Ultimate Algorithmic Trading System

John Holland’s 1975 book titled Adaptation in Natural and Artificial Systems is the
bible on GA and his work has paved the way for a majority of researchers in this
field. Holland has stated, 1 ‘‘Living organisms are consummate problem solvers.
They exhibit a versatility that puts the best computer programs to shame.’’
Computer speed and power has come a long way since 1975 and you may think
these biologically based optimization/search algorithms have become obsolete. The
exponential growth rate of multiple variable optimizations proves that this is not the
case. Also humans are constantly creating more and more complex problems that
continually exceed our technology. The relationship between GA and computers is
as close as it has ever been.
The principles that make up GA, if taken one concept at a time, are easy to
understand. The combining of these concepts into a complete algorithm can be
daunting if each piece is not broken down. I programmed these concepts in a
modular manner using VBA for Excel in a very small amount of time. This same
software was used to illustrate the concepts and solve the initial problem in this first
part of the chapter. Snippets of VBA code will be sprinkled throughout the first part
of the chapter to help explain the most important components of genetic algorithms.
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GENETIC OPTIMIZATION, WALK FORWARD
■ Computers, Evolution, and Problem Solving
Genetic algorithms borrow the following concepts from biology:
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Population and generations
Selection of the fittest
Reproduction
Mutation
All these terms are easily understood in a biological framework but may not seem
initially translatable into a computer/software/math paradigm. The application of
these concepts to solve a complex problem can be difficult but we need not worry
ourselves with this because we know our objective with TradeStation, AmiBroker,
or Excel—building the world’s best trading algorithms! However, a simple problem
needs to be solved so we can demonstrate the eloquence of GA. The ideas and
processes that will be discussed for applying a GA to finding a solution to a simple
equation were derived directly from Denny Hermawanto’s paper titled ‘‘Genetic
Algorithm for Solving Simple Mathematical Equality Problem.’’ The problem that
we will be solving utilizes this very simple equation:
a+2b+3c+d= 40
1 John H. Holland. Genetic algorithms. Scientific American, 267(1):44–50, 1992.
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