BayesianGain.java
package org.drip.sample.trend;
import org.drip.execution.bayesian.*;
import org.drip.execution.cost.*;
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.
*/
/**
* BayesianGain demonstrates the Gains achieved from using an Optimal Trajectory for a Price Process with
* Bayesian Drift, Arithmetic Volatility, and Linear Temporary Market Impact across a Set of Drifts. 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 BayesianGain {
public static final void main (
final String[] astrArgs)
throws Exception
{
EnvManager.InitEnv ("");
int iN = 50;
double dblT = 1.;
double dblX0 = 1.;
double dblNu = 1.;
double dblEta = 0.07;
double dblSigma = 1.5;
double dblAlphaBar = 0.7;
double dblTime = 0.;
double dblTimeWidth = dblT / iN;
double dblXUnconstrained = dblX0;
ParticipationRateLinear prlTemporary = ParticipationRateLinear.SlopeOnly (dblEta);
PriorDriftDistribution pdd = new PriorDriftDistribution (
dblAlphaBar,
dblNu
);
double[] adblAlpha = pdd.realizedDrift (iN);
System.out.println();
System.out.println ("\t|-----------------------------------------------------------------------||");
System.out.println ("\t| L -> R ||");
System.out.println ("\t|-----------------------------------------------------------------------||");
System.out.println ("\t| - Time ||");
System.out.println ("\t| - Realized Drift ||");
System.out.println ("\t| - Realized Price Change ||");
System.out.println ("\t| - Estimated Drift ||");
System.out.println ("\t| - Unconstrained Trade Rate ||");
System.out.println ("\t| - Unconstrained Holdings ||");
System.out.println ("\t| - Transaction Cost ||");
System.out.println ("\t| - Transaction Cost Gain ||");
System.out.println ("\t|-----------------------------------------------------------------------||");
for (int i = 0; i < iN - 1; ++i) {
dblTime = dblTime + dblTimeWidth;
ConditionalPriceDistribution cpd = new ConditionalPriceDistribution (
adblAlpha[i],
dblSigma,
dblTime
);
double dblPriceSwing = cpd.priceVolatilitySwing();
double dblRealizedPriceChange = adblAlpha[i] * dblTimeWidth + dblPriceSwing;
PriorConditionalCombiner pcc = new PriorConditionalCombiner (
pdd,
cpd
);
LinearTemporaryImpact lti = LinearTemporaryImpact.Unconstrained (
dblTime,
dblT,
dblXUnconstrained,
pcc,
dblRealizedPriceChange,
prlTemporary
);
double dblUnconstrainedInstantaneousTradeRate = lti.instantaneousTradeRate();
dblXUnconstrained = dblXUnconstrained - dblUnconstrainedInstantaneousTradeRate * dblTimeWidth;
System.out.println (
"\t| " + FormatUtil.FormatDouble (dblTime, 1, 2, 1.) + " => " +
FormatUtil.FormatDouble (adblAlpha[i], 1, 3, 1.) + " | " +
FormatUtil.FormatDouble (dblRealizedPriceChange, 1, 3, 1.) + " | " +
FormatUtil.FormatDouble (lti.driftExpectationEstimate(), 1, 3, 1.) + " | " +
FormatUtil.FormatDouble (dblUnconstrainedInstantaneousTradeRate, 1, 3, 1.) + " | " +
FormatUtil.FormatDouble (dblXUnconstrained, 1, 3, 1.) + " | " +
FormatUtil.FormatDouble (lti.staticTransactionCost(), 1, 3, 1.) + " | " +
FormatUtil.FormatDouble (lti.transactionCostGain(), 1, 3, 1.) + " ||"
);
}
System.out.println ("\t|-----------------------------------------------------------------------||");
}
}