cmt lvl iii 2018 the integration of technical analysis 5 november 29, 2017 5:55 pm index 5...

bindex 1 November 29, 2017 5:55 PM

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I n d e x

A

Absolute true range (ATR), 796Act of God, 36Actualized rate of return, 25Adaptive stops, 49ADX. See Average directional movement indexAffinity bias, 582Alexander’s filter rule, 112Algorithms

genetic, 79–81, 737, 741–747Alpha, 542–544, 634–636

positive, 535Alternative hypothesis

versus null hypothesis, 287AMEX oil index (XOI), 339–342, 340AMR. See Average maximum retracementAnchoring and adjustment bias, 573–574ANN. See Artificial neural networksAnnualized rate of return, 88Annualized volatility, 181–182, 708Appel, Gerald, 715–716, 721Aristotle, 390Arnold, Curtis, 658Artificial neural networks (ANN), 733–735Assets. See Regression

allocation of, 632–634demand for, 620

ATR. See Absolute true range; Average true range

Availability bias, 575–576Average directional movement index (ADX),

206–207Average maximum retracement (AMR),

178–179

Average net return per trade, 87Average time-to-recovery, 88Average trade net profit, 18Average true range (ATR), 48, 197Averaging

test results, 77–78Averaging down, 221, 224–225Axons, 732

B

Backward- (or forward-) adjusted data, 59–60Balance of trade, 736Balance sheets, 634–635Band width, 56Basket of Unhedged Gold Stocks (BUGS),

345–347Bearish engulfing, 812–813Bearish Harami, 815–816Behavioral biases, 567–583. See also Investor

psychologybubbles and, 604–615cognitive biases, 569–578emotional biases, 578–582information processing biases, 573–578learning objective statements, 567overview, 567–569

Behavioral finance. See also Behavioral biases; Behavioral techniques; Bubbles; De-bubbling; Investor psychology

description of, 545Behavioral techniques, 638–667

commitment of traders report, 656–661commitment of Traders Sentiment Index,

665

Page numbers followed by n indicate note numbers.

CMT LVL III 2018 The Integration of Technical Analysis


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2

fraud and, 628historic examples of, 614in the laboratory, 617–618learning objective statement, 604overview, 604–605revulsion, 611–615, 629–632swindles in, 610

BUGS. See Basket of Unhedged Gold StocksBuilding permits, 502–503Bullish engulfing, 822–823Bullish Harami, 818–819Buy-and-hold return, 19Buy-and-hold strategy, 906

C

CAC40 index, 326–328Calmar ratio, 179Canada’s Venture Index, 383Canadian dollar, 104, 111Candlestick analysis, 789–837. See also

Progressive chartingbearish reversal candlestick patterns,

806–824candlestick continuation patterns, 824–827candlestick to bar chart correspondence, 795chart patterns and, 827–828color, 800–801composite candlestick, 790–791construction and classification of Japanese

candlesticks formations, 789–790cycle analysis and, 828elements of, 789–803Fibonacci retracements and, 829–831filtered candlesticks, 832–833FOREX candlesticks, 837Ichimoku Kinko Hyu analysis and, 829integrating, 827–832internal proportions, 802–804learning objective statements, 789location, 801–802Marubozu, 799–801moving averages and, 832multi-timeframe based candlestick

confirmation, 791–792oscillator analysis and, 829overview, 789popular patterns and their psychology,

804–827preceding activity, 802

Behavioral techniques (continued)conditional analysis of shocks, 655–656contrary opinion about, 661–665Dogs of the Dow, 666event trading, 644–656learning objective statements, 638market selectivity, 643measuring the news, 639–644media indicators, 644overview, 638–639public opinion, 661put-call ratios and, 665–666ranking and measuring, 641–642trading on the news, 642–643watching big block transactions, 666–667

Belief perseverance biases, 569–573Benchmarks, 86

inaccuracies in, 86Bernoulli, Daniel, 171, 233Beta, 532–538, 538–546, 562n5

description of, 533–534drivers of, 536–538estimating, 534–535sector weights and, 544–546from solid fundamentals, 537–538

Bierovic, Thomas, 713–714Big block transactions, 666–667Binomial probability, 233–234Black Swan, 36Bloomberg, 128Bollinger Bands, 401–402Bonds, 218Bootstrap method, 298, 299–301

generating confidence intervals with, 310–311

testing rule performance with, 304Breakeven level, 48Breakeven stop, 48Breakout systems, 10, 588–590Bretton Woods fixed exchange rate agreement,

336–337Bubbles, 604–615

bursting, 616–617cash flow and, 626credit creation, 606–607, 620–622de-bubbling, 616–637displacement of, 605, 619–620“euphoria,” 607–608, 622–624in the field, 618–619financial distress and, 609–611, 624–629


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3

Chicago Mercantile Exchange (CME), 128, 330, 345

Chi-square test, 234–237CME. See Chicago Mercantile ExchangeCMT Association. See Chartered Market

Technician’s AssociationCoded parameters, 58Cognitive biases, 569–578

belief perseverance biases, 569–573information processing biases, 573–578overview, 569

Cognitive dissonance bias, 572–573conflicted analytics trader and, 585at market tops and bottoms, 593–595

Cognitive errors, 567–569. See also Behavioral biases

Coincident Economic Index (CEI), 503–507Coincident indicators, 487COMEX. See New York Commodities

ExchangeCommercials, 188–189Commitment of Traders Report (COT),

656–661capturing the cycle in, 660–661creating an oscillator for the COT numbers,

659–661trading signals, 660

Commodex system, 158–159Commodities, 325–350

correlations with equity and gold, 375–379

S&P 500 and, 358–364Commodity channel index (CCI), 16Commodity selection index (CSI), 204,

207–208Commodity Systems Inc., 128Commodity Trading Advisor (CTA) Index, 86,

177Composite indicator, 786–788Computers. See also Technical analysis

acquiring data, 128–129for analysts, 54–55application of computer intensive methods

to back-test of a single rule, 303–305computer-intensive methods for generating

sampling distribution, 298–305life span of, 127problem-solving with, 130software, 127–128use and abuse of, 127–135

price action guide for analyzing candlestick action, 795–803

price-error analysis, 833–836relative proportions, 802reversal and continuation candlestick

patterns, 794–795sentiment bias, 795–797single, double, triple, and multiple

candlestick formations, 793–794size, 799–800strengths and weaknesses of Japanese

candlestick charting, 792–793support and resistance with, 829trading cyclic-barrier confluences, 833trading with, 833–837trend interruptions, 802trend sentiment, 798–799volume analysis and, 831–832

