CONTENTS 1 FEATURES - The Technical Analyst · 2016-11-23 · 1.2980 on 5th June, EURUSD has been...

© 2006 Clements Biss Economic Publications Limited. All rights reserved. Neither this publication nor any partof it may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic,mechanical, photocopying, recording or otherwise, without the prior permission of Clements Biss EconomicPublications Limited. While the publisher believes that all information contained in this publication was correctat the time of going to press, they cannot accept liability for any errors or omissions that may appear or loss suffered directly or indirectly by any reader as a result of any advertisement, editorial, photographs or othermaterial published in The Technical Analyst. No statement in this publication is to be considered as a recommendation or solicitation to buy or sell securities or to provide investment, tax or legal advice. Readersshould be aware that this publication is not intended to replace the need to obtain professional advice inrelation to any topic discussed.

CONTENTS 1 FEATURES

Interpreting the COT reportRecent changes to the Commitments of Traders

report mean that more attention should befocused on the commercial shorts.

Waiting for EURUSDWith volatility at all time lows, John Noyce of

Citigroup prepares for the breakout in EURUSDby identifying key support and resistance levels.

In the meantime, while EURUSD remainsrangebound, GBPJPY offers a more interesting

trading opportunity.

InterviewJim Rogers

The legendary investor talks candidly about thebull market in commodities, the future of the US

dollar, and why he “wouldn’t put a nickel intoIndia.”

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WELCOMEThe launch of the automated trading section in our last issue coincided with our Automated

Trading conference in October, both of which were well received by our readers. Whilst hedgefunds and trading houses that are using technical automated trading systems are understandablyreluctant to divulge too much about how their systems were built, valuable research is being done

and we will endeavour to bring the very latest thinking to our readers via the magazine and ourconferences and workshops.

Our second February conference takes place in London in February and will look to cover asmany ‘alternative’ TA subjects as possible including the much talked about Ichimoku charting and

DeMark Indicators, as well as some more familiar topics. Full details can be found inside.

We hope you enjoy this issue of the magazine.

Matthew Clements, Editor.

November/December 2006 THE TECHNICAL ANALYST 1

Editor: Matthew ClementsManaging Editor: Jim BissConsultant Editor: Trevor Neil Advertising & subscriptions:Louiza Charalambous Marketing: Vanessa GreenEvents: Adam CooleDesign & Production:Paul Simpson & Thomas Prior

The Technical Analyst is published byClements Biss Economic Publications LtdUnit 201, Panther House,38 Mount Pleasant, London WC1X 0AN

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INDUSTRY NEWS

MARKET VIEWS EUR/USD: Waiting for the breakoutOil: Black gold should regain its lustreNikkei 225: Bullish longer term outlook

SPECIAL FEATUREIFTA conference 2006 roundup

TECHNIQUES Interpreting the COT reportExamining trend characteristicsCross market indictatorsResearch update

INTERVIEWJim Rogers

SOFTWAREInterbank FX Trader 4

BOOKSReview: Mapping the Markets, Griffiths & OwenRecent & forthcoming releases

AUTOMATED TRADING SYSTEMSUsing percentage trailing stoplossesIdentifying a consistent edge

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15

19222529

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CONTENTS 2 REGULARS>


Exploring the relationship between trendand volatility

22

Percentage Trailing Stops

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4 THE TECHNICAL ANALYST November/December 2006

Industry News

BOB PRECHTER RESPONDS TO CASS FIBONACCI STUDYRobert Prechter, CEO at ElliottWave International in the US, hasissued commentary on the recentpaper published by Roy Batchelorand Richard Ramyar at CassBusiness School in London. Theirresearch, which appeared in theEconomist and on Dow JonesNewswires in October, looked forevidence of Fibonacci retracementsand projections in the Dow stockmarket and rejected the idea thatthey occur more often than expectedby random chance.

Prechter says that the study sub-stantially agrees with his commentsin his book, Elliott Wave Principle,stating, "Retracements come in all

sizes." He challenges the notion,however, that the study pertains inany meaningful way to Elliott wavetheory and its observations ofFibonacci relationships among spe-cific types of Elliott waves and wavegroupings. His colleague, DeepakGoel, also questions their statisticalanalysis and argues that their owndata show a non-random biastowards Fibonacci multiples.

Their analysis can be found at:www.socionomics.net/FiboStudy andwww.cass.city.ac.uk/magicnumbers.The Technical Analyst will publish adetailed article on the Cass paperand Prechter's analysis of Fibonacciin the next issue of the magazine.

FX markets the most inefficient According to Jessica James atCitigroup, speaking at the FX WeekEurope conference in London, theFX market is far less efficient com-pared to other financial marketsdespite its huge size and liquidity.Citing a recent Bank of InternationalSettlements survey, Dr James said

that only around 10% of the market- the active currency managers - aretruly concerned with real returns (incontrast to the equity market wheremost participants are profit seeking).The rest of the flows are driven bypassive hedging, travel, cash & assettransfers, and flows related to other

asset classes. Even sophisticatedhedgers are restricted by their ownhedging policies and can only profitwithin certain ranges. This inefficien-cy, James argues, presents profitabletrading opportunities in FX marketsfor speculators and active traders.

DKR Capital in Stamford US hasannounced the launch of a new sys-tematic program that will pursue abehavioural finance approach totrading. DKR Fusion, headed byPascal Magnollay, will consist ofvarious models running equity mar-ket-neutral strategies. Assisting withthe fund will be Dr. Owen Lamont,who is a Professor of Finance at theYale School of Management and aspecialist in behavioural financetechniques.

DKR CAPITAL LAUNCHBEHAVIOURAL FINANCEFUND

Goldman to launch automatedtrading platform Goldman Sachs is set tolaunch an off-exchangeautomated trading plat-form in Europe thatwill provide clients withaccess to ‘dark liquiditypools’. The new plat-form will provide cus-tomers with access todeals that are notoffered publicly on anyexchange. The bank isthought to have recruit-

ed three other marketmakers with which itwill share informationabout deals available inits liquidity pool.

Currently EU rulesrequire all trades to gothrough an exchangebut new legislation willenable banks to tradeshares internally (off-exchange) as long asthey publish the prices

of intended trades tothe rest of the marketbeforehand. GoldmanSachs is also one of sixWall Street banks thatare teaming up tolaunch Block InterestDiscovery Service(Bids), an electronicplatform that will pro-vide users with accessto dark liquidity pools.

Bob Pretcher

Here’s an intriguing question with a simple answer:Name the analyst who:

Published specific gold and silver forecasts for 22 years during one of the metals’ most historically baffling periods, Correctly called nearly every major turn and trend during that entire time, andNow offers that complete body of work for public scrutiny and your personal education?

Robert Prechter. How to Forecast Gold and Silver, Bob’s 15th book, really does tell a story. All markets do, but this story is unique.

It also reminds you of what matters – and what does not – when you think about the markets you follow.

This is not some abstract “How To” book, using pristine drawings of an idealized world. It is a tale that shows “How It Was Done” in real market, in real time, for two decades. The book includes every single chart in its original form, exactly as subscribers saw them.

No trader or technician’s library is complete without this exciting metals saga. It’s like sitting next to an expert while he forecasts the market you’re watching. That’s how you’ll learn to do it yourself.

Go to www.elliottwave.com/wave/GSBTA for details.

•

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“Robert Prechter has been right on target for almost the whole of the gold bear market, having called previous major turning points almost to the exact dollar, which is a feat I believe to be un-equalled by any other forecaster (and I read most of them).”- Australian Investors Digest

“ I’ve been reading Bob’s thoughts on gold for over four years. After a while you conclude he must be reading next year’s newspapers.”- A Non-Random Walk

“Elliott wave forecasting has enabled Prechter to make some remarkably accurate calls. In January of last year, for example, he predicted gold would soon peak at just over $500 an ounce. It hit $511 on Feb. 15 and within two weeks fell to $408.”- Money Magazine

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Market Views

EURUSDWAITING FOR BREAKOUT FROM LOW VOLATILITYby John Noyce

Since hitting the 2006 high at1.2980 on 5th June, EURUSDhas been trading within a broad

range (Figure 1). During this periodvolatility has continued to move steadi-ly lower hitting all-time lows, with vol-ume also moving lower as market par-ticipants effectively lose interest in themarket. At this point it's a good time totake a step back and look at both thepivot points that we should be watch-ing, i.e. to indicate that the market islikely to begin a trending move when

they are broken, and also at the under-lying longer-term outlook.

Support and resistanceThe answer to the first questionappears relatively clear as there areareas of both clustered support andresistance. By clustered we mean thatthere are a number of different supportor resistance points that are centred onone small region. This is important as itconfirms that a break of either of theseclusters should be a material develop-

ment for the market. Put another way, itreduces the risk of a false break, whichis far more likely when you are watch-ing just one individual point as supportor resistance, i.e. an independent trend,retracement or moving average. Thetop of the range is probably the mostsimple to spot. From 1.2897 to 1.2980there are three points; the August andJune '06 highs and the trend acrossthem. On the downside there are fivepoints in the range from 1.2508 to1.2456; the 38.2% retrace of the

Figure 1.

→→


Market Views

Figure 2.

bounce from the November '05 lows,the recent low at 1.2484, the July '06low, the July '04 high and the uptrendfrom the January '02 lows.

These levels can also be used to givea high level of objectivity to a view onthe actual directional outlook forEURUSD. As we have described in pre-vious articles for the Technical Analyst,the underlying structure continues tohave a positive bias. The near 76.4%retracement of the initial bounce fromthe November '05 corrective low andsubsequent sharp rally is a classic wayfor EURUSD to form a base. SeeJanuary '02 and September '89; the lowshit in these two months being 76.4%retraces of the bounce from the correc-tive low in June '89 and the consolida-tion low in July '01 respectively. Bothwere precursors to significant rallies.EURUSD's price action since the June'06 high at 1.2980 also appears as a con-tinuation pattern; it having a flat baseand gradually downward sloping resist-ance line across the highs, this being thetextbook definition of a bull-flag orpennant continuation pattern.

Alternative outlookThe downside pivot that has to be

watched very closely is as detailedabove, the region from 1.2508 to1.2456. If this region was to break itwould force us to take a step back andseriously reconsider our underlyingpositive view, at least in the medium-term. Very quickly, the simple implica-tion of a break of this range would bethat EURUSD was to begin a "C wave"of correction, i.e. a sharp and signifi-cant decline to complete the multi-month correction following the cyclehigh at 1.3670, but that's a story foranother day if it becomes necessary.

Other currency opportunitiesGiven that at this stage EURUSD, eventhough eventually biased higher, doesremain within the broader range it's aninteresting time to look for opportuni-ties in other "smaller" and uncorrelatedcurrency pairs. One that springs tomind is GBPJPY, which has beentrending higher for some months. It isshowing clear signs of being stretchedand exhausted. First the monthly chart(Figure 2) where momentum hasmoved to the most extreme levels seensince the highs in '98 and '90, in bothcases preceding sharp downside correc-tions, in fact trends. The weekly chart is

forming an extremely clear EveningStar candle pattern (three period pat-tern where the market closes sharplyhigher in week one, doesn't move inweek two i.e. forms a Doji, and movessharply lower in week three). This is aclassic exhaustion pattern. Finally thedaily chart; here the market has beenabove the 55-day moving average since27th April - both a very extended peri-od and enabling a very large gap toopen to the 200-day moving averagewhich stands at 213.65. In conclusion,is GBPJPY about to make a sharpdownside correction? This appears amarket to watch closely.

John Noyce is technical analyst forCitigroup Foreign Exchange

Copyright © 2006 Citibank N.A. All rightsreserved. Any unauthorized use, duplicationsor disclosure is prohibited by law and mayresult in prosecution. CitiFX, Citigroup andthe Umbrella Device and trademarks are serv-ice marks of Citicorp or its affiliates and areused and registered throughout the world.


