GA Signals is useful in situations where you are not sure which system to use to generate a signal, but have an idea that several systems and measurements, when combined, could create a good entry.  That is, by combining 3 Systems and 3 Indicators (for example), some combination thereof would generate a signal - you just don't know what that combination is. 

GA Signals iterates on the problem space using a Genetic Algorithm process, until it has converged on numerous "rules" which are then added to a Knowledge Base, such as "Long Signal when MAC-M fires a signal and Volatility is between 2.5 and 3.5 and Close is more than 5% above the 21 period Moving Average."

Test case #1:  Futures Knowledge Base on Currencies

We generated a Knowledge Base on by using continuous contracts on currencies going back 30 years (that is, our Back Test for training was set to use all 30 years of data).  We reserved a one-year Forward Test at the end of training as an "out of sample" test period:

Currencies Used:
EuroDollar
British Pound
Australian Dollar
Canadian Dollar
American Dollar
Japanese Yen
Swiss Franc

After training, we changed the Back Test to one year so we could compare similar time frames (one year Back Test and one year Forward Test).  The resulting performance report  is shown here:

Here is a chart for the Swiss Franc:

Test Case #2: Knowledge Base for Grains

Here is a chart for Wheat: