Market Technician No 57

What may now fairly be described as the annual dinner of the STA

took place in the National Liberal Club on 21st September. After

not having held a dinner for a number of years, it was decided

to organise one last year to see what the level of interest was

amongst the membership. It was such a resounding success

that another one was arranged this year. All that really needs to

be said about this year’s dinner is that dates are already being

discussed for next year and, as we all know, once there are three

points of contact the trend becomes firmly established. This

year’s speaker was David Murrin, who has 20 year’s experience

in proprietary trading and financial market analysis. In 1997 he

joined Emergent Asset Management Ltd as a Principal and Chief

Investment Officer. David gave an amusing talk explaining

amongst other things how his first job living and working with

local tribes in the Sepik Basin in Papua New Guinea was a

formative experience and shaped his theories on behavioural-

based analysis techniques that could be applied to the financial

markets. After the dinner, Clive Lambert organised a hybrid

version of heads and tails and musical chairs. Spotting the

trend was the key and it was noteworthy that there was a very

strong female representation amongst those left standing in the

last few rounds. Cathryn Lyall, Managing Director of the

European Office of the Chicago Board of Trade, was the winner

and elected to give the £500 charitable donation to the Global

Crop Diversity Trust. It was a very balmy night and everyone

then repaired to the terrace overlooking the river to discuss the

markets and/or the meaning of life.

Ally McKinnon, the new head of the Scottish Chapter, organised

a meeting on 5 October, attended by some 20 people and

sponsored by NCB Stockbrokers. It was held at the Scotch Malt

Whisky Society in Edinburgh, and began with a tasting of three

types of whisky! Thereafter Richard Crossley, of NCB

Stockbrokers, gave a talk on the markets. Ally hopes to

organise two to three meetings a year, so Scottish members

should look out for the announcement of the next one.

The Swiss Association of Market Technicians (SAMT) hosted the

IFTA conference in Lugano in October. The stunning location

and beautiful weather provided an ideal backdrop for a very

stimulating three days. The theme was inter-market analysis

and John Murphy, one of its early proponents, noted that

China’s industrialisation has had a significant impact on

correlations between different markets. It has exaggerated the

strength of the upturn in commodities and, at the same time,

exported deflationary pressures, thereby keeping bond yields

unusually low for an extended period. Most participants at the

conference felt the equity markets were in the grips of a strong

uptrend but there was concern that the increasingly

overbought condition of these markets might prompt some

short term correction. There was a general consensus that the

decline in oil prices was near its end but opinion was very

divided about the future outlook for interest rates and the

dollar.

Membership of the Society has been growing steadily and we

now have 857 members – 415 full members, 421 associate

members and 21 fellows. Just over 10 per cent of the

membership are based outside the UK in 34 different countries.

IN THIS ISSUE

D. Watts Bytes and Pieces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

G. Gandolfi, Variable MACD – adapting to financial M. Rossolini market dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3& A. Sabatini

A. Nasser Real technical analysis . . . . . . . . . . . . . . . . . . . . . . . . 5

A. Angeli Smartview. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

M. Bain Gold – a point and figure perspective . . . . . . . 8

COPY DEADLINE FOR THE NEXT ISSUE 30TH JANUARY 2007

PUBLICATION OF THE NEXT ISSUE MARCH 2007

FOR YOUR DIARY

Wednesday 17th January Monthly Meeting

Tuesday 13th February Monthly Meeting

N.B. Unless otherwise stated, the monthly meetings will take

place at the Institute of Marine Engineering, Science and

Technology, 80 Coleman Street, London EC2 at 6.00 p.m.

December 2006 The Journal of the STAIssue No. 57 www.sta-uk.org

MARKET TECHNICIAN

MARKET TECHNICIAN Issue 57 – December 20062

CHAIRMAN

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The Barbican library contains our collection. Michael buys new books for it

where appropriate. Any suggestions for new books should be made to him.

EDUCATION

John Cameron: [email protected]

IFTA

Robin Griffiths: [email protected]

MARKETING

Clive Lambert: [email protected]

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MEMBERSHIP


REGIONAL CHAPTERS

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Please keep the articles coming in – the success of the Journal depends

on its authors, and we would like to thank all those who have supported

us with their high standard of work. The aim is to make the Journal a

valuable showcase for members’ research – as well as to inform and

entertain readers.

The Society is not responsible for any material published in The Market

Technician and publication of any material or expression of opinions

does not necessarily imply that the Society agrees with them. The

Society is not authorised to conduct investment business and does not

provide investment advice or recommendations.

Articles are published without responsibility on the part of the Society,

the editor or authors for loss occasioned by any person acting or

refraining from action as a result of any view expressed therein.

NetworkingWHO TO CONTACT ON YOUR COMMITTEE

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AVG has also released a home user edition of its Anti-Spywareprotection tool. If you browse the web, it is essential that you checkyour computer for spyware. AVG now has another free home usertool to keep your computer free of Spyware and Trojans. These areboth excellent tools for the home computer user.

Professional editions provide the additional protection ofcontinuous updates.

For the free edition see: http://free.grisoft.com/doc/avg-anti-spyware-free/lng/us/tpl/v5

MultichartsWith the dollar in fast decline it’s an opportunity to pick up a cheaperdollar priced technical analysis and charting package. Muticharts withits system testing and Omega Easy Language compatibility is one ofthe products of choice. Compatible with a wide variety of data feeds– IB (TWS), eSignal, Patsystems (J-Trader), IQFeed, etc, it has the abilityto run Tradestation studies and systems.The soon-to-be-releasedupdate to Version 1.9 includes many essential features for a programof this type while still at the old Version 1.9 pricing of $399. There is a30 day free trial edition available.

Dynamic TrendDynamic Trend is a multi-timeframe approach to technical analysistrading, brought to you by Advanced GET developer Tom Joseph.When three or more timeframes align in the direction of themarket it confirms this via coloured signals. So Dynamic Trendtrades in the direction of the major timeframes to increase profitpotential. It is not a charting program, but for those who useconfirmations by looking at longer timeframes, it is an essentialtime saving tool.

I see that Advanced GET has now been upgraded too with a similar“Dashboard” showing multiple time frame confirmations.Advanced GET is supported by Interactive Data Corporation thesupplier of Esignal data and charting software. See the link belowfor details of the upgrade.

Dynamic Trend: http://www.dynamictrend.com/

Advanced GET Upgrade:http://www.advancedget.com/ads/0106/realtime/default.asp

Dual Core ProcessorsI’m impressed with Intel’s new dual core processors, fast efficientand cool. By expanding the processors’ resources true multi-taskingis a reality. The leap in technology is sufficient for me to make arecommendation and it is well worth specifying “Dual Core” if youare considering a new computer or laptop. The mobile processorsare also impressive with power saving modes that effectivelydouble the battery life over that of a single core processor.

http://www.intel.com/technology/computing/dual-core/

Bytes and Pieces

Issue 57 – December 2006 MARKET TECHNICIAN 3

AbstractOne of the major limits of technical analysis tools is its ability tosupply operative signals in all types of market conditions. LaggingIndicators are efficient primarily in trending markets, whereasleading Indicators exhibit better performance in sideways marketconditions. This article introduces a new technical instrument –Variable MACD. The advantage of Variable MACD is that itdemonstrates high efficiency both in trending and non-trendingmarkets. The method for constructing Variable MACD is shownbelow. Empirical results and considerations based on historicaldata of major indices and financial time series data aredemonstrated and show encouraging results: Variable MACD isnot only more efficient than MACD, it also performs very stronglyin a range of market conditions.

IntroductionIn order to better appreciate the effect of Variable MACD onfinancial markets time series, it is important to outline theconcept of phase lagging and leading. A wave, and in general, anydynamic series whose motion is oscillatory, is defined by thefollowing three parameters:

1. AMPLITUDE: measure of the height or intensity of the wave;

2. PERIOD: measure of the time interval between two minima(or two maxima);

3. PHASE: measure of the positioning along the time axis oftwo minima.

Given the time series x = x1, x2, x3,...xn, the time series movingaverage (MA) is defined as:

MA is a lagging indicator. On the other hand, the Rate of Change(ROC) indicator, defined as ROC = [100(x2-x1)/ x1]% is a leadingindicator.

Mathematically, for a function ƒ(t), if every occurrence of t isreplaced by t-a or t+a, “a” being a constant,ƒ(t-a) is a lagging function with delay “a”;ƒ(t+a) is a leading function with lead “a”.

Operators, also, may affect the phase. For example, integration haslagging effects, whereas differentiation has leading characteristics.

MethodThe two indicators used in the construction of Variable MACD arethe MACD (Moving Average Convergence Divergence) and VIDYA(Variable Index Dynamic Average).

The aim of the VIDYA dynamic indicator is to automatically reducethe moving average time period when volatility increases andautomatically increase the moving average time period whenvolatility decreases. In this work, volatility is defined as a measureof trending vs. trading range behaviour. High volatility indicates astrong trending market. By contrast, low volatility indicates themarket is range bound. Market Volatility can be measured by theuse of Chande’s proprietary indicator called Chande MomentumIndicator (CMO) as follows:

CMO = [(Su – Sd)/(Su + Sd)];

whereSu = Sum of Moves up of n previous bars;Sd = Sum of Moves down of n previous bars;Moves up = difference between two price bars when currentclose is higher than previous close;Moves down = difference between two price bars when currentclose is lower than previous close;

The CMO indicator ranges between +1 and -1 The absolute valueof the CMO will, therefore, vary as follows:

Abs(Cmo) approaching 0 when there is Low VolatilityAbs(Cmo) approaching 1 when there is High Volatility

Vidya is defined as:Vidyat = a * k * Ct + (1- a * k) * Vidyat-1

where:Ct= Current Close;K = Volatility Indicator (|CMO|);a = constant determined by Analyst (a = 0.5);Vidyat-1 = Previous-bar-Vidya.

True Vidya Length, N, is:N = (2-k*a) / (k*a)

Inferring thatIf |CMO| r 1 Then N decreases;If |CMO| r 0 Then N increases;

VARIABLE MACD = Vidya[12] – Vidya[26] =(a * |CMO| * Ct + ((1- a) * |CMO| * Vidya[12]t-1) – b *|CMO|* Ct +(1- b) *|CMO|* Vidya[26]t-1)

wherea = 0.154;b = 0.074;

|CMO| = Absolute value of CMO;Ct = Current Close;Subscript t-1 indicates previous bar;

a =2/(N+1) with N=12,a =0.154b =2/(N+1) with N=26,b =0.074

Variable MACD: adapting to financial market dynamicsThis article is a short summary of a paper presented at the IFTA Conference, Lugano, 2006 By G. Gandolfi, M. Rossolini and A. Sabatini

Fig. 01: Leading and Lagging Curves


To calculate the True Length, N, we have to consider volatility:

N12 = (2 – |CMO| * a) / (|CMO|* a) for 12-day-Vidya

N26 = (2 – |CMO| * b) / (|CMO|* b) for 26-day-Vidya

If |CMO| r 1 Then

N12 r 12

N26 r 26

If |CMO| r 0 (i.e. 0.005) Then the two Vidya will have a higherLengthN12 r 2596N26 r 5404

Interpreting the SignalsSignal interpretation is the same as for MACD. The advantages ofusing Variable MACD are the following:

• False signal reduction;

• Good trading range management;

• Less frequent trading;

• Better performance than MACD.

The major disadvantage is exhibited by a delay in triggering thebeginning of a new trend.

Fig. 02: NIKKEI225 MACD trade signals (vertical grey lines) Fig. 03: NIKKEI225 Variable MACD trade signals (vertical grey lines)

ConclusionVariable MACD adapts better to market conditions. It modulates the phase lagging and leading effect efficiently.

• G. Gandolfi, Professor of Financial Markets and Institutions – University of Parma

• M. Rossolini, Ph.D Candidate in “Banking and Finance” Tor Vergata University Rome; Researcher and Lecturer – University of Parma

• A. Sabatini, Electrical Engineer (MIT B.S. – MS), Portfolio Manager – MIT EC, Finbest, CEO, CFO, Florence

Signal Number of Trades Performance (%) Max Drawdown (%)

MACD on NIKKEI 225 12 +29.71% -15.31%

VARIABLE MACD on NIKKEI 225 3 +49.76% -6.00%

MACD on MIB30 18 -21.92% -22.06%

VARIABLE MACD on MIB30 5 +10.52% -7.39%

Results

Society of Technical Analysts Ltd (STA)

DIPLOMA COURSE

For the eleventh year running, the Society of Technical Analysts Ltd (STA) Education Committee is holding itsDiploma course in Technical Analysis. This year it will be held at London School of Economics in Aldwych.

Technical analysis has become an important part of most investment house activity. The STA is the professional body associated withtechnical analysis. All teaching is by STA members. The courses may be suitable for the annual PIA Continuous ProfessionalDevelopment Programme.

The course runs from: 11 January – 3 April 2007It prepares students for the Diploma examination in April 2007. The Course consists of 11 Thursday evenings and is followed by a fullRevision Day (including Report writing), on Tuesday 3 April 2007. The sessions are from 6.00pm to 9.00pm and the Revision Day, whichincludes lunch, runs from 9.30am to 5.00pm. The Exam itself lasts three hours and will be held Thursday 19th April 2007.

If you would like further information please contact Katie Abberton on 07000 710207

Society of Technical Analysts Ltd


Pick up any national newspaper or lifestyle magazine, and in ityou are sure to find an advert by the latest market "guru" enticingyou to learn his or her sure fire way to make millions trading themarkets. Go to one of their "free evening seminars" and you willlearn to spot triangles, bottoms and tops paving the way to yourfuture riches. With technical analysis being so oversold and freelyavailable, has it become a victim of its own success? Has technicalanalysis become an exercise in simply spotting pretty patterns ona chart or drawing imaginary lines that have mystical meaning tothe artist?

Whenever I teach students of "modern" technical analysis astaught by these "gurus", I always begin by showing them thefollowing chart and ask what each of the five figures represent.Without fail, the resounding answer is always five "Doublebottoms"! Although each one of the five images is different, thepre-programmed modern student of technical analysis will seeonly one thing – a double bottom chart pattern. How can eachimage, looking very different from the other, have the samemeaning and possible outcome?

In addition to pattern spotting, the modern student of technicalanalysis is taught to draw imaginary lines on a chart and whenthose imaginary lines are breached in what is commonly termed a"breakout", to buy the stock. Recent research, carried out by LarryConnors on price breakouts of the S&P500 index in his book "Howmarkets really work", concluded that only 53% of the time did theindex continue to trade higher a week after making a new onemonth high. Furthermore, only in 49% of cases did the indextrade higher the day after it made a new one month high. Doesthat mean that our beloved breakout trade has about the samechance of success as flipping a coin?

As a technical "trader" (i.e. one of those who actually utilisestechnical analysis to make money trading, rather than thesemodern gurus teaching people to draw imaginary lines oncharts), I am reading an ever increasing number of failedbreakouts and failed chart patterns on my charts. The two chartsbelow provide a simple example of failed "support/resistance"lines and chart patterns that are now more and more common.

At the beginning of this article I asked whether technical analysishas become a victim of its own success? Are breakout andpattern failures due, paradoxically, to the success of technicalanalysis in reaching the wider retail investor audience?

To answer this question, I believe we need to take a step back andat the most basic level consider the definition of a "market". Amarket is a place where buyers and sellers meet to exchange theirgoods or services. With this definition in mind, let us assume you –as a market participant – have a large number of goods to sell.You know that, if you simply offered this large amount of stock onthe open market, it is likely that there will not be sufficientdemand at the current price and the probability is that you willreceive progressively less as you sell your goods.

Now, assume you know that there is a large pool of buyers of yourgoods that have been taught to draw imaginary lines at a previousprice point and to buy goods once the price of goods rises abovethat level. Above this imaginary price point, you have a ready marketto offload all the goods you have at a very attractive high price.

In our story so far, let us assume that you are a financial institutionwith a large sell order to fill and let us assume that the artists of

Real technical analysisThis article is a summary of a talk given to the Society on 11th October, 2006 By Aboudy Nasser

Figure 1

Figure 2

Figure 3


the imaginary lines are the thousands of people that have beentaught by technical analysis "gurus" to buy the breakouts and Ibelieve you will find the answer to why so many chart patternsand breakouts fail. I believe that the success of technical analysisin reaching the wider retail investor audience has not goneunnoticed by the "smart money", and it is this wider audiencethat provides liquidity for smart money to fill orders at levelswhich are easily calculated.

Program TradingReportedly, over 30% of all trades on the NYSE are undertaken bycomputers in what is known as "program trading". As computertechnology continues to improve and algorithmic tradingbecomes more popular, this figure is likely to continue rising. Iftechnical analysis is about understanding how human psychologyand emotions such as greed, fear and panic affect stock pricemovements, what effect will a market dominated by machineshave on technical analysis?

I should be surprised if the program trading algorithms of largeinstitutions are set up to buy breakouts. On the contrary, I believethat the more people learn about technical analysis and the morewe move to a program trading environment, the more we will seepattern and support/resistance break formulas.

Who is buying and who is selling?I am regularly asked how you spot when "smart money" is buyingor selling. My answer is that the smart money acts as a gatekeeper – price breaks will work when smart money is behind themove and will fail when it is not.

In the chart below, we see the stock break below an imaginary"support" line which also happens to be around the otherimaginary "whole number support" level of $48. As the stockbreaks below this level, the large volume spikes tell us that a largenumber of stocks are changing hands. However, despite thebreak below the imaginary line, there was very little movement inprice. In fact, the price stayed just below $48 for the best part ofhalf an hour on high volume.

With this in mind, the question needs to be asked "Who is buyingand who is selling"? Is it likely that smart money is selling orshorting on the break below $48, or is the smart money likely tobe using the break as an opportunity to buy up stock? Theanswer you will agree is the latter.

What is also particularly interesting about this chart is that, oncethe price moved back up through the imaginary line, a scrambletook place to cover the short positions resulting in a $2 move inless than one hour. The price then rallied through anotherimaginary resistance line where no doubt new unsophisticatedmoney entered the market to buy on the breakout providing thesmart money with the liquidity to offload some of the stockpurchased earlier.

What about technical indicators?In addition to the sport of spotting chart patterns, "modern"technical analysis taught by "gurus" now appears to be pre-occupied with designing mathematical derivations of priceknown as "technical indicators".

Such technical indicators, of which there are many, are alldesigned to provide the technical analyst with "supportinginformation" to assist in the trading decision. The key problemwith nearly all these indicators is that they appear to work someof the time and fail wholeheartedly at other times. Put differentindicators together, as many people do, and interpretations of theindicators can lead to a confused and contradictory view of themarket.

Without going into the "interpretation" of each indicator in thechart below – I shall leave that to the pattern spotters – you willsee a range of conflicting views based on some of the morepopular indicators. Are we trending or not? Are we overboughtor are we at resistance? Are we touching the top of the band orare we breaking out? I shall stop there.....

What is clear, if I may use such a term in the above chart, is thatprice and the action of price are almost impossible to see. If weaccept the definition of technical analysis as the "study of buyerand seller behaviour, through the use of charts, to give meaningto price action", then why do so many of us litter our price chartswith mathematical derivations of price. Price is what we trade,not a derivation of it.

