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Examine the performance data, run some inferential statistics, plot the equity curve, and the system is ready to be traded Everything is clean and mathematically unimpeachable Corrections for shrinkage or multiple tests, worries over excessive curve-fitting, and many of the other concerns that plague traditional optimization methodologies can be forgotten Moreover, with today s modem computer technology, walk-forward and self-adaptive models are practical and not even difficult to implement The principle behind walk-forward optimization (also known as walk-forward testing) is to emulate the steps involved in actually trading a system that requires periodic optimization It works like this: Optimize the system on the data points 1 through M Then simulate trading on data points M + I through M + K Reoptimize the system on data points K + 1 through K + M Then simulate trading on points (K + M) + 1 through (K + M) + K.

Lost Opportunities A toddler, student, or prodigy with no additional purchase 18 months after last purchase Inactives No additional purchase 18 months after initial purchase

Advance through the data series in this fashion until no more data points are left to analyze As should be evident, the system is optimized on a sample of historical data and then traded After some period of time, the system is reoptimized and trading is resumed The sequence of events guarantees that the data on which trades take place is always in the future relative to the optimization process; all trades occur on what is, essentially, out-ofsample data In walk-forward testing, M is the look-back or optimization window and K the reoptimization interval Self-adaptive systems work in a similar manner, except that the optimization or adaptive process is part of the system, rather than the test environment.

Some carcinogens, such as aflatoxin, require only low exposure for a short time to cause cancer Other carcinogens, such as benzene, are relatively weaker and require higher doses for longer periods of time People with genetically inherited high levels of detoxification enzymes obviously deal with relatively more carcinogens than those with genetically inherited lower levels The same can be said for our DNA repair capability and immune systems, the next lines of defense: the better they function, the more they protect us Age is also a very important factor Animal studies have shown that because of higher levels of cell growth and turnover, younger animals are more susceptible to cancer induction than older animals, where cell growth and turnover is lower Humans behave the same way Cancer development has three stages: initiation, promotion, and progression.

Toddler Made a second purchase in the first 18 months Precocious Purchased more than starter kit initially Newborn Just purchased starter kit

As each bar or data point comes along, a self-adaptive system updates its internal state (its parameters or rules) and then makes decisions concerning actions required on the next bar or data point When the next bar arrives, the decided-upon actions are carried out and the process repeats Internal updates, which are how the system learns about or adapts to the market, need not occur on every single bar They can be performed at fixed intervals or whenever deemed necessary by the model The trader planning to work with self-adapting systems will need a powerful, component-based development platform that employs a strong language, such as Cf f, Object Pascal, or Visual Basic, and that provides good access to thirdparty libraries and software components Components are designed to be incorporated into user-written software, including the special-purpose software that constitutes an adaptive system.

Challenged Infants No additional purchase in 6 months after starter kit purchase No additional purchase in 12 months after starter kit purchase

The more components that are available, the less work there is to do At the very least, a trader venturing into self-adaptive systems should have at hand genetic optimizer and trading simulator components that can be easily embedded within a trading model Adaptive systems will be demonstrated in later chapters, showing how this technique works in practice There is no doubt that walk-forward optimization and adaptive systems will become more popular over time as the markets become more efficient and difficult to trade, and as commercial software packages become available that place these techniques within reach of the average trader..

Conversations Total Zap registrant surveys Zap registrant nonbuyers Buyers invited to training CSR survey Corporate billing contract usage 5516 300 430 2195 137 Active 792 207 170 142 14 Messages Sent 15 209 123 772 147 Opened 5 50 0 368 0 Responses to questions 3 10 0 149 0

OPTIMIZER TOOLS AND INFORMATION Aerodynamics, electronics, chemistry, biochemistry, planning, and business are just a few of the fields in which optimization plays a role Because optimization is of interest to so many problem-solving areas, research goes on everywhere, information is abundant, and optimization tools proliferate Where can this information be found What tools and products are available Brute force optimizers are usually buried in software packages aimed primarily at tasks other than optimization; they are usually not available on their own In the world of trading, products like TradeStation and SuperCharts from Omega Research (800-2923453), Excalibur from Futures Truth (828-697-0273), and MetaStock from Equis International (800-882-3040) have built-in brute force optimizers If you write your own software, brute force optimization is so trivial to implement using in-line progranuning code that the use of special libraries or components is superfluous.