Market Views

OILBLACK GOLD SHOULD REGAIN ITS LUSTER by Ron William

Oil prices have declined bysome 27% since July, havingpunched through the 52-week

moving average and a sequence ofhigher reaction lows, which were thedefining characteristic of the uptrend.However, a study of oil's countertrendmoves since late 2001 tells us this latestcorrection, despite being nominally sig-nificant, is still only the third largest inpercentage terms, with an average peakto trough drawdown of 29% (seeFigure 1). Moreover, a semi-logarithmicchart (see inset in Figure 1) better illus-trates the current correction, whilsthighlighting medium-term supportaround the $55-57/bl zone and signal-ing a potential bottoming out process.

Speculative FlowsWhile sentiment and liquidity play a keyrole in determining price dynamics, oneshould also observe market positioning.The Commitment of Traders Report(COT) shows that de-leveraging was amajor driver of the recent oil pricedrop. The reversal in speculative posi-tioning was triggered by the ceasefire inthe Middle East and the absence of anymajor hurricanes this year. Figure 2shows that following the accelerativepeaks of speculative activity in May andAugust, extreme long positions havebeen scaled back aggressively. But, withspeculative positions now at their low-est level since early 2006 (presently innegative territory), momentum is look-ing distinctly oversold. Furthermore, alikely increase in seasonal demand (withthe onset of winter in the NorthernHemisphere), could point to a potentialupside reassertion.

Secular TrendsIn 1999 oil broke out of its nineteenyear secular decline and OPEC's oldtarget range ($22-28/bl). Originatingfrom this multi-year base, the resilientpattern of higher peaks and troughs

took form, yielding accelerated swingsto the July 2006 high of 78$/bl (animpressive 658% increase from thelows). Nevertheless, a measure of oil inreal terms (adjusted for inflation usingCRB CPI), shows that we have still

Figure 1 - Oil's largest countertrend moves since 2001, with an average peak to trough draw-down of 29%. Inset - Semi-logarithmic chart highlights medium-term support at $55-57/bl.

→→

Figure 2. De-leveraging was a major driver of oil's recent price decline. Note extreme longpositions have scaled back aggressively, presently in negative territory.


Market Views

only scaled about half the level of thelate 1970s upswing that culminated atthe $100/bl mark. Moreover, a basicmeasurement of the chart's range since1970 tells us that the latest capitulationphase has seen prices merely revert to

the long term mean of $57/bl.

ConclusionThe major uptrend in oil remains dom-inant, with the latest correction stillonly the third largest in the sequencesince late 2001. A semi-logarithmicchart highlights support at the $57 to$55/bl zone, with this proving to be aconfluence for support as it alsoequates with the average drawdown of29% and oil's long-term mid-range.

Settlement below $55/bl would putthe long-term theme on hold and open$47/bl to test on the downside, which

would mark a half-way retracement ofthe entire bull market rise since 1999.But geo-political risks aside, further(potential) output cuts by OPEC couldprovide a floor to current price actionand in the context of stretched

momentum these corrective setbacksshould therefore present longer-terminvestors with potential buying oppor-tunities.

In terms of the big picture, it is worthremembering that after a nineteen yearsecular bear market, the phenomenonof cycle alternation now favours a bullmarket of similar duration. Oil has onlybeen rising for a little under sevenyears, which is less than half the lengthof prior cycles (see Figure 3). The factremains that global oil reserves arefinite and developing country demand,particularly from China and India, is

unlikely to abate anytime soon. Indeed,supply inelasticity, coupled with risingdemand, is a theme that will keep thecurrent oil bull trend intact.

The clearest signal, other than a bear-ish cover story in print media, would be

a sharp upside confirmation on theprice chart, above the recent high of$64/bl. Arguably, the current wave ofbearish forecasts serves as a contrarianindicator and is diametrically oppositeto the "$100/bl plus" forecasts voicedat the July 2006 highs. Ultimately, oil isoverextended within a medium-termcorrection phase, but remains withinthe rising tide of a secular bull market.

Ron William is a TechnicalStrategist at Investors Intelligence,a division of Stockcube ResearchLtd.

Figure 3. Secular trends in oil (inflation-adjusted terms)

“...FOLLOWING THE ACCELERATIVE PEAKS OF SPECULATIVEACTIVITY IN MAY AND AUGUST, EXTREME LONG POSITIONS HAVE

BEEN SCALED BACK AGGRESSIVELY”


Long term outlookThe Nikkei's strong 3-year rally fromthe 7604 low of April 2003 pausedmomentarily beneath 12000 before theprotracted range cracked, reassertingstrong equity demand and reaching themulti-year trendline barrier from 1991.The trendline break however hasproved to be short-lived as risingmonthly RSI studies failed to make newhighs. This negative divergence, (whichis of considerable importance, as ithighlights probable exhaustion withinthe trend), along with the 200 month

MA, provided some selling interest, andso promoted profit-taking from theJuly 2005 bull signal. A corrective set-back is now developing from the 17560high of April 2006 but this is provingto be shallow with the 13760, (38.2%),retracement of the 2003-2006 rally, notbeing reached.

Coupled with strong, multi-year risingtrendlines, (the first drawn from the2003 low at 7604 , and the second fromthe 2005 low at 10785, Figure 1.), andthe increasingly impulsive price struc-ture in the direction of the dominant

trend, this shallow retracement hints ata strong underlying bullish tone, withexpectations for still higher levels totrade. Ultimately, we believe the 17560high of April 2006 will be cleared, witha multi-month rally then confirmed.

A later break above the 16902 highputs prices back into the April/May2006 top (base at 17000) with 17560 tofollow. Further clearance of here isexpected to be the catalyst for a freshrun higher with renewed buying inter-est helping to confirm the next signifi-cant equity rally. This will strength-

NIKKEI 225

Figure 1.

Market Views

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BULLISH LONGER TERM OUTLOOK By Mike MacDonald


Market Views

en both the 2005 bull trend, as well asthe stronger 2003 bull trend, with con-gestion around 18000 the first stop. Stillhigher is the 19565~, (38.2%), retrace-ment of the December 1989 to April2003 fall, with strong psychological

reactions at 20000 not too far away.

Short term viewImmediate price action is being cappedby the 16902 monthly high of 24October 2006, with a test not yetlooked for as overbought weekly sto-chastics and cycles begin to unwind.Congestion around 16000 provides ini-tial significant focus with a later breakopening up the 15514 monthly low ofSeptember 2006 ahead of the 2005

trendline (currently around 15250Figure 2.). Further congestion around15000 should follow. This latter levelshould underpin any tests but if profit-taking from the late July 2006 weeklybull signal continues to increase, deeper

reactions will ensue with lows down tothe 14437 of June 2006. Beneath hereis the 14046 level and congestionaround 14000.

This area needs to hold to keep theunderlying multi year bullish toneintact. If further slippage does unfold,then the 2003 trendline, currentlyaround 13100, comes into view withthe 12000 break level and 2004-2005trading range centered around 11375 tofollow. Preferences however are for

both the weekly and monthly bull sig-nals to remain intact, with accumula-tion to limit further downside testsbefore fresh gains are seen.

In summary, with the June 2006 lowat 14046 seen firm, losses for the

Nikkei should remain limited in furtherrange extension. Moving further intoQ4, and into Q1 next year a push aboverecent highs is looked for, with a test ofthis year's peak to follow. A still furtherbreak should follow with renewed gainsthen confirmed into 2007.

Mike MacDonald is technical analyst with 4Cast in Singapore.

Figure 2.

“WITH THE JUNE 2006 LOW AT 14046 SEEN FIRM, LOSSESFOR THE NIKKEI SHOULD REMAIN LIMITED IN FURTHER

RANGE EXTENSION”

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Who should attend: + Traders+ Fund managers+ Hedge funds+ Market analysts+ Risk managers+ Brokers

The Technical Analyst magazine is proud to present its 2nd Annual European Conference for traders and investment managers. This year’s event brings together the very best domestic and international experts to speak on a wide range of important strategies in the world of technical trading. Including talks on mechanical trading plus a panel discussion taking pre-submitted questions from delegates, this is an essential event for Europe’s trading and investment community.

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This advertisement is intended for institutional investors who subscribe to The Technical Analyst magazine. It is directed at persons having professional experience in matters relatingto investments, and it relates to investments which are only available to, and will only be engaged in with, such investment professionals. Persons who do not have professionalexperience in matters relating to investments should not rely on this advertisement. This advertisement has been issued by Barclays Global Investors Limited (“BGI”), which is authorised andregulated by the Financial Services Authority in the United Kingdom (“FSA”). iShares plc and The Exchange Traded Fund Company plc (the “Companies”) are investment companies with variable capitalincorporated in Ireland and are authorised by the Financial Regulator in Ireland. The iShares funds are authorised by the Financial Regulator in Ireland. Any application for shares in any fund is on theterms of the relevant prospectus. The iShares funds may not be registered in your jurisdiction. In particular, none of the shares has been or will be registered under the United States Securities Act of1933, or as an investment company under the 1940 Act, or the laws of any of the states of the United States, and, therefore, may not be offered or sold, directly or indirectly, in the United States or toor for the account of any U.S. Person, as defined by the 1933 Act, except pursuant to an exemption form, or in a transaction not subject to, the regulatory requirements of the 1933 Act, the 1940 Actand any applicable state security laws. In addition, the Companies have not been, nor will they be, qualified for distribution to the public in Canada as no prospectus for the Companies has been filedwith any securities commission or regulatory authority in Canada or any province or territory thereof. This document is not, and under no circumstances is to be construed, as an advertisement, or anyother step in furtherance of a public offering of shares in Canada. No person resident in Canada for the purposes of the Income Tax Act (Canada) may purchase or accept a transfer of shares in theCompanies unless he or she is eligible to do so under applicable Canadian or provincial laws. This advertisement is not intended to constitute an offer to sell or a solicitation of an offer to buy shares ofany fund, nor shall any such shares be offered or sold to any person in any jurisdiction in which an offer, solicitation, purchase or sale would be unlawful under the securities laws of such jurisdiction.BGI and/or its affiliated companies and/or their employees may, from time to time, hold shares in the funds included in this publication or any underlying shares heldwithin the funds, and may as principal or agent buy or sell the funds or securities. Copies of the relevant prospectus can be obtained from www.iShares.net or by calling+44 (0)20 7668 8007. “iShares” is a trademark owned by Barclays Global Investors N.A. © 2006 Barclays Global Investors Limited. All rights reserved.

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The annual conference of the International Federation of Technical Analysts (IFTA) gave the opportunity for delegatesto meet many of the major names in the industry and to speak with them in a friendly and informal atmosphere. JohnMurphy, Martin Pring, John Bollinger and Perry Kaufman were among more than 20 speakers who presented their lat-

est thoughts and techniques to delegates from all corners of the globe. Amid the glorious setting of Lugano the lakeside HotelEden was an ideal venue, and the welcome cocktail evening and closing gala dinner gave delegates a perfect opportunity tobuild new relationships and discuss the subjects of the day. The theme of the conference was focussed around intermarketanalysis.

Martin Pring, Pring ResearchIdentifying emerging industry groups or sectors

Martin looked at the relative performance of bonds, stocks and commodities within the economiccycle and showed techniques to interpret the relationships between these different asset classes. Hesplit the economic cycle into six stages, showed how to recognise these stages and illustrated the per-formance of each asset class within each stage. Regarding sector rotation, he noted that stock sectorstended to lead their respective sectors of the economy and showed how ETF's can be used to enableinvestors to trade by sector or industry group without the need to select individual stocks within aparticular sector.

IFTA CONFERENCE 2006Trevor Neil and Paddy Osborn report on the speaker highlights from IFTA's latest annual conference in Lugano held in October.