So where does this leave "technical analysis"?What I have been discussing so far is what I have termed"modern" technical analysis as taught by the modern "gurus".Let's go back to "traditional" technical analysis since I believe it isonly through the "spirit" of technical analysis as laid down by the

Figure 4

Figure 5


early pioneers of technical analysis that will help us make sense ofthe modern markets and future markets where program tradingwill play a greater part.

The early pioneers of technical analysis were not concerned withthe latest fad indicator. These pioneers stressed the importanceof seeing a chart as a picture telling a story. Charts depict a storyabout buyers and sellers in the market and the pressures theyboth exert as played out in price action. It is the skill in readingthe story that for me is the spirit of technical analysis that seemsto be lost amongst the modern pattern spotters and technicalindicator junkies.

Real technical analysis is concerned with understanding theforces of supply and demand, reading the story of whether buyerswant the stock so much that they are willing to hold price up andtake it higher, or whether sellers have had enough and are willingto sell the stock at any price.

The skill required for real technical analysis is not one of spottingpatterns or indicators. On the contrary, the skill required is that ofputting aside all pre-conceptions and beliefs, and opening ourminds to being in tune with the market, and listening to what it istelling us about the forces of demand and supply.

In the chart below, pattern spotters and indicator junkies will tellyou that the stock is heavily overbought and is setting up for a"double top" reversal. Leaving such pre-conceptions aside andreading the story, the chart is telling us that there are very fewsellers interested in selling the stock after the significant gap upand rally. We also read that even at this high price level, buyerscontinue to demand the stock and keep it trading near the highs.The lack of selling pressure and the continued buyer demand tella very different story to what many pattern spotters/ indicatorjunkies would say.

The lit wickNo discussion about "modern" technical analysis would becomplete without discussing charting that dates back well beforethe days of Charles Dow but which has gained great interestamongst the pattern spotters – candlestick charting.

A myriad of candlestick patterns have been "identified", eachgiving the modern technical analyst a clue as to future pricedirection. "Modern" technical analysts now have even morepatterns to play with in their quest to spot patterns on charts.

I believe candlestick charting is essential to understanding the

story about demand and supply in one period. However, it is not

a candlestick chart pattern itself that is important in my view, but

the story behind price action as played out within each

candlestick pattern or formation.

Let us take the example of the "hanging man" candlestick.

According to modern technical analysis literature, the hanging

man gives a good reversal indication as a market top. I fail to see

how that can be, for if we read the story of the hanging man, a

different picture emerges.

Price opened and collapsed early during the period as supply

pressure overwhelmed demand. At some point during the

period, new demand entered the market and halted the price

collapse, soaking up the remaining supply. As supply was

absorbed, increasing demand caused price to rise. What little

supply was left continued to be absorbed and price kept rising.

By the end of the period, demand soaked up all available supply

and closed near where it opened for the period.

If this is the story behind a hanging man and if the hanging man

appears at the top of a strong price move, led by strong buying,

does this not indicate buyers still want the stock? For my trading,

a hanging man at the top of a strong price move is a great buying

opportunity, with the more hanging men the better, as in the

example below.

In his classic book, "Where are the customer's yachts?" Fred

Schwed Jr begins by saying:

""Wall Street" reads the sinister old gags, "is a street with a river at

one end and a graveyard at the other". This is striking, but

incomplete. It omits the kindergarten in the middle"

As technical analysts, we need to protect our art and teach our art

in the way it was meant to be taught. In kindergarten, children

are taught to spot pictures in books. At school, they are taught to

read the stories in books.

Aboudy Nasser is a founder of www.via-trader.com, an automated

real time technical news service designed to provide readers with the

full real time holistic story behind price action. He may be reached

on [email protected]

Figure 6

Figure 7


The purpose of this article is to bring a new, reasoned and logicalargument to the plotting of moving averages on price-chartswhich I have called the SmartView model. Further on, I’ll try toprove that the use of the SmartView model, in every marketcondition, gives the analyst a better interpretation of what ishappening on the price-chart than the one he or she would havegot just by using a closing moving average in relation to theclosing price itself.

1 An introduction to the SmartView modelI have always been amazed by the capacity of a closing movingaverage to provide, during trending markets, support andresistance areas and profitable buy and sell signals. But, aware ofthese limitations, I have tried over the last four years to developsomething, without departing too much from the basic idea ofmoving averages, that does not perform any worse duringtrending markets and does much better during other kinds ofmarket conditions.

After reading many technical analysts’ reports and specific books,the first thing I have noticed is that, generally, closing movingaverages are plotted on bar-charts or candle-charts and not onclose-only charts.The second thing I have observed is that analystsusually ask the closing moving averages to provide, during bullishtrends, support areas near the lows of the price-bars and, duringbearish trends, resistance areas near the highs. Furthermore, it isgenerally accepted that a buy signal is generated when the pricecloses above the closing moving average and that a sell signal isgenerated when the price closes under the closing moving average.

In my opinion these interpretations are not wrong, but theycontain something that doesn’t persuade me completely. Whyshould I use an algorithm (the moving average) based on theclosing price hoping it will provide support areas near the lows ofthe price-bars and resistance areas near the highs? Closing pricesare usually above the lows and under the highs; and in any casethey are different. Rather than focus on closing prices, I believethat support and resistance should be determined using high andlow prices, since these are specifically geared to ideas of supportand resistance. So, rather than examine moving averages ofclosing prices for support or resistance, it might be better to usemoving averages of lows and highs to determine support andresistance respectively.

And something else could be observed also about the classicaldefinitions, explained above, of the buy and sell signals. Surelythey are correct because everything is based on the closing price.But, if we try to imagine a buy signal when the price closes abovethe high-moving average and a sell signal when the price closesunder the low-moving average, the signals can be consideredmuch stronger.

2 Moving averagesThe moving average is one of the most versatile and frequentlyused of all technical indicators. It is very often the basis for manytrend-following systems. A moving average is an indicator whichshows the average value of a price over a period of time. The term"moving" means the average changes or moves. For example, in a

5-day average of closing prices only the latest five prices are usedin the calculation.

The most popular method of interpreting a moving average is tocompare the relationship between a moving average of theclosing price and the closing price itself. A sell signal is generatedwhen the closing price falls below its moving average and a buysignal is generated when the closing price rises above its movingaverage. Furthermore, a moving average tends to be a support inan uptrend and to become a resistance in a downtrend.

Usually, before plotting a moving average, it’s necessary to set atleast three parameters; the price field, the number of time periods(the length) and the calculation method.