Capitalconservation of, 171, 186expenditure, 635–636initial, 42

Capital Asset Pricing Model (CAPM), 32, 45Capital goods, 501CAPM. See Capital Asset Pricing Model“Carry trade,” 360Cascade, 600–601Case studies

of designing “HAL” (2001: A Space Odyssey), 16–20, 28–29

Cash data, 61CCI. See Commodity channel indexCEI. See Coincident Economic IndexCentral Limit Theorem, 282n36CFTC. See Government regulatory agencyChannel breakout, 112, 314n14Channel systems, 164Chaos pattern, 728–729Chartered Market Technician’s Association, ix

about, ixexam topics and question weightings, xv–xvilevel III content selections, xiiiprogram, xi

Charts. See also Progressive chartingbehavioral elements associated with chart

patterns, 586–588conditional formatting, 201real-world, 879–904

Chicago Federal Reserve’s National Credit Index, 499–500


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4

d

Dark cloud cover, 809–810Data

accuracy of, 128–129acquiring, 128–129backward- (or forward-) adjusted data,

59–60cash, 61cleaning, 12fixed, 60–61intraday, 128mining, 479older, 105, 131problems with adjusted data series, 61–62selecting test data, 58–63series, 62special data problems for futures systems,

14–15synthetic, 62–63testing with clean data, 13–14

Data mining, 13Davis, R. E., 99–100DAX index, 146–152, 325–326, 366–367.

See also European indicesfutures, 397

Dax Volatility Indices (VDAX), 326De-bubbling, 616–637

learning objective statement, 616overview, 616–617

Defensive sectors, 486Deflation, 626–627DEH. See Domestic Economic HealthDe Kempenaer, Julius, 421. See also Relative

rotation graphsDeMark, Tom, 754Dendrites, 732Descriptive statistics, 259–261

frequency distributions, 259statistical measures of variability, 260–261statistics that measure central tendency,

259–260Detrending, 314n8Deutsche Börse, 325, 326DFA. See Dimensional Fund AdvisorsDial Data, 128Diffusion index, 487Dimensional Fund Advisors (DFA), 469–470Directional indicator, 112, 205–206, 911Directional movement, 112, 204–208

retesting, 208–209

Conditional probability, 295–296Confidence intervals, 283–314, 307–313

connection to sampling distribution, 309–310

description of, 307–308generating with the bootstrap method,

310–311versus hypothesis tests, 312–313for the TT-4-91 rule, 313

Confirmation bias, 570, 590Congestion index, 416–420Connors, Larry, 714Conservatism bias, 569–570Constant forward contracts, 14Constant n-day forward, 61Consumer installment credit/personal

income, 509Consumer price index (CPI)

for services, 510Continuous contract, 14–15Continuous parameters, 58Continuous test series, 58–59Contracts

individual full, 59number of, 39

Contrarianism, 586Corporations

GDP, 608profits, 491–492

Correlated risk, 44Cost of doing business, 154COT. See Commitment of Traders ReportCountertrends

delayed entry into a run, 152–153CPI. See Consumer price indexCQG Data Factory, 128CRB index, 342–343, 383Credit

creation of, 620–622Critical threshold stop, 48Crossover of two simple moving averages,

109–111CSI. See Commodity selection indexCTA. See Commodity Trading Advisor

IndexCurrencies, 336–339

comparison of, 413Curve-fitting, 13, 20“Cyclical” bulls markets, 500Cyclical sectors, 486


Ind

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5

conditional, 730–731description of, 729–730

Equitycorrelations with commodity assets and

gold, 375–379Equity curve, 51

in system design and testing, 26Equity market risk premium (RMRF), 461Equity trends, 225–229

trading on, 227–229ER. See Efficiency ratioES. See e-mini futuresEstimation, 305–313

interval estimates, 307point estimates, 306–307

Euro. See European Currency UnitEurodollar, 146–152Euronext (MONEP), 327European Currency Unit (Euro), 97European futures, 368–369European indices, 366–373. See also DAX

index; International Indicescorrelation with stocks, 367–368European futures, 368–369intraday, 371–373learning objective statement, 366time factor, 370–371

Euro Stoxx 50 index, 317, 321, 329EURUSD

highs and lows, 763–764time patterns, 760–761

Evening star, 814–815Event trading, 644–656. See also Price shocks

finding recent patterns, 651–655market reactions to reports, 645–647measuring an event, 647Rashke and the news, 651reaction to unemployment reports, 650results of event studies, 648–651trading reaction of treasuries to economic

reports, 650–651trading the event lag, 647–648trigger for, 644–645

Exogenous signal systems, 12Exponential moving average (EMA), 400

F

Fading, 12Faith, Curtis, 7Fast market, 129

Directional parabolic method, 112Discrete parameters, 58Displacement, 605Diversifiable risk, 44–45Diversification

a portfolio, 528–530risk and, 526–528

Dividendyield, 538–546yield by sector, 541–542

Dogs of the Dow, 666Doji, 802Dollar index, 334–339, 383. See also GoldDollar stop, 46–47Domestic Economic Health (DEH), 734Donchian channel breakout system, 10Dow Jones Euro Stoxx 50, 329Dow Jones Stoxx 50, 329Dow Jones Transportation Index, 303DP. See Probability of drawdownDrawdown, 32, 36Dual moving average crossover, 112

e

Earningsquality of, 635

ECN. See Electronic Communication NetworkEconomics

analysis of, 485–490expansion of, 486lagging economic indicators, 507–512Nobel Prize in, 519, 532

Edwards, Albert, 631–632Efficiency factor, 25Efficiency ratio (ER), 211, 727Efficiency ratio selection

results of, 211–213Efficient frontier, 173–174Efficient Market Hypothesis, 645–646Ego defensive bias, 589–590Elder, Dr. Alexander, 232, 779–782

triple-screen trading system, 779–782Elder-Ray technique, 781Electronic Communication Network (ECN),

642–643EMA. See Exponential moving averagee-mini futures (ES), 371, 393–394Emotional biases, 578–582Endowment Bias, 581Entropy, 8, 729–731. See also Technical analysis


