BayesianDriftTrajectoryDependence.java
package org.drip.sample.trend;
import org.drip.execution.bayesian.*;
import org.drip.execution.cost.LinearTemporaryImpact;
import org.drip.execution.impact.ParticipationRateLinear;
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.
*/
/**
* BayesianDriftTrajectoryDependence demonstrates the Dependence of the Trading Trajectory achieved from
* using an Optimal Trajectory for a Price Process as a Function of the Bayesian Drift Parameters. The
* References are:
*
* - Bertsimas, D., and A. W. Lo (1998): Optimal Control of Execution Costs, Journal of Financial Markets 1
* 1-50.
*
* - Almgren, R., and N. Chriss (2000): Optimal Execution of Portfolio Transactions, Journal of Risk 3 (2)
* 5-39.
*
* - Brunnermeier, L. K., and L. H. Pedersen (2005): Predatory Trading, Journal of Finance 60 (4) 1825-1863.
*
* - Almgren, R., and J. Lorenz (2006): Bayesian Adaptive Trading with a Daily Cycle, Journal of Trading 1
* (4) 38-46.
*
* - Kissell, R., and R. Malamut (2007): Algorithmic Decision Making Framework, Journal of Trading 1 (1)
* 12-21.
*
* @author Lakshmi Krishnamurthy
*/
public class BayesianDriftTrajectoryDependence {
static final void RunScenario (
final double dblAlphaBar,
final double dblNu,
final double dblSigma,
final double dblT,
final ParticipationRateLinear prlTemporary)
throws Exception
{
PriorDriftDistribution pdd = new PriorDriftDistribution (
dblAlphaBar,
dblNu
);
double dblTimeWidth = 0.5 * dblT;
double[] adblAlpha = pdd.realizedDrift (2);
ConditionalPriceDistribution cpd0 = new ConditionalPriceDistribution (
adblAlpha[0],
dblSigma,
1.0 * dblTimeWidth
);
double dblPriceSwing0 = cpd0.priceVolatilitySwing();
double dblRealizedPriceChange0 = adblAlpha[0] * dblTimeWidth + dblPriceSwing0;
PriorConditionalCombiner pcc0 = new PriorConditionalCombiner (
pdd,
cpd0
);
LinearTemporaryImpact lti0 = LinearTemporaryImpact.Unconstrained (
1.0 * dblTimeWidth,
dblT,
1.,
pcc0,
dblRealizedPriceChange0,
prlTemporary
);
double dblInstantanenousTradeRate0 = lti0.instantaneousTradeRate();
double dblX0 = 1. - dblInstantanenousTradeRate0 * dblTimeWidth;
ConditionalPriceDistribution cpd1 = new ConditionalPriceDistribution (
adblAlpha[1],
dblSigma,
2.0 * dblTimeWidth
);
double dblPriceSwing1 = cpd1.priceVolatilitySwing();
double dblRealizedPriceChange1 = adblAlpha[1] * dblTimeWidth + dblPriceSwing1;
PriorConditionalCombiner pcc1 = new PriorConditionalCombiner (
pdd,
cpd1
);
LinearTemporaryImpact lti1 = LinearTemporaryImpact.Unconstrained (
1.0 * dblTimeWidth,
dblT,
dblX0,
pcc1,
dblRealizedPriceChange1,
prlTemporary
);
double dblInstantanenousTradeRate1 = lti1.instantaneousTradeRate();
double dblX1 = 1. - dblInstantanenousTradeRate1 * dblTimeWidth;
System.out.println (
"\t|[" +
FormatUtil.FormatDouble (dblAlphaBar, 1, 1, 1.) + "," +
FormatUtil.FormatDouble (dblNu, 1, 1, 1.) + "," +
FormatUtil.FormatDouble (dblSigma, 1, 1, 1.) + "] => " +
FormatUtil.FormatDouble (dblX0, 1, 3, 1.) + " | " +
FormatUtil.FormatDouble (dblX1, 1, 3, 1.) + " || " +
FormatUtil.FormatDouble (dblInstantanenousTradeRate0, 1, 3, 1.) + " | " +
FormatUtil.FormatDouble (dblInstantanenousTradeRate1, 1, 3, 1.) + " || " +
FormatUtil.FormatDouble (lti0.driftExpectationEstimate(), 1, 3, 1.) + " | " +
FormatUtil.FormatDouble (lti1.driftExpectationEstimate(), 1, 3, 1.) + " || " +
FormatUtil.FormatDouble (lti0.driftVolatilityEstimate(), 3, 0, 100.) + "% | " +
FormatUtil.FormatDouble (lti1.driftVolatilityEstimate(), 3, 0, 100.) + "% || "
);
}
public static final void main (
final String[] astrArgs)
throws Exception
{
EnvManager.InitEnv ("");
double dblT = 1.;
double dblEta = 0.07;
double[] adblNu = new double[] {
0.5,
1.0,
2.0
};
double[] adblSigma = new double[] {
0.5,
1.7,
2.9
};
double[] adblAlphaBar = new double[] {
0.2,
0.7,
1.2
};
ParticipationRateLinear prlTemporary = ParticipationRateLinear.SlopeOnly (dblEta);
System.out.println();
System.out.println ("\t|-------------------------------------------------------------------------------------------||");
System.out.println ("\t| BAYESIAN GAIN INPUT DRIFT DISTRIBUTION DEPENDENCE ||");
System.out.println ("\t|-------------------------------------------------------------------------------------------||");
System.out.println ("\t| ||");
System.out.println ("\t| Inputs L -> R: ||");
System.out.println ("\t| - Alpha Bar ||");
System.out.println ("\t| - Sigma ||");
System.out.println ("\t| - Nu ||");
System.out.println ("\t| ||");
System.out.println ("\t|-------------------------------------------------------------------------------------------||");
System.out.println ("\t| ||");
System.out.println ("\t| Outputs L -> R: ||");
System.out.println ("\t| ||");
System.out.println ("\t| - Phase #1 Outstanding Holdings ||");
System.out.println ("\t| - Phase #2 Outstanding Holdings ||");
System.out.println ("\t| - Phase #1 Instantaneous Trade Rate ||");
System.out.println ("\t| - Phase #2 Instantaneous Trade Rate ||");
System.out.println ("\t| - Phase #1 Drift Expectation Estimate ||");
System.out.println ("\t| - Phase #2 Drift Expectation Estimate ||");
System.out.println ("\t| - Phase #1 Drift Volatility Estimate ||");
System.out.println ("\t| - Phase #2 Drift Volatility Estimate ||");
System.out.println ("\t| ||");
System.out.println ("\t|-------------------------------------------------------------------------------------------||");
for (double dblAlphaBar : adblAlphaBar) {
for (double dblNu : adblNu) {
for (double dblSigma : adblSigma)
RunScenario (
dblAlphaBar,
dblNu,
dblSigma,
dblT,
prlTemporary
);
}
}
System.out.println ("\t|-------------------------------------------------------------------------------------------||");
}
}