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Ruggiero, Murray A., Jr. (1997). Cybernetic Trading. New York: John Wiley & Sons. Rug&m, Murray A., Jr. (May 1998). Unholy Search for the Grail. Funcres Magazine. Schwager, Jack (October 1992). Selecting the Best Futures for Computer Testing. Technical Analysis of Stocks and Commodities, pp. 65-71. Sharp+ William E (Fall 1994). The Shape Ratio. Journal of Porffolio Management. Space Science Institute (1996). A Magnetic Storm Rips through !&ah s Atmosphere. A news release on their web site: www@www-ssi.colorado.edu. Star, Barbara (July 1993). WI Variations. Technical Analysis ofSlocks rind Commodities, pp. 5460. Stendahl, David (1999). Evaluating Trading Performance. Computerized Trading. New Jersey: New York Institute of Finance/Prentice Hall, pp. 137-162. Sweeney, John (1993). Where to Put Your Stops. Technical Analysis of Srocks and Commodities (Bonus Issue), pp. 3&32. Sweeney, John (April 1998). Applying Moving Averages. Technical Analysis of Stocks and Commodities, pp. 48-50. Tilley, D.L. (September 1998). Moving Averages with Resistance and Support. Technical Analysis of Srocks and Commodities, pp. 62-87. Trippi, Robert R., and Turban, Efraim (Eds.) (1993). Neural Nenvorks in Finance and Investing. Chicago: Pmbus Publishing. White, Adam (April 1993). Filtering Breakouts. Technical Analysis of Sfocks and Commodities, pp. 3w1. Wilder, J. Welles (1978). New Concepfs in Technical Trading Systems. Trend Research. Williams, Larry (1979). How Z Made One Million Dollars Last Year Trading: Commodities. New York: Windsor Books. Yuret, D&z, and de la Maza, Michael (June 1994). A Genetic Algorithm System for Predicting the OEX, Technical Analysis of Stocks and Comnwdiries, pp. 5864.

Total sales Hoses Fasteners Valves Pipe Wire Switches Connectors Total $ 6,001,000 $ 778,001 $ 1,002,000 $ 9,001,000 $ 339,000 $ 603,000 $ 46,001 $17,770,002 Total Average buyers per buyer 220 198 18 144 201 22 32 415 $27,277 $ 3,929 $55,667 $62,507 $ 1,687 $27,409 $ 1,438 $42,819 Company G $ 889,554 $ 19,646 $ 478,003 $ 312,535 $ 8,433 $ 276,881 $ 7,188 $1,992,240 Company B $227,665 $ 3,929 $ 55,667 $ 62,507 $ 1,687 $545,667 $ 2,434 $899,555 Company C $ 19,882 $ 1,965 $ 27,833 $ 37,253 $ 843 $ 13,705 $ 719 $102,200

AI (see Genetic algorithms, Neural networks) Alexa. der, Colin, 113 All-past-years technique, 158 Analysis, 39 Analytic opti li7m, 39, lo. 48 Annealing, 38 Annualized risk-to-reward ratio (ARRR), 15.60 Appel. Gerald, 133 Artificial intdligence (see Genetic algorithms, Neural networks) Astrology, ,79 (See ok0 Lunar and Bohr rhythms) Author s conclusions, 353-363 Average directional mowmenl index (see ADX trend filter) Average tree rmgc, 86 Back-adjustment, 3.4 Bad curve-fitting, S4 Band-pass ,i,tcr, 207 Barrier ai s, 287 Bmm & 12 Bars, 109 Basic C~SSOW mode,: lunar activity, 191-194 seasonality, 166--170 Basic mome tum model:

ha activity, 194. I95 seasonality, 1 7 0 , 171 Bernstein, Me, 154

Company G B C Total Similar Sales companies $1,992,001 6 $ 889,445 8 $ 102,004 20 34 Best-selling Average product annual buys Hoses $889,554 Switches $545,667 Pipe $ 37,253

B E Y O N D B A S I C S : B R E A K T H R O U G H S U P P L E M E N T S F O R D E T O X I F I C AT I O N

Breakout models (Conr,): breakout type. 104 channel breakouts, W-997 characterisdcfi of breakouts, 84, 85 dose only channel breakouts, 8692 currencies only, 101, 102 eney orders, 104-106 highest high/lowest low breakouts, 92-97 interactions. IO-5 lessons learned, 107, 10R long positions only, 100, 101 res ictio sJfilters, la5 summary of results, ,0+,07 testing, X5-104 types of breakouts, 83. 84 volatility breakout variations. lW104 volatility bmkows, 97-100 Breasert, 203 Brnte force optimizers, S-34.47 Burke. Gibbons, 257

each company. He also sees a list of the 10 best prospects, along with the product that he should sell to them. By clicking on each prospect, he gets the name and address of the company, the contact information, company data, and so on. Let us suppose that this particular agent has already canvassed some of these prospects and knows that he cannot win them over. They are already locked into the competition for some solid reason. The agent needs more prospects. No problem; he clicks on a Do Not List column opposite each of these prospects, and then clicks on a Refresh button. The top 10 prospect software goes back to the prospect pool and redoes the analysis, dropping the Do Not List companies and adding new companies from the pool. Within a few minutes, the agent has a refreshed list of top 10 prospects. He can do this as often as he wishes, working his way down all the prospects reported by D&B within his territory. This is an example of the way modern database analysis supports business-to-business marketing and sales. The information shown in these tables could not have been developed without a customer marketing database. Once you have that database, the software to produce next best product and top 10 prospects is complex, but achievable.