2532983 Break Out Strategy

Breakout Trading Technique article collections: BASIC and ADVANCED" Technical tool insight: Price breakout"BY ACTIVE TRADER STAFF (Active Trader, March 2001) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2"More bang for your buck: Patterns within patterns"BY ACTIVE TRADER STAFF (Active Trader, October 2000) . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5"Anticipating breakouts and beating slippage"BY STEVE WENDLANDT (Active Trader, August 2000) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .9"Trading System Lab: 100-20 channel breakout system" BY DION KURCZEK (Active Trader, June 2003) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .13"Futures Trading System Lab: 100-20 channel breakout system" BY DION KURCZEK (Active Trader, June 2003) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .16"Futures Trading System Lab: 60-minute breakout system" BY VOLKER KNAPP (Active Trader, January 2004) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .18"Futures Trading System Lab: Four-percent breakout system" BY VOLKER KNAPP (Active Trader, September 2004) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .20"Broadening patterns: Clues to breakout direction"BY THOMAS N. BULKOWSKI (Active Trader, April 2004) . . . . . . . . . . . . . . . . . . . . . . . . . . . . .23"High, tight flag helps squeeze out profits"BY THOMAS N. BULKOWSKI (Active Trader, December 2004) . . . . . . . . . . . . . . . . . . . . . . . . . .28"Mastering two-minute breakouts"BY KEN CALHOUN (Active Trader, September 2001) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .32"Swing trading 10-day channel breakouts"BY KEN CALHOUN (Active Trader, March 2002) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .35"Trading System Lab: Volatility breakout system"BY THOMAS STRIDSMAN (Active Trader, October 2002) . . . . . . . . . . . . . . . . . . . . . . . . . . . . .39"Futures Trading System Lab: Futures volatility breakout system"BY THOMAS STRIDSMAN (Active Trader, October 2002) . . . . . . . . . . . . . . . . . . . . . . . . . . . . .41"Better breakout trading: The noise channel system"BY DENNIS MEYERS, PH.D. (Active Trader, September 2001) . . . . . . . . . . . . . . . . . . . . . . . . . .42"The long and short of it: The noise channel breakout system 2"BY DENNIS MEYERS, PH.D (Active Trader, October 2001) . . . . . . . . . . . . . . . . . . . . . . . . . . . .47"The multibar range breakout system"BY DENNIS MEYERS, PH.D (Active Trader, January 2004) . . . . . . . . . . . . . . . . . . . . . . . . . . . .53"Trading System Lab: DeMark variation"BY THOMAS STRIDSMAN (Active Trader, September 2001) . . . . . . . . . . . . . . . . . . . . . . . . . . .58"Trading System Lab: Dynamic breakout system"BY THOMAS STRIDSMAN (Active Trader, February 2003) . . . . . . . . . . . . . . . . . . . . . . . . . . . .60"Futures Trading System Lab: Dynamic breakout system"BY THOMAS STRIDSMAN (Active Trader, February 2003) . . . . . . . . . . . . . . . . . . . . . . . . . . . .62"Futures Trading System Lab: Experimenting with exits"By VOLKER KNAPP (Active Trader, June 2004) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .64"Futures Trading System Lab: Monthly breakout"BY DION KURCZEK AND VOLKER KNAPP (Active Trader, March 2004) . . . . . . . . . . . . . . . . . . . . . . . . . .66"Trading System Lab: 60-minute breakout system"BY VOLKER KNAPP (Active Trader, January 2004) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .68

ACTIVE TRADER • www.activetradermag.com 1

The price “breakout” is one of thesimplest — and most powerful —concepts in trading. It occurs

when price moves forcefully out of aconsolidation or trading range (a periodof relatively narrow, sideways pricemovement) or pushes above or below anestablished price level (support or resist-ance), initiating either temporary follow-through or a sustained trend.

The act of pushing to new highs orlows (especially if the price level in ques-tion has been repeatedly tested in thepast) is evidence of strong momentumand suggests the market has the poten-tial to continue in that direction. In otherwords, the basic logic behind pricebreakouts is that a market making newhighs (and with potential for furtherprice gain) is exhibiting strength andshould be bought, while a market mak-ing new lows (and with potential for fur-ther price decline) is exhibiting weak-ness and should be sold.

For example, the reason new 52-weekhighs or lows in stocks are so commonlyreferenced is because of the implied sig-nificance of price breaking through theselevels. This concept of price movement isvalid on intraday time frames as well asdaily or monthly ones.

Donchian breakout levelsThe term “breakout” is often associatedwith Richard Donchian, the first personto popularize the systematic use ofbreakout levels. His basic approach wascalled the Donchian “four-week rule,”which consisted of the following:

1. Go long (and cover short positions)when the market makes a new four-week high (that is, when price exceedsthe highest price of the previous fourweeks).

2. Go short (and cover long positions)when the market makes a new four-week low (that is, when price dropsbelow the lowest price of the previousfour weeks).

The four-week highs or lows simplyrepresent natural resistance and supportlevels.

This kind of trading system is oftenreferred to as stop-and-reverse (SAR),because when a trade signal is generated,the existing position is liquidated(stopped out) and a new position (areverse of the previous one) is established.

This basic trading rule — which gainedwidespread popularity as the “20-daybreakout” — was integral to many popu-lar mechanized trading strategies, mostfamously those of futures trader RichardDennis group of trend-followers knownas the “Turtles.” Trend-following traders(especially in the futures markets) usedthis simple technique, or a variation of it,to exploit strong trends in the 1970s and’80s. However, the widespread populari-ty of the 20-day breakout level has dimin-

ished its effectiveness to the point thatmany traders look for false breakouts(when price pushes through a breakoutlevel, only to reverse back through it) atthese levels, to take positions against thedirection of the initial breakout (referredto as “fading” the breakout).

Breakouts are not limited strictly tomoves to new highs of a certain numberof bars (i.e., 10-bar, 20-bar or 40-barbreakouts). As mentioned, price can also“break out” through the support andresistance levels of trading ranges, orother past technical milestones such aslong-standing highs or lows.

Figure 1 shows 40-day breakout levelson a daily chart. Figure 2 shows 20-barbreakout levels on a 10-minute chart.Figure 3 shows a breakout above theresistance level defined by a past signifi-cant high.

2 www.activetradermag.com • March 2001 • ACTIVE TRADER

Technical tool insight: Price breakout

TRADING Basics

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Source: QCharts by Quote.com

40-day Donchian breakout levels, both high and low. A basic breakoutapproach is to buy when price exceeds the n-bar (in this case, the 40-day)high and sell when it falls below the n-bar low.

FIGURE 1 DONCHIAN BREAKOUT CHANNELS, DAILY

Oracle Corporation (ORCL), daily

Price breakouts are the basis of many of the most successful

trading approaches. We explain the basics of this trading technique.

The Donchian-type breakout is alsocommonly referred to as a “price chan-nel” breakout.

ApplicationTraders using breakouts are basing theirtrades on the following principle: Ifprice momentum is strong enough(either up or down) to push through asignificant technical level, there is agood chance price will continue in thatdirection for at least a while. As a result,these price levels represent logical tradeentry and exit levels with well-definedrisk, both for traders who expect followthrough in the direction of the breakoutand, as will be described shortly, traderswho are looking to fade breakouts.

Key points Price breakouts are typically used astrend-following signals. The greater thenumber of days (or price bars) used todetermine the breakout, the longer-termtrend the trading system will reflect andattempt to exploit. For example, a 20-day(or 20-bar) breakout would capture short-er trends than a 40-day breakout, whichin turn would reflect shorter trends thanan 80-day breakout. Generally, in terms oftrend-following approaches, the longer-term the breakout, the more significantthe price move and the greater the likeli-hood of sustained follow through.

Breakout trading can also simplifyrisk control because stop-loss levels areoften easy to identify. For example, ifprice breaks out of the upside of a trad-

ing range, traders who go long on thebreakout can place protective stops in anumber of technically logical places, inrelation to the range. First, the stopcould be placed below the low of thetrading range. Second, a more conserva-tive stop placement would be in themiddle of the trading range (or in theupper 25 percent of the trading range,etc.). Finally, the most conservative alter-native is a stop just below the originalbreakout level, which might be used by

ACTIVE TRADER • March 2001 • www.activetradermag.com 3

GlossaryA false breakout occurs when pricepushes through a support or resist-ance level in the anticipated direc-tion, suggesting a new price thrust ortrend, only to (relatively) quicklyreverse direction when no real follow-through materializes. Because traderswho bought or sold on the initialbreakout may all scramble at once toget out of their trades when the mar-ket fails to follow through, the rever-sal can be quite forceful. For this rea-son, contrarian traders sometimesfade initial breakouts to capitalize onthese short-term reversals.

Stop-and-reverse (SAR) refers to atrading approach that is always in themarket, long or short. The existingposition is liquidated (stopped out) anda new position (a reverse of the previ-ous one) is established, using the samesignal in the opposite direction. Forexample, a simple 40-day SAR break-out system would buy when priceexceeds the highest high of the last 40days and sell when price falls belowthe lowest low of the last 40 days.

Support and resistance. Support is aprice level that acts as a “floor,”preventing prices from droppingbelow that level. Resistance is theopposite: a price level that acts as a“ceiling;” a barrier that preventsprices from rising higher.

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The breakout concept is applicable to any time frame. Here, the highest 20-bar highs and lowest 20-bar lows are shown by the channel lines.

FIGURE 2 DONCHIAN BREAKOUT CHANNELS, INTRADAY

Oracle Corporation (ORCL), 10-minute

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Breakout above previously tested high

Jan. 1997 Apr. July Oct. Jan. 1998 Apr. July Oct. Jan. 1999


A prior high creates a resistance level that is tested multiple times beforeprice breaks out to the upside. A significant trending move follows.

FIGURE 3 BREAKOUT ABOVE PRIOR HIGH

Sun Microsystems Inc. (SUNW), Weekly

a very short-term trader. All these choices have one thing in

common: The placement of the stop cor-responds to a price move that negatesthe validity (to varying degrees) of theoriginal breakout. Whenever the original

reason for a trade is nullified, that posi-tion should be eliminated. (Note also,the second and third options would belikely short entry points for traders look-ing to fade the upside breakout.) Figure4 shows a downside breakout out of a

trading range and possible stop points.Figure 5 shows the reverse situation.

The stock first breaks out to the down-side of the trading range, but this turnsout to be a false breakout. The stockreverses back into the trading range andeventually breaks out through theupside of the trading range. Again, theboundaries (and the midpoint) of thetrading range provide logical stop levels— both for the initial downside breakoutand the subsequent upside breakout.

Because of the possibility of falsemoves at popular breakout levels,traders looking to capture trendingmoves sometimes use confirming sig-nals to improve the likelihood of success.For example, after an initial upsidebreakout, the trader may wait for themarket to stay above the breakout level(or close above it) for a certain number ofbars, or penetrate it by a certain percent-age. Such techniques delay entry andlimit profit potential (and will result insome missed trades), but they can alsocut down on false signals.

Bottom lineThe breakout concept is one of the mostimportant in technical trading. Buyingmarkets showing strength (upsidebreakouts) with further potential forupside movement, and selling marketsshowing weakness (downside break-outs) with further potential for down-side movement is the basis of many trad-ing plans and systems on many timeframes. Similarly, false breakouts are thefoundation of some counter-trend trad-ing techniques. The breakout concept isalso easily mechanized for traders inter-ested in a systematic approach.�


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The boundaries of a trading range provide logical stop levels for a breakouttrade. After a downside breakout of the range, a trader, depending on howconservative he was, could place a stop-loss order at the original breakoutlevel, the midpoint of the range (or some other point within the range) orthe upper level of the range.

FIGURE 4 TRADING RANGE BREAKOUT WITH STOP LEVELS

American Express Inc. (AXP), 2-minute

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In this case, the stock first breaks out below the bottom of the trading range, only to reverse back into the trading range and eventually break outthrough the top of the range. In either case, the stop-loss levels are againeasily identified.

FIGURE 5 FALSE BREAKOUT AND REVERSAL

Microsoft Corporation (MSFT), daily

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Additional research:Trading for a Living by Alexander Elder John Wiley & Sons, 1993

Trading Systems and Methods by Perry Kaufman 3rd edition, John Wiley & Sons, 1998

Technical Analysis of the FinancialMarketsby John Murphy New York Institute of Finance, 1999

Street Smarts by Linda Raschke and Laurence A. Connors M. Gordon Publishing Group, 1995

Schwager on Futures: Technical Analysisby Jack Schwager John Wiley & Sons, 1996

5 www.activetradermag.com • October 2000 • ACTIVE TRADER

W hat makes a goodtrade? Well, in retro-spect, most traderswould say a nice prof-

it makes a good trade. But when you’reputting a position on, the outcome isunpredictable. We’d all like to know atrade will be good in advance, but alas,the markets are not so accommodating.

What you look for when you’re get-ting in a trade is an entry point wherethe odds of a move in your favor are bet-ter than average. Then, by having a plan

that determines when and where you’llexit with either a loss or a profit, you tryto structure a trade where the potentialreward is greater than the known risk.

The advantage of trading breakouts ofcongestion patterns such as tradingranges, triangles, flags and pennants isthat these formations allow you to clear-ly define the risk on your trades. Forexample, if a stock moves into a tradingrange after a rally, you may look to buyan upside breakout of the range in antic-ipation of a continuation of the uptrend.The logical place to put an initial protec-tive stop is below the low of the tradingrange, because a downside reversalthrough the support of the range wouldbe a bearish development.

Figure 1 provides an example. In lateJune, Microsoft (MSFT) established a rel-atively narrow trading range afterapproximately a 16-point rally. The stockbroke out of the upside of the range(around 80 1⁄8) on July 6. The initial pro-tective stop would have been placed justbelow the support level of the tradingrange, around 76 1⁄2. A move back belowthis level would suggest the upsidethrust was actually a false breakout andthat the trade should be exited.

That’s exactly what happened. Twodays after entry the stock had pulledback into the trading range. It movedsideways to lower over the next severaldays before, on July 19, penetrating thedownside of the range and stopping outthe long trade.

The risk on this trade was a moderate3 5⁄8 points. But what do you do when atrading range is much wider and a stopbased on either the support or resistancelevel represents too large a risk? Figure 2

TRADING Strategies

FIGURE 1 FALSE BREAKOUT

Microsoft Corporation (MSFT), daily

12 19 26 3 10 17 24 31 7July Aug.

Upsidebreakout

Stopped outSupport level used as initial stop

A trading range develops in the aftermath of a sharp rally. After an initialupside breakout, the stock reverses to the downside, stopping out the longposition.

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Source: Qcharts by Quote.com

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More bang for your buck: PATTERNS

WITHIN PATTERNS

shows a much more volatile trading rangethan that in Figure 1. Using the sameapproach as in the previous example —buying on an upside breakout of the trad-ing range and placing an initial protectivestop below the low of the range — wouldrepresent considerable risk.

As a result, some traders place the ini-tial stop in the middle of the tradingrange. This more conservative method isbased on the idea that a strong breakoutmove should follow through immediate-ly and not reverse back into the trading

range. Another way to reduce risk onbreakout trades is to look for shorter-term patterns within larger patterns thatallow you to place your initial stop-losscloser to your entry point.

Patterns within patternsWhen the risk implied by a particulartrading range is exceptionally large, youcan look for smaller congestion patternsnear the support or resistance levels ofthe range. Basing entry and stop pointson the levels defined by the smaller pat-tern can reduce the risk on the trade aswell as provide the opportunity for earlyentry into the position.

Figure 3 shows the formation of awide trading range in Oracle (ORCL) atthe beginning of this year. A trader look-ing to enter long on an upside breakoutof this range would have to accept a riskof more than 16 points, assuming thebottom of the range was used for the ini-tial stop-loss.

However, a much narrower tradingrange developed in February. Using thisrange as the basis of an upside breakouttrade would have offered the same entry

ACTIVE TRADER • October 2000 • www.activetradermag.com 6

How to create trade

opportunities with

increased reward

and decreased risk by

trading patterns within

patterns.

FIGURE 3 CONGESTION WITHIN CONGESTION

Oracle Corporation (ORCL), daily

Narrow range

Wider trading range

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A shorter, narrower trading range forms just at the resistance level of a larg-er range. Using the support level of the smaller range as a protective levelfor an upside breakout substantially reduces the trade’s initial risk.

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FIGURE 2 RANGE RISK

International Business Machine Corp. (IBM), daily

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Using the opposite side of a trading range as a stop for a breakout trade canresult in large initial risk if the trading range is wide.

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point but a much closer stop. In thiscase, placing a stop one tick below thelow of the narrower trading rangewould have reduced the risk to 6 3⁄4points. For a short-term trader, this rep-resents a large stop, but it’s still a dra-matic improvement and the profitpotential for the move out of the largertrading range is still intact. (Later, we’lllook at the practical risk-reward impactthis can have on a trade.)

Figure 4 provides another example. Inthis case, EMC Corp. (EMC) repeatedlypulled back from resistance around 72 1⁄2.Because a well-defined horizontal trad-ing range did not develop (the stockswung back and forth in an increasinglywider range), the most recent swing lowaround 51 would be the reference pointfor the initial stop-loss — a risk of morethan 20 points.

However, as the stock bounced off thatlow and made another run at the resist-ance level, it formed a flag consolidationfrom June 7 to June 12 with a high around69 7⁄8 (the highs of the bars in the flagswere within 1⁄16 of each other) and a lowaround 66 13⁄16. The upside breakout of thisflag provided an early entry to the subse-quent surge that pushed the stock pastthe 72 1⁄2 resistance level to new highs.

Figure 5 shows a 15-minute chart ofthe Nasdaq 100 tracking stock (QQQ).The stock formed a large bottoming pat-


FIGURE 4 FLAG NEAR RESISTANCE

EMC Corporation (EMC), daily

Flag

Resistance

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A small flag forms just below a well-defined resistance level, offering earlyentry into the upside thrust move.

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FIGURE 5 NARROW FLAG

Nasdaq 100 Index (QQQ), 15-minute

Narrowflag

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A narrow flag consolidation forms near the resistance level of an intradayhead-and-shoulders bottom pattern. The low of the flag provides a lower-riskstop level than the most recent swing low.

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The advantage of trading breakoutsof congestion patterns such astrading ranges, triangles, flags and pennants is thatthese formationsallow you to clearlydefine the risk onyour trades.

tern (a head-and-shoulders bottom pat-tern; the preceding sell-off is not shown)with resistance around 82 5⁄8. As the stockapproached the resistance level for thesecond time, on May 30, it consolidated ina narrow flag pattern with resistancearound 82 7⁄32 and support around 81 5⁄8.Playing an upside breakout of this pat-tern and using its support level for theinitial stop (rather than the most recentswing low around 76) reduced the risk ona long trade to less than a point.

A final example is shown in Figure 6.Here, in the middle of a larger tradingrange with resistance around 32 3⁄8,Motorola (MOT) formed a flag consolida-tion in late-October 1999 that offered theopportunity to trade an upside move withlower risk. The stock gapped out of theflag (a bullish sign) above 31 1⁄2 and contin-ued to run past the resistance of the largertrading range. Placing a stop just belowthe flag support at 29 15⁄16 would havereduced the initial risk on the trade to lessthan two points. As was the case withFigure 4, the smaller pattern allowed youto both use a tighter stop and get in earli-er on an upside breakout.

Structuring a tradeFigure 3 provides a good example of howthis approach can work in the context of acomplete trade plan. The rally from thelate-October 1999 low to the early-

January 2000 high was 41 7⁄32. The stockthen moved sideways, forming the largertrading range. A trader looking to buy onan upside breakout of the range could usethe measured move approach, whereby thesize of the previous price move is addedto the current price, to project a price tar-get. Adding the size of the price movepreceding the trading range to the low ofthe larger trading range (around 46 5⁄8)results in an upside target of 87 27⁄32.

Using the measured move approachon the smaller price swing from Jan. 28low of 46 5⁄8 to the Feb. 14 high of 64 3⁄4 (18 1⁄8points) sets up a shorter-term price targetof 77 7⁄16. This level would mark a goodspot to take at least partial profits on theposition and raise the stop on the balanceof the position. The stock actually formed

another flag after hitting a high of 76 1⁄2 onFeb. 28. This consolidation marked anopportunity to exit part of the positionwith a profit; the stop on the remainder ofthe position could then be moved up tothe breakeven point, locking in a profit onthe trade. (For more information on tak-ing profits and moving stops, see“Opening day opportunities,” p. 42.) Thebottom line: The development of thesmaller trading range allowed the estab-

lishment of a trade with a price targetbased on the larger, longer-term price pat-tern with a risk based on the smaller,shorter-term price pattern.

Another general advantage of thisapproach is that it increases your flexi-bility. Even if you are stopped out on amove through the support of the smallercongestion pattern, you can still re-entera long position if the market reversesagain and breaks out above resistance asecond time. For example, a trader whowent long on the intraday upside thrustabove resistance (say, at 62 5⁄8) on Feb. 14and used the low of the smaller tradingrange (around 58 5⁄8) as the stop level,would have been stopped out on theintraday downside thrust on Feb. 22.However, as mentioned earlier, this lossis much smaller than the one that wouldhave occurred had the stop been placedbelow the low of the larger tradingrange, which was nearly 12 points lower.

These patterns may develop relativelyinfrequently, but they fulfill the primarygoals of smart trading: They allow youto establish trades with shorter-term riskand longer-term profit potential. Infuture articles we’ll expand on theseideas by looking at additional measuringobjectives and ways to put breakoutsinto context in relation to underlyingtrends of different magnitudes.�


FIGURE 6 EARLY ENTRY

Motorola, Inc. (MOT), daily

Flag

Trading range

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A flag forms in the middle of a larger trading range. Even though pricegapped above the flag, playing the upside of this smaller pattern offeredearly entry and a tighter stop on a long-side trade.

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When the risk implied by a particular trading range is exceptionally large,you can look for smaller congestion patternsnear the support or resistance levels of the range.

9 www.activetradermag.com • August 2000 • ACTIVE TRADER

Anticipating BREAKOUTSand beating SLIPPAGE

Trading breakouts is a tried-and-true

approach on all time frames. But intraday

and other short-term traders

can sometimes give up

precious points because

of slippage.

Here’s one trader’s take

on finding setups that allow

you to enter early and beat

the breakout crowd.

TRADING Strategies

One of the most importantaspects of short-termstock trading is some-thing you almost never

hear about: Slippage. Slippage is the difference between

where you expect, or want, to be filledon a trade and where your order is actu-ally executed. If you don’t understandthis concept, try to enter a market orderwith a browser-based online broker thefirst day of a hot IPO and see what hap-pens. That’s slippage! Slippage can becaused by a number of factors: Poor exe-cution by a broker, communication fail-ure or other technical problems, or fastmarket conditions.

While it’s true that we all try to keepour costs down to the bare minimumwithout sacrificing service or technolo-gy, slippage is probably the most over-looked and significant cost in trading.But through a little-known tendency,you can make slippage work for youinstead of bleeding you dry. In fact, ifmost of your trading techniques arebreakout related, you can use this trickon almost every trade you enter. Butfirst, let’s look at why it works.

One tick at a timeTom DeMark, a highly regarded tradingsystem developer who has worked withsuch top traders as George Soros, PaulTudor Jones and Steve Cohen, wrote abook (his second) called New MarketTiming Techniques: Innovative Studies inMarket Rhythm and Price Exhaustion(1997, John Wiley & Sons, New York). Init, he explained what probably is one ofthe most significant discoveries in themarkets: the TD One-Tick, One-TimeRule.

This rule states if a market makes anew high or low just once (a single print)and backs off from that point, that newhigh or low should hold for a significantperiod of time. In fact, most significanthighs and lows only print one time at theextreme price.

It makes sense that the opposite also istrue: If a price prints more than once at acertain high or low, then that high or lowwill be broken in short order almostevery time. From that, it follows the

more a particular level is tested, theweaker it becomes.

In layman’s terms, if a stock continu-ally prints or finds support or resistanceat a certain price, the odds are extremelygood that price level will be brokenshortly. That is invaluable informationfor any trader who uses breakouts aspart of his or her strategy.

Figure 1 is a five-minute chart ofCMGI. The stock bounced off support at50 six times (and who knows how manyprints actually occurred at that level).Every time a stock tests a support orresistance level, that level gets weakerand weaker, as if a hammer and chiselwere chipping away at it.

Fortunately, most people view sup-port levels as opportunities to go long,while breakout traders view tests of sup-port as fuel to propel an eventual break-out. In this example, not only are tradersestablishing new long positions withtheir stops just below the support levelat 50, there are also many traders wait-ing to short the stock once it does breakdown. Don’t forget that all the people

who bought the stock around $50 willeither be stopped out or will wait for anopportunity to breakeven on theirtrades. The bottom line is that when sup-port at 50 is penetrated it quickly turnsinto significant resistance.

Here’s the question: If, because ofrepeated tests of the support level, theodds are very good the 50 level will bebroken (and the broader market indicessupport this view), why wait for thebreakout? Doing so increases the odds ofhaving to chase the market or missing thetrade. In this case, if you wait for the stockto trade at 49 15⁄16 and then try to establisha short position, you’ll probably end upmissing the trade waiting for an uptick.

Let’s look at a second example. InFigure 2, Netro Corp. (NTRO) wasbouncing off the 82 1⁄2 level for about twoweeks. The day it finally broke that sup-port level (March 30, 2000) was a veryweak day in the broader market indices,which helped the stock to finally breakdown. A good opportunity to shortNTRO came at the prior day’s closewhen NTRO closed right at the support

ACTIVE TRADER • August 2000 • www.activetradermag.com 10

FIGURE 1 CHISELING AT SUPPORT

CMGI (CMGI), 5-minute 61

59

57

55

53

51

49

47

350,500

10:00 11:00 12:00 13:00 14:00 15:00 16:0010:00 11:00 12:00 13:00 14:00 15:00 16:00

51 1⁄8

Stop placed at mostrecent swing high (50 3⁄4)

CMGI repeatedly tests supportat 50 in a weak market

Repeated tests of a support level increase the odds of a downside breakout.A short position can be established in anticipation, with a stop just abovethe most recent swing high to protect against an upside reversal.

Source: CyberTrader by CyberCorp.

BY STEVE WENDLANDT

level for the second day in a row. Thenext morning NTRO gapped lower andcontinued to drop dramatically. It wouldhave been difficult to get short after the

market opened for trading on the day ofthe breakdown (although, there weresome upticks in the pre-market).

All breakout traders know it’s very

difficult to get short once a stock breaksthrough support, if the trade is anygood. You must either wait for an uptick(which may not happen) or offer it short1⁄16 higher than the inside bid (for Nasdaqstocks). But if the stock is dropping like arock, who is going to hit your offer?

