Ultimate Algorithmic Trading System

For generation = 1 To 100 ' total generations
For i = 0 To 5 'Six Chromos
FObj(i) = 0
For j = 0 To 3 ' Four Genes
'Calculate each expression in equation and accumulate
If j = 0 Then FObj(i) = Cells(3, 3) * chromo(i, j)
If j = 1 Then FObj(i) = FObj(i) + Cells(3, 4)
* chromo(i, j)
If j = 2 Then FObj(i) = FObj(i) + Cells(3, 5)
* chromo(i, j)
If j = 3 Then ' Test the summation against 40
FObj(i)=Abs((FObj(i)+Cells(3, 6)*chromo(i,j))
-Cells(3,7))
'if summation = 40 then solution found
If (FObj(i) = 0) Then foundSolution = i
End If
Next j
Next i
If we rank the chromosomes in order of fitness, then we would have the following:
■ Selection
Chromosome(2) = 64—closest to the solution (24 away)
Chromosome(3) = 90—second closest (50 away)
Chromosome(5) = 93
Chromosome(1) = 94
Chromosome(0) = 111
Chromosome(4) = 164
Now that we have our initial population and the respective chromosome fitness, we
can select the chromosomes that should be carried forward and used for reproduction
and mutation. It’s easy to see that the top four ranking chromosomes should be
selected because they are closer to our solution than the last two (the absolute
difference between the chromosome fit score and 40, our solution, is smaller).
However, the computer makes the decisions and it makes its selection based on
probabilities. This task is accomplished by converting each chromosome’s fitness in
terms of probability. We can’t make the selection decision for the computer, but
we can make sure the computer understands that some chromosomes are more fit
than others. If you remember probabilities from an old math or stat class, you know
the basic premise: Rolling a dice once results in one out of six potential outcomes.
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GENETIC OPTIMIZATION, WALK FORWARD
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