Rules based Optimization
Before I discuss my second Automated Trading System (ATS), I need to explain the principal in which it operates. A rule based ATS depends on carefully chosen thresholds and parameters to determine when a particular stock should be entered and exited (long or short). Experience and theory can provide an excellent starting point, but to perform really well for a particular stock, it is useful to maximize these parameters on historical data.
Think about your set of rules as though they are a function.
function rules (double shortMATime, double longMATime)
{
“Enter Long when the shortMATime minute moving average crosses above the longMATime minute moving average.”
“Exit when the longMATime minute moving average crosses above the shortMATime minute moving average.”
}
In this example, there are two rules which are executed over live or historical data, with two parameters, shortMATime and longMATime. We would like to select values for these parameters such that they would have made the most money over the last week (or any time frame), assuming this represents closely what values will make the most money tomorrow. This function is very difficult to maximize, as it is not continuous. Small adjustments to either parameter can cause huge swings in the profitability of the system.
In this particular case, you may consider running the entire variable space through the function, setting each parameter from 0 to 1000 minutes, incrementing by one second, and taking the maximum output when you are done. This turns out to be roughly (1000 * 60)2 = 3,600,000,000 runs. Assuming a very fast 5 seconds per run for a week’s worth of data, this would take 570.77 years to process. Clearly a better maximization function is needed, but it cannot depend on the derivative of the function, nor can it require continuity of the first order. This is exactly where genetic algorithms shine.
My next series of articles will cover Genetic Optimization in detail. Stay tuned for more updates.
