TrajectoryShortfallAggregate.java
package org.drip.execution.capture;
/*
* -*- 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>TrajectoryShortfallAggregate</i> aggregates the Execution Short-fall Distribution across each Interval
* in the Trade. The References are:
*
* <br><br>
* <ul>
* <li>
* Almgren, R., and N. Chriss (1999): Value under Liquidation <i>Risk</i> <b>12 (12)</b>
* </li>
* <li>
* Almgren, R., and N. Chriss (2000): Optimal Execution of Portfolio Transactions <i>Journal of
* Risk</i> <b>3 (2)</b> 5-39
* </li>
* <li>
* Bertsimas, D., and A. W. Lo (1998): Optimal Control of Execution Costs <i>Journal of Financial
* Markets</i> <b>1</b> 1-50
* </li>
* <li>
* Chan, L. K. C., and J. Lakonishak (1995): The Behavior of Stock Prices around Institutional
* Trades <i>Journal of Finance</i> <b>50</b> 1147-1174
* </li>
* <li>
* Keim, D. B., and A. Madhavan (1997): Transaction Costs and Investment Style: An Inter-exchange
* Analysis of Institutional Equity Trades <i>Journal of Financial Economics</i> <b>46</b>
* 265-292
* </li>
* </ul>
*
* <br><br>
* <ul>
* <li><b>Module </b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/ProductCore.md">Product Core Module</a></li>
* <li><b>Library</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/TransactionCostAnalyticsLibrary.md">Transaction Cost Analytics</a></li>
* <li><b>Project</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/src/main/java/org/drip/execution/README.md">Optimal Impact/Capture Based Trading Trajectories - Deterministic, Stochastic, Static, and Dynamic</a></li>
* <li><b>Package</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/src/main/java/org/drip/execution/capture/README.md">Execution Trajectory Transaction Cost Capture</a></li>
* </ul>
*
* @author Lakshmi Krishnamurthy
*/
public class TrajectoryShortfallAggregate {
private java.util.List<org.drip.execution.discrete.ShortfallIncrementDistribution> _lsSID = null;
/**
* TrajectoryShortfallAggregate Constructor
*
* @param lsSID List of the Incremental Slice Short-fall Distributions
*
* @throws java.lang.Exception Thrown if the Inputs are Invalid
*/
public TrajectoryShortfallAggregate (
final java.util.List<org.drip.execution.discrete.ShortfallIncrementDistribution> lsSID)
throws java.lang.Exception
{
if (null == (_lsSID = lsSID))
throw new java.lang.Exception ("TrajectoryShortfallAggregate Constructor => Invalid Inputs");
int iNumInterval = _lsSID.size();
if (0 == iNumInterval)
throw new java.lang.Exception ("TrajectoryShortfallAggregate Constructor => Invalid Inputs");
for (org.drip.execution.discrete.ShortfallIncrementDistribution sid : _lsSID) {
if (null == sid)
throw new java.lang.Exception ("TrajectoryShortfallAggregate Constructor => Invalid Inputs");
}
}
/**
* Retrieve the List of the Interval Cost Distributions
*
* @return The List of the Interval Cost Distributions
*/
public java.util.List<org.drip.execution.discrete.ShortfallIncrementDistribution> list()
{
return _lsSID;
}
/**
* Generate the Total Cost R^1 Normal Distribution
*
* @return The Total Cost R^1 Normal Distribution
*/
public org.drip.measure.gaussian.R1UnivariateNormal totalCostDistribution()
{
double dblTotalCostMean = 0.;
double dblTotalCostVariance = 0.;
for (org.drip.measure.gaussian.R1UnivariateNormal r1un : _lsSID) {
dblTotalCostMean = dblTotalCostMean + r1un.mean();
dblTotalCostVariance = dblTotalCostVariance + r1un.variance();
}
try {
return new org.drip.measure.gaussian.R1UnivariateNormal (dblTotalCostMean, java.lang.Math.sqrt
(dblTotalCostVariance));
} catch (java.lang.Exception e) {
e.printStackTrace();
}
return null;
}
/**
* Generate the Array of Incremental Expectation Sequence
*
* @return The Array of Incremental Expectation Sequence
*/
public double[] incrementalExpectation()
{
int iNumInterval = _lsSID.size();
double[] adblIncrementalExpectationSequence = new double[iNumInterval];
for (int i = 0; i < iNumInterval; ++i)
adblIncrementalExpectationSequence[i] = _lsSID.get (i).mean();
return adblIncrementalExpectationSequence;
}
/**
* Generate the Array of Cumulative Expectation Sequence
*
* @return The Array of Cumulative Expectation Sequence
*/
public double[] cumulativeExpectation()
{
int iNumInterval = _lsSID.size();
double[] adblCumulativeExpectationSequence = new double[iNumInterval];
for (int i = 0; i < iNumInterval; ++i)
adblCumulativeExpectationSequence[i] = 0 == i ? _lsSID.get (i).expectation() :
adblCumulativeExpectationSequence[i - 1] + _lsSID.get (i).expectation();
return adblCumulativeExpectationSequence;
}
/**
* Generate the Array of Incremental Variance Sequence
*
* @return The Array of Incremental Variance Sequence
*/
public double[] incrementalVariance()
{
int iNumInterval = _lsSID.size();
double[] adblIncrementalVarianceSequence = new double[iNumInterval];
