OptimalMeasuresReconciler.java
package org.drip.sample.principal;
import org.drip.execution.dynamics.*;
import org.drip.execution.impact.*;
import org.drip.execution.nonadaptive.ContinuousPowerImpact;
import org.drip.execution.optimum.PowerImpactContinuous;
import org.drip.execution.parameters.*;
import org.drip.execution.principal.*;
import org.drip.execution.profiletime.*;
import org.drip.function.r1tor1.FlatUnivariate;
import org.drip.numerical.common.FormatUtil;
import org.drip.service.env.EnvManager;
/*
* -*- mode: java; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*-
*/
/*!
* Copyright (C) 2018 Lakshmi Krishnamurthy
* Copyright (C) 2017 Lakshmi Krishnamurthy
* Copyright (C) 2016 Lakshmi Krishnamurthy
*
* This file is part of DRIP, a free-software/open-source library for buy/side financial/trading model
* libraries targeting analysts and developers
* https://lakshmidrip.github.io/DRIP/
*
* DRIP is composed of four main libraries:
*
* - DRIP Fixed Income - https://lakshmidrip.github.io/DRIP-Fixed-Income/
* - DRIP Asset Allocation - https://lakshmidrip.github.io/DRIP-Asset-Allocation/
* - DRIP Numerical Optimizer - https://lakshmidrip.github.io/DRIP-Numerical-Optimizer/
* - DRIP Statistical Learning - https://lakshmidrip.github.io/DRIP-Statistical-Learning/
*
* - DRIP Fixed Income: Library for Instrument/Trading Conventions, Treasury Futures/Options,
* Funding/Forward/Overnight Curves, Multi-Curve Construction/Valuation, Collateral Valuation and XVA
* Metric Generation, Calibration and Hedge Attributions, Statistical Curve Construction, Bond RV
* Metrics, Stochastic Evolution and Option Pricing, Interest Rate Dynamics and Option Pricing, LMM
* Extensions/Calibrations/Greeks, Algorithmic Differentiation, and Asset Backed Models and Analytics.
*
* - DRIP Asset Allocation: Library for model libraries for MPT framework, Black Litterman Strategy
* Incorporator, Holdings Constraint, and Transaction Costs.
*
* - DRIP Numerical Optimizer: Library for Numerical Optimization and Spline Functionality.
*
* - DRIP Statistical Learning: Library for Statistical Evaluation and Machine Learning.
*
* 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.
*/
/**
* OptimalMeasuresReconciler reconciles the Dependence Exponents on Liquidity, Trade Size, and Permanent
* Impact Adjusted Principal Discount for the Optimal Principal Horizon and the Optional Information Ratio
* with Almgren and Chriss (2003). The References are:
*
* - Almgren, R., and N. Chriss (1999): Value under Liquidation, Risk 12 (12).
*
* - Almgren, R., and N. Chriss (2000): Optimal Execution of Portfolio Transactions, Journal of Risk 3 (2)
* 5-39.
*
* - Almgren, R. (2003): Optimal Execution with Nonlinear Impact Functions and Trading-Enhanced Risk,
* Applied Mathematical Finance 10 (1) 1-18.
*
* - Almgren, R., and N. Chriss (2003): Bidding Principles, Risk 16 (6) 97-102.
*
* - Almgren, R., C. Thum, E. Hauptmann, and H. Li (2005): Equity Market Impact, Risk 18 (7) 57-62.
*
* @author Lakshmi Krishnamurthy
*/
public class OptimalMeasuresReconciler {
public static final void main (
final String[] astrArgs)
throws Exception
{
EnvManager.InitEnv ("");
double dblS0 = 50.;
double dblDailyVolume = 1000000.;
double dblBidAskSpread = 0.;
double dblPermanentImpactFactor = 0.;
double dblTemporaryImpactFactor = 0.01;
double dblDailyVolumeExecutionFactor = 0.1;
double dblDrift = 0.;
double dblVolatility = 1.;
double dblSerialCorrelation = 0.;
double dblX = 100000.;
double dblFinishTime = 1.;
double dblLambda = 5.e-06;
double[] adblK = new double[] {
0.5,
1.0,
2.0
};
double[][] aadblOptimalHorizonDependenceReconciler = new double[][] {
{0.810, 2.0, 1.0, 0.0, -2.0},
{1.000, 1.0, 1.0, 0.0, -1.0},
{1.134, 0.5, 1.0, 0.0, -0.5}
};
double[][] aadblOptimalInformationRatioDependenceReconciler = new double[][] {
{1.063, -2.0, -1.0, -1.0, 3.0},
{0.707, -1.0, -1.0, -1.0, 2.0},
{0.449, -0.5, -1.0, -1.0, 1.5}
};
System.out.println();
System.out.println ("\t|-----------------------------------------------------||");
System.out.println ("\t| Optimal Market Parameters Horizon Dependence ||");
System.out.println ("\t|-----------------------------------------------------||");
for (double dblK : adblK) {
PriceMarketImpactPower pmip = new PriceMarketImpactPower (
new AssetTransactionSettings (
dblS0,
dblDailyVolume,
dblBidAskSpread
),
dblPermanentImpactFactor,
dblTemporaryImpactFactor,
dblDailyVolumeExecutionFactor,
