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INDEX

Breakout models (Conr,):

breakout type. 104

channel breakouts, W-997

characterisdcfi of breakouts, 84, 85

dose only channel breakouts, 8692

AI (see Genetic algorithms, Neural networks) currencies only, 101, 102

Alexa.“der, Colin, 113 eney orders, 104-106

All-past-years technique, 158 highest high/lowest low breakouts, 92-97

Analysis, 39 interactions. IO-5

Analytic opti”li7m, 39, ˜lo. 48 lessons learned, 107, 10R

Annealing, 38 long positions only, 100, 101

Annualized risk-to-reward ratio (ARRR), 15.60 res”ictio”sJfilters, la5

Appel. Gerald, 133 summary of results, ,0+,07

Artificial intdligence (see Genetic algorithms, testing, X5-104

Neural networks) types of breakouts, 83. 84

˜Astrology, ,79 volatility breakout variations. lW104

(See ok0 Lunar and Bohr rhythms) volatility bmkows, 97-100

Author™s conclusions, 353-363 Breasert, 203

Average directional mowmenl index (see ADX trend filter) Brnte force optimizers, S-34.47

Average tree rmgc, 86 Burke. Gibbons, 257

Butterworth filters, 206, 207

Back-adjustment, 3.4

C++, ,4, 15.24-26,46

Bad curve-fitting, S4

c++ Builder, 26

Band-pass ,i,tcr, 207

C++ Oenetic Optimizer, 49

Barrier ai˜s, 287

Bmm”˜& 12 C-Trader toolkit, 14, 15, 19.27,36,99, 114,214

Bars, 109 Calendar effects chart, 154

Basic C˜SSOW mode,: Catastrophe SOD, 288

lunar activity, 191-194 CCI (cohmdi& channel index), 135, 136

seasonality, 166--170 CD-ROM, 364

Basic mome”tum model: centered smaothing, 155,181

ha activity, 194. I95 Cemal limit theorem, 61.68

seasonality, 1 7 0 , 171 ChandqTusbar S., 110-112

Bernstein, Me, 154 Channel breakouts, 8697

Best exit strategy (market-by-market results), 33S-332 chroI”osome. 258

Best possible solution to a problem. 30 clipping, 164

Beta weights, 55 Close only channel breakouts, 8692

Bb”, Willianl, 1 3 4 Code listmgs:

Bonneville Market lnfomation @MI), 1, cycle-based entries, 21&2219

Borland, 24.25 dynamic stops, 317-321

genetic *gorhhms., 262-268

Bottom turning-point mode,, 243.249.250

Bouncing tick, 25 genetic exit Stmtegy˜ 342-345

BreakO”, r”odels. 74.83-108 lunar activity. 183-189

ADX tx”d filter, 102104 moving average models. 115-l Ifi

analysis b y mwkct, 106, 1 0 7 MSES, 3 0 2 3 0 5

369

370

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, results),

330-332

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

372

HHLL breakouts, 92-97 Lunar and solar rhythms (Cont.):

HHLL stop, 322 test methd”l”gy, 183-190

High-pass Bumworth filter, 206 test results. 19%,97

Highest higNlowcst low breakouts, 92-97 Lunar “lo”len”“n series, 181

Highest highnowest low stop, 322 Lupa, Louis M., ,34

Histograms. 22.23

Holland, John, 257 MACD, 134

MACD divergence models, 150

Implicit optimizers, 3 1 MACD Histogram (MACD-H). 134

IMSL, 25,26,48 MACD signal lines models, 148

Individual co”tmti data, 3 Marder, K&n, 11

Inferential statistics (see Statistics) Market orden, 72.73

I”f”r”mi”” soun;es: Masters, Timothy, 48.49

data., II, 11 MathWorks. The, 48

optbnizers, 48.49 Mating, 258

htemational Mathematics and Statistics Libm™y (IMSL), MM-LAB, 48

25.26,48 Maximum enmpy (MEM), 203.204

Inmday pricing data, 4 Maximum encmpy spectral analysis (MESA), 203

Inversions, 156, 182 Mayo, 1.. 179

(See also Cotimtion-and-inversion mode,) McComick,Do”“aL., 57,76,77, 154, 170.181, 197, 199.

,““esr”r™s ,9winesr Doily, 12 202,204,227,252.257,258,262,332,363

McWhorkr, W. Lawson, 136

Jackknife, 158 Mean, 58

Mean squared deviation, 58

Meibahr, Stuart. 135, 139

MEM, 104

Katz, Jeffrey Owe”. 51.76.77, 154, IlO, 181, 197,199, MEMA dynamic stop, 323,324

202,2c4,227,252,257,258,262,332.363 MESA, 203

Klein, R.A., 227 MESA96.76

Knight, Sheldon, 11 MetaSfmk, 25347

Metasystems, 15, 26

Lag, 110 Modified exponential moving average (MEMA), 323,324

Lane™s stochastic, 135 Modified standard exit smategy (MSES), 302-307

Leave-one-out “ledxd, 158 Momentum, 133, 155. 18,

Ledemm, I., 227 (See also Basic mome”t”m mode,)

Litit orders, 12.13 Money management, 182

Linear band-pass filters, 134 Money management exits, 283,284

Linear programming, 40.41 Money ma”ager”e”t stop, 78

Low-pass B”ttcrw”rtb filter, 206 Monte Carlo simulations, 67

Low-pass t3vr. 110 Modcf wavelet, 207, 208

Lunar and solar rhythms. 75.76, 179-202 Moving average, 109

basic crossover mode,, 191-194 Moving average convergence divergence oscillator

basic momentum mode,, 194, 195 e”fACD), 134

code listing. 183-189 Moving average crossover, 113

cr”ss”“er model with c”nlimlati”“, 195, ,96 Moving average models. 74, 109-132

crossover model with confirmation and ADX vend filters, 131

inversions, 196 code listing, 115-I 18

generating lunar entries, 181, 182 counter-trend models, 113, 114, U-130

lusons learned, 201,202 equity cm-ves. 130

solar activity, 197-20, lag, 110,111

sm”“ary analyses, 196, 197 lessons Learned, 131, 132

sunsp.3, 180 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, 4 3 4 5

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