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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.
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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:
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ha activity, 194. I95 seasonality, 1 7 0 , 171 Bernstein, Me, 154
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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
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Butterworth filters, 206, 207 C++, ,4, 15.24-26,46 c++ Builder, 26 C++ Oenetic Optimizer, 49
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Best exit strategy (market-by-market results), 33S-332 Best possible solution to a problem. 30 Beta weights, 55 lnfomation @MI), 1, Borland, 24.25 Bottom turning-point mode,, 243.249.250 Bouncing tick, 25
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BreakO , r odels. 74.83-108 ADX tx d filter, 102104 analysis b y mwkct, 106, 1 0 7 Bb , Willianl, 1 3 4 Bonneville Market
C-Trader toolkit, 14, 15, 19.27,36,99, 114,214 Calendar effects chart, 154 Catastrophe SOD, 288 CCI (cohmdi& channel index), 135, 136 CD-ROM, 364 centered smaothing, 155,181 Cemal limit theorem, 61.68 ChandqTusbar S., 110-112 Channel breakouts, 8697 chroI osome. 258 clipping, 164 Close only channel breakouts, 8692 Code listmgs: cycle-based entries, 21&2219 dynamic stops, 317-321
genetic *gorhhms., 262-268 genetic exit Stmtegy~ 342-345 lunar activity. 183-189 moving average models. 115-l MSES, 3 0 2 3 0 5
Code listings (Cont.): neural exit strategy, 337-339 neural networ!c% 233-237 oscillator-based entries. 140-143 pmfit target (fixed stop). 311-313 reverse slow %K model, 233-237 seasonaMy, W-163 shrinkage profit target, 326328 standard exit strategy (SES), 295-297 turning-point *odds, 241,242 Commodities channel index (021). 135, 136 Co lmodities pricing data, 3,4 Commodities Systems Incqwrafed (CSI), 1 I Conlpanion software a ailahle, 364 C mpurerized ml& * (lurik,, 227 Conclusions, 353-363 Confidence interval, 60 Confim*ltion-and-inversion model: lunar activity, 182, 196 seasonality, 156, 173 Constant-i estr e t model, 8 1 Continuous contract, 3 Co tralim crosso er model. 125 CO trtia. tmiing, 289 Correlational statistics, 52 Cost function, 30 Counter-trend moving average entry models, 113, 125-130 CRlTBWOM function, 60 Critical threshold exits, 283 C~OSSO ~~: genetic algorithms, 258,259 lunar activity. 181, 82 seasonality, 155, 156 (See also Basic crn~~~ver model) Crossover-with-confirmation mdel: lunar activity, 182, 195, 196 seasonality. 156. 171-173 CSI K2xnmodities systems Incorporated), 11 C-Trader toolkit, 14, 15, 19,X,. 36,99. L14.214 Cumulative t-distribution, 58, 59 Curve-flttmg: bad. 54 gmd, 54 neural networks. 230,255 optimization. and 54-57 Cycle, 203 Cycle-based entries, 76.203-226 B tterwo* filters, 206,207 characteristics. 2,3, 214 code testing, 2, &2,9 filter banks. 206213
Entry methods (Conr.): dollar volatility equalization. 78-81 genetic algorithms. 257-280 good entry, 71 inmoduction, 71-82 lunar/solar phenomena. 179-202 moving average models, 109-132 CUEi, networks, 227-256 orders used in entries, 72-74 osciUators, 133-152 seasonality, 153-177 standard portfolio, 81. 82 standardized exits, 77.78 Equis International. 25,47 Evol~ti0n.q model building (see Genetic algorithms) Evolver, 47.49
Excalih,41,48
Exit strategies, 28L-351 barrier exits. 287 best exit stn,egy (market-by-marke,
330-332
results),
contraian trading, 289 critical threshold exits, 283 dynamic AIR-based stop, 322, 323 dynamic stops, 316-324 extended time limit, 328 fined stop,,-,rof,t target, 311-316
geneticcomponents,34,-348
