-3.
11
Currency Futures
Table 2-1 Currency Contract SpecificationsCommodity
Contract size
Months
Hours
AustraliandollarBritish pound
100,000 AUD
H, M, U, Z
7:20–14:00
62,500 GBP
H, M, U, Z
7:20–14:15
CanadiandollarEuro
100,000 CAD
H, M, U, Z
7:20–14:00
62,500 EUR
H, M, U, Z
7:20–14:15
Japanese yen
12,500,000 JPY
H, M, U, Z
7:00–14:00
Mexican peso
500,000 MXN
All months
7:00–14:00
New ZealanddollarRussian ruble
100,000 NZD
H, M, U, Z
7:00–14:00
2,500,00 RUR
H, M, U, Z
7:20–14:00
South AfricanrandSwiss franc
5,00,000 ZAR
All months
7:20–14:00
62,500 CHF
H, M, U, Z
7:20–14:15
Minimumfluctuation0.0001AUD  $10.000.0002GBP  $12.500.0001CAD  $10.000.0001EUR  $6.250.0001JPY  $12.500.0025MXN  $12.500.0001NZD  $10.000.0001RUR  $25.000.0025ZAR  $12.500.0001CHF  $12.50
Note: “Contract Size” represents one contract requirement, although somebrokers offer minicontracts, usually one-tenth the size of the standard contract.“Months” identify the month of contract delivery. The tick symbols H, M, U, andZ are abbreviations for March, June, September, and December, respectively.“Hours” indicate the local trading hours in Chicago. “Minimum Fluctuation” represents the smallest monetary unit that is registered as 1 pip in price movementat the exchange and usually is one ten-thousandth of the base currency.
IMM currency futures traders monitor the U.S. Dollar Index togauge the dollar’s overall performance in world currency markets.If the U.S. Dollar Index is trending lower, then it is very likely thata major currency that is a component of the U.S. Dollar Index istrading higher. When a currency trader takes a quick glance at theprice of the U.S. Dollar Index, it gives the trader a good feel forwhat is going on in the Forex market worldwide.For traders who are interested in more details on commodityfutures, we recommend Todd Lofton’s paperbound book, GettingStarted in Futures (Wiley, 1993).
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Part 1: Currency Markets
Table 2-2 Futures Volume and Open InterestMarket
Ticker symbol
Exchange
Volume
OI (000)
ESNQEDSPECYMTYGCFVUSJYCDNDBPSISFMEDJADTUHG
CMECMECMECMECMECBOTCBOTNYMEXCBOTCBOTCMECMECMECMENYMEXCMECMECBOTCMECMENYMEX
489.1237.693.959.349.548.143.133.729.625.918.618.013.312.210.09.38.88.77.87.04.2
377.9158.4772.5531.4112.930.2676.4163.0582.8324.1132.164.265.458.384.245.630.529.555.7108.632.8
S&P 500 E-MiniNasdaq 100 E-MiniEurodollarS&P 500EurocurrencyMini-Dow10-year T-noteGold5-year T-note30-year T-bondJapanese yenCanadian dollarNasdaq 100British poundSilverSwiss francMexican pesoDow JonesAustralian dollar2-year T-noteCopper
Source: Active Trader Magazine, January 16, 2004; www.activetradermag.com.
Table 2-3 U.S. Dollar Index WeightsCurrencyEuroJapanese yenBritish poundCanadian dollarSwedish kronaSwiss franc
Weight (%)57.613.611.99.14.23.6
2
PARTTechnicalAnalysis
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Chapter
3
PatternRecognitionOVERVIEWProbably the most successful and most used means of makingdecisions and analyzing Forex markets is technical analysis.The difference between technical analysis and fundamental analysis is that technical analysis is applied only to the price action ofthe market. While fundamental data often can provide only along-term forecast of exchange-rate movements, technical analysis has become the primary tool to analyze and trade short-termprice movements successfully, as well as to set profit targets andstop-loss safeguards, because of its ability to generate price-specificinformation and forecasts. Technical analysts are by naturechart mongers. The more charts there are, the better is theforecast.Historically, technical analysis in the futures markets hasfocused on the six price fields available during any given periodof time: open, high, low, close, volume, and open interest. Sincethe Forex market has no central exchange, it is very difficult to estimate the latter two fields, volume and open interest. In thissection, therefore, we will limit our analysis to the first fourprice fields.
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Part 2: Technical Analysis
In this section, the technical analysis methods have been categorized not only be the underlying techniques used but also by thetype of output that each category generates. We will begin thissummary with pattern recognition, probably the most popular andeasiest to use technique within the technical analysis family. Thismethod involves scanning a raw open-high-low-close (OHLC)chart (such as a vertical bar chart or a candlestick chart) from leftto right searching for identifiable price formations.Technical analysis consists primarily of a variety of technicalstudies, each of which can be interpreted to predict marketdirection or to generate buy and sell signals. Many technical studies share one common important tool: a price-time chart thatemphasizes selected characteristics in the price motion of theunderlying security. One great advantage of technical analysis is its“visualness.”
IDENTIFYING PRICEFORMATIONSProper identification of an ongoing trend can be a tremendousasset to a trader. However, the trader also must learn to recognizerecurring chart patterns that disrupt the continuity of trend lines.Broadly speaking, these chart patterns can be categorized as reversalpatterns and continuation patterns.
REVERSAL PATTERNSReversal patterns are important because they inform the traderthat a market entry point is unfolding or that it may be time to liquidate an open position. Figures 3-1 through 3-4 display the mostcommon reversal patterns.
CONTINUATION PATTERNSA continuation pattern implies that while a visible trend was inprogress, it was interrupted temporarily and then continued in the
Pattern Recognition
17
Figure 3-1 Double Top.
Figure 3-2 Double Bottom.
direction of the original trend. The most common continuationpatterns are shown in Figures 3-5 through 3-9.The proper identification of a continuation pattern may preventa trader from entering a new trade in the wrong direction or fromexiting a winning position too early.
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Part 2: Technical Analysis
HeadLeft ShoulderRight Shoulder
Neckline
Figure 3-3 Head and Shoulders Top.
Figure 3-4 Head and Shoulders Bottom.
Pattern Recognition
Figure 3-5 Flag or Pennant.
Figure 3-6 Symmetrical Triangle.
19
20
Figure 3-7 Ascending Triangle.
Figure 3-8 Descending Triangle.
Part 2: Technical Analysis
Pattern Recognition
Figure 3-9 Rectangle.
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Chapter
4
EconometricModelsOVERVIEWWithin the technical analysis family, econometric models areunique because they belong to the only category that generates acontinuous stream of discrete numeric values as the forecast. Forexample, if the analyst has determined that a particular timeseries exhibits distinctly linear properties, then the followinglinear regression model should be used:Y (x ) = Ax + B + ε
where
x  the independent variable, timeY(x)  the dependent variable, the price at time index xA  the slopeB  the interceptε  the error factor whose sum approximates zero
By solving for the regression coefficients A and B, the trader canestimate the next value in the time series Y(·) by incrementing thevalue of x in the linear model.
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Part 2: Technical Analysis
SIMPLE SINUSOIDAL MODELIf security prices were not cyclical, they would tend to go off thetop or bottom of the charts. This alone justifies the examinationof a simple sinusoidal model. The current method identifies themost dominant sinusoidal in the time series using the conventionalmodel:Y (x ) = A * cos(x * θ ) + B * sin(x * θ ) + µwherex  the independent variable, timeY(x)  the dependent variable, the price at time index xA  cosine amplitudeB  sine amplitude  frequency, expressed as cycles per time unit  the arithmetic mean of the time seriesThe crux of this regression is based on a fundamental trigonometric identity, specifically the following multiple-angle relationship:cos n θ = 2 cos θ cos(n − 1)θ − cos(n − 2)θOnce the frequency has been isolated and extracted, the twoamplitudes can be calculated relatively simply.Unfortunately, very few security time series exhibit a distinctsingle-cycle property for prolonged periods of time. However, thesinusoidal regression may be applied iteratively. That is, calculate theprimary cycle coefficients, and remove that cycle from the originaltime series. Then perform the regression a second or third time.
