BlackLittermanProcessor.java

package org.drip.json.assetallocation;

/*
 * -*- 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>BlackLittermanProcessor</i> Sets Up and Executes a JSON Based In/Out Processing Service for the Black
 * Litterman Bayesian View Incorporation/Parameter Estimation.
 *
 *  <br><br>
 *  <ul>
 *      <li><b>Module </b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/ComputationalCore.md">Computational Core Module</a></li>
 *      <li><b>Library</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/ComputationSupportLibrary.md">Computation Support</a></li>
 *      <li><b>Project</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/src/main/java/org/drip/json">RFC-4627 Compliant JSON Encoder/Decoder (Parser)</a></li>
 *      <li><b>Package</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/src/main/java/org/drip/json/assetallocation">JSON Based In/Out Service</a></li>
 *  </ul>
 *
 * @author Lakshmi Krishnamurthy
 */

public class BlackLittermanProcessor {

    /**
     * JSON Based in/out Bayesian Co-variance/Returns Estimation Thunker
     * 
     * @param jsonParameter Bayesian Co-variance/Returns Estimation Parameters
     * 
     * @return JSON Bayesian Co-variance/Returns Estimation Response
     */

    @SuppressWarnings ("unchecked") public static final org.drip.json.simple.JSONObject Estimate (
        final org.drip.json.simple.JSONObject jsonParameter)
    {
        java.lang.String[] astrAssetID = org.drip.json.parser.Converter.StringArrayEntry (jsonParameter,
            "AssetSet");

        double[][] aadblAssetSpaceViewProjection = org.drip.json.parser.Converter.DualDoubleArrayEntry
            (jsonParameter, "AssetSpaceViewProjection");

        double dblTau = java.lang.Double.NaN;
        double dblRiskAversion = java.lang.Double.NaN;
        double dblRiskFreeRate = java.lang.Double.NaN;
        org.drip.measure.gaussian.R1MultivariateNormal viewDistribution = null;
        org.drip.portfolioconstruction.bayesian.BlackLittermanCombinationEngine blce = null;
        int iNumView = null == aadblAssetSpaceViewProjection ? 0 : aadblAssetSpaceViewProjection.length;
        java.lang.String[] astrProjectionName = 0 == iNumView? null : new java.lang.String[iNumView];

        double[] adblAssetEquilibriumWeight = org.drip.json.parser.Converter.DoubleArrayEntry (jsonParameter,
            "AssetEquilibriumWeight");

        double[][] aadblAssetExcessReturnsCovariance = org.drip.json.parser.Converter.DualDoubleArrayEntry
            (jsonParameter, "AssetExcessReturnsCovariance");

        double[] adblProjectionExpectedExcessReturns = org.drip.json.parser.Converter.DoubleArrayEntry
            (jsonParameter, "ProjectionExpectedExcessReturns");

        double[][] aadblProjectionExcessReturnsCovariance =
            org.drip.json.parser.Converter.DualDoubleArrayEntry (jsonParameter,
                "ProjectionExcessReturnsCovariance");

        for (int i = 0; i < iNumView ; ++i)
            astrProjectionName[i] = "PROJECTION #" + i;

        try {
            dblTau = org.drip.json.parser.Converter.DoubleEntry (jsonParameter, "Tau");

            dblRiskAversion = org.drip.json.parser.Converter.DoubleEntry (jsonParameter, "Delta");

            dblRiskFreeRate = org.drip.json.parser.Converter.DoubleEntry (jsonParameter, "RiskFreeRate");

            viewDistribution = org.drip.measure.gaussian.R1MultivariateNormal.Standard (new
                org.drip.measure.continuous.MultivariateMeta (astrProjectionName),
                    adblProjectionExpectedExcessReturns, aadblProjectionExcessReturnsCovariance);