Ind

ex

6

modified VIX futures contract, 678–679VIX futures as an indicator, 675–677

FX. See Foreign exchange markets

G

Gammage, Kennedy, 185n11Gann swings, 783–784Gaps

close-to-close, 775–776in futures markets, 767–772opening, 765–776stocks and, 772–775

Gartley, H. M., 422Gas

prices, 514Gaussian distribution, 63GDP. See Gross domestic productGeneralized VaR calculation, 183–184Genetic algorithms, 79–81, 737, 741–747

converging on a solution, 746fitness, 743–744initial chromosome pool, 742–743mating, 745multiple seeding, 747mutation, 745–746overview, 741propagation, 744–745representation of, 741–742simulated performance of, 746

German mark, 114Globalization

S&P 500 and, 356–357of world futures markets, 640

Globex, 642Gold, 55, 345, 374–389, 408. See also Dollar

indexcorrelations with equity and commodity

assets, 375–379impact of, 104inflation and, 375leading prices or lagging prices, 379–385learning objective statement, 374as a long-term investment, 375overview, 374–375patterns, 379price of, 681silver and, 377time frame and, 385–389VIX index and gold price indicator, 680–683

Fat tail, 185, 238Fear and greed index, 671Federal Reserve Economic Data (FRED), 486,

517n1, 546–547Feedback loops, 585–586Fibonacci retracements, 829–831Fidelity, 86Financial crises

of 1929 (U.S.), 618–619of 1988 (U.S.), 84, 140–142, 187, 359, 497of 1997 (Asian), 359

Financial cycle, 486Financial distress, 604–605, 624–629Financial market participants (FMPs), 569Fisher, Irvine, 618, 619Fisher, Sir Ronald, 274–275Fixed data, 60–61Flight to safety, 135, 648FMPs. See Financial market participantsForce Index, 780–781Foreign exchange (FOREX) candlesticks, 837Foreign exchange (FX) markets, 648, 703Forex

S&P 500 and, 358–364FOREX. See Foreign exchange candlesticksForward-shifted technical indicators, 736Fractals, 726–731

efficiency of, 727–728fractal dimension, 726–727

Fractional area, 280n6Fraction Same Sign, 63Framing bias, 575

mental framing and anchors, 590–591FRED. See Federal Reserve Economic DataFrench, Kenneth, 462, 466–467, 474, 474n17,

478Frequency

distribution of sample statistic, 250distributions, 259equivalence of frequency and area, 250–252relative frequency distribution, 252–253

FTSE index, 328–329Futures

combining VIX index and, 679–680DAX, 397European, 368–369gaps in futures markets, 767–772globalization of world futures markets, 640hedging with VIX futures, 696–699markets, 768–772


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7

Herrick Payoff Index, 661Heuristic bias, 589High, low, and close (OHLC)

prices, 789High Low Logic Index (HLLI),

185“High minus low” (HML), 462Hill, John E., 13Hindenburg Omen, 185–186Hindsight bias, 572, 589HLLI. See High Low Logic IndexHML. See “High minus low”HSI. See Hang Seng indexHUI index, 345–347Hypothesis tests, 283–314

computer-intensive methods for generating sampling distribution, 298–305

conclusions and errors, 297–298versus confidence intervals, 312–313decisions about, 291–292estimation, 305–313evidence, 284–285falsifying a hypothesis with improbable

evidence, 286–287improbability of, 292–296versus informal inference, 284–288learning objective statements, 283mechanics of, 292–298null hypothesis versus the alternative

hypothesis, 287rationale of, 288–292statistical hypothesis, 285–286statistical significance of, 296–297types of statistical inference, 283–284

I

Ichimoku Kinko Hyu analysis, 829Illusion of control bias, 571–572Immediate practical future, 256Implied volatility, 710–712Indexed data series, 61Index of industrial production,

506, 517Indicators

combining, 910Individual full contracts, 59Inference, 258

hypothesis tests versus, 284–288reliability of, 258–259

Goldman Sachs Commodity Index (GSCI), 344–345

Gold Standard, 374Gorbachev, Mikhail (Russian Premier),

136–137Government

U.S. federal debt, 514Government regulatory agency (CFTC), 164Greenspan, Alan, 374Grice, Dylan, 628Grid, 72Gross domestic product (GDP), 491, 733

corporate, 608ratio, 514–515

Gross loss, 18Gross profit, 18Group bias, 596–603

deliberative groups, 599devil’s advocates, 602groupthink characteristics, 601–602learning objective statement, 596overcoming, 602polarization and, 597respect for other group members, 602secret ballots, 602statistical groups, 598

GSCI. See Goldman Sachs Commodity IndexGulf War (1991–1992), 137–138, 187

H

Hadady, R. Earl, 664–665Hammer, 819–820Hang Seng index (HSI), 330Hard money stop, 46–47HDAX index, 326Heat map, 201Hedge funds

popularization of, 422–423replication of, 747–748

Hedging, 687–700alternative to, 693–694decision, 696learning objective statements, 687overview, 687University of Massachusetts study, 699–700with VIX futures, 696–699with VIX options, 687–696

Heikin Ashi candlesticks, 832–833Herd behavior, 586


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8

HUI, 345–347L-DAX index, 326learning objective statement, 325MDAX, 325Nikkei 225, 329–330, 367OIX, 339–342overview, 325SDAX, 326S&P 500 correlation with, 351–358TecDAX, 326trading hours, symbols, and volatility,

330–334VDAX-NEW, 326VIX, 326, 347–350XAU, 345–347, 382

International Standard Asset Management (ISAM), 168

Internet, 605Intraday correlations, 390–398

intermarket regression, 392–393lagging prices or leading prices, 395–398learning objective statement, 390relationships between different time frames,

390–392time frame, 393–395

Intraday data, 128European indices and, 371–373

Intraday patternsEURUSD highs and lows, 763–764EURUSD time patterns, 760–761intraday highs and lows, 762–765Merrill’s, 757refining patterns for trading, 761–762Tubbs’, 756updating, 2000–2011, 757–762using the high and low patterns for trading,

764–765Intraday volatility and volume, 712–713Investing, 170

decisions, 584–585rational investor, 173–174

Investor psychology, 584–595. See also Group bias

behavioral aspects of, 584–586behavioral aspects of market consolidations,

591–593behavioral aspects of market reversals,

593–595behavioral elements associated with chart

patterns, 586–588

Inflationadjustments for, 490as economic indicator, 513gold and, 375public awareness of, 162–163