FOURIER TRANSFORMThe fast Fourier transform is another popular method amongtechnical analysts for extracting cycles from a time series. The basicassumption is that any (well-behaved) curve can be approximatedas the sum of a finite number of sinusoidals and is based on thefollowing Fourier series:Yx = A0 /2 + ΣAn cos(nπ x /L ) + ΣBn sin(nπ x /L )
25
Econometric Models
The transform operations calculate the values for the cosineamplitudes A and the sine amplitudes B in a similar fashion to thesimple trigonometric regression above. Most analysts prefer todownload an Internet utility to handle the complexities ratherthan code it themselves. Traders who are interested in more detailsshould refer to Fourier Analysis, by Murray R. Spiegel, in theSchaum Outline Series (1974).
AUTOREGRESSIONThe premise behind autoregressive methods is that previous valuesin the time series directly influence the current value in the timeseries. Mathematically, this can be expressed asYx +1 = AYx + BYx −1 + CYx −2 + εwhere
x  the time incrementY(x)  the price at time index xA  the first regression coefficientB  the second regression coefficientC  the third regression coefficientε  the error factor, whose sum approximates zero
This equation infers that the time-series closing price on anygiven day is the sum of the closing prices on the three previousdays, all adjusted by regression coefficients. The number of independent variables on the right side of the equation determines theautoregressive order of the model.Autoregression has numerous supporters in the realm of technical analysis. It also has several variations and enhancements, such asthe autoregressive integrated moving-average (ARIMA) time-seriesmodel introduced by George Box and Gwilym Jenkins in the early1970s. This model frequently is designated as the ARIMA(p, d, q)model, where p is the autoregressive order, d is differencing order,and q is the moving-average order. Traders interested in more information should refer to Box and Jenkins’ book, Time Series Analysis:Forecasting & Control (Prentice-Hall, 1994). Readers who prefer a less
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Part 2: Technical Analysis
advanced compendium should start with The Analysis of Time Series:An Introduction, by Chris Chatfield (CRC Press, 2003).
OTHER ECONOMETRIC MODELSThere exist a number of other econometric models that have beenapplied to financial time series. For example, the Holt-Wintersmodel is a combination of a linear trend model and a seasonalmodel. A recent addition to time-series analysis is the generalizedautoregressive conditional heteroskedacity model (GARCH),which attempts to improve on the ARIMA model by incorporating skew analysis of the data.In addition, several statistical regression models (such as logistical and exponential) have been performed on securities data,but most return low correlation coefficients except over very shortperiods of time. See Chatfield’s book (referenced above) forfurther details.
Chapter
5
Crossover TradingSystemsOVERVIEWCrossover trading systems consist of various indicators andoscillators and are unique in the technical analysis of securityprices. Rather than predicting future numeric values, they signala particular market action to execute, such as (1) initiate a longposition, (2) initiate a short position, (3) liquidate a long position,(4) liquidate a short position, (5) reverse a long position equivalent to (3) and (2), or reverse a short position equivalent to (4)and (1).
MOVING AVERAGESMoving averages (MAs) are an important instrument used to studytrends and generate market entry and exit signals. An MA is thearithmetic mean of the closing prices over a given period. Thelonger the period studied, the weaker is the magnitude of the moving-average curve. The number of closes in the given period iscalled the moving-average index. Market signals are generated by calculating the residual-difference value:Residual = close(x ) − MA(x )27Copyright © 2007 by The McGraw-Hill Companies. Click here for terms of use.
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Part 2: Technical Analysis
Figure 5-1 Daily Close with Five-day Moving Average.
In the chart shown in Figure 5-1, the curve with higher peaksand lower valleys is the daily close, whereas the smoother curve isa five-day moving average of the closes.In the chart shown in Figure 5-2, called a moving-average convergence/divergence (MACD) histogram, the following signals are triggered:1. When the residual difference rises above zero, a buy signal isgenerated.2. When the residual difference falls below zero, a sell signal isgenerated.A significant refinement to this residual-difference method,called moving-average convergence/divergence (MACD), involves the useof two moving averages. When the MA with the shorter MA index,called the oscillating MA index, crosses above the MA with the longerMA index, called the basis MA index, a sell signal is generated.MACD residual = basis MA(x ) oscillating MA(x )
Crossover Trading Systems
29
Figure 5-2 Residual Difference.
