A "Self-Adaptive System" is quite simply one that has the internal ability to dynamically alter its analytical equations in real time to more closely adapt the system to ever changing market conditions. The systems accomplish this task by the diligent application of parallel functions.

What makes Parallel User Function driven systems unique?

Recall the old college days, just after that chemistry exam when you realized you should have spent more time reviewing the section on oxidation and reduction and less time on electron shell configuration? Or after the English exam when you found the test emphasis on sentence construction rather than proper pronoun usage, which you had spent all night studying?

How about that last trade, when just waiting a few more minutes for your entry or exit would have turned the result into a profitable experience rather than another one of those annoying losses?
Obviously, it’s not possible to turn the clock back and alter previous decisions. However, we have all, hopefully, over the years, learned from our previous experiences and have become better traders as the result of this learning process.

Parallel Function Technology operates in much the same fashion as our own learning process. While there is no computer in existence, or even on the horizon, which can come close to the analytical capability of the human mind, we can, with our Parallel Function Technology, enable our trading systems to learn from their past experiences and become more effective as a result.

The ultimate objective of all trading is to buy the low and sell the high. As you know this is much easier said that done. In fact it is, in all likelihood, altogether impossible. It is possible however to buy and sell in areas where price action determines that the trade has a higher probability of being profitable rather than losing.

In this attempt, our Parallel Function based systems are always trying to identify optimum buy and sell areas. If the identification of this buy and sell area can be improved upon, the system recognizes this fact and will “self-adapt” to take this new understanding into account the next time a similar market situation arises.
For example, let’s consider a trade placed by the Symphony System. A set of specific mathematical equation is used to calculate all trading signals.

Obviously, all the signals from this system are not perfect. In many instances, moving the entry point forward or back a few bars would improve the quality of the signal issued by this trading tool.

Using historical data, we can easily determine the top or the bottom of the price move where the placement of our buy or sell signal would have been optimally placed.

The system component of our Parallel Function programming examines the relationship of all the orders placed over a given period of time and compares the placement of the signals to what would have been the perfect placement of each order. The computer program then makes alterations to the base system equation in an attempt to more accurately place the proper buy and sell signals as they are issued by the system when similar chart patterns present themselves in the future.

One might ask, at this point, with the self-adaptive nature of this system discussed above, why all the signals aren’t always perfect after the examination of an adequate amount of past data. The best answer to this question requires a more detailed examination of the forces that are responsible for the creation a price chart.

Price charts are ultimately the expression of random human behavior in the market place. Much of this random activity is largely the result of analytical inputs, such as supply and demand, earnings and other hard numbers which are objective in nature and can be analyzed mathematically. The balance of the origin of market behavior is the result of human emotion, intuition and other non-analytical data and therefore much less repetitive and much more difficult to analyze from an objective approach.

It is relatively simple to analyze, from a mathematical perspective, activity which arises from the repetitive activity generated by hard data.

It is quite difficult, if not impossible, to objectively analyze and therefore predict the subjective result of emotion and intuition.

Ultimately, therefore, it is mathematically possible to only predict a portion of the activity which goes into the creation of a price chart. In a sense, you are always shooting at a moving target from a mathematical standpoint, thus markedly decreasing the accuracy of perfect market prediction.

However, with all of the above qualifications, the Parallel Function Technology which drives all of the Systems offered on this site has been shown by our research to be much more responsive to the ever changing conditions of today’s active markets than the standard indicator packages which use fixed mathematical processes to calculate their respective buy and sell signals.