The first thing to do is to choose the price field to use whencalculating the moving average. This is also one of the mostimportant points of my research; in fact most technical analystsuse just the closing price and they prefer to concentrate onmodifying the other parameters. Instead, the SmartView model isbased on two moving averages: the first one uses the high pricesand the second one the low prices.

The number of time periods used in calculating the average isgenerally considered as the critical element in a moving average.Many analysts think that a good choice of the length is the realkey to make a moving average consistently profitable. So theyusually do their best to look for the perfect length for eachsecurity. Many analysts also believe that it’s necessary to adaptmanually or automatically this number if the market conditionschange for example from volatile to non-volatile. I will try todemonstrate that while the choice of a number of time periods issurely very important, it is not as critical as many people think.

The last parameter to set is the calculation method which wil bediscussed in the next section.

Moving averages’ calculation methods

There are several types of methods for calculating movingaverages’. These four most popular are simple, weighted,exponential and triangular. The only significant differencebetween these various types of moving averages is the weightassigned to the most recent data. Simple moving averages applyequal weight to prices. Exponential and weighted averages applymore weight to recent prices. Triangular averages apply moreweight to prices in the middle of the time period.

What you should ask and what you shouldn’t ask of movingaverages

Moving averages are generally considered as trend-followingindicators because their signals are profitable during trending-markets and they are less effective when the market movessideways.

So the first thing you shouldn’t expect from a closing movingaverage is that it works well during trading-ranges. But another thingyou shouldn’t expect is that a particular closing moving average (forexample a 15-period one) will work well on all the trends which aredeveloping on the chart. First of all, note that I define a trend asbullish if it’s possible to identify on the chart rising highs and lows

SmartviewThis article is a summary of a paper presented to the IFTA conference, Lugano, 2006 By Alessandro Angeli


and I define a trend as bearish if it’s possible to identify falling highsand lows. However, in order to get good results, trends have to beextended. In fact, if this doesn’t happen, a long term moving averageis unlikely to become an efficient support or resistance point in ashort-term movement.This happens because the change ofdirection of the closing moving average is generally quite slow.

Furthermore, in order to obtain profitable buy and sell signals,uptrends and downtrends should have more or less the sameslope. In fact, if the trends have consistently different slopes it isvery difficult for a particular closing moving average (for examplethe 15-period ones) to be able to provide good signals.

Figure 1 illustrates this point on the weekly chart of the DJIA withthe 20-period closing EMA. On the left side of the chart it’s possibleto identify a bull trend with rising highs and lows; the 20-periodclosing moving average becomes a support from October 1998 toSeptember 1999 and its buy and sell signals are very profitable. Bycontrast, on the right side of the chart, the market begins to movein a trading-range from 10.000 to 11.500 points.The movingaverage’s signals become much less indicative and it’s impossible tomake a profit from them during this sideways movement.

Figure 2 shows the weekly chart of the Eur/Usd with the 15-period closing EMA. Clearly the signals are profitable during long-time or middle-time trends with more or less the same slope (Band D) and they become less good at identifying trends with adifferent slope (A), in short-time trends (C) and during sidewaysmovements (E).

Some solutions to improve the moving averages’ signals

Aware of the limits of the moving average in relation to theclosing price, many technical analysts have conducted extensiveresearch in order to develop a moving average which would beable to work well in almost all market conditions. They have

concentrated their work mainly on two of the three parametersdiscussed earlier: the number of time periods (the length) and thecalculation method. The two people who have obtained the bestresults in this way are Tushar Chande and Perry Kaufman. Theyrespectively have developed the Variable Index DYnamic Average(VIDYA) and the Kaufman’s Adaptive Moving Average (KAMA).

Another person who has also worked with moving averages isJake Bernestein who has concentrated on the third parameter, theprice field, developing in this way the Moving Average Channel(MAC).

Chande’s VIDYA

Tushar Chande explains in his first book that one of the principalproblems of moving averages is due to the fixed number of timeperiods chosen to calculate the average. This parameter is staticand it can’t change when market conditions vary, for examplewhen volatility increases or decreases. So, he has had the idea ofdeveloping a dynamic exponential moving average (VIDYA) whichadjusts its effective length using a market variable or, moreprecisely, a volatility index (k).

VIDYA0 = alpha * k * C0 + (1 – alpha * k) * C-1

When the volatility index (k) = 1, we have an exponential averagedetermined by alpha; when k > 1, we take a larger bite of the newdata and the effective length of the average decreases; when k <1, we take a smaller bite out of the new data, and the effectivelength of the average then increases.

The result of this process is that the VIDYA tends to increase itsangle when the volatility increases (because the effective lengthof the average decreases) in order to follow as closely as possiblethe probable incoming trend. Further on, it tends to flatten whenthe volatility decreases (because the effective length of theaverage increases) in order to identify as well as possible theprobable incoming sideways movement.

For instance, VIDYA can be indexed to the standard deviation ofclosing prices or to other indicators such as the absolute value ofthe Chande Momentum Oscillator (AbsCMO).The softwareMetastock indexes the VIDYA to the AbsCMO but, in my opinion,this choice is disputable because the AbsCMO can oscillate onlyfrom 0 to 1. Personally, I would prefer to index the VIDYA to thestandard deviation using the formula presented in Chande’s book:

k = standard deviation (9-periods) / standard deviation (30-periods).

Figure 3 shows the same Eur/Usd chart of figure 2, except theVIDYA is plotted.

Figure 1: DJIA (weekly data) and the 20-period closing EMA.

Figure 2: Eur/Usd (weekly data) and the 15-period closing EMA.

Figure 3: Eur/Usd (weekly data) and the Variable Index DYnamicAverage (default settings).

MARKET TECHNICIAN Issue 57 – November 200610

Kaufman’s AMA

Perry Kaufman has worked more or less in the same direction asTushar Chande to develop the KAMA, an adaptive movingaverage indexed to a particular “noise” indicator called EfficiencyRatio (ER) which he also developed. In the book in which heintroduced the KAMA, Kaufman defines the price direction as thenet price change over “n” periods and the volatility as the sum ofall the day-to-day or hour-to-hour price changes (each taken as apositive number) over the same n periods the price direction wascalculated. Then he defines the Efficiency Ratio (ER) as the ratiobetween the price direction and the volatility. This indicatorobviously varies from 0 to 1. When the market moves in the samedirection for all the n days then the price direction = volatility andthe ER = 1; in this particular case the fastest possible movingaverage is surely the best choice to follow the market because it’smoving very fast in a clear direction. If the volatility is muchgreater than the price direction, the ER will be probably very closeto 0 which means that the market is going nowhere and the moresuitable moving average to follow is surely one of the slowest. Inorder to transform the ER into the trend speed, Kaufman haschanged the ratio into a smoothing constant (c) for use in anexponential moving average. The smoothing constant and theKAMA formulas are then:

c = [ER * ( fastest – slowest ) + slowest]2

KAMA0 = KAMA-1 + c * (C0 – KAMA-1)

The fastest value in the (c) formula is 0.667 and the slowest0.0645.

