UnboundedMarkovitzBullet.java

package org.drip.sample.efficientfrontier;

import java.util.*;

import org.drip.feed.loader.*;
import org.drip.measure.statistics.MultivariateMoments;
import org.drip.numerical.common.FormatUtil;
import org.drip.portfolioconstruction.allocator.*;
import org.drip.portfolioconstruction.asset.AssetComponent;
import org.drip.portfolioconstruction.mpt.MarkovitzBullet;
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>UnboundedMarkovitzBullet</i> demonstrates the Construction of the Efficient Frontier using the
 * Unconstrained Quadratic Mean Variance Optimizer.
 * 
 * <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/efficientfrontier/README.md">Efficient Frontier Markovitz Bullet Variants</a></li>
 *  </ul>
 * <br><br>
 *
 * @author Lakshmi Krishnamurthy
 */

public class UnboundedMarkovitzBullet
{

    private static void DisplayPortfolioMetrics (
        final HoldingsAllocation optimalOutput)
        throws Exception
    {
        AssetComponent[] globalMinimumAssetComponentArray =
            optimalOutput.optimalPortfolio().assetComponentArray();

        String strDump = "\t|" + FormatUtil.FormatDouble (
                optimalOutput.optimalMetrics().excessReturnsMean(), 1, 4, 100.
            ) + "% |" + FormatUtil.FormatDouble (
                optimalOutput.optimalMetrics().excessReturnsStandardDeviation(), 1, 4, 100.
            ) + " |";

        for (AssetComponent assetComponent : globalMinimumAssetComponentArray)
        {
            strDump += " " + FormatUtil.FormatDouble (
                assetComponent.amount(), 3, 2, 100.
            ) + "% |";
        }

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

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

        int frontierSampleUnits = 20;
        double riskToleranceFactor = 0.;
        String seriesLocation =
            "C:\\DROP\\Daemons\\Feeds\\MeanVarianceOptimizer\\FormattedSeries1.csv";

        CSVGrid csvGrid = CSVParser.NamedStringGrid (
            seriesLocation
        );

        String[] variateHeaderArray = csvGrid.headers();

        String[] assetIDArray = new String[variateHeaderArray.length - 1];
        double[][] variateSampleGrid = new double[variateHeaderArray.length - 1][];

        for (int assetIndex = 0;
            assetIndex < assetIDArray.length;
            ++assetIndex)
        {
            assetIDArray[assetIndex] = variateHeaderArray[assetIndex + 1];

            variateSampleGrid[assetIndex] = csvGrid.doubleArrayAtColumn (assetIndex + 1);
        }

        MarkovitzBullet markovitzBullet = new QuadraticMeanVarianceOptimizer().efficientFrontier (
            new HoldingsAllocationControl (
                assetIDArray,
                CustomRiskUtilitySettings.RiskTolerant (riskToleranceFactor),
                new EqualityConstraintSettings (
                    EqualityConstraintSettings.FULLY_INVESTED_CONSTRAINT,
                    Double.NaN
                )
            ),
            AssetUniverseStatisticalProperties.FromMultivariateMetrics (
                MultivariateMoments.Standard (
                    assetIDArray,
                    variateSampleGrid
                )
            ),
            frontierSampleUnits
        );

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

        System.out.println ("\t|                     GLOBAL MINIMUM VARIANCE AND MAXIMUM RETURNS PORTFOLIOS                    ||");

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

        String header = "\t| RETURNS | RISK % |";

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

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

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

        DisplayPortfolioMetrics (markovitzBullet.globalMinimumVariance());

        DisplayPortfolioMetrics (markovitzBullet.longOnlyMaximumReturns());

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

        TreeMap<Double, HoldingsAllocation> frontierPortfolioMap = markovitzBullet.optimalPortfolioMap();

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

        System.out.println ("\t|         EFFICIENT FRONTIER: PORTFOLIO RISK & RETURNS + CORRESPONDING ASSET ALLOCATION         ||");

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

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

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

        for (Map.Entry<Double, HoldingsAllocation> me : frontierPortfolioMap.entrySet())
        {
            DisplayPortfolioMetrics (
                me.getValue()
            );
        }

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

        EnvManager.TerminateEnv();
    }
}