The reliability of the MACD method depends ultimately on theMA indices chosen. These two indices can be optimized by a computer program that performs a brute-force search for the mostprofitable parameters on the most recent daily closes. However, asmarket conditions change in the underlying time series, theindices must be adjusted accordingly.It should be noted that some traders prefer to use exponentiallysmoothed moving averages rather than arithmetically smoothedmoving averages, although this is usually a subjective decision on thepart of the investor. In addition, we note that the MACD method iscredited to Gerald Appel in the early 1960s in his book, TechnicalAnalysis: Power Tools for Active Investors (Prentice-Hall, 1961).
RELATIVE STRENGTH INDEXThe relative strength index (RSI) was introduced by J. WellesWilder in the June 1978 issue of Commodities (now known as
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Part 2: Technical Analysis
Figure 5-3 Relative strength Index.
Futures) magazine and later in his book, New Concepts in TechnicalTrading (Trend Research, 1978). The index is designed to followthe momentum of price as an oscillator that ranges between 0 and100. The index tracks recent price to itself and therefore is a measure of velocity.RSI is a front-weighted momentum indicator that measures acommodity’s price relative to its past performance, and therefore,it gives a better velocity reading than other indicators. RSI is lessaffected by sharp rises or drops in a commodity’s price performance. Thus it filters out some of the white noise in a security’strading activity (Figure 5-3).The RSI formula is as follows:RSI = 100 − [100/(1 − U/D)]whereU  average of up closesD  average of down closes
Crossover Trading Systems
31
For a nine-day RSI calculation, the following steps are involved:1. Add the closing values for the up days, and divide this total by 9.2. Add the closing values for the down days, and divide this totalby 9.3. Divide the up-day average by the down-day average. Store thisas the RS factor in the formula.4. Add 1 to the RS factor.5. Divide 100 by the number arrived at in step 4.6. Subtract the number arrived at in step 5 from 100.Repeat steps 1 through 6 for day number 10. Drop day number1 from the calculation.Wilder originally proposed a 14-day RSI and later a 9- and a 25day period. In modern times, this index can be optimized by abrute-force software program.RSI values range from 1 to 100. Traditionally, buy signals are triggered at 30, and sell signals are triggered at 70. However, many analysts are now using 20 for buy signals and 80 for sell signals. RSIlends itself to support and resistance studies such as trend-line penetration and price patterns. Overbought and oversold conditionsare suppose to be an asset in interpreting the RSI, but as you cansee, overbought and oversold conditions do poorly in a strongtrending environment.The RSI shows whether a currency is overbought or oversold.Overbought indicates an upward market trend because the financial operators are buying a currency in the hope of further rateincreases. Sooner or later, saturation will occur because the financial operators have already created a long position. They showrestraint in making additional purchases and try to make a profit.The profits made can very quickly lead to a change in the trendor at least a consolidation.Oversold indicates that the market is showing downward trendconditions because the operators are selling a currency in the hopeof further rate falls. Over time, saturation will occur because thefinancial operators have created short positions. They then limittheir sales and try to compensate for the short positions with profits. This can rapidly lead to a change in the trend.
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Part 2: Technical Analysis
STOCHASTIC OSCILLATORSIn a strictly mathematical sense, the term stochastic signifies a processinvolving a randomly determined sequence of observations, each ofwhich is considered as a sample of one element from a probabilitydistribution. In technical analysis, the term has evolved to signify anindicator that compares the current close with the highest high andthe lowest low over a predetermined number of days.The stochastic oscillator was developed by George C. Lane in thelate 1950s. It is used most commonly to identify overbought andoversold conditions, as well as divergence between the oscillatorand the price. The original stochastic plot consisted of two lines.The curve with higher peaks and lower valleys is referred to as %K,and the other (more smoothed) line is called %D (Figure 5-4).The stochastic oscillator compares where a security’s price closedrelative to its price range over a given time period. The basicformula is as follows:%K = 100(C − L )/(H − L )
Figure 5-4 Stochastic Oscillators.
