This presentation is designed to detail an additional tool which can be used to make trading systems more self adaptive and therefore more responsive to current market conditions.
If a system uses back data to any degree it can be regarded as being self adaptive to one degree or another. Moving averages, standard deviations, breakouts, neural networks, etc. all rely on some historical price movement to generate buy and sell signals.
This programming technique takes the self adaptive concept one step farther by using the system itself to adjust its own trading parameters for each trade.
I wish to emphasize at the beginning that this is a programming technique which must be applied in a different manner to each and every system on which it is used. It is not a canned function or add on program which can be applied to any system.
Also, since the programming involved in the application of this technique can be quite involved and extensive, it should be emphasized that this is not a fix - all for mediocre or poor systems. In fact, it will probably worsen the results of a poor system since the variables will constantly be reset to extreme values, making the equity swings of the system even more pronounced.
Systems which respond best to this technique are those which are considered robust in nature and remain profitable over a progressive set of input variables. Such a system should show a bell curve pattern when the results of an optimization over the critical inputs is performed. Systems which respond well to frequent optimization will find this technique useful in improving performance and smoothing out equity curves.