at-1210

How stock splits can multiply profits p. 50

Uncovered: A critical look at the covered call p. 40

trAding trend trAnsitions:getting in early p. 28

Finding tHe best gAp trAdes p. 34

risk and reward: catching the stock market’s big swings p. 14

welcome to the (systems) mAtrix p. 20

Decem

ber 2010

Active Trader

Trends, trends, trends

$4.95 Printed in the U.S.A. www.activetradermag.com

• TRADING STRATEGIES FOR THE FINANCIAL MARKETS •

December 2010 • Volume 11, No. 12

®

®

2 www.activetradermag.com • December 2010 • ACTIVE TRADER

CONTENTSDecember 2010 • VOLUME 11, NO. 12

6 Contributors

8 Opening TradesTrends and events moving

the markets.

64 Trading Calendar

68 Stocks SnapshotVolume, volatility, and momentum

statistics for stocks.

69 ETF SnapshotVolume, volatility, and momentum

statistics for exchange-traded funds.

70 Futures SnapshotVolume, volatility, and momentum

statistics for futures.

73 Trader’s Bookshelf

74 Trader’s Marketplace Classified Advertising

76 Upcoming Events

76 Advertising Index

78 Key Concepts

In every issue

Trading Strategies14 Catching longer-term market swings

Buying sell-offs in the stock market provides an edge,

but it takes money, nerves, and persistence.

By Active Trader Staff

20 Gauging performance with the system matrix

How do you know if your trend-following system —

or trend-following CTA — is pulling its weight or

underperforming? Compare it to the benchmark

strategies in the “system matrix.”

By Thomas Stridsman

28 Trading trend transitions

Recognizing a few simple patterns can help you get

into new trends early.

By Dave Landry

34 Trading gaps with the most potential

Filtering for tradable up gaps with volatility and

volume.

By Chris Kacher & Gil Morales

40 Uncovering the covered call

A review of the most misunderstood and overused

strategy in the options business.

By Larry Shover

toc1210 10/13/10 8:06 AM Page 2

Advanced Strategies44 Not all carry trades are alike

More analysis of the consequences of free

money shows that in the battle between

Main Street and Wall Street, Wall Street

won.

By Howard L. Simons

Trading System Lab50 Profiting with stock splits

Trading splits can offer a longer-term edge

for stock traders.

By Robert Sucher Jr.

The Face of Trading 55 Finding a niche

This full-time stock trader relies solely on

technical analysis.


Trading Basics56 The subjectivity trap

Vague concepts and ambiguous guidelines

are impossible to translate into real-world

trading ideas. Start with market facts and

build from there.



The Business of Trading60 Trader tax reporting strategies

Sort through the various trader tax forms

and review important strategies for your 2010

return.

By Robert A. Green, CPA

The Economy66 U.S. economic briefing

Updates on economic numbers and the

market’s reaction to them.

Trade Diary80 One bad trade is all it takes to ruin a day of

careful trading.

Contact Active Trader:Editorial inquiries: [email protected]

Comments, suggestions:[email protected]

For advertising or subscription information, log on to: www.activetradermag.com

Contents

toc1210 10/13/10 8:06 AM Page 4

For all subscriber services: Active Trader Magazine

P.O. Box 17015 N. Hollywood, CA 91615

•(800) 341-9384

•www.activetradermag.com

This Month’s

CONTRIBUTORS®

Editor-in-chief:

Mark Etzkorn

[email protected]

Managing editor:

Molly Goad

[email protected]

Contributing editor:

Howard L. Simons

Contributing writers:

Marc Chandler, Keith Schap,

Robert A. Green, Chris Peters

Editorial assistant and webmaster:

Kesha Green

President:

Phil Dorman

[email protected]

Publisher, ad sales:

Bob Dorman

[email protected]

Classified ad sales:

Mark Seger

[email protected]


Howard Simons is president of Rosewood Trading Inc. and astrategist for Bianco Research. He writes and speaks frequently on awide range of economic and financial market issues.

Thomas Stridsman is a private trader, trading-strategy developer,and lecturer. Previously, he was the senior researcher for RotellaCapital in Chicago, and a systems-developer specialist for CQG inDenver. He also is a long-time contributing editor for Active Tradermagazine and a former editor at Futures magazine. He has authoredtwo books: Trading Systems that Work (McGraw-Hill, 2000) and TradingSystems and Money Management (McGraw-Hill, 2003). He has a degreein macroeconomics from Uppsala University, Sweden.

Larry Shover has been a firm and proprietary options trader formore than 25 years and taught courses at a variety of exchangesincluding the Chicago Mercantile Exchange (CME) for more than 20years. A large part of his career has been dedicated to developing hisown proprietary trading firm, and he has also served as director ofeducation, senior vice president of trading, and director of global trad-er development at several commodities and options firms. Shover is amember of the CME and the Chicago Board Options Exchange

(CBOE) and holds several Financial Industry Regulatory Authority (FINRA) licenses.He most recently published a book: Trading Options in Turbulent Markets(Bloomberg/Wiley).

Chris Kacher and Gil Morales are managing directors of MoKa Investors, LLC,and authors of the book Trade Like an O’Neil Disciple: How We Made 18,000 percent inthe Stock Market. They are also the authors of www.VirtueofSelfishInvesting.com.

Dave Landry has been actively trading the markets since the early90s. In 1995 he founded Sentive Trading, LLC (www.davelandry.com)— a trading and consulting firm. He is author of Dave Landry on SwingTrading (2000), Dave Landry’s 10 Best Swing Trading Patterns & Strategies(2003), and The Layman’s Guide to Trading Stocks (2010). His books havebeen translated into many languages including Russian, Italian, French,and Chinese (pending 2010). He has made several television appear-ances, has written articles for several publications including Active

Trader and Traders Journal-Singapore. He has been publishing daily web-based com-mentary on technical trading since 1997. He has spoken at trading conferences bothnationally and internationally. He holds a bachelor’s in computer science and has anMBA.

Robert Sucher holds a M.S.E.E. in signal processing from C.S.U. Northridge(1992). After working 12 years in the military aircraft industry, he moved to theCanary Islands (Spain) where he began actively trading stocks and futures in 1999. In2002, he started an ongoing journey with Wealth-Lab.com, assisting customers withtrading tools and solutions.

Robert A. Green, CPA, is CEO of Green & Company(GreenTraderTax.com), a CPA firm focused on traders and investment-management businesses. Green is also founder and CEO of theGreenTraderTax Traders Association. He is the author of The Tax Guidefor Traders (McGraw-Hill, 2004) and Green’s 2010 Trader Tax Guide.GreenTrader provides tax preparation, accounting, consulting, entity,and retirement-plan formation services; IRS/state tax exam representa-tion; and trade-accounting software. For more information or to par-

ticipate in free conference calls, visit www.greencompany.com or call (877) 662-2014or (646) 216-8061.

Jim Kharouf is editor of Environmental Markets Newsletter and a freelancereporter who has covered the derivatives markets since 1996.

Volume 11, Issue 12 Active Trader is published month-ly by TechInfo, Inc., PO Box 487, Lake Zurich, IL60047-0487. Copyright © 2010 TechInfo, Inc. All rightsreserved. Information in this publication may not bestored or reproduced in any form without written per-mission from the publisher. Annual subscription rate is$59.40.

The information in Active Trader magazine is intendedfor educational purposes only. It is not meant to rec-ommend, promote or in any way imply the effective-ness of any trading system, strategy or approach.Traders are advised to do their own research and test-ing to determine the validity of a trading idea. Tradingand investing carry a high level of risk. Past perform-ance does not guarantee future results.

contributors1210 10/14/10 12:13 PM Page 6

OPENING Trades


In late September and early October,

U.S. equities pushed decisively out

of the roughly four-month consoli-

dation that followed the early 2010

sell-off. The S&P 500 (SPX) punc-

tured the resistance represented by

the June and August highs around

1130 and, having reached 1180 by

Oct. 3, had no remaining chart bar-

rier between it and the April high

around 1220. The upside breakout

was aided by mostly positive earn-

ings announcements as the Q2

reporting period got underway.

The move padded the year’s gains

for the major U.S. indices, some of

which, after spending much of the

year underwater or barely afloat,

pushed into double-digit territory.

By Oct. 13 the Russell 2000 index of

small-cap stocks was up more than

13 percent, the Nasdaq 100 was up

nearly 11 percent, and the Dow and S&P 500 were up around 6

and 7 percent, respectively.

Meanwhile, the CBOE volatility index (VIX) dropped below

18 by Oct. 13, the lowest it had been since the April high.

Despite the recent rally and the continued growth in high-fre-

quency trading, volume continued to sag: The week ending Oct.

15 was the 14th consecutive week with S&P 500 volume below

the 52-week median.�

U.S. stocks break out of range

Bullish September carries over into mid-October as earnings seasons begins.

Treasuries rocket into OctoberDecember 10-year T-note futures (TYZ10) traded up to 127-22/32 on

Oct. 12 — more than four full points above the September pullback low, and

approaching levels not seen since the depths of the 2008 financial panic when

T-note prices briefly topped

130. Yields on the 10-year

Treasuries dipped below

2.4 percent.

Dollar poised to challenge 2009 lowsIn mid-October the U.S.

dollar index (DXY) closed

at its lowest level in nearly

a year, extending the sell-

off that began in June and

setting itself up for a challenge to last year’s bottom below 75.00.

opening_trades1210 10/18/10 8:06 AM Page 8

ACTIVE TRADER • December 2010 • www.activetradermag.com 9

Commodity indices challenge 2010 highsCommodity futures rallied close to their highest levels of the year, driven by

blistering moves in a handful of markets. The Deutsche Bank Liquid

Commodity Index (DBL-

CIX) hurdled above its

spring highs in October,

marking its seventh con-

secutive week of higher

highs and higher closes

as of Oct. 15.

Besides big runs in

metals, especially silver

(see “Gold’s golden run,” p. 10), continued strength in grain markets and

select soft commodities spurred commodities higher as a group. In grains,

corn (C) continued to assert its domi-

nance over former front-runner wheat

(W), while soybeans (S), which had

lagged the other two markets much of the

year, made a push of their own.

The rally in December coffee futures

(KC) would have been eye-catching

despite their pullback from highs just

below 200, but sugar’s continued recovery

from the massive sell-off that ended in the

spring has grabbed most of the attention

in the softs. As of Oct. 13, December

sugar (SBZ10) had jumped nearly 56 per-

cent from its August low close — and

more than 100 percent since June.

Cotton remained kingly into mid-

October, tricking many traders who bet

the end-of-September sell-off was the bull

move’s death-knell. After dipping below

100, however, December cotton (CTZ10)

leaped above 110, reaching 114 by

Oct. 14 — more than 52 percent above

July levels. �

BBaarrccllaayy TTrraaddiinngg GGrroouupp’’ss mmaannaaggeedd ffuuttuurreess ppeerrffoorrmmaannccee aass ooff AAuugg.. 3311

TToopp 1100 ttrraaddeerrss mmaannaaggiinngg mmoorree tthhaann $$1100 mmiilllliioonn

AAuugg.. 22001100 YYTTDD $$ UUnnddeerr

TTrraaddiinngg aaddvviissoorr rreettuurrnn rreettuurrnn mmggmmtt..