Information processing biases, 573–578Information ratio, 87, 177–178In-sample (IS) data, 21, 64–65

comparisons, 24Insider trading, 666–667Institute for Supply Management New

Manufacturing Orders Index (ISM), 494–495, 516–517

Interest rates, 146–152, 703average prime rate, 507–509S&P 500 and, 358–364spread, 496–498trends and interest rate carry, 726zero interest rate policy, 509

Intermarket indicators, 399–420Bollinger Band divergence, 401–402congestion index, 416–420intermarket disparity, 402–404intermarket LRS divergence, 404–405intermarket momentum oscillator, 407intermarket moving average, 415–416intermarket regression divergence, 405–406learning objective statements, 199multiple intermarket divergence, 409–410multiple regression divergence, 410–415overview, 399relative strength, 399–401Z-score divergence, 407–409

International Indices, 325–350. See also European indices

AMEX oil index, 339–342, 340CAC40, 326–328Canada’s Venture Index, 383CRB index, 342–343, 383DAX, 325–326Dax Volatility Indices, 326Dollar index, 334–339, 382Dow Jones Euro Stoxx 50, 329Dow Jones Stoxx 50, 329Euronext (MONEP), 327Euro Stoxx 50, 329FTSE, 328–329Goldman Sachs Commodity Index, 344–345Hang Seng, 330HDAX, 326


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9

Learning objective statements, 518–519Legislation

Sarbanes-Oxley Act, 666Securities and Exchange Act, 666

Lehman Brothers Treasury Index, 86LEI. See Leading economic indicators indexLength of the average winning trade, 25Leverage, 42

based on exposure, 191risk control and, 188–191

Leveraged inflation funds, 163Levy, Robert, 422LIFFE. See London International Futures &

Options ExchangeLIM. See Logical Information MachinesLimit up/limit down rule, 160Linearly weighted average, 105Linear regression slope, 105Liquidity, 175–176, 722Loans

commercial and industrial, 510Locked-limit day, 161Logical Information Machines (LIM),

641, 650, 729n12Lognormal, 701

calculation, example of, 706London International Futures & Options

Exchange (LIFFE), 329London Stock Exchange, 328Longest flat time, 25Long-only funds, 636Loss-aversion bias, 578–579Losses

consecutive, small, 11gross, 18large, 25maximum consecutive, 25, 26rationalizing, 134versus wins, 214–216

Lovell, Robert, 374L-S-O price channel, 112Lynch, Peter, 540

M

MACD. See Moving average convergence/divergence

Macroeconomicsstatistical representations of risk,

524–532

behavioral elements associated with market trends, 588–590

learning objective statements, 584mental framing and anchors, 590–591overview, 584

IS. See In-sample dataISM. See Institute for Supply Management New

Manufacturing Orders Index

J

Japan, 622–624, 627–628construction and classification of Japanese

candlesticks formations, 789–790strengths and weaknesses of Japanese

candlestick charting, 792–793JdK RS-momentum, 431–433JdK RS-ratio, 429–431Jegadeesh, Narasimhan, 422Jiler, William L., 656Jobs

creation of, 492

K

Kahneman, Daniel, 578–579Kaufman

on stops and profit-taking, 200–203strategy selection indicator, 210–213

Kelly, John L., Jr., 40Kelly formula, 40–41Keynes, John Maynard, 374, 520, 627–628Kindleberger, Charles, 618Kipling, Rudyard, 639Kissinger, Henry, 366Krausz, Robert

multiple time frames, 782–785Kurtosis, 185

L

LAG. See Lagging economic indicators indexLagging economic indicators index (LAG),

487, 507–512Large losses, 25Law of large numbers, 263, 275, 281n18,

281n20L-DAX index, 326Leading economic indicators index (LEI), 487,

488, 492–503


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10

ranking of markets for selection, 203–213selectivity, 643strategies for, 111–114testing in only one market, 90–91time in, 88tops, 593trading across a wide range of markets,

99–114trends, 585–586volatility of, 134

MarketSci Blog, 714–715Markowitz, Harry, 519

framework, 532Markup phase, 660MAR ratio, 21, 25, 37Martingale betting system, 37, 144–146,

145n2anti-Martingales, 146, 150–152fractional, 152–153within a trend, 147–150

Marubozu candlestick, 799–801Masters, Dr. Timothy, 301–302Mathematical optimization, 67–68Mating, 745Maximum adverse excursion, 19, 47, 179, 203Maximum consecutive losses, 25Maximum cumulative drawdown, 25Maximum drawdown (MDD), 25, 36, 87Maximum favorable and adverse excursions, 26Maximum holding period, 56Maximum loss, 26Maximum percentage drawdown (MDD), 19Maximum winning adverse excursion, 47Maximum winning favorable excursion, 49Maxwell, Joseph R., 100–101McCallum rule, 620, 620n1McClellan Oscillator, 186MCP. See Monte Carlo permutation methodMCS. See Monte Carlo simulationMDAX index, 325MDD. See Maximum drawdownMean

of a large sample, 275sampling distribution of, 275–279trimmed, 281n17

Mean-reverting systems, 155–156Media

indicators, 644Mendoza, Mario, 472n15Mendoza line, 472, 472n15

Macro-finance environment, 484–517coincident economic indicators, 503–507corporate profits, 491–492economic analysis, 485–490job creation in, 491–492lagging economic indicators, 507–512leading economic indicators, 492–503learning objective statements, 484national income and, 491–492nominal and real time series, 490overview, 484sector rotation, 486–489supplemental economic indicators, 513–515

Macrotrends, 104Major, John, 137Manufacturing

average length of workweek, 493–494inventory to sales ratio, 510ISM New Orders Index, 494–495new orders for consumer goods and

materials, 498new orders for nondefense capital goods,

501unit labor cost, 511

Margincalls, 174initial, 202minimum, 188rules and volatility of, 189trading on, 188–189

Market-adjusted returns, 465n13Market risk, 169. See also Risk controlMarkets

advertised systems for one market, 91behavioral aspects of market consolidations,