Crossover Trading Systems
33
where
C  current closeH  highest high over given period of timeL  lowest low over same period of timeThe full stochastic oscillator has four variables:
1. %K periods. This is the number of time periods used in the stochastic calculation.2. %K slowing periods. This value controls the internal smoothingof %K. A value of 1 is considered a fast stochastic; a value of 3is considered a slow stochastic.3. %D periods. This is the number of time periods used when calculating a moving average of %K. The moving average is called%D and usually is displayed as a dotted line on top of %K.4. %D method. The smoothing method (i.e., exponential, simple,time series, triangular, variable, or weighted) that is used to calculate %D.To calculate a 10-day %K, first find the security’s highest highand lowest low over the last 10 days. As an example, let’s assumethat during the last 10 days the highest high was 46, and the lowest low was 38, a range of 8 points. If today’s closing price was 41,%K would be calculated as:100 * (4138)/(4638)  37.5%The 37.5 percent in this example shows that today’s close wasat the level of 37.5 percent relative to the security’s trading rangeover the last 10 days. If today’s close was 42, the stochastic oscillator would be 50 percent. This would mean that the security closedtoday at 50 percent, or the midpoint, of its 10-day trading range.This example used a %K slowing period of one day (no slowing).If you use a value greater than one, you average the highest highand the lowest low over the number of %K slowing periods beforeperforming the division.A moving average of %K then is calculated using the number oftime periods specified in the %D periods. This moving average iscalled %D.The stochastic oscillator always ranges between 0 and 100 percent. A reading of 0 percent shows that the security’s close was the
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Part 2: Technical Analysis
lowest price that the security has traded during the preceding xtime periods. A reading of 100 percent shows that the security’sclose was the highest price that the security has traded during thepreceding x time periods.Popular interpretations of the stochastic oscillator include• Buy when the oscillator (either %K or %D) falls below a specificlevel (e.g., 20) and then rises above that level. Sell when the oscillator rises above a specific level (e.g., 80) and then falls belowthat level.• Buy when the %K line rises above the %D line, and sell whenthe %K line falls below the %D line.Look for divergences, for example, where prices are making aseries of new highs and the stochastic oscillator is failing to surpassits previous highs. Ways to use the stochastic oscillator as a confirming signal generator include• A buy is indicated when the %K or %D falls below a specifiedlevel (typically 30) and then rises above that level. A sell is indicated when the line rises above a specified level (typically 70) andthen goes below that level.• A buy is indicated when the %K line rises above the %D line. Asell is indicated when the %K line falls below the %D line.When prices are making new highs and the stochastic does notexceed its previous highs, a divergence occurs, often indicating achange in the current trend.The buy/sell signals are triggered when the %K line crosses the%D line after the %D line has changed direction. At the bottom,the buy signal is generated. At the top, the sell signal is generated.
BOLLINGER BANDSThis indicator was developed by John Bollinger and is explainedin detail in his book, Bollinger on Bollinger Bands (McGraw-Hill,2001). The technique involves overlaying three bands (lines) on
Crossover Trading Systems
35
Figure 5-5 Bollinger Bands.
top of an OHLC bar chart or a candlestick chart of the underlying security.The center line is a simple arithmetic moving average of the dailycloses using a trader-selected moving-average index. The upper andlowers bands are the running standard deviation above and belowthe central moving average. Since the standard deviation is a measure of volatility, the bands are self-adjusting: widening during volatilemarkets and contracting during calmer periods. Bollinger recommended 10 days for short-term trading, 20 days for intermediate-termtrading, and 50 days for longer term trading. These values typicallyapply to stocks and bonds; thus shorter time periods will be preferredby commodity and currency traders (Figure 5-5).Bollinger Bands require two trader-selected input variables: thenumber of days in the moving-average index and the number ofstandard deviations to plot above and below the moving average.Over 95 percent of all the daily closes will fall with three standarddeviations of the mean of the time series. Typical values for the second parameter range from 1.5 to 2.5 standard deviations.As with moving-average envelopes, the basic interpretation ofBollinger Bands is that prices tend to stay within the upper and
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Part 2: Technical Analysis
lower bands. The distinctive characteristic of Bollinger Bands isthat the spacing between the bands varies based on the volatilityof the prices. During periods of extreme price changes (i.e., highvolatility), the bands widen to become more forgiving. During periods of stagnant pricing (i.e., low volatility), the bands narrow tocontain prices.Bollinger notes the following characteristics of Bollinger Bands:• Sharp price changes tend to occur after the bands tighten asvolatility lessens.• When prices move outside the bands, a continuation of the current trend is implied.• Bottoms and tops made outside the bands followed by bottomsand tops made inside the bands call for reversals in the trend.• A move that originates at one band tends to go all the way tothe other band. This observation is useful when projecting pricetargets.Bollinger Bands generally do not trigger buy and sell signalsalone. They should be used with another indicator, usually the RSI.This is so because when the price touches one of the bands, itcould indicate one of two things: a continuation of the trend or areaction the other way. Thus Bollinger Bands used by themselvesdo not provide all of what technicians need to know, which is whento buy and sell. MACD can be used in conjunction with BollingerBands and the RSI.