1. Clarke Cap’l Mgmt. (Gl. Basic) 24.46% 7.90% 19.0

2. Clarke Cap’l Mgmt. (Millennium) 17.27% 5.54% 29.3

3. DUNN Capital Mgmt. (WMA) 16.96% 21.04% 254.0

4. Clarke Cap’l Mgmt. (Gl. Magnum) 16.56% 26.65% 17.9

5. Commodity Fut. Services (IPATS) 16.00% 33.23% 21.4

6. Mulvaney Capital Mgmt. (Gl. Markets) 14.59% -20.12% 115.0

7. Global Ag 13.07% 36.50% 42.0

8. Superfund Trading Mgmt (Gold C) 12.30% -12.58% 65.2

9. Quicksilver Trading, Inc. 11.78% 3.38% 211.7

10. Brummer & Partners (Lynx) 10.38% 14.47% 2267.1

TToopp 1100 ttrraaddeerrss mmaannaaggiinngg lleessss tthhaann $$1100 mmiilllliioonn aanndd aatt lleeaasstt $$11 mmiilllliioonn

1. Clarke Cap’l Mgmt. (Jupiter) 21.26% 11.15% 9.0

2. Persistent Cap Mgmt (Perseverance 2X) 19.46% 1.98% 2.9

3. Clarke Cap’l Mgmt. (FX-Plus) 14.73% 35.06% 4.0

4. Valu-Trac Invest. Mgmt (Strat. 2.5) 14.28% 3.45% 1.9

5. Clarke Cap’l Mgmt. (Orion) 13.27% 11.03% 2.0

6. Persistent Cap Mgmt (Perseverance) 10.00% 1.94% 4.2

7. IMFC (Multi-Strategy) 9.60% 3.86% 4.1

8. Vermillion Asset Mgmt (Indigo) 9.53% -1.49% 9.0

9. Pardo Capital Ltd. (XT99 Divers.) 9.50% 25.60% 7.6

10. Bayside Pacific Advisors (Futures) 9.42% 7.17% 1.1

Based on estimates of the composite of all accounts or the fully funded subset method. Does not reflect theperformance of any single account. PAST RESULTS ARE NOT NECESSARILY INDICATIVE OF FUTURE PERFOR-MANCE. Source: Barclay Hedge (www.barclayhedge.com)


Opening Trades


Gold topped $1,300/ounce for the first time in its history in late

September after a nearly uninterrupted two-month/13-percent

rally took the metal well past its December 2009 and June 2010

record highs. As of Oct. 15, gold futures had strung together 11

consecutive weeks of higher highs, and 10 out of 11 higher clos-

es (Figure 1).

December gold futures (GCZ10) hit an intraday high of

$1,388 on Oct. 14, eclipsing the June high by approximately

$120. As of Oct. 15, the run of 11 higher weekly highs had

been equaled or exceeded just two other times over the past 31

years, with all instances occurring during the current gold bull

market or at the end of the last gold explosion in 1979-1980, as

shown in Table 1. Twenty-three years separated the 10-week run

ending the week of Jan. 25, 1980 (that bull market’s all-time

high) and the first 10-week run of the current bull in February

2003.

One of the more interesting but overlooked aspects of the

current gold bull is that over the past several months, as well as

over most of the past decade, gold has failed to keep pace with

silver on a percentage basis, and trails copper by an even wider

margin. The Oct. 14 high marked a 400-percent increase from

the December 2001 gold futures closing price of $277 — a

major rally, certainly, but less than silver’s 459-percent gain over

the same period, and much less than the 472-percent jump in

copper, which was trading around 68 cents/pound in December

2001 and in mid-October was around $3.85/pound. (Also,

crude oil gained 365 percent between December 2001 and early

October 2010 — and that was after a more than 50-percent sell-

off from its 2008 bubble peak, at which point it had gained 731

percent in less than seven years.)

More recently, the December gold and copper futures con-

tracts both gained a little more than 17 percent from their July

28 closes and their early October highs, while December silver

gained more than 50 percent.

More room on the upside?Each new gold high has brought out more gold bugs calling for

$2,000 (or $3,000) gold, as well as more market watchers warn-

ing of a collapse in the market. “The market with the golden

arm” (Active Trader, February 2010) noted that gold rallied more

than 420 percent on a closing basis from 1974 to the beginning

TABLE 1: 10 WEEKS OF HIGHER HIGHS

Week ending

No. consecutive weekly highs

Oct. 15, 2010 11

Nov. 9, 2007 11

Feb. 7, 2003 10

Jan. 25, 1980 10

July 27, 1979 14

Gold futures have put together runs of 10 or more

consecutive higher weekly highs just five times over the

past three decades.

Gold’s golden runMetal reaches for $1400 in mid-October; $1500 now in sights.

Gold (top) established another milestone in September,

but silver (middle) and copper have outgained it during

the recent rally.

FIGURE 1: PEDALS TO THE METALS

““ The bubble is in money-printing,

not in gold.””

— Howard Simons, president of Rosewood Trading

continued on p. 12


Opening Trades


BY JIM KHAROUF

Chicago Board Options Exchange announced it will launch its

second options market, the C2 Options Exchange in late

October. The new exchange will use a form of the “maker-taker”

pricing model designed to compete with other options

exchanges, such as NYSE Arca, the Nasdaq Options Market, and

BATS Options Exchange, that use a similar pricing structure.

Other exchanges have also adopted maker-taker models at vari-

ous levels, including the International Securities Exchange (ISE)

and the Boston Options Exchange (BOX). The maker-taker pric-

ing model, first introduced in the equity markets, give rebates to

those who provide liquidity to the exchange and charges cus-

tomers who take liquidity from it.

CBOE president and chief operating officer Ed Joyce says the

new electronic market is designed to be complementary to the

CBOE, which offers a traditional pro-rata pricing model.

“It provides CBOE with more flexibility by offering customers

different market models and different choices,” Joyce says. “It

expands the menu and complements what we do.”

Joyce says the new exchange, which operates separately from

CBOE, will start slowly by offering a few multiple-listed names

and expand from there. C2 may also offer the CBOE’s exclusive

anchor contract, S&P 500 options, although Joyce says “we will

be very careful how we roll that out.”

Maker-taker pricing models are considered more user-friendly

to high-speed, high-frequency traders looking for the best prices

across multiple markets, and who are responsible for an increas-

ingly large portion of volume.

“You need the right systems and the right price,” Joyce says.

“We’re adding another line on our menu for that business we’re

not getting a shot at right now.”

The CBOE has reason to address the ongoing competition

from maker-taker pricing models. The CBOE’s option market

share in August was 30 percent, down from 32.4 percent a year

earlier. Chief rival ISE, which is still largely a traditional pro-rata

pricing model, has watched its market share erode to 18.2 per-

cent in August, down from 27.3 percent a year earlier.

Meanwhile, maker-taker options market NYSE Arca watched

its market share rise to 11.6 percent from 10.9 percent, and the

Nasdaq Options Exchange increased its share from 3.25 to 4.61

percent.

The CBOE has not provided the details of its maker-taker

pricing, but sources say it will likely be a mix of traditional pro-

rata pricing and maker-taker.

“Overall, CBOE is looking at C2 as almost a hedge on maker-

taker,” says Paul Zubulake, senior analyst, futures and options,

at Aite Group. “They want to keep their customer priority model

in place but they also want to pay for order flow. So it’s not just

a pure, cut-and-dried maker-taker model going forward.”

Some market participants see potential for C2 going forward,

especially for firms and customers looking for platforms that fea-

ture speed and a pricing structure that suits their trading styles.

“C2 looks interesting to us,” says Jeff Wecker, president and

CEO of Lime Brokerage, which specializes in high-frequency

trading and broke into the options brokerage business in

September with the launch of a low-latency service. “It’s the kind

of market active traders like. And it has the model that would

attract the kind of traders we tend to work most closely with —

high-volume, black-box, and gray-box traders.” �

CBOE looks to double upExchange launches new trading centering catering to high-frequency sector.

of 1980. On an inflation-adjusted basis, it’s still valued well

below its 1980 high of $850 — roughly $2,230 in today’s dol-

lars when adjusted with the Consumer Price Index (CPI).

Howard Simons, president of Rosewood Trading and Active

Trader contributing editor, argues those using the word “bubble”

to describe the gold market are off base, but not for the reason

you might expect. There is, he says, a very simple fuel driving

the market: The extraordinary steps many countries, including

the U.S., continue to take in their efforts to jolt life into their

still-struggling economies — specifically, slashing interest rates

and engaging in so-called “quantitative easing” programs.

“As central banks around the world try to reflate by printing

paper money without a link to underlying economic value-

added, the value of that paper is zero,” he says. “Gold is not ris-

ing — paper is falling. This is not — I repeat, not — a bubble

so long as money is being printed. The bubble is in money-

printing, not in gold.”

Which means the question looming for gold traders is, when

will the money presses be switched off? �

GOLD continued from p. 10


The U.S. stock market’s direc-

tionless, volatile trajectory

in much of the second half

of 2010 has been a wash for

trend followers and buy-and-hold

investors — the market ulti-

mately went nowhere between

May and early October — and

it has likely been challenging for

shorter-term traders of all

stripes.

The gyrations that have dominated the

market recently have typically lasted between

two and six weeks, falling somewhere between swing-trading

and position-holding time frames. Sharp sell-offs (aside from the

anomalous May 6 flash crash) have been followed by rela-

tively brisk rallies, scaring many traders as the market tested

supported and cheating the hope of bulls as it reached resist-

ance (Figure 1, p. 16). Riding these waves is easier said than

done, but let’s see if we can model this price action in fairly sim-

ple terms and test it historically. For example, the late-August

low would have, with hindsight, been an excellent buying

opportunity. How could you have identified it proactively?

Pattern experimentationThere are many ways to define the

price action that preceded this bot-

tom, but let’s begin with two broad

characterizations: In late August

the market reached its lowest

level in more than 20 days

and it suffered a sharp decline

over the preceding one to two

weeks. At a glance, the early July

bottom appears to have had similar qualities.

Studying similar patterns in the S&P 500

ETF (SPY) led to the following general descrip-

tion, which will be referred to as Pattern 1:

1. Today’s low is lower than the previous 15 lows.

2. Price has dropped at least 5 percent from the highest high

of six to eight days ago to today’s low.

Although this definition is extremely basic, it is also objective

and specific — a good starting point for the analysis.

Not surprisingly, Pattern 1 formed relatively frequently. From

Jan. 1, 2000, through Oct. 4, 2010, there were 213 instances; in

BY ACTIVE TRADER STAFF

Buying sell-offs in the stock market provides an edge,

but it takes money, nerves, and persistence.


TRADING Strategies

Catching longer-term market swings

continued on p. 16

etzkorn1210.qxd 10/12/10 1:15 PM Page 14

Trading Strategies

many cases, several consecutive days fulfilled the pattern crite-

ria, which means multiple “patterns” were often signaled within

a single, larger down move (in other words, the pattern often

signaled several times before the market bottomed). For exam-

ple, Pattern 1 signaled completion not just on Feb. 5, 2010 (the

conspicuous spike low), but also on Jan. 22, Jan. 26, Jan. 27.

Jan 28, and Jan. 29, while the market pushed lower and lower.

Table 1 shows SPY’s gains or losses one, two, five, 10, 15, 20,

25, 30, 35, and 40 days after the pattern’s conclusion, measured

from the close of the final bar of the pattern to the closes at each

interval. The results are positive — both the median and average

moves at each interval are above zero (the averages are smaller

than the medians, reflecting the influence of a smaller number of

large losers) — but the high standard deviations and relatively

modest winning percentages indicate the post-pattern market

performance is rather volatile. The median and average gains,

total point gain/loss, and probability of gains peak 20 to 35 days

(between one and two months) after the pattern’s conclusion.

To potentially remove some of the pat-

tern’s early and repetitive signals, a third

criterion was added that required the

market to make a relatively large down

move on the final day of the pattern:

1. Today’s low is lower than the previ-

ous 15 lows.

2. Price has dropped at least 5 percent

from the highest high of six to eight

days ago to today’s low.

3. Today’s low is at least 1 percent

lower than yesterday’s close.

While observation of several patterns

suggested this might be a distinguishing

characteristic of more-successful exam-

ples, the change didn’t amount to much.

The number of signals declined only by

17 percent (to 177), but Table 2 shows

Pattern 2’s performance was very similar

to Pattern 1’s.

Studying the relationships between the

price bars within the original pattern led

the analysis in a different direction.