591–593behavioral aspects of market reversals,

593–595behavioral elements associated with market

trends, 588–590bottoms, 593evolving, 134expectations of, 584–585fast, 129, 175hindsight and, 136–139illiquid, 175–176inactive, 175knowledge of, 44money and, 135noise, trends, and frequency of stops, 202, 210


Ind

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11

money-management risk strategies, 45–50monitoring systems and portfolios, 50–51overview, 31–33testing money-management strategies, 34–35unusual risks, 43–45

Money-management stop, 46Money point

versus technical point, 48Monte Carlo permutation method (MCP),

298, 301–303procedure, 303results, 305testing rule performance of, 305

Monte Carlo sampling, 81–82, 229–230Monte Carlo simulation (MCS), 34–35, 42, 43,

62–63Moving average

of relative strength, 428Moving average calculation period, 56

intermarket, 415–416Moving average convergence/divergence

(MACD), 12, 416Moving average systems, 10, 164

comparison of, 167–168relative risk of, 155

Moving average with % price band, 112Multifactor performance models, 546–549

based on macro factors, 546–547based on style factors, 547–549managing a portfolio using, 549

Multiple time frames, 777–788Elder’s triple-screen trading system,

779–782Krausz’s, 782–785laws of, 783–785learning objective statements, 777overview, 777–778Pring’s KST system, 785–788selection of, 785tuning two time frames to work together,

778–779Mutation, 745–746Mutual funds

popularization of, 422–423

n

Najarian, Jon, 12NAPM. See National Association of Purchasing

Managers

Menken, H. L., 639Mental accounting bias, 574–575Merrill, Arthur, 751, 757MetaStock, 53, 69, 121, 126Miekka, Jim, 185n11MII price channel, 112Minsky, Hyman, 605˚, 618Misinterpretation bias, 590Models

limited life of, 98–99Minsky/Kindleberger model, 619momentum investing behavioral models,

480–481multifactor performance models, 546–549predictive, 395risk characteristics of a trading model,

176–177simple moving average model, 103testing, 8

Momentum investing, 459–483behavioral models, 480–481critics of, 463, 464, 476description of, 460n2disappearing returns, 472–475learning objective statement, 459long-only versus short side, 465–466overview, 459–461results, 478–479returns, 461–464screens versus a direct factor, 471–472small cap stocks versus large cap stocks,

466–468for a taxable investor, 470–471theory behind, 479–481trading costs, 468–470volatility, 476–478

Momentum/oscillators, 164intermarket, 407

MONEP. See EuronextMoney

determining optimal position size, 39–41final position size, 41money-management risks, 35–45normal risks, 38–43reward to risk, 37testing money-management strategies,

34–35Money and portfolio risk management, 31–52

learning objective statements, 31money-management risks, 35–45


Ind

ex

12

skepticism about, 289–290as target of the test, 288–289

NYBOT. See New York Board of Trade

O

Objective function, 21Occam’s razor, 479OHLC. See High, low, and close pricesOil, 653

prices, 514OIX INDEX, 339–3421-parameter optimization, 56O’Niel, William, 399OOS. See Out-of-sample optimizationOpening gaps, 765–776Optimal f, 40–41, 229–232

finding, 230–231observations of, 232

Optimization, 541-parameter, 562-parameter, 56description of, 116–120mathematical, 67–68sequential, 67–68worst results, 95–96

Outcome bias, 577Outlier-adjusted profit, 24Outliers, 89Out-of-sample data, 64–65Out-of-sample optimization (OOS), 13, 20,

21, 22–23comparisons, 24

Out-of-sample testing, 65Overconfidence bias, 579

P

Paper trading, 131Parabolic system, 112Parameter set, 15ParisBourse, 326Pattern recognition systems, 12, 751–776

comparing ranges, 754DeMark’s projected ranges, 754intraday highs and lows, 762–765learning objective statements, 751Merrill’s intraday patterns, 757opening gaps, 765–776overview, 751–753

NASDAQ 100, 90silver trading and, 162–163

National Association of Purchasing Managers (NAPM), 649

N-day breakout, 105–107Net losses, 87Net profit, 18Net profits, 87Networks, 732Neural networks, 22, 732–741

artificial, 733–735closed system, 740–741description of, 732modeling human behavior, 740reducing number of decision levels and

neurons, 739–740selecting and preprocessing inputs,

735–736selecting success criteria, 736success criteria, 739terminology of, 732–733training, example of, 737738training process, 736–737

Neurons, 732News

discounting, 643measuring, 639–644media indicators, 644professional analysis of, 641–642Rashke and, 651trading on the news, 642–643

New York Board of Trade (NYBOT), 343New York Commodities Exchange

(COMEX), 374Neyman, Jerzy, 274–275Nikkei index, 329–330, 3679/11, 135, 138, 170. See also Price shocksNinja Trading, 69, 121Nobel Prize in economics, 519, 532Noble, Grant, 644No cap method, 151–152Noise, 210, 302

description of, 723trends and, 723–725

Nonagricultural payrolls, employees on, 504

Null hypothesisversus the alternative hypothesis, 287rejecting, 296–297simplicity of, 290–291


Ind

ex

13

Portfolio risk and performance attribution, 518–563

answers to selected questions and problems, 554–562

beta, 532–538components of volatility, 523diversification a portfolio, 528–530expected value and expected returns,

520–521mean-variance framework, 532–538multifactor models, 546–549performance attribution, 538–546questions and problems, 550–554risk and expected return, 519–523standard deviation of returns, 521–522statistical representations of macroeconomic

and firm-specific risk, 524–532Position

averaging into, 217–218compounding, 221–225entering, 170, 216–221exiting, 170initial risk, 195leveraged, 910–911long, 344size, 34, 38, 191–194using timing for a better price, 220–221waiting for a better price, 218–220

Positive alpha, 535PPI. See Producer Price IndexPrice-error analysis, 833–836Prices

asymmetry of price movement, 236–237corrections, 662gold, 379–385of gold, 681lagging versus leading, 395–398for oil and gas, 514ranking trends using prices, 210of stocks, 500

Price shocks, 114–116, 135–142, 170, 644–645. See also Event trading

conditional analysis of shocks, 655–656crisis management of, 140–142distributions and, 184–185identifying, 139–140trading limits and, 162

Price-volatility relationship, 701, 702–703. See also Volatility analysis

exceptions to, 703–704

pivot points, 753–754projecting daily highs and lows, 753–754time of day, 755–765trading habits forming lasting patterns,

