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Katz, Jeffrey Owen, and McCormick, Donna L. (May 1997). “Cycles and Trading Systems.”
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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

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