Pattern analysis often incorporates the


TABLE 1: PATTERN 1

213 1 2 5 10 15 20 25 30 35 40

Median 0.45 0.69 0.65 0.52 1.03 1.46 2.48 2.82 2.25 2.01

Average 0.28 0.50 0.63 0.22 0.18 0.88 0.86 0.80 1.04 0.91

Total 60.49 107.07 134.83 46.03 38.36 186.90 183.60 170.11 218.31 189.55

StD 2.48 3.25 4.33 5.12 6.96 8.19 8.85 9.69 9.80 10.04

Max 12.85 11.35 14.64 12.48 14.31 18.85 17.01 18.28 17.86 21.96

Min -8.75 -10.31 -21.84 -17.13 -29.37 -34.81 -35.92 -36.14 -37.38 -34.65

Win % 56.34% 58.69% 56.34% 53.99% 57.28% 59.62% 60.56% 61.79% 60.95% 57.89%

Pattern 1's median price moves are positive at all intervals, but the high standard deviations and relatively modest winning

percentages indicate the post-pattern market performance is volatile.

FIGURE 1: WIDE-RANGING SWINGS

The market’s recent swings, here represented by the S&P 500 tracking stock

(SPY), have lasted roughly between two and six weeks. Buying into the

market’s sharp sell-offs can be difficult to do, especially when volatility and

uncertainty are high.



changes from one bar to the next or the number of consecutive

price milestones over a certain period — for example, a series of

consecutive lower lows, highs, or closes. However, such bar-to-

bar comparisons can be restrictive, especially when analyzing

longer time periods. Instead, Pattern 3’s new rule compares each

day’s low to the low two days ago:

1. Today’s low is lower than the previous 15 lows.

2. Price has dropped at least 5 percent from the highest high

of six to eight days ago to today’s low.

3. Today’s low is the ninth consecutive low that is lower than

the low two days earlier.

This time the change was more significant, as shown in Table

3. The number of signals was more than cut in half, to 100, the

median/average gain at most intervals increased (especially at

days 30 and 35), and the winning percentage was above 60 per-

cent for all intervals. Thirty-five days after pattern conclusion,

the median gain at the close was 3.50 points — 50-percent more

than Pattern 1, with the close being higher than the closing

price of the last day of the pattern nearly 69 percent of the time.

The pattern vs. the marketBefore analyzing the three pattern variations in greater detail,

let’s look at what the market did overall during the January

2000-October 2010 analysis period. SPY closed at 146.88 on

Dec. 31, 1999 and closed at 113.53 on Oct. 4, 2010, a decline

of 22.71 percent, although the market twice pushed to record

highs during that time span.

Figure 2 (p. 18) shows the analysis period began a few

months before the peak in the bull market that began in the

1990s. The 10 years and 10 months that followed were domi-

nated by the bull market that began in late-2002 or early 2003

(pick your bottom), and book-ended by the 2000-2002 bear

market and the 2008-2009 financial collapse. As of Oct. 4,

2010, the market was trading around 2004 levels after having

fallen to 1996 levels in March 2009. The horizontal line marks

the closing price on Dec. 31, 1999 — relatively close to the

highs SPY set in 2000 and 2007.

Figure 3 (p. 18) shows the median performance after the

three patterns, along with the median and average values for all

moves of the same size (one to 40 days) in the analysis period

continued on p. 18

TABLE 2: PATTERN 2

177 1 2 5 10 15 20 25 30 35 40

Median 0.17 0.47 0.63 0.65 1.21 1.89 2.90 2.48 2.25 2.01

Average 0.19 0.42 0.63 0.32 0.16 0.94 0.81 0.45 0.83 0.82

Total 33.58 75.08 111.58 56.77 27.68 165.95 143.61 78.79 143.69 141.08

StD 2.63 3.43 4.59 5.40 7.34 8.67 9.38 10.27 10.37 10.58

Max 12.85 11.35 14.64 12.48 14.31 18.85 17.01 18.28 17.86 21.96

Min -8.75 -10.31 -21.84 -17.13 -29.37 -34.81 -35.92 -36.14 -37.38 -34.65

Win % 52.54% 57.63% 55.37% 54.24% 58.19% 61.02% 61.58% 59.89% 58.19% 58.19%

The additional rule didn’t significantly alter the results from Pattern 1.

TABLE 3: PATTERN 3

100 1 2 5 10 15 20 25 30 35 40

Median 0.72 0.89 1.25 1.09 1.68 1.73 2.83 3.11 3.50 2.32

Average 0.38 0.70 0.69 0.39 0.26 0.87 1.35 1.09 1.68 1.31

Total 38.25 70.01 69.36 39.33 25.55 87.38 135.31 107.49 166.73 129.31

StD 2.26 3.13 4.21 5.04 7.21 8.23 8.54 9.76 9.04 10.14

Max 5.51 10.65 7.47 11.56 14.31 18.85 17.01 18.28 17.86 21.96

Min -6.05 -10.31 -21.84 -17.13 -23.30 -27.43 -26.24 -36.14 -30.82 -32.33

Win % 62.00% 61.00% 63.00% 61.00% 62.00% 61.00% 62.00% 65.66% 68.69% 60.61%

Requiring nine consecutive lows that are lower than the lows two days earlier dramatically reduced the number of trades,

boosted the typical gain, and increased the winning percentages.


Trading Strategies

(dashed lines). While SPY’s overall median

moves are slightly positive, the average

moves are slightly negative, reflecting the

large, concentrated losses that occurred

during the two bear phases. Pattern 3 had

the largest median gains at most intervals,

especially at days 30, 35, and 40, but all

three patterns outperformed the market by

a wide margin.

Figure 4 compares winning percent-

ages — i.e., the percentage of times the

market closed higher than the close of a

pattern’s last day. Again, Pattern 3 had the

best performance, especially at the short-

est and longest intervals. As was the case

with the median gains, the winning per-

centages of all three-pattern variations

converge around days 20 to 25. The

dashed line shows the winning percentage

for the overall market during the analysis

period.

Reality checkThe relatively small differences between

the pattern’s winning percentages and the

market overall in Figure 4 is a reminder of

the stock market’s inherent bullish bias.

Even during a period containing the two

most severe bear moves of a generation,

the odds of a higher close at any of the

given intervals was never less than 52 per-

cent. The market’s total loss during the

past decade is simply a function of a

minority of large losses overwhelming a

majority of gains. From this perspective,

only Pattern 3 shows a dramatic improve-

ment over the market’s tendency to close

higher at any of the given intervals.

Figure 5 offers one more necessary

glimpse into the reality of trading this kind

of pattern. This chart shows equity curves

for the three patterns based on an initial

account size of $25,000 and buying

$25,000 worth of SPY at each trade signal


FIGURE 3: PATTERN PERFORMANCE — MEDIAN GAINS

Pattern 3 had the best returns of the three pattern variations, all of which

outperformed the market.

FIGURE 2: ANALYSIS PERIOD

The period over which the pattern will be analyzed encompasses the era’s

two major bear markets, but also a multi-year uptrend. Between Dec. 31,

1999 and Oct. 4, 2010, SPY declined approximately 22 percent, despite

twice establishing record highs.


(representing an average trade size of

approximately 210 shares). For Pattern 1

and Pattern 3, trades were exited on the

close 35 days after entry; for Pattern 2,

after 20 days. These holding periods were

selected based on the most-favorable total

profit and winning percentage figures from

Tables 1, 2, and 3. The black line toward

the bottom of the chart represents a buy-

and-hold position in SPY.

Pattern 1 had the highest ending profit,

but this is a function of it signaling more

trades than Patterns 2 or 3 (twice as many

as Pattern 3, as mentioned). All the pat-

terns outperformed buy-and-hold by a

wide margin, but this is not more impor-

tant than the risk a trader would have

been subjected to: The drawdowns during

the 2000-2002 bear market and 2008-

2009 were huge — in some cases, worse

than the overall market.

Interestingly, all three patterns carried

equity highs into early September 2008,

but they completely fell apart as the market

collapsed in October. Pattern 1’s drawdown

reached 65 percent by February 2009 —

much more than the S&P 500’s decline —

while Pattern 2 lost 54 percent. Pattern 3

suffered the least damage, declining “only”

48 percent.

A once-in-a-lifetime market event, you

say? Not for Patterns 1 and 2, which lost

even more on a percentage basis in 2002.

Only Pattern 3 managed to avoid the

calamity of the decade’s first bear market,

losing around 22 percent in 2001 (a little

less than $9,000) before treading water

through the worst of the bear move.

Figure 6 (p. 72) shows Figure 1’s price

action but highlights each pattern varia-

tion’s entry signals. Pattern 3 signaled at

the February, July, and August lows, plus


continued on p. 72

FIGURE 5: EQUITY CURVE COMPARISON

Pattern 1 had the highest ending profit, but it was also the riskiest of the

patterns, losing more than 60 percent during the 2000-2002 and 2008-

2009 market drops. Pattern 3 had the best risk-adjusted performance.

FIGURE 4: WINNING PERCENTAGE

This chart shows the percentage of times SPY closed above the closing

price of the last day of the pattern. Pattern 3 had the best performance,

especially at the shortest and longest intervals.


Even though all trend-following systems have the same

purpose — cut losses short and let profits run — the

results between systems can vary significantly over

shorter time periods. Differences in excess of 3 to 5

percent over any three- to 12-month period are not uncommon

among systems applied to the same markets.

This is mostly a result of the different distances between entry

and exit points from system to system. For example, if trend-fol-

lowing systems A and B usually enter at the same price, but sys-

tem A has a tighter stop than system B, then system A is likely

to marginally outperform B in the short term if the market goes

against both systems immediately. System A might also do better

in very steady, long-term trends because it will both enter and

exit trades before system B.

However, in the intermediate-term, and in more volatile mar-

ket conditions, system B will likely perform better because it

will avoid getting stopped out time and time again. (In this case,

even a relatively large loss might be preferable to several smaller

ones.) In those instances when the market takes off after having

produced a short-term loss for system A, system A will fall

behind system B not only in terms of the initial loss, but also in

how far it needs price to move before it can enter the market

again.

Other factors that can make a difference between systems

include trade frequency, position (trade) sizing, and asymmetric

rules for the long and short sides of the market.

If you plan on developing or trading a trend-following system

yourself, or if you are interested in investing with a trend-fol-

lowing commodity trading advisor (CTA), it would be immense-

ly helpful to be able to compare performance with that of cer-

tain benchmark systems. This trend-following system analysis

may also help you discover one or two secrets of the profession-

als, or perhaps provide ideas for your own strategies.

The systemsTo get a feel for what works when, we will track and analyze six

trend-following systems:

System 1: A two-standard deviation volatility breakout with a

moving-average trailing stop.

System 2: A 1.5-standard deviation volatility stop-and-reverse

breakout.

System 3: A highest-high/lowest-low (HH/LL) breakout, with

a center-line trailing stop.

System 4: A highest-high/lowest-low (HH/LL) stop-and-

reverse breakout.

System 5: A constant-period average true range (ATR) break-

out, with a median stop.

BY THOMAS STRIDSMAN

How do you know if your trend-following system — or trend-following

CTA — is pulling its weight or underperforming?

Compare it to the benchmark strategies in the system matrix.

20 www.activetradermag.com •• December 2010 •• ACTIVE TRADER

TRADING Strategies

Gauging performance with the system matrix

continued on p. 22

KC Go to “Key concepts” on p. 78

for more information about:

• Compounded Annual

Geometric Return (CAGR)

• Stop-and-reverse (SAR)

stridsman1210.qxd 10/8/10 2:26 PM Page 20

Trading Strategies

System 6: A constant-period ATR stop-and-reverse breakout.

(For more information about using the center line and medi-

an, see “Baseline primer.”)

Five versions (short term to long term) of each system will be

tracked, for a total of 30 system variations.

System parametersThere are three basic systems (1-2, 3-4, 5-6), each traded with

and without trailing stops and using different look-back periods.

The look-back periods for the volatility breakout and HH/LL

breakout systems using trailing stops (systems 1 and 3) will be

30, 60, 120, and 240 days. The look-back periods for the

volatility and the HH/LL stop-and-reverse systems (systems 2

and 4) will be 20, 40, 80, and 160 days.

The two volatility breakout systems maintain constant volatili-

ty multipliers of two and 1.5 standard deviations, respectively.

The ATR systems use constant look-back periods of 240 and 80

days, respectively. Instead of varying their look-back periods,

they vary their ATR multipliers in steps of 0.75, 1.5, three and

six ATRs. (All parameter settings were decided more or less arbi-

trarily to create similar long-term performance data and trades.)