gunning, 288 highest higbilowest low stop, 322 importance, 281,282 impmvemenu on standard exit, Xl%333 MEMA dynamic stop. 323,324 modified standard exit strategy (MSES), 302-307 money mana~emc"texits,283,284 neuralnetworks, 336-34,
profittargetexits,285,*86,311-316,324-328
protective stops. 288. 289 random entry model, 291,292 shrinking profit rarget. 324-328 signalcrit9, 287, 288 slippage, 289 standard exit strategy (SESS), 293-302 time-based exits, 286
tmilingexits.284,285
volatility exits, 287 Eroge o s, 76 Exponential moving average, 111, 112 Extended time limit (exit strategies), 328 Eysenck, H.J.. 179 Eysenck, S.B.F., 180
HHLL breakouts, 92-97 HHLL stop, 322 High-pass Bumworth filter, 206 Highest higNlowcst low breakouts, 92-97 Highest highnowest low stop, 322 Histograms. 22.23 Holland, John, 257 Implicit optimizers, 3 1 IMSL, 25,26,48 Individual co tmti data, 3 Inferential statistics (see Statistics) I f r mi soun;es: data., II, 11 optbnizers, 48.49 htemational Mathematics and Statistics Libm y (IMSL), 25.26,48 Inmday pricing data, 4 Inversions, 156, 182 (See also Cotimtion-and-inversion mode,) , esr r s ,9winesr Doily, 12 Jackknife, 158
Lunar test test Lunar Lupa,
and solar rhythms (Cont.): methd l gy, 183-190 results. 19%,97 lo len n series, 181 Louis M., ,34
Katz, Jeffrey Owe . 51.76.77, 154, IlO, 181, 197,199, 202,2c4,227,252,257,258,262,332.363 Klein, R.A., 227 Knight, Sheldon, 11 Lag, 110 Lane s stochastic, 135 Leave-one-out ledxd, 158 Ledemm, I., 227 Litit orders, 12.13 Linear band-pass filters, 134 Linear programming, 40.41 Low-pass B ttcrw rtb filter, 206 Low-pass t3vr. 110 Lunar and solar rhythms. 75.76, 179-202 basic crossover mode,, 191-194 basic momentum mode,, 194, 195 code listing. 183-189 cr ss er model with c nlimlati , 195, ,96 crossover model with confirmation and inversions, 196 generating lunar entries, 181, 182 lusons learned, 201,202 solar activity, 197-20, sm ary analyses, 196, 197 sunsp.3, 180
MACD, 134 MACD divergence models, 150 MACD Histogram (MACD-H). 134 MACD signal lines models, 148 Marder, K&n, 11 Market orden, 72.73 Masters, Timothy, 48.49 MathWorks. The, 48 Mating, 258 MM-LAB, 48 Maximum enmpy (MEM), 203.204 Maximum encmpy spectral analysis (MESA), 203 Mayo, 1.. 179 McComick,Do aL., 57,76,77, 154, 170.181, 197, 199. 202,204,227,252.257,258,262,332,363 McWhorkr, W. Lawson, 136 Mean, 58 Mean squared deviation, 58 Meibahr, Stuart. 135, 139 MEM, 104 MEMA dynamic stop, 323,324 MESA, 203 MESA96.76 MetaSfmk, 25347 Metasystems, 15, 26 Modified exponential moving average (MEMA), 323,324 Modified standard exit smategy (MSES), 302-307 Momentum, 133, 155. 18, (See also Basic mome t m mode,) Money management, 182 Money management exits, 283,284 Money ma ager e t stop, 78 Monte Carlo simulations, 67 Modcf wavelet, 207, 208 Moving average, 109 Moving average convergence divergence oscillator e fACD), 134 Moving average crossover, 113 Moving average models. 74, 109-132 ADX vend filters, 131 code listing, 115-I 18 counter-trend models, 113, 114, U-130 equity cm-ves. 130 lag, 110,111 lessons Learned, 131, 132 moving average, defmed, 109
object Pascal, M-26,46 Omega Research, 14.41 GpiE ol e, 41,259.345 Gptimal f,81 Gptimimio . 29,54 optimizers, 29-49 alternatives to lmditional optimimion, 45.46 analytic, 3Y,40 brute force, 32-34 choosing the right one, 48.49 failure of, 4143 genetic. 35-38 Ilaw used, 30.31 implicit, 3 I linear pmgmmming, 40,41 parameters, 4345 sample size, 41-U hnulated annealing, 38. 39 sources of toolslinfommion, 41.48 Steep-a ascent, and, 39 S eCeSS of, 43-45 types, 3141 user-guided optimization, 34, 35 crification, 43.45 what thw do. 29.30
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