PriorDriftDistribution.java
package org.drip.execution.bayesian;
/*
* -*- 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>PriorDriftDistribution</i> holds the Prior Belief Distribution associated with the Directional Drift.
* The References are:
*
* <br><br>
* <ul>
* <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>
* Almgren, R., and N. Chriss (2000): Optimal Execution of Portfolio Transactions <i>Journal of
* Risk</i> <b>3 (2)</b> 5-39
* </li>
* <li>
* Brunnermeier, L. K., and L. H. Pedersen (2005): Predatory Trading <i>Journal of Finance</i> <b>60
* (4)</b> 1825-1863
* </li>
* <li>
* Almgren, R., and J. Lorenz (2006): Bayesian Adaptive Trading with a Daily Cycle <i>Journal of
* Trading</i> <b>1 (4)</b> 38-46
* </li>
* <li>
* Kissell, R., and R. Malamut (2007): Algorithmic Decision Making Framework <i>Journal of
* Trading</i> <b>1 (1)</b> 12-21
* </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/bayesian/README.md">Bayesian Price Based Optimal Execution</a></li>
* </ul>
*
* @author Lakshmi Krishnamurthy
*/
public class PriorDriftDistribution extends org.drip.measure.gaussian.R1UnivariateNormal {
/**
* Construct an Instance of Prior Drift Distribution
*
* @param dblExpectation Expectation of the Prior Drift
* @param dblConfidence Confidence of the Prior Drift
*
* @throws java.lang.Exception Thrown if the Inputs are Invalid
*/
public PriorDriftDistribution (
final double dblExpectation,
final double dblConfidence)
throws java.lang.Exception
{
super (dblExpectation, dblConfidence);
}
/**
* Retrieve the Expectation of the Prior Drift Distribution
*
* @return The Expectation of the Prior Drift Distribution
*/
public double expectation()
{
return mean();
}
/**
* Retrieve the Confidence of the Prior Drift Distribution
*
* @return The Confidence of the Prior Drift Distribution
*/
public double confidence()
{
return java.lang.Math.sqrt (variance());
}
/**
* Generate the given Number of Bayesian Drift Realizations
*
* @param iNumRealization The Number of Realizations to be generated
*
* @return Array of the Drift Realizations
*/
public double[] realizedDrift (
final int iNumRealization)
{
if (0 >= iNumRealization) return null;
double[] adblRealizedDrift = new double[iNumRealization];
double dblConfidence = confidence();
double dblExpectation = mean();
for (int i = 0; i < iNumRealization; ++i) {
try {
adblRealizedDrift[i] = dblExpectation + dblConfidence *
org.drip.measure.gaussian.NormalQuadrature.InverseCDF (java.lang.Math.random());
} catch (java.lang.Exception e) {
e.printStackTrace();
return null;
}
}
return adblRealizedDrift;
}
}