[Quantproject-developers] QuantProject/b4_Business/a2_Strategies/InSample GeneticChooser.cs, NONE,

SourceForge Headquarters 1320 Columbia Street Suite 310 San Diego, CA 92101 +1 (858) 422-6466

Update of /cvsroot/quantproject/QuantProject/b4_Business/a2_Strategies/InSample
In directory sc8-pr-cvs16.sourceforge.net:/tmp/cvs-serv4013

Added Files:
	GeneticChooser.cs 
Log Message:
Added GeneticChooser, an abstract class that should be inherited
by all the IInSampleChoosers that use genetic optimization.

--- NEW FILE: GeneticChooser.cs ---
/*
QuantProject - Quantitative Finance Library

GeneticChooser.cs
Copyright (C) 2008
Marco Milletti

This program is free software; you can redistribute it and/or
modify it under the terms of the GNU General Public License
as published by the Free Software Foundation; either version 2
of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program; if not, write to the Free Software
Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA  02111-1307, USA.
*/

using System;
using System.Collections;

using QuantProject.ADT;
using QuantProject.ADT.Messaging;
using QuantProject.ADT.Optimizing.Genetic;
using QuantProject.Business.DataProviders;
using QuantProject.Business.Strategies;
using QuantProject.Business.Strategies.Eligibles;
using QuantProject.Business.Strategies.EquityEvaluation;
using QuantProject.Business.Strategies.Optimizing.Decoding;
using QuantProject.Business.Strategies.Optimizing.FitnessEvaluation;
using QuantProject.Business.Strategies.ReturnsManagement;
using QuantProject.Business.Timing;
using QuantProject.Business.Strategies.OutOfSample;

namespace QuantProject.Business.Strategies.InSample
{
	/// <summary>
	/// Abstract GeneticChooser to be used for
	/// in sample optimization
	/// </summary>
	public abstract class GeneticChooser : IInSampleChooser
	{
		public event NewProgressEventHandler NewProgress;
		public event NewMessageEventHandler NewMessage;

		protected int numberOfPortfolioPositions;
		protected int numberOfBestTestingPositionsToBeReturned;
		protected bool choosePositionsWithAtLeastOneDifferentTicker;
		//the genetic chooser will return the requested number
		//of TestingPositions that have a different tickers'composition
		//(that is TestingPositions with a different HashCodeForTickerComposition)
		protected TestingPositions[] bestTestingPositions;
		protected Benchmark benchmark;
		protected IDecoderForTestingPositions decoderForTestingPositions;
		protected IFitnessEvaluator fitnessEvaluator;
		protected IHistoricalQuoteProvider historicalQuoteProvider;
		protected IGenomeManager genomeManager;
		protected double crossoverRate;
		protected double mutationRate;
		protected double elitismRate;
		protected int populationSizeForGeneticOptimizer;
		protected int generationNumberForGeneticOptimizer;
		protected int seedForRandomGenerator;

		protected GeneticOptimizer geneticOptimizer;

		public string Description
		{
			get
			{
				string description = "PopSize_" + this.populationSizeForGeneticOptimizer +
					"_GenNum_" + this.generationNumberForGeneticOptimizer +
					"_FitnEval_" + this.fitnessEvaluator.Description +
					"_DecoderForTestingPositions_" + this.decoderForTestingPositions;
				return description;
			}
		}

		/// <summary>
		/// Abstract GeneticChooser to be used for
		/// in sample optimization
		/// </summary>
		/// <param name="numberOfBestTestingPositionsToBeReturned">
		/// The number of TestingPositions that the
		/// AnalyzeInSample method will return
		/// </param>
		/// <param name="choosePositionsWithAtLeastOneDifferentTicker">
		/// If true, the returned TestingPositions 
		/// will be different in TickerComposition
		/// (there won't be two TestingPositions with 
		/// exactly the same tickers).
		/// If false, there will be returned just the best TestingPositions
		/// ordered by fitness by the genetic optimizer
		/// </param>
		public GeneticChooser(
			int numberOfPortfolioPositions , int numberOfBestTestingPositionsToBeReturned ,
			bool choosePositionsWithAtLeastOneDifferentTicker ,
			Benchmark benchmark ,
			IDecoderForTestingPositions decoderForTestingPositions ,
			IFitnessEvaluator fitnessEvaluator ,
			IHistoricalQuoteProvider historicalQuoteProvider ,
			double crossoverRate , double mutationRate , double elitismRate ,
			int populationSizeForGeneticOptimizer ,
			int generationNumberForGeneticOptimizer ,
			int seedForRandomGenerator )
		{
			this.numberOfPortfolioPositions = numberOfPortfolioPositions;
			this.numberOfBestTestingPositionsToBeReturned = numberOfBestTestingPositionsToBeReturned;
			this.bestTestingPositions = new TestingPositions[numberOfBestTestingPositionsToBeReturned];
			this.benchmark = benchmark;
			this.decoderForTestingPositions = decoderForTestingPositions;
			this.fitnessEvaluator =	fitnessEvaluator;
			this.historicalQuoteProvider = historicalQuoteProvider;
			this.crossoverRate = crossoverRate;
			this.mutationRate = mutationRate;
			this.elitismRate = elitismRate;
			this.populationSizeForGeneticOptimizer = populationSizeForGeneticOptimizer;
			this.generationNumberForGeneticOptimizer =
				generationNumberForGeneticOptimizer;
			this.seedForRandomGenerator = seedForRandomGenerator;
		}