The longest-term versions of all the systems were also tested

with a twice-monthly rebalancing (every 10 days) of all open

positions, to reset each trade’s risk as it was at the start of the

trade. This modification approximately triples the trade frequen-

cy. Regular rebalancing also shortens the average time in the

market per contract traded, and gives the system a shorter-term

character.

Test settingsWe conducted tests to see how the systems performed in the

recent past, especially this year. The amount of account equity

risked per trade for each system in 2010 was based on a back-

test on historical data from January 1990 through December

2009. The position size was set in such a way that the back-test-

ed average Compounded Annual Geometric Return (CAGR)

came as close as possible above 16 percent, net of trading costs,

management fees, and earned interest on account equity. Also,

the position size in any market could not surpass 25 percent of

the most recent average daily volume.

The initial account equity for all systems was set to $10 mil-

lion. As of December 2009, all systems had an account balance

of approximately $200 million. This is a reasonable total equity

for most trend-following CTAs to handle efficiently, so this will

also be the amount we assume is in the accounts as we conduct

quarterly analysis. As an example of the historical back-tested

performance, Figure 1 shows the equity growth of system 2,

which has performed the best so far this year.


FIGURE 1: SYSTEM 2 EQUITY CURVE

System 2 had the best performance in 2010 as of August.



Because slippage is a function of both

volatility and volume, shorter-term systems

and the ones with the tightest stops will have

the most slippage. Most systems will suffer

around $30 per round-turn trade because of

volatility. The the most expensive systems in

terms of estimated slippage will suffer in

excess of $20 slippage because of traded vol-

ume, while this will only affect the cheaper

ones by a few bucks. For all systems the aver-

age slippage comes out to three to five ticks

per contract traded. Slippage was also deduct-

ed for rollovers, as well as for any eventual

position-size adjustments. Commissions were

set to $5 per round turn, and were also

deducted for rollovers and position-size

adjustments.

To make comparison easier to CTA perform-

ance, the management fee was set to 1 percent

per year, deducted monthly. The incentive fee

was deducted at the end of each quarter that

ended higher than the previous highest-ending

quarter. The fee is 20 percent of the difference

between said quarters. The systems also earn a

small interest payment each day, based on the

90-day T-bill rate.

The marketsThe test portfolio contains 49 markets in seven

sectors:

CCuurrrreenncciieess:: Australian dollar, British pound,

Canadian dollar, Euro, Japanese yen,

Mexican peso, Swiss franc.

IInntteerreesstt rraatteess:: Canadian 10-year bond, Euro

German bund, Long gilt (British), Japanese

10-year bond, American 10-year T-note,

American 30-year bond, Australian 10-year

bond.

SSttoocckk iinnddiicceess:: S&P 400 Midcap, CAC 40

(France), DAX (Germany), FTSE 100

(Britain), Hang Seng (Hong Kong), Nikkei

225 (Japan), E-Mini Russell 2000.

EEnneerrggiieess:: crude oil, heating oil, Brent crude

oil, gas oil, natural gas, gasoline, EUA emis-

sion rights.

MMeettaallss:: gold, copper, aluminum (LME for-

ward), nickel (LME forward), palladium,

platinum, silver.

GGrraaiinnss:: wheat (CBOT), soybean oil, corn,

wheat (KCBT), rough rice, soybeans, soy-continued on p. 24

Baseline primerA baseline represents the best estimate of the current “equilibrium” price,

around which price should (theoretically) fluctuate. The most common

example is the moving average, but others include the center, median,

and mode.

1. Center: The center line is the midpoint of the high-low range in a look-

back period: (HighestHigh+LowestLow)/2. In most cases, the center-line

price will only be implied, and not used as an input in other calcula-

tions.

2. Median: The median is the middle value of all values in the look-back

period — half the values are above the median and the other half are

below it. If there is an even number of values, the median is the aver-

age of the middle two.

3. Mode: The mode is the most frequently occurring value in a sample of

values. For example, in the group of prices 12, 10, 12, 29, 47, 33, 25,

16, 47, 12, and 20, the mode is 12. When dealing with price data, it is

more useful to estimate the mode using the following formula:

Estimated mode = (3*median) – (2*mean)

This formula is derived from another formula that describes the relationship

between the mean, median, and mode for any skewed distribution:

Mean – mode = 3*(mean – median)

In our data sample, the estimated mode would be 12.18, which is very

close to the actual, observed mode. Figure A shows examples of the differ-

ent baseline calculations.

FIGURE A: SAMPLE BASELINES

Day Price Prices (sorted low to high)

1 12 102 10 123 12 124 29 125 47 166 33 207 25 258 16 299 47 33

10 12 4711 20 47Average: 23.90Median: 20Center: 28.5Actual mode: 12Estimated mode: 12.18

The average is

the most com-

monly used

baseline price,

but the medi-

an, center, and

mode prices all

provide addi-

tional informa-

tion about

price action

and can be

used in trading

indicators and

systems.


Trading Strategies

bean meal.

MMeeaattss aanndd ssooffttss: feeder cattle, live cattle, lean hogs, coffee,

lumber, orange juice, sugar.

Performance analysisTable 1’s system matrix shows the short-term systems have per-

formed best in 2010 overall, and also during the last three

months of the test (through July). For example, the average

return for the short-term systems for the three months ending

July was +5.65 percent, while the longer term systems lost in

excess of -13 percent over the same period (far-right column).

The same holds true for most of the systems that go flat

(those with trailing stops — systems 1, 3, and 5) rather than

automatically reversing position (systems 2, 4, and 6). This indi-

cates one of two things: Either the markets have been very

trendy, or they have been very choppy. As the negative perform-

ance of most systems indicates, the markets have been choppy

enough that even the long-term systems using the widest stops

have gotten whipsawed.

In short, mastering market volatility — as opposed to catch-

ing trends — has been the key issue over the past year. This is

indicated by the relatively small losses suffered by the volatility

breakout systems (1 and 2) relative to the larger losses of the

two ATR systems (5 and 6). Thus, the systems that have per-

formed the best are the ones with short look-back periods and a

high sensitivity to volatility. This makes sense because the more

frequently a system trades, the more precise its position sizing

will be over the lifetime of a trade, which should result in better


TABLE 1: SYSTEM MATRIX

System 1 System 2 System 3 System 4 System 5 System 6

2-StD breakout w/mean stop

1.5-StD breakoutreversal

HH/LL breakout w/center stop

HH/LL breakout reversal

240-day ATRbreakout w/median stop

80-day ATR breakout

reversal

Avg. per-tradereturn & risk, all systems

Look-back (days) 30 20 30 20 0,75 ATR 0,75 ATR

Risk / trade 0.25 0.3 0.35 0.5 0.15 0.25 0.3

Return, 09 -21.2 -16.6 -29.8 -13.2 0.4 -14 -15.73

Return, YTD 7.6 11.9 5.9 -1.6 -16.6 -9.7 -0.42

Return, 3 months 14.38 18.2 12.19 11.96 -18.82 -4.01 5.65

Look-back (days) 60 40 60 40 1,5 ATR 1,5 ATR

Risk / trade 0.45 0.5 0.5 1.2 0.25 0.45 0.56

Return, 2009 -13.2 -14.9 -4.8 -32.7 -5.1 -9.5 -13.37

Return, YTD -2.1 7.2 -9.8 -16 -11.5 -12.3 -7.42

Return, 3 months 3.27 9.01 -4.86 3.28 -15.9 -7.68 -2.15

Look-back (days) 120 80 120 80 3 ATR 3 ATR

Risk / trade 0.6 0.6 0.75 1 0.45 0.9 0.72

Return, 2009 1.1 3.4 -1.9 2.6 -3.3 4.3 1.03

Return, YTD -10.1 -13.4 -10.2 -20.9 -9.6 -24 -14.7

Return, 3 months -7.59 -9.7 -12.67 -13.89 -16.4 -17.7 -12.99

Look-back (days) 240 160 240 160 6 ATR 6 ATR

Risk / trade 0.85 0.9 1 1.6 1 2.2 1.26

Return, 2009 -18.9 -2.9 -13.2 -6.8 -7.4 -13 -10.37

Return, YTD -4.5 -14.8 -3.9 -7.5 -5.7 -10.2 -7.77

Return, 3 months -8.6 -15.4 -9.81 -16.3 -10.64 -17.65 -13.07

Longest period systems, with twice monthly rebalancing of open positions

Risk / trade 1.1 1.1 1.2 1.7 1.2 2.4 1.45

Return, 2009 -9.6 -0.3 -11.2 -6.5 -1.7 -12.8 -7.02

Return, YTD -9.6 -17.5 -2.4 -10.7 -8.2 -11.1 -9.92

Return, 3 months -11.7 -17.54 -8.83 -16.91 -12.52 -16.29 -13.97

Average of all look-back periods per system

Return, 2009 -12.36 -6.26 -12.18 -11.32 -3.42 -9 -9.09

Return, YTD -3.74 -5.32 -4.08 -11.34 -10.32 -13.46 -8.04

Return, 3 months -2.048 -3.086 -4.796 -6.372 -14.856 -12.666 -7.30

The short-term systems have performed best in 2010, a reflection of the prevailing choppiness in many markets. In contrast, these

systems underperformed in 2009.


risk-reward characteristics for the system.

Unfortunately, this isn’t always the case, as viewing last year’s

returns reveals. The situation was almost the opposite in 2009:

Again, most of the systems lost money, but the short-term,

volatility-sensitive systems were the worst performers, while the

longer-term ones (look-back periods in the 80- to 120-day

range) fared best.

The performance of the longest-term systems that also rebal-

ance their trades biweekly fell somewhere between the 40- to

60-day systems and the 80- to 120-day systems, which makes

sense because the rebalancing triples the trade frequency, mak-

ing the systems intermediate-term in nature. (By the way, it is

very hard to give exact figures for trade frequency or length, but

generally, the average trade frequency varied between one and

10 trades per market per year — approximately 500 to 1,500

round-turns per million dollars in equity — from the longest-

term to the shortest-term systems.)

So, does two years of lackluster performance for so many dif-

ferent trend-following systems prove such systems no longer

work? No, it doesn’t. We must remember that trend-following

systems are designed not only to make money in trending mar-

kets, they are in essence designed to lose money in choppy mar-

kets. From this perspective, the systems are doing just fine. The

markets are choppy and the systems are losing money — which

means they are performing exactly as intended. As soon as a

trend or two develops they will also start fulfilling the primary

design goal.

Using the matrixThat said, one interesting anomaly is the performance and per-

trade risk of the 40-day HH/LL stop-and-reverse system (system

4). Note that both its -32.7 percent loss in 2009 and its -16 per-

cent loss this year are way out of proportion to the same system’s

performance using different look-back periods, as well as the

performance of different systems with similar look-back periods.

Its 1.2 percent per-trade risk means it is also risking significantly

more than almost all the other systems to reach the desired

return target. Higher-high/lower-low stop-and-reverse breakout

systems with look-back periods of roughly 30 to 60 days proba-

bly are the most common systems used in trading, so maybe this

truly is a case of a system that has ceased to work optimally

because everyone is using it. Figure 2 shows its back-tested

equity growth.

When the trends return, the systems will still produce vastly

different results, even though most of them will be profitable in

the long run. But if you’re like most investors, you will feel the

pain or joy in the short run. Therefore, before you start trading a

system or invest with your first CTA, it’s a good idea to decide

on a system type that best fits with how you’d like to experience

your pain and joy. Start by comparing your system or CTA with

the systems in the matrix to get a feel for its trading style and

volatility, then decide whether its reward-risk profile fits your

investment style. Or, if you’re looking to diversify into several

systems or CTAs, compare them with all systems in the matrix


continued on p. 26

FIGURE 2: 40-DAY HH/LL SAR SYSTEM

The exceptionally poor performance of the 40-day breakout system could be a case of a strategy that has

ceased to work optimally because everyone is using it.


Trading Strategies


to make sure you will cover several reward-risk profiles.