Figure 4 shows the same Eur/Usd chart of figures 2 and 3, exceptthe KAMA is plotted.

Kaufman shows that squaring the value of (c) greatly improvesthe results by virtually stopping the KAMA from moving duringsideways markets. This process selects very slow trends duringsideways markets, and speeds up to a very fast trend duringhighly trending periods.

At the end it’s important to note that Kaufman has also provideda personal way to work with the KAMA which is not based on itsrelationship with the price but is fully based on the KAMA’s angle.

Bernstein’s MAC

Jake Bernstein has tried to improve moving average signals inrelation to the price working with another parameter, the pricefield. In one of his first books in which he presented the MAC heexplains that, inspired by concepts originally introduced in the1950s by Richard Donchian, he departed from the traditional use

of the moving average having conducted intensive research onmoving average channels (MACs) which consist of a movingaverage of high prices and a moving average of low prices. Ratherthan focus on closing prices, he felt that support and resistanceshould be determined using high and low prices because usuallyresistance tends to be found near previous highs and supporttends to be found near previous lows. His technique uses amoving average of high prices and a moving average of lowprices which in conjunction form a Moving Average Channel thatis used to determine support and resistance. More precisely, whenthe trend of prices is up, the Moving Average of Lows (MAL) tendsto act as support and when the trend of the prices is down, theMoving Average of Highs (MAH) tends to act as resistance.

Jake Bernstein provides many examples in his book but almost allof them are about intraday markets. He suggests many differentinterpretations in order to use the MAC during trending orsideways markets by both aggressive and more conservativetraders.

Figure 5 shows the same Eur/Usd chart of figures 2, 3 and 4, withthe MAC also plotted.

Although the MAC is not easy to use because the two lines tendsometimes to clutter the chart, it is possible to notice that, veryoften, the two averages provide efficient support and resistanceareas. Have a look also at figure 6 which shows the daily chart ofthe S&P500 Index.

3 The SmartView model explainedThe SmartView model is my contribution to the research toimprove moving average signals in relation to the price. It could

Figure 4: Eur/Usd (weekly data) and the Kaufman’s Adaptive MovingAverage (default settings).

Figure 5: Eur/Usd (weekly data) and the MAC or the 10-periodexponential moving average of the high prices (MAH) and the 8-periodexponential moving average of the low prices (MAL).

Figure 6: S&P500 Index (daily data) and the MAC (10-period MAH and 8-period MAL).


also be intended as an attempt to complete Bernstein’s MAC inorder to simplify its use and to identify more easily the key levelsof support and resistance.

Two is better than one

I believe that the use of two moving averages, a moving averageof the high prices and a moving average of the low prices, couldgive analysts a better interpretation of what is happening in theprice-chart than the one they would have by just using a closingmoving average in relation to the closing price itself.

There are two reasons for this. The first is that this argument islogical and rational. As mentioned before, usually resistance tendsto be found near previous highs and support tends to be foundnear previous lows. So, it’s surely reasonable that rather thanexamine moving averages of closing prices for support orresistance, it might be better to use moving averages of lows andhighs to determine support and resistance respectively.Furthermore, if we consider the buy signal when the price closesabove the high-moving average and the sell signal when theprice closes under the low-moving average, these signals can beconsidered much stronger in relation to the classical movingaverages’ buy and sell signals.

The second reason is that, in my experience, this idea works wellin practice. I’ve tested it on stocks, currencies, indexes,commodities and bonds in different market places and inmultiple time frames (from 5-minute data to quarterly data) and Ihave obtained encouraging results.

But, using of the MAC does require accurate and careful analysis.For example, during an uptrend analysts could pay attention onlyto the MAL (which tends to act as support) ignoring the fact thatthe MAH could also be very useful. In the same way, during adowntrend, analysts could pay attention only to the MAH (whichtends to act as resistance) and they could overlook that the MALcould also be very powerful. But it is also true that there are oftenoccasions when one of the two averages is completely redundantand its presence on the chart is superfluous. During high-volatilitysideways movements the two averages are continuouslypenetrated. In conclusion we could say that it is easier to workwith a closing moving average rather than with the MAC,although its signals are probably less indicative.

The real key of my research, therefore has been to develop a new-elaboration of the MAC based on programming a technicalanalysis software to show the moving average of the low prices(MAL), the moving average of the high prices (MAH) or both ofthem exclusively when I really need them. The output of thisprogramming is the SmartView model which I like to define as anew way of looking at price-charts.

The SmartView model is based on the relationship between theMAC, the closing price and the opening price in each tradingperiod.

The position of the closing price

The closing price has surely the major weight compared to theother three prices (open, high and low) which form a price-bar.Consequently, it is very important to consider the closing price inrelation to the MAC in each trading period.

The MAH, can be defined as a resistance moving average. Then, ifthe closing price is above the MAH, we can presume that the

resistance has been broken. When this happens, there is no needto see the MAH on the chart because in an uptrend prices areexpected to rise and it is not helpful to see a resistance lineunder prices. We need to see the MAH on the chart only if theclosing price is under the MAH. In the same way the MAL can bedefined as a support moving average. Similarly, if the closingprice is under the MAL, we can presume that the support hasbeen broken and there is no need to see the MAL on the chartbecause there is little point in showing a support line above theprice line. The MAL is only shown on the chart if the closing priceis above the MAL.

These are the two first conditions of the SmartView model andthey have a heavier weighting than the second ones which areconsidered in the next section.

The position of the opening price

Even if the opening price probably has a lesser significancecompared to the other three prices (high, low and close) whichform a price-bar, it is in any case very important to take it intoaccount in relation to the MAC.

For example, if the opening price of a trading period is above theMAH, it means that it is not working very well as resistance. If theclosing price of the same trading period is also above the MAL, itwill be useless to show the MAH on the chart because it representsa resistance which hasn’t done its job well. However, if the closingprice is under the MAL, the MAH should be plotted on the chartbecause we can presume that in the next trading period themarket will fall (a support has been broken) and it will be useful tohave in advance a reference resistance value. In the same way, if theopening price of a trading period is under the MAL, it means it isnot working very well as support. Later, if the price in the sametrading period closes under the MAH, it will be useless to show theMAL on the chart because it represents a support which hasn’tdone its job well. But, again, if the closing price is above the MAH,the MAL should be plotted on the chart because we can presumethat in the next trading period the market will rise (a resistance hasbeen broken) and it will be useful to have in advance a referencesupport value.