The bottom line is that if you want totrade a stock when the overall market istrending in the direction of your poten-tial trade, and the stock repeatedly testsa support or resistance level, you shouldenter before the breakout. Most times,you even can avoid paying the spreadbecause the stock will be whipsawingback and forth between the bid and offer.If you wait until the stock breaks out youare almost always forced to pay thespread — if you can get it at all.

But, you may ask, what if the stocknever breaks out? Should you hold theposition until it does, or should you exitthe position on the close? One approachto reduce risk is to use the last swing lowor high as your initial stop-loss point. Inthe CMGI example, you could haveplaced an initial stop loss at 50 3⁄4 whichwas the last swing high on the five-minute chart. With a stop in place, youcan simply wait for the breakout to mate-rialize. The only reason not to hold theposition is if the overall market begins tomove counter to the trade (i.e., you’relong, waiting for the breakout, and themarket begins to drop precipitously).

But you must use caution when enter-ing breakout trades early; you neverwant to enter a trade that is counter tothe overall market momentum. Forexample, before entering the CMGItrade on the short side, you should havechecked to make sure the Nasdaq andS&P 500 were both weak on the day andtrending lower. The weakness of theseindices would help pull the stock belowthe support level.

Figure 3 shows one last example. OnMay 25, Warner Lambert (WLA) openedfor trading at 121 1⁄2, just under the downtrendline of a nice triangle pattern. Thepre-opening call was for the Nasdaq andS&P 500 to go higher that morning, andthey both began to rally from the open.

This created a setup to go long beforethe actual breakout above the trendline.As soon as WLA began to move towardthe trendline, a buy order was entered at

11 www.activetradermag.com • August 2000 • ACTIVE TRADER

FIGURE 2 EARLY OPPORTUNITIES

Netro Corp. (NTRO), daily122

109 1⁄2

97

841⁄2

72

591⁄2

47

341⁄2

Jan. Feb. Mar. Apr. May

25 11⁄16

Stock tests support in weak market. Short trade entered at 82 9⁄16

A close at the low of the bar preceding the downside breakout, just at the support level, offers an early entry opportunity for a short position.


5,820,000

FIGURE 3 GOING WITH THE MARKET

Warner Lambert (WLA), daily130

1231⁄2

117

1101⁄2

104

971⁄2

91

841⁄2

Jan. Feb. Mar. Apr. May125 9⁄64

Stock tests trendlineresistance in strongoverall market. Entered long at 122 1⁄4

(before the breakout).

Pre-breakout entry should be confirmed by the broader market indices. In thiscase, establishing a position in advance of a breakout above the trendline wassupported by strength in the S&P 500 and Nasdaq indices.


8,434,900

122 1⁄4, well before the 123 1⁄16 breakoutpoint. Not long after, the overall marketstrength helped pull WLA through thetrendline; it continued to rally for therest of the day.

Had you waited for WLA to print at123 1⁄16, you would have been filled at aminimum of 13⁄16 worse than the earlyentry price. Those extra fractions add upquickly. You can usually gain an extra 1⁄8(sometimes as much as a point) simplyby realizing that support and resistancealmost always get broken. Try the fol-lowing experiment: Multiply 50 percentof all the shares you have traded over agiven time period by 1⁄8 and see what youcome up with. That’s being conservative.

You can use this entry technique onany breakout-related trade in any time-frame, including breakouts from dailyand intraday cup-and-handle patterns,triangles, trendline breakouts and spikeand ledge patterns (see Figure 4). Veryrarely should you wait for the actual

breakouts to materialize on any of thesepatterns. Remember, slippage affectsyou whether or not you make a profit onthe trade. Most traders don’t even thinkabout the effect of slippage on their win-ning trades; they only think about thelosers. And don’t forget about the tradesyou missed completely because thestock just ripped through the support orresistance level and you couldn’t evenget a partial fill.

We tend to forget about those missedopportunities completely, but those areusually the most potentially profitabletrades because the stock is moving soforcefully. This approach will also helpyou on the breakout trades that don’tmaterialize because you’ll have a betterentry price and may even be able to stillgarner a small profit or, at worst, scratcha trade from these false breakouts.

No approach is without risk, but incertain situations entering early canyield excellent trading results.�

ACTIVE TRADER • August 2000 • www.activetradermag.com 12

FIGURE 4 BREAKOUT PATTERNS

Cup and handle breakout Trendline breakout

Spike and ledge breakout Triangle breakout

A sampling of the breakout patterns short-term traders can use on any timeframe. They provide well-defined support or resistance levels you can use toanticipate breakouts.

System concept: This is a classic trend-following system thatbuys when price moves above the highest high of the last xdays and sells when price falls below the lowest low of the lasty days. The number of days used to calculate the breakout levelis called the “channel length.”

Breakout systems are based on the logic that by making anew price high (or low), a market is demonstrating it has themomentum to establish a trend, and price will likely continuein that direction.

In this test, one long channel length (100 days) was used forentries, and a short channel length (20 days) was used for exits.The exit strategy allows the system to follow large moves untilprice makes a significant reversal.

We will also examine the results of using a range of channellengths and how a “walk-forward optimization” couldimprove the results of the system for the most recent year.

Rules:1. Enter long on the next bar at the highest 100-day high.2. Exit long on the next bar at the lowest 20-day low.3. Enter short on the next bar at the lowest 100-day low. 4. Exit short on the next bar at the highest 20-day high.

Money management: Risk a maximum of 2 per-cent of total account equity per trade. The positionsize is based on the difference between the entryprice and the initial stop level. Trade the numberof shares that would result in a 2-percent loss ofaccount equity if the stop level were hit.

Starting equity: $100,000. Deduct $10 slippage andcommission per trade.

Test data: The system was tested on the ActiveTrader Standard Stock Portfolio, which containsthe following 18 stocks: Apple Computer (AAPL),Boeing (BA), Citibank (C), Caterpillar (CAT),Cisco (CSCO), Disney (DIS), General Motors(GM), Hewlett Packard (HPQ), InternationalBusiness Machines (IBM), Intel (INTC),International Paper (IP), JP Morgan Chase (JPM),Coke (KO), Microsoft (MSFT), Sears (S), Starbucks(SBUX), AT&T (T) and Wal-Mart (WMT).

Test period: January 1993 through February 2003.

System results: The system’s performance wasmediocre, at best: It returned only 12.61 percentover 10 years, while buy and hold would havereturned more than 253 percent. Furthermore, thesystem was exposed to the market nearly 75 per-

13 www.activetradermag.com • June 2003 • ACTIVE TRADER

100-20 channelbreakout system

190,000180,000170,000160,000150,000140,000130,000120,000110,000100,00090,00080,00070,00060,00050,00040,00030,00020,00010,000

0

Equity Cash Linear reg Long Short

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FIGURE 1 EQUITY CURVE

3/3/93 3/2/94 3/1/95 2/6/96 2/3/97 2/2/98 1/7/99 1/3/00 1/2/01 1/2/031/2/02

The long- and short-only equity curves, along with the overall equity curve, are shown here. The long side of the system substantially outperformed the short sideduring the 10-year test period

FIGURE 2 SAMPLE TRADES

Source for all figures: Wealth-Lab Inc. (www.wealth-lab.com)

Boeing (BA), daily

Volume

Short

Cover

August 2002 September October November

50.00

48.00

46.00

44.00

42.00

40.00

38.00

36.00

34.00

32.00

30.00

10.00 M

5.00 M

This short trade was triggered when price crossed below the 100-day low.The exit occurred when price crossed above the 20-bar high. The 100- and20-day high/low channels are plotted as gray lines.

cent of the time, whichmeans we are squeezing justabout as much performanceout of this system as possi-ble, short of using margin orsome other form of leverage.

It is interesting to note,however, that the long sideof the system performedmuch better than the shortside. The net return for longtrades was 56 percent, withonly 38-percent marketexposure. Maximum draw-down for the long side of thesystem was only 18 percent,while buy and hold experi-enced a devastating 66 per-cent maximum drawdown.

These results confirmshort trading in equities canbe tricky. We measured theresults of the short side of thesystem after the broad mar-

ket began to fall in the year 2000, and although this period didproduce a small profit, it was also accompanied by extremevolatility.

System parameters: One way many traders attempt toimprove a system is to “optimize” its parameters (in this case,the number of days used to determine the channel lengths).This involves testing various parameter combinations to find arange of values that result in the greatest profit over a givenperiod.

Although this technique can result in a system that showstremendous profit over a historical testing period, the odds thatyou would have known to use those specific parameter values

Disclaimer: The Trading System Lab is intended for educational purposes only to provide a perspective on different market concepts. It is not meant to recommend orpromote any trading system or approach. Traders are advised to do their own research and testing to determine the validity of a trading idea. Past performance does notguarantee future results; historical testing may not reflect a system’s behavior in real-time trading.

LEGEND: Net profit — profit at end of test period, less commission •Exposure — the area of the equity curve exposed to long or short positions, asopposed to cash • Profit factor — gross profit divided by gross loss • Payoffratio — average profit of winning trades divided by average loss of losingtrades • Recovery factor — net profit divided by max. drawdown • Max DD(%) — largest percentage decline in equity • Longest flat days — longestperiod, in days, the system is between two equity highs • No. trades — num-ber of trades generated by the system • Win/Loss (%) — the percentage oftrades that were profitable • Avg. profit — the average profit for all trades •Avg. hold time — the average holding period for all trades • Avg. profit(winners) — the average profit for winning trades • Avg. hold time (win-ners) — the average holding time for winning trades • Avg. loss (losers) —the average loss for losing trades • Avg. hold time (losers) — the averageholding time for losing trades • Max. consec. win/loss — the maximumnumber of consecutive winning and losing trades

LEGEND: Avg. return — the average percentage for the period • Sharpe ratio— average return divided by standard deviation of returns (annualized) •Best return — best return for the period • Worst return — worst return forthe period • % Profitable periods — the percentage of periods that were prof-itable • Max. consec. profitable — the largest number of consecutive prof-itable periods • Max. consec. unprofitable — the largest number of consec-utive unprofitable periods

Trading System Lab strategies are tested on a portfolio basis (unlessotherwise noted) using Wealth-Lab Inc.’s testing platform.

If you have a system you’d like to see tested, please send the trad-ing and money-management rules to [email protected].

PERIODIC RETURNS

Avg. Sharpe Best Worst Percentage Max. Max.return ratio return return profitable consec. consec.

periods profitable unprofitableWeekly 0.04% 0.15 11.49% -8.47% 49.42% 11 9Monthly 0.19% 0.15 13.53% -8.31% 50.83% 6 6Quarterly 0.51% 0.15 22.09% -13.63% 48.78% 5 4Annually 2.19% 0.17 33.91% -10.28% 50.00% 3 2

TABLE 1 BEST PARAMETER VALUES FOR EACHSTOCK

Symbol Long Short period period

AAPL 70 16BA 70 14C 130 26CAT 130 14CSCO 80 24DIS 70 18GM 80 18HPQ 90 24IBM 90 18INTC 70 16IP 130 14JPM 130 26KO 130 16MSFT 120 18S 70 14SBUX 120 16T 90 26WMT 110 14

FIGURE 3 DRAWDOWN CURVE

3/3/93 3/3/94 3/1/95 2/9/96 2/3/97 2/2/98 1/8/99 1/3/00 1/2/01 1/2/031/2/02

0%

-5%

-10%

-15%

-20%

-25%

-30%

-35%

The system was never able to overcome the drawdown that began in mid-1995.

Profitability Trade statisticsNet profit ($): 12,608 No. trades: 330Net profit (%): 12.61 Win/loss (%): 38.79Exposure (%): 73.36 Avg. gain/loss (%): 0.09Profit factor: 1.05 Avg. holding time: 34.09Payoff ratio: 0.25 Avg. profit (winners): 12.67Recovery factor: 0.35 Avg. hold time (winners): 53.33

Drawdown Avg. loss (losers) %: -7.88

Max. DD (%): 35.29 Avg. hold time (losers): 21.91Longest flat days: 1,766 Max. consec. win/loss: 6/14

STRATEGY SUMMARY

ACTIVE TRADER • June 2003 • www.activetradermag.com 14

at the start of the period are about the same as picking thewinning lottery numbers for tomorrow. The parameters thatworked best in the past years are unlikely to be those thatwork best in the future.

However, there are ways to use optimization effectively. Onetechnique is called “walk-forward optimization.” First, systemparameters are optimized on an initial (“sample”) data period.Second, the best-performing parameters are used to execute thesystem on a new, historical (“out-of-sample”) data period afterthe sample period. This allows you to find out if the optimizedparameters would have improved the results going forward,without cheating by using hindsight.

We performed a walk-forward optimization on the 100-20channel breakout system by first optimizing the long and theshort channel periods for the first nine years of historicalprice data. We then used the best-performing parameter val-ues for each stock in the portfolio (see Table 1) and appliedthem to the last year of historical price data.

Figure 4 is the equity curve for this optimized system. Thewalk-forward optimized system lost 1.54 percent during theone-year period, but buy and hold lost 30.57 percent. (Thesystem lost nearly 9 percent during this same year using thedefault parameter values of 100 and 20.) The walk-forwardoptimization was effective in this case.

The 100-20 channel breakout performs much better on thelong side than on the short side in stocks. Although it may bepossible to improve the system’s performance by optimizingthe channel periods for each stock, optimization must be used

with caution. The walk-forward technique described here canhelp you find more realistic optimized parameters that have abetter chance of performing well in real trading.

— Compiled by Dion Kurczek of Wealth-Lab Inc.


120,000115,000110,000105,000100,00095,00090,00085,00080,00075,00070,00065,00060,00055,00050,00045,00040,00035,00030,00025,00020,00015,00010,0005,000

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Equity Cash Linear reg Long Short Buy & holds

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FIGURE 4 WALK-FORWARD OPTIMIZATION RESULTS

3/1/02 4/3/02 5/7/02 6/12/02 7/22/02 8/28/02 10/7/02 11/15/02 2/6/031/2/03

After finding the optimal long and short channel lengths for eachstock over the first nine years of historical data, we tested theparameters on the most recent year of data. The system outper-formed buy and hold (as well as the un-optimized parameters).


Trading System LabTrading System LabFUTURES

100-20 channel breakout systemSystem concept: The channel breakout is probablyone of the oldest trend-following systems around(see the stock Trading System Lab on p. 46), andone that has been especially popular in futuresmarkets over the years, for better or worse.

The system results published here are based ona 100-day channel length for trade entries and a 20-day channel length for exits. The channel lengthsare relatively long, because the system is intendedto catch long-term moves.

This system goes long and short. The stop levelsfor both long trades and short trades play animportant role, because they are used to calculatethe position sizes in the different contracts.

Rules1. Enter long on the next bar at the highest

100-day high.2. Exit long on the next bar at the lowest

20-day low.3. Enter short on the next bar at the lowest

100-day low.4. Exit short on the next bar at the highest

20-day high.(All trades are executed as stop orders.)

Money management1. Risk a maximum of 2 percent of account equity

per trade. (Results will also be discussed for a 6-percent maximum risk version of the system.)2. To determine the position size (number of con-tracts to trade), multiply the difference between theentry price and the stop-loss price by the dollarvalue of a one-point move in the contract, anddivide the result by the contract’s minimum margin.

For example, assume the contract being tradedhas a point value of $250 and a $1,000 marginrequirement. Next, assume the initial entry buystop is at $100 (the value of the 100-day high) andthe initial stop-loss level is at 80 (the lowest 20-day low). In this case, you would buy five [{(100 –80)* $250}/$1000 = 5] contracts.

The $5,000 maximum loss this five-contracttrade represents should not be more than 2 per-cent of the current portfolio equity. As a result,unless the account equity is in excess of $250,000,the system would not be able to take this position.

Starting equity: $100,000. Deduct $10 slip-page/commission per trade.

Test data: The system was tested on the ActiveTrader standard futures portfolio, which containsthe following 20 futures: DAX30 (AX), corn (C),crude oil (CL), German bund (DT), Eurodollar(ED), Euro Forex (FX), gold (GC), copper (HG),Japanese yen (JY), coffee (KC), live cattle (LC),lean hogs (LH), Nasdaq 100 (ND), natural gas(NG), soybeans (S), sugar (SB), silver (SI), S&P 500

FIGURE 1 EQUITY CURVE: 2 PERCENT MAXIMUM RISK


3/25/93 3/1/94 2/1/95 1/4/96 1/2/97 1/2/98 1/4/99 1/3/00 1/2/01 1/2/02

The system equity curve with the 2-percent maximum loss setting has arelatively stable uptrend.

220,000

210,000

200,000

190,000

180,000

170,000

160,000

150,000

140,000

130,000

120,000

110,000

100,000

90,000

80,000

70,000

60,000

50,000

40,000

30,000

20,000

10,000

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FIGURE 2 EQUITY CURVE: 6 PERCENT MAXIMUM RISK

3/25/93 3/1/94 2/2/95 2/1/96 1/8/97 1/2/98 1/4/99 1/3/00 1/2/01 1/2/02

The equity curve using a 6-percent maximum per-trade loss highlightslarge returns accompanied by high volatility and large drawdowns.

3,400,000

3,200,000

3,000,000

2,800,000

2,600,000

2,400,000

2,200,000

2,000,000

1,800,000

1,600,000

1,400,000

1,200,000

1,000,000

800,000

600,000

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(SP) and10 year T-Notes (TY). The test used RatioAdjusted data from Pinnacle Data Corp

Test period: August 1993 to November 2002.System results: Both the long and short sides of the sys-tem were profitable, and the ratio of winning to losingtrades was fairly balanced. The equity curve (Figure 1)using the 2-percent maximum loss setting shows a rela-tively smooth, steady uptrend. The 6-percent maximumloss version (Figure 2) posts a much larger gain, withmuch higher volatility.

The position-sizing method keeps the system out ofmany risky positions, although it resulted in no trade sig-nals in some markets because the risk was too highthroughout the entire test period.

To show the effect of the amount of risk taken, comparethe drawdown curves in Figures 3 and 4. Figure 3 is thedrawdown curve using a maximum risk of 2 percent. The maximumdrawdown during this period was approximately 18 percent. Figure4 shows the result of increasing the maximum per-trade risk to 6percent. The effect is dramatic: The drawdown increased to 50 per-

cent.The system results on the futures portfolio were fair-

ly good when the maximum risk was set to 2 percentper trade. The system returned an average profit of 8.42percent per year, with the largest losing year being -8.91percent. The system’s market exposure was low — onaverage, about 30 percent.

Based on this information, the idea of increasing therisk and taking more contracts for each signal mightsound like a good idea, especially because there is stillplenty of margin available. Even though the systemreached an account value of more than $3 million (refer

to Figure 2), the accompanying drawdown would have been near-ly impossible to stomach. Exposure climbed near 70 percent, andthe longest wait between new equity highs was more than 750trading days.

The 100-20 channel breakout performed fairly well in this test. Asdiscussed in the stock Trading System Lab, you can experimentwith the system by optimizing the channel periods for each market.

— Compiled by Dion Kurczek of Wealth-Lab Inc.

LEGEND: Avg. return — the average percentage for the period • Sharperatio — average return divided by standard deviation of returns (annualized)• Best return — best return for the period • Worst return — worst returnfor the period • % Profitable periods — the percentage of periods that wereprofitable • Max. consec. profitable — the largest number of consecutiveprofitable periods • Max. consec. unprofitable — the largest number ofconsecutive unprofitable periods



Profitability Trade statisticsNet profit ($): 99,997.48 No. trades: 292Net profit (%): 100.00 Win/loss (%): 45.21Exposure (%): 29.99 Avg. gain/loss (%): 0.73Profit factor: 1.50 Avg. holding time: 37.60Payoff ratio: 1.64 Avg. gain (winners) %: 6.22Recovery factor: 3.21 Avg. hold time (winners): 56.30

Drawdown Avg. loss (losers) %: -3.80

Max. DD (%): 19.59 Avg. hold time (losers): 22.17Longest flat days: 685 Max. consec. win/loss: 5/7

STRATEGY SUMMARY

LEGEND: Net profit — profit at end of test period, less commission •Exposure — the area of the equity curve exposed to long or short positions, asopposed to cash • Profit factor — gross profit divided by gross loss • Payoffratio — average profit of winning trades divided by average loss of losingtrades • Recovery factor — net profit divided by max. drawdown • Max DD(%) — largest percentage decline in equity • Longest flat days — longestperiod, in days, the system is between two equity highs • No. trades — num-ber of trades generated by the system • Win/Loss (%) — the percentage oftrades that were profitable • Avg. gain — the average profit for all trades •Avg. hold time — the average holding period for all trades • Avg. gain(winners) — the average profit for winning trades • Avg. hold time (win-ners) — the average holding time for winning trades • Avg. loss (losers) —the average loss for losing trades • Avg. hold time (losers) — the averageholding time for losing trades • Max. consec. win/loss — the maximumnumber of consecutive winning and losing trades


PERIODIC RETURNSAvg. Sharpe Best Worst Percentage Max. Max.

return ratio return return profitable consec. consec.periods profitable unprofitable

Weekly 0.15% 0.64 7.29% -6.14% 52.07% 6 9

Monthly 0.66% 0.61 12.32% -9.25% 55.08% 6 6

Quarterly 1.99% 0.55 24.09% -8.25% 40.00% 4 6

Annually 8.58% 0.58 29.99% -8.91% 66.67% 3 2

0.00%

-2.00%

-4.00%

-6.00%

-8.00%

-10.00%

-12.00%

-14.00%

-16.00%

-18.00%

FIGURE 3 DRAWDOWN CURVE: 2 PERCENT MAXIMUM RISK

3/25/93 3/1/94 2/1/95 1/9/96 1/2/97 1/2/98 1/4/99 1/3/00 1/2/01 1/2/02

The maximum drawdown was about 18 percent.

0.00%

-5.00%

-10.00%

-15.00%

-20.00%

-25.00%

-30.00%

-35.00%

-40.00%

-45.00%

-50.00%

3/25/93 3/1/94 2/1/95 1/9/96 1/2/97 1/2/98 1/4/99 1/3/00 1/2/01 1/2/02

The drawdown increased both in depth and length in this version of the system.

FIGURE 4 DRAWDOWN CURVE: 6 PERCENT MAXIMUM RISK

Market: Futures (indices).

System concept: This is an intraday systemthat trades on breakouts of the range estab-lished in the first hour of trading. For a detailedexplanation of the strategy please read thestock Trading System Lab on p. 50.

The intention was to see how the system per-formed on stock index futures as opposed toindividual stocks. In this test the S&P 500 (SPY)and Nasdaq 100 (QQQ) index-tracking stockswere used as proxies for the S&P 500 andNasdaq 100 futures.

Entry rules: Long trades: Buy if the closing price of the

third 30-minute bar is above the high of thefirst 60 minutes of the day.

Short trades: Sell short if the closing price ofthe third 30-minute bar is below the low of thefirst 60 minutes of the day.

Exit: Exit all positions on signals in theopposite direction or at the end of the day.

Money management: To equalize the weight ofboth markets, 49 percent of the current portfo-lio capital is allocated for every trade. Forexample, if the total equity moves up to $22,000and our strategy generates a new signal, wewould invest $10,780 for the next signal. Weuse 49 percent to give us some leeway for com-mission. Please keep in mind that we use theportfolio result and not the individual result.This is very important and should always beused since only this method reflects what youwould actually experience later in your trad-ing.

Starting equity: $20,000 (nominal). Deduct$0.01 per share slippage and commissions.

Test period: October 2001 until October 2003.

Test data: SPY and QQQ. The SPY is designedto trade at one-tenth the level of the S&P 500;the QQQ is designed to trade at one-fortieth of the Nasdaq 100.

Like futures, the uptick rule to enter short positions does notapply to these instruments. QQQ and SPY can be traded intra-day but have the advantage that no rollover occurs every threemonths.

We downloaded more than two years of 30-minute bars fromthe QCharts historical intraday database for SPY and QQQ.There are a few interesting things to note. For the first hourrange we take the prices from 9:30 a.m. to 10:30 a.m. and for theclosing time we use 4:15 p.m. This is important because we will




10/15/01 1/8/02 4/1/02 6/24/02 9/23/02 12/30/02 4/2/03 6/26/03 9/25/03

The system produced a modest profit, with long trades outperforming shorts.

24,000

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)

Equity Cash Long Short Buy & hold

60-minute breakout system

18 www.activetradermag.com • January 2004 • ACTIVE TRADER


10/15/01 1/2/02 3/14/02 5/30/02 8/9/02 10/28/02 1/21/03 4/2/03 6/13/03 8/29/03

The largest drawdown occurred early in the test period. The system’sbiggest string of losing trades was seven.

0%-1%-2%-3%-4%-5%-6%-7%-8%-9%

-10%-11%-12%-13%

close all positions not triggered by an opposite signal at theclose of the day.

Test results: The results for the two years are very encourag-ing: a profit of 19.88 percent on the starting capital in twoyears, compared to an unchanged result for the combinedequities of the two indices (see Figure 1).

The system generated its largest drawdown (-13.52 per-cent) on Feb. 21, 2002 (see Figure 2). Buy and hold’s largestdrawdown (on Oct. 9, 2002) was -44.87 percent.

Because of the low average profit per trade, the systemrequires more fine tuning. Nevertheless, the ratio of tradesthat kept their original position for the whole day makes thisstrategy worthy of further investigation.

— Volker Knapp of Wealth-Lab Inc.

ACTIVE TRADER • January 2004 • www.activetradermag.com 19

On the downside, the average profitper trade was just 0.05 percent, or $4.46.This may be too little to really trade thesystem. Looking at the statistics it is inter-esting to note that out of the 892 tradesover the last two years, only 102 werestopped out by the opposite signal whilethe rest stayed with the initial direction. Itseems that in most cases, once the marketbegins an intraday trend, it continues inthat direction throughout the day.

Figure 3 shows a short trade on Sept.10, 2003, that was exited at the close of theday. On the following day it appeared themarket was continuing its down move.We received a short signal but gotstopped out after the market bouncedback.

Bottom line: There is a big differencebetween indices and stocks in regard tothis system. Individual stocks tend to bemuch more volatile than an index; also,with an index you hardly see largeovernight gaps. This might be one reasonthe 60-minute breakout system performs much better on theETFs than it does on the individual stocks.

