Security Genetics presents

Genetic System Search for Technical Analysis

Automate Your Trading System Development

Background Information for the Genetic System Search for Technical Analysis Program

Technical Analysis

Many investors study the movement of market prices and volumes and base their decisions upon such technical analysis.

A Typical Investment System

  • Enter a long order when the closing price of the stock is greater than the 60-day exponential moving average of the high price.
  • Close the order when the closing price of the stock falls below the 30-day simple moving average of the closing price.

The construction of an successful investment system is often time-consuming and must be performed periodically as market conditions change. Furthermore, a system that works well for one stock may not be profitable for another stock.

Commentary on Technical Analysis in the Blog

Read more in the Technical Analysis category of The GSSTA Blog.

Evolutionary Computation and Genetic Programming

Genetic programming and the related genetic algorithms are forms of evolutionary computation, a subset of artificial intelligence (AI). These automated methods use the Darwinian mechanism of evolution to arrive at a solution more quickly than techniques such as exhaustive search.

The genetic algorithm randomly creates a large population of candidate systems. Then by a Darwinian process, the program searches through several generations of evolution for the most fit solution. The program ends when no improvements can be made.

The Power of the Fitness Function

As an additional benefit, further enhancements are possible by adding terms to the fitness formula of the genetic programming algorithm. In the case of GSSTA the program is tuned to minimize drawdown and the volatility of trade returns. That’s not possible with an exhaustive search. The technique of evolutionary computation combined with modern computer programming results in a clever solution to an often unwieldy problem.

Additional Information in the Blog

Read more in the Genetic Programming category of The GSSTA Blog.