Lugano, Switzerland

Special Feature

November/December 2006 THE TECHNICAL ANALYST 15→→

Perry Kaufman Essentials of intermarket analysis

Perry also used ETF's to compare baskets of correlated markets. He used pairs trading(e.g. gold vs. inflation, interest rates vs. stock indices) to illustrate the mechanics of eval-uating two different but linked markets using indexing, correlations and hedge ratios. Hedemonstrated how to calculate hedge ratios in Excel and made a point of incorporatingfundamental influences into his strategies.

Rolf Wetzer (MEAG Asset Management) & Manfred Huebner (DekaInvestment)A systematic technical approach to intermarket asset allocation

They presented a systematic approach for managing an intermarket portfolio. Thestrategies they use for the allocation criteria are based on both relative strength andmomentum and they showed how to take advantage of the interdependences betweendifferent markets and sectors. They discussed different exit strategies, based on typicalinvestment behaviour, which are integrated in their approach. They also discussed riskmanagement strategies and position sizing. This was a very thought provoking talk.

Japan HourThe Japan Hour - actually The Japan two hours - introduced a wide variety of Japanesetechnical analysis theories and techniques. The six different speakers made a range ofpresentations, from an investment philosophy based on "Yagyu Shinkage-Ryu" to a cut-ting edge technique of neural network application. The presentations included some ofthe more imaginative thoughts of up-and-coming technical analysts who have recentlyobtained the MFTA qualification.

Hank Pruden, Professor Golden Gate UniversityWyckoff: a method for all markets

Hank considers Wyckoff as the "master of the trading range". Wyckoff considers thatthe notion of exchange is central to all markets and he considers the market in terms ofthe "composite man", or smart money, on one side of the exchange. The Wyckoffmethods analyse the psychology and actions of this composite man and the aim is totrade in sympathy with those actions as Wyckoff pattern recognition signals are given.Hank outlined the Wyckoff method's principles and tools and presented the classicWyckoff Laws. In particular, he looked at Effort (i.e. volumes) vs. Results (i.e. prices),looking for divergence to give warning of potential reversals. He uses OBV and point-&-figure charts to identify the accumulation phase of a new trend in an effort to iden-tify the point at which composite man is entering the market. The Wyckoff method canbe applied to charts from different markets around the globe, while Wyckoff marketselection relies upon comparative strength and weakness.

Special Feature


Alberto Vivanti, (Vivanti Analysis)Rising and falling stars: how to find them using an adaptive indicatorAlberto said relative strength can dramatically increase yourinvestment returns. Yet, timing is the most difficult aspectwhen trading relative trends. Reversals are hard to find as toomuch smoothing gives late signals and pares most of thegains. How strong is too strong? He showed how a normal-ized, trend-adjusted indicator can help to find the excesses andturning points and also rank stocks and sectors within a sys-tematic approach.

Robin Griffiths, (Rathbones)Intermarket mapping - a global overviewRobin gave another of his no nonsense talks on the world andthe future expectation for global stock markets. He gave theaudience what they wanted, his views and his reasons for theseviews. While his thinking is very long term and strategic,everyone will benefit from knowing how the big picture lookslike unfolding.

Richard Arms The ARMS IndexRichard gave a brief talk on his breadth measure, the ARMSIndex and announced it was going to be rolled out in the nearfuture in Australia and Italy. It is currently available only forthe US stock market. Some people know his index as theTRIN Index although Richard was doubtless the originator.Bloomberg confirmed they would carry the ARMS Index fornon-US markets.

Francesco Caruso, (Journalist) How to use technical and quantitative tools in improving alpha andabsolute returns in an operative multi-asset management modelHaving taken 15 minutes to read out the exotic title of thispresentation, Francesco went on to explain the conceptualdefinition of an algorithm as a procedure or formula for solv-ing a problem. The problem here is defining the rules andconstantly improving alpha through technical tools in a multi-asset allocation model, based only on the action of the mar-kets. These are the key points of the presentation: why usetechnical and quantitative tools in improving alpha; ATDmodel: a four-step process to determine asset allocation - 1ststep - the rules; 2nd step - the algorithms; 3rd step - the explo-rations and 4th step - and the results.

Bruno Estier & Richard Arms Intermarket factors linked to the four-year cycle in US equities, and Internal Factors, such as the Arms Index.Bruno used the (usually reliable) four-year cycle in major global equities to predict a significant low in 2006. The recent lowthis summer (following the mid year 8-10% correction) did not qualify as he expects the low to be formed around 25% offthe highs. In light of the recent strong performance in global equities, he anticipated that this low may not arrive until the endof Q1 2007. Dick Arms then explained the origin of his Arms Index and illustrated how it is calculated. He works predom-inantly on the analysis of volume as a useful tool to confirm price action. He demonstrated how the Arms Index often pre-dicts moves in the underlying market and he hopes to extend his study shortly to analyse additional worldwide markets.

Robin Griffiths

Special Feature


Programme:

Presents

Cost per delegate: £1795 + VAT

A 2-day Automated Trading Workshop24 & 31 January 2007

London Chamber of Commerce, London EC4

www.ta-conferences.com +44 (0)20 7833 1441

[email protected]

This 2-day workshop will instruct delegates in all aspect of building an automated trading system. Beginning at an introductory level, the course will provide a practical and in-depth grounding in all aspects of system building including model development, data analysis, backtesting and optimisation. The course will cater to traders and fund managers of all asset classes and trading time scales who are looking to learn how to develop their own proprietary automated trading system.

Each delegate will also be equipped with a PC along with the appropriate charting, data analysis and programming software.

Day 1 (January 24): System building, data analysis, programming, optimisation and backtesting.

Day 2: (January 31):Portfolio selection, system testing, correlation, money management and portfolio enhancements

Course tutor:Nick Wesolowski

Techniques


TA: Can you give us an overview of the breakdown of theCOT report?

FU: The COT data represents the long and short positionsof its participants and is collected after the market close onTuesdays and released every Friday by the CommodityFutures Trading Commission (CFTC). There are three partic-ipants in futures: Commercial hedgers (producers and con-sumers), Non-Commercials (funds and large traders) andNon-Reported Participants (small traders and others).

TA: How do you use the data of these different participants?

FU: I use the Commitments of Traders data to determinewhat the underlying fundamentals of the market are doing. Ibelieve that the Commercials who deal in the cash marketson a daily basis know much more about the fundamentalsthan anyone else and they have the money and the motive topay the salaries of forecasters and analysts. The core of mysystem focuses on this information. I analyse both past andpresent COT reports to gain insight into the current condi-tion of the markets and what the commercials are anticipat-ing in the near future.

I use the COT data to determine if we should get into atrade. We then use technical analysis to determine our entryand stops. Our system tracks the supply and demand balanceby breaking down the open interest by individual participantand then tracking each participant's behaviour. There arethree participants in futures. The public, funds and commer-cial hedgers. Using a proprietary statistical formula we devel-oped, we are able to determine when the dominant marketparticipants (commercial institutions) are building statistical-ly significant positions. They represent the largest single com-ponent in the futures market and generally accumulate largepositions over a period of time (weeks to months). Our sys-tem, (the IMPA) identifies the markets that are most likely tobreak out to the upside or downside and trend significantly.We do not care if the commercials are long or short, or byhow much. We do care about the size of their net-positionhowever and how that relates to their normal size position.

TA: What about the Non-Commercials, the funds?

FU: The funds are the large speculators category of theCOT. We know something about this type of participant.They do not take or make delivery of the underlying physi-cals for example. They tend to follow and/or create the pricetrends. When prices are moving higher (or believed to bemoving higher) speculators will buy. Eventually the marketwill reach a point where the majority of speculative buyinghas occurred. At that point the market is also out of com-mercial balance. In other words, commercial producers areholding a substantially larger or smaller number of positionsthan the commercial consumers, resulting in a short or longnet position in the commercials. If that is indeed the

Floyd Upperman is recognized as a leading expert and analyst of the Commitments of Traders(COT) report and is the author of the book, "Commitments of Traders" (Wiley and Sons).Based in Ohio, he is an active futures trader using COT report data in his proprietary tradingsystem, Individual Market Participant Analysis (IMPA).

INTERPRETING THE COT REPORT

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Techniques


case, it's an indication that the market may be at or near amajor turning point. Now, if we have a large fund positionwithout the imbalance between the producers and con-sumers, then we may simply see a correction in the marketfollowed by a resumption of the previous trend. That occursa great deal of the time too.

TA: So the hedgers lead?

FU: Yes: the commercial participants (hedgers) tend to antic-ipate changing fundamental conditions before speculators.Cash (spot) prices may have already started changing toreflect changing fundamentals. This is something very fewsmall speculators in commodities understand or even follow.Fundamental changes in supply and demand typically surfacefirst in the cash markets. Hedging and bets are placed in thefutures. This is why we see large shifts in commercial posi-tions at market tops and bottoms. This is widely understoodas commercial producers and consumers obviously havemore information and knowledge about fundamentals.Smaller traders and speculators tend to focus primarily onprices and price patterns, reacting to changes in prices.Managed money (large speculators) tends to focus on bothfundamentals and price patterns and again look for situationswhere prices do not reflect fundamental conditions.

TA: How does this fit in with your trading approach?

FU: In our IMPA (Individual Market Participant Analysis)trading system we combine both fundamental and technicalanalysis. From my point of view it is imperative in a tradingsystem to use both forms of analysis. Fundamental analysisis your leading indicator (we use the COT data to determineif we should get into a trade), and technical analysis is usedfor entry and position management. A strategy that com-bines fundamental analysis with technical analysis is far supe-rior versus a strategy that focuses on price indicators or fun-damental information only. Trading opportunities exist whenthe true fundamentals are out of line with price. Once this isrealized, prices adjust as investors and traders begin recogniz-ing discrepancies between market prices and fundamentals.

TA: How does the proprietary statistical formula identifywhen the commercial institutions are building significantpositions?

FU: This is based on a normal distribution pattern. My pro-gram calculates the average size position for each group ofparticipants over a period of 5 years. That average is thenused to calculate the normal distribution for each group.Some additional programming is done to address extremeoutliers; addressing the outliers is important and impacts thenumber of statistical events in the future.

TA: How do you use the COT data to identify a change in

trend, for example? FU: We never know with 100% certainty when an old trendhas ended but, there are things we can look at to help usunderstand the overall likelihood that a change in trend isoccurring. First, the make up of the participant conditionsshould support it. That is, at a trend change from down toup, we ought to see a good number of speculative shorts inthe market (lots of records numbers). This is fuel for a turnup and new trend higher as speculative shorts must bebought back at some point. Thus, by monitoring and track-ing the positions in the market and sorting the positions byparticipant type we can make an assessment as to the condi-tion of the market based on the market positions and who'sholding the positions. Our IMPA trading system breaks theCOT data into commercial producers, commercial con-sumers, and funds and detects position imbalances that couldbe a forerunner of major trend changes.

TA: Are there any problems or drawbacks with relaying onCOT data?

FU: Maybe I should clear up a rumour first. There has beentalk that the COT report will be abolished. I have talked withthe CFTC about this and they made it clear to me that theydo not plan to discontinue the report. In fact they are plan-ning to expand the data published and improve the presenta-tion format.

As a result of the many new electronic exchanges that areappearing across the globe providing access to many of thesame markets traded in the US, there is some concern thatthe COT report may no longer capture as much of the totalactivity as it has in the past. This is because the CFTC onlyhas jurisdiction over trading on US exchanges. In addition,some markets are becoming available via equity markets (theETF in gold and silver for example). This too has generateda new kind of trading in the underlying futures via long onlyfund positions that must be established in order to managevarious ETF's and commodity indexes. This creates a uniqueparticipant position (unlike a traditional commercial or tradi-tional speculator). This type of participant doesn't care aboutmarket prices and simply needs to establish a number ofpositions to properly manage an index. Since the index posi-tions are long only positions they impact the commercialconsumer data (traditional commercial consumers). To helpaddress this issue we must focus more attention on commer-cial shorts now as well as this tends to be the most pure com-mercial measurement (true traditional commercial produc-ers).