Profitability Trade statisticsNet profit ($): 3,976.80 No. trades: 892Net profit (%): 19.88 Win/loss (%): 54.60Exposure (%): 44.95 Avg. gain/loss (%): 0.05Profit factor: 1.11 Avg. hold time: 7.21Payoff ratio: 0.93 Avg. profit (winners) %: 0.80Recovery factor: 1.38 Avg. hold time (winners): 7.35Drawdown Avg. loss (losers) %: -0.86

Max. DD (%): -13.52 Avg. hold time (losers): 7.04Longest flat days: 1,742 Max. consec. win/loss: 10/7

STRATEGY SUMMARY

LEGEND: Net profit — Profit at end of test period, less commission •Exposure — The area of the equity curve exposed to long or short positions,as opposed to cash • Profit factor — Gross profit divided by gross loss •Payoff ratio — Average profit of winning trades divided by average loss of los-ing trades • Recovery factor — Net profit divided by max. drawdown •Max. DD (%) — Largest percentage decline in equity • Longest flat days —Longest period, in days, the system is between two equity highs • No. trades— Number of trades generated by the system • Win/Loss (%) — The per-centage of trades that were profitable • Avg. gain — The average profit for alltrades • Avg. hold time — The average holding period for all trades • Avg.gain (winners) — The average profit for winning trades • Avg. hold time(winners) — The average holding time for winning trades • Avg. loss (los-ers) — The average loss for losing trades • Avg. hold time (losers) — Theaverage holding time for losing trades • Max. consec. win/loss — The max-imum number of consecutive winning and losing trades

PERIODIC RETURNS


periods profitable unprofitable

Weekly 0.19% 0.80 7.31% -3.36% 53.85% 7 8

Monthly 0.77% 0.88 6.97% -3.17% 52.00% 2 4

Quarterly 2.07% 1.39 6.19% -2.19% 66.67% 6 2

LEGEND: Avg. return — The average percentage for the period • Sharperatio — Average return divided by standard deviation of returns (annual-ized) • Best return — Best return for the period • Worst return — Worstreturn for the period • Percentage profitable periods — The percentage ofperiods that were profitable • Max. consec. profitable — The largest num-ber of consecutive profitable periods • Max. consec. unprofitable — Thelargest number of consecutive unprofitable periods




9/10/03 9/11/03

The average hold time for both winning and losing trades was around seven days.

Nasdaq 100 index-tracking stock (QQQ), 30-minute

Sell

Buy

Sell

Buy

34.10

34.00

33.90

33.80

33.70

33.60

33.50

33.40

33.30

33.20

33.10

33.00


Market: Nasdaq 100 index-tracking stock (QQQ).

System concept: This system is from the book Tradelike a Hedge Fund (John Wiley & Sons, 2004) byJames Altucher.

Both day traders and hedge-fund managers loveto “fade” (i.e., trade in the opposite direction of)sharp intraday moves. The thought behind thistype of trading is price is unlikely to go much high-er after an extreme up move. Traders take shortpositions anticipating a reversal.

In some situations, however, the market ignoresthe contrarians and continues to rise. Traders whofade the up move must cover their short positions,which leads to panic buying and further upwardmomentum.

The four-percent breakout system is an attemptto quantify and profit from this market scenario.The system goes long when price rises four percentfrom the previous close — in this case, assumed tobe the point at which short sellers must concedethey were wrong and cover their positions, drivingprices even higher during the trading day. The sys-tem is long only; no short trades are made.

Rules:Entry — Buy today if price gains four percent

from the previous trading day’s closing price.Exit — Exit on the open of the next trading day.

Figure 1 shows sample trades in QQQ from Marchand April 2003.

Risk control and money management: This systemtests only one market, and enters only one position ata time, so 100 percent of equity should be tied up oneach trade.

Starting equity: $100,000. Deduct $20 per round-turntrade for slippage and commissions.

Test data: The system was initially tested only on theQQQ. It was also tested on the Active Trader StandardStock Portfolio, which contains the following 18stocks: Apple Computers (AAPL), Boeing (BA),Citigroup (C), Caterpillar (CAT), Cisco Systems(CSCO), Disney (DIS), General Motors (GM), Hewlett-Packard (HPQ), International Business Machines(IBM), Intel (INTC), International Paper (IP), J.P.Morgan Chase (JPM), Coca-Cola (KO), Microsoft(MSFT), Sears (S), Starbucks (SBUX), AT&T (T) andWal-Mart (WMT).

Test period: March 1999 through June 2004 for theQQQ test; July 1994 to June 2004 for the Active Traderportfolio.

Four-percent breakout system

20 www.activetradermag.com • September 2004 • ACTIVE TRADER


The equity grew steadily from 2000 through 2002, but the systemhas been stagnating since mid-2002.

Equity Cash

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3/10/99 9/1/99 3/1/00 9/1/00 3/1/01 9/4/01 3/8/02 9/5/02 3/6/03 9/3/03 3/3/04

250,000240,000230,000220,000210,000200,000190,000180,000170,000160,000150,000140,000130,000120,000110,000100,00090,00080,00070,00060,00050,00040,00030,00020,00010,000

0


March and April 2003 were very active months for the four-percentbreakout system. There was one large winner, two mid-size winnersand one large losing trade.

Nasdaq 100 index-tracking stock (QQQ), daily

Volume

Buy

Buy

Buy Buy

Sell

Sell

Sell

Sell

April 2003

27.4027.2027.0026.8026.6026.4026.2026.0025.8025.6025.4025.2025.0024.8024.6024.4024.2024.0023.8023.6023.40

100M

50M



LEGEND: Net profit — Profit at end of test period, less commission • Exposure— The area of the equity curve exposed to long or short positions, as opposed to cash• Profit factor — Gross profit divided by gross loss • Payoff ratio — Averageprofit of winning trades divided by average loss of losing trades • Recovery factor— Net profit divided by max. drawdown • Max. DD (%) — Largest percentagedecline in equity • Longest flat days — Longest period, in days, the system isbetween two equity highs • No. trades — Number of trades generated by the sys-tem • Win/Loss (%) — the percentage of trades that were profitable • Avg. trade— The average profit/loss for all trades • Avg. winner — The average profit forwinning trades • Avg. loser — The average loss for losing trades • Avg. hold time— The average holding period for all trades •Avg. hold time (winners) — Theaverage holding time for winning trades • Avg. hold time (losers) — The aver-age holding time for losing trades • Max. consec. win/loss — The maximumnumber of consecutive winning and losing trades

ACTIVE TRADER • September 2004 • www.activetradermag.com 21

Trading System Lab strategies are tested on a portfolio basis (unless otherwise noted) using Wealth-Lab Inc.’s testing platform.

If you have a system you’d like to see tested, please send the trading and money-management rules to [email protected].

Profitability Trade statisticsNet profit ($): 121,023 No. trades: 105Net profit (%): 121.09 Win/loss (%): 64.76Exposure (%): 7.81 Avg. trade (%): 0.80Profit factor: 1.82 Avg. winner (%): 2.38Payoff ratio: 1.13 Avg. loser (%): -7.55Recovery factor: 4.05 Avg. hold time: 1.00

Drawdown ($): 29,900 Avg. hold time (winners): 1.00Max. DD (%): -11.93 Avg. hold time (losers): 1.00Longest flat days: 420 Max. consec. win/loss: 11/5

STRATEGY SUMMARY

LEGEND: Avg. return — The average percentage for the period • Sharpe ratio —Average return divided by standard deviation of returns (annualized) • Best return— Best return for the period • Worst return — Worst return for the period •Percentage profitable periods — The percentage of periods that were profitable •Max. consec. profitable — The largest number of consecutive profitable periods •Max. consec. unprofitable — The largest number of consecutive unprofitable periods

Test results: Figure 1 shows the first trade was a suc-cess. Price rose 40 cents after entry and the marketmade a small gap open the following day for a two-percent profit. The next two trades, which occurredonly a few days later, were not as successful butnonetheless booked modest profit.

However, the next trade wiped out the previousprofit and then some. Price gapped up at the marketopen, beyond the four-percent threshold, and theentry order was filled (this particular trade wouldhave probably been subject to negative slippage because of thevolatility at the open).

Price then suddenly reversed, and the result was a large lossupon the exit the following day. The fact that the initial losingtrade occurred on a day when prices gapped above the entrylevel on the open suggests the system might benefit from a fil-ter that ignores the signal if price opens with a greater thanfour-percent gain.

The equity curve (Figure 2) provides a better indication ofthe system’s overall performance. After a small loss in 1999,profits began in early 2000 and lasted until mid- to late 2002.

The drawdown curve (Figure 3) confirms this, as the 12-per-cent drawdown began in late 2002. The system is more or lessflat from April 2003 forward. The only trade after that was inJuly 2003, resulting in a loss of 0.08 percent.

Portfolio test results: While it is still too early to tell if this sys-tem is worth trading on the QQQ (because it’s possible the sys-tem was subconsciously designed to take advantage of what

the designers knew of previous QQQ price movement), it wastested on other markets in an attempt to determine its validity.

Our starting equity for the Active Trader portfolio test wasalso $100,000, although only 10 percent of equity was commit-ted per trade.

This equity curve (Figure 4, p. 60) shows fairly steadygrowth from the beginning of the test period through mid-2002. From that point, there is a slight decline in capital and ageneral stagnation as fewer trades take place.

This equity curve mirrors the QQQ equity curve. However,the fact that the system was profitable on a portfolio of stocks(8.95 percent annualized gain) and not just one stock is evi-dence the system is based on a valid core assumption.

System variation: James Altucher publishes a variation of thesystem that adds one additional entry rule: Price must be downtwo percent on the day before entering a trade. This rule isintended to avoid entering when a price move is nearlyexhausted, and allows the system to capture solid rebound


periods* profitable unprofitableWeekly 0.30% 1.18 11.89% -9.23% 22.10% 4 63

Monthly 1.30% 1.35 13.34% -6.40% 50.00% 10 15

Quarterly 3.93% 1.03 25.74% -6.98% 59.09% 8 5

Annually 16.71% 0.56 75.51% -2.52% 66.67% 4 2

*The system remains flat much of the time. A flat period is considered unprofitablefor purposes of this report.

PERIODIC RETURNS


The drawdown phase from mid-2002 to the present dominates thedrawdown curve.

0%-1%-2%-3%-4%-5%-6%-7%-8%-9%

-10%-11%

3/10/99 9/1/99 3/2/00 9/1/00 3/5/01 9/4/01 3/8/02 9/6/02 3/7/03 9/5/03 3/5/04

Dra

wdo

wn


moves. This generally increases the efficiencyof the system while reducing the number ofactual trades.

The bottom equity curve in Figure 4 showsthe results of the system variation on theActive Trader portfolio. Since the new filterreduces the number of trades, the positionsize was changed to 25 percent for each trade.The shape of the equity curve is similar to theprevious run, but actual profit is higher andthe system does not enter as many losingtrades during the stagnation period of mid-2002 to present.

Bottom line: The four-percent breakout sys-tem could not be much simpler. Simpler sys-tems are often the most effective, and thisone is no exception. However, there needs tobe sufficient “post-publication” data to pro-vide a reliable test for the QQQs.

This system is from James Altucher’s Tradelike a Hedge Fund.

— Compiled by Volker Knapp of Wealth-Lab

FIGURE 4 EQUITY CURVE: ACTIVE TRADER PORTFOLIO

The upper equity curve shows the results of the four-percent breakoutsystem on the Active Trader standard stock portfolio using 10 percent ofequity per trade. The lower equity curve is a system variation that entersafter a down move and uses 25 percent of equity per trade.

Equity Cash

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7/15/94 7/31/95 6/7/96 6/2/97 6/1/98 5/6/99 5/1/00 5/1/01 5/1/02 5/1/03 4/5/04

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A

B

T rying to determine when abreakout will occur inbroadening chart patterns,which are expanding rather

than contracting price formations, can bedifficult. However, partial rises (PRs) or

partial declines (PDs) can improve theodds of making a correct decision.

These signals predict immediatebreakouts and indicate their direction,too, allowing you to increase your prof-its and reduce your losses. However,

because a PR or PD often slows overallmomentum, the size of the eventualbreakout is not as large as when a PR orPD does not appear.

Broadening tops and bottomsFigure 1 shows two broad-ening bottom patterns.These are different frombroadening tops becauseprice enters the pattern fromthe top. In both patterns,price touches each trendlineat least two times andswings in a progressivelywider range. That is, theminor highs get higher andthe minor lows get lower.

The July pattern shows aPR, which occurs after thepattern is established — thatis, there were at least twotouches of each trendlinebefore the PR. Price makes apartial rise when it leaves thebottom trendline and worksits way higher but fails totouch or come too close tothe top trendline before turn-ing away.

How close is “close”? Usethe figures in this article andyour common sense asguides. For example, theJuly broadening bottom hasthree top trendline “touch-

ACTIVE TRADER • April 2004 • www.activetradermag.com 23

BROADENING PATTERNS:Clues to breakout directionA partial rise or decline can predict the direction of a breakout.

Learn to use these signals to increase profits when trading broadening patterns.

The July broadening bottom pattern appeared midway through the down move. A partialrise accurately signaled a downside breakout from the pattern.

FIGURE 1 PARTIAL RISE

Milacron Inc. (MZ), daily

Partial rise

1998 Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec.

1 2 34

34

32

30292827262524232221201918

17

16

15

14

13

12

Source: Proprietary software (Thomas Bulkowski)

BY THOMAS N. BULKOWSKI

TRADING Strategies

es,” not two or four: The second minorhigh (point 2) comes close enough to callit a touch, but the third (point 3) doesnot.

Analysis of 77 broadening bottoms on

500 stocks from mid-1991 to mid-1996, abull market, showed that a PR correctlypredicted a downward breakout 67 per-cent of the time. The accuracy rate ofPDs predicting upside breakouts was

even better — 80 percent (see Table 1, topright, for more statistics).

Notice how the July pattern is mid-way between the price at the start of thedowntrend (around 32) and its low(around 14). The middle of the pattern isaround 23, the center of the 32-14 range.Although broadening patterns some-times act as “half-staff patterns” thatform in the middle of moves, broaden-ing bottoms usually function as reversalsin a downtrend, not as continuation pat-terns within those trends, as they do inFigure 1.

Figure 2 includes two broadeningtops with PDs. In a partial decline, priceleaves the top trendline and descendsbut does not come close to or touch thebottom trendline. An upward breakoutusually follows immediately. Again, thebroadening top pattern must touch each

trendline at least two timesbefore a PD signal can occur.

Table 1 shows PDs inbroadening tops correctlypredicted an upward break-out 65 percent of the time,while partial rises were 86-percent accurate in predict-ing downside breakouts.

In a larger combinedstudy of broadening topsand bottoms, PDs worked 77percent of the time. When aPD occurred, the post-break-out up move was 32 percent;without a PD, the rise meas-ured 36 percent. Thus, thePD affected momentum byreducing the eventual rally.PDs not resulting in break-outs occurred just nine per-cent of the time, whichmeans false signals are com-paratively rare.

For PRs, the post-breakoutdecline measured 15 percent;without a PR, the declinesaveraged 17 percent, indicat-ing a partial rise steals ener-gy from the resulting downmove.

24 www.activetradermag.com • April 2004 • ACTIVE TRADER

The percentages reflect how often partial rises and partial declines predictedbreakout direction.

TABLE 1 PARTIAL RISES AND DECLINES: SUCCESS RATES

Chart pattern Partial decline Partial rise

Broadening bottom 80% 67%

Broadening top 65% 86%

Broadening wedge, ascending Not measured 84%

Broadening wedge, descending 76% Not measured

Right-angled and ascending Not measured Not measured

Right-angled and descending 78% 58%

Both of these broadening tops included partial declines, which predicts an upward break-out of the pattern.

FIGURE 2 BROADENING TOPS

Newport Corporation (NEWP), daily

Partial decline

Partial decline

1997 June July Aug. Sept. Oct. Nov. Dec. 1998 Feb. Mar. Apr. May June

7

6

5

4

3

2


False breakout signals forPRs (i.e., when a partial riseoccurred inside a broadeningtop pattern after pricetouched each trendline twicewithout triggering a break-out) occurred just 11 percentof the time in the 350 pat-terns examined.

A broadening top usuallyacts as a continuation patternwithin the prevailing pricetrend, as shown in Figure 2.

Right-angled broadeningformationsFigure 3 shows a “right-angled,” ascending broaden-ing formation. The top trend-line slopes upward (ascends)and the bottom trendline ishorizontal or nearly so. Likeother broadening patterns,the breakout can occur inany direction, but this pat-tern usually reverses thetrend. The figure shows this,as prices rise into the patternand exit out the bottom.

After two touches of eachtrendline occur, look for apartial rise or decline. Thelate-May decline in Figure 3does not show a partialdecline. Why? Because thepattern at that point did nothave at least two minortouches of each trendline.Price touches at point 1 but itis not a minor high or low, soit does not count as a touch.Point 2 is valid, as is point 3.Only after price touchespoint 3 can you draw thehorizontal trendline. By thattime, the three touches onthe top connect an up-slop-ing trendline. The partial risethat follows correctly pre-dicts a downward breakout.

Figure 4 shows a descend-ing right-angled broadening


Look for a partial rise or decline only after price touches each trendline of the broaden-ing pattern at least twice. Here, a partial rise formed in this ascending, right-angledbroadening formation.

FIGURE 3 TRENDLINE TOUCHES

Tommy Hilfiger (TOM), daily

Partial rise

Not a partial decline

1998 Feb. Mar. Apr. May June July Aug. Sept. Oct.

1 2 3

37

35

33

313029282726252423222120

19

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Although broadening formations are often continuation patterns, descending, right-angledbroadening formations like the one shown here usually act as reversal patterns.

FIGURE 4 REVERSAL PATTERN

CDI Corp. (CDI), dailyPartial rise

1994 Nov. Dec. 1995 Feb. Mar. Apr. May June July Aug. Sept.

26

2524

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pattern. The top trendline ishorizontal and the bottomone slopes down. Pricetouches the bottom trend-line, bounces up but does notcome close to or touch thetop trendline before retrac-ing its gains. This PR predict-ed a downward breakout. Asis the case with this example,the descending, right-angledbroadening pattern usuallyacts as a price reversal. In thedescending pattern, partialrises worked just 58 percentof the time and partialdeclines worked 78 percentof the time in descending,right-angled broadening pat-terns.

Broadening wedgesFigure 5 shows a descendingbroadening wedge, whichconsists of two down-slop-ing trendlines (think of adownward-tilting mega-phone). The rules for wedgesare the same as other broad-ening patterns: There mustbe at least two minor hightouches of the top trendlineand at least two minor lowtouches of the bottom trend-line. Only then is the patternvalid and only then shouldyou look for a partial rise ordecline.

The pattern usually acts asa continuation, rather than areversal, of the prevailingprice trend. However, thetwo wedges shown in Figure5 are reversal patterns. In theAugust pattern, pricesclimbed into the pattern andbroke out to the upside, butthe overall trend (except fora few days after the break-out) was downward after thepattern. Prices in the Octoberwedge were trending down-ward into the pattern andexited out its top. The trendafter the pattern ends is pre-dominantly upward.

In the August patternexample, the slight dip in

26 www.activetradermag.com • April 2004 • ACTIVE TRADER

The August pattern did not produce a valid partial decline because price must drop fromthe top trendline and curl around. Here, price rose from the bottom trendline. The firstpartial decline in the October pattern fails to predict an immediate upward breakout, butis correct in the longer term.

FIGURE 5 DESCENDING BROADENING WEDGE

Transocean Inc. (RIG), daily

Partial declines

Not a partial decline

1999 June July Aug. Sept. Oct. Nov. Dec. 2000 Feb. Mar.

525048464442

4038

36

34

323130292827262524232221

20


After the pattern is established, a partial decline fails to correctly predict an upwardbreakout. Later, a partial rise precedes a downside breakout.

FIGURE 6 ASCENDING BROADENING WEDGE

WPS Resources Corp. (WPS), daily

Partial rise

Failed partial decline

1999 Aug. Sept. Oct. Nov. Dec. 2002 Feb. Mar. Apr. May June

32

31

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28

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25

24

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early September was not a partialdecline. Price in a PD must start from thetop trendline, bow downward (withoutcoming close to or touching the bottomtrendline) and rejoin the top trendline. Inthis case, price leaves the bottom trend-line, not the top one.

In the October pattern, the first partialdecline is a failure because price doesnot breakout upward immediately aftertouching the top trendline. Instead, pricedrops down again and finally shoots outthe top.

A partial decline correctly predicts anupward breakout 76 percent of the time.Not enough samples were found for par-tial rises in descending broadeningwedges.

Figure 6 (p. 28) shows an ascendingbroadening wedge. Both trendlinesslope upward and minor highs andminor lows touch each trendline at leasttwice. The January partial decline failedbecause price did not break out to theupside — it touched the top trendline,then reversed.

The partial rise does better when itleaves the bottom trendline, bounces upand then plunges through the bottomtrendline. A PR correctly predicts adownward breakout 84 percent of thetime.�


Additional research

Books by Thomas Bulkowski:Encyclopedia of Chart Patterns (John Wiley & Sons, 2000)

Trading Classic Chart Patterns (John Wiley & Sons, 2002)

Active Trader articles: “Technicals meet fundamentals in the earnings flag,” February 2004, p. 30

“A different breed of scallop,” January 2004, p. 32

“The three rising valleys pattern,” December 2003, p. 28

“Pipe bottom reversals,” November 2003, p. 28

“Grabbing the bull by the horns,” September 2003, p. 46

“Head-and-shoulders bottoms: More than meets the eye,” August 2003, p. 32

“The high-low game,” July 2003, p. 28

“Tom Bulkowski’s scientific approach,” September 2002, p. 32

A high, tight flag (HTF) is aconsolidation pattern thatforms after a stock’s pricedoubles. When price breaks

out above the pattern, it signals the riseis not over.

Figure 1 shows an example of an HTFthat formed in January-February 2000.The uptrend started in October at a lowof 5.50 and reached a high of 11.35 at theHTF’s starting point — a doubling ofprice in less than a month.

Although many HTFs have irregularshapes, you can usually draw a trendlinealong the top of the pattern to signal abreakout. In this example, parallel trend-lines mark the flag’s upper and lowerboundaries. Volume slopes downwardover the course of the flag, as it did in 90percent of HTFs in a recent study.

The basic HTF trade strategy is to buyat the close of the day after price breaksout above the pattern’s upper trendline.In Figure 1, the stock rallied 52 percentfrom the closing price the day after pricepierced the HTF’s upper trendline to theultimate high.

Although it is sometimes difficult tobuy high and sell higher, the price

moves following HTFs show how suchan approach can work.

Flag criteriaWhat should you look for when select-ing HTFs? That depends on whom youask. William O’Neil, who popularizedthe pattern, has several selection criteria(see “Additional reading,” p. 33). He haswritten the rally preceding the patternshould measure 100 to 120 percent andtake less than two months; the flagshould move sideways for three to fiveweeks. Finally, the flag should retrace nomore than 20 percent of the precedingrally.

Applying these rules to 252 patternsfound in price data of approximately 500stocks between mid-1991 and early 2004filtered out all of them! (An earlier studyfound only six of 81 patterns met his crite-ria; these patterns did, however, produceaverage gains of 69 percent.) The flagshown in Figure 1 actually does not meetO’Neil’s criteria because it retraced 52percent of the prior rise (most flags failedO’Neil’s filter because they retraced morethan 20 percent) and the flag durationlasted more than seven weeks .

28 www.activetradermag.com • December 2004 • ACTIVE TRADER

HIGH, TIGHT FLAGhelps squeeze out profitsThis bullish formation boasts excellent

post-breakout performance and a low failure rate —

exactly the type of pattern traders should look

for in bull markets.

BY THOMAS N. BULKOWSKI

TRADING Strategies

Another study that useddifferent selection criteriafor HTFs also showed anaverage post-breakout gainof 69 percent. The criteria forthis study was simply a neardoubling (a rise of 90 per-cent or more in less than twomonths) of the stock price,which is easy to find bylooking for stocks that havemoved up sharply in lessthan two months, thensearching for a nearby con-solidation region. The studyplaced no limit on flaglength, although HTFs equalto or shorter than the 14-daymedian length performedbetter (71 percent) thanthose that were longer (66percent). The study alsoignored the size of theretracement.

HTF examples Figure 2 shows two HTFsidentified in the study. Thetrend start point was deter-mined by finding a 20-per-cent reversal of the existingtrend, measured from aprior low to the most recentclose. The ultimate high wasidentified by finding a sub-sequent 20-percent trendchange, measured from aprior high to the recentclose.

HTF 1 was preceded by a156-percent rally that lasted40 days. The flag retraced 38percent of the rally and last-ed 15 days. After the break-out, price climbed 54 percentto the ultimate high, whichoccurred at a resistance areaestablished by price peaks asfar back as mid-1995 (notshown).

Price rose 121 percentleading up to HTF 2, taking51 days to make the climb.

ACTIVE TRADER • December 2004 • www.activetradermag.com 29

National Semiconductor Corp. (NSM), dailyUltimate high

Ultimate high

HTF 1

HTF 2

Trend start

1999 May June July Aug. Sept. Oct. Nov. Dec. 2000 Feb. Mar.

8476686256504440363228

242220181614

12

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8

6

The first flag looks like a descending, broadening wedge and the second like a fallingwedge. Both HTFs show good gains after the breakout with the first pattern hitting overhead resistance at the ultimate high.

FIGURE 2 TWO HTFS


Trend start

Alkermes (ALKS), daily

Ultimate high

High, tight flag

Trend start

1998 Aug. Sept. Oct. Nov. Dec. 1999 Feb. Mar. Apr. May June

1716151413

12

11

10

9

8

7

6

5

4

This pattern does not meet William O’Neil’s HTF criteria, but the post-breakout rise was52 percent. Such a well-defined flag shape is unusual.

FIGURE 1 A LARGE HIGH, TIGHT FLAG


The flag retraced 45 percentof the rally and was 29 dayslong. After the breakout,price rallied 92 percent. Thispattern did not have over-head resistance to overcomeon its way to the ultimatehigh. That may explain whyprice nearly doubled beforetrending downward.

Notice the irregularshapes of these two HTFs.The first looks like a smallbroadening wedge and thesecond looks like a regularfalling wedge. Volumetrends downward in both.

Figure 3 shows two moreexamples. Starting from aflat base in late 1999, priceclimbs 116 percent leading toHTF 1 and soars 129 percentafterward.

The stock did not performas well after HTF 2 becausethe market changed frombull to bear between the twopatterns (the bear marketstarted in March 2000, nearHTF 1’s ultimate high). Thesecond HTF pattern waspreceded by a 136-percentprice rise and followed by a61-percent rally after thebreakout.

The measure ruleThe second pattern in Figure3 is typical of the rise youcan expect after an HTF in abull market. For all 252 pat-terns in the study, the climbleading to the pattern aver-aged 124 percent, but thepost-breakout gain was just69 percent.

To determine an approxi-mate target, compute thepercentage change from thelow of the trend start pointto the high at the top of theflag. After the breakout, themove from the flag’s lowestlow should measureapproximately half this

30 www.activetradermag.com • December 2004 • ACTIVE TRADER

Vertex Pharmaceuticals (VRTX), daily

Ultimate high

Ultimate high

HTF 1

HTF 2

Trend start

1999 Dec. 2000 Feb. Mar. Apr. May June July Aug. Sept.

938577716559534945413733

29

2523211917

15

13

11

9

The first HTF launches from a flat base and price soars to the ultimate high. The secondtrade is more typical with the rise to the ultimate high about half the distance, on a per-centage basis, from the trend start to the flag.