755–756Tubbs’ intraday patterns, 756updating intraday time patterns, 2000–

2011, 757–762Patterns. See also Candlestick analysis

behavioral elements associated with chart patterns, 586–588

candlestick continuation patterns, 824–827chaotic patterns and market behavior, 728–729continuity in test results, 71–72finding recent events and, 651–655gold and, 379predictability and, 284–285in return, 226short-term, 12

Payoff ratio, 25, 37PCE. See Personal consumption expenditure

deflatorPercentage of profitable trades, 87Percent profitable, 18Perfect profit correlation, 21Performance

attribution, 538–546long-term, 168

Perpetual contracts, 14Personal consumption expenditure (PCE)

deflator, 506, 517n2Personal income

less transfer payments, 505–506ratio of consumer installment credit, 509

Philadelphia Exchange (PHLX), 345PHLX. See Philadelphia ExchangePiercing line, 823–824Pivot points, 753–754PMs. See Portfolio managersPopulation, 246, 256

parameter, 256–257Portfolio management. See also Macro-finance

environment; Momentum investing; Portfolio risk and performance attribution

alpha and beta, 542–544description of, 457three-stock risk and expected return, 530–532using multifactor models, 549value of, 908–909

Portfolio managers (PMs), 422–423


Ind

ex

14

R

Random numbers and distributions, 63, 82, 314n9

Range quotient, 112Raschke, Linda, 651, 778Real estate

new private housing units, 502–503Real-world charts, 879–904

learning objective statements, 879overview, 879–880

Recency bias, 577–578Recovery ratio, 25Reference deviation, 112Regression, 317–324

assumptions, 323intermarket, 392–393intermarket divergence, 405–406learning objective statements, 317multiple divergence, 410–415multiple regression, 318, 321–323nonparametric regression, 323–324overview, 317regression equation, 317–320studies, 22

Regret-aversion bias, 581–582Relative rotation graphs (RRGs)

angles, 441combining heading and angles, 444–446constructing, 433–435derived indicators, 440–447distance, 442–444heading, 444quadrant, 440rotational sequence, 433–435standard deviation, 443–444users, 438–439velocity, 444visualization versus trading system, 439weights, 447–456

Relative strength (RS), 399–401analysis of, 423–426constructing a relative rotation graph, 433–435difference between institutional and private

investors, 423history of, 422–423indicators, 427–433interpretation of, 435–439JdK RS-momentum, 431–433JdK RS-ratio, 429–431

Pring, MartinKST system, 785–788

Probability, 261–264of chance performance, 240–243conditional, 295–296density, 240, 280n5, 281n21distributions of random variables,

264–267experiments and random variables,

246–255law of large numbers, 263relationship between probability and

fractional area of the probability distribution, 267–269

theoretical versus empirical probability, 264types of, 264

Probability of drawdown (DP), 180–181Producer Price Index (PPI), 114–115, 650Professional development, 489–490Profit

adding on, 223–224gross, 18measures in system design and testing,

24–25net, 18percentage of, 197targets, 198–200

Profit factor, 18, 24, 37, 281n29Profits

corporate, 491–492targets, 716

Progressive charting, 838–878learning objective statements, 838market observations, 855–878using candlestick analysis, 838–855

Protective stop, 46–47Proximity risk, 202Pruitt, George, 13Psychological risk, 43–44Psychology. See also Candlestick analysis;

Investor psychologyPullbacks, 220, 588–590Put-call ratios, 665–666

VIX option and, 683–686p-value, 296–297Pyramiding, 42–43

Q

Quant investing, 477n20


Ind

ex

15

optimal f, 229–232overlay, 202–203overview, 169potential risk, 180–186probability of success and ruin, 213–216proximity risk, 202ranking of markets for selection, 203–213reserves and targeted risk levels, 189–191risk aversion, 171–175risk characteristics of a trading model,

176–177risk characteristics of systems, 186–187risk preference, 172–173risk protection, 201–202value at risk, 182–183

Risk management. See also Money and portfolio risk management; System design and testing; System evaluation and testing

Risk of ruin (ROR) formula, 39–40RMRF. See Equity market risk premiumRobustness, 13, 23, 85–86, 108

description of, 120–135Roll date, 59, 60–61ROR. See Risk of ruin formulaRotational sequence, 433–435RRGs. See Relative rotation graphsRS. See Relative strengthRSI. See Relative strength indexRussell, Bertrand, 325RV. See Relative volatility

S

S5INFT. See S&P Information Technology sectorSampling, 246–250

example of, 254–255frequency distribution of sample statistic,

250knowledge gained from, 253, 255one sample, 273–274statistic, 281n24variability, 248

Sampling distribution, 269–274approach to, 274–280definition of, 269–270dispersion of, 276–277as foundation of statistical inference, 270–272of the sample mean, 275–279toward normal, 277–279of trading performance, 272

learning objective statements, 421overview, 421–422studies, 426–427values, 427–429visualizing, 421–456weights on RRG, 447–456

Relative strength index (RSI), 12Relative volatility (RV), 710Repren, 590Representativeness bias, 570–571Reserves

description of, 190–191targeted risk levels and, 189–191

Resting orders, 201–202Retail sales

nominal and real total, 504Return on account, 19

stability of, 187Return retracement ratio, 26Reversion to the mean, 12Rising three method, 796, 824–825Risk. See also Portfolio risk and performance

attributionadjustments to, 77definition of, 33diversification and, 526–528entry strategies for, 36execution strategies, 50exit strategies for, 46measurements of, 187reputational, 599in system design and testing, 25–26temporal, 45tolerances, 575

Risk-adjusted returns, 186–187Risk assessment, 229–230Risk control, 9, 169–237

common sense management of risk, 174–175comparing expected and actual results,

232–237equity trends, 225–229individual trade risk, 191–200Kaufman on stops and profit-taking, 200–203learning objective statements, 169leverage, 188–191leverage based on exposure, 191liquidity, 175–176luck versus skill, 170managing risk without stops, 202–203measuring return and risk, 176–188


Ind

ex

16

“Small minus big” (SMB), 461SMB. See “Small minus big”Smith, Adam, 607Sontag, Susan, 351Sortino ratio, 26, 180S&P 100, 347S&P 500, 347, 351–365

correlation between stocks and, 364–365correlation with international indices,

351–358daily changes in, 333–334globalization and, 356–357highs and lows, 762–763interest rates, 358–364learning objective statement, 351overview, 351VIX index and, 672–673