Other factorsHowever, keep in mind that CTAs can alter their individual

reward-risk profiles by using different proprietary money man-

agement (i.e., position sizing) rules, and by incorporating factors

such as macroeconomic trends as well as trade and sector-alloca-

tion rules.

For example, Figure 3 shows the performance of the same

system on the same markets as Figure 2, except this time it used

a CTA’s proprietary position-sizing algorithm. (For 2009 it had a

loss of -6.8 percent, for 2010 the loss was -0.1 percent through

the end of July). Perhaps we can figure out what’s at work here

in upcoming articles, in which we will start tracking the per-

formance of a group of trend-following CTAs, and also examine

how some of the systems in this article have performed going

long and short in different market sectors.�

Related reading

Books and articles by Thomas Stridsman:

Trading Systems That Work (2000, McGraw-Hill)

Trading Systems and Money Management(2003, McGraw-Hill)

““Building a volatility-momentum system””Active Trader, October 2010

Systematizing volatility and momentum concepts

produces compelling test results.

““New approaches to volatility””Active Trader, September 2010

Just as there are alternatives to the moving aver-

age when defining trends, there are better ways

to measure volatility than the tools you may be

used to relying on.

““A baseline trend strategy””Active Trader, August 2010

Experimenting with moving medians, modes, and

center lines — in addition to moving averages —

in a robust trend system.

For information on the author, see p. 6.

All tests were done with TradingBlox system-testing software

(www.tradingblox.com) using Unfair Advantage data by CSI

(www.csidata.com), with the kind and invaluable help of Roger Rines

([email protected]), independent trader and system-developer

consultant.

FIGURE 3: THE IMPACT OF POSITION SIZING

Applying a position-sizing rule to the same system and markets represented in Figure 2 produced a much

more stable equity curve.


Although trends don’t last forever, they often last

much longer and go much further than most peo-

ple anticipate, which makes trying to buy a stock

because it’s low or short a stock because it’s high a

loser’s game.

Fortunately, a stock will often leave clues the trend is turning

and will usually make a minor correction before resuming its

new trend. Entering after that minor correction — and only if

the new trend shows signs of resuming — is the goal of “transi-

tional” patterns, as shown in Figure 1.

When you catch a new trend early, the payoff can be huge.

Unfortunately, since you are trading what could turn out to be a

correction in a longer-term trend, this approach will also have a

higher failure rate than trading pullbacks in established trends.

Let’s look at three transitional patterns: First Thrusts,

Gatekeepers, and Bow Ties.

First ThrustsMarkets in major trend transitions often begin with a bang,

making a sharp thrust in the new direction. This tends to catch

traders off guard. Trapped on the wrong side of the market, they

find themselves waiting for the market to reverse so they can get

BY DAVE LANDRY

Recognizing a few simple patterns — and trading

them correctly — can help you get into new trends early.


TRADING Strategies

Trading trend transitions

FIGURE 1: TRANSITIONAL PATTERNS

Trading trend transitions requires identifying a correc-

tion as the market appears to be making a major turn.continued on p. 30

Uptrend

Downtrend

begins

Uptrend

begins

Uptrend

continues

Downtrend

resumes

First correction

First

correction

Downtrend

Shorts

Longs



• Fibonacci numbers

• Weighted and exponential

moving averages

landry1210.qxd 10/12/10 7:23 AM Page 28

Trading Strategies

off the hook. Bottom pickers and top pickers who missed the

top or bottom and do not want to pay up are also waiting for

some sort of meaningful correction.

Unfortunately, a meaningful correction may never come for

these traders. Often, markets making a sharp thrust in a new

direction pull back only briefly before resuming their new trend.

The old market participants will soon be forced out at unfavor-

able prices and the bottom or top pickers must pay up or risk

being left behind. By waiting for the market to make a sharp

thrust in the new direction, you avoid the pitfalls associated

with trying to pick highs or lows. By entering at the first signs of

a correction rather than waiting for something more substantial,

there is the potential for the position to be helped along by the

predicament of the aforementioned traders.

Let’s look at the pattern. Figure 2 shows how after making a

significant new low (1), the market should make a sharp thrust

in the new direction (2) followed by a lower low and a lower

high — in other words a one-bar pullback (3). Entry occurs

above the high of the pullback bar (4).

The best transitional patterns form in markets making major

new lows (for longs) or major new highs (for shorts). This helps

ensure the maximum number of people are on the wrong side of

the market when the trend turns. In Figure 3 the stock was at its

lowest level in more than a decade (1) when it thrust higher in

March 2009 (2). The stock made a lower low and a lower high

at point 3; in this case, entry would occur at point 4, above the

high of the pullback.

In Figure 4 the stock made multi-year highs in late-April (1)

and then began to sell off (2). It made a higher high and higher

low at point 3 to complete the setup. A short was triggered

when the stock turned back down at point 4. Notice the stock

made two more higher highs and

higher lows after point 3 before

turning lower. Entry occurs only

when price makes a lower low (for

a short setup) or a higher high (for

a long setup) after an initial pull-

back bar completes.

Notice that the retracement in

this example is fairly sharp. This is

similar in vein to another transi-

tional pattern, the Gatekeeper.

GatekeepersMarkets forming tops after a strong

trend often sell-off sharply before

making one last attempt to resume

their uptrends. This resumption is

caused by bargain hunters buying

at what they perceive to be low lev-

els and by shorts taking profits.

(Also, the move can be accelerated


FIGURE 3: LONG FIRST THRUST

After making its lowest low in more than a decade, the stock made a sharp up

move.

FIGURE 2: FIRST-THRUST PATTERN

First Thrusts begin with a sharp move that reverses the

previous trend. A long trade would occur after the

initial pullback in the new up move.

(4)

(3)

(2)

(1)



by shorts being squeezed.)

However, this move often exhausts

itself before price makes it back to

the old high. When this occurs, a

true top is then formed.

The Gatekeeper is a Fibonacci-

retracement reversal pattern

designed to identify when a market

has completed this “final gasp.”

Fibonacci trader and author Derrik

Hobbs refers to 78.6 percent as the

“gatekeeper” of Fibonacci numbers,

claiming that markets often stop

(and reverse) at that number. After

big downthrusts, markets often

stall after retracing between 61.8

percent and 78.6 percent of the

move. In some cases, the market

will reverse right at the 78.6-per-

cent retracement level.

The advantage of this pattern is

that its risk is well-defined (at worst, the trade is stopped out on

a move above the old high), while the potential reward of cap-

turing the occasional major top or bottom can be great. The pat-

tern is especially helpful for determining when an extended rally

could be topping out. Let’s look at the rules for short sales.

As shown in Figure 5, the market should make a new high

(1) followed by a sharp sell-off (2). It should then make a move

back toward the old high but stall somewhere between the 61.8-

percent and 78.6-percent (3) retracement levels of the sell-off

(i.e., the move from point 1 to point 2). Ideally, the sell-off and

retracement should unfold over 10 to 11 days, giving the move

a sharp “V” appearance (a reverse check mark). Entry occurs

when the market turns back down (4).

Figure 6 (p. 32) shows the S&P 500 during before and after

the May 6 “flash crash.” After making one-year-plus highs at

point 1, the index sold off hard to point 2, the day of the crash.

It then retraced sharply (3), giving players trapped on the wrong

(long) side of the market false hope. However, notice price

FIGURE 4: SHORT FIRST THRUST

The short trade is triggered only when the stock turns back down after making an

initial bar with a higher high and higher low.

continued on p. 32

FIGURE 5: GATEKEEPER PATTERN

The Gatekeeper pattern looks to enter after the

market retraces the move away from a major top

or bottom by a certain percentage.

(4)

78.6%

61.8%

(3)

(2)

(1)

10-11 days


Trading Strategies

stalled just shy of the 78.6-percent

retracement. Short entry occurs

when the market turned back

down at point 4.

Bow TiesFirst Thrusts and Gatekeepers are

fairly abrupt patterns that form rel-

atively quickly and accompany

new trends that begin with a bang.

Sometimes, though, new trends

start more gradually; price will

accelerate in the new direction only

after the market goes through a

distribution phase.

The Bow-Tie pattern uses a

series of moving averages to signal

such transitions. Although all indi-

cators are prone to lag, the Bow-Tie

moving averages can often alert

you to a trend change in markets

that have been going through

extended consolidations, especially

those that have recently made a

major high or low.

For this pattern, you can use a

10-day simple moving average

(SMA) and 20-day and 30-day

exponential moving averages

(EMAs). These averages often come

together and then spread out in the

opposite direction right before a

market makes a major transition.

That is, they go from “proper”

downtrend order (the faster mov-

ing average lengths below the slow-

er moving average lengths) to

proper uptrend order (the faster

moving averages above the slower

moving averages).

When this happens over a short


FIGURE 6: GATEKEEPER: AFTER THE FLASH CRASH

A Gatekeeper pattern formed after the May 6 “flash crash” when price sold off

and the subsequent rally retraced only between 61.8 and 78.6 percent of the

sell-off before turning down.

FIGURE 7: BOW-TIE PATTERN

Bow Ties form when the three moving averages reverse their order, signaling a turn

in the market.

(3)

(2)

(1)30-day EMA

20-day EMA

20-day EMA

30-day EMA

10-day SMA

10-day SMA


time period, it gives the appearance of a Bow Tie, as shown in

Figure 7. Notice the moving averages are in proper downtrend

order (10-bar SMA < 20-bar EMA < 30-bar EMA), but quickly

invert after point 1 to proper uptrend order (10-bar SMA > 20-

bar EMA > 30-bar EMA). Ideally, this should happen over a peri-

od of three to four days. The inversion suggests the market has

made a major trend shift.

However, the market is still prone to correct in this situation.

Therefore, wait for the market to make at least a one-bar pull-

back (2) and then enter above it (3).

Like all transitional patterns, those that follow major highs or

lows are preferable. For example, in Figure 8, as the stock made

a six-year-plus low the moving averages were in proper down-

trend order (10-bar SMA < 20-bar EMA < 30-bar EMA). As the

stock began to bottom, the moving averages came together and

then inverted to proper uptrend order (10-bar SMA > 20-bar

EMA > 30-bar EMA) over just a few days, forming the Bow Tie

(1). The stock then made three consecutive lower lows and

lower highs (2). A long trade was triggered when price took out

the high of this pullback (3).

Staying on the right side of the marketTransitional patterns can often alert you that an old trend is

coming to an end and a new one is emerging, especially when

the market is making a longer-term

high or low. If you study major

market turning points — such as

the stock tops in 2000 and 2007,

or the bottoms in 2003 and 2009

— you’ll notice transitional setups

occurred on many time frames as

the market turned.

Trying to pick tops or bottoms is

a loser’s game. You’re much better

off waiting for the market to show

signs the trend is turning and then

look to enter after the first correc-

tion. First Thrusts, Gatekeepers,

and Bow-Tie patterns can be used

to catch new trends early. The best

setups occur after major highs and

lows — multi-year or even lifetime

highs or lows work best — because

it increases the odds that many

traders are trapped on the wrong

side of the market. Not every transitional pattern will turn into a

major top or bottom, but all major tops or bottoms will have

some sort of transitional pattern — that’s what makes watching

for them so worthwhile. �


FIGURE 8: BOW-TIE: MOVING AVERAGE INVERSION

The reversal of the moving averages that forms the Bow Tie should unfold relatively

quickly (just a few bars).

Related reading““Trading the Bow-Tie pattern”” by Dave Landry

Active Trader, November 2000

Illustration of the bow-tie setup in the stock market.

““Bow-Tie variation””Active Trader, February 2008

A Trading System Lab article that tests a version of the

Bow-Tie pattern with a filter that requires the shortest

and longest moving averages to be within a certain dis-

tance of each other when an entry is triggered, and

extends the trade’s default holding period.


Some of the strategies in this article are applied to the forex market in

Dave Landry’s article in the October issue of Currency Trader maga-

zine (www.currencytradermag.com).


When investors see a stock gapping to new

high ground on huge volume they immedi-

ately think, “Well, I can’t buy that now – the

train has left the station.” However, up-gaps

that occur on massive volume can be some of the most poten-

tially profitable price-volume signals you will come across.