FIGURE 3 TWO MORE HTFS


Trend start

Noven Pharmaceuticals (NOVN), daily

Ultimate high

Dead-cat bounce

Resistance

HTF

2000 Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov.

4442403836343230

28

26

2423222120191817161514

13

12

This HTF fails to travel far due to overhead resistance and a change in company fundamentals. The stock tumbles on an earnings warning.

FIGURE 4 HTF FAILURE


Trend start

amount. This “measure rule” works 90percent of the time in a bull market.

Pattern failuresFigure 4 illustrates two types of patternfailures. The first is a rise blocked byoverhead resistance. Underlying sup-port or overhead resistance (look for asolid mass of horizontal price movementor peaks and valleys stopping near thesame price area) spells death to mostchart-pattern breakout trades. In thisexample, price climbed 31 percent (thepre-pattern rally was 128 percent) afterthe HTF breakout. That is a significantrally by most standards, but it fails tocome close to the 64-percent gain usingthe measure rule.

The second failure comes from the fun-damentals. The company issued an earn-ings warning for the quarter and said full-year earnings would suffer as well. Thestock tumbled 43 percent in one session.Price bounced up during the next monthbefore “rounding over” and making alower low in a classic “dead-cat bounce”(DCB) pattern. Three months later, thestock dropped another 32 percent on awarning about flat annual revenues.

Problems cannot always be fixed inone quarter. Avoid a stock showing aDCB for at least six months — preferablya year. That will give the company timeto get its act together.

Trading the patternAn HTF triggers a buy signal after astock has made a significant up moveand, thus, will appear “overbought” tomany traders. This is a momentum play,buying high and selling higher. Tradingan HTF is like standing on the edge of thecliff and jumping off, hoping the water atthe bottom is deep enough. You need agood dose of courage to take the plunge.

To trade the pattern, wait for price toeither close above the flag trendline or, ifthe HTF has an irregular shape, use a

close above the highest high in the pat-tern as the buy signal. This is important:If you buy before the breakout, pricemight drop instead. Only 10 percent ofthe 252 patterns in the study failed toclimb at least 20 percent, and none failedto climb less than 5 percent. Those arevery low failure rates.

For protection, use progressive stops.For example, once price makes a new

high, raise the stop to just below theprior minor low, provided it is not toofar away; otherwise use 1.5 times thedaily volatility, which is 1.5 times theaverage intraday trading range over theprior month. Keep raising the stop asprice climbs. Use the measure rule tofind a target price (half the price riseleading to the HTF, projected upwardfrom the flag low). Most of the gains (35percent, on average) will come in thefirst week, so enter as soon as you get thesignal.

Also, the time from the trend start tothe flag start will be slightly less (by sixdays on average) than the time from theflag’s end to the ultimate high. �

ACTIVE TRADER • December 2004 • www.activetradermag.com 31

Additional readingBooks:How to Make Money in Stocks (McGraw-Hill, 1988) by William O’Neil

Books by Thomas Bulkowski:Encyclopedia of Chart Patterns (John Wiley & Sons, 2000)

Trading Classic Chart Patterns (John Wiley & Sons, 2002)

Active Trader articles:

“Trading ‘busted’ patterns,” November 2004, p. 42

“Half-staff patterns: Profiting from flags and pennants,” September 2004, p. 48

“Three falling peaks: Bearish trend change pattern,” August 2004, p. 32

“Chart patterns: Does size matter,” June 2004, p. 44

“Trading disaster: the dead-cat bounce,” May 2004, p. 44

“Broadening patterns: Clues to breakout direction,” April 2004, p. 36

“Technicals meet fundamentals in the earnings flag,” February 2004, p. 30

“A different breed of scallop,” January 2004, p. 32

“The three rising valleys pattern,” December 2003, p. 28

“Pipe bottom reversals,” November 2003, p. 28

“Grabbing the bull by the horns,” September 2003, p. 46

“Head-and-shoulders bottoms: More than meets the eye,” August 2003, p. 32

“Tom Bulkowski’s scientific approach,” September 2002, p. 32

You can purchase past articles at www.activetradermag.com/purchase_arti-cles.htm and download them to your computer.

An HTF triggers a buy signal after a stockhas made a significant up move and, thus,will appear overbought to many traders.


BY KEN CALHOUN

I t is often a struggle to find themost appropriate indicator for agiven trading situation. A toolthat works in one environment

may not be appropriate in another.Momentum oscillators or the Nasdaq

and S&P 500 futures may provide earlysignals of shifts in the stock market, butthese tools also are often unreliable.Moving average crossovers providetrend confirmation but generally lagprice action, and you cannot count onsustained trends in consolidating mar-kets. Further, market makers frequentlydisguise their intentions via ElectronicCommunications Networks (ECNs) orLevel II head fakes, which render theLevel II screen more or less useless. So,what’s a trader to do?

Watch price action. Trading breakoutsand breakdowns of chart patterns is areliable and simple trading techniquethat can help you limit risk. A relativelyconsistent short-term, pattern-based

trade is to buy upside or downsidebreakouts of the previous day’s high orlow, respectively, avoiding trades in themiddle of the day’s range. We’ll showhow to apply this technique using two-minute charts.

The toolsFor this approach, use a two-minute can-dlestick chart encompassing a two-daytime horizon (today and yesterday). Fora long trade, buy the stock once it hascleared the whole number closest to theprevious day’s high.

For example, assume a stock made ahigh of 47.9 yesterday. In this case, youwould enter a buy order when the stockhits 48.5 (having cleared 48, the nearestwhole number) and when time andsales shows that most trades are beingexecuted at the ask price, which wouldsuggest strong demand for the stock.Reverse the logic for short trades.

The reason for placing the entry a cer-tain amount above the previous day’shigh — in this case, 48.5 — is to makesure the trade safely “clears the hurdle”of the previous day’s trading range,accounting for any market noise thatmay be present. We don’t want to buy adouble top, we want to buy a breakoutabove the previous day’s high. Entering0.3 to 0.5 above the whole number helpsavoid false breakouts.

This approach works because manyprofessional traders and institutionalbuyers buy such breakouts. In addition,some institutional buy programs alsofactor in the open, high, low and closingprices. When such programs trigger buysignals and money starts flowing into a

stock, you can ride the coattails of thelarge money on the way up. To makesure, however, don’t enter the trade untilthe stock also has cleared the requirednoise level.

We also use the time and sales win-dow to confirm that any large blocktrades are going our way and that mosttrades are executed at the ask price forlong trades, or the bid price for shortsales. It also is good if a directional chartpattern — i.e., one that implies a moveeither up or down — confirms the break-out. A simple example is successive clos-es at the high (or low) of the price barsleading up to, or coinciding with, thebreakout. Also, many traders use specif-ic candlestick chart patterns to indicatelikely price direction.

The rulesThe best time to use this method is theprofitable and volatile 9:40 a.m. to 11a.m. (EST) time period. Trades typicallylast several to 20 minutes. Here are step-by-step guidelines for applying this

MasteringTWO-MINUTE breakouts

How can you find consistent trade opportunities?

One way is to trade breakouts through yesterday’s high and low —

but only after the stock has shown its true colors.

TRADING Strategies

Strategy snapshotStrategy: “Two-day” breakout

Market: Stocks

Entry: Go long (short) on move .3 to .5-points above (below) wholenumber closest to previous day’s high(low).

Exit: Exit with trailing stop or on close.

Risk control: Stop-loss of no more than0.4 points. Trail stop at this interval if market moves in direction of trade.

technique.1. Define the day’s breakout and

breakdown entry levels before each mar-ket open. Set up one of your tradingscreens to plot a single, large two-minutecandlestick chart covering two days(today and the previous trading day) oftrading activity, as shown in Figure 1.Make sure you start charting by 8:30a.m. so you can spot any pre-market topor bottom formations, price gaps andtrends. Identify the previous day’s highand low.

2. Enter 0.3 to 0.5 points above the pre-vious day’s high (for long trades) or low(for short trades).

3. All intraday trades should have amaximum stop-loss of 0.4 points.Combined with entering 0.3 to 0.5 pointsabove the previous day’s high, this pro-vides an excellent risk management tool.In effect, we will exit if the reason for thetrade is negated, i.e., the stock moves

back into the trading range near thewhole number and the entry price levelis violated.

4. Trail the stop to protect profits. 5. Because the market often reverses

around 10 a.m. each day, it is useful totighten the stop during this time to threeor four “spreads” (the colored bands ofbid and ask levels on the Level II screen)behind the current inside bid. With deci-mal trading, this allows active traders tokeep even tighter stops than was previ-ously possible.

Trade examplesFigure 1 shows that on April 27, Ebay(EBAY) made a high of 48 and a low of45.5. Based on the guideline to place theentry points 0.3 to 0.5 points above orbelow the previous day’s high and lowprices, on April 30 we set long entry at48.5 (0.5 points above the previous day’shigh of 48, which was a whole number).


A buy signal occurs in EBAY when the stock moves .5 points above the wholenumber nearest to yesterday’s high.

FIGURE 1 BUY SIGNAL

9:30 10:00 10:30 11:00 11:30 12:004/27/01 4/30/01

52.00

51.50

51.00

50.50

50.00

49.50

49.00

48.50

48.00

47.50

47.00

46.50

46.00

45.50

45.00

0

Current dayPrevious day(compressed)

Previous day’shigh: 48.00

Buy signal is generated when price exceeds

previous day’s high +.5 points.

Source: Data Broadcasting Corp.

EBay Corp. (EBAY), two-minute

GlossaryTime and sales: The real-time, official record ofexecuted trades (as opposed tobids and offers) throughout theday. Most trading platformsinclude a time and sales windowto monitor this activity.

Noise:Random, meaningless price fluc-tuations that can knock tradersout of the market.

Buy programs (program trading):Computer-based tradingapproach whereby institutions orlarge trading operations executelarge volume in related marketsto take advantage of discrepan-cies between them (i.e., buyingS&P stocks and selling S&Pfutures). See “Program tradingand fair value,” Active Trader,Jan./Feb. 2001, p. 28, for moreinformation.

Uptick rule: Securities and ExchangeCommission rule that requiresshort sales to be executed whenthe last recorded price in a stockis higher than (or equal to,depending on the circumstances)the immediately preceding price.(The rule varies slightly for NYSEand Nasdaq stocks, although theprinciple is the same.) See “Awalk on the short side,” ActiveTrader, July 2000, p. 32, formore information.

We trailed a stop no more than 0.4points behind the current price level. Inthis trade, the trailing stop was triggeredat 49.375, yielding a net profit of 0.875points in less than 20 minutes.

Figure 2 (left) is an example on theshort side of the market. Adobe (ADBE)traded between 42.4 and 45.4 on May 2.The next day (May 3) we thereforelooked to go short if the market fell to41.6, 0.4 points below the whole number(42) closest to the previous day’s low.

However, ADBE gapped down to 41.6in pre-market trading. When this hap-pens, it’s a good idea to move the initialentry point farther away from the priceaction to avoid being caught on the

wrong side when the market opens.Therefore, we adjusted the entry to 41.4to clear the gap with as small a distanceas possible. This is not an exact science.Sometimes you will jump into a tradetoo soon despite this step; other timesthis precaution will save you from tak-ing an unnecessary loss.

Because of the uptick rule, it may takeseveral attempts to execute a short trade.Don’t be afraid to hit the short button onyour trading platform software severaltimes (assuming you are using a direct-access broker) so you can get in on anuptick. Check your trade confirmationwindow to make sure you are executinga single short trade, and not mistakenly

entering multiple trades.Because the stock already has traded

at or close to this price in the pre-market,it also is important that the time andsales window confirms large blocktrades are going our way and that mosttrades are being executed at the bid price(indicating selling pressure).

After the entry at 41.4, we trailed astop a few spreads behind the opentrade, without exceeding the 0.3-pointstop we’ve set for this trade. Note thatthe stop is slightly tighter in this tradethan in the first example. Because of thesupport-resistance level created by thepre-market gap to 41.6, this trade will beinvalidated as soon as the market tradesabove this level, which will happen at41.7 — 0.3 points away from the entryprice. Most trades entered before 10 a.m.should not last any longer than five toeight minutes. Trades entered after 10a.m. can last a little longer, but nevermore than 20 minutes. This trade wascovered at 40.75 for a .65-point profit.

Bottom lineSuccessful trading is much more difficultthan it first appears. It requires a longprocess of market watching and practic-ing chart pattern recognition. In time,you can learn to avoid low-potential sit-uations and focus on entries based onspecific chart pattern breakouts andbreakdowns.

Planning ahead to trade breakoutsshould be done daily using the previousday’s high and low to set trade alerts.Trading with the trend on breakouts usingthese criteria will help traders avoid over-trading and selectively trade the strongestand most powerful chart patterns.

The only exceptions to trading break-outs of the previous day’s trading rangeare those rare occasions when a stockmakes a rapid multi-point drop from theprevious day’s high and bounces off theprevious day’s low. But this is a trade forexperienced traders only, and youshould not expect to capture more than50 percent of the retracements followingthe bounce.

In fact, buying bottoms and shortingtops is largely a failing method, despite theamazing predisposition of most newtraders to attempt these types of trades.Your trades should be at least 80 percentbreakouts and no more than 20 percentbottom bounces, not the other way around.

It’s a good idea to tape that to yourmonitor, along with the words, “Tightstops — no exceptions!”�


ADBE had already reached the pre-determined entry price in pre-markettrading. The stock kicked off the official trading session with a two-minuterally. Had we sold immediately on the open without adjusting the entry priceto take this price action into account, we would have been stopped out witha loss.

FIGURE 2 SELL SIGNAL

9:30 10:00 10:30 11:00 11:30 12:005/2/01 5/3/01

45.6045.4045.2045.0044.8044.6044.4044.2044.0043.8043.6043.4043.2043.0042.8042.6042.4042.2042.0041.8041.6041.4041.2041.0040.8040.6040.4040.2040.0039.80Current dayPrevious day

(compressed)

Previous day’slow: 42.20

Short signal isgenerated at

41.40.

Source: Data Broadcasting Corp.

Adobe Systems Inc. (ADBE), two-minute

To trade breakouts successfully, you have to line up as many market factors

as possible. Incorporating volume and momentum into your trading plan can put

you on the inside track to breakout trades that won’t break apart.

TRADING Strategies

Swing trading 10-day CHANNEL BREAKOUTS


T rying to outguess the mar-ket by picking bottoms andtops is usually unsuccess-ful, and more often than not

results in a large numbers of whipsawtrades. By contrast, professional tradersand institutions favor breakout trading.

Combining 10-day support and resist-ance lines with confirming signals suchas volume breakouts and reversals is apractical approach to identifying swingtrade opportunities. These 10-day “chan-nels“ provide clear criteria for enteringbreakout trades once these price levelsare triggered.

Why swing trading?Swing trading is a shorter-term tradingstyle in which positions are held any-where from one to 10 days. Swing tradinghas been increasingly popular ever since

the Securities and Exchange Commission(SEC) raised the minimum marginrequirement for pattern day traders(PDTs) to $25,000 on Sept. 28, 2001 (see“New rules for the intraday trader,”Active Trader, October 2001). Traders withless than the $25,000 can make no morethan four intraday trades in a five-dayperiod; those who exceed this limit mustmeet the new day trading marginrequirements or face potential positionliquidation or account closure.

Day trading, in which trades often areentered and exited in a matter of seconds,can be highly stressful and requires a sig-nificant initial investment — not just intrading funds, but in computer hardwareand software, and training in direct-access trading methods as well.

Swing trading, by contrast, is general-ly less stressful and does not require as

large an upfront investment in capital,software or equipment. Because it doesnot require a trader to watch the marketall day, swing trading can be done on apart-time basis using online discountbrokers. Professional day tradingrequires a full-time commitment and afast direct-access broker.

This makes swing trading a viablealternative for active traders who areunwilling to meet the new marginrequirements and/or uncomfortablewith the technology and capital demandsof day trading. Swing trading is also aneffective way to learn many of the “clas-sic” technical indicators and limit riskwith small-share or paper trades.

The following strategy uses simplevolume and sector-strength filters todetermine when to trade breakouts of10-day price channels.

BY KEN CALHOUN

The toolsFor this approach, use a 15-minute chartencompassing the most recent 10 days(i.e., today and the previous nine tradingsessions). The following rules are givenin terms of upside breakouts and longtrades; reverse the rules for short trades.However, this strategy is better for longswing entries.

For a breakout swing trade, buy whena stock breaks out at least 50 cents overthe whole number above the 10-dayhigh, accompanied by volume that ishigher than the previous day’s volume atthe same time. For example, assume thatduring the previous nine trading ses-sions plus today, the highest price a stocktraded at was 37.8, which was set on theprevious trading day. The trigger for a10-day breakout long trade would be38.5, as long as the volume in the currentsession is higher than it was at the sametime in the previous session. If the stockopened today at 37.6 and traded up to38.5, this would trigger a long trade.

The only exception is when the entryprice would contain a “9” — e.g., 19.5,29.5, 39.5, and so on; in such cases, waituntil the stock clears the nearest multipleof 10, which would result in long tradetriggers at 20.5, 30.5, 40.5, etc. The ration-ale is prices with a “9” tend to look expen-sive and often meet resistance, choppyprice action, or both near multiples of 10.

The rulesThe best types of stocks to trade with thisapproach are Nasdaq or NYSE stockspriced between $5 and $60, with averagedaily volume of at least 800,000 shares,and average intraday trading ranges of 1to 4 points. Here are the rules:

1. Define the 10-day high and low forthe stock using a 15-minute candlestickor bar chart. Be sure to include volumebars on the chart.

2. Enter 50 to 60 cents above the near-est whole number above the highest highof the past 10 days, including today.

3. Look for volume breakouts on the10-day chart. Compare volume bars onthe current trading day to previous trad-ing days. The best entries are those forwhich volume is higher than in the pre-vious session.

4. Confirm entries using market indi-cators such as the Arms Index (TRIN) aswell as the time of day.

The TRIN measures the net buyingpressure vs. selling pressure in the mar-ket at a given point in the trading day.The formula is:

{number of advancing issues/numberof declining issues}/{volume of advanc-ing issues/volume of declining issues}

The TRIN, versions of which are avail-able for both NYSE and Nasdaq stocks,can help determine whether a trade isadvisable by highlighting whethermomentum is bullish or bearish at agiven time. A TRIN reading of 1 meansbuying/selling volume and the numberof advancers/decliners are equallymatched; a TRIN reading above 1 isbearish; a TRIN under 1 is bullish. SeeIndicator Insight, Active Trader,December 2000, for more information onthis indicator.

It’s also helpful to enter at times of theday when the market is the strongestand most volatile. It’s usually best toenter 10-day channel long trades in theearly morning, from 9:45 until 11 a.m.EST, or during late-afternoon rallies —between 2:30 and 3:30 p.m., for example.

Certain cautionary indicators (“redflags”) can be used to eliminate poortrades. For long entries, avoid highsreached on lower-than-average volumeor those reached by a stock in a weak sec-tor that day. Compare sector indices suchas the SOX, NBI, GHA and GSO to deter-mine which are strongest, and give pref-erence to entries in the strongest sectors


Strategy snapshotStrategy: 10-day channel breakout

Markets: Nasdaq or NYSE stocks trading between $5-$60, with average daily volume of at least 800,000 shares and average daily range of 1 to 4 points.

Entry (for longs; Go long 50 to 60 cents above the nearest whole number reverse for shorts): above the highest high of the past 10 days.

Confirmation: The best entries are those in which volume is higher than in the previous session and/or when the stock is in a strongly trading sector. Avoid highs that are reached on lower-than-average volume or those reached by a stock in a weak sector on the entry day.

Exit/risk control: The widest initial stop should be the closer of 1.5 points or the previous day’s low. The most conservative initial stop-loss is the previous day’s high. Once in a profitable trade, trail the stop .5 points below the current trading range.

If a stock gaps

open more than

10 to 15 percent

from its previous

close, it will often

reverse and fill

the gap, in which

case it’s necessary

to take your profit

before the market

does.

— e.g., those up 1.5 to 3 percent or so onthe day at the time of the trade entry.

Also check to see if sectors are conver-gent (all green or red — i.e., moving upor down) or divergent (mixed). It’s bestto enter swing trades on days where allsectors are convergent and the broadmarket has strength in a single direction.

Mixed, choppy days are poor days forswing trade entries.

5. Stop-loss values are determined bythe previous day’s high and low. Eitherof these price points can provide youwith an initial stop-loss value, depend-ing on the intraday market trend and

your risk tolerance. A good initial stop-loss is the previous

day’s low or 1.5 points, whichever issmaller. For example, let’s say the 10-daychannel range for EBAY is bounded by alow (support level) of 61.8 and a high(resistance level) of 66.8. If the previousday’s range was 65.5 to 66.8, we wouldenter EBAY on a breakout above 67.5.The initial maximum stop-loss for thistrade would be at 66, 1.5 points belowentry (roughly 2 percent).

If you trade on a shorter time frame,say three- or five-minute charts, youmight consider setting a tighter stop atthe previous day’s high.

6. Trail a stop to protect open profits at2 percent (generally from .5 to 1.5 points)below the current level of the open trade,or use a time stop of no longer than 10days (i.e., exit all remaining open posi-tions after 10 days). Whenever one of theexit signals appears, the position shouldbe closed with a profit. Re-enter on sub-sequent breakouts after retracementshave occurred.

How to handle gapsManaging gap opens on swing trades isalways a challenge. When a stock gapsopen significantly above the previousday’s high (in your favor for a longswing trade), trail a stop no more than 50cents below the current pre-market trad-ing range to lock in your profit.

However, if a stock gaps open morethan 10 to 15 percent from its previousclose, it will frequently reverse and fillthe gap, in which case it’s necessary totake your profit before the market does.This is especially true if the stock gapsup above the previous day’s high.

Conversely, when a stock gaps downsignificantly against you, it’s often bestto wait until 15 to 20 minutes after theopen to exit the position, because downgaps frequently attract buyers who canbring the price back up. Again, use astop-loss of no more than 50 cents belowthe current pre-market trading range.

It is sometimes helpful to wait untilapproximately 9:45 a.m. EST to seewhere the stock trades before exiting aposition. It is frustrating to panic out of agap-down swing trade only to see thestock turn around and fill the gap in thefirst few minutes of the trading day.Calmly give it a few minutes to establisha trend and see if it consolidates and


Nvidia (NVDA), 15-minute

Ascending triangle

Support

Resistance

Volume

55.5055.0054.5054.0053.5053.0052.5052.0051.5051.0050.5050.0049.5049.0048.5048.00

6 million4 million2 million

NVDA traded in a channel from 48 to 55 for 10 days, forming an ascending triangle toward the end of the period as it challenged the resistance levelanother time. Entry would occur at 55.50, 50 cents above the highest high of the past 10 days.

FIGURE 1 POISED TO BREAK OUT

Source: eSignal

12:00 12:00 12:00 12:00 12:00 12:00 12:00 12:00 12:00 12:0011/16/01 11/19/01 11/20/01 11/21/01 11/23/01 11/26/01 11/27/01 11/28/01 11/29/01 11/30/01

Intuit Inc. (INTU), 15-minute

Volume

Entry

44.00

43.50

43.00

42.50

42.00

41.50

41.00

40.50

40.00

39.50

39.00

600,000400,000200,000

After the stock fulfills the entry requirements and breaks out above resistance, a trailing stop is used to lock in profits.

FIGURE 2 AFTER THE BREAKOUT

Source: eSignal

12:00 12:00 12:00 12:00 12:00 12:00 12:00 12:00 12:00 12:0011/16/01 11/19/01 11/20/01 11/21/01 11/23/01 11/26/01 11/27/01 11/28/01 11/29/01 11/30/01

Support

Resistance

reverses this initial gap move.

Trade examplesFigure 1 shows Nvidia (NVDA) tradingin a 10-day channel (between 48 to 55)from Nov. 16, 2001, to Nov. 30,2001. Based on the guideline to enter 50cents over the nearest higher whole num-ber above the highest high of the 10 days,a long entry would be triggered at 55.5.(If, however, the stock gaps up to, say,55.8 in pre-market trading, the entrywould be reset over the next number up,at 56.5 or higher).

The initial stop would be placed at 54,1.5 points below the entry, because 1.5points is a tighter stop than the previousday’s low (see trade rule No. 5). Thealternate, more conservative stop-losslevel is the previous day’s high — in thiscase 54.60.

Notice this stock forms an ascendingtriangle pattern following an initialdownturn earlier in the 10-day channel,and is poised to break out to new highsif it clears the 55 resistance area. If thevolume when the trade is entered (onDec. 1, not shown) is higher than it wasat the same time on the previous day,this would provide additional confirma-tion for a long trade.

Figure 2 provides an example of howto manage a profitable long swing trade.On the afternoon of Nov. 29, 2001, thestock cleared the 10-day high and a longtrade was entered at 42.50. The initialstop loss was set at 41 (42.5-1.5).

On Nov. 30, the stock continued torally throughout the session to a high of44, at which point it consolidated. Usinga trailing stop approach raises the stopto 43.5 (44-.5), which was not triggered.The “time stop” is 10 days from Nov. 29.In this case the stock is up more than apoint on an overnight hold. We continueto trail a stop .5 behind the current trad-ing range until the stop is taken out.

Dynamic position sizing using cup breakoutsDynamic position sizing is the process ofadding to an initial position once a stockhas broken out and is continuing toattract buyers. For example, a tradermay buy 200 shares initially and addanother 100 shares on a subsequentbreakout as the stock continues to climb.

The key to using dynamic positionsizing is to add no more than half thenumber of the initial trade size on subse-quent 10-day high cup-pattern break-outs. When adding to an initial position,

it is sound risk management to “extendyourself” only on the strongest of pat-terns. This keeps the average entry pricetoward the low end of the total position.

Cup-pattern breakouts, like ascend-ing-triangle and consolidation breakouts,are much stronger than cases where astock simply trades to a new high with-out penetrating any kind of resistancelevel. The test of sellers that occurs at aresistance level prior to a long cup-pat-tern breakout validates the entry andprovides a support level after the trade.

Cup patterns, which are extended,saucer-shaped retracements, appear fair-ly frequently. The key to trading them isto apply volume and other filters toavoid false breakouts that turn out to bedouble tops (i.e., when price falls backfrom the right side of the cup instead ofbreaking out above the resistance levelof the cup).

Figure 3 shows a cup pattern thatstarted on Dec. 3, pulled back from theresistance level of 28.59 on Dec. 5 (form-ing a short-lived double top), formedanother cup and finally broke out abovethe resistance on the afternoon of Dec. 7.A long trade was triggered at 30.5(because the entry price would havebeen 29.5, and in such cases entry ismade above the nearest multiple of 10).