for (int i = 0; i < iNumInterval; ++i)
adblIncrementalVarianceSequence[i] = _lsSID.get (i).variance();
return adblIncrementalVarianceSequence;
}
/**
* Generate the Array of Cumulative Variance Sequence
*
* @return The Array of Cumulative Variance Sequence
*/
public double[] cumulativeVariance()
{
int iNumInterval = _lsSID.size();
double[] adblCumulativeVarianceSequence = new double[iNumInterval];
for (int i = 0; i < iNumInterval; ++i)
adblCumulativeVarianceSequence[i] = 0 == i ? _lsSID.get (i).variance() :
adblCumulativeVarianceSequence[i - 1] + _lsSID.get (i).variance();
return adblCumulativeVarianceSequence;
}
/**
* Generate the Array of Incremental Market Dynamic Expectation Sequence
*
* @return The Array of Incremental Market Dynamic Expectation Sequence
*/
public double[] incrementalMarketDynamicExpectation()
{
int iNumInterval = _lsSID.size();
double[] adblIncrementalMarketDynamicExpectationSequence = new double[iNumInterval];
for (int i = 0; i < iNumInterval; ++i)
adblIncrementalMarketDynamicExpectationSequence[i] = _lsSID.get (i).marketDynamicExpectation();
return adblIncrementalMarketDynamicExpectationSequence;
}
/**
* Generate the Array of Cumulative Market Dynamic Expectation Sequence
*
* @return The Array of Cumulative Market Dynamic Expectation Sequence
*/
public double[] cumulativeMarketDynamicExpectation()
{
int iNumInterval = _lsSID.size();
double[] adblCumulativeMarketDynamicExpectationSequence = new double[iNumInterval];
for (int i = 0; i < iNumInterval; ++i)
adblCumulativeMarketDynamicExpectationSequence[i] = 0 == i ? _lsSID.get
(i).marketDynamicExpectation() : adblCumulativeMarketDynamicExpectationSequence[i - 1] +
_lsSID.get (i).marketDynamicExpectation();
return adblCumulativeMarketDynamicExpectationSequence;
}
/**
* Generate the Array of Incremental Permanent Impact Expectation Sequence
*
* @return The Array of Incremental Permanent Impact Expectation Sequence
*/
public double[] incrementalPermanentImpactExpectation()
{
int iNumInterval = _lsSID.size();
double[] adblIncrementalPermanentImpactExpectationSequence = new double[iNumInterval];
for (int i = 0; i < iNumInterval; ++i)
adblIncrementalPermanentImpactExpectationSequence[i] = _lsSID.get
(i).permanentImpactExpectation();
return adblIncrementalPermanentImpactExpectationSequence;
}
/**
* Generate the Array of Cumulative Permanent Impact Expectation Sequence
*
* @return The Array of Cumulative Permanent Impact Expectation Sequence
*/
public double[] cumulativePermanentImpactExpectation()
{
int iNumInterval = _lsSID.size();
double[] adblCumulativePermanentImpactExpectationSequence = new double[iNumInterval];
for (int i = 0; i < iNumInterval; ++i)
adblCumulativePermanentImpactExpectationSequence[i] = 0 == i ? _lsSID.get
(i).permanentImpactExpectation() : adblCumulativePermanentImpactExpectationSequence[i - 1] +
_lsSID.get (i).permanentImpactExpectation();
return adblCumulativePermanentImpactExpectationSequence;
}
/**
* Generate the Array of Incremental Temporary Impact Expectation Sequence
*
* @return The Array of Incremental Temporary Impact Expectation Sequence
*/
public double[] incrementalTemporaryImpactExpectation()
{
int iNumInterval = _lsSID.size();
double[] adblIncrementalTemporaryImpactExpectationSequence = new double[iNumInterval];
for (int i = 0; i < iNumInterval; ++i)
adblIncrementalTemporaryImpactExpectationSequence[i] = _lsSID.get
(i).temporaryImpactExpectation();
return adblIncrementalTemporaryImpactExpectationSequence;
}
/**
* Generate the Array of Cumulative Temporary Impact Expectation Sequence
*
* @return The Array of Cumulative Temporary Impact Expectation Sequence
*/
public double[] cumulativeTemporaryImpactExpectation()
{
int iNumInterval = _lsSID.size();
double[] adblCumulativeTemporaryImpactExpectationSequence = new double[iNumInterval];
for (int i = 0; i < iNumInterval; ++i)
adblCumulativeTemporaryImpactExpectationSequence[i] = 0 == i ? _lsSID.get
(i).temporaryImpactExpectation() : adblCumulativeTemporaryImpactExpectationSequence[i - 1] +
_lsSID.get (i).temporaryImpactExpectation();
return adblCumulativeTemporaryImpactExpectationSequence;
}
/**
* Generate the Expected Short-fall
*
* @return The Expected Short-fall
*/
public double shortfallExpectation()
{
int iNumInterval = _lsSID.size();
double dblExpectedShortfall = 0.;
for (int i = 0; i < iNumInterval; ++i)
dblExpectedShortfall = dblExpectedShortfall + _lsSID.get (i).expectation();
return dblExpectedShortfall;
}
/**
* Generate the Short-fall Variance
*
* @return The Short-fall Variance
*/
public double shortfallVariance()
{
int iNumInterval = _lsSID.size();
double dblShortfallVariance = 0.;
for (int i = 0; i < iNumInterval; ++i)
dblShortfallVariance = dblShortfallVariance + _lsSID.get (i).variance();
return dblShortfallVariance;
}
}