dblK
);
LinearPermanentExpectationParameters lpep = ArithmeticPriceEvolutionParametersBuilder.Almgren2003 (
new ArithmeticPriceDynamicsSettings (
dblDrift,
new FlatUnivariate (dblVolatility),
dblSerialCorrelation
),
new UniformParticipationRateLinear ((ParticipationRateLinear) pmip.permanentTransactionFunction()),
new UniformParticipationRate ((ParticipationRatePower) pmip.temporaryTransactionFunction())
);
Almgren2003Estimator a2003e = new Almgren2003Estimator (
(PowerImpactContinuous) ContinuousPowerImpact.Standard (
dblX,
dblFinishTime,
lpep,
dblLambda
).generate(),
lpep
);
OptimalMeasureDependence omd = a2003e.optimalMeasures().omdHorizon();
System.out.println (
"\t| " +
FormatUtil.FormatDouble (dblK, 1, 3, 1.) + " | " +
FormatUtil.FormatDouble (omd.constant(), 1, 3, 1.) + " | " +
FormatUtil.FormatDouble (omd.liquidityExponent(), 1, 3, 1.) + " | " +
FormatUtil.FormatDouble (omd.blockSizeExponent(), 1, 3, 1.) + " | " +
FormatUtil.FormatDouble (omd.volatilityExponent(), 1, 3, 1.) + " | " +
FormatUtil.FormatDouble (omd.adjustedPrincipalDiscountExponent(), 1, 3, 1.) + " ||"
);
}
System.out.println ("\t|-----------------------------------------------------||");
System.out.println ("\t|-----------------------------------------------------||");
for (int i = 0; i < adblK.length; ++i)
System.out.println (
"\t| " +
FormatUtil.FormatDouble (adblK[i], 1, 3, 1.) + " | " +
FormatUtil.FormatDouble (aadblOptimalHorizonDependenceReconciler[i][0], 1, 3, 1.) + " | " +
FormatUtil.FormatDouble (aadblOptimalHorizonDependenceReconciler[i][1], 1, 3, 1.) + " | " +
FormatUtil.FormatDouble (aadblOptimalHorizonDependenceReconciler[i][2], 1, 3, 1.) + " | " +
FormatUtil.FormatDouble (aadblOptimalHorizonDependenceReconciler[i][3], 1, 3, 1.) + " | " +
FormatUtil.FormatDouble (aadblOptimalHorizonDependenceReconciler[i][4], 1, 3, 1.) + " ||"
);
System.out.println ("\t|-----------------------------------------------------||");
System.out.println();
System.out.println ("\t|-----------------------------------------------------||");
System.out.println ("\t| Optimal Market Parameters Information Ratio ||");
System.out.println ("\t|-----------------------------------------------------||");
for (double dblK : adblK) {
PriceMarketImpactPower pmip = new PriceMarketImpactPower (
new AssetTransactionSettings (
dblS0,
dblDailyVolume,
dblBidAskSpread
),
dblPermanentImpactFactor,
dblTemporaryImpactFactor,
dblDailyVolumeExecutionFactor,
dblK
);
LinearPermanentExpectationParameters lpep = ArithmeticPriceEvolutionParametersBuilder.Almgren2003 (
new ArithmeticPriceDynamicsSettings (
dblDrift,
new FlatUnivariate (dblVolatility),
dblSerialCorrelation
),
new UniformParticipationRateLinear ((ParticipationRateLinear) pmip.permanentTransactionFunction()),
new UniformParticipationRate ((ParticipationRatePower) pmip.temporaryTransactionFunction())
);
Almgren2003Estimator a2003e = new Almgren2003Estimator (
(PowerImpactContinuous) ContinuousPowerImpact.Standard (
dblX,
dblFinishTime,
lpep,
dblLambda
).generate(),
lpep
);
OptimalMeasureDependence omd = a2003e.optimalMeasures().omdInformationRatio();
System.out.println (
"\t| " +
FormatUtil.FormatDouble (dblK, 1, 3, 1.) + " | " +
FormatUtil.FormatDouble (omd.constant(), 1, 3, 1.) + " | " +
FormatUtil.FormatDouble (omd.liquidityExponent(), 1, 3, 1.) + " | " +
FormatUtil.FormatDouble (omd.blockSizeExponent(), 1, 3, 1.) + " | " +
FormatUtil.FormatDouble (omd.volatilityExponent(), 1, 3, 1.) + " | " +
FormatUtil.FormatDouble (omd.adjustedPrincipalDiscountExponent(), 1, 3, 1.) + " ||"
);
}
System.out.println ("\t|-----------------------------------------------------||");
System.out.println ("\t|-----------------------------------------------------||");
for (int i = 0; i < adblK.length; ++i)
System.out.println (
"\t| " +
FormatUtil.FormatDouble (adblK[i], 1, 3, 1.) + " | " +
FormatUtil.FormatDouble (aadblOptimalInformationRatioDependenceReconciler[i][0], 1, 3, 1.) + " | " +
FormatUtil.FormatDouble (aadblOptimalInformationRatioDependenceReconciler[i][1], 1, 3, 1.) + " | " +
FormatUtil.FormatDouble (aadblOptimalInformationRatioDependenceReconciler[i][2], 1, 3, 1.) + " | " +
FormatUtil.FormatDouble (aadblOptimalInformationRatioDependenceReconciler[i][3], 1, 3, 1.) + " | " +
FormatUtil.FormatDouble (aadblOptimalInformationRatioDependenceReconciler[i][4], 1, 3, 1.) + " ||"
);
System.out.println ("\t|-----------------------------------------------------||");
}
}