The stock gapped open higher on Dec.10 and the trade was exited on Dec. 11(when the trailing stop was hit) at 34.1for a 3.6-point profit.

Bottom lineSwing trading provides traders withopportunities to manage multiple posi-tions and entries at a more leisurely trad-ing pace than is possible in the hecticworld of day trading. However, everytrader should research and experimentwith different trading styles to helpdetermine his or her preference and levelof comfort.

Using 10-day trading channels to iden-tify entries on volume breakouts can helpyou better define support and resistancelevels and provide techniques you canintegrate with other technical indicatorsto develop a swing-trading plan.

Using a comprehensive, measured andspecific strategy to trade breakouts con-tinues to produce entries that are moreconsistent than intra-range or “bounce”trade approaches. Using volume andprice action filters will help you avoidfalse breakouts in choppy markets.�


Invision Technologies (INVN), 15-minute

Volume

40.00

38.00

36.00

34.00

32.00

30.00

28.00

26.00

24.00

22.00

20.00

18.00

16.00

1 million

500,000

Like channels and ascending triangles, cup patterns also provide well-definedresistance levels for breakout trades. The stock broke out above a second cuppattern on Dec. 7, and was stopped out with a 3.6 point profit on Dec. 11.

FIGURE 3 OVERFLOWING CUP

Source: eSignal

12:00 12:00 12:00 12:00 12:00 12:00 12:00 12:00 12:00 12:0011/29/01 11/30/01 12/3/01 12/4/01 12/5/01 12/6/01 12/7/01 12/10/01 12/11/01 12/12/01

Resistance

Initial cup Second cup

Breakoutentry

Exit

System logic: The volatility breakout system is a classictrading strategy based on identifying situations when amarket is about to burst out of a congestion area and poten-tially establish a new long-term trend. It also can signal atrade if the market is already trending in one direction butquickly reverses to establish a new trend in the oppositedirection.

This system uses Bollinger Bands (complemented bymoving averages), which are lines typically plotted twostandard deviations above and below a moving average.Bollinger Bands expand during high-volatility periods and contractduring low-volatility periods.

When volatility is high, the system is designed to stay out of themarket to avoid taking any unnecessary risk, but if an entry is trig-gered anyway, the system will work to keep you in the trade toavoid being stopped out prematurely with a loss. A long entry istriggered when price moves above its 60-day moving average andbreaks the upper Bollinger Band. To exit, price must move below its30-day moving average and break a lower Bollinger Band that is setone standard deviation away (instead of the usual two). Because thenext entry cannot occur until price moves back above the lowerband, above its moving average and above the upper band, therewill be times when the system is out of the market completely.

Markets: This system will be tested on stocks and also on futures (p. 70).

Rules:1. Go long tomorrow if price moves above its average price for thelast 60 days and breaks theupper Bollinger Band.2. Exit with a profit or loss ifprice moves below its 30-daymoving average and penetratesa lower Bollinger Band set onestandard deviation away.

Reverse the rules for shorttrades.

Money management:1. Risk 4 percent of availableequity per stock traded.2. The number of shares to trade(ST) is calculated using the fol-lowing formula:

ST = AC * PR / RwhereAC = Available capitalPR = Percent riskedR = Distance between entryprice and exit price (stop-loss).

Test period: November 1992 toJune 2002.

SAMPLE SIGNALS

Source: Omega Research ProSuite

Volatility breakout system


Philip Morris (MO), daily

LX = Long exit

LX

LX

BuySell

Buy

February March April May June July

58.00

56.00

54.00

52.00

50.00

48.00

46.00

44.00

42.00

Test data: Daily prices for 14 Dow Jones Industrial Average stocks(AXP, C, CAT, DIS, GM, HWP, IBM, INTC, JPM, KO, MO, MRK,MSFT, T), with $10 deducted per trade for slippage and commis-sions.

Starting equity: $1 million (nominal).

Buy-and-hold stats:Total Maximum Longest

Index return drawdown flat periodDJIA 175% 31.5% (current) 29 months (current)S&P 500 120% 40% (current) 26 months (current) Nasdaq 182% 80.5% (current) 26 months (current)

Test results: The system was originally tested on all 30 stocks inthe DJIA; the 14 in this test were selected because they were theones that showed a profit. Singling out these stocks, though,

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EQUITY CURVE

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In this system, though, the large distance betweenthe entry and exit prices requires trading rather smallpositions. Doing otherwise would run the risk ofusing all the capital on only a few positions. This inturn will result in relatively small dollar gains despitelarge price swings. Even though the system tradesonly 14 stocks, close to 75 percent of available capitalis tied up.

Also, the current drawdown has gone on for 42months. This is likely a reflection of the disappearanceof the stock market’s pre-2000 trending characteristics,and it is not very likely that a system like this will startproducing a profit anytime soon.

This system is also a bit passive in its trade fre-quency. To make it more aggressive, the lookback period can beshortened and/or the standard deviation boundariestightened.�


LEGEND: End. equity ($) — equity at the end of test period • Total return(%) — total percentage return over test period • Avg. annual ret. (%) —average continuously compounded annual return • Profit factor — grossprofit/gross loss • Avg. tied cap (%) — average percent of total available cap-ital tied up in open positions • Win. months (%) — percentage profitablemonths over test period • Max. DD (%) — maximum drop in equity •Longest flat — longest period, in months, spent between two equity highs •No. trades — number of trades • Avg. trade ($) — amount won or lost bythe average trade • Avg. DIT— average days in trade • Avg. win/loss ($)— average wining and losing trade, respectively • Lrg. win/loss ($) —largest wining and losing trade, respectively • Win. trades (%) — percentwinning trades • TIM (%) — amount of time there is at least one open posi-tion for entire portfolio, and each market, respectively • Tr./Mark./Year —trades per market per year • Tr./Month — trades per month for all markets

LEGEND: Cumulative returns — Most recent: most recent return from start toend of the respective periods • Average: the average of all cumulative returnsfrom start to end of the respective periods • Best: the best of all cumulative returnsfrom start to end of the respective periods • Worst: the worst of all cumulativereturns from start to end of the respective periods • St. dev: the standard devia-tion of all cumulative returns from start to end of the respective periods

Annualized returns — The ending equity as a result of the cumulative returns,raised by 1/n, where n is the respective period in number of years


Send Active Trader your systemsIf you have a trading system or idea you’d like tested, send it tous at the Trading System Lab. We’ll test it on a portfolio ofstocks or futures (for now, maximum 60 markets, using the last2,500 trading days), using true portfolio analysis/optimization.

Most system-testing software only allows you to test one mar-ket at a time. Our system-testing technique lets all marketsshare the same account and is based on the interaction withinthe portfolio as a whole.

Start by e-mailing system logic (in TradeStation’sEasyLanguage or in an Excel spreadsheet) and a short descriptionto [email protected], and we’ll get back to you.

Note: Each system must have a clearly defined stop-loss leveland a suggested optimal amount to risk per trade.

Profitability Trade statisticsEnd. equity ($): 2,491,172 No. trades: 456Total return (%): 149 Avg. trade ($): 3,270Avg. annual ret. (%): 10.00 Avg. DIT: 35.0Profit factor: 1.34 Avg. win/loss ($): 31,090 (13,546)Avg. tied cap (%): 73 Lrg. win/loss ($): 391,627(109,437)Win. months (%): 53 Win. trades (%): 38.8

Drawdown TIM (%): 100 57.7 Max. DD (%): 25.8 Tr./Mark./Year: 4.3Longest flat (m): 41.5 Tr./Month: 4.0

ROLLING TIME WINDOW RETURN ANALYSIS

Cumulative 12 24 36 48 60 months months months months months

Most recent: 16.70% -0.91% -5.86% 15.22% 22.04%Average: 10.76% 24.50% 42.81% 64.40% 83.77%Best: 58.88% 87.95% 102.78% 162.29% 155.19%Worst: -15.08% -19.33% -16.50% 4.57% 16.53%St. dev.: 17.29% 22.72% 30.43% 36.41% 42.26%

Annualized 12 24 36 48 60 months months months months months

Most recent: 16.70% -0.45% -1.99% 3.61% 4.06%Average: 10.76% 11.58% 12.61% 13.23% 12.94%Best: 58.88% 37.10% 26.57% 27.26% 20.61%Worst: -15.08% -10.19% -5.83% 1.12% 3.11%St. dev: 17.29% 10.78% 9.26% 8.07% 7.30%

STRATEGY SUMMARY

DRAWDOWN CURVE

0%

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failed to create results as good as the ones produced by the test oncurrency futures (see Futures System Lab, p. 44).

One thing to keep in mind is that portfolio composition is morecomplex than simply eliminating instruments that don’t performwell. In a dynamic portfolio such as this one, where a group ofstocks interacts within a single trading account, the ability of anindividual stock to turn a profit or loss also depends on the behav-ior of all the other stocks.

As proof of this, three stocks that were profitable when all 30stocks were tested showed a loss when the field was pared to 14. Ifwe were to test only the remaining 11 that showed a profit, it’s like-ly a few more would turn into losers.

This correlation also makes it extremely difficult to trade a long-term system on the stock market. The individual stocks eithertrend well, almost all at once, or they don’t, which results in a largeamount of whipsaw losing trades and rather severe drawdowns.The way to overcome this is to diversify by trading as many stocksfrom different sectors and groups as possible.

System logic: The system uses Bollinger Bands and a moving aver-age to trade volatility breakouts. The logic and tools for this systemare described in the Trading System Lab on p. 56.Markets: Most trending futures markets, such as currencies, energiesand interest rates. This system was also tested on stocks (see TradingSystem Lab).Rules:1. Go long tomorrow if price moves above the 60-day moving aver-age and breaks the upper Bollinger Band. 2. Exit with a profit or loss if price moves below its 30-day movingaverage and penetrates a lower Bollinger Band set one standarddeviation away (instead of the usual two).

Reverse the rules for short trades.Money management1. Risk the following percentages of available equity per market: 2percent for Australian dollar, British pound, Canadian dollar, Dollarindex and Swiss franc, and 4 percent for Japanese yen, D-mark andEuro. (The yen and the Euro are traded with twice the risk becausethey are the most liquid currencies.)2. The number of contracts to trade (CT) is calculated with the following formula:

CT = (AC * PR) / (R * PV)whereAC = Available capitalPR = Percent riskedR = Distance between entry price and exit price (stop-loss).PV = Dollar value of a one-point move.

Test period: November 1992 to June 2002Test data: Daily futures prices for eight currency futures: Japaneseyen, Australian dollar, Canada dollar, British pound, Dollar index,Swiss franc, and D-mark (until Dec. 31, 1999)/Euro (after Dec. 31,1999).Starting equity: $1 million (nominal).Test results: If there ever was a system built for the currency markets,this is it. The reason is the smooth, long-term trends currencies some-times exhibit, probably because they are mostly influenced by theglobal, long-term economical and political climate. In contrast, thestock market is very sensitive to all other markets; while the stockmarket cares a great deal about the currency market, the currencymarket doesn’t care all that much about the stock market.

The risk for this system was 2 or 4 percent per trade, which result-ed in both a drawdown and flat time that would be deemed unac-ceptable by most professional money managers. These numbersshould stay under 30 percent and 18 months, respectively.

The major disadvantage of a trend-following system is most mar-kets that work well with this type of a system are usually correlated.In other words, they will either all work well or perform poorlysimultaneously. This is reflected in the erratic look of the system’sequity curve. Because of this, the drawdowns can be both deep andlong, and it usually requires a couple of very good trades to get thesystem profitable again. Research has shown that a trend-followingsystem will work best when traded on 15 to 20 select markets fromvarious sectors of the economy.

The system ties up an average of 11 percent of capital. This meansthere is plenty of room to add markets, and even trade the systemmore aggressively, without extending ourselves too much. This isbecause futures have much smaller margin requirements thanstocks. Theoretically, as many as 40 to 50 different futures contractscould be traded before reaching the same level of margin fewer than20 stocks would require.

Finally, the most recent drawdown of approximately 30 percent isthe only one over the last 10 years of such magnitude. Most of theprevious drawdowns bottomed between 10 to 20 percent. Therefore,the latest drawdown is quite possibly an anomaly. That said, how-ever, there are no guarantees in the market, and your worst draw-down is always still to come.

The system also has a relatively low trade frequency. To make itmore aggressive, the lookback period can be shortened and/or thestandard deviation boundaries tightened.�


System Lab&FUTURES OPTIONS

Profitability Trade statisticsEnd. equity ($): 4,550,290 No. trades: 303Total return (%): 355 Avg. trade ($): 11,717Avg. annual ret. (%): 17.13 Avg. DIT: 35.8Profit factor: 1.32 Avg. win/loss ($): 53,057 (29,536)Avg. tied cap (%): 11 Lrg. win/loss ($): 345,600 (131,350)Win. months (%): 51 Win. trades (%): 42.1Drawdown TIM (%): 97 54.1 Max. DD (%): 31.6 Tr./Mark./Year: 4.0Longest flat (m): 19.7 Tr./Month: 2.6

STRATEGY SUMMARY

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ROLLING TIME WINDOW RETURN ANALYSISCumulative 12 24 36 48 60

months months months months monthsMost recent: 34.31% 58.39% 65.73% 114.82% 138.14%Average: 15.07% 34.15% 55.68% 80.53% 109.16%Best: 74.75% 76.50% 108.66% 137.94% 202.94%Worst: -28.10% -1.33% 4.12% 28.76% 38.77%St. dev.: 18.17% 16.26% 23.27% 22.74% 37.29%Annualized 12 24 36 48 60

months months months months monthsMost recent: 34.31% 25.85% 18.34% 21.06% 18.95%Average: 15.07% 15.82% 15.90% 15.91% 15.90%Best: 74.75% 32.86% 27.78% 24.20% 24.82%Worst: -28.10% -0.67% 1.36% 6.52% 6.77%St. dev: 18.17% 7.82% 7.22% 5.26% 6.54%

Futures volatility breakout system

System Lab

LEGEND: End. equity ($) — equity at the end of test period • Total return (%) — totalpercentage return over test period • Avg. annual ret. (%) — average continuously com-pounded annual return • Profit factor — gross profit/gross loss • Avg. tied cap (%) —average percent of total available capital tied up in open positions • Win. months (%) —percentage profitable months over test period • Max. DD (%) — maximum drop in equi-ty • Longest flat — longest period, in months, spent between two equity highs • No.trades — number of trades • Avg. trade ($) — amount won or lost by the average trade• Avg. DIT— average days in trade • Avg. win/loss ($) — average wining and losingtrade, respectively • Lrg. win/loss ($) — largest wining and losing trade, respectively •Win. trades (%) — percent winning trades • TIM (%) — amount of time there is at leastone open position for entire portfolio, and each market, respectively • Tr./Mark./Year —trades per market per year • Tr./Month — trades per month for all markets

LEGEND: Cumulative returns — Most recent: most recent return from start to end of therespective periods • Average: the average of all cumulative returns from start to end of therespective periods • Best: the best of all cumulative returns from start to end of the respectiveperiods • Worst: the worst of all cumulative returns from start to end of the respective peri-ods • St. dev: the standard deviation of all cumulative returns from start to end of the respec-tive periods

Annualized returns — The ending equity as a result of the cumulative returns, raised by 1/n,where n is the respective period in number of years

Better breakout trading:THE NOISE CHANNEL SYSTEMBetter breakout trading:THE NOISE CHANNEL SYSTEM

TRADING Strategies

BY DENNIS MEYERS, PH.D.

P rice trends begin with a breakout of a previoushigh or previous low. Unfortunately, many break-outs are random — mere market noise. Falsemoves and reversals can repeatedly whipsaw

traders who act immediately on typical breakout signals.As a result, traders sometimes attempt to use filters to

improve the odds of catching a successful breakout trend. Oneexample of a simple filter is to wait for consecutive closesabove or below a breakout level. Another example is waitingfor price to penetrate a breakout level by x percent or pointsbefore acting on the signal.

The following discussion will analyze a variation on thesimple channel breakout system that uses the latter type of fil-ter to minimize whipsaws on an intraday basis. The strategywill be tested on International Business Machines (IBM). Thediscussion is broken into two parts, covering 1) the systemrules and data selection and 2) testing procedures. This willgive you the necessary tools for performing similar researchand tests on other markets.

The noise channel breakout systemThe basic system we will use here is a fairly simple and effec-tive breakout system that has been in the public domain formany years: the channel breakout system, which goes long ona move above the highest high of the last n bars and goes shorton a move below the lowest low of the last n bars.

In the tests that illustrate this strategy, we’ll use five-minutebars of IBM from Feb. 21 to April 6. (For an important point ontesting stock trading strategies, see “A note on price data anddividends,” p. 75). Intraday data has a high noise level, mean-ing it contains a great deal of random price movement thatlooks significant but turns out to be meaningless. Withoutsome kind of filter, the losses generated by the random pricemovement (that is, whipsaws) can completely overwhelm atrading system. To help eliminate such random movement, wewill add a noise filter, designated by the symbol f, to the basicchannel breakout system.

There are three system parameters to find:• nhi, which is the number of bars in the lookback period

used to determine the highest high price (hhp).• nlo, which is the number of bars in the lookback period

used to determine the lowest low price (llp).• f, which is the amount price must exceed the hhp or llp to

trigger a buy or sell.

The rules for the resulting noise channel breakout system(NCBS) are:

Buy rule: If price crosses above the highest high of the last n

All breakout traders have to deal

with the reality of false moves

and whipsaws. The noise channel

breakout system shows how a filter

can improve the performance

of intraday breakout trading.

bars (nhi) by an amount greater than orequal to f, buy at market. For example, ifn = 20 and f = 2 (points), you would golong when price moved 2 points abovethe highest high of the last 20 bars.

In addition, when short, and whencalculating the highest high price (hhp),it cannot be higher than the previouslycalculated hhp as previous highs aredropped out of the lookback window.Otherwise, a situation can occur wherethere is a higher hhp without the pricefilter f being hit. Therefore, when shortthe stock, the hhp can only stay the sameor go lower. It cannot go higher.

Sell rule: If price crosses below thelowest low price of last n bars (nlo)minus an amount greater than or equalto f, sell at market. In addition, whenlong and when calculating the lowestlow price (llp), it cannot be lower thanthe previous calculated llp as previouslows are dropped out of the lookbackwindow. Again, to avoid the situationwhere a lower llp occurs without theprice filter f being hit, when long thestock, the llp can only stay the same orgo higher. It cannot go lower.

Exit rule: Close the position five min-utes before the NYSE close (no trades arecarried overnight).

Testing the strategyThe “walk-forward testing” approachwill be used to test this strategy becauseof the volatile nature of intraday stockprices. Intraday price dynamics are con-stantly changing because of economicsurprises, events and trader sentiment.Also, the time of year — such as the sea-son, holidays, vacation time, etc. —affects the character of intraday markets.As a result, tests performed on intradaydata three months ago may no longer berepresentative of today’s intraday priceaction. For more information on walk-forward testing and how it was used forthis strategy, see “Proper system test-ing,.”

The best parameters will be defined asthose values that generate the best netprofits combined with the minimumdrawdown and minimum largest losingtrades. In addition, the results should be

stable — i.e., theprofits, wins anddrawdowns shouldnot change much asthe parameters moveby a small amountaway from their opti-mum values. In otherwords, the system


TABLE 1 OPTIMUM PARAMETER VALUES FOR TEST DATA

Start date End date nhi nlo f

2/21/01 3/23/01 8 4 1

2/28/01 3/30/01 8 4 1

Performance summary for noise channel breakout system: IBM, five-minutebars from Feb. 28 to March 30. Statistics based upon trading 1,000 shares of IBM.

TABLE 2A TEST PERIOD 1

TABLE 2B TEST PERIOD 2

Performance summary: All tradesTotal net profit ($): 13,890 Open position P/L ($): 0

Gross profit ($): 39,260 Gross loss ($): -25,370

Total no. of trades: 48 Percent profitable (%): 54

Number winning trades: 26 Number losing trades: 22

Largest winning trade ($): 5,940 Largest losing trade ($): -2,060

Average winning trade ($): 1,510 Average losing trade ($): -1,153.18

Ratio avg. win/avg. loss: 1.309 Avg. trade(win & loss) ($): 289.38

Max. consec. winners: 4 Max. consec. losers: 3

Avg. no. bars in winners: 39 Avg. no. bars in losers: 21

Max intraday drawdown ($): -8,470

Profit factor: 1.547 Max. no. contracts held: 1






Average winning trade ($): 1,613.64 Average losing trade ($): -1,000.40




Max. intraday drawdown ($): -9,660


Source: TradeStation by TradeStation Group Inc.

Performance summary for noise channel breakout system: IBM, five-minutebars from Feb. 21 to March 23. Statistics based upon trading 1,000 shares ofIBM.

performance using an nhi of 10 barsshould be similar to that using nine barsor 11 bars. Also, in choosing the “best”parameters, we considered only thoseresults with four or less maximum con-secutive losses.

Test resultsTable 1 shows the optimum parametervalues for the test window described in“Proper system testing.” The nhi waseight bars, the nlo was four bars and fwas 1 point. Tables 2a and 2b show testresults using these parameters.

Table 3 summarizes the combinedperformance of the two out-of-sampledata segments from March 26 to April 6.This performance represents whatwould have happened in real time if youused the system parameters found in thetest section (not including slippage andcommissions). By comparison, the samenhi and nlo values tested without anyfilter resulted in a loss of $1,150.

Table 4 is a trade-by-trade summary

from March 26 to April 6. The trades in thistime period are the out-of-sample trades

generated from the optimized parametersfrom the two test sections of Feb 21 to


Combined walk-forward out-of-sample performance summary for the noisechannel breakout system: IBM five-minute bars from March 26 to April 6.Statistics based upon trading 1,000 shares of IBM.

TABLE 3 OUT-OF-SAMPLE RESULTS






Average winning trade ($): 1,807.50 Average losing trade ($): -758.75







This trade summary for the out-of-sample test (five-minute bars, March 26 to April 6) of the noise channel breakoutsystem shows the strategy actually worked better on the short side than the long side.

TABLE 4 TRADE-BY-TRADE SUMMARY

Entry Entry Buy Entry Exit Exit Exit # bars P&L ($) P&L (%) Max. Time Max. Time Date time or price date time price in profit drawdown

sell trade ($)3/26/01 10:20 Sell 93.75 3/26/01 15:55 94.52 67 (770) -0.82% 0 10:20 (1,620) 10:35

3/27/01 10:15 Buy 95.59 3/27/01 15:55 99.59 68 4,000 4.18% 4,300 15:50 0 10:15

3/28/01 9:40 Sell 97.92 3/28/01 15:55 94.50 75 3,420 3.49% 3,420 12:00 (380) 9:40

3/29/01 10:05 Buy 96.05 3/29/01 15:05 94.90 60 (1,150) -1.20% 950 10:30 (1,160) 11:20

3/29/01 15:05 Sell 94.90 3/29/01 15:55 94.88 10 20 0.02% 390 15:15 (500) 15:45

3/30/01 9:40 Buy 96.70 3/30/01 13:05 96.20 41 (500) -0.52% 800 11:55 (1,190) 10:00

3/30/01 13:05 Sell 96.20 3/30/01 15:55 96.25 34 (50) -0.05% 220 13:15 (840) 14:35

4/2/01 9:40 Buy 97.75 4/2/01 10:55 96.40 15 (1,350) -1.38% 350 10:05 (1,350) 10:55

4/2/01 10:55 Sell 96.40 4/2/01 15:55 94.50 60 1,900 1.97% 2,600 15:40 (1,300) 11:40

4/3/01 10:00 Sell 93.00 4/3/01 15:55 90.50 71 2,500 2.69% 2,740 15:40 0 10:00

4/4/01 9:45 Buy 92.00 4/4/01 13:50 92.00 49 0 0.00% 1,900 11:20 (1,890) 10:30

4/4/01 13:50 Sell 92.00 4/4/01 15:55 91.85 25 150 0.16% 380 14:00 (500) 14:20

4/5/01 9:40 Buy 95.68 4/5/01 15:55 98.15 75 2,470 2.58% 3,040 15:25 (10) 9:40

4/6/01 9:40 Sell 97.30 4/6/01 11:55 98.24 27 (940) -0.97% 550 11:15 (940) 11:55

4/6/01 11:55 Buy 98.24 4/6/01 12:35 97.30 8 (940) -0.96% 1,660 12:05 (940) 12:35

4/6/01 12:35 Sell 97.30 4/6/01 15:55 97.67 40 (370) -0.38% 300 12:35 (1,960) 13:55

March 23 and Feb. 28 to March 30. Figure 1 is a five-minute chart with

the noise channel superimposed, as wellas some of the buy and sell signals fromthe Table 4 trade-by-trade summary.

Breaking down the numbersWith respect to average winning and los-ing trades, drawdowns and profit factor,the out-of-sample performance (Table 3)was better than the test sample perform-ance (Tables 2a and 2b) The better per-formance of the out-of-sample sectioncould have been coincidental, but it doesindicate that four weeks of test data wasenough to capture the intraday pricedynamics of this stock.

When testing any trading strategy, the importantpoint is how well it will perform on price data it hasnot been optimized on — that is, out-of-sample

data. In short, without out-of-sample testing, it’s nothingmore than a hope and a prayer to believe that system per-formance in the future will be anywhere near the optimizedperformance.

For example, it’s possible to take a trading strategy withfour independent variables, or parameters, and with hind-sight, find the values for each of them that give the best(optimized) results on a specific historical period — say, thelast three years (using daily price data).

However, these optimized parameter values have, inessence, been cherry-picked for this particular data period (aprocess known as “curve-fitting”), and are unlikely to per-form as well on other historical test periods, or in actual trad-ing in the future.

A walk-forward testing procedure was applied to the noisechannel breakout system as follows: Five-minute bars from aperiod of four weeks from the start of the test period — Feb.21 to March 23 — were chosen and system parameter valueswere found through optimization on this intraday data seg-ment. In other words, the “best-performing” system parame-ters (e.g., number of days in lookback period, noise filtervalue) were determined by testing a range of values for each.At this stage of system development, the only thing indicatedby the optimum values in the test portion is that the data hasbeen “curve-fitted” as best it can with this system. Withoutfurther testing on out-of-sample data, there is no way to tell ifthe system will work in the future.

These parameter values were then applied to an out-of-sample data period following the test segment (March 26 toMarch 30). This walk-forward process was repeated by movingthe test data window forward one week, to Feb. 28 to March30, and again finding the parameters values through optimiza-tion on this new data. These optimized parameter values arethen applied to the next out-of-sample five-minute intradaydata window (April 2 to April 6). An important (but unspoken)point in walk-forward testing is that if you cannot get goodresults in the out-of-sample data segments, real-time systemperformance will be random.

