Nerds on Wall Street: Math, Machines, and Wired Markets

8.2. Genetic Algorithms

Genetic algorithms have been used successfully in many contexts, including meteorology, structural engineering, robotics, econometrics, and computer science. The genetic algorithm is particularly appealing for financial applications because of its robust nature and the importance of the payoff in guiding the process.

The genetic algorithm is robust in the sense that very few restrictions are placed on the form of the financial model to be optimized. It can include conventional models (e.g., time series regressions), but is not restricted to them (allowing, for example, heuristic decision rules). Almost any form of constraint can be applied, including self-referential constraints involving the form of the model, its complexity, and similarity to other known models.

Genetic algorithms are particularly well-suited for financial modeling applications for three reasons:

1. They are payoff driven. Payoffs can be improvements in predictive power or alpha. There is an excellent match between the tool and the problems to be addressed.

2. They are inherently quantitative, and well-suited to parameter optimization (unlike most symbolic machine learning techniques).

3. They are robust, allowing a wide variety of extensions and constraints that cannot be accommodated in traditional methods.

The key aspect of the genetic algorithm's appeal in trading is clearly made in Dave Goldberg's book: It is payoff driven. (See Figure 8.1.) Think of flipping the switches to maximize payoff. ...