Another potential problem is that commodities havebecome more attractive for large managed funds. These enti-ties gain exposure to commodities through SWAP dealers viathe OTC market. This particular trend has been the majorconcern regarding the COT report in recent years. The longpositions that are entered on behalf of pension funds (via anindex SWAP) end up being counted as commercial longs in

the COT report because the banks report the long positionsas hedged positions, not speculative. This is skewing thecommercial data. No one knows for sure (except the CFTC)just how much pension fund influence is occurring in com-modities. If this data were public I believe it would be moreof a headline in the media. The solution, which the CFTC tomy understanding is considering, is to create a new categoryof data in the COT report. The new category would be cre-ated to track the index fund positions and to preserve theoriginal commercial category (which should consist of com-mercial consumers and commercial producers) only.

TA: How have you gotten around the pension fund problem?

FU: We now have to focus on both commercial sides and notjust the net-commercial position. That means we focus onthe longs and shorts separately and apply separate indicatorsto each entity. While many if not most others that botherlooking at the COT are scrambling around to find a solutionto this issue, we've been prepared via our individual commer-cial consumer and commercial producer studies. In addition,since the index funds are long only funds, we know the com-mercial shorts continue to remain the purest position, whichmeans these positions (commercial shorts) are primarily pro-ducer positions. There is no contamination from the indexfunds as they are long only positions. Because of this issuehowever, we can no longer assume that all commercial longsare primarily commercial consumers. Thus, our traditionalmethod of using commercial consumer and commercial pro-ducer positions to understand, measure and track the balancebetween supply and demand (based on commercial hedging)also now has to be adjusted as well. The adjustment is notperfect because we are kept in the dark to the extent of theindex funds infiltrating the commercial long data. We justdon't know how much of the data consists of commercialindex funds and traditional commercial consumers. This is abig issue in the area of transparency as well.

TA: Which instruments of technical analysis do you use?

FU: A pattern I often use is the plunger pattern. Under theright conditions a plunger pattern can provide an excellentpoint of entry via the logical stop. The logical stop is whatthe plunger provides (the entry being defined by the stop).This may provide an entry into a short-term swing trade or alonger-term IMPA setup. Learning to recognize this is farmore important than being tossed a dozen recommendationsto choose from. I really can't stress that enough.

TA: What backtesting have you done on your system?

FU: I enter the data into my program and do some backtest-ing to see what the performance is. Where people run intoproblems is to develop a system based on backtesting. Inother words, they don't have an idea about the concept yet

and they go looking through the data to find some pattern.What that does is create curve fitting because now you foundsomething that is not really a concept, it is something thatjust existed in the data. There is no way to know for sure if itis going to repeat in the future.

TA: What are the pros and cons of using the COT dat a in atrading system?

FU: Roughly 85% or more of all trading systems are basedon some form of price analysis (technical analysis). Othertrading systems are based on fundamental data such as eco-nomic data, crop yields and so forth. While the COT reportis readily available, many traders do not completely under-stand all the benefits of tacking and thoroughly analyzing thisdata. I have developed a proprietary, automated computerprogram designed to analyze COT data. It should be remem-bered that COT data is more for position trading. We don'tget big position trade set-ups every day but when they dothey are significant. The pros of automation are that it keepsyou consistent in following an approach and it can help youkeep your losses under control. It also allows you to manageprofits. The cons are you can get stuck waiting for conditionsthat may not arrive. You might also miss a lot of tradesbecause some things might happen in the market that yoursystem might not pick up.

TA: Do you recommend buying a system off the shelf ?

FU: You must check it out first. You must make sure it is notcurve fitted. This is very important as most systems are curvefitted. You also want a system that you can adjust to yourposition size and fits your trading time scale. If you are trad-ing very short term you want a system that has signals everyday, if you are a position trader you want a system that waitsfor the correct set ups.

“I USE COT DATA TODETERMINE IF WE

SHOULD GET INTO ATRADE. WE THEN USE

TECHNICAL ANALYSIS TODETERMINE OUR ENTRY

AND STOPS”

Techniques


By Karsten Schröder & Florian Leder


In this paper we develop a trend indi-cator (TI) which overcomes some ofthe weaknesses of other trend con-cepts. We use this trend indicator toexplore the close but crucially differentrelationship between trends and volatil-ity. We find that in periods of lowvolatility there are not many trends, butin highly volatile markets trends are stillno stronger than in medium volatileperiods.

We then use the trend indicator con-cept to examine simple trend followingsystems (in this case a simple MACDsystem). We find that we can use the TIconcept to determine the underlyingtrading timeframe that is used in atrend following system. Finally, werepeat this procedure for a real FXtrend following fund and find the twotimeframes that it operates on.

Existing conceptsThere are different ways to describe thetrend characteristics of price move-

ments. A common indicator is theAverage Directional Indicator (ADX)which describes a trading or trendingmarket environment. The ADX is intu-itively a measure of the differencebetween the increase of daily highs andthe decrease of daily lows over a cer-tain timeframe. One downside of theADX is that it may indicate a trendingmarket where the market is showinghigher volatility but no real trend.

The ADX may indicate a strongupward trend when it is actually moreof range trading market. In order toavoid this drawback, one could use theabsolute value of the slope of the lin-ear regression as a measure of trend.This alone would be misleading, how-ever, since there may be an upwardtrend in the regression line but notrend. We therefore propose takingaccount of the volatility around thetrend, measured for example by thestandard error of the slope. These twoaspects are incorporated into our pro-posed trend indicator (TI). This indica-tor will be defined in such a way that itcan be calculated easily over anytimescale.

Trend indicator: TIWe propose a trend indicator (TI) fortrends of n minutes. The TI (n) for aspecific time t is calculated by carryingout a linear regression on the n minutesaround t (n/2 before t and n/2 after),with the following variables;

n: time scale in minutes (e.g. 30 for 30minutes, 1440 for 1 day,…)t: point in time at which to calculatethe indicator (e.g. 14/2/2002 16:45) a: the slope of the linear regressionfrom t-n/2 to t+n/2

?sa: the error of this regressionR: a small summand is added to pre-vent that single outliers (with nearlyno error of the slope) disturbing theresult. In this paper R=10-11

The TI over n minutes at t is defined as:

Volatility and trendsOften trend and volatility are referredto in the same context and even used assynonyms for each other. It is normallyassumed that one is strongly correlatedto the other. Nonetheless it can beobserved that despite high volatility,strong trends cannot always be found.This calls into doubt the belief in ahigh correlation between trend andvolatility.

In this section we will explore therelationship between trends and volatil-ity - using our trend indicator as a proxyfor trends. We have used EUR/USDminute price data between 1990 and2004. The 1 day (1440 minute) volatili-ty and 1 day TI's were calculated andplotted in the frequency graph (Figure1). Different colours represent the den-sity or frequency of occurrence. Due tothe extreme range of data, the naturallogarithm of density is used.

Figure 1 is challenging to interpret. Inorder to unravel its meaning, the graphwill be modified. For a particular levelof volatility all the data is normalisedand the relative frequencies are calcu-lated.

This procedure is carried out for allvolatilities and the results are displayed

EXAMINING TREND CHARACTERISTICS

Techniques

Many funds in the man-aged futures and themacro space try to gen-

erate profits by identifying trendsand taking the appropriate shortor long positions. Strong marketmoves usually lead to profitabletrades while a narrow-range trad-ing market environment createsharder conditions to generateprofitable transactions. In order todescribe this behaviour quantita-tively, an appropriate index oftrend indication needs to be iden-tified as existing trend indicatorconcepts have their limitations.

in Figure 2. Figure 2 shows that for lowvolatility there is a correlation betweenvolatility and the TI. Above approxi-mately 0.4%, the average TI seems tostay constant. If one plots the averageTI versus volatility this is even moreobvious (Figure 3).

For low volatility the expected valueof the trend indicator increases asvolatility increases. But for high valuesof volatility this relation doesn't holdanymore. Above about 0.5%, theexpected value of the trend indicator isbasically independent of volatility.

Therefore at volatilities above 0.5% theknowledge of the exact volatility holdsno more information than the fact thatit is higher than 0.5%. It should benoted however that daily volatilityabove 0.5% rarely happens.

A simple trend following systemHaving explored the relationshipbetween trends and volatility, we canstart to use the trend indicator to inves-tigate simple trend following systems.Specifically, we will use a relatively stan-dard smoothed moving average conver-gence divergence indicator (MACD) asan example of a simple trend followingsystem. Clearly we would expect theMACD indicator to be correlated withthe TI if both are on a similar timescale. The MACD indicator is the dif-ference between two moving averages(with periods t1 and t2) and this resultis smoothed (over a third time periodt3).

Let us consider two MACD systemson EUR/USD exchange rate data.

MACD 1: Moving averages over 16 and32 hours and a smoothing time of 10hours.

MACD 2: Moving averages over 5 and10 days and a smoothing time of 3days.

Figure 4 shows the correlation of theMACD1 and MACD2 indicators withTI (n) (the trend indicator with a timeframe of n) at the y-axis, and n on thex-axis.

The chart shows a peak of correlationbetween MACD1 and TI (n) at about24 hours, and a peak for MACD2 atbetween 4 and 10 days. Thus in princi-ple, this sort of analysis can show usthe underlying timeframe of trends thata particular systematic system isdesigned to pick up. MACD1, by virtueof its parameterisation is designed topick up 1 day trends, and MACD2 isdesigned to pick up 1-2 week trends. Inthe next section, we will use this tech-nique to reverse engineer the broadtrend following parameters of a partic-ular fund.

Figure 1. Plot of logarithmic Density

Figure 2. Volatility of Relative Frequency of Trend Indicator→→


Techniques


Reverse engineeringWe have seen recently in the trend fol-lowing space new strategies that claim

to operate on short time frames. Itwould be interesting for an investor toreconfirm independently the time

frame that a fund operates on. Usingthe analysis of the previous section, wecan try to uncover the underlying trad-ing timescale of a fund.

First of all, we need to identify a fundthat claims to operate on particulartime frames. Often this information ishard to obtain. A prerequisite for aninvestor to run this analysis is to havereturn information on astrategy/instrument level. Most CTAsinvest in a number of markets; theunderlying instrument level informa-tion is usually proprietary and rarelydisclosed. In this case study, a specificfund has been selected that claims totrade on multiple timeframes in the FXmarkets. The correlation of the fund'smonthly returns with the TI of theEUR/USD exchange rate over differ-ent time scales is plotted in Figure 5using the data from 1/2002 till12/2004. Figure 5 shows strong corre-lation in the two day time frame and atthe longer end, a finding which is in linewith the fund’s communicated srategy.

ConclusionThrough this research paper we haveconsidered the nature of trends andhow to develop an efficient indicator ofthem. Our proposed indicator was usedto probe the relationship betweentrends and volatility - two concepts thatare usually held to be interchangeable -and we found that one does not neces-sarily imply the other. We then consid-ered a simple trend following systemand used the trend indicator to deter-mine the underlying timeframes ofthese trend following systems. Finally,this was repeated for a real fund wherewe found that the fund operated withtwo primary trading time frames. Webelieve that the trend indicator conceptcan be a powerful tool to analyse sys-tematic traders.

Karsten Schroeder is portfolio man-ager and Florian Leder is quantita-tive analyst at Amplitude Capital inLondon.