		#region AnalyzeInSample
		private void analyzeInSample_checkParameters(
			EligibleTickers eligibleTickers ,
			ReturnsManager returnsManager )
		{
			if ( eligibleTickers.Count <	this.numberOfPortfolioPositions )
				throw new Exception( "Eligible tickers for driving positions contains " +
					"only " + eligibleTickers.Count +
					" elements, while NumberOfDrivingPositions is " +
					this.numberOfPortfolioPositions );
			if ( this.numberOfBestTestingPositionsToBeReturned > 
			    this.populationSizeForGeneticOptimizer )
				throw new Exception( "Number of BestTestingPositions for " +
				                     "out of sample testing is too high with " +
				                     "respect to the population size of the " + 
				                     "genetic optimizer" );
		}

		#region newGenerationEventHandler
		private void sendNewProgress( NewGenerationEventArgs e )
		{
			if ( this.NewProgress != null )
				this.NewProgress( this ,
					new NewProgressEventArgs( e.GenerationCounter , e.GenerationNumber ) );
		}
		#region sendNewMessage
		private string getProgressMessage(
			int generationCounter , int generationNumber )
		{
			string progressMessage =
				generationCounter.ToString() + " / " +
				generationNumber.ToString() +
				" - " +
				DateTime.Now.ToString();
			return progressMessage;
		}
		private void sendNewMessage( NewGenerationEventArgs e )
		{
			string message = this.getProgressMessage(
				e.GenerationCounter , e.GenerationNumber );
			NewMessageEventArgs newMessageEventArgs =
				new NewMessageEventArgs( message );
			if( this.NewMessage != null )
				this.NewMessage( this , newMessageEventArgs );
		}
		#endregion sendNewMessage
		private void newGenerationEventHandler(
			object sender , NewGenerationEventArgs e )
		{
			this.sendNewProgress( e );
			this.sendNewMessage( e );
		}
		#endregion newGenerationEventHandler

		//it returns a hashCode for the given genome
		protected virtual string getHashCodeForGenome(Genome genome)
		{
			string returnValue;
			if (this.choosePositionsWithAtLeastOneDifferentTicker)
				returnValue = ((TestingPositions)genome.Meaning).HashCodeForTickerComposition;
			else
				returnValue = genome.Meaning.GetHashCode().ToString();

			return returnValue;
		}

		private void setBestTestingPositions()
		{
			GeneticOptimizer GO = this.geneticOptimizer;
			int addedTestingPositions = 0;
      int counter = 0;
      Genome currentGenome = null;
      string currentGenomeHashcode;
      Hashtable genomesCollector = new Hashtable();
      while(addedTestingPositions < this.numberOfBestTestingPositionsToBeReturned && 
            counter < GO.PopulationSize)
      {
      	currentGenome = (Genome)GO.CurrentGeneration[GO.PopulationSize - 1 - counter];
      	currentGenomeHashcode = this.getHashCodeForGenome(currentGenome);
      	if( counter == 0 || !genomesCollector.ContainsKey(currentGenomeHashcode) )
      	{
					this.bestTestingPositions[addedTestingPositions] = 
						(TestingPositions)currentGenome.Meaning;
      		genomesCollector.Add(currentGenomeHashcode, null);
      	}
      	counter ++ ;
      }  
		}

		protected abstract void setGenomeManager();
		//sets the member genomeManager in inherited classes

		private TestingPositions[] getBestTestingPositionsInSample(
			EligibleTickers eligibleTickers ,
			ReturnsManager returnsManager
			)
		{
			this.setGenomeManager();
			this.geneticOptimizer = new GeneticOptimizer(
				this.crossoverRate ,
				this.mutationRate ,
				this.elitismRate ,
				this.populationSizeForGeneticOptimizer ,
				this.generationNumberForGeneticOptimizer ,
				this.genomeManager ,
				this.seedForRandomGenerator );
			this.geneticOptimizer.NewGeneration +=
				new NewGenerationEventHandler( this.newGenerationEventHandler );
			this.geneticOptimizer.Run( false );
			this.setBestTestingPositions();

			return this.bestTestingPositions;
		}

		/// <summary>
		/// Returns the best TestingPositions with respect to the in sample data
		/// </summary>
		/// <param name="eligibleTickers"></param>
		/// <returns></returns>
		public object AnalyzeInSample(
			EligibleTickers eligibleTickers ,
			ReturnsManager returnsManager )
		{
			this.analyzeInSample_checkParameters( eligibleTickers ,
				returnsManager );
			TestingPositions[] bestTestingPositionsInSample =
				this.getBestTestingPositionsInSample( eligibleTickers ,
				returnsManager );
			return bestTestingPositionsInSample;
		}
		#endregion AnalyzeInSample
	}
}

[Quantproject-developers] QuantProject/b4_Business/a2_Strategies/InSample GeneticChooser.cs, NONE,

[Quantproject-developers] QuantProject/b4_Business/a2_Strategies/InSample GeneticChooser.cs, NONE, 1.1