When a stock gaps higher — and exhibits certain characteris-

tics — the train is, in fact, often just leaving the station.

Although the crowd is afraid of buying up-gaps because the

sudden jump gives the stock the illusion of being “too high,”

massive-volume up-gaps are exactly the type of rocket-fueled

move that signals big money is moving into a stock — particu-

larly if it occurs in the earlier stages of a leading stock’s larger

potential price move. In essence, massive-volume up-gaps work

because the crowd doesn’t believe them.

Two simple rules, based on volume and volatility when an

up-gap occurs, help identify the trade setups with the most

potential.

Identifying viable gapsYou can use some simple rules to screen for tradable up-gaps.

First, the up-gap must be at least 75 percent (0.75) of the stock’s

40-day Average True Range (ATR).

Figure 1 (p. 36) shows Apple’s (AAPL) 40-day ATR at the

time of its big earnings-related up-gap on Oct. 14, 2004, was

0.51; 75 percent of this ATR is 0.75*0.51 = .3825. The stock

more than exceeded this number when it gapped up 1.605 on

open of that day.

Another prerequisite for a valid up-gap is strong volume,

which in this case is defined as volume that is at least 1.5 times

the 50-day average daily volume. On Oct. 14, around 98.9 mil-

lion shares traded on the up-gap day — nearly seven times the

50-day average volume of 14.14 million shares on the previous

day. (Note how AAPL also made another buyable up-gap as the

stock made its big rally into the end of 2004.)

When most investors watch one of their favorite stocks gap

higher on a favorable earnings announcement, they usually

assume the stock has simply rallied too far to buy. However,

buying a stock aggressively prior to an earnings announcement

with the intention of participating in a possible up is simply

spinning the roulette wheel. The true high-probability buy point

occurs when the up-gap takes place, because price and volume

parameters can be determined and well-defined risk manage-

ment boundaries established.

In AAPL, the October 2004 major up-gap was the starting

point for a long-term price move that has continued to this day,

as AAPL remains at or near all-time highs, some 27 times higher

today than the price it hit on Oct. 14, 2004.

Although gap size and volume are key factors in determining

BY CHRIS KACHER & GIL MORALES

Screening stocks with volume and volatility

criteria can help make trading up-gaps less of a guessing game.


TRADING Strategies

Trading gaps with the most potential

continued on p. 36



• Simple moving average

• True range (average true

range)

morales1210.qxd 10/8/10 1:54 PM Page 34

Trading Strategies


whether a gap is tradable, there are other qualitative factors to

consider in the stock’s chart pattern. For example, confirm from

a quick check of the price chart that the stock is an uptrend or

coming out of a roughly sideways price consolidation several

days or weeks long. up-gaps that occur in downtrends are typi-

cally not high-probability buy points because they are often tem-

porary, news-related countertrend moves that eventually give

way to the stock’s overall macro trend. In general, the up-gap

should occur in a constructive, fundamentally sound, leading

stock.

The AAPL example might seem part of remote market history,

but the up-gap rules were still working in August 2010 when

Priceline.com (PCLN) rocketed higher after announcing earnings

(Figure 2). This up-gap move met the trade guidelines, and the

chart shows the gap was followed by a price move that took

PCLN above $300 for the first time.

Selling rulesFigure 3 shows another big earnings-related up-gap that fulfilled

the pattern criteria in mid-July 2009. Figures 1, 2, and 3 all

show how the stocks held well above the up-gap day’s low (dot-

ted line in Figure 3). Failure to do so is a potential sell signal.

However, you can wait for the stock to close before deciding

whether to sell on an intraday move below the up-gap day’s low.

Higher-volatility stocks can be given a little more room to fluctu-

ate intraday than lower-volatility ones. Sometimes a stock will

undercut the gap day’s low in subsequent trading days by a

small amount (e.g., less than 2 percent), in which case the posi-

tion could possibly be held.

Also, see if the gap day’s low is close to major support, such

FIGURE 1: VALID UP-GAP BUY OPPORTUNITY

The Oct. 14, 2004 up-gap was a valid buy opportunity because the gap more was than 75 percent of the 40-day

ATR, and volume was greater than the 50-day average volume the day before.

Source for all figures: eSignal


as the 10-day or 50-day moving

average. These moving averages

may “catch up” to the price pat-

tern and function as support.

The idea is to keep in-line with a

maximum stop loss, while avoid-

ing selling prematurely if the

stock undercuts the gap day’s

low by just a small amount. This

is Selling Rule No. 1. Figure 4

(p. 38) shows another recent

example of a viable up-gap in

Salesforce.com (CRM). As of

early October the stock has con-

tinued to trade above the up-gap

day’s low, thus avoiding Selling

Rule No. 1.

If a stock has gapped up and

is trending higher, you can

implement Selling Rule No. 2,

which uses two moving averages

as guides for unloading a posi-

tion, depending on the stock’s

“character.” Powerful up-gaps

often generate strong trends that

follow, or “obey,” the 10-day

moving average for at least seven

weeks at a time. Once a stock

has obeyed its 10-day moving

average for at least seven weeks,

a violation of the average consti-

tutes a sell signal. (A violation of

a moving average is defined here

as a close below the moving

average, followed in the next few

days by an intraday drop below

the low of the day that first

closed below the moving aver-

age.) This is called the Seven-

Week Rule. There are three

exceptions to this rule:

1. The stock, prior to the up-gap, has tended to violate the

10-day moving average in intervals of less than seven

weeks as a matter of course in its price history;

2. The stock is in one of the following industry groups: semi-

conductors, retailers, or commodities (including oils and

precious metals);

3. The stock has a market capitalization greater than

$5 billion.

In these cases it is better to use a violation of the 50-day mov-

ing average as a sell signal — i.e., if the stock doesn’t obey its

10-day moving average for at least seven weeks, use a 50-day

moving average violation. A violation of the 10-day moving aver-

age can be used to sell at least half the position for stocks that

meet the Seven-Week Rule. A subsequent violation of the 50-day

moving average can be used to sell the balance of the position.

Let’s look at two examples to see how all this works. Figure 5

(p. 38) shows Chinese Internet leader Baidu (BIDU) formed a

viable up-gap in early January 2010, but that move quickly


continued on p. 38

FIGURE 2: SPARKING THE MOMENTUM

After this up-gap the stock pushed above $300 for the first time.

FIGURE 3: STAYING ABOVE THE GAP-DAY’S LOW

After a valid signal, price should not violate the low of the day immediately after the

gap.


Trading Strategies


failed when the stock dropped

below the low of the up-gap

day and violated the 50-day

moving average. But in the first

half of February BIDU came

right back and staged another

tradable up-gap, and this time

the stock rallied without look-

ing back. As BIDU continued to

trend higher, it never violated

its 10-day moving average

(pink). As the stock reached the

mid-$60s in April, it twice

closed below its 10-day moving

average, but in each instance

the stock failed to subsequently

trade below lows of each of the

days that closed below average.

As a result, they never met the

definition of a moving-average

violation.

In Figure 6, a tradable up-

gap in late-July 2010 took F5

Networks, Inc. (FFIV) up and

out of an up-trending price

channel. Notice that once the

stock gapped up and began to

move higher, the stock broke

down through the 10-day mov-

ing average a couple of weeks

later, which means it did not

obey its 10-day moving average

for seven weeks or more. Based

on this, you would ignore the

10-day average and instead use

the 50-day moving average as a

selling guide. One other point:

Although it fell below the 10-

day moving average, FFIV never

fell below the gap day’s low, so

it didn’t trigger Selling Rule No.

1. Nor did it trigger Selling Rule

No. 2, since it also remained

well above the 50-day moving

average (blue). Three weeks

later the stock had completely

recovered and moved to new

FIGURE 4: STAYING ABOVE THE AVERAGE

The stock should also remain above the 10-day moving average.

FIGURE 5: AVOIDING VIOLATION

The stock quickly failed after the first gap, but it rallied strongly after the second.

Although it closed below its 10-day moving average twice in April, in both cases the

stock did not subsequently penetrate the low the day that closed below average.


highs.

In practice, big-volume up-

gaps, which often appear to be

out-of-reach trade opportunities,

are often the first cannon shots

marking the start of a strong

upside price move. Having a

methodology in place for identify-

ing and capitalizing on these

trades is critical for success.

The approach outlined here is

fairly straightforward, and pro-

vides traders with an edge that is

likely to be overlooked by the

crowd. Using a few simple rules

makes such trades less risky, and

helps skew the reward-risk equa-

tion in your favor. �


For information on the authors,

see p. 6.

FIGURE 6: USING THE 50-DAY MOVING AVERAGE

Approximately two weeks after gapping higher the stock traded below the 10-day

moving average, which means it failed to “obey” the average for seven weeks or more.

As a result, the 50-day moving average would be used as a selling guide.

Related reading

Book:

Trade Like an O’Neil Disciple: How We Made 18,000percent in the Stock Market by Gil Morales and ChrisKacher.

Website:

www.VirtueofSelfishInvesting.comIncludes more information from the authors on buyableup-gaps and other technical methods.

Other articles:

““Opening gap locations””Active Trader, December 2008Historical testing attempts to identify the best setups forfading the opening gap.

““Double gaps””Active Trader, March 2008Analysis of the performance of double (and triple) pricegaps.

““Opening gap trader”” Active Trader, August 2007Further analysis points to a new direction for trading anopening-gap signal.

““Gauging gap opportunities””Active Trader, January 2007A different look at an “old” pattern offers insights intoprice behavior in the S&P 500.

““Gap trading techniques: Five-article set””A discounted collection of the following five ActiveTrader articles, published between 2001 and 2004:

1. “Morning reversal strategy” by Bryan C. Babcock andArthur Agnelli (May 2003). A strategy that takes itscue from historical tests revealing the tendency of themajor stock indices to revert to the previous day’s clos-ing price in the early minutes of the trading session.

2. “Trading the overnight gap” by David Nassar (March2001). Learn how to spot the early warning signs ofopening gaps and how to take advantage of them.

3. “Trading the opening gap” by John Carter (December2004). Watching pre-market volume is a good way todetermine whether to trade or fade the openingmove.

4. “Trading System Lab: Gap closer (stocks)” by DionKurczek (May 2003). This system test is designed tosee if the “all gaps are eventually closed” axiom holdswater (tested on a portfolio of stocks).

5. “Trading System Lab: Gap closer (futures)” by DionKurczek (May 2003). The above gap-based systemtested on a portfolio of futures markets.


The covered call is the one option strategy people

seem to grasp. New brokers with freshly minted

Series 7 licenses eagerly take their enhanced-

income investment approach to their client bases.

It’s plain to see that using the covered call offers the possibility

for immediate income — something investors are so attracted

to. It is heralded as a safe investment choice, from the perspec-

tive of both a brokerage house and the investor who is clamor-

ing to generate additional income for his portfolio.

Yet for all the history and salesmanship, it’s worthwhile to

step back and take another look at the covered call. Option

traders agree there is a season for any strategy. But is the invest-

ing public being duped with a short-sighted method, or is the

covered call truly a four-season strategy?

The covered call definedIn its most basic form, a covered call position is created when a

trader who owns an underlying security sells a near at-the-

money or slightly out-of-the-money call. The strategy is “cov-

ered” if the trader sells enough calls to cover the existing long

position in case of assignment of the short call.

Although the covered call is often referred to as a “buy-write,”

it’s important to recognize they differ in implementation.

Generally speaking, a covered call applies when a trader, for

whatever reason, simply sells an equivalent number of calls

against an already existing underlying position; buy-write

applies when the trader simultaneously buys the underlying

market and sells the call — as a package. Either way, the trader

typically holds the underlying in the same account from which

he sells the calls. The underlying provides collateral for the trad-

er’s requirement to deliver the stock if he gets assigned on the

option position.

For example, let’s say a trader buys 1,000 shares of XYZ stock

at $40 per share. He wants to sell a call option that offers him a

satisfactory amount of premium within a specific time horizon.