Figure 3. Average Trend Indicator versus Volatility

Figure 4. Correlation of the MACD with TI

Figure 5. Correlation versus Time Scale

Techniques


Techniques

Intermarket analysisBased on their research into intermar-ket relationships, it is possible thatanalysis on asset co-movement can takea new direction by creating proprietarycross market indicators. These indica-tors could be defined as an attempt toinvestigate the strength of externalforces that affect internal marketdynamics by employing a combinationof auxiliary (related) markets, rather

than relying on the assumption thatthere is a simple one-to-one cause andeffect relationship between the variousmarkets. These proprietary marketcombinations are able to synthesize dis-parate data by detecting and quantifyingsubtle but complex relationshipsbetween numerous related markets.

These auxiliary markets are the mar-kets employed to form a cross-marketindicator (CMI) in order to expose therelative attractiveness of the TargetMarket. The auxiliary markets canbelong to the same (CMI Type A) ordifferent asset classes (CMI Type B).The two examples given below rely onthe multiplication of the two auxiliarymarkets to obtain the CMI. To obtainthe CMIs for other target markets itmay be necessary to add/subtract ordivide the auxiliary components.

Example 1: T-bond futuresAn example of a cross market indicator(type A) is given in Figure 1. The targetmarket is the 10-year Treasury bondfuture. The auxiliary markets are cop-per and crude oil. We will be employingthese commodities in order to gaugetheir inflationary effects on the bondmarket.

The CMI is created by the multiplica-tion of the price of copper by the priceof crude oil (both continuous futures).The underlying logic behind employinga combination of the two commoditiesis that rather than relying on single assetcomparisons between bonds and crudeor bonds and copper, we include multi-ple inputs to provide a tree of indica-tors, rather than swinging on a single-indicator branch. The weight of theevidence from all indicators dic-tates whether it is time to raise

Martin Pring and JohnMurphy have shed lighton inter-market analysis

by establishing the connectionbetween the various asset classes,their rotational sequence andmore importantly on why marketsare so highly integrated duringperiods of relative stability.

CROSS MARKET INDICATORS by Alex Spiroglou

→→

Figure 1. Top: 10-year T-bond future (1989 - 2006). Bottom: Copper v. crude 6-month ROC

HOW COMBINING DIFFERENT MARKET PRICES CAN PROVIDE AN INDICATOR FOR US TREASURYFUTURES AND THE DOW COMMODITY INDEX.

Techniques


exposure or cut back. As the bottompane in Figure 1 suggests, when the rateof change of the indicator reaches+100% on a 6 month basis, the bondmarket forms a significant bottom(1989, 1990, 1994, 2000, 2004 and2006).

Example 2: Dow Jones AIGcommodity index An example of CMI (type B) is given inFigure 2. The target market is theDJAIG Index (cash). The auxiliarymarkets are 13wk T-bill yields and theUS Dollar Index. The underlying logicbehind the construction (T-bills multi-plied by USD Index) of this cross mar-ket indicator is that a surging commod-ity market eventually "convinces" theFed to push rates upwards. At the sametime however, a rising commodity mar-ket coincides with a falling (inflation-ary) USD. Eventually higher rateschoke economic demand (commoditiesfall) and also pull the US dollar upwardsby increasing its attractiveness vis-à-visother currencies. By synthesizing 13wkyields and the USD we are able toobtain a dual-market view of the com-modity market and quantify relation-

ships between numerous related mar-kets that exhibit nonlinear interdepend-ence characteristics.

Research and trading applicationsThe study of co-movement acrossasset classes and understanding thebehaviour of international financialmarket linkages is extremely importantfor many other reasons other than justoutright directional trading. Cross assetanalysis proves useful in:

(a) Portfolio optimization and assetallocation decisions. The study of howcross-market correlations affect thevolatilities of portfolios and impact onasset allocation policy. Portfolio opti-mization and international diversifica-tion hinges on the concept of correla-tion.

(b) Derivative pricing. Cross-marketcorrelations are important for the pric-ing of derivative securities, especiallyexotic options, whose payoffs dependon more than one underlying assetprice.

(c) Risk management. Correlation is acentral issue in risk management andhedging.

(d) Correlation / Relative ValueTrading.

Cross asset research is an imperativein today's world as it can provide a mul-tidimensional trading structure byleveraging an existing analytical frame-work. This results in the expansion ofthe range of trading opportunities andan amplified level of observation ofcross market correlation causalities.More importantly it is an independentsource of alpha; alphas from wellknown inefficiencies diminish by imita-tion. Since cross-market research isunique, imitation risk is small and con-sequently lengthens the lifespan of thestrategies, thereby avoiding "strategycontagion" effects.

Alex Spiroglou is an institutionaltrader and has created an extensivedatabase of proprietary cross assetindicators, employed as a decision

Figure 2. Top: DJAIG (1994 - 2006). Bottom: combination of 13wk yields and USD Index

e-yield

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Techniques



Techniques

Michael Cooper, JohnMcConnell, and AlexeiOvtchinnikov consider

whether the performance of the U.S.stock market in January is a good indi-cator of its performance in the remain-ing eleven months of the year. Theirpaper, which is entitled "The OtherJanuary Effect" has recently been pub-lished in the Journal of FinancialEconomics.

Published streetlore regarding thevirtues of returns in January as a pre-dictor of returns during the remainderof the year dates back to the early1970s. Publications such as YaleHirsch's Stock Traders Almanac makestatements such as "we doubt that anytechnique ever devised has been soremarkably accurate as the JanuaryBarometer." The authors give the term"the other January effect" to this phe-nomena to distinguish it from the well-known "January effect" (where smalland low priced firms that have per-formed poorly in the prior year out-perform the market in January), but it isreferred to in this article as the "JanuaryBarometer" - its more familiar marketname.

Cooper et al's core tests use U.S.value-weighted (VW) and equallyweighted (EW) indices over the 1940-2003 period. They find, using the VWindex, that when the January return ispositive the VW market return over the

next 11 months averages 14.8%, butwhen the January return is negative thereturn over the next 11 months aver-ages 2.92%, giving rise to a spread ofalmost 12%. Applying the samemethodology to the EW index resultsin an even bigger spread of 18%.

The authors conduct numerousrobustness checks around their coreresults. They establish that the JanuaryBarometer occurs over the period1825-2003 and in all sub-periods exceptthe market crash and Depressiondecade of 1929-1939. They verify thatthe January Barometer is not subsumedby well-known macroeconomic predic-tors of stock returns such as dividendyields, default spreads, term-spreads,and short-term interest rates. Investorsentiment has been shown to predictstock returns and so they check that theJanuary Barometer is not being drivenby positive investor sentiment inJanuary spilling over to subsequentmonths. They find no evidence of this.

The authors show that the JanuaryBarometer is not explained by the"Presidential Puzzle", which refers tothe general out-performance of thestock market under Democrat presi-dents, or variations in the businesscycle. Finally, Cooper et al prove thatthe January Barometer is a good predic-tor of returns for both small and largeand growth and value stocks.

There are several avenues for→→

RESEARCH UPDATE by Ben Marshall

I) TESTING THE JANUARY BAROMETERII) BIG SPORTING EVENTS, INVESTOR SENTIMENT

AND THE STOCK MARKET

Latest research strongly supports the existence of the January Barometerbut fails to find any statistical evidence to support existing explanations.And when a country loses a big football game, research confirms that itsstock market is also likely to suffer.


Techniques

future research into the JanuaryBarometer. Researchers might like toinvestigate whether this effect is a goodpredictor of returns in other stock mar-kets around the world. Further workon whether behavioural biases are driv-ing this effect is also warranted.

In a paper entitled "Sports Sentimentand Stock Returns", which is soon tobe published in the Journal of Finance,Alex Edmans of the Sloane School ofManagement at MIT, Diego Garcia ofthe Tuck School of Business atDartmouth, and Oyvind Norli of theNorwegian School of Management,investigate the stock market reaction tosudden changes in investor mood. Theresearchers use soccer results and othersporting event outcomes to proxy forinvestor mood.

Their research builds on the substan-tial body of work which shows vari-ables that influence investor mood canbe used as predictors of stock returns.Kamstra, Kramer, and Levi (2000) con-sider the impact of disruptions to sleeppatterns caused by daylight saving,Hirshleifer and Shumway (2003) studythe impact of sunshine, while Cao andWei (2005) examine temperature.

Using a cross-section of 39 countries,the authors document a strong negativestock market reaction to losses bynational soccer teams. The size of thedrop in stock markets exceeds 7% inmonthly terms. This effect is morepronounced in countries where socceris especially important and for more

important games such as World Cupelimination matches. Edmans et al finda corresponding loss effect for interna-tional cricket, rugby, and basketballgames. There is no evidence of posi-tive reactions to wins in any of thesesports.

The sporting loss effect is concentrat-ed in Western European countries withdeveloped stock markets, but there areseveral reasons why this effect might bedifficult to exploit via a trading strategy.Firstly, the sporting events that lead tothese losses occur relatively infrequent-ly. Secondly, the losses are concentrat-ed in small stocks, which tend to bemore illiquid, so it may be impossible toshort-sell these stocks at reasonablecost.

References:Cao, N and Wei, J. (2005). Stock marketreturns: A note on the temperatureanomaly. Journal of Banking andFinance, 29, 1559-1573.

Cooper, M.J., McConnell, J.J., andOvtchinnikov, A.V. (2006). The otherJanuary effect. Journal of FinancialEconomics, 82(2), 315-341.

Edmans, A., Garcia, D. and Norli, O.(2006. Sports sentiment and stockreturns. Journal of Finance - forthcom-ing.

Hirsch, T. (1974). Stock trader'salmanac. The Hirsch Orgnaisation,

Nyack, NJ.

Hirschleifer, D. and Shumway, T.(2003). Good day sunshine: Stockreturns and the weather. Journal ofFinance, 58, 1009-1032.Kamstra, M.J., Kramer, L.A. and Levi,

M.D. (2003). Winter blues: A SADstock market cycle. AmericanEconomic Review, 93, 324-343.

Ben Marshall is a Senior Lecturer inthe Department of Finance,Banking and Property, MasseyUniversity, New Zealand.([email protected]). Hisresearch interests include investi-gating the profitability of technical

“WE FIND THAT JANUARY STOCKRETURNS ARE A SURPRISINGLY ROBUSTPREDICTOR OF MARKET RETURNS OVER

THE FOLLOWING 11 MONTHS.”(COOPER ET AL, 2006)

www.tradingtechnologies.com

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Harris Brumfield, CEOVeteran Trader

JJiimm RRooggeerrss talks to theTechnical Analyst

Interview



Interview

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TA: How much time do you spend actively trading thesedays?

JR: Very little. I've never been an active trader as such.Whatever success I've had has come from buying things thatare very cheap and holding on to them, for the long-term.I'm a stockbroker's nightmare.

TA: Long-term? Do you mean two or three years?

JR: I'm talking decades for both long positions. I don't liketo sell at all. I've recently sold out of a lot of commodities,not because I wanted to but because I was being so overexploited. Otherwise, I haven't sold for 15 years or so.

TA: Would you describe yourself as a contrarian?

JR: No, not at all, but I'm often described that way. I look forthings that are cheap where there is a fundamental changetaking place. I don't buy or sell just because other people aredoing the opposite. The difference is that most people buyhigh because they think it is going to go higher. I buy longbefore that point is reached. However, I can take a differentview on some things. India is one of them. If I'm wrong itwill one of the great investment opportunity of our lifetimebut I wouldn't put a nickel into India. It's almost impossibleto do business there; they have very poor infrastructure, loweducation levels and a very anti-capitalist attitude. The primeminister seems happy with the way things are so it's difficultto see where further changes will come from.

TA: But you believe, generally speaking, that most players areon the wrong side of the market?

JR: That's not just my opinion, that's been proven a milliontimes by academics, researchers and the like. Its commonknowledge that index investing consistently outperforms anykind of active management.