OTHER CROSSOVER SYSTEMSThe techniques and methods just listed in no way represent all thecrossover trading systems available to technical analysts. Numerousrange and momentum oscillators also have been devised ascrossover triggers, as well as several volume oscillators.
Chapter
6
Wave TheoryOVERVIEWWave theory is one of the most intriguing and perplexing studieswithin the entire technical analysis complex. It is also the centralsubject of the remainder of this book.
WAVE CHARTSWave theory normally does not generate discrete numeric forecasts, as do the econometric models discussed previously. Nor doeswave theory trigger specific market actions, as do the crossovertrading systems.Instead, wave theory converts the raw data into a series of alternating interconnected diagonal lines whose vertices accentuatelocal peaks and valleys based on the parameters of a reversal algorithm (Figure 6-1).
WAVE FORECASTINGThe object of wave analysis is to discern the heights (y axis) andthe widths (x axis) of subsequent waves based on mathematicalingenuity, ratio analysis, and the frequencies of preceding wavepatterns called cycles. This is obviously an ambitious task, but givenadequate data and resources, we feel that this is an achievable goal.
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Part 2: Technical Analysis
Figure 6-1 Simple Wave Chart.
CAVEATIn addition to the four basic types of technical analysis describedin this part (i.e., pattern recognition, econometric models,crossover trading systems, and wave theory), there exist other, lessfrequently employed techniques. Unfortunately, some of theseinvolve such esoteric methods as astrology, numerology, and thelike. Let the trader beware.
3
PARTReversalCharts
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Chapter
7
Point and FigureChartsOVERVIEWThe point and figure chart is a member of the genre of chartsnormally referred to as reversal charts (Figure 7-1). A reversal chartis any chart that filters the raw OHLC data in order to accentuatesignificant points of interest while ignoring points of less interest. Alltechnical analysts find peaks and valleys of great interest, whereasthey find areas of lateral price movements less interesting. Peaksand valleys are those points of inflection where price directionsreverse and the slope of an existing trend changes its arithmeticsign (minus to plus and plus to minus).
HISTORYThe point and figure chart (also called the three-box reversal method),created in the late nineteenth century, is roughly 15 years older thanthe bar chart and is probably the oldest Western method of charting prices in existence. Its roots date way back in trading lore, andit has been intimated that this method was used successfully by thelegendary trader James R. Keene during the merger of U.S. Steel in1901. Mr. Keene was employed by Andrew Carnegie to distributeownership, because Carnegie refused to take stock as payment for41Copyright © 2007 by The McGraw-Hill Companies. Click here for terms of use.
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Part 3: Reversal Charts
Figure 7-1 Point and Figure Chart.
his equity interest in the company. Keene, using point and figurecharting and tape readings, managed to promote the stock and getrid of Carnegie’s sizable stake without causing the price to crash.The point and figure method derives its name from the fact thatprice is recorded using figures (X’s and O’s) to represent a point,hence point and figure. Charles Dow, the founder of the Wall StreetJournal and the inventor of stock indexes, was rumored to be apoint and figure user, and the practice of point and figure charting is alive and well today on the floor of the Chicago Board ofTrade (CBOT). Its simplicity in identifying price trends, support,and resistance, and its ease of upkeep have allowed this methodto endure the test of time, even in the age of Web pages, personalcomputers, and the information explosion.
ANATOMYPrice advances in a point and figure chart are represented as vertical columns of X’s, whereas price declines are represented ascolumns of O’s (Figure 7-2).
Point and Figure Charts
43
Figure 7-2 Point and Figure Chart Anatomy.
Two user-supplied variables are required to plot a point andfigure chart