He finds a three-month $45 call trading for $2 per share and

decides to sell 10 of these $45 three-month call options, receiv-

ing a $2 premium for the 10 contracts ($2,000) sold against his

long position:

$2 premium per stock share *

100 shares per options contract

* 10 contracts = $2,000

This position is considered covered in that the trader sold 10

call options against the thousand shares of stock he holds in his

BY LARRY SHOVER

Market realities make the popular covered call strategy

more difficult to pull off than most people think.


TRADING Strategies

Uncovering the covered call

• At the money

• Call option

• Implied volatility

• In the money

• Naked put

• Out of the money

• Premium

• Skew

• Strike price

KC Go to “Key concepts” on p. 78 for more

information about:

shover1210.qxd 10/8/10 12:47 PM Page 40


account. The premium received from

the options sale ($2 per share) effective-

ly lowers the stock’s cost basis from $40

per share to $38 per share.

There are three possible outcomes for

this example. First, the stock could

close above $45 per share at expiration.

In this case, the short $45 call will auto-

matically exercise, resulting in the stock

being delivered to an exerciser of a long

$45 call at $45 per share. The maxi-

mum profit in this situation is:

Strike price – purchase price +

option premium received, or

$45.00 - $40.00 + $2 = $7

Second, the stock could close at $45 per share at expiration.

In this case, the call will expire worthless, leaving the trader

with the original stock holding and $2,000 in realized option

premium profit.

Finally, if the stock closes below $45 per share at expiration,

the call will expire worthless and the trader will enjoy both the

original stock position and $2,000 in premium profit. However,

the strategy offers no protection below the original cost basis of

$38 per share (the $40 per share purchase price of the stock

minus the $2 in premium received). Table 1 summarizes cov-

ered call performance across a range of stock prices at expira-

tion.

Reviewing the construction of the covered call strategy and its

possible outcomes, it’s easy to see why the strategy is a starting

point for both trader and investors: It’s simple and the risk asso-

ciated with it is both defined and limited. But keep these risks

in mind.

The four realities of the covered call strategyFirst, don’t assume you can consistently pick stocks (or futures)

that have both a high amount of option premium and a stable

price. In fact, the opposite is generally true. Only volatile instru-

ments are likely to have large option premiums. In the case of

stocks, the truly safe, stable, established blue-chip issues are the

ones with relatively low option premiums, and there’s a very

good reason for that. A relatively high implied volatility suggests

the underlying share price is or will soon be extremely volatile

and, therefore, quite risky for a short-term investment strategy.

Second, using a covered call position as a long-term trading

approach usually results in poor performance. A trader invests

in a variety of stocks and, hoping to generate extra income, sells

near at-the-money call options against them. After several

months some of the stocks have gone up, some have gone

down, and some have remained unchanged. The stocks that

went up were, unfortunately, called away. The ones that have

gone down are more than likely well below the option’s strike

price. What remains is a portfolio that is worth far less than

before the covered call strategy was attempted, because a trader

is always forced to sell the best-performing stocks. In short, the

covered call strategy can be a painfully effective way of sorting

out the good from the bad — and keeping the bad.

Third, covered call writing is not necessarily safe — even in a

bull market. First, diagnosing exactly what the market is doing

sometimes involves pure guesswork. For example, when a bear

market ends and a new bull market (or at least an upward

TABLE 1: COVERED CALL PROFIT/LOSS AT EXPIRATION

Covered call trade:Bought 1,000 shares XYZ at $40/share: ($40,000)

Sold 10 XYZ 3-month $45 calls at $2/share: $2,000

Result at expiration

Stock price Stock P/L$45 call

valuePremium received

Net P/L

$100 $60,000 ($55,000) $2,000 $7,000

$75 $35,000 ($30,000) $2,000 $7,000

$65 $25,000 ($20,000) $2,000 $7,000

$47 $7,000 ($2,000) $2,000 $7,000

$40 $0.00 $0.00 $2,000 $2,000

$38 ($2,000) $0.00 $2,000 $0.00

$25 ($15,000) $0.00 $2,000 ($13,000)

$10 ($30,000) $0.00 $2,000 ($28,000)

$0 ($40,000) $0.00 $2,000 ($38,000)

The covered call caps profit in the event of a rising stock price, but offers only

partial downside protection.

continued on p. 42


Trading Strategies

trend) begins, it takes time before it clearly is considered a bull

market. Who exactly decides a bull market is a bull market, any-

way? It can take a lifetime for everyone to agree a long-term

market trend has developed, and by the time unanimity is

achieved, the up move has ended. Second, there will always be

stocks that underperform in a rising market and vice versa. To

base a covered call strategy solely on broad market assumptions

is nothing short of living by faith.

Finally, covered call writing is not as simple as it appears. The

complexity is not so much in the strategy itself but rather in

addressing the two primary challenges the strategy presents:

downside risk is reduced but not eliminated, and potential profit is

capped. Given those challenges, it would appear this strategy

could potentially violate the fundamentals of conservative

options trading, the primary objectives of which are maximizing

income while using leverage to limit portfolio risk. There are

stocks and various circumstances for which the covered call

makes sense, but you must apply the strategy correctly and be

fully aware of its risks.

Three reasons to reconsider the covered call strategy Would you sell a put option naked? A covered call’s risk profile

looks a lot like selling a naked put. The only difference is that

the underlying will not expire. As a result, as the underlying

price begins to fall, agony is prolonged, and losses are increased.

Table 2 compares covered call and naked put positions.


TABLE 2: RISK PROFILE COMPARISON: NAKED PUT VS. COVERED CALL

Stock price = $30 Naked put trade: Covered call trade:

2-month $30 call = $1Buy 1,000 shares at

$30,000

2-month $30 put = $1Sell 10 two-month

$30 puts at $1

Sell 10 two-month $30

calls $1.00

Naked put P/L at expiration

Stock price Stock P/L2-month $30 put

valuePut premium

receivedNet P/L

$80 N/A $0.00 $1,000 $1,000

$70 N/A $0.00 $1,000 $1,000

$60 N/A $0.00 $1,000 $1,000

$50 N/A $0.00 $1,000 $1,000

$40 N/A $0.00 $1,000 $1,000

$30 N/A $0.00 $1,000 $1,000

$20 N/A ($10,000) $1,000 ($9,000)

$10 N/A ($20,000) $1,000 ($19,000)

$0 N/A ($30,000) $1,000 ($29,000)

Covered call P/L at expiration

Stock price Stock P/L2-month $30

call valueCall premium

receivedNet P/L

$80 $50,000 ($50,000) $1,000 $1,000

$70 $40,000 ($40,000) $1,000 $1,000

$60 $30,000 ($30,000) $1,000 $1,000

$50 $20,000 ($20,000) $1,000 $1,000

$40 $10,000 ($10,000) $1,000 $1,000

$30 $0.00 $0.00 $1,000 $1,000

$20 ($10,000) $0.00 $1,000 ($9,000)

$10 ($20,000) $0.00 $1,000 ($19,000)

$0 ($30,000) $0.00 $1,000 ($29,000)

The risk profile of a covered call is essentially the same as that for a naked put.



Why cap the upside? All active traders will at some point

wistfully tell the same story of the great stock that got away

because they bought it to cover a short options position and

the contract was exercised against them. Many professionals

feel picking market direction, option strategies, or stocks is

rocket science; to succeed one needs to be as brainy as a

nuclear physicist. Sorry. Some trading firms these days do, in

fact, employ engineers and people with math and physics

Ph.D.s to build computer models, and many traders like to

think of themselves as brilliant.

But this is not the case. The beautiful notion of random-

ness means that much what goes on in the world of choosing

stocks is nothing more than luck. The reality is we are very poor

decision makers, and to think we can consistently pick success-

ful stocks is foolhardy at best. To be consistent, traders need to

ride winners and cut losses. The problem with the covered call

is there will always be the one stock that got away.

Why sell an option into the hole? “Skew” is the contour, or

the unevenness, in a distribution of values. The negative skew

seen in equity and index options reflects the reality that the

prospect of losing money, maybe a great deal of money, is much

more likely than taking home large gains (Table 3). In a theoreti-

cally precise, ideal, normal distribution, the probability of enjoy-

ing strong gains or suffering large losses is the same. Equity

options, however, typically have a built-in negative skew. Out-

of-the-money call options cost less than equally out-of-the-

money put options, and more often than not, at-the-money

options have implied volatility somewhere in between, for two

reasons. First, the market always insists a trader is more likely to

lose money with any strategy or position. More important, the

investing public joins the investment distress. People panic, or at

least get uneasy — even financial analysts with Ph.D.s in

physics. That means that the public normally sells out-of-the-

money calls and buys of out-of-the-money puts to protect

against potential losses.

Effective call writingVolatile stocks with high option premiums are needed to get the

kind of returns covered call investors are looking for. But that’s

the problem. A low-priced, highly volatile stock is needed to

make this strategy work from a cash-flow perspective. The share

prices for these stocks, however, tend to go up and down, some-

times in stunning fashion. When the share price rises, traders

miss out on profiting from that increase by putting a cap on the

strike price for the options they sold.

In the end, why buy a stock and then cap its upside poten-

tial? When looking at a long-term investment, if a stock’s price

isn’t likely to go up soon, why tie up cash to buy it now?

Keep in mind human frailty. Trading is all about possible loss

that can’t be predicted and controlled. It’s the result of living in

an imperfect world with imperfect people who become greedy

and short-sighted, who panic, who make blunders and then try

to hide them, who try to protect their jobs, who have more con-

fidence than experience, or who have too much experience and

grow complacent. Trading rises from an unpredictable world

with too many human factors to count. And nothing is more

unpredictable in markets and trading than the humans who are

behind it all. �

TABLE 3: TYPICAL IMPLIED STRIKE VOLATILITY FOR 30-DAY SPX CASH-SETTLED OPTIONS

SPX = $1,064.00

Interest rate = 1.75%

Strike price Call value Put value Strike volatility$1,170 $0.80 $106.50 18.50%

$1,150 $1.75 $87.50 19.00%

$1,130 $3.60 $69.40 19.50%

$1,110 $7.25 $53.00 20.00%

$1,090 $12.95 $39.00 20.50%

$1,070 $21.80 $27.80 21.50%

$1,050 $33.35 $19.35 23.00%

$1,030 $47.00 $13.05 24.50%

$1,010 $63.00 $8.85 26.00%

$990 $80.00 $6.00 27.50%

$970 $98.00 $4.00 29.00%Because of negative skew, the out-of-the-money equity call

option cost less than the equally out-of-the-money put option.




All professions develop a

shorthand sooner or

later. Not only does it

facilitate communication

amongst those with a shared knowl-

edge (or ignorance) base, it excludes

outsiders. For years, bond traders

have referred to “the yield curve” as

the spread between 10- and two-year

Treasury notes. The choice of a two-

year note may seem a little odd to

outsiders, which suits the pros just

fine. The two-year represents a one-

year forward rate agreement stacked

on the end of a one-year money mar-

ket strip, and is thus linked to the

cost of rolling a one-year money mar-

ket strip forward for another year.

The global drive toward zero per-

cent interest rates in 2008-2010 com-

pressed the yield on the two-year note

down to a limit where lenders found

resistance. Moreover, as low as the

two-year note yields got (below 55

basis points in July 2010), they could

not compete with short-term strips of

federal funds in what is known as the

overnight index swap (OIS) market. A

three-month OIS hovered below 20

basis points in July 2010. As the

Federal Reserve kept promising to

keep short-term rates low for “an

extended period,” carry traders found

it increasingly attractive to switch

their funding source from the more

traditional two-year note to the three-

month OIS.

We can compare the two yield

curves by their forward rate ratios

(FRR) over time.

This is the rate at

which we can lock

in borrowing for

either 9.75 years

(OIS) or eight years

(two-year note)

starting either three

months or two years

from now, divided

by the 10-year note

yield. The more

these FRRs exceed

1.00, the steeper the

yield curve.

Figure 1 shows

the OIS-based FRR

is both flatter and

smoother than the

FRR2,10. The period-

ADVANCED Concepts

Not all carry trades are alike

Much of the free money in the aftermath of the financial crisis fattened the balance

sheets of investment banks, regional banks, and asset managers rather than

flowing into the economy as job-creating credit.