The next three figures (7, 8 and 9) show the daily chart of Procterand Gamble, the MAC (blue lines) and the SmartView model(coloured dots). It is easy to see that they always coincide exceptthe SmartView model plots the MAC levels only when theexplained conditions are fulfilled.

Figure 7: Procter and Gamble (daily data), the MAC (10-period MAH and80-period MAL) and the EMA-Smartview


The SmartView model

The SmartView model can be defined as a new way of looking atprice-charts with moving averages. Its first aim is to help analyststo follow as easily and as clearly as possible financial marketmovements. The SmartView model appears on the price chartcombining each trading period with alternatively: a green dotplaced under the price-bar (the MAL value), a red dot placedabove the price-bar (the MAH value) or two dots of two colours.

Briefly, the model signals the probable presence of:

• a bullish trend, when the SmartView model shows just green dots;

• a bearish trend, when the SmartView model shows just red dots;

• a trading range when the SmartView model shows dots of boththe two colours.

Besides providing information about the market direction and thereal existence of a trend, the SmartView model also furnishessupport and resistance levels which can be identified observingthe exact position of each dot on the chart. As a consequence, themodel is able to provide in every period, both stop loss and longand short entry levels. A buy signal occurs in the first tradingperiod when the model plots only a green dot; a sell signal occursat the first trading period in which the model plots only a red dot.

Before going on, it is very important to notice that price levelsprovided by the SmartView model when the market is still open,

should be broken by the closing price in order that the signal beconsidered valid. For this reason, it is useful to consider the modelonly at the end of each trading period and not during itsconstruction. Obviously this condition must be fulfilled by alltechnical indicators which are based on the closing price.However, in the case of the SmartView model this aspect has alower weighting. In fact, the resistance and the support levelsdepend exclusively on the highs and on the lows and they are notbased on the closing price. Therefore, in some cases, if the marketis trading sufficiently away from the highs or from the lows, it ispossible to have an effective resistance or support reference valuebefore the end of each period.

Before plotting the SmartView model, it is possible to choose themoving averages’ calculation method (simple, weighted,exponential, triangular, Chande’s Vidya and Kaufman’s AMA) andtheir length. My default settings are: 10-periods exponential MAHand 8-periods exponential MAL. The Exponential SmartView modelcan be written as an indicator in Omega code as:

{EMA-SmartView : SmartView of Exponential Moving AveragesProvided By: Alessandro Alberto Angeli (c) Copyright 2004. Allrights reserved.}Inputs: PeriodH(10), PeriodL(8);If(C>Xaverage(H,PeriodH)) then

plot1(Xaverage(L,PeriodL),"Support")

else

if (O<Xaverage(H,PeriodH)) thenplot2(Xaverage(H,PeriodH),"Resistance");

If(C<Xaverage(L,PeriodL)) thenplot3(Xaverage(H,PeriodH),"Resistance")

else

if (O>Xaverage(L,PeriodL)) thenplot4(Xaverage(L,PeriodL),"Support")

I prefer to use the EMAs but combining the VIDYA or the KAMAwith the SmartView it is possible to obtain a double result in theprocess to improve moving average’s signals.

Figure 10 shows the same Eur/Usd chart as figures 2, 3, 4 and 5,except the EMA-SmartView is plotted. The model shows thesupport and the resistance levels exactly when I want and needthem.

Figure 8: the first condition of the SmartView model is that the MAH(red dots) is plotted on the chart only if it is above the closing price. Inthe same way the MAL (green dots) is plotted on the chart only if itunder the closing price.

Figure 9: the second condition of SmartView model is that the MAH(red dots) is plotted on the chart if it is above the opening price. In thesame way the MAL (green dots) is plotted on the chart if it is under theopening price. However the closing price condition (the first) is strongerthan the opening price one (the second).

Figure 10: Eur/Usd (weekly data) and the EMA-SmartView

(default settings).


Figure 11 shows again the same Eur/Usd chart, except the VIDYA-SmartView is plotted.

4 Advantages of using the SmartView modelI believe that the use of the SmartView model leads analysts to abetter interpretation of what is happening on the price-chartthan they would have by just using a closing moving average inrelation to the closing price itself. If the logical and reasonedarguments presented in section 3 haven’t yet persuaded you, I’llnow try to prove my point with some real examples in multipletime frames taken from different markets.

5 During trending marketsFigure 12 shows the monthly chart of the German DAX Index, theEMA-SmartView and the 15-period closing EMA. The 15-periodlength has been chosen because, during trending movements,this average tends to be very similar to the EMA-SmartView withdefault settings and in this way it is possible to do a comparison.Observing carefully the chart it is possible to notice that, whenthe market is trending, the EMA-SmartView provides buy and sellsignals very similar to the closing EMA ones even if, in my opinion,the EMA-SmartView works better. However, from this point ofview, we could say that the use of the EMA-SmartView instead ofthe closing EMA is not a handicap.

Considering now the function of support and resistance, the twoindicators confirm that the EMA-SmartView works better. Have alook at the rectangles in the second part of 2000 and 2003. TheEMA-SmartView furnishes correct resistance and support levelssooner than the 15-period closing EMA. Further on, during bullish

(bearish) trends the EMA-SmartView provides interestingresistance (support) areas which, if broken, confirm the originaltrend. Consider the ovals in figure 12; this information is not givenby the 15-period closing EMA.

During major reversal patterns

During major reversal patterns (double bottoms, head andshoulder, broadening formation, etc.) prices very often movesideways for a time even if the volatility remains quite high. Forthis reason a closing moving average usually provides wrong buyand sell signals.

Figure 13 shows the 60-minutes chart of the S&P500 Future (04-Sep), the EMA-SmartView and the 15-period closing EMA. In themiddle of the chart it is possible to identify a double bottompattern.The buy and sell signals that the two indicators provideduring this reversal formation are very similar. However, in therectangle, which underlines when prices are moving sideways, theEMA-SmartView gives interesting and correct support and resistancelevels, especially in the oval, providing precious information whichthe 15-period closing EMA is not able to furnish at all.