S&P 500 Index, 500S&P Index, 86, 317, 321S&P Information Technology (S5INFT) sector,

424Spread-adjusted contract, 14–15Standard deviation, 19, 197

of relative rotation graphs, 443–444stop, 197–198in volatility analyses, 718

Standard deviation of returns, 521–522Standard error, 77Standard error of the mean, 276, 279–280Statistical analysis, 238–282, 562n1

descriptive statistics, 259–261learning objective statements, 238need for, 243–244overview, 238–239probability, 261–264probability distributions of random

variables, 264–267probability experiments and random

variables, 246–255relationship between probability and

fractional area of the probability distribution, 267–269

sampling, 246–250sampling and statistical inference, example

of, 244–246sampling distribution, 269–274statistical reasoning and, 239–243statistical theory and, 255–259

Statistical hypothesisdescription of, 285–286

Sarbanes-Oxley Act, 666Scaled-down buying, 221Scaling, 50Scatter diagrams, 75–77Schwager, Jack D., 14–15SDAX index, 326SEC. See Securities and Exchange CommissionSector rotation, 485Sector weights, 538–546

beta and, 544–546dividend yield by sector, 541–542returns by sector, 540–541under- and overweights, 539–540

“Secular” bear markets, 500Secure f, 41Securities

quality of, 45Securities and Exchange Act, 666Securities and Exchange Commission (SEC),

160, 331, 333SEHK. See Stock Exchange of Hong KongSelf-attribution bias, 576–577Self-control bias, 580Sensitivity testing, 89–90

in n-space, 89–90Sentiment bias, 795–797Sequential optimization, 67–68Sequential testing, 66–67Shares

number of, 39Sharpe, William, 530Sharpe ratio, 19, 26, 177–178, 272, 463, 530

definition of, 281n28Shooting stars, 807–808Short sale, 210Short-term patterns, 12SIF. See Student Investment FundSignals. See also Pattern recognition systems

following, 905–906interpretation of, 910systematic trading signals similarity,

164–168trading, 660, 749

Signal stop, 49Silver, 345, 383

gold and, 377NASDAQ and, 162–163

Simple moving averagemodel, 103

Slope of periodic returns, 88


Ind

ex

17

Systematic risk, 169. See also Risk controlSystem design and testing, 3–30

benefits of a nondiscretionary, mechanical system, 6

best type of system, 13case study of “HAL,” 16–20, 26–29as a complete trading system, 7discretionary versus nondiscretionary

systems, 4–7exogenous signal systems, 12good system, 29initial decisions for designing, 8–9learning objective statements, 3measuring system results for robustness,

23–29necessity of, 4–7optimization, 20–29overview, 3–4pattern recognition systems, 12pitfalls to a nondiscretionary, mechanical

system, 6–7profit measures, 24–25requirements for designing, 7–8reversion to the mean, 12risk measures, 25–26screening for parameters, 23smoothness and the equity curve, 26–27special data problems for futures systems,

14–15testing components, 24testing methods and tools, 15testing with clean data, 13–14test parameter ranges, 15types of technical systems, 9–12

System evaluation and testing, 53–125alternate ways of visualizing results, 73–77average results, 88–89best system result, 91–95common sense versus statistics, 84–85comparing methods of calculation across

selected markets, 101–107comparing results of two systems, 91–95distribution of values to be tested, 57evaluating results, 122–123expectations, 55–56identifying parameters, 56–58large-scale testing, 79–82learning objective statements, 53measuring success, 55–56measuring test results, 8–88

Statistical inferencedistributions of, 273types of, 283–284

Statistical reasoning, 239–243. See also Statistical analysis

Statistical significance, 296Statistical theory, 255–259

elements of a statistical inference problem, 255–259

inference, 258population, 256population parameter, 256–257reliability of the inference, 258–259sample, 256sample statistic, 257–258

Statisticsdefinition of, 281n16description of, 292statistical representations of macroeconomic

and firm-specific risk, 524–532Status quo bias, 580Step-forward testing, 65–66

feedback, 66short-term bias, 66

Sterling ratio, 26Stochastic, 12Stock Exchange of Hong Kong (SEHK), 330Stocks

buy-and-hold strategy, 906European indices and, 367–368gaps and, 772–775prices, 500results, 909S&P 500 and, 364–365

Stop–loss orders, 716triple-screen trading system and, 779–782

Stop-loss value, 56, 130, 195–200, 592frequency of stops, 202initial, 196managing risk without stops, 202–203

Stopsadaptive, 49slippage, 46trailing, 165

Student Investment Fund (SIF), 538–540, 542, 544, 545

Surface plot, 73–74Surowiecki, James, 597Synapses, 732Synthetic data, 62–63


Ind

ex

18

gambling techniques, 142–153indicators for, 906isolating the problems, 133learning objective statements, 126, 905outcome of, 131–132overview, 126–127, 905price shocks and, 135–142programming a new idea, 130selective trading, 153–154silver trading and NASDAQ, 162–163systematic trading signals similarity,

164–168system disconnect during a crisis, 140–142system trade-offs, 154–160trading limits and disconnected markets,

160–162transparent or complex solutions, 132–133vertical or integrated solutions, 132–134

Technical pointversus money point, 48

Testing. See System evaluation and testingThe Conference Board, 486–487Theory of runs, 36, 40, 142–153

applied to trading, 146–153Thiel, C. C., Jr., 99–100Three black crows, 817–818, 829Three white soldiers, 820–821Tick Data Inc., 128Time in the market, 88, 370–371

series, 521–522Time of day, 755–765Time stop, 49Time to recovery, 25, 187Time zones, 395, 398Timing

position and, 218–221Titman, Sheridan, 422Tokyo Stock Exchange (TSE), 329Topological relief map, 73–74Townsend, Robert, 127TR. See Treynor ratioTrade

inventory to sales ratio, 510Trade MAR, 19Traders

conflicted analytical, 585insider, 666–667real-time, 14types of, 5under-informed, 586