BY HOWARD L. SIMONS

FIGURE 1: TWO DIFFERENT YIELD CURVES

The OIS-based FRR is both flatter and smoother than the FRR2,10.

simons1210 10/11/10 4:29 PM Page 44

ic outbursts of fear regard-

ing short-term rates tend

to make the highly expec-

tational two-year note

yield jump around vio-

lently, while the OIS rate

stayed anchored by the

Federal Reserve.

Stock market impactThe federal government

made the financial sector

its special project during

and after the financial cri-

sis of 2008. The low bor-

rowing costs it created

allowed banks and other

financial institutions to

rebuild their balance

sheets by borrowing low

at the short end and lend-

ing high at the long end.

Indeed, this engineered

carry trade allowed the

Federal Reserve to mone-

tize Treasury debt by let-

ting member banks buy

Treasuries at auction

rather than having the

Federal Reserve do more

than its $300 billion of

Treasury and $1.25 trillion

of mortgage security pur-

chases financed out of thin

air.

Not that this free

money led to stock market


continued on p. 46

FIGURE 2: FINANCIAL INDUSTRY GROUP TOTAL RETURNS NOV. 20, 2008 – JULY 30, 2010

Of the 12 S&P 1500 financial sector industry groups, the favored groups of the banking sector

underperformed, and the mortgage-finance group most of all.

FIGURE 3: RELATIVE PERFORMANCE & TWO CARRY TRADES: OTHER DIVERSIFIED FINANCIAL SERVICES

For most of 2010, the OIS carry was more important for big banks, suggesting the major banks have

fattened up somewhat on their ability to borrow at a term federal funds rate.

simons1210 10/11/10 4:29 PM Page 45

Advanced Concepts


outperformance. If we

go back to the day in

November 2008 when

Timothy Geithner (pres-

ent at the creation dur-

ing his stay as president

of the Federal Reserve

Bank of New York) was

appointed to be

Secretary of the Treasury

and Citigroup got back-

stopped once again by

the Paulson Treasury,

and compare the total

returns for the 12 indus-

try groups of the S&P

1500 financial sector,

the favored groups of

the banking sector have

underperformed, with

the mortgage-finance

group underperforming

the most (Figure 2,

p. 45).

Which carry, the OIS-

based or the two-year

note-based, was the bet-

ter explanatory variable

for some of these key

banking groups? We can

answer this by modeling

the total return of each

financial group relative

to the S&P 1500

Supercomposite on a

log-linear basis:

ln(Rel.Perf) = f(FRR).

Let’s take the big

banks first (Figure 3,

p. 45). Here the behavior

FIGURE 4: RELATIVE PERFORMANCE & TWO CARRY TRADES: REGIONAL BANKS

The relative performance of regional banks has not been a strong function of either carry trade.

FIGURE 5: RELATIVE PERFORMANCE & TWO CARRY TRADES: INVESTMENT BANKS & BROKERAGES

For the investment bank and brokerage group the OIS carry produces the better statistical fit, but

the investment banks’ reliance on free money started to wear off after the financial crisis began to

dissipate.

simons1210 10/11/10 4:29 PM Page 46


of the industry was so

dominated by external

factors, such as the

question of national-

ization and the repay-

ment of TARP funds,

the actual answer to

which carry trade was

more important must

be, for most of 2010,

the OIS carry. This

does suggest the major

banks have fattened

up somewhat on their

ability to borrow at a

term federal funds

rate.

The answer is different

for the regional banks,

however (Figure 4).

These banks have had

greater exposure to real

estate portfolios and

depend more on their

loan portfolios as

opposed to trading and

fee income than the

major banks do. Their

relative performance is

not at all a strong func-

tion of either carry trade.

What about the invest-

ment bank and brokerage

group (Figure 5)? Here

the answer lies in

between. The OIS carry

clearly produces the bet-

ter statistical fit, but the

investment banks’

reliance on free money

started to wear off after

the financial crisis began

to dissipate. In retrospect,

the best thing that hap-

pened to the investment

continued on p. 48

FIGURE 6: RELATIVE PERFORMANCE & TWO CARRY TRADES: CONSUMER FINANCE

The consumer finance group is almost a mirror-image of the investment bank group.

simons1210 10/11/10 4:29 PM Page 47

Advanced Concepts

banks was their post-2008 status as com-

mercial banks and members of the

Federal Reserve system, which gave them

direct access to federal funds. As an aside,

the pattern for asset managers and custo-

dial banks is similar to that seen for the

investment banks.

The final group we will discuss directly

is consumer finance (Figure 6, p. 47).

This group is almost a mirror image of

the investment banks. Its relative per-

formance turned into a close function of

the FRR in the Treasury market, while its

link to the OIS carry disappeared in early

2010. They are yield curve-dependent, to

be sure, but as they are not a direct player

in the federal funds market, they have to

rely on a different carry trade.

It must be emphasized the variable

being modeled here is the relative stock

market performance of financial groups,

not their profitability. A stock can rise in

the face of poor earnings if there is a rea-

sonable belief business will improve or

the firm will be rescued. Conversely, a

stock with strong earnings can do poorly

if the earnings derive from special cir-

cumstances, such as free money and gov-

ernment protection. The simple fact of

the matter is modeling anything in the

financial sector based on earnings was

impossible during this period; you had to

account for massive operating losses, cap-

ital raised, assets written off, government

capital infusions, all manner of extraordi-

nary items, the elimination of FAS 157

mark-to-market accounting, etc. A stock

price, in contrast, is observable and more

or less beyond dispute.

We can infer from the observations

above of relative stock performance the

carry based on the much shorter and

much more dangerous OIS (because the

funding must be rolled over every three

months instead of every two years)

became more important than the tradi-

tional yield curve spread between the two

and 10-year note. Recent evidence sug-

gests the one-month OIS rate has become

more important now than the three-

month OIS rate. Such dependence on

ever-shorter funding is reminiscent of the

overnight funding employed by the late,

great Bear Stearns and Lehman Brothers.

What did we fail to learn, besides every-

thing?

We can also infer much of the free

money went to fatten the balance sheets

of investment banks, regional banks and

asset managers as opposed to flowing into

the economy as job-creating credit. In the

battle between Main Street and Wall

Street, Wall Street won. Where is the

adage, “Don’t fight the Fed” better

known? �



A stock can rise in

the face of poor

earnings if there is a

reasonable belief

business will improve

or the firm will be

rescued.

““Investing under a constant expectation””Active Trader, November 2010

Will 2010 be remembered as Year

One of America’s “Lost Decade”?

““The risks of risk-free bonds””Active Trader, October 2010

History shows governments cannot

indefinitely abuse their currencies and

creditors through irresponsible poli-

cies.

““How Japan lost more than a decade””Active Trader, September 2010

A warning to countries that adopted

Japanese policies during the 2008-

2009 financial crisis: The end result of

20 years of monetary and fiscal excess

is failure.

““Which stocks and what dollar?””Active Trader, August 2010

Watch as the U.S. dollar index is

deconstructed and the relationship

between currencies and U.S. stocks is

clarified.

““Natural gas and contango limits””Active Trader, July 2010

Explore the relationship between

different contract months in the ener-

gy futures market.

““China starts setting the pace””Active Trader, June 2010

Data is beginning to suggest China is

leading global financial markets, not

reacting to developments elsewhere.

““Financial markets and inflation””Active Trader, May 2010

As we attempt to grapple with the

risk of inflation and its implication for

markets, we find we are working with

outdated concepts.

““Inflation’s macro myths””Active Trader, April 2010

Everything you think you know about

inflation is wrong.

Related readingOther Howard Simons articles:

simons1210 10/11/10 4:29 PM Page 48

This Trading System Lab focuses on determining if

stock splits are useful in identifying stocks likely to

outperform in the future. The system idea is derived

from a 1996 Rice University study by David

Ikenberry, who showed that a group of stocks that had split

between 1975 and 1990 performed significantly better (up to

three years following splits) than a control group of comparable

stocks that had not split. (The study, and this test, uses standard

“forward” splits, not reverse splits.)

This result might seem somewhat surprising since a stock

split does not create value for investors. If a stock splits two for

one (2:1), a corporation doubles the number of outstanding

shares while simultaneously halving the share price. The result-

ing market capitalization is the same before and after the split

(although the costs of this corporate action must be absorbed).

If splitting stock shares doesn’t create value and it costs

money to do it, why split? Because splits reduce share price, it

makes the stock easier (less expensive) to trade round lots (mul-

tiples of 100 shares). More so in the past than today, higher fees

or commissions associated with “odd-lot” transactions could

influence an investor’s decision.

Also, lower stock prices are gener-

ally accompanied by tighter bid-

ask spreads. On the other hand, if

price is too low, the shares will

not attract institutional invest-

ment.

In short, for our purposes it’s

interesting to consider that a com-

pany can essentially manage (per-

haps even optimize) the range of

its stock price to make it attractive

to the maximum number of mar-

ket participants, even though

recent high-flying examples such

as AAPL, BIDU, GOOG, and

PCLN would seem to contradict

the theory that an optimum range

exists.

The system’s basic strategy is

simple: Each month, it gathers a

list of symbols that have split with

a ratio 1.5 (a 3:2 split) or higher.

The list is ranked by current ratio

TRADING System Lab


Profiting with stock splits



• Current ratio BY ROBERT SUCHER JR.

FIGURE 1: EQUITY CURVE

As the worst of the financial crisis approached, the strategy had already increased its

cash position, which served to cushion the blow. The S&P 500 index, by compari-

son, declined approximately 58 percent.

Source for all figures: Fidelity Wealth-Lab Developer 6.0

tsl1210 10/8/10 12:58 PM Page 50


and 4 percent of the account equity is

allocated to buy the top two stocks

each month. Any viable ranking strat-

egy could be used; current ratio was

selected for this test to give priority to

companies with stronger capital posi-

tions.

The maximum holding period is

three years, but once a position has

attained a 25-percent gain on an intra-

day basis, a 5-percent “profit stop” is

placed below the market (i.e., 5-per-

cent above the entry price). While the

strategy doesn’t use stops after open-

ing positions, the idea behind the

profit stop is straightforward: Prevent

a solid gain from turning into a loss.

Note that entering two positions

each month (when candidates exist)

with 4 percent of equity will result in

approximately 100-percent invest-

ment with 25 positions just after the

end of the first two years of trading.

Thereafter, the strategy will naturally

make room for new split candidates as

the oldest positions are exited or

when a profit stop is hit. This phase-

in approach forces you to not commit

to 100-percent exposure all at once,

something that could make it difficult

to stick with the strategy during

volatile periods.

System entry rules:1. EEnntteerr lloonngg on the first trading day

of the month following a split with

a ratio of 1.5 (3:2 split) or higher.

2. When multiple candidates existcontinued on p. 52

FIGURE 2: ANNUAL RETURNS

Having a large exposure during the cyclical bull that started in 2003 helped the

system outperform the market, but the strategy was not entirely immune to the

2008 sell-off.

FIGURE 3: TRADE EXAMPLE

This highly profitable trade came less than two years after a reverse split, which

generally occur in struggling stocks.

tsl1210 10/8/10 12:58 PM Page 51

(the usual case), priority is given to the two stocks with the

highest current ratio.

System exit rules:1. SSeellll after three years or,

2. When the stock achieves a 25-percent gain, set a stop 5 per-

cent above the entry price.

Starting equity: $100,000. Deduct $8 per trade in commis-

sions.

Test data: The system was tested on all S&P 500 component

stocks that were in the index as of Sept. 8, 2010. Dividend-

adjusted price data provided by Yahoo.com (an important man-

ual correction to FHM’s split on 9/10/08 is required). Current


PERIODIC RETURNS

Avg. Sharpe Best Worst % profitable Max. consec. Max. consec. return ratio return return periods profitable unprofitable

Monthly 1.11% 3.89 10.63% -11.66% 56.0 7 9

Quarterly 3.0% 0.80 14.85% -12.31% 65.9 9 3

Annually 12.02% 0.67 40.91% -20.69% 72.7 5 1

Trading System Lab

STRATEGY SUMMARY

Profitability Original Enter before ex-date Trade statistics Original