TA: You're obviously known as a commodities investor. Arewe anywhere approaching a bubble scenario in the commodi-ties markets at the moment?

JR: The people who are saying we are in a bubble are thesame guys who couldn't spell the word 'commodities' twoyears ago and failed to spot the bull market. If you look atthe 40 or 50 commodities, most of them are 60-90% belowtheir all time highs. How can this be a bubble? Very few com-modities have reached an all time high and even those thathave, when you adjust the price for inflation, they still have along way to go. For stocks and bonds there are 70,000 mutu-al funds but for commodities there are fewer than 10. A bub-ble exists when everybody is shrieking to buy and the WallStreet Journal has a whole section devoted to commodities.Right now it has eight paragraphs devoted to commodities.When it makes the front page in capital letters, then you havea bubble. We are nowhere near that with copper, maize,sugar, rubber and the rest. Coffee is 70% below its all timehigh and that isn't even adjusted for inflation. This is not abubble.

TA: But if commodities were so obviously cheap in the latenineties, why weren't people buying sooner?

JR: Most people just didn't spot the market and everybody

THE TECHNICAL ANALYST TALKS TO...

Jim Rogers runs a fund out of his New York office specialising in commodities investing.Basing his investment decisions almost entirely on underlying long-term fundamentals, he isfamous for embarking on year long world trips to far flung copper mines and oil fields aroundthe world in order to discover the forces that may impact on future market prices. He talks tothe Technical Analyst about his trading style, asset bubbles and the outlook for commodities.

“A BUBBLE EXISTS WHEN EVERYBODY IS SHRIEKING TO BUYAND THE WALL STREET JOURNALHAS A WHOLE SECTION DEVOTEDTO COMMODITIES. RIGHT NOW IT

HAS EIGHT PARAGRAPHS DEVOTEDTO COMMODITIES.”


Interview

was excited by the dotcom boom. If you watched CNBC inthe late 90s all everybody was talking about was the techstocks and dotcoms. We have just said that most investors getit wrong. This proves my point. If I mentioned the futuregrowth of China and its potential impact on the demand forcommodities back then no one was interested. Why? I don'tknow. A while back I was shorting Fannie Mae and housingstocks. Everybody called me a damn fool but look nowwhat's happened to the market.

TA: Most hedge funds have failed to outperform the marketand yet the market continues to expand, the increased num-ber of failures not withstanding. How can this be explained?

JR: Fees remain high enough that the incentive for newfunds to set up remains high. We all know that 80% of man-agers under perform the index but the search goes on for theother 20%. I wouldn't put a penny into a hedge fund unlessI knew them very well. There are 20,000 hedge funds inworld now but there aren't 20,000 smart managers out thereso something must be wrong.

TA: Are there any fund managers in the UK that you admire?

JR: There are some managers in the UK with an excellenttrack record and this has to be admired. Whether they will beadmired going forward is another matter. Paul Tudor Jones inparticular is a fantastic trader and is spectacularly successfulat what he does. If I worked for him, I wouldn't last five min-utes.

TA: What qualities would you look for if you were employ-ing a fund manager?

JR: Scepticism. You need the ability to question everything.It has all happened before so you need knowledge of histo-ry. I guess this is where technical analysis has a part to play.

TA: Are there any particular commodities markets you arebullish about at the moment?

JR: If you look at the equity bull market between 1980 and2000, not everything went up every day. Some sectors madetheir highs in 1996 and others before and after. Technicalanalysts know better than anybody that there are many differ-ent patterns going on at any one time so you need to take alonger term view. I'm more bullish on agriculture than saybase metals. Some metals have gone up 600% or so, whereascotton or sugar still have a long way to go and so I would sug-gest there are greater opportunities in those areas.

TA: What technical indicators, if any, have you used to formyour views?


JR: I haven't looked at a commodities chart for a long timeand I'm not in any way a technician. However, there aretraders out there who have come to the same view as meusing nothing but technical analysis. We come to the sameconclusion but in different ways. If they make money thenwho cares how someone comes to their view. I suppose youhave to remember that a fundamental trader and technicaltrader can both come to exactly the same outlook for a par-

ticular market using completely different data.

TA: You have expressed strong views on the US Fed before.How do you compare Greenspan and Bernanke?

JR: What Greenspan did to the stock market was terrible. Hecaused a technology bubble, then a housing bubble and thena consumption bubble. The man has never been right aboutanything. I'm not alone in this view incidentally. There havebeen a couple of books published recently that say the sameas me. If you look back at Greenspan's history you will seethat he has failed at everything he has ever done. He is essen-tially a bureaucrat with little market experience. Not the idealperson to put in charge of the Fed. As for Bernanke, he is anamateur who doesn't understand the markets or the econo-my. His whole intellectual career has been based on learninghow to print money. He is eager to print money and he isgoing to be a disaster. I would even say he could lead to thedemise of the Fed and the dollar. The US has had three cen-tral banks in its history; the first two failed and this one willfail too.

TA: Who would you put in the Fed?

JR: I'd abolish the Fed. Many other countries have done finewithout a central bank. It is only very recently that centralbanks have adopted this halo. They have never been verygood at managing the economy and this hasn't changed.

TA: Turning to everybody's favourite talking point, what doyou see as the major risks in China at the moment?

JR: There are lots of risks. The government are doing theirbest to cool things off and they need to, especially with theconstruction market. This will lead to a slowdown and theywill have their setbacks. However, lets suppose China isgrowing at 10% a year (and I don't trust their figures anymore than I trust US data) then even if growth slows to 2%,demand, especially commodities, from their huge populationwill still be significant. In the last commodities bull market,

Asia and especially China didn't even figure in the equation.This time round things are different and there are 3 billionextra people to take into account when assessing futuredemand for raw materials. What is more, supply is nowunder serious pressure.

TA: How do you see the dollar moving ahead?

JR: I urge everyone to sell dollars because the currency is inserious trouble and Bernanke will contribute to this. I shouldemphasise however that I'm not talking about this week butthe bigger picture over the next decade or so. It's a terriblyflawed currency now as it's losing its place as the worldreserve currency as sterling did in the past.

TA: What's you outlook for the US markets?

JR: The US is overextended, politically, militarily, financially,economically and there are bad days ahead.

TA: Do you have any more trips planned?

JR: Not at the moment. I may move to Asia in the future butmy trips to Asia and Africa etc are done for business reasonsas I always come back with a clearer idea of where to put mymoney.

Editor's note: sections of this interview were alsorecently published in Monetary Policy Review.

“I WOULDN’T PUT A PENNY INTO A HEDGE FUNDUNLESS I KNEW THEM VERY WELL. THERE MUST

BE 20,000 HEDGE FUNDS...BUT THERE AREN’T20,000 SMART MANAGERS OUT THERE SO

SOMETHING MUST BE WRONG.”

Interview


Software

AUTOMATED TRADING WITHINTERBANK FX TRADER 4

The trading industry has longfocused on providing their cus-tomers with strategies. Take any

major trading software and you willfind a slew of "plug-ins" and "add-ons", with the names of Elliott Wave,Bollinger Bands, candlestick patternsand many others. These add-ons willdisplay signals and sometimes recom-mendations on the end user's charts.One still had to pull the trigger; theprogram went only as far as telling youwhat to trade, what stop losses andprofit targets to use, but it never actual-ly executed the trade. In the last fewyears more attention has been given toautomated trading but only a handfulof trading platforms offer this technol-ogy today. One such is Interbank FXtrader 4.

What is automated trading?Automated trading is the ability to letyour computer place trades based onyour strategy. In Interbank FX trader 4,automated trading is done throughwhat we call an "Expert Advisor". Anexpert advisor (expert) is the programyou attach to a chart to execute tradesfor you. The expert has to be pro-grammed. However, an expert advisorwill not modify a stop loss because itbelieves the market will turn aroundand try to give its trade one last chance.It won't modify the profit target of atrade because suddenly it thinks it canmake more money. The execution isdone within seconds; it simply sees asignal and places the trade.

There are other interesting facets toautomated trading. It is not just aboutemotions. If you have been trading FX,with an expert attached to your chartyou can set a trailing stop or a stop lossand a profit target. This brings upanother advantage of automation; the24 hour market: your strategy could

trigger many signals and make profitduring times you could not trade atbefore.

Scanning opportunitiesAnother interesting benefit is that youcan program your expert to scan foropportunities on any and all availableinstruments and periodicities. Nolonger will you need to open 15 chartsto make sure you don't miss out on agood trade, the expert can make sureyou see any available opportunity andtrade them for you.

Automated trading has also broughtup new strategies that could not havebeen profitable in the past due tohuman limitations. In theory, if you hada strategy with 51% accuracy and youtraded for small profits/losses (a fewpips, and I'm not referring to scalpinghere) and traded very often (hundredsof trades a day) then you would in the-ory make money every day. Only acomputer program could execute hun-

dreds of trades every day.

How to write an Expert Advisor?How does one write an automatedstrategy? A big mistake made by tradersis to look for an expert that was creat-ed by some other party and try to see ifit makes money or matches their ownstrategy. I personally think of an auto-mated expert not as my replacement ora money making machine but more asmy backup or substitute. If I can't tradefor any reason then I will let my substi-tute go in for me.

Before using an automated system,experience of discretionary trading isrequired in order to understand the sig-nals produce by automation. An experi-enced trader will also be familiar withseveral indicators; in effect they arealready using a system but automationrequires programming. What most pro-grammers would tell anyone interestedin automating their strategy is to writedown why they placed a trade on paper.

Software


Keep a journal of your trades formaybe a month. See what trades weregood, which were not but most impor-tantly, why a trade was entered. Whatdid the user see on the chart that madehim/her place a trade and set the stoplosses and profit target as they did.?

The programming languages are pow-erful and very flexible. You can decideto only trade during specific hours orspecific days. Some people have createdexperts based on moon cycles; nothingis impossible as long as you can specif-ically define it. Of late, "news trading"has been very popular and some pro-grammers have created experts to reada text file containing dates and time ofnews announcements and used that totrigger trades. It can be very complexbut it is my belief that most strategiescan be programmed.

How to test the expert advisor?Many platforms will provide you with a"back tester" or a "simulator" of somesort. In my opinion these should becalled "debuggers" but I will come backto this later. So you run your simulationand get a report. Regardless of whetheror not you have made profit, I encour-age you to only look at the trades. Didit place the entry and exit pointsaccording to your criteria? If not,spend some time adjusting the code tofix the errors, regardless of profit. Thisis another pitfall common to program-mers - curve fitting. Do not get lured bysome extraordinary results; stay thecourse and stick to your original design.The same goes with bad results; do notjust give up on your expert advisor

because the results look poor. At thispoint in the design process you shouldonly focus on getting entry and exit sig-nals to match your original strategy,nothing else. Do not waste your timeoptimizing and curve fitting.

No matter how great a platformclaims their back testing or simulationsto be, nothing will be as good as thereal thing. Run the strategy live on adummy account. That is why I ask peo-ple not to waste time curve fitting theirsystems and why I call the "back tester"the debugger because its only useshould be to check that the signals areaccurate. The only people who careabout simulation results are businesseswho are trying to sell you an add-onand are trying to prove it works.

How to use an expert advisor onthe Interbank FX Trader 4 plat-form?Expert Advisors are located in theNavigator window. It should be on theleft side of the platform window. Ifyou do not see it, click on "View" andthen "Navigator". In this window youshould see 5 groups (Accounts,Indicators, Expert Advisors, CustomIndicators and Scripts). Click on theplus sign to the left of expert advisor toexpand the group and see the list ofexperts available to you. To apply anexpert to a chart you simply need todrag and drop one of the experts fromthe navigator list to a chart.

A pop up window will then appear.You will be asked whether to placeboth Long and Short trades triggered.There will be options about alerts that

are self explanatorybut the mostimportant input tolook for is "AllowLive Trading" and"Ask ManualConfirmation". Inorder for yourexpert to executetrades live you haveto check "AllowLive Trading". Askmanual confirma-tion should be

unchecked; however you could use thisoption for debugging and testing. Nowon your chart, in the top right corneryou should see the name the expert youattached and most likely a cross (Youwill see a cross, a happy or a sad smi-ley). The cross means that automatedtrading was not enabled. To enable it,click on Tools, Options, and ExpertAdvisors and check the very first boxtitled: Enable Expert Advisors. Nowyou should see a happy smiley next tothe name of the expert. This meansthat now your expert is running andwill place trades. If you see a sad smileyit means you did not check the "AllowLive Trading" option.

What can I write with theMetaEditor? What are the limita-tions?The programming language used inInterbank FX Trader 4, is similar to C.Any C based programmer will be ableto pick up the language fairly quickly. Itis very powerful and flexible. I have yetto run into something that I could notaccomplish using functions alreadybuilt in the language and best of all Iknow that if I ever ran into such limita-tions I can create my own set of toolsin a Dynamic Link Library and importthem.

I have created an expert template tohelp newcomers program their firstexpert advisor. It can be found onwww.interbankfx.com; click on Forum,go to our tools library and select theCode Snippet index, there pick"Patrick's Expert Template". There isalso a training video on how to use thetemplate in our Education Libraryforum. With the Interbank FX frame-work, users only need to come up withthe logic for the entry and exit signalsto reap the benefits of automated trad-ing.

Patrick Nouvion is a software engi-neer at Interbank FX.

Figure 2.

Mapping the Markets By Robin Griffiths and Deborah Owen

The Bloomberg PressISBN: 1861979371224 pages. £20

Mapping the Markets can be purchased from the TechnicalAnalysis bookshop. To orderplease call 01730 233870 andquote "The Technical AnalystMagazine".

This book is the joint venture of two well known figures in the world oftechnical research. Deborah Owen runs a technical research house andedits the UK Society of Technical Analyst's Journal. Robin Griffiths is a

former head of technical analysis at HSBC and is now head of asset allocationat Rathbones, a private wealth management house in London. Between them,they have written an unusual book that combines many disparate elements ofanalysis into a convincing and highly readable overview of the factors impactingon medium and longer term stock market trends.

Griffiths has a formidable reputation in the technical analysis community andspeaks regularly on TA and asset allocation at various financial conferencesaround the world. His analytical approach is unique and highly persuasive, com-bining both technical and fundamental techniques into a strategy he calls"Mapping the Markets". Moreover Griffiths always has something new and orig-inal to say and his recent speeches have drawn attention to, amongst otherthings, the importance of Canada's oil tar sands, global demographics and theascent of the Indian economy in assessing a longer term outlook for stocks. Thisnew book expands on all this with invaluable contributions from Owen.

So this is not really a book about technical analysis: whilst the book containssome familiar descriptions of standard TA techniques, you will have to searchhard to find any publication that explains descending triangles on one page andlater goes on to discuss the possible mining of Helium isotopes on the moon asa source of nuclear fuel for future generations. But this is the strength of thebook and illustrates the foresight of the authors in looking beyond the merelyprosaic in identifying long term trends. For example, demographics is a topicthat receives less attention than it deserves. As the authors explain, the futuredemographic profile of most western economies, being highly skewed towardsolder age and retirement, presents a potentially bleak outlook for economicgrowth and productivity in the years ahead. This in turn suggests that stockmar-kets will struggle to find sources of growth. Meanwhile, China and India willenjoy demographics that favour a young and productive population and so theirmarkets will benefit accordingly. However, it is not all doom and gloom for thedeveloped economies. As Griffiths has often explained, immigration may be theanswer for western economies, particularly the US, and his outlook for the nextdecade or so suggests US stocks will continue to lead the way in the global mar-ket.

The author's appreciation of the importance of economic history in lookingahead combined with the selective use of practical and proven technical tradingtechniques make this an important publication for anyone involved in asset allo-cation and the equity markets.

MAPPING THE MARKETS


Books

HedgehoggingBarton Biggs Wiley and Sons ISBN: 0471771910

Commodities Rising Jeffrey M. ChristianWiley and Sons ISBN: 0471772255

Future Storm:The Dynamics Unlocking the FutureWilliam Houston and Robin GriffithsHarriman House ISBN: 1897597983

Technical AnalysisCharles D. KirkpatrickFT Prentice HallISBN: 0131531131

Master Traders Fari Hamzei and Steve ShobinWiley and Sons ISBN: 0471790621

Trend Trading Kedrick BrownJohn Wiley and Sons ISBN: 0471980218

Technical Analysis of the Currency MarketBoris SchlossbergJohn Wiley and Sons ISBN: 0471745936

Long/Short Market Dynamics:Trading Strategies for Today's MarketsClive Corcoran John Wiley and Sons ISBN: 047005728 9

Commitments of TradersFloyd UppermanJohn Wiley and Sons ISBN: 047171965X

Entries and Exits:A Visit to 16 Trading RoomsAlexander ElderJohn Wiley and Sons ISBN: 0471678058

Beat the Odds in Forex Trading:How to Identify and Profit from High Percentage Market Patterns I. R. ToshchakovJohn Wiley and SonsISBN: 0471933317

Market Wizards:Interviews with Top TradersJack SchwagerMarketplace BooksISBN: 0887306101

Investment MegatrendsBob FroehlichJohn Wiley and Sons ISBN: 0471769991

RECENTLY PUBLISHED / COMING SOON


Books

The logic of a good exit strategyRobin Griffiths, one of the city's toptechnical experts, notes, "Once you'rein, you're in…what makes you themoney then is how you see your tradeto completion." While good entrystrategies are primarily designed tosteer a trade in the right direction, thetruth is that the outcome of everytrade, thereafter, is dependent on theexit strategy. A trade, entered in a time-ly manner but exited poorly will, moreoften than not, result in a loss.Conversely, a poorly entered trade canstill be salvaged to yield a profit using agood exit strategy.

Charles Dow, early editor of the WallStreet Journal and founder of the DowJones indices, left us with invaluableinvesting lessons a century ago.According to Dow, "The [best] rule isto cut losses short but let profits run."While it sounds easy to do, traders, whoare firstly humans, are at the mercy ofpowerful emotions which override thislogical dictate. Instead they end upclutching onto losing positions in thehope that they will reverse and takingprofits as soon as they crystallise in the

fear that they could turn into losses. Agood exit strategy attempts to curtailthis emotive behaviour.

The primary purpose of trading is toproduce profits, but losses are a realitythat no trader is immune to. As such,good exit strategies should attempt tofocus on both. Considering the twoseparately, profit exits should ideally beobjective (offering clear, logical andprecise profit-take rules), whilst lettingprofits ride and preserving existingprofits. A good risk exit (stoploss) onthe other hand should protect tradingcapital in the event of a trade goingwrong. In order to achieve this, itshould also be objective (providingclear, logical and precise abort rules)and get a trader out of a losing tradewithout severe capital erosion. Finally,it should not be activated frequently bynoise and it should ideally get you outof a lingering trade for better capitalemployment.

Percentage trailing stops - onestop shopPercentage Trailing Stops (PTS) are anall-in-one exit strategy that bears the

genetics blueprints of a good exit strat-egy. The number of industry expertswho advocate their use is very large andit's not until we study the logic behindtheir functioning that we can trulyappreciate their value.

Unlike an ordinary stop whichremains fixed throughout a trade, a PTSis an adjusting stoploss that readjustsevery day (or period) as the trade pro-gresses to reflect changing risk andprofit levels. As the trade progresses inthe direction of favourable price move-ment, the trailing stop is ratcheted upthereby protecting existing profits andlimiting losses to a preset amount if themarket falls thereafter. In the event ofthe price remaining stationary orfalling, the PTS remains prostrate, giv-ing the trade the "benefit of the doubt"up until the point at which price revers-es adversely below the PTS, whence thetrade is "cut short" to prevent furtherlosses and profits taken - if they exist.By reacting as it does, the PTS providesclear and precise exit points for bothprofitable and losing trades, preventinga trader from taking profits as soon asthey materialise or hanging onto losing

USING PERCENTAGE TRAILINGSTOPLOSSESBy Rashpal Sohan

When designing automated trading systems, system designers areoften caught in the trap of over-perfecting their entry strategy,blinded by the search for the holy-grail entry, while sacrificing the

exit strategy to a blind leap of faith.Without underplaying the importance of the entry strategy towards the

long-term success of the system, by no means should it be taken to be thepanacea. Like a house, a trading system is supported by critical pillars, oneof which just happens to be labelled the 'entry strategy.' But there are oth-ers, without which the house would be very weak and prone to crumbling.The 'exit strategy', another one of these pillars, is the stanchion that actual-ly determines whether your house remains standing or not.


trades. In essence, this conforms toDow's cardinal rule of "cutting yourlosses and letting your profits run",explaining why they work so well.

Figure 1 illustrates how a 7% PTStrails the price action of the FTSE 100Index as it trends higher and remainslevel as the Index either falls or ranges,whereas Figure 2 reveals how thevolatility of a stock determines howtight the PTS should be.

Calculating a percentage trail-ing stoplossCalculating a PTS is done as soon as atrade is initiated. The formulae for cal-culating the PTS for a long trade isrevealed in box 1.

While it may appear to be very elabo-rate and intricate, the formula is actual-ly quite simple if you follow throughwith the earlier stated logic of how thePTS functions. On entering a long posi-tion, we begin by calculating the initialstoploss (Y0). In order to set the initialstoploss below the purchase price(Closing Price (0)) we multiply the clos-ing price with a factor of (1-PercentStoploss / 100), where PercentStoploss is your risk tolerance. Going

forward, we repeat this process everytime period (daily, if the trade is moni-tored daily) using the most recent price(Closing Price (t)). If the most recentlycalculated stoploss is greater than theprevious days' stoploss (or other timeperiod), we set the trailing stoploss tobe the most recent value, otherwise weset it to the previous value. As the pricemoves higher, the PTS trails higher andif price remains flat or falls, the PTSequals the previous value, but at notime should it dip lower than where italready is as long as the trade is active.

Testing the value addedIn order to test the value added by thePTS as an exit strategy, I devised a sim-ple test. Using a portfolio of theFTSE100 stocks, I simulated buying thestocks at random (based on the Excelrandom number generator function(RAND)) - assuming a trader had theworst timing method possible! A ran-dom number between one and ten wasgenerated and if the number wasgreater than or equal to 5, the stock waspurchased and added to the portfolio,with a 15% PTS. An exit was initiated

Figure 1. FTSE 100 within a 7% trailing stoploss

Box 1. Formulae for calculating the PTS


as soon as the price fell below its 15%PTS and no stock was added to theportfolio if it was currently held or ifthe total number of stocks in the port-folio exceeded 40. Commission andslippage were accounted for in the areaof 50 basis points per trade and the testperiod was January 1999 - November2006 (incorporating both a bear marketand a bull market in UK equities).Finally, the test was repeated using a25%, 35% and 45% PTS to gauge theperformance difference of a loose sto-ploss against a tight stoploss. Figure 3 isa graphical depiction of the results.

Analysing the resultsThe results are eye-opening. Despite

utilising a random entry, the perform-ance of all three (equity) simulationsbeats the buy and hold strategy withflying colours. As noticeable from theequity curves generated by these simu-lations (see Figure 3), the propensitytowards greater risk tolerance isrewarded with better long run perform-ance, albeit with greater drawdowns.Running the system using a 45% PTSyields jaw dropping results, eventhough the ability to live with a 45%risk tolerance is psychologically har-rowing. Comparatively, the 15% PTSsimulation reveals that while draw-downs are kept fairly small, the longrun results are hardly worth praising.

One key point to take home from this

is that while tight trailing stops meansmaller drawdowns, it also means hav-ing to accept much smaller profits.Very tight stops defy the concept ofletting your profits run, because tradesare frequently stopped out by noise anddaily fluctuations resulting in smallerprofits. Many individual UK stocks aremore volatile than 15% in that they fre-quently retrace more than 15% over thelong run (including bull markets).There needs to be room for the trade tobreathe.

Separating fact from fictionAdmittedly, the PTS is not perfect: Youhave to give back some profits beforebeing stopped out; knowing what risklevel to use can be tricky and the systemis better suited to a trend-followingmethod than a countertrend one, butthe advantages definitely outweigh thedisadvantages. The important thing, ofcourse, is to have a sell discipline and tostick with it.

Warren Buffett's first rule of invest-ing is 'never lose money'. Using ourtime we come very close to achievingthis by ensuring that we never let asmall loss become an unacceptable loss.Figure 3 reveals that over the period2000-2003, in all three cases, the simu-lated equity curves hardly dipped belowthe watermark, whereas the FTSE 100Index incurred a drawdown greaterthan 40%, peak to trough. Controllingthe downside risk is a key strength ofthe PTS exit strategy.

Anyone can buy a stock, but knowingwhen to sell it is the true art of invest-ing. By using trailing stops we haveobjective, logical, clear and precise sellrules to protect both our profits andour trading capital, removing emotionfrom the investment process. Havingproven that you can still salvage a poorentry with a good exit, think of thePTS as that "second chance" if youever get your entry strategy miserablywrong - and just think how much youwould have saved during the last bearmarket even if you'd had used a 25%trailing stoploss on all your stocks!

Figure 2. BP with a 5% and 15% trailing stop, illustrating the looser the stop, thegreater the risk incurred, but the less likely you are to be chopped out in a strong uptrend.

Figure 3. FTSE 100 trading model with a random entry strategy with four levels of per-centage trailing stoploss: 15%, 25%, 35% and 45% and the FTSE 100 index overlaid.


IDENTIFYING A CONSISTENT EDGE BY ALAN CRARY

Money management cannot improve the profitabil-ity or consistency of a method. It can only mul-tiply the effects of whatever edge the method has

so that you can compound the profits at a rapid rate. Thus,once you think you have developed a trading method, it'simportant to know if it has a genuine edge over a randomtrading model. I have developed a test for this purpose,which I think is worth looking at during the developmentphase of a trading model.

The Edge TestThis is a test that should be applied to one trading model ata time. First you backtest on the data you're using to devel-op the model. Then, when you're satisfied with the overallresults you separate the trades by long and short by year.

It'll look something like this:

Your Trading Model, e.g 1996Long +3.00 hold 1 dayShort -2.00 hold 2 daysLong -1.00 hold 2 daysShort +4.00 hold 1 dayetc.

Then you process the year of data (in this case 1996) andpull out random individual trades with the same length ofhold (being careful to avoid reuse of any one day):

Random Pass - 1996Long -2.00 hold 1 dayShort +1.00 hold 2 daysLong -2.00 hold 2 daysShort +1.00 hold 1 dayetc.

When you've finished you total up the results of the longand short trades for the random pass for the entire period:

Results Random Pass - 1996Long -4.00Short +2.00

You do this random pass thousands of times (Monte Carlo)and rank each of the passes so that you have a distributionfrom 1% - 99% for both longs and shorts for each year ofthe tests:

Long 1% -16.00etc.Long 99% +21.00

Then you compare the total you have for your tested tradesversus the distribution to rank where your trades are ascompared to the random trades, doing this for both longsand shorts for each year. If both longs and shorts rank 70%or better (20%+ better than random) then you might

While there are some money man-agement techniques that can improvea marginal trading method, you ulti-mately need an edge to avoid the riskof ruin. Otherwise as you increasethe number of trades in random trad-ing it's just a matter of time beforeyou hit a drawdown from whichyou'll never recover.


→→

looking at an edge.Do the same test with out of sample data and shorten the

time period to 3 months (so that you can view multiple for-ward time periods). If the numbers continue to be 70% orbetter on both longs and shorts then you probably are trad-ing with a edge.

You do this test every 3 months after you start trading itto make sure the edge is not deteriorating. If it drops below70% then stop trading it.

The edge test may also be useful in deciding which mar-ket to trade, although note I don't look for a market to fit asystem. Each system I've developed is targeting somebehavior and if that behavior is present in multiple markets,then I could test it to see if my system captures the behav-ior better than random. If so, I'd just trade it on that mar-ket where the edge is superior and check to make sure thebehavior was persistent. For instance I had a volatilitybreakout model that I used successfully in the S&P market.I tested it against the DAX market and found the edge(ability to capture profits at better than random) was betterin the DAX than the S&P.

Checking for dependencyThe number of winners or losers in a row has no impor-tance to me as long as the trades are independent. If I seenumbers outside of normal bounds then I search fordependency (loss begets loss, etc.). To know if the numberof wins or losses is outside of the normal bounds I creat-ed a formula to estimate the number of winners or losersin a row I should expect to see.

The basic formula for figuring the expected maximum los-ing streak is:

S = ln(1/T)/ln(L) where:L = % losersS = Streak T = # trades

Example:T = 500 tradesL = .6 or 60% losers

S = ln(1/500)/ln(.6)S = -6.21461/-.51083S = 12.16581 or a expected max. losing streak of 13trades

If you increase the number of trades to 1,000 then:

S = ln(1/1000)/ln(.6)S = -6.90776/-.51083S = 13.52273 or a expected max. losing streak of 14trades

confidence level is 1 - (1 / number of trades)ex. 500 trades = 1 - ( 1/ 500) = .998 confidence level

In this case if I were to see 20 losers in a row from a testsample of say 400 trades, then I'd check other streak levelssuch as the number of 3 in a row or 4 in a row to see if theyare also outside the bounds. If so, I'd look for dependency.

As long as the model is operating with a consistent edge Iwould not pull it. The range of drawdowns is somewhatpredicatable using the Monte Carlo tests and these tests,combined with the ideas mentioned here, should ensurefew and far between losing months.

Combining modelsOne of the ways to improve the consistency of results is touse several trading models simultaneously. But how do weknow which models to combine?

First steps require that the models/instruments are non-(or weakly) correlated. It’s worthwhile creating a table ofcorrelations with rolling periods (e.g. monthly 12 periods).If the correlation isn't consistent and smooth then you'reprobably looking at fools gold and you can expect the cor-relation to revert to random over time in the future.

As an aside, others have suggested looking at stress cor-relation rather than or in addition to normal correlation.This, for example, involves extracting only the days wherex equity curve was negative and finding the correspondingvalue for the y equity curve on the same day. Now you havetwo time series - one with the negative x values and onewith corresponding y values – which can then be correlat-ed. This is believed to be an important indicator becausewhat we fear most is +1 correlation in times of crisis. Stresscorrelation should point that out.

It’s worth pointing out that instead of combining severaldifferent models, you can also use one model on severalnon-correlated instruments. I do this for some marketswith longer holding timeframes mainly because the furtherout in length of trade you go the better a single trend fol-lowing system seems to work.

Once you've used correlation analysis to determine youruniverse of possible models, you then need to answer thequestion of how much of model 1 should we trade withhow much of model 2 and how much of model 3? Also ifI had a model 4 how could I tell if I should add it in as well?

One of the nice tools in trading are the sharpe ratioswhich are designed to measure consistency. One of them isthe modified sharpe ratio. It's defined as average return /std. deviation. For example, if the average return is $100and the std. deviation is $50 then the sharpe ratio is 2.0. Soin other words for me to breakeven or start to lose moneyI'd have to have a return that was 2 std. deviations worsethan the average return. If you look up 2 std. deviations ina stats book under z score you'll note that it equates to


deviations in a stats book under z score you'll note that itequates to 95.44%. So if my returns are normally distrib-uted then I have a 95.44% chance of breaking even or mak-ing a profit.

The modified sharpe ratio can be thought of as a z score.The higher the number, the closer we are to achieving con-sistent profitability. In my case I want to be 99% sure ofmaking a profit each month. So if I were to look it up in anormal distribution table I'd know the Z score I need isapproximately 2.58 or a modified sharpe ratio of 2.58.

We also know that the returns are not going to be nor-mally distributed. There will be fat tails so whatever sharperatio we come up with, it will be higher than we shouldexpect in normal trading. This is where using the modifiedsharpe ratio and non-correlated methods pays off. I don'thave a way to measure directly the benefit of using non-correlated methods but I know it improves the smoothnessof the equity curve. If I combine that with a high modifiedsharpe ratio I can have a high degree of confidence that mymodels will be consistently profitable.

In a test that I carry out, I take each of say three modelsand apply the modified sharpe ratio (the normal method ofcomputing the modified sharpe ratio is to use 36 periods).I now have a way of doing comparisons. If I combine saymodel 1 and model 2 and run the same tests I'll get oneresult. If I combine model 2 and 3 I'll get another. And ifI combine 1 and 3 still another. Then if I do many runs inwhich I weight each pass say 1 unit of model 1 and 2 unitsof model 2 I'll get more results. In the end what I found Ihad to do was develop a program to do all these passes. Itweights each model from 1 - 100 units and determines theoptimal modified sharpe ratio. Then it determines the ratiobetween each of the methods to determine how much ofeach should be traded. If I have 4 models it'll do tests onall four of them, five, six, 10, 50, etc. All it takes is computetime. If I choose to do all my models, I leave it for a cou-ple of days. In the end it gives me the optimal balance forthe best modified sharpe ratio. For my trading I had to use8 very good models to get the number up to 2.58 so that'swhy I trade against 8 models.

In my three model example I ran my program and theresults can be found in the chart below.

From this test I can see I should be trading all the mod-els in the ratio shown to achieve the maximum consistency.At 1.461 the zscore translates into 85.56% winning months(about 1 3/4 losing months per-year). Not up to the 99%level I desire but a definite improvement over any combi-nation of two systems. If I had a fourth model I could dothe same test and see if it should be included, and if so,what the optimal ratio should be.

It's worth repeating the tests again but with a lower prof-it factor, lower win %, and lower total profit, to test the sta-

bility of the results. Essentially, however, if you used 1, 2,and 3 in the ratios provided it would be more successfulthan using 1 & 3 by itself. Also, I noticed in my tests thatthe largest losing month for all 3 models combined is lowerthan any of the models individually.

As such, even below-par models with low trade frequen-cy can be morphed into an above-par model when com-bined with other non-correlated sub-par models (assumingthey're not too sub-par), thus increasing frequency, consis-tency, and lowering the need for a higher profit factor

CaveatIn the abovementioned tests, in digging through the data Inoted that model 3 didn't have very consistent results. Infact model 3 lost money for all of 1999. This just showsthat the concept is only as good as the underlying modelsit's built on. With win % ranges from 35 - 60 and the prof-it factor ranges from .79 - 2.89, model 3 was a bad choiceof model to include in the tests. If the models are inconsis-tent, then everything else that flows from them will be sus-pect.

Alan Crary is a regular contributor to Elite Trader(www.elitetrader.com), a web-based forum for traders.

Article extracted form the thread "System Development with acrary"with permission from Elite Trader.

Combined Best SR Quantity

model 1 & 2 1.395 1 unit model 2 and 1.5 units model 1

model 1 & 3 1.166 1 unit m1 and 1.333 units m3

model 2 & 3 1.057 1 unit m2 and 2.04 units m3

model 1 & 2 & 3 1.461 1 unit m2, 1.26 units m1, and 1.08units m3


“THE NUMBER OF WINNERSOR LOSERS IN A ROW HASNO IMPORTANCE TO ME ASLONG AS THE TRADES ARE

INDEPENDENT.”

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