During trading-range movements

Figure 14 shows the weekly chart of the DJIA, the EMA-SmartViewand the 15-period closing EMA.When the market moves sideways,the closing EMA tends to flatten and to place itself in the middle ofthe trading-range, becoming completely unable to provide efficientsupport and resistance areas. Further on it gives a lot of wrong buyand sell signals, in particular when the volatility is quite low, as it’spossible to see in the rectangles drawn on figure 16. On the otherhand the EMA-SmartView provides very good resistance andsupport areas which really help analysts to identify the trading-

Figure 11: Eur/Usd (weekly data) and the VIDYA-SmartView (VIDYAdefault settings).

Figure 12: Dax Index (monthly data), the EMA-SmartView (defaultsettings) and the 15-period closing EMA.

Figure 13: S&P500 Future 04-Sep (60-minutes data), the EMA-SmartView(default settings) and the 15-period closing EMA.

Figure14: DJIA (weekly data), the EMA-SmartView (default settings) andthe 15-period closing EMA.


range bands.When the volatility increases but the marketcontinues to move sideways (in the ovals) the two indicatorsprovide more or less the same buy and sell signals even if it’s rightand honest to say that in some (unlikely) cases the 15-period EMAworks better.

What you shouldn’t ask to the SmartView model

The SmartView model is first of all a trend-following indicatorwhich seems to work very well, or at least no worse than a closingmoving average. However I could prove that, with the defaultsettings, the EMA-SmartView gives good signals in all the trendsthat develop on the price-chart. Have a look at figure 10 incomparison to figure 2. The EMA-SmartView always works better,except for the last oval (E).

Furthermore it provides a powerful information during low-volatility trading-ranges. In these situations it is much more usefulthan a closing moving average but, unfortunately, it is not perfect.In fact, it becomes less reliable during high-wave movements andhigh-volatility trading-ranges but even during these periods it isno worse than a closing moving average.

In order to reduce the false signals we could try to work on thesystem in different ways as for example:

• modifying the length of the two averages (the MAH and theMAL);

• working with multiple data compressions for example daily andweekly or weekly and monthly;

• combining the SmartView model with other technical indicators;

• developing low-risk strategies, for example using thepyramiding technique or combining the SmartView long andshort entry signals with exit signals which are function of theWilder’s Average True Range;

• simply accepting the faults and consequently the losses.

About the first point I have to say that in general I don’t like tomodify manually the length of technical indicators looking for themore suitable number. However, the increase of the length of thetwo averages from the default settings sometimes can bring animprovement. Figure 15 shows the daily chart of General Electricand the EMA-SmartView with the default settings. The modelworks very well, except for the drawn ovals; in fact in thoseperiods there were high-wave movements or high-volatilitytrading-ranges. Figure 16 shows the same chart, except the EMA-SmartView with the 50-period EMAH and the 30-period EMAL isplotted. Indeed, some of the previous problems have been solved.But I don’t like to work in this way. I would prefer to use theVIDYA-SmartView or to work with the data compression withoutmodifying the default settings even if it’s important to rememberthat in any case it is not possible to always obtain good resultswith these techniques. Consider figure 17. It shows the weeklychart of General Electric and the EMA-SmartView; the ovals arecorresponding to the ones in figure 15. In the second oval awrong signal would have been given in the weekly chart too.

Another way to try to reduce SmartView model wrong signals isto combine it with other technical indicators. Personally, I use theEMA-SmartView with the stochastic oscillator. It helps me toidentify possible reversals and to understand if the market is in apositive or in a negative cycle.

Other solutions could be used to develop low-risk strategies. Insection 7 I’ve provided one of them based on the pyramidingtechnique.

Finally, the last way I propose is to accept the model’s faults and,as a consequence, the losses. Maybe this is the real key to makeenough money and live in peace!

6 The SmartView model in over-underweightdecisionsIn this section I’ve provided some simplified examples whichshow how the SmartView model could be used during 2004 toarrive at over-underweight decisions. All the six stocks have beentaken from the Stoxx50 Index. (Figure 18 – Figure 23)

7 A low-risk strategy to use the SmartView modelThis section looks at a low-risk trading strategy which could beutilized to use the SmartView model as a trading method.Consider figure 24.

Figure15: General Electric (daily data) and the EMA-SmartView(default settings).

Figure16: General Electric (daily data) and the EMA-SmartView(50-period EMAH and 30-period EMAL).

Figure17: General Electric (weekly data) and the EMA-SmartView(default settings).


It consists in opening a 33% position at the end of the first periodcombined with just one dot in a colour different from the previousone (A). Later it is possible to increase the position with a further 33%when the market tests the support or resistance area where the dotsof the initial colour are placed and it seems to be able to continuethe trend; this obviously happens when the SmartView model plots

again just dots of the initial colour (B and C).The exit level will be thefirst closing price where there are no dots of the initial colour. At thispoint (D) a 33% position in the opposite direction should be opened.

Using this strategy, most wrong signals should be faced with only33% of total exposure because generally, during high-wavemovements or high-volatility trading-ranges, the SmartViewmodel doesn’t give confirmation to act as support or resistance.Consider the drawn ovals in figure 10, 14 and 15. However thisprotection has a cost. In fact no trend will be fully exploitedbecause positions will only lead be increased to the totalexposure when the trend is already well established.

8 ConclusionThe SmartView model, in every market condition, brings theanalyst a better interpretation of what is happening on the price-chart than the one he would have obtained by just using aclosing moving average in relation to the closing price itself.

Alessandro Angeli is a Strategist at T&F Asset Management SA inLugano, part of the Tax and Finance Group, a global provider ofinternational tax consultancy, trust and financial services.

Figure 18: ABN Amro (weekly chart).

Figure 19: Credit Suisse (weekly chart).

Figure 20: E. ON (weekly chart).

Figure 21: HSBC (weekly chart).

Figure 22: Swiss Re (weekly chart).

Figure 23: Total Fina (weekly chart).

Figure 24: S&P500 Future 04-Jun (60-minutes data) and the EMA-SmartView (default settings).


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actio

ntw

ice.

It th

eref

ore

follo

ws

that

a b

reac

h of

the

sup

por

ting

line

(S) a

t $56

5,fa

lling

thro

ugh

the

five-

year

sup

por

t of u

ptr

end

line

(T),

wou

ld h

eral

d a

larg

e d

ownw

ard

pha

se fo

r the

gol

d p

rice.

Mea

nw

hile

,th

e tr

end

is s

till

up

war

ds.

Go

ld –

his

tori

c p

rice

ch

art

sin

ce J

an 1

980

Dai

ly c

losi

ng

pri

ce s

ince

Jan

198

0 w

ith

200

day

& 5

0 d

ay e

xp.m

ovin

g a

vera

ges

.

Market Technician No 57

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Transcript of Market Technician No 57