[Quantproject-developers] QuantProject/b7_Scripts/TickerSelectionTesting GenomeManagerForEfficientPortfolio.cs,1.7,1.8

[Marco M. <mi...@us...>]

Update of /cvsroot/quantproject/QuantProject/b7_Scripts/TickerSelectionTesting
In directory sc8-pr-cvs1.sourceforge.net:/tmp/cvs-serv7317/b7_Scripts/TickerSelectionTesting

Modified Files:
	GenomeManagerForEfficientPortfolio.cs 
Log Message:
the variance and the average rate of return of portfolio are now computed in a different way (that seems to be right, now ...)

Index: GenomeManagerForEfficientPortfolio.cs
===================================================================
RCS file: /cvsroot/quantproject/QuantProject/b7_Scripts/TickerSelectionTesting/GenomeManagerForEfficientPortfolio.cs,v
retrieving revision 1.7
retrieving revision 1.8
diff -C2 -d -r1.7 -r1.8
*** GenomeManagerForEfficientPortfolio.cs	26 Apr 2005 19:02:13 -0000	1.7
--- GenomeManagerForEfficientPortfolio.cs	4 May 2005 18:29:00 -0000	1.8
***************
*** 167,188 ****
      {
        double returnValue = 0;
!       double portfolioVariance = this.getPortfolioVariance(genome.Genes());
!       double portfolioRateOfReturn = this.getPortfolioRateOfReturn(genome.Genes());
!       
!       if(!Double.IsInfinity(portfolioVariance) ||
!          !Double.IsInfinity(portfolioRateOfReturn) ||
!          !Double.IsNaN(portfolioVariance) ||
!          !Double.IsNaN(portfolioRateOfReturn))
!       //variance and rate of return are 
!       //double values computed in the right way
        {
  	      this.variance = portfolioVariance;
!       	this.rateOfReturn = portfolioRateOfReturn;
!       	NormalDistribution normal = new NormalDistribution(portfolioRateOfReturn, Math.Sqrt(portfolioVariance));
  	      if(this.portfolioType == PortfolioType.OnlyLong ||
        	   this.portfolioType == PortfolioType.ShortAndLong)
  	        //the genome fitness is evaluated as if
        		//the portfolio was long
!       		//ALTERNATIVE: returnValue = normal.GetProbability(this.targetPerformance*0.75,this.targetPerformance*1.25);
  	      	returnValue = 1.0 - normal.GetProbability(this.targetPerformance);
  	      else//only short orders are permitted
--- 167,197 ----
      {
        double returnValue = 0;
!       //OLD IMPLEMENTATION double portfolioRateOfReturn = this.getPortfolioRateOfReturn(genome.Genes());
!       //OLD IMPLEMENTATION double portfolioVariance = this.getPortfolioVariance(genome.Genes());
!       double[] portfolioRatesOfReturn = this.getPortfolioRatesOfReturn(genome.Genes());
!       double averagePortfolioRateOfReturn = 
!             BasicFunctions.SimpleAverage(portfolioRatesOfReturn);
!         
!       double portfolioVariance = 
!             BasicFunctions.Variance(portfolioRatesOfReturn);
! 
!       if(!Double.IsInfinity(portfolioVariance) &&
!          !Double.IsInfinity(averagePortfolioRateOfReturn) &&
!          !Double.IsNaN(portfolioVariance) &&
!          !Double.IsNaN(averagePortfolioRateOfReturn))
!       //both variance and rate of return are 
!       //double values computed in the right way:
!       // so it's possible to assign fitness using normal distribution class
        {
  	      this.variance = portfolioVariance;
!       	this.rateOfReturn = averagePortfolioRateOfReturn;
!       	NormalDistribution normal = 
!       		new NormalDistribution(this.rateOfReturn,
!       		                       Math.Sqrt(this.variance));
  	      if(this.portfolioType == PortfolioType.OnlyLong ||
        	   this.portfolioType == PortfolioType.ShortAndLong)
  	        //the genome fitness is evaluated as if
        		//the portfolio was long
!       		//returnValue = normal.GetProbability(this.targetPerformance*0.75,this.targetPerformance*1.25);
  	      	returnValue = 1.0 - normal.GetProbability(this.targetPerformance);
  	      else//only short orders are permitted
***************
*** 250,277 ****
      protected double getPortfolioVariance(int[] tickerIdx)
      {
!       double sumOfVariances = this.getSumOfVariances(tickerIdx);
!       double sumOfCovariances = this.getSumOfCovariances(tickerIdx);
        double returnValue = sumOfVariances + sumOfCovariances; 
        return returnValue;
      }
      
