseStatisticalProperties
                    )
                ), 2, 2, 100.
            ) + "% ||"
        );

        System.out.println ("\t|------------------------------------------------||\n");

        EnvManager.TerminateEnv();
    }
}