MeanVarianceOptimizer.java
package org.drip.portfolioconstruction.allocator;
/*
* -*- 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>MeanVarianceOptimizer</i> exposes Portfolio Construction using Mean Variance Optimization Techniques.
*
* <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/portfolioconstruction/README.md">Portfolio Construction under Allocation Constraints</a></li>
* <li><b>Package</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/src/main/java/org/drip/portfolioconstruction/allocator/README.md">MVO Based Portfolio Allocation Construction</a></li>
* </ul>
*
* @author Lakshmi Krishnamurthy
*/
public abstract class MeanVarianceOptimizer
{
protected abstract org.drip.portfolioconstruction.allocator.HoldingsAllocationControl
constrainedPCP (
final org.drip.portfolioconstruction.allocator.HoldingsAllocationControl
designPortfolioConstructionParameters,
final double returnsConstraint);
/**
* Allocate the Long-Only Maximum Returns Portfolio
*
* @param portfolioConstructionParameters The Portfolio Construction Parameters
* @param assetUniverseStatisticalProperties The Asset Universe Statistical Properties Instance
*
* @return The Long-Only Maximum Returns Portfolio
*/
public abstract org.drip.portfolioconstruction.allocator.HoldingsAllocation
longOnlyMaximumReturnsAllocate (
final org.drip.portfolioconstruction.allocator.HoldingsAllocationControl
portfolioConstructionParameters,
final org.drip.portfolioconstruction.params.AssetUniverseStatisticalProperties
assetUniverseStatisticalProperties);
/**
* Allocate the Global Minimum Variance Portfolio without any Returns Constraints in the Parameters
*
* @param portfolioConstructionParameters The Portfolio Construction Parameters
* @param assetUniverseStatisticalProperties The Asset Universe Statistical Properties Instance
*
* @return The Global Minimum Variance Portfolio
*/
public abstract org.drip.portfolioconstruction.allocator.HoldingsAllocation
globalMinimumVarianceAllocate (
final org.drip.portfolioconstruction.allocator.HoldingsAllocationControl
portfolioConstructionParameters,
final org.drip.portfolioconstruction.params.AssetUniverseStatisticalProperties
assetUniverseStatisticalProperties);
/**
* Allocate the Optimal Portfolio Weights given the Portfolio Construction Parameters
*
* @param portfolioConstructionParameters The Portfolio Construction Parameters
* @param assetUniverseStatisticalProperties The Asset Universe Statistical Properties Instance
*
* @return The Optimal Portfolio
*/
public abstract org.drip.portfolioconstruction.allocator.HoldingsAllocation allocate (
final org.drip.portfolioconstruction.allocator.HoldingsAllocationControl
portfolioConstructionParameters,
final org.drip.portfolioconstruction.params.AssetUniverseStatisticalProperties
assetUniverseStatisticalProperties);
/**
* Generate the Efficient Frontier given the Portfolio Construction Parameters
*
* @param portfolioConstructionParameters The Portfolio Construction Parameters
* @param assetUniverseStatisticalProperties The Asset Universe Statistical Properties Instance
* @param frontierSampleUnits The Number of Frontier Sample Units
*
* @return The Efficient Frontier
*/
public org.drip.portfolioconstruction.mpt.MarkovitzBullet efficientFrontier (
final org.drip.portfolioconstruction.allocator.HoldingsAllocationControl
portfolioConstructionParameters,
final org.drip.portfolioconstruction.params.AssetUniverseStatisticalProperties
assetUniverseStatisticalProperties,
final int frontierSampleUnits)
{
if (0 >= frontierSampleUnits)
{
return null;
}
org.drip.portfolioconstruction.allocator.HoldingsAllocation globalMinimumVarianceOptimizationOutput =
globalMinimumVarianceAllocate (
portfolioConstructionParameters,
assetUniverseStatisticalProperties
);
if (null == globalMinimumVarianceOptimizationOutput)
{
return null;
}
org.drip.portfolioconstruction.allocator.HoldingsAllocation longOnlyMaximumReturnsOptimizationOutput
= longOnlyMaximumReturnsAllocate (
portfolioConstructionParameters,
assetUniverseStatisticalProperties
);
if (null == longOnlyMaximumReturnsOptimizationOutput)
{
return null;
}
double globalMinimumVarianceReturns =
globalMinimumVarianceOptimizationOutput.optimalMetrics().excessReturnsMean();
double longOnlyMaximumReturns =
longOnlyMaximumReturnsOptimizationOutput.optimalMetrics().excessReturnsMean();
double returnsConstraintGridWidth = (longOnlyMaximumReturns - globalMinimumVarianceReturns) /
frontierSampleUnits;
double returnsConstraint = globalMinimumVarianceReturns + returnsConstraintGridWidth;
org.drip.portfolioconstruction.mpt.MarkovitzBullet markovitzBullet = null;
try
{
markovitzBullet = new org.drip.portfolioconstruction.mpt.MarkovitzBullet (
globalMinimumVarianceOptimizationOutput,
longOnlyMaximumReturnsOptimizationOutput
);
}
catch (java.lang.Exception e)
{
e.printStackTrace();
return null;
}
while (returnsConstraint <= longOnlyMaximumReturns)
{
try
{
markovitzBullet.addOptimalPortfolio (
allocate (
constrainedPCP (
portfolioConstructionParameters,
returnsConstraint
),
assetUniverseStatisticalProperties
)
);
}
catch (java.lang.Exception e)
{
e.printStackTrace();
return null;
}
returnsConstraint += returnsConstraintGridWidth;
}
return markovitzBullet;
}
}