            blce = new org.drip.portfolioconstruction.bayesian.BlackLittermanCombinationEngine
                (org.drip.portfolioconstruction.allocator.ForwardReverseHoldingsAllocation.Reverse
                    (org.drip.portfolioconstruction.asset.Portfolio.Standard (astrAssetID,
                        adblAssetEquilibriumWeight), aadblAssetExcessReturnsCovariance, dblRiskAversion), new
                            org.drip.portfolioconstruction.bayesian.PriorControlSpecification (false,
                                dblRiskFreeRate, dblTau), new
                                    org.drip.portfolioconstruction.bayesian.ProjectionSpecification
                                        (viewDistribution, aadblAssetSpaceViewProjection));
        } catch (java.lang.Exception e) {
            e.printStackTrace();

            return null;
        }

        org.drip.portfolioconstruction.bayesian.BlackLittermanCustomConfidenceOutput blo = blce.customConfidenceRun();

        if (null == blo) return null;

        org.drip.measure.bayesian.R1MultivariateConvolutionMetrics jpm = blo.jointPosteriorMetrics();

        org.drip.measure.continuous.R1Multivariate r1mPrior = jpm.prior();

        org.drip.measure.continuous.R1Multivariate r1mJoint = jpm.joint();

        org.drip.measure.continuous.R1Multivariate r1mPosterior = jpm.posterior();

        if (null == r1mPrior || !(r1mPrior instanceof org.drip.measure.gaussian.R1MultivariateNormal) || null
            == r1mJoint || !(r1mJoint instanceof org.drip.measure.gaussian.R1MultivariateNormal) || null ==
                r1mPosterior || !(r1mPosterior instanceof org.drip.measure.gaussian.R1MultivariateNormal))
            return null;

        org.drip.measure.gaussian.R1MultivariateNormal r1mnPrior =
            (org.drip.measure.gaussian.R1MultivariateNormal) r1mPrior;
        org.drip.measure.gaussian.R1MultivariateNormal r1mnJoint =
            (org.drip.measure.gaussian.R1MultivariateNormal) r1mJoint;
        org.drip.measure.gaussian.R1MultivariateNormal r1mnPosterior =
            (org.drip.measure.gaussian.R1MultivariateNormal) r1mPosterior;

        org.drip.json.simple.JSONObject jsonResponse = new org.drip.json.simple.JSONObject();

        jsonResponse.put ("Tau", dblTau);

        jsonResponse.put ("Delta", dblRiskAversion);

        jsonResponse.put ("RiskFreeRate", dblRiskFreeRate);

        jsonResponse.put ("ScopingSet", org.drip.json.parser.Converter.Array (astrAssetID));

        jsonResponse.put ("ScopingExpectedExcessReturns", org.drip.json.parser.Converter.Array
            (r1mnPrior.mean()));

        jsonResponse.put ("ScopingExcessReturnsCovariance", org.drip.json.parser.Converter.Array
            (r1mnPrior.covariance().covarianceMatrix()));

        jsonResponse.put ("ProjectionExpectedExcessReturns", org.drip.json.parser.Converter.Array
            (adblProjectionExpectedExcessReturns));

        jsonResponse.put ("ViewScopingProjectionLoading", org.drip.json.parser.Converter.Array
            (aadblAssetSpaceViewProjection));

        jsonResponse.put ("JointExcessReturnsCovariance", org.drip.json.parser.Converter.Array
            (r1mnJoint.covariance().covarianceMatrix()));

        jsonResponse.put ("PosteriorExcessReturnsCovariance", org.drip.json.parser.Converter.Array
            (r1mnPosterior.covariance().covarianceMatrix()));

        jsonResponse.put ("PriorExpectedExcessReturn", org.drip.json.parser.Converter.Array
            (r1mPrior.mean()));

        jsonResponse.put ("PosteriorExpectedExcessReturn", org.drip.json.parser.Converter.Array
            (r1mPosterior.mean()));

        return jsonResponse;
    }
}