System evaluation and testing (Continued)in only one market, 90–91ordering test parameters, 57overview, 53–55parameter values, 123–124performance criteria, 86–89performance monitoring, 124–125price shocks, 114–116profiting from worst results, 95–96range of parameter settings, 56–57refining strategy rules, 83retesting for changing parameters, 97–99review outliers, 89robustness, 85–86searching for best result, 66–69selecting test data, 58–63setting objectives, 55–56significance of “significant,” 90standardizing test results, 77–79success criteria, 122test continuity, 68–69, 71–72testing decisions, 121–122testing integrity, 63–66testing tools and methods, 121trading across a wide range of markets, 99–114two-parameter tests, 72–73types of test variables, 58validity of test results, 84–91visualizing and interpreting test results, 69–79

System MAR, 19Szado, Edward, 690

T

TA. See Technical analysisTable of uniform numbers, 144–146Targets, 49–50

multi levels, 200profit, 198–200volatility of, 189

Tasker, Peter, 622–623, 629–630Taylor rule, 620–621TecDAX index, 326Technical analysis (TA), 126–168, 281n13.

See also Computers; Entropyassumptions for, 133–134combining standard techniques into a

system, 129–130control of, 134–135correlation at best and worst times, 168


Ind

ex

19

Trend line, 49Trends

behavioral elements associated with market trends, 588–590

combining trading ranges and, 159–160interest rate carry and, 726interruption of, 591–593, 802Martingales and, 147–150measuring, 203price noise and, 723–725ranking trends using prices, 210trade-offs, 156–157

Trend sentiment, 798–799Treynor ratio (TR), 178, 544–546Triple-screen trading system, 779–782

intermediate move, 780–781major move, 780stop-loss, 781–782timing, 781

TSE. See Tokyo Stock ExchangeTurtle rules, 7Tversky, Amos, 578–579Tweezers top, 810–8122-parameter optimization, 56Two-parameter averaging, 78–79Two-parameter tests, 72–73

U

UI. See Ulcer IndexUlam, Stanislaw M., 81–82, 301Ulcer Index (UI), 180, 281–282n30UMD. See “Up minus down”Underwater curve, 26–27Unemployment

average duration of, 512event trading as reaction to unemployment

reports, 650rate, 513

Unemployment insuranceaverage weekly claims for, 501–502

United Statesbusiness cycle, 485–486federal debt, 514–515financial crisis of 1929, 618–619financial crisis of 1988, 84, 140–142, 187,

359, 497University of Massachusetts study, 699–700University of Michigan Consumer Sentiment

Index, 495–496

Traders Commitment Index, 665Trades

average monthly return and standard deviation, 19

average weeks in winning and losing positions, 19

frequency of, 45maximum consecutive losing, 18–19number of, 18, 87, 91–94percentage winning, 24

TradeStation, 53, 69, 121, 126Trading methods, 112

candlestick analysis and, 833–837effects of limits on trading systems, 161Elder’s triple-screen trading system, 779–782filtering out losing trades, 154long positions versus short positions,

907–908paper trading, 131refining patterns for trading, 761–762risk characteristics of a trading model,

176–177sampling distribution of trading

performance, 272selective, 153–154short-term, 220strategies for, 153theory of runs and, 146–153trading habits forming lasting patterns,

755–756trading limits and disconnected markets,

160–162trading on equity trends, 227–229trading on margin, 188–189trading rules combining PDM, MDM, and

ADX, 210using volatility for trading, 712–716VIX trading systems, 714–716

Trading range systems, 12combining trends and, 159–160trading across a wide range of markets,

99–114Trading signals, 660, 906–907

description of, 749Trailing stop, 47–49, 196–197Transaction costs, 93–95, 909Treasury yield curve, 496Trend-following systems, 9–10, 154–155

market trends, 585–586problems with, 10–12


Ind

ex

20

forecasting, 729–731fractals, chaos, and entropy, 726–731hedge funds, replication of, 747–748implied volatility, 710–712intraday volatility and volume,

712–713learning objective statements, 701liquidity, 722measuring volatility, 701–712neural networks, 732–741on-balance rue range, 713–714overview, 701predicting volatility with trading ranges,

713profit targets and stop–loss orders, 716ranking based on volatility, 721ratio measurements, 708–710reducing the risk with high volatility exits,

720–721relative volatility, 710standard deviation measurement,

718time interval, 705–706trade selection using volatility,

717–721trends and interest rate carry, 726trends and price noise, 723–725using volatility for trading,

712–716volatility measures, 706–708volatility system, 716

Volatility pattern, 647Volatility stop, 48

components of volatility, 523risk and, 521–522

Volume-weighted average price (VWAP), 755–756

Von Neumann, Johann, 81–82VWAP. See Volume-weighted average price

W

Walk forward optimization, 13, 23Weather markets, 643Wells, H. G., 238Whipsaws, 11White, Dr. Halbert, 299Whole sample optimization, 21–22Williams, Larry, 10, 41

“Up minus down” (UMD), 462Upside Tasuki gap, 826–827U.S. dollar index, 334–339U.S. federal debt, 514–515

V

Validitycommon sense versus statistics, 84–85

Value at risk (VaR), 182–183Values, 478n21, 481n29

annualizing, 77simple value at risk, 184

VaR. See Value at riskVariables

probability distributions of random variables, 264–267

VDAX-NEW, 326Vince, Ralph, 215, 230VIX index, 326, 347–350

as an indicator, 673–675combining VIX futures and, 679–680filter for, 674–675futures as an indicator, 675–677gold price indicator and, 680–683hedging and, 687–696learning objective statements, 671modified VIX futures contract,

678–679overview, 671–672and the S&P 500, 672–673S&P 500 and, 358–364as a stock market indicator, 671–686VIX option put-call ratio, 683–686

VIX trading systems, 714Volatility analysis. See also Hedging;

VIX indexadjusting for a base price, 703advanced techniques, 701–748comparing annualized volatility and average

true range, 708constructing a volatility filter,

717–718description of, 669determining the base price,

704–705entry filter results, 718–720exceptions to the price-volatility

relationship, 703–704


Ind

ex

21

Y

Yates, Frank, 237correction, 237

Z

Zero interest rate policy (ZIRP), 509ZIRP. See Zero interest rate policyZ-score divergence, 407–409, 634

Winsversus losses, 214–216

World Gold Index. See GoldWriting

guidelines, 489–490

x

XAU index, 345–347, 382XOI. See AMEX oil index

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