!     protected double getSumOfVariances(int[] tickerIdx)
      {
        double returnValue = 0;
!       double tickerCoeff = 1.0/this.genomeSize;
        foreach(int idx in tickerIdx)
        {
!         returnValue += BasicFunctions.Variance((float[])this.setOfTickers.Rows[idx]["ArrayOfRatesOfReturn"]);
        }
-       returnValue = returnValue * tickerCoeff * tickerCoeff;
        return returnValue;
      }
  
!     protected double getSumOfCovariances(int[] tickerIdx)
      {
        double returnValue = 0;
        float[] ticker_i;
        float[] ticker_j;
!       double tickerCoeff = 1.0/this.genomeSize;
        for(int i = 0; i<this.genomeSize ; i++)
        {
--- 259,286 ----
      protected double getPortfolioVariance(int[] tickerIdx)
      {
!       double sumOfVariances = this.getWeightedSumOfVariances(tickerIdx);
!       double sumOfCovariances = this.getWeightedSumOfCovariances(tickerIdx);
        double returnValue = sumOfVariances + sumOfCovariances; 
        return returnValue;
      }
      
!     protected double getWeightedSumOfVariances(int[] tickerIdx)
      {
        double returnValue = 0;
!       double tickerCoeff = 1.0/this.genomeSize;  
        foreach(int idx in tickerIdx)
        {
!         returnValue += tickerCoeff * tickerCoeff * 
!         				BasicFunctions.Variance((float[])this.setOfTickers.Rows[idx]["ArrayOfRatesOfReturn"]);
        }
        return returnValue;
      }
  
!     protected double getWeightedSumOfCovariances(int[] tickerIdx)
      {
        double returnValue = 0;
        float[] ticker_i;
        float[] ticker_j;
!       double tickerCoeff = 1.0/this.genomeSize; 
        for(int i = 0; i<this.genomeSize ; i++)
        {
***************
*** 282,290 ****
            {
              ticker_j = (float[])this.setOfTickers.Rows[j]["ArrayOfRatesOfReturn"];
!             returnValue += BasicFunctions.CoVariance(ticker_i, ticker_j);
            }
          }
        }
-       returnValue = returnValue * tickerCoeff * tickerCoeff;
        return returnValue;
      }
--- 291,299 ----
            {
              ticker_j = (float[])this.setOfTickers.Rows[j]["ArrayOfRatesOfReturn"];
!             returnValue += tickerCoeff * tickerCoeff * 
!             				BasicFunctions.CoVariance(ticker_i, ticker_j);
            }
          }
        }
        return returnValue;
      }
***************
*** 321,328 ****
        return ratesOfReturns;
      }
!     
!     
!     
! 
      protected double getPortfolioRateOfReturn(int[] tickerIdx)
      {
--- 330,334 ----
        return ratesOfReturns;
      }
!    
      protected double getPortfolioRateOfReturn(int[] tickerIdx)
      {
***************
*** 330,338 ****
        foreach(int idx in tickerIdx)
        {
!         returnValue += BasicFunctions.SimpleAverage((float[])this.setOfTickers.Rows[idx]["ArrayOfRatesOfReturn"]);
        }
        //the investment is assumed to be equally divided
        return (returnValue/this.GenomeSize);
      }
    }
  
--- 336,365 ----
        foreach(int idx in tickerIdx)
        {
!         returnValue += 
!         		BasicFunctions.SimpleAverage((float[])this.setOfTickers.Rows[idx]["ArrayOfRatesOfReturn"]);
        }
        //the investment is assumed to be equally divided
        return (returnValue/this.GenomeSize);
+       
+     }
+     
+     protected double[] getPortfolioRatesOfReturn(int[] tickerIdx)
+     {
+       int numberOfExaminedReturns = 
+           ((float[])this.setOfTickers.Rows[tickerIdx[0]]["ArrayOfRatesOfReturn"]).Length;
+       double[] returnValue = new double[numberOfExaminedReturns];
+       for(int i = 0; i<returnValue.Length; i++)    
+       {  
+         foreach(int idx in tickerIdx)
+         {
+           float[] tickerRatesOfReturn = (float[])this.setOfTickers.Rows[idx]["ArrayOfRatesOfReturn"];
+           returnValue[i] += tickerRatesOfReturn[i]/this.genomeSize; 
+           //the investment is assumed to be equally divided for each ticker
+         }
+       }
+       return returnValue;
+       
      }
+     
    }