Part 3: Genetic Algorithms & Cycles
scheduled release date: Q4 2014

Table of Content: n.a.

First Edition
approx. 200 pages
Page Size: 7.44 x 9.69 inch
Formats: pdf Download, Hardcover Print

Abstract / Summary:

A genetic algorithm is a promising way to detect and follow the status of dominant cycles. It is a new alternative to using digital signal processing for detecting possible cycles. Akin to chromosomes in a genome, genetic algorithms will check possible cycle length settings for long, short, and exit signals. The genome transforms based on an evolutionary process that involves mutation, crossover, and survival of the fittest. Similarly, based on a random population of cycle genomes, the genetic algorithm will evolve to detect useful cycles at different starting points and optimize these cycles based on the rules of natural evolution. Each genome is measured against a special fitness function that simply checks the equity curve that would result if you had traded these volume cycles. The smoothest upward sloping equity curve will have the highest fitness score and the best rating for the cycles in the evolution process.

This third book of the series explains how to combine cycle analysis with the use of genetic algorithms to develop entry and exit scripts for real-time trading.