Almost any period of historical prices can be curve fittedeasily to give the false illusion of future profitability.However, these performance measures in no way reflect howa system will perform on price data it has not been optimizedon. Only out-of-sample testing — that is, testing on price datathe system parameters were not originally derived from — candetermine if a system is robust and has a chance of perform-ing well in real trading.

Despite these facts, many market pundits still make theunproven claim that statistics generated solely from optimizedbuy and sell trades in the test section (the initial period ofprice data)have value in predicting whether or not the systemwill perform well in the future. Nothing could be further fromthe truth. The only thing the statistics from the test sectiontell you is how well you have curve-fitted the data in the testsection. As a matter of fact, using optimization, it’s almostimpossible not to get an excellent fit with great statisticalresults.

Proper system testing

A note on price data and dividends

An overlooked aspect of testing a stock trading strategy is the effect ofdividends. For example, IBM pays dividends on a quarterly basis, usu-ally on the “dividend payable dates” of March 10, June 10, Sept. 10

and Dec. 10. On the “Ex-dividend dates” (approximately one month beforethe payable date), the price of the stock is adjusted down by the value of thedividend.

Thus, over the course of a year, IBM has a small downward price bias equalto the amount of the yearly dividend. If you were an owner of IBM, you wouldreceive those dividends in cash, making up for the small downward bias.

However, when developing and testing a system using historical stock data,prices are not adjusted for dividend payments. This creates a small distortionin parameter selection and forward-adjusted results.

Because no dividends were paid in the data sample used for the test in thisarticle, no adjustment needs to be made. However, if the intraday time peri-od fell on an ex-dividend date, an adjustment would have to be made toavoid distortion.


The out-of-sample trade summary(Table 4) shows the system did better onshort trades than it did on long trades. Onone hand, this could indicate a “negative”bias for the system. On the other hand,given the current bear market environ-ment, the ability to cash in on the shortside has value. There were no big winnersor big losers, indicating steady returns.Average wins were 2.4 times averagelosses in the out-of-sample section.

Figure 1 shows how the system wasable to efficiently capture intradaytrends in IBM. Also, the system con-straint of not carrying positionsovernight eliminated many negative

opening surprises. Overall, traded onIBM, the NCBS did a good job of mini-mizing the whipsaw losses prevalent inbreakout trading systems and maximiz-ing the profits from major intraday trendmoves.

Building on the resultsTo use this system in real-time trading, atleast 10 additional test and out-of-samplewindows should be examined to ensurethe performance summarized here wasnot the result of chance.

To determine if this approach can beused on other stocks or markets it wouldbe necessary to follow the same proce-

dures and determine the appropriateparameters for each. Every market hassubtle differences because the partici-pants vary from market to market. Also,market activity can change over time.Consequently, you should continue toperform walk-forward testing to deter-mine if there is a shift in the system’seffectiveness and whether better param-eters have emerged.

Any trading method should be testedbefore you risk capital on the technique.Granted, there is a considerable amountof work involved, but without taking thetime to adequately research a technique,the chances of success are low.�

A few of the buy and sell signals generated by the noise channel breakout system are shown. The system successfullycaptured intraday trends.

FIGURE 1 NOISE CHANNEL AND TRADE SIGNALS

3/26 11:20 12:15 1:10 2:05 3:00 3/27 11:15 12:10 1:05 2:00 2:55 3/28 11:10 12:05 1:00 1:55 2:50

101

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Source: TradeStation by TradeStation Group

International Business Machine (IBM), five-minute



The long and short of it:The Noise Channel BreakoutSYSTEM 2

The long and short of it:The Noise Channel BreakoutSYSTEM 2

TRADING Strategies


A lthough price breakouts are the basis for manytrading approaches, breakout systems areplagued by false signals — when price initiallybreaks out, triggering a buy or sell, but quickly

retraces, resulting in a losing trade.To combat this problem, traders often apply filters to break-

out systems, delaying trade entry until the initial breakout hasbeen confirmed by a price move of a certain size or duration inthe direction of the breakout.

“Better breakout trading: The noise channel breakout sys-tem“ (Active Trader, September 2001, p. 70) showed how a sim-ple channel breakout system, with an additional noise filter tominimize whipsaws, could be developed to trade IBM on anintraday basis using five-minute bars. The noise filter delaystaking a breakout signal until the market provides some con-firmation the breakout is sustainable, thus avoiding falsebreakouts.

One aspect the original noise channel breakout system(NCBS) did not address is whether to treat the long and shortsides of the market the same — that is, whether the filtershould be different for long and short trades, since uptrendsand downtrends have different characteristics.

Here we will use the NCBS, again applied to IBM five-minute price bars, to see if some improvement can be made byusing different filters for long and short trades, respectively. Tocompare the new version of the system to the previous one, thefollowing tests will use the same five-minute bar prices of IBMfrom Feb. 21, 2001, to April 6, 2001.

First, we’ll review the basics of breakout systems in generaland the NCBS in particular.

NCBS refresherThe basic channel breakout system goes long on a move abovethe highest high of the last n bars and goes short on a movebelow the lowest low of the last n bars. For example, a 40-daychannel breakout goes long when price moves above the high-est high of the last 40 days and goes short when price fallsbelow the lowest low of the last 40 days.

Breakout systems can be used on intraday price data, as wellas daily or weekly data. The NCBS is an intraday breakout sys-tem based on five-minute bars. Because intraday price actioncan be very volatile, without some kind of filter the losses gen-erated by the random price movement (that is, whipsaws) cancompletely overwhelm a trading system. To help eliminate

Traders often use additional

rules or filters to prevent

being whipsawed by

breakout trading strategies.

Find out if using different

filters for long and short

trades improves the

performance of an intraday

breakout strategy.

WALK-FORWARD: Proper system testing

When testing any trading strategy, theimportant point is how well it willperform on data on which it has not

been optimized — that is, out-of-sample data.If a certain system is first tested on a “sample”piece of historical price data (say, daily barsfrom 1993 up to 1998), the system’s perform-ance results have no implication outside thissample data set; all you know is how well yoursystem parameters performed during this par-ticular period.

To get an idea of how the system might actu-ally perform, the system parameters used forthe sample data should be applied to different“out-of-sample” price data (say, daily barsfrom 1998 to the present). This “walk-forwardprocess” simulates the application of a systemto future data, as would occur in actual trad-ing. In short, without out-of-sample testing,it’s nothing more than hope to believe that sys-tem performance in the future will be any-where near the optimized performance.

For example, it’s possible to take a tradingstrategy with four independent variables, orparameters, and with hindsight, find the valuesfor each of them that give the best (optimized)results on a specific historical period — say, thelast three years (using daily price data).

However, these optimized parameter valueshave been, in essence, cherry-picked for thisparticular data period (a process known as“curve-fitting”), and are unlikely to perform aswell on other historical test periods, or in actu-al trading in the future. An important (butunspoken) point in walk-forward testing is thatif you cannot get good results in the out-of-sample data segments, real-time system per-formance will be random.

such random movement, the NCBS adds a “noise filter,” designat-ed by the symbol f, to the basic channel breakout system. The threesystem parameters for the NCBS are:

• nhi, which is the number of bars in the lookback period used to determine the highest high price (hhp).

• nlo, which is the number of bars in the lookback period used to determine the lowest low price (llp).

• f, which is the amount price must exceed the hhp or llp to trigger a buy or sell.

The Noise Channel Breakout System 2The Noise Channel Breakout System 2 (NCBS-2) uses different fil-ter values (f, from the original system) for the long and short sidesof the market. As a result, there are four system parameters for theNCBS-2:

• nhi, which is the number of bars in the lookback period used to determine the highest high price (hhp).

• nlo, which is the number of bars in the lookback period used to determine the lowest low price (llp).


• xoU, which is the amount price must exceed the hhp to trigger a buy signal.

• xoD, which is the amount price must fall below the llp to trigger a sell signal.

Think of the symbols xoU and xoD as the “crossover Up”and “crossover Down” values.

The logic behind modifying the filter values is because mar-ket behavior associated with buys and sells is quite different,the noise channels associated with buys and sells should beindependent of each other. The NCBS-2 rules are:

Buy rule: If price crosses above the highest high price of thelast nhi bars by an amount greater then or equal to xoU, thenbuy at the market. In addition, when short, and when calculat-ing the highest high price (hhp), the hhp can only stay the sameor go lower than its most recent value, it cannot go higher.

Sell rule: If price crosses below the lowest low price of lastnlo days by an amount of greater than orequal to xoD, then sell at the market. Inaddition, when long and when calculat-ing the lowest low price (llp), the llp canonly stay the same or go higher than itsmost recent value, it cannot go lower.

Exit rule: Close any position five min-utes before the New York Stock Exchangeclose (no trades are carried overnight).

Testing the strategyAs in last month’s article, “walk-for-ward” optimization will be used here.The same data will also be used so we canjudge whether this new modification canimprove performance.

The walk-forward testing procedurewas applied as follows: A four-week peri-od from the start of the IBM five-minutebar data from Feb. 21 through March 23was chosen and system parameter valueswere found through optimization on thisintraday data segment. (Optimizationrefers to the search for the parameter val-ues that give the best results.) It should benoted that in this stage of system devel-opment, the only thing indicated by theoptimum values that are found in the testportion is that the data has been curve fit-ted as best it can with this system.Without further testing on out-of-sampledata, there is no way to tell if the systemwill work in the future.

The parameter values found were thenapplied to an out-of-sample period, inthis case March 26 to March 30. Thisprocess was repeated by moving the testdata window forward one week to Feb. 28through March 30 and again finding theparameters values through optimizationon this new data test window. The param-eter values found were then applied tothe next out-of-sample data set, which inthis case was April 2 to April 6. See “Walk-forward: Proper system testing” for addi-


Performance summary for NCBS-2, IBM five-min. bars. Feb. 28 to March 30.Statistics based upon buying and selling 1,000 shares of IBM.

FIGURE 1A TEST PERIOD 1

FIGURE 1B TEST PERIOD 2














Total no. of trades: 38 Percent profitable (%): 52.63










Performance summary for NCBS-2, IBM five-min. bars, Feb. 21 to March 23.Statistics based upon buying and selling 1,000 shares of IBM.

TABLE 1 OPTIMUM PARAMETER VALUES FOR TEST DATA

Start date End date nhi xoU nlo xoD

2/21/01 3/23/01 8 1 4 1

2/28/01 3/30/01 18 1.25 12 0.25

tional information on optimization and walk-forward testing. Of the four system parameters to find (nhi, nlo, xoU and

xoD), the “best” parameters are defined as those values thatgive the best net profits and best total winning bars/total los-ing bars ratio with the minimum drawdown and minimumlargest losing trades. In addition, the results should be stable— e.g. the profits, wins and drawdowns should not change bymuch as the parameters move by a small amount away fromtheir optimum values. Also, in choosing the best parameters,we considered only those parameter sets with maximum con-secutive losses of four or less.

ResultsTable 1 shows the optimum parametersfor the IBM five-minute data series. Thelookback periods refer to number of barsand the filters values are given as dollaramounts.

Figures 1a and 1b) show the perform-ance summary of the test windows usingthe optimum parameters shown in Table 1.

Figure 2a shows the combined per-formance summary of the two out-of-sample data segments from March 26 toApril 6 for NCBS-2. This performancerepresents what would have happenedin real time if the parameters found inthe test sections (Table 1) were used.Slippage and commissions are notincluded. For comparison, Figure 2b(bottom, right) shows the combined per-formance summary of the two out-of-sample data segments from March 26 toApril 6 for the original NCBS.

Figure 3 shows a specialized percent-age trade-by-trade summary from March26 to April 6. Note that the trades fromMarch 26 to April 6 are the out-of-sampletrades generated from the optimizedparameters from the two test sections ofFeb. 21 to March 23 and Feb. 28 to March30. The in-sample trades are, by defini-tion, curve-fit and are not of interest here.

In addition, for comparison withFigure 3, Figure 4 contains the special-ized trade-by-trade summary from theoriginal NCBS for the same out-of-sam-ple dates.

Figure 5 is a five-minute chart of IBMwith the NCBS-2 channels superimposedand some of the buy and sell signalsfrom the Figure 3 trade-by trade summa-ry indicated on the charts. (All the sig-nals, as well as expanded performancestatistics, can be found at www.activetra-dermag.com.) Also included at the bot-tom of the chart is the bar-by-bar profitor loss of each trade.

Improved performance?The optimum parameters in Table 1 show the first test data sec-tion produced the same optimum parameters as the originalNCBS. This can been seen by observing that both xoU and xoDare exactly the same and are equal to f of the original NCBS.

The sample performance summaries in Figures 1a and 1b,and the out-of-sample performance summary of Figure 2a,show the out-of-sample performance was better than the testsample performance with respect to average winning and los-ing trades, drawdowns and profit factor. This improved per-formance in the out-of-sample section could have been due to


Combined walk-forward out-of-sample performance summary for NCBS, IBMfive-min. bars, March 26 to April 6. Statistics based upon buying and selling1,000 shares of IBM.

FIGURE 2A TEST PERIOD 1

FIGURE 2B TEST PERIOD 2



Total no. of trades: 15 Percent profitable (%): 0.47





















Combined walk-forward out-of-sample performance summary for NCBS-2,IBM five-min. bars, March 26 to April 6. Statistics based upon buying andselling 1,000 shares of IBM.


IBM five-min.; NCBS-2; Trade size = 1,000 shares; March 26 to April 6

FIGURE 3 OUT-OF-SAMPLE TRADE BY TRADE SUMMARY

Entry Entry Entry Exit Exit Exit Bars Trade Trade Trade Time Trade Timedate time price date time price in trade $ % Max Max

P&L P&L $Pft $DD3/26/01 10:20 Sell 93.75 3/26/01 15:55 94.52 67 (770) (0.82) 0 10:20 (1,620) 10:35

3/27/01 10:15 Buy 95.59 3/27/01 15:55 99.59 68 4,000 4.18 4,300 15:50 0 10:15

3/28/01 9:40 Sell 97.92 3/28/01 15:55 94.50 75 3,420 3.49 3,420 12:00 (380) 9:40

3/29/01 10:05 Buy 96.05 3/29/01 15:05 94.90 60 (1,150) (1.20) 950 10:30 (1,160) 11:20

3/29/01 15:05 Sell 94.90 3/29/01 15:55 94.88 10 20 0.02 390 15:15 (500) 15:45

3/30/01 9:40 Buy 96.70 3/30/01 13:05 96.20 41 (500) (0.52) 800 11:55 (1,190) 10:00

3/30/01 13:05 Sell 96.20 3/30/01 15:55 96.25 34 (50) (0.05) 220 13:15 (840) 14:35

4/2/01 10:55 Sell 96.45 4/2/01 15:55 94.50 60 1,950 2.02 2,650 15:40 (1,250) 11:40

4/3/01 9:40 Sell 93.33 4/3/01 15:55 90.50 75 2,830 3.03 3,070 15:40 (670) 9:45

4/4/01 10:55 Buy 92.99 4/4/01 12:55 92.55 24 (440) (0.47) 910 11:20 (660) 11:00

4/4/01 12:55 Sell 92.55 4/4/01 15:55 91.85 36 700 0.76 930 14:00 (520) 13:20

4/5/01 9:40 Buy 95.68 4/5/01 15:55 98.15 75 2,470 2.58 3,040 15:25 (10) 9:40

4/6/01 9:40 Sell 97.30 4/6/01 12:00 98.75 28 (1,450) (1.49) 550 11:15 (1,450) 12:00

4/6/01 12:00 Buy 98.75 4/6/01 12:35 97.02 7 (1,730) (1.75) 1,150 12:05 (1,730) 12:35

4/6/01 12:35 Sell 97.02 4/6/01 15:55 97.67 40 (650) (0.67) 20 12:35 (2,240) 13:55

Total Average Average Average8,650 0.61% 1,493 (948)

IBM five-min.; NCBS; Trade size = 1,000 shares; March 26 to April 6

FIGURE 4 OUT-OF-SAMPLE TRADE BY TRADE SUMMARY

Entry Entry Entry Exit Exit Exit Bars Trade Trade Trade Time Trade Timedate time price date time price in trade $ % Max Max

P&L P&L $Pft $DD3/26/01 10:20 Sell 93.75 3/26/01 15:55 94.52 67 (770) (0.82) 0 10:20 (1,620) 10:35

3/27/01 10:15 Buy 95.59 3/27/01 15:55 99.59 68 4,000 4.18 4,300 15:50 0 10:15

3/28/01 9:40 Sell 97.92 3/28/01 15:55 94.50 75 3,420 3.49 3,420 12:00 (380) 9:40

3/29/01 10:05 Buy 96.05 3/29/01 15:05 94.90 60 (1,150) (1.20) 950 10:30 (1,160) 11:20

3/29/01 15:05 Sell 94.90 3/29/01 15:55 94.88 10 20 0.02 390 15:15 (500) 15:45

3/30/01 9:40 Buy 96.70 3/30/01 13:05 96.20 41 (500) (0.52) 800 11:55 (1,190) 10:00

3/30/01 13:05 Sell 96.20 3/30/01 15:55 96.25 34 (50) -0.05) 220 13:15 (840) 14:35

4/2/01 9:40 Buy 97.75 4/2/01 10:55 96.40 15 (1,350) -1.38) 350 10:05 (1,350) 10:55

4/2/01 10:55 Sell 96.40 4/2/01 15:55 94.50 60 1,900 1.97 2,600 15:40 (1,300) 11:40

4/3/01 10:00 Sell 93.00 4/3/01 15:55 90.50 71 2,500 2.69 2,740 15:40 0 10:00

4/4/01 9:45 Buy 92.00 4/4/01 13:50 92.00 49 0 0.00 1,900 11:20 (1,890) 10:30

4/4/01 13:50 Sell 92.00 4/4/01 15:55 91.85 25 150 0.16 380 14:00 (500) 14:20

4/5/01 9:40 Buy 95.68 4/5/01 15:55 98.15 75 2,470 2.58 3,040 15:25 (10) 9:40

4/6/01 9:40 Sell 97.30 4/6/01 11:55 98.24 27 (940) (0.97) 550 11:15 (940) 11:55

4/6/01 11:55 Buy 98.24 4/6/01 12:35 97.30 8 (940) (0.96) 1,660 12:05 (940) 12:35

4/6/01 12:35 Sell 97.30 4/6/01 15:55 97.67 40 (370) (0.38) 300 12:35 (1,960) 13:55

Total Average Average Average8,390 0.55% 1,475 (911)

Source: Meyers Analytics

Source: Meyers Analytics

chance but does indicate that four weeks of test data wereenough to capture the intraday price dynamics of IBM.

The performance summaries in Figures 2a and 2b showthere is very little difference between the NCBS and NCBS-2.The less-complicated NCBS, while having a slightly lower netprofit and average win/average loss ratio, has a smaller draw-down and a smaller largest losing trade. Comparison ofFigures 2a and 2b favors the simpler NCBS.

The out-of-sample trade-by-trade summary of Figure 3shows the system did better on short trades than on longtrades. This could indicate a negative bias for the system, orperhaps, given the current bear market, this could be normal.Whatever the reason, this bias warrants further investigation.

There were no big winners or big losers, indicating steadyreturns. Average wins were 2.6 times average losses in the out-of-sample section. Average trade run-ups were $1,493, average tradedrawdowns were -$948 and the average trade net profit was $576.

It’s also instructive to compare Figure 3 with Figure 4 to

determine if the more complicated NCBS-2 offers any advan-tage in the trade-by-trade figures. There seems to be littleadvantage: Both systems’ totals and averages are nearly thesame. The NCBS-2 had one less trade and slightly better num-bers. However, the difference wasn’t enough to claim anysuperiority or to justify the added complication of anotheroptimization parameter.

The NCBS-2 did very well in catching every major intradaytrend of IBM. The charts show the system constraint of not car-rying positions overnight eliminated many negative openingsurprises. Overall, the system did a good job in minimizing thelosses resulting from the inevitable whipsaws that will occur inany trading system and maximizing the profits from the majorintraday trend moves of IBM.

To use NCBS-2 in real time trading, the results from at least10 to 20 more tests and out-of-sample periods would have to beexamined to make sure that the results above were not due topure chance.�


Trade signals for the NCBS-2 are shown on a five-minute chart of IBM. The blue and red lines are the long and short filter levels, respectively.

FIGURE 5 NCBS-2 SIGNALS

International Business Machine (IBM), five-minute

9:55 10:50 11:45 12:40 1:35 2:30 3/27 10:50 11:45 12:40 1:35 2:30 3/28 10:50 11:45 12:40 1:35 2:30

0

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2,500

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The multibar range BREAKOUT SYSTEMBreakouts of price channels can be

profitable — if the volatility is there

and you’re on the right side of the

trade. This stop-and-reverse system

tries to capture intraday trends in

the S&P E-Mini contract by recognizing

differences in the characteristics

of up moves and down moves.

FIGURE 1 TRADESTATION CODE FOR THE MULTIBAR RANGE BREAKOUT SYSTEM

{Strategy: #MultiBarRangeBO}

Input: n(45),bx(0.45),m(15),sx(0.45),XTime(1515); vars: hhv1(h),llv1(l),hhv2(h),llv2(l),ii(0),xb(c),xs(c);

hhv1=h; llv1=l; for ii=1 to n-1 begin

if h[ii]>hhv1 then hhv1=h[ii]; if l[ii]<llv1 then llv1=l[ii]; end; value1=hhv1-llv1;

hhv2=h; llv2=l; for ii=1 to m-1 begin

if h[ii]>hhv2 then hhv2=h[ii]; if l[ii]<llv2 then llv2=l[ii]; end; value2=hhv2-llv2;

xb= c + (Value1 * bx); xs= c - (Value2 * sx);

if time<XTime then begin if marketposition<=0 then Buy Next Bar xb stop; if marketposition>=0 then Sell Short Next Bar xs stop;

end;

if XTime<>0 then SetExitOnClose;

ADVANCED Strategies


B reakout systems are popular when markets arevolatile. Such systems typically identify supportand resistance levels when price has been movingin a range or channel, and enter trades when price

breaks out of either the up side or down side of a channel. There are two simple ways to define support and resistance

levels for price channels. In both cases, it is first necessary todefine a lookback period. The first way is to use the highesthigh and the lowest low of the lookback period. The secondway is to determine the range of each bar (high minus low) andadd that range (or a percentage of it) to, or subtract it from, thecurrent close.

In either case, the upper and lower boundaries represent theprice channel. One advantage to the second method is it betterreflects the volatility of the market — it will expand and con-tract as the volatility changes.

Breakout strategies require the market to be in a high-volatil-ity period; a trade will become profitable only if it continues tomove in the direction of the breakout. Volatility and emotion gohand in hand. As volatility increases, traders have to cope withmore risk; hence, the more emotional the market becomes. Thisis often reflected by the fact markets fall faster than they rise.

In the following system, the channel is determined by usingthe range of the price bars in the lookback period. A breakoutabove or below the channel’s resistance or support creates buyor sell signals.

However, the parameters for the buy signals will bedifferent than those for the sell signals, because of thepropensity for markets to fall faster than they rise. Therange for the last x bars will be defined as the highesthigh of the last x bars (including the current bar) minusthe lowest low of the last x bars (including the currentbar).

The buy price is determined by adding a percentageof the range of the last n bars to the current close — thepreviously described volatility-adjusted technique. Ifthe next bar’s price exceeds the buy price, the systemissues a buy signal. The sell price is determined bysubtracting a percentage (a different percentage thanthe buy percentage) of the range of the last m bars fromthe current close. If the next bar’s price falls below thesell price, the system issues a sell signal.

The resulting Multibar Channel Breakout systemwill trade the S&P 500 E-Mini futures on an intradaybasis using one-minute bars. The TradeStation Code isshown in Figure 1 (opposite page).

Multibar Channel Breakout rulesThis is a stop-and-reverse system, meaning it is alwaysin the market: When a sell signal occurs, long tradesare exited and a short trade is entered; when a buy sig-nal occurs, short trades are exited and a long trade isentered. These are the system’s parameters:

ES = E-Mini price;BRange = the price range over the last n bars;SRange = the price range over the last m bars;bx = the percentage multiplier of the BRange for buy

signals;sx = the percentage multiplier of the SRange for sell

signals;c = the current price;buyCh = c + bx*BRange; sellCh = c - sx*SRange

wheren = The number of lookback bars (including the cur-

rent bar) for buy signals.m = The number of lookback bars (including the

current bar) for sell signals.

Notice that not only are the percentage multipliersfor long (bx) and short trades (sx) different, the look-back periods the system references for buys (n) andsells (m) are also different. The trade rules are simple:

1. Buy rule: Buy the next bar at buyCh, stop. 2. Sell rule: Sell the next bar at sellCh, stop. 3. Intraday bar exit rule: Exit the position on the

close (no overnight trades).

Although it may not be immediately obvious, this sys-tem avoids the opening gap whipsaw problem —trades being triggered because of large gap openings


The system triggered more short trades during the test, but pro-duced profits on long trades, as well.

TABLE 1 MULTIBAR RANGE BREAKOUT SYSTEM PERFORMANCE SUMMARY, JULY 7 TO AUG. 1, 2003

Source: TradeStation

All trades Long trades Short trades

Total net profit $4,912.50 $1,450.00 $3,462.50

Gross profit $6,637.50 $1,912.50 $4,725.00

Gross loss ($1,725.00) ($462.50) ($1,262.50)

Profit factor 3.85 4.14 3.74

Open position P/L $0.00 $0.00 $0.00

Total number of trades 55 20 35

Percent profitable 58.18% 55.00% 60.00%

Winning trades 32 11 21

Losing trades 22 9 13

Even trades 1 0 1

Avg. trade net profit $89.32 $72.50 $98.93

Avg. winning trade $207.42 $173.86 $225.00

Avg. losing trade ($78.41) ($51.39) ($97.12)

Ratio avg. winning/avg. losing 2.65 3.38 2.32

Largest winning trade $700.00 $362.50 $700.00

Largest losing trade ($300.00) ($125.00) ($300.00)

Largest winner as % of gross profit 10.55% 18.95% 14.81%

Largest loser as % of gross loss 17.39% 27.03% 23.76%

Net profit as % of largest loss 1,637.50% 1,160.00% 1,154.17%

Max. consecutivewinning trades 5 5 4

Max. consecutive losing trades 5 2 3

Avg. bars in total trades 134.96 45.8 185.91

Avg. bars in winning trades 166.88 69.18 218.05

Avg. bars in losing trades 91.95 17.22 143.69

Max. drawdown (intraday peak to valley) ($887.50) ($862.50) ($975.00)

Max. drawdown (trade close to trade close) ($300.00) ($175.00) ($400.00)

Max. trade drawdown ($475.00) ($475.00) ($362.50)


This list contains each trade in the test period. Overall, 58.18 percent of trades were profitable.

TABLE 2 TRADE-BY-TRADE SUMMARY: JULY 7 TO AUG. 1, 2003

Source: Meyers Analytics, LLC

Entry Entry Entry Exit Exit Exit Bars Trade Trade Trade date time price ($) date time price ($) in trade $P&L max$Pft Time max$DD Time 7/7/03 10:36 Sell 1,003.75 7/7/03 12:35 1,002.50 119 $62.50 $175.00 12:06 ($12.50) 10:36 7/7/03 12:35 Buy 1,002.50 7/7/03 12:52 1,001.50 17 ($50.00) $0.00 12:35 ($50.00) 12:38 7/7/03 12:52 Sell 1,001.50 7/7/03 13:01 1,002.75 9 ($62.50) $25.00 12:55 ($62.50) 13:01 7/7/03 13:01 Buy 1,002.75 7/7/03 13:24 1,002.50 23 ($12.50) $37.50 13:23 ($25.00) 13:08 7/7/03 13:24 Sell 1,002.50 7/7/03 15:15 1,002.75 111 ($12.50) $87.50 14:22 ($112.50) 13:47 7/8/03 9:51 Sell 1,002.00 7/8/03 14:09 1,004.25 258 ($112.50) $62.50 11:21 ($175.00) 10:19 7/8/03 14:09 Buy 1,004.25 7/8/03 15:15 1,007.50 66 $162.50 $187.50 14:48 ($62.50) 14:11 7/9/03 9:33 Sell 1,007.75 7/9/03 15:15 1,001.00 342 $337.50 $525.00 11:03 $0.00 9:33 7/10/03 8:58 Sell 992.50 7/10/03 15:15 988.75 377 $187.50 $512.50 13:48 ($62.50) 9:04 7/11/03 9:05 Sell 993.25 7/11/03 15:15 997.75 370 ($225.00) $62.50 9:08 ($337.50) 11:23 7/14/03 9:54 Sell 1,012.25 7/14/03 15:15 1,002.75 317 $475.00 $575.00 14:46 ($112.50) 10:01 7/15/03 9:05 Sell 1,002.75 7/15/03 15:15 1,000.75 370 $100.00 $362.50 14:05 ($275.00) 9:46 7/16/03 8:48 Sell 1,000.50 7/16/03 12:10 993.25 202 $362.50 $625.00 10:12 $0.00 8:487/16/03 12:10 Buy 993.25 7/16/03 12:22 992.25 12 ($50.00) $0.00 12:10 ($50.00) 12:18 7/16/03 12:22 Sell 992.25 7/16/03 15:15 995.25 173 ($150.00) $187.50 14:30 ($150.00) 15:10 7/17/03 9:04 Sell 988.25 7/17/03 11:01 986.25 117 $100.00 $275.00 9:39 $0.00 9:04 7/17/03 11:01 Buy 986.25 7/17/03 11:02 983.75 1 ($125.00) $0.00 11:01 ($125.00) 11:02 7/17/03 11:02 Sell 983.75 7/17/03 15:15 980.50 253 $162.50 $337.50 14:01 ($25.00) 11:06 7/18/03 8:49 Sell 986.00 7/18/03 11:27 985.25 158 $37.50 $287.50 9:20 ($25.00) 9:027/18/03 11:27 Buy 985.25 7/18/03 12:02 985.50 35 $12.50 $62.50 11:31 ($12.50) 11:27 7/18/03 12:02 Sell 985.50 7/18/03 13:01 985.50 59 $0.00 $50.00 12:19 ($25.00) 12:03 7/18/03 13:01 Buy 985.50 7/18/03 14:35 991.50 94 $300.00 $375.00 14:11 $0.00 13:01 7/18/03 14:35 Sell 991.50 7/18/03 15:15 990.00 40 $75.00 $75.00 14:53 ($87.50) 14:417/21/03 8:32 Sell 988.00 7/21/03 15:15 978.25 403 $487.50 $700.00 14:10 ($12.50) 8:32 7/22/03 9:20 Sell 976.75 7/22/03 10:01 978.50 41 ($87.50) $112.50 9:50 ($87.50) 10:01 7/22/03 10:01 Buy 978.50 7/22/03 11:37 985.75 96 $362.50 $500.00 11:10 ($75.00) 10:18 7/22/03 11:37 Sell 985.75 7/22/03 14:37 987.25 180 ($75.00) $237.50 12:37 ($150.00) 14:02 7/22/03 14:37 Buy 987.25 7/22/03 15:15 986.75 38 ($25.00) $25.00 14:38 ($87.50) 14:49 7/23/03 8:35 Sell 986.25 7/23/03 12:51 984.75 256 $75.00 $412.50 11:16 $0.00 8:35 7/23/03 12:51 Buy 984.75 7/23/03 13:38 986.50 47 $87.50 $187.50 13:35 ($37.50) 12:55 7/23/03 13:38 Sell 986.50 7/23/03 14:27 986.75 49 ($12.50) $100.00 14:14 ($50.00) 13:39 7/23/03 14:27 Buy 986.75 7/23/03 15:15 987.75 48 $50.00 $62.50 15:13 ($62.50) 14:35 7/24/03 9:21 Sell 995.50 7/24/03 12:45 993.75 204 $87.50 $162.50 9:50 ($50.00) 10:27 7/24/03 12:45 Buy 993.75 7/24/03 13:10 994.25 25 $25.00 $62.50 12:57 $0.00 12:45 7/24/03 13:10 Sell 994.25 7/24/03 15:15 980.25 125 $700.00 $762.50 14:57 ($25.00) 13:11 7/25/03 8:43 Buy 982.50 7/25/03 9:01 983.25 18 $37.50 $150.00 9:00 ($37.50) 8:45 7/25/03 9:01 Sell 983.25 7/25/03 13:01 989.25 240 ($300.00) $375.00 9:52 ($337.50) 12:33 7/25/03 13:01 Buy 989.25 7/25/03 15:08 996.00 127 $337.50 $412.50 14:54 ($87.50) 13:25 7/25/03 15:08 Sell 996.00 7/25/03 15:15 997.00 7 ($50.00) $0.00 15:08 ($62.50) 15:12 7/28/03 8:33 Sell 995.50 7/28/03 12:37 996.50 244 ($50.00) $175.00 8:52 ($187.50) 10:29 7/28/03 12:37 Buy 996.50 7/28/03 12:57 996.25 20 ($12.50) $100.00 12:55 ($12.50) 12:37 7/28/03 12:57 Sell 996.25 7/28/03 15:15 993.50 138 $137.50 $187.50 14:42 ($87.50) 14:04 7/29/03 9:01 Sell 991.25 7/29/03 10:34 986.75 93 $225.00 $450.00 9:34 $0.00 9:01 7/29/03 10:34 Buy 986.75 7/29/03 11:28 992.00 54 $262.50 $450.00 10:58 ($12.50) 10:34 7/29/03 11:28 Sell 992.00 7/29/03 15:15 989.00 227 $150.00 $300.00 14:17 ($250.00) 12:33 7/30/03 8:34 Sell 990.00 7/30/03 11:38 989.25 184 $37.50 $275.00 10:32 ($37.50) 9:56 7/30/03 11:38 Buy 989.25 7/30/03 11:56 988.00 18 ($62.50) $0.00 11:38 ($62.50) 11:52 7/30/03 11:56 Sell 988.00 7/30/03 13:02 988.75 66 ($37.50) $62.50 12:19 ($50.00) 12:01 7/30/03 13:02 Buy 988.75 7/30/03 13:04 987.00 2 ($87.50) $0.00 13:02 ($87.50) 13:04 7/30/03 13:04 Sell 987.00 7/30/03 15:15 986.25 131 $37.50 $125.00 13:14 ($25.00) 14:21 7/31/03 9:00 Buy 995.75 7/31/03 11:20 1,001.25 140 $275.00 $387.50 10:14 ($400.00) 9:08 7/31/03 11:20 Sell 1,001.25 7/31/03 13:07 1,003.00 107 ($87.50) $37.50 11:40 ($112.50) 12:14 7/31/03 13:07 Buy 1,003.00 7/31/03 13:22 1,002.25 15 ($37.50) $12.50 13:19 ($50.00) 13:16 7/31/03 13:22 Sell 1,002.25 7/31/03 15:15 988.50 113 $687.50 $725.00 14:56 ($12.50) 13:22 8/1/03 8:46 Sell 983.50 8/1/03 15:15 979.50 389 $200.00 $300.00 9:35 ($125.00) 8:51

that quickly reverse and stop out the position. With this sys-tem, if there is a gap on the opening bar, the buy and sell rangesare expanded and no trades are made until the buy and sellranges contract or the price breaks the expanded ranges.Breaking the expanded ranges takes time and avoids the open-ing gap whipsaw.

TestingThe system was tested from July 7 through Aug. 1, 2003, usingSeptember 2003 E-Mini futures (ESU03) one-minute bars. Awide range of parameter values was tested to find the optimalones for the system. The parameter ranges tested for the initialoptimization test were:

n =10 to 50 in steps of 5;bx = 0.4 to 1 in steps of 0.05;m = 10 to 50 in steps of 5;sx = 0.4 to 1 in steps of 0.05;

After the initial test, we had to choose one set of parametersthat produced the most realistic results. To avoid curve fitting,we eliminated all results that had profit factors (gross profitdivided by gross loss) greater than 4.0, since such performancewas unlikely to be duplicated in the future. Also, because it is

difficult to sustain more than a handful of consecutive losses,we eliminated all cases that had more than five losing trades ina row. Of the remaining test results, we chose the one that hadthe highest total net profit and the lowest drawdown. The opti-mization procedure produced the following system parame-ters:

n = 45;bx = 0.45;m = 15;sx = 0.45;

Table 1 (p. 43) shows the performance summary for the four-week test period (slippage and commissions not included).Table 2 (opposite page) is a trade-by-trade summary of all thetrades. The average net profit per trade was $89 — well aboveslippage and commissions for a typical S&P E-Mini trade. Thelargest losing trade was $300, and the biggest intraday draw-down was $887. These losses are small compared to the totalnet profit of $4,912.

Figures 2 and 3 are one-minute charts of the S&P E-Minithat span July 31 to Aug. 1. The Multibar Range Breakout chan-nels are superimposed on the price series, and all the buy andsell signals are marked. Finally, the bottoms of Figures 2 and 3


September 2003 S&P E-Mini futures (ESU03), one-minute

Short

Buy

Buy

Short

Short

End of dayexit

End of dayexit

7/30 9:11 9:33 9:55 10:17 10:39 11:01 11:23 11:45 12:07 12:29 12:51 13:13 13:35 13:57 14:19 14:41 8/1

1,010

1,005

1,000

995

990

985

980

During this period, the system caught one intraday uptrend, one intraday downtrend, and produced small losses on twosignals when the market was flat.

FIGURE 2 RIDING THE TREND


600

400

200

0

-200

include the bar-by-bar profit or loss ofeach trade.

Figure 4 is a daily chart of the S&P E-Mini futures from July 7 to Aug. 1, andshows the market moved up, down andsideways during this period. The systemwas able to produce profits on both thelong and short sides of the market, andaside from a streak of five losing tradesnear the outset of the test period, neverhad more than three consecutive losses.

The Multibar Channel Breakout sys-tem’s positive performance warrantsfurther investigation. If you considerfollowing this system in real-time, payclose attention to how the real-time sta-tistics compare to the hypothetical num-bers shown here. If the numbers begin todeviate, another review of the systemparameters are in order. �

Individual articles can be purchased anddownloaded from www.activetradermag.com/purchase_articles.htm.


September 2003 S&P E-Mini futures (ESU03), one-minute

14:19 14:41 8/1 9:02 9:24 9:46 10:08 10:30 10:52 11:14 11:36 11:58 12:20 12:42 13:04 13:26 13:48 14:10 14:32 14:54

1,002

1,000

998

996

994

992

990

988

986

984

982

980

978

976

600

400

200

0

If no signal in the opposite direction is triggered, the system will stay in the same direction the entire day. All tradesare exited at the close — no positions are held overnight.

FIGURE 3 ONE DAY, ONE TRADE


September 2003 S&P E-Mini futures (ESU03), daily

7 14 21 28

1,015

1.010

1,005

1,000

995

990

985

980

975

The daily chart of the test period shows the system was able to profit onboth sides of the market when conditions shifted from uptrend to downtrendto consolidation.

FIGURE 4 DAILY PERSPECTIVES


End of day exit

End of day exit

Sell


Markets: Stocks, stock index futures, index stocks(SPDRs, DIAs, QQQs), futures and currencies

System logic:This system is based on a simple pattern, named TDCarrie, described by Tom DeMark in his book NewMarket Timing Techniques (John Wiley & Sons, 1997).It trades a move above or below the true high (thehighest of one bar’s high and the previous bar’sclose) or the true low (the lowest of one bar’s lowand the previous bar’s close) of the bar four daysprior to the current (active) bar. However, for thebreakout to be valid it must be qualified by a few cri-teria. (The following rules are described in terms ofa long trade; reverse for short trades.)

First, to identify a strongly trending market, thetrue high of four days ago must be higher than the high five days ago.If this requirement is not met, it’s still possible to get an entry signal ifthe market has made a correction counter to the direction of an even-tual trade (i.e., a downward correction in the case of a long trade). Inan uptrend this correction is identified by the highs of either two orthree days ago being lower than the true high of four days ago.

Second, the close of the bar prior to the anticipated breakoutneeds to be lower then the previous bar’s close. This is to ensure thatmost traders still have a short-term bearish outlook prior to theupside breakout. That will increase the force of the up move as thetraders are caught on the wrong side of the market and scramble toget out of the market.

Finally, the breakout must take place intraday and exceed the truehigh of four days ago by a sufficient amount. That the trade needsto take place intraday means, for a valid upside breakout, the open-ing price of the day for the breakout must be lower than the truehigh of four days ago. This is to avoid entering into too strong anopening, which often marks the end of the current trend. Also, by

requiring the breakout to take place intraday, we enter into anorderly market instead of a highly volatile one.

Rules:1. Prepare to go long today if

a. the true high from four days ago is higher than the high fromeither two, three or five days ago, and

b. yesterday’s close is lower than the close two days ago, and c. today’s open is lower than the high four days ago.

2. Prepare to go short today if a. the true low from four days ago is lower than the low from

either two, three or five days ago, and b. yesterday’s close is higher than the close two days ago, and c. today’s open is higher than the low four days ago

3. Go long today with a stop order at the true high of four days ago, plus 0.1 percent.4. Go short today with a stop order at the true low of four days ago, minus 0.1 percent.

5. Risk 2 percent of available equity per trade.6. Exit all trades with a loss if the market moves against the position by 4 percent or more.7. Exit all trades with a profit if the market moves in favor of the position by 12 percent ormore.8. Exit all trades after five days, counting theday for the entry as day one, and no matter howlate in the day the trade was made (i.e., a tradeexecuted at 2:50 p.m. on Monday would be exit-ed Friday the same week).

Test period: November 1991 to June 2001

Test data: Daily stock prices for the 30 highestcapitalized stocks in the Nasdaq 100 (excludingIntel and Microsoft, which are also part of theDow Jones Industrial Average). $10 commissiondeducted per trade.

Starting equity: $100,000 (nominal)

Buy-and-hold stats:DJIA: Total return – 254 percent; Max DD – 22.5

600,000

500,000

400,000

300,000

200,000

100,000

0

Acco

unt b

alan

ce ($

)

EQUITY CURVE

SAMPLE TRADES

71.00

69.00

67.00

65.00

63.0062.00

60.00

58.00

56.0055.00

53.00

51.00

Amgen (AMGN), daily

2 9 16 23 30 7 14 21 28 4 11April May June


SX#3

SX

Buy

BuyBuy

Buy

Buy

LXLX#3

LX#3LX#3

Sell

Sell

Sell

The TRADING Systems Lab

11/12/91 11/12/92 11/12/93 11/12/94 11/12/95 11/12/96 11/12/97 11/12/98 11/12/99 11/12/00

DeMark variation



LEGEND: End. equity ($) — equity at the end of test period • Total return(%) — total percentage return over test period • Avg. annual ret. (%) —average continuously compounded annual return • Profit factor — grossprofit/gross loss • Avg. tied cap (%) — average percent of total available cap-ital tied up in open positions • Win. months (%) — percentage profitablemonths over test period • Max DD (%) — maximum drop in equity •Longest flat — longest period, in months, spent between two equity highs •No. trades — number of trades • Avg. trade ($) — amount won or lost bythe average trade • Avg. DIT— average days in trade • Avg. win/loss ($)— average wining and losing trade, respectively • Lrg. win/loss ($) —largest wining and losing trade, respectively • Win. trades (%) — percentwinning trades • TIM (%) — amount of time there is at least one open posi-tion for entire portfolio, and each market, respectively • Tr./Mark./Year —trades per market per year • Tr./Month — trades per month for all markets

LEGEND: Cumulative returns — Most recent: most recent return from start toend of the respective periods • Average: the average of all cumulative returns fromstart to end of the respective periods • Best: the best of all cumulative returns fromstart to end of the respective periods • Worst: the worst of all cumulative returnsfrom start to end of the respective periods • St. dev: the standard deviation of allcumulative returns from start to end of the respective periods


go with the entry strategies. We therefore arbitrarilyattached a 4-percent stop-loss and a 12-percent prof-it-exit target, plus a time-based stop that exits alltrades after five days, no matter what. (All thesestops are completely un-optimized, which means alittle optimization should increase performance con-siderably.)

Also, note the system operates with no trend filter,such as a long-term moving average. Such filters,which allow only those trades that are in the directionof the underlying trend, also improve performance.

Finally, note that many of the stocks traded in thisexample weren’t tradable until a few years ago,which explains the initial large drawdown andexceptionally long flat period. Had we been able totest the same 30 stocks throughout the entire period,

it’s highly likely performance would have improved considerably.

Send Active Trader your systemsIf you have a trading system or idea you’d like to see tested,send it to us at the Trading System Lab. We’ll test it on aportfolio of stocks or futures (for now, maximum 30 markets,using daily data starting Jan. 1, 1990), using true portfolioanalysis/optimization.

Most system-testing software only allows you to test onemarket at a time. Our system-testing technique lets all mar-kets share the same account and is based on the interactionwithin the portfolio as a whole.

Start by e-mailing system logic (in TradeStation’sEasyLanguage or in an Excel spreadsheet) and a short descrip-tion to [email protected], and we’ll get back toyou.

Note: Each system must have a clearly defined stop-losslevel and a suggested optimal amount to risk per trade.

Profitability Trade statisticsEnd. equity ($): 415,573 No. trades: 3,529Total return (%): 316 Avg. trade ($): 158Avg. annual ret. (%): 16.03 Avg. DIT: 3.0Profit factor: 1.13 Avg. win/loss ($): 1,150 (1,393)Avg. tied cap (%): 58 Lrg. win/loss ($): 12,674 (8,131)Win. months (%): 53 Win. trades (%): 39.4

Drawdown TIM (%): 97 /15.1Max DD (%): 37.5 Tr./Mark./Year: 12.3Longest flat (m): 57.2 Tr./Month: 30.7

STRATEGY SUMMARY

ROLLING TIME WINDOW RETURN ANALYSISCumulative 12 24 36 48 60

months months months months monthsMost recent: 4.27% 34.67% 81.32% 157.51% 336.03%Average: 21.73% 59.85% 115.57% 185.15% 254.23%Best: 87.23% 206.04% 294.97% 393.04% 494.23%Worst: -26.11% -25.61% -21.38% -15.55% 12.15%St. dev.: 29.26% 61.15% 95.27% 125.03% 137.24%


Most recent: 4.27% 16.05% 21.94% 26.68% 34.25%Average: 21.73% 26.43% 29.18% 29.95% 28.78%Best: 87.23% 74.94% 58.07% 49.01% 42.82%Worst: -26.11% -13.75% -7.70% -4.14% 2.32%St. dev: 28.26% 26.94% 24.99% 22.48% 18.86%

DRAWDOWN CURVE

0.00%

-5.00%

-10.00%

-15.00%

-20.00%

-25.00%

-30.00%

-35.00%

-40.00%

11/12/91 11/12/92 11/12/93 11/12/94 11/12/95 11/12/96 11/12/97 11/12/98 11/12/99 11/12/00

percent (current); Longest flat – 18 months (current).S&P 500: Total return – 216 percent; Max DD – 30.4 percent (cur-rent); Longest flat – 15 months (current).Nasdaq: Total return – 519 percent; Max DD – 72 percent (current);Longest flat – 15 months (current).

System analysisIn DeMark’s original work, the amount by which the price had toclear the breakout level was set to the smallest price increment forthe market in question. In this version, this is changed to one-tenthof a percent to make the system consistent across all markets. Thismeans that for a stock that trades around $50, this amount is aboutfive cents; for a stock that trades around $100, it comes out toapproximately 10 cents.

DeMark did not suggest any exit strategies or stop-loss levels to

60 www.activetradermag.com • February 2003 • ACTIVE TRADER

Markets: Any market with a propensity totrend.

System logic: This system enters a long (short)trade if the last closing price is above (below) thehighest high (lowest low) of the lookback peri-od. The word “dynamic” refers to the fact thatthe lookback period will change based on thevolatility of the market.

This is similar to the system tested in theJanuary Trading System Lab (p. 50), whenBollinger Bands were used to trigger trades.Since the Bollinger Bands moved farther awayfrom price as the volatility of the market increased, the higherthe volatility, the more difficult it was to enter and exit trades.

This month’s system functions in a related manner. The high-er the volatility, the longer the lookback period will be. Becausebuys and sells will be based on price making a new high or lowfor the lookback period, the longer the lookback period, themore difficult it will be to enter or exit a trade.

In this case, the lookback period can range from 20 to 60 daysand will change daily depending on the level of volatility.(Volatility reading is the daily standard deviation of the closingprice over the last 30 days.)

You can read more about the logic of the system in theFutures System Lab (p. 70), where we have tested it on 15 dif-ferent commodity futures markets.

Rules:1. Go long on the open if yester-day’s close is higher than the high-est high of the lookback period.2. Exit by reversing the position.

Reverse the rules for short trades.

Money management:1. Risk 2 percent of availableequity per market traded. 2. The number of shares to tradewas calculated with the followingformula:

ST = AC * PR / DistwhereST = Shares to tradeAC = Available capitalPR = Percent riskedDist = Distance between the entryprice and the exit price on the dayof entry.

Test period:April 1993 to October 2002.

Test data: Daily prices for 30 of the most widely tradedNasdaq 100 stocks. $10 per trade deducted for slippage andcommission.

Starting equity: $100,000 (nominal).

Buy-and-hold stats:Total Maximum Longest

Index return drawdown flat period

DJIA 146% 39% (current) 33 months (current)S&P 500 100% 51% (current) 30 months (current)Nasdaq 180% 83% (current) 30 months (current)

Test results: Although the system has fared no better thanbuy-and-hold over the life of the test period, it has fared much

$300,000

$250,000

$200,000

$150,000

$100,000

$50,000

$0Ac

coun

t bal

ance

($)

EQUITY CURVE

3/26/93 3/26/94 3/26/95 3/26/96 3/26/97 3/26/98 3/26/99 3/26/00 3/26/01 3/26/02

Dynamic breakout system

SAMPLE TRADES


Cisco (CSCO), daily

Sell

Sell

Buy

Buy

May June July August September October

17.00

16.00

15.00

14.00

13.00

12.00

11.00

10.00

9.00

ACTIVE TRADER • February 2003 • www.activetradermag.com 61

maximum open profit of each trade. Overall,this would translate into more profitablemonths, a smoother equity curve and a high-er average annual return.

Another way to improve this system couldbe to use different lookback periods for longand short trades. Most likely, the lookbackperiod for the short side would be shorterthan that for the long side. As with all sys-tems tested in the Trading Systems Lab, thisone is totally unoptimized. As a result, youshould be able to increase profits consider-ably by experimenting with different settingsfor the indicators and adding a few ideas ofyour own.�


LEGEND: End. equity ($) — equity at the end of test period • Total return(%) — total percentage return over test period • Avg. annual ret. (%) —average continuously compounded annual return • Profit factor — grossprofit/gross loss • Avg. tied cap (%) — average percent of total available cap-ital tied up in open positions • Win. months (%) — percentage profitablemonths over test period • Max. DD (%) — maximum drop in equity •Longest flat — longest period, in months, spent between two equity highs •No. trades — number of trades • Avg. trade ($) — amount won or lost bythe average trade • Avg. DIT— average days in trade • Avg. win/loss ($)— average winning and losing trade, respectively • Lrg. win/loss ($) —largest winning and losing trade, respectively • Win. trades (%) — percentwinning trades • TIM (%) — amount of time there is at least one open posi-tion for entire portfolio, and each market, respectively • Tr./Mark./Year —trades per market per year • Tr./Month — trades per month for all markets

LEGEND: Cumulative returns — Most recent: most recent return from start toend of the respective periods • Average: the average of all cumulative returnsfrom start to end of the respective periods • Best: the best of all cumulativereturns from start to end of the respective periods • Worst: the worst of all cumu-lative returns from start to end of the respective periods • St. dev.: the standarddeviation of all cumulative returns from start to end of the respective periods






Profitability Trade statisticsEnd. equity ($): 164,849 No. trades: 1,133

Total return (%): 65 Avg. trade ($): 57

Avg. annual ret. (%): 5.36 Avg. DIT: 63.3

Profit factor: 1.15 Avg. win/loss ($): 1,053 (435)

Avg. tied cap (%): 74 Lrg. win/loss ($): 37,484 (2,209)

Win. months (%): 50 Win. trades (%): 34.1

Drawdown TIM (%): 100 92.8 Max. DD (%): 43.1 Tr./Mark./Year: 3.9

Longest flat (m): 31.3 Tr./Month: 9.9



Most recent: 8.69% -19.18% 15.28% 63.22% 26.23%

Average: 8.77% 20.27% 34.69% 46.45% 58.78%

Best: 104.96% 106.03% 131.24% 148.03% 186.41%

Worst: -27.93% -38.42% -14.94% 5.25% 4.40%

St. dev.: 27.06% 30.46% 28.77% 33.48% 40.51%


Most recent: 8.69% -10.10% 4.85% 13.03% 4.77%

Average: 8.77% 9.67% 10.44% 10.01% 9.69%

Best: 104.96% 43.54% 32.24% 25.49% 23.42%

Worst: -27.93% -21.53% -5.25% 1.29% 0.86%

St. dev.: 27.06% 14.22% 8.79% 7.49% 7.04%

STRATEGY SUMMARY

DRAWDOWN CURVE

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better than a Nasdaq 100 buy-and-hold strategy over the last 30months (a 43 percent drawdown compared to the Nasdaq’s 83percent).

It is quite difficult to succeed over the long term with a sys-tem that sells short because of the inherent upside bias of thestock market, and the high volatility associated with bear mar-kets. For this system, the high volatility makes it difficult toenter potential winning short trades and exit losing trades.

The best way to improve the results for this system wouldprobably be to reverse the logic for the exit so that it would beeasier to exit during times of high volatility. This would mostlikely result in more and smaller losing trades, but also in larg-er winning trades because the system would exit closer to the

62 www.activetradermag.com • February 2003 • ACTIVE TRADER

Markets: Any markets with a propensity to trend.

System logic: This is the same system tested on 30Nasdaq stocks (p. 60, where you can read more aboutthe system’s logic).

The system is based on the Donchian breakout sys-tem, which enters a trade as soon as the market tradesabove or below the highest or lowest price of the lastfour weeks (approximately 20 days).

The Donchian breakout system was invented byRichard Donchian in the 1970s and refined by Richard Dennis inthe 1980s. The more popular it became, the more other tradersmodified the system by varying the lookback periods, applyingother types of filters and attaching various money managementrules.

The dynamic breakout system is a modified version of theDonchian system that alters the lookback period between 20and 60 days depending on the volatility of the market. This sys-tem and the volatility breakout system used in the JanuaryTrading System Lab are likely the most commonly used strate-gies of all time, particularly in the commodity futures market.

This is a result of commodity futures markets’ historical ten-dency to trend. The idea is that capturing a strong trendingmove should more than make up for a large number of smalllosing trades produced during times of consolidation and stag-nant prices. Applying the system to many different futures mar-kets should result in a steady profit, as it is highly likely that at

Dynamic breakout system

least some of the markets traded should be in strong trends.These markets should be able to produce profits large enoughto make up for the whipsaw losses produced in the other mar-kets plus enough additional profits to make trading worth-while.

Rules:1. Go long on the open if yesterday’s close is higher than

the highest high of the lookback period.2. Exit by reversing the position.

Reverse the rules for short trades.

Money management:1. Risk 2 percent of available equity per trade.2. The number of contracts to trade was calculated with the

following formula:

CT = AC * PR / DistwhereCT = Contracts to tradeAC = Available capitalPR = Percent riskedDist = Distance between the

entry price and the exit price on theday of entry.

Test period: April 1993 to October2002.

Test data: Daily prices for 15 com-modity futures markets: cocoa, cof-fee, corn, cotton, feeder cattle, lum-ber, oats, orange juice, pork bellies,soybeans, soy meal, soy oil, roughrice, sugar and wheat.

Starting equity: $100,000 (nomi-nal); $50 deducted for slippage andcommission per contract traded.

Test results: It is fair to say this sys-tem did not work very well from1996 to 1999. However, since 1999 it

SAMPLE TRADES


Oats (O), daily

Buy

Sell

May June July August September October

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EQUITY CURVE

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Trading System LabTrading System Lab&FUTURES OPTIONS

$160,000

$140,000

$120,000

$100,000

$80,000

$60,000

$40,000

$20,000

$0

ACTIVE TRADER • February 2003 • www.activetradermag.com 63

However, as the performance summary shows,this system is potentially poised to launch itself intoa new period of prosperity. This shows the marketworks in cycles, and just when you’re about tothrow in the towel, things often take a turn for thebetter.

Aside from the various ways of improving thisstrategy suggested on p. 60, the best way to opti-mize it for the futures markets is to trade it in manymarkets. Trading a large number of markets is pos-sible thanks to the relatively low margin require-ments of the futures markets (often only 5 to10 per-cent of the total contract value).�


LEGEND: End. equity ($) — equity at the end of test period • Total return(%) — total percentage return over test period • Avg. annual ret. (%) —average continuously compounded annual return • Profit factor — grossprofit/gross loss • Avg. tied cap (%) — average percent of total available cap-ital tied up in open positions • Win. months (%) — percentage profitablemonths over test period • Max. DD (%) — maximum drop in equity •Longest flat — longest period, in months, spent between two equity highs •No. trades — number of trades • Avg. trade ($) — amount won or lost bythe average trade • Avg. DIT— average days in trade • Avg. win/loss ($)— average winning and losing trade, respectively • Lrg. win/loss ($) —largest winning and losing trade, respectively • Win. trades (%) — percentwinning trades • TIM (%) — amount of time there is at least one open posi-tion for entire portfolio, and each market, respectively • Tr./Mark./Year —trades per market per year • Tr./Month — trades per month for all markets

LEGEND: Cumulative returns — Most recent: most recent return from start toend of the respective periods • Average: the average of all cumulative returnsfrom start to end of the respective periods • Best: the best of all cumulative returnsfrom start to end of the respective periods • Worst: the worst of all cumulativereturns from start to end of the respective periods • St. dev.: the standard devia-tion of all cumulative returns from start to end of the respective periods






Profitability Trade statisticsEnd. equity ($): 123,221 No. trades: 199Total return (%): 23 Avg. trade ($): 117Avg. annual ret. (%): 2.20 Avg. DIT: 90.1Profit factor: 1.03 Avg. win/loss ($): 2,028 (1,045)Avg. tied cap (%): 40 Lrg. win/loss ($): 21,163 (2,832)Win. months (%): 52 Win. trades (%): 32.2

Drawdown TIM (%): 100 51.2 Max. DD (%): 32.9 Tr./Mark./Year: 1.4Longest flat (m): 82.6 Tr./Month: 1.7



Most recent: 9.97% 17.00% 20.46% 0.25% 2.89%Average: 2.53% 4.64% 4.24% 2.94% 2.18%Best: 44.61% 47.38% 47.95% 50.34% 49.68%Worst: -20.11% -19.85% -25.71% -28.62% -24.67%St. dev.: 11.36% 17.85% 21.91% 22.75% 21.23%


Most recent: 9.97% 8.17% 6.40% 0.06% 0.57%Average: 2.53% 2.30% 1.40% 0.73% 0.43%Best: 44.61% 21.40% 13.95% 10.73% 8.40%Worst: -20.11% -10.48% -9.43% -8.08% -5.51%St. dev.: 11.36% 8.56% 6.83% 5.26% 3.93%

STRATEGY SUMMARY

has gained ground little by little, slowly increasing its averageannual return. For example, the average return for the lastthree years has been 6.4 percent. However, the return for thepast 12 months has been 9.97 percent, and the return is close to30 percent for the last six months.

Granted, there still is a long way to go before the system canfind its way out of a drawdown that has lasted for almostseven years, but the recent upward trend confirms our findingsfrom last month that the long-term trend-following strategy isready to stage a comeback as a profitable trading system.

In the late 1990s, many analysts claimed that long-termtrend-following systems would no longer make money. Thereason, they argued, was that the markets had become sosophisticated over the last several years that whatever ineffi-ciencies made the strategy profitable during the 1970s and1980s had been eliminated.

DRAWDOWN CURVE

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Experimenting with exits

FIGURE 2 EQUITY CURVE (MODIFIED EXIT RULE)

3/22/94 5/1/95 6/3/96 7/3/97 8/3/98 9/1/99 11/1/00 1/2/02 2/3/03

The modified exit rule produced modest profits over the test period.

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Equity Cash Linear Reg. Long Short

FIGURE 3 EQUITY CURVE (SIMPLE EXIT RULE)

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The simple 55-day low exit technique actually outperformed the modified stop.

1,000,000950,000900,000850,000800,000750,000700,000650,000600,000550,000500,000450,000400,000350,000300,000250,000200,000150,000100,00050,000

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Equity Cash Linear Reg. Long Short

Rules:1. Enter long on the next bar’s open if today’s high is

greater than the highest high of the last 55 days.2. Exit on the next day’s open if the lowest low is less than

the lowest low of the last 55 days plus 10 percent of the actual average true range for each day in the market.

This is a long-only system.

Market: Futures.

System concept: Some traders believe exit signals are moreimportant than entry signals, while others believe successhinges on money management and diversification. Thetruth is most likely somewhere in the middle and, as aresult, all components of a trading system must be ade-quately developed and tested.

This system’s entry technique is simply a breakout abovea 55-day high. The more important part of the strategy is atrailing stop technique that is designed to capture as muchof the trend as possible by tightening the stop relative to thenumber of days the trade has been open.

After initiating a trade, a recent low point, such as thelowest low of the past 55 days, is selected. To determine thestop level, the 20-day average true range (ATR) is multi-plied by 10 percent, and then multiplied by the number ofdays the trade has been open. This amount is then added tothe recent low used as the initial reference point.

For example, if a trade has been open for 12 days, wewould multiply the 20-period ATR by 0.1 (10 percent), mul-tiply this result by 12 (days) and, finally, add the result tothe lowest low of the past 55 days. The next day, the 20-day ATRwould be multiplied by 0.1, multiplied by 13, added to the lowestlow of the past 55 days, and so on.

We will compare the results of this stop with using the lowestlow of the past 55 days. Figure 1 shows the difference between thetwo stops. The magenta line represents the 55-day low stop and theblue line is the modified exit strategy. The blue line tracks the priceaction more closely.

FIGURE 1 COMPARING THE STOPS

December 1997 January 1998 Feb. 1998

Notice how the modified exit rule approaches price at an acceleratedrate as time passes.

Sell

Nasdaq 100 index (ND), daily 900.00

880.00

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Profitability Trade statisticsNet profit ($): 338,282.00 No. trades: 351Net profit (%): 135.31 Win/loss (%): 42.17Exposure (%): 33.88 Avg. gain/loss (%): 0.65Profit factor: 1.32 Avg. hold time: 40.03Payoff ratio: 1.70 Avg. profit (winners) %: 7.97Recovery factor: 2.56 Avg. hold time (winners): 58.09Drawdown Avg. loss (losers) %: -4.69Max. DD (%): -28.70 Avg. hold time (losers): 26.87Longest flat days: 760 Max. consec. win/loss: 6/9

STRATEGY SUMMARY

LEGEND: Net profit — Profit at end of test period, less commission •Exposure — The area of the equity curve exposed to long or short positions,as opposed to cash • Profit factor — Gross profit divided by gross loss •Payoff ratio — Average profit of winning trades divided by average loss of los-ing trades • Recovery factor — Net profit divided by max. drawdown •Max. DD (%) — Largest percentage decline in equity • Longest flat days —Longest period, in days, the system is between two equity highs • No. trades— Number of trades generated by the system • Win/Loss (%) — The per-centage of trades that were profitable • Avg. gain — The average profit for alltrades • Avg. hold time — The average holding period for all trades • Avg.gain (winners) — The average profit for winning trades • Avg. hold time(winners) — The average holding time for winning trades • Avg. loss (los-ers) — The average loss for losing trades • Avg. hold time (losers) — Theaverage holding time for losing trades • Max. consec. win/loss — The max-imum number of consecutive winning and losing trades

PERIODIC RETURNS


periods profitable unprofitableWeekly 0.21% 0.59 11.95% -9.16% 55.19% 9 8Monthly 0.88% 0.59 16.74% -9.95% 52.63% 6 4Quarterly 2.60% 0.57 22.78% -10.91% 56.41% 4 3Annually 9.86% 0.63 40.57% -8.30% 70.00% 7 1

LEGEND: Avg. return — The average percentage for the period • Sharperatio — Average return divided by standard deviation of returns (annual-ized) • Best return — Best return for the period • Worst return — Worstreturn for the period • Percentage profitable periods — The percentage ofperiods that were profitable • Max. consec. profitable — The largest num-ber of consecutive profitable periods • Max. consec. unprofitable — Thelargest number of consecutive unprofitable periods



Money management: Risk a maximum 3 per-cent of total account equity per trade (“stop-based risk %”). Setting the “maximum riskpercent” to 3 means at any given time weshould not lose more than 3 percent of the totalaccount equity. Of course, overnight gaps andlimit down moves could lead to higher losses.

Starting equity: $250,000 (nominal). Deduct$20 slippage/commission per round-turntrade.

Test period: March 1994 to August 2003.

Test data: The system was tested on the ActiveTrader Standard Futures Portfolio, which con-tains the following 19 futures: DAX30 (AX),corn (C), crude oil (CL), German bund (DT),euro dollar (ED), euro forex (FX), gold (GC),copper (HG), Japanese yen (JY), coffee (KC),live cattle (LC), lean hogs (LH), Nasdaq 100 (ND), natural gas(NG), soybeans (S), sugar (SB), silver (SI), S&P 500 (SP) and T-Notes 10 year (TA). The test used ratio adjusted data from PinnacleData Corp.

System results: Figure 2 shows the equity curve when risking 3percent of the total portfolio equity per trade. The system returneda total profit of 135.31 percent over approximately 10 years and9.54 percent annually, with the worst year being a loss of 8.3 per-cent in 1999. The single largest annual drawdown of 19.99 percentoccurred in 1996. The system produced 351 trades with an averageprofit of $963.77 per trade.

Figure 3 shows the equity curve of the comparison system that

uses the simple 55-day low exit rule. This version had fewer trades(208) than the modified system because the stop maintains a con-stant distance from price action regardless of volatility changesand how long the trade has been open. The average profit per tradeincreased to $3,073 and the average holding time increased from 40days to 95 days. The simple system’s net profit was much higher($639,204 compared to $338,282), although its drawdown was alsomarkedly higher (36.65 percent compared to 28.7 percent).

Bottom line: The trailing exit strategy enabled the system toreduce drawdown, but it also reduced profits. Comparison to thesimple stop approach suggests the modified version exited tradestoo quickly and did not give the system enough time to ride thetrend.

However, the concept behind this exit idea is worthy of exper-imentation and could prove to be a more effective exit strategywhen combined with other trading methods.

— Volker Knapp of Wealth Lab

FIGURE 4 DRAWDOWN

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The modified exit rule succeeded in reducing the system’s risk (28 percent compared to 36 percent).

Draw

dow

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Market: Futures.

System concept: Some traders believe designing a robust tradingsystem requires dividing your data into two sets. The first half(referred to as “in-sample” data) is used to develop trading rulesand optimize their parameters; the second (“out-of-sample” data)is used to simulate trading the system and see if the results areboth favorable and consistent with the initial test.

If the system performs well on the second data set (which rep-

resents new, “unseen” price action), the system is considered tohave a better chance of working in real trading. The process isreferred to as “walk-forward testing” because the system can beprogressively applied to new data to see if it continues to perform.

We will explore that concept here. To illustrate the principlesin a straightforward fashion, we test a basic, long-only monthlybreakout system on a single market, the T-Bond.

Even though the monthly highs and lows define theentry points, we will use end-of-day data to takeopening gaps into consideration.

Whenever performing out-of-sample testing,expect the performance to be worse than the in-sam-ple tests. This is not a reflection of the quality of thesystem. It is simply because the system was devel-oped and optimized on a different data set.

Rules:1. Entry: Buy at the highest high of the last x

months.2. Exit: Sell at the lowest low of the last y months.

Risk control: The system does not use a fixed dollar,point or percentage stop. Instead, a reversal below they-month low is used to indicate the market is nolonger in an uptrend, at which point all positions are liquidated,win or lose.

Money management: Each position will consist of one T-bondcontract.


Monthly breakout


Optimization parameters: The x and y parameters will beoptimized from one to eight months.

Test data: Daily continuous T-Bond futures (US). No com-missions or slippage were deducted.

Test periods: Initial “in-sample” period: Jan. 1, 1985, throughDec. 31, 1995. Second “out-of-sample” period: Jan. 1, 1996, toDec. 31, 2002.

Test results — In-sample: All parameter combinations wereprofitable. Figure 1 (above) shows the top combinations sortedby “Recovery Factor,” which is the absolute value of the sys-tem’s net profit divided by its maximum drawdown (see thestock Trading System Lab on p. 56 for the significance of thisstatistic).

For the 10 years covered in the test, the best parameterswould be to buy at the highest high of the past month andsell at the lowest low of the past two months. As expected,the shorter the breakout length, the higher the number oftrades. However, the optimal settings did not produce manytrades (19) over the test period.

FIGURE 2 PROFIT CURVE

1/2/85 12/2/85 1/2/87 1/4/88 1/3/89 1/2/90 1/2/91 1/2/92 1/4/93 1/3/94 1/3/95

The optimized monthly breakout system slightly underperformedbuy-and-hold, but it had a much lower risk level.

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Total profit Buy & hold Linear reg


FIGURE 1 OPTIMIZATION RESULTS FROM IN-SAMPLE TEST

The results for different parameter combinations are sorted by “recovery factor,” which is the net profit divided by the maximumdrawdown.

Bottom line: The testing process illustrated hereshows how the performance of even fundamentallysound trading systems can vary over time. The bestparameter set of the first 10 years became the thirdworst in the following seven years. However, all theparameter combinations were profitable in both peri-ods, suggesting the basic trading approach is sound.

The best-performing parameters in one period willalmost never be the best in future data. Because of this,it is more important to look for parameter stability. Abroad range of parameter values should be profitableand deliver consistent results.

Optimizing is a very useful tool, but it should beused to confirm parameter stability rather than to

try to find the parameter combination with the highest netprofit.

— Dion Kurczek and Volker Knapp of Wealth-Lab Inc.


The equity curve (dark green) in Figure 2 (opposite page) forthe most part follows the underlying market curve (blue line).Even though the buy-and-hold profit ($44,006) over the 10-yearin-sample period was higher than the system’s profit ($40,012),the buy-and-hold drawdown was more than 50 percent higher($10,886) than the system’s drawdown ($6,428).

Test results — Out-of-sample data: Testing the parameters on theout-of-sample data set produced significantly different results.The best parameter combination from the in-sample test (buyabove the one-month high and sell below the two-month low)became the third worst combination in the out-of-sample test.

Figure 3 (top) sorts the 11 worst parameter combinations byrecovery factor. Two combinations from the former top 11 arenow in the bottom eleven. On the other hand, all the parame-ter combinations have remained profitable, which indicatesthe system rules have some merit.

Figure 4 (above) shows the equity curve for the optimizedparameters from the in-sample data period tested on the out-of-sample period. Even though the performance was positive,it was quite volatile compared to the smooth ride of the opti-mal parameter combination (buying above the one-monthhigh and selling below the eight-month low) for this test peri-od, a sample trade of which is shown in Figure 5 (right).



FIGURE 5 SAMPLE TRADE

August 2001 September 2001 October 2001 November 2001

The system performed best in the out-of-sample test when it boughtabove the one-month high and sold below the eight-month low.

T-bonds (US), daily106.00105.50105.00104.50104.00103.50103.00102.50102.00101.50101.00100.50100.0099.5099.0098.5098.0097.5097.0096.5096.0095.50

Volume

Sell

Buy


FIGURE 4 PROFIT CURVE

1/2/96 7/29/96 3/31/97 12/1/97 7/28/98 3/30/99 12/1/99 7/27/00 3/28/01 12/3/01 8/1/02

The optimized parameters from the in-sample test performedmuch worse on the out-of-sample data.

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Total profit Buy & hold Linear reg

FIGURE 3 OUT-OF-SAMPLE TEST RESULTS

The system behaved much differently on the out-of-sample data. The best parameter combination from the in-sample test was the thirdworst in the out-of-sample test.

Markets: Stocks.

System concept: This intraday system takes a tradewhen price breaks out of the trading range establishedin the first hour of the session. Early in the morning themarket often is trying to establish its direction, and amove above or below the 60-minute range might sig-nal a trend in that direction. Also, a breakout of theearly trading range is sometimes caused by a specificnews item that will cause the trend to continue.Because the highest volatility of the day often occurs inthe first trading hour, there are no trades in the first 60minutes.

After the first 60 minutes have ended, if the closingprice of the current 30-minute bar is above the high ofthe range, we will go long on the next open. A shortposition is established if the closing price is below therange. A signal in the opposite direction is used to exitthe current position. All open positions are exited atthe close of the day. There will be only one trade perday.

Entry rules:Long trades: Buy if the closing price of the third 30-minute

bar is above the high of the first 60 minutes of the day.

Short trades: Sell short if the closing price of the third 30-minute bar is below the low of the first 60 minutes of the day.

Exit: Exit all positions on signals in the opposite direction orat the end of the day.

Money management: Each trade is sized at five percent of thecurrent account equity. This will allow all trades thesystem is generating to be executed. Increasing thep e rcentage would re q u i re dropping trades thatexceeded the available cash limit.

Starting equity: $100,000 (nominal). Deduct $0.01 pershare slippage and commissions.

Test data: The system was tested on 30 minute bars ofthe Active Trader Standard Stock Portfolio, which con-tains the following 18 stocks: Apple Computer(AAPL), Boeing (BA), Citibank (C), Caterpillar (CAT),Cisco (CSCO), Disney (DIS), General Motors (GM),Hewlett Packard (HPQ), International BusinessMachines (IBM), Intel (INTC), International Paper(IP), JPMorgan Chase (JPM), Coke (KO), Microsoft(MSFT), Sears (S), Starbucks (SBUX), AT&T (T) andWal-Mart (WMT). Data from www.qcharts.com.

1 6 0 , 0 0 0

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E q u i t y Cash Linear reg L o n g Short Buy & hold


We tested the portfolio of 18 stocks on 30-minute bars (more than 3,500total trades). Each signal used five percent of the portfolio equity value.Long trades were slightly profitable.

1 0 / 1 0 / 0 2 1 1 / 2 5 / 0 2 1 / 1 4 / 0 3 2 / 2 8 / 0 3 4 / 1 4 / 0 3 5 / 2 9 / 0 3 7 / 1 4 / 0 3 8 / 2 6 / 0 3 1 0 / 8 / 0 3

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FIGURE 2 ADDING A FILTER

The bars show the average per-trade profit that would have been captured by adding the CMO as a trade filter.

- 8 0 . 0 0 - 6 0 . 0 0 - 4 0 . 0 0 - 2 0 . 0 0 0 . 0 0 2 0 . 0 0 4 0 . 0 0 6 0 . 0 0 8 0 . 0 0

CMOlevel

60-minute breakout system

Test period: October 2002 through October 2003.

Initial test results: The system’s performance was disappoint-ing — an overall loss of -3.25 percent. With 3,546 trades in thetest period, this is one of the more active systems tested here.However, even setting the commission at one cent per share(advisable for a system that generates this many trades) stillresulted in $13,576 in commission charges. Figure 1 showslong trades were slightly profitable, generating a 4.22-percentprofit during this period.



LEGEND: Net profit — Profit at end of test period, less commission • Exposure— The area of the equity curve exposed to long or short positions, as opposed to cash• Profit factor — Gross profit divided by gross loss • Payoff ratio — Averageprofit of winning trades divided by average loss of losing trades • Recovery factor— Net profit divided by max. drawdown • Max. DD (%) — Largest percentagedecline in equity • Longest flat days — Longest period, in days, the system isbetween two equity highs • No. trades — Number of trades generated by the sys -tem • Win/Loss (%) — the percentage of trades that were profitable •Avg. prof-it — The average profit for all trades •Avg. hold time — The average holding peri -od for all trades •Avg. profit (winners) — The average profit for winning trades• Avg. hold time (winners) — The average holding time for winning trades •Avg. loss (losers) — The average loss for losing trades •Avg. hold time (losers)— The average holding time for losing trades • Max. consec. win/loss — Themaximum number of consecutive winning and losing trades


Trading System Lab strategies are tested on a portfolio basis (unless otherwise noted) using Wealth-Lab Inc.’s testing platform.

If you have a system you’d like to see tested, please send the trading and money-management rules to [email protected].

PERIODIC RETURNS


periods profitable unprofitable

Weekly -0.06% -0.56 1.70% -2.11% 47.17% 4 6

Monthly -0.24% -0.60 2.48% -2.57% 38.46% 2 5

Quarterly -0.63% -0.51 1.99% -4.47% 60.00% 1 1

P r o f i t a b i l i t y Trade statisticsNet profit ($): - 3 , 2 4 7 . 4 1 No. trades: 3 , 5 4 6Net profit (%): - 3 . 2 5 Win/loss (%): 4 9 . 5 5Exposure (%): - 3 . 2 6 Avg. gain/loss (%): - 0 . 0 2Profit factor: 0 . 9 6 Avg. hold time: 6 . 4 6Payoff ratio: 0 . 9 8 Avg. profit (winners) (%): 0 . 8 5Recovery factor: 0 . 4 2 Avg. hold time (winners): 6 . 7 1

D rawdown Avg. loss (losers) (%): - 0 . 8 7

Max. DD (%): - 7 . 4 5 Avg. hold time (losers): 6 . 2 2Longest flat days: 3 , 0 7 8 Max. consec. win/loss: 1 8 / 2 3

STRATEGY SUMMARY

LEGEND: Avg. return — The average percentage for the period • Sharpe ratio —Average return divided by standard deviation of returns (annualized) • Best re t u r n— Best return for the period • Worst return — Worst return for the period •P e rcentage profitable periods — The percentage of periods that were pro f i t a b l e •Max. consec. profitable — The largest number of consecutive profitable periods •Max. consec. unprofitable — The largest number of consecutive unprofitable peri -o d s

Although some individual stocks produced very goodequity curves, the total portfolio itself did not. This illustrateshow testing a strategy on a single instrument or very limitedportfolio can lead to the wrong conclusions about a system’svalue.

— Volker Knapp of Wealth-Lab Inc.

Adding a filter: To see if we could reduce the number of tradesand improve performance, we added a filter — a 14-barChande Momentum Oscillator (CMO) to the basic system (seethe December 2003 Trading System Lab, p. 48, for more infor-mation on this indicator). The filter consisted of taking tradesonly when the CMO reading for the previous day’s bar wasabove 60, indicating bullish momentum. The filter reduced thetotal number of trades and eliminated short trades. Figure 2(opposite page) shows waiting for a CMO reading above 60before taking trades would have increased profitability.

Testing our portfolio with the additional filter turned the los-ing system into a winner — although not a spectacular one.The number of trades decreased to 251 from 3,546, the averageprofit was 0.12 percent and total profit was 1.48 percent.

Bottom line: Although the strategy concept sounds reasonable,it did not produce satisfactory results. There is plenty of roomfor further experimentation, though — such as trying other fil-ters, including those that would differentiate between long andshort trades. Another alternative is to use shorter bars (e.g., fiveminutes) to get into positions faster.

FIGURE 3 SAMPLE TRADE

Cisco Systems (CSCO), 30-minute

The green bars are the first trading hours of the days shown here.Subsequent black bars have closing prices within the first-hourrange; red bars indicate the closing price is below the range andblue bars show bars with closing prices above the range.

2 1 . 5 02 1 . 4 02 1 . 3 02 1 . 2 02 1 . 1 02 1 . 0 02 0 . 9 02 0 . 8 02 0 . 7 02 0 . 6 02 0 . 5 02 0 . 4 02 0 . 3 02 0 . 2 02 0 . 1 02 0 . 0 01 9 . 9 0

Short [email protected]

Buy [email protected]

Cover [email protected]

9/24/03 9/25/03

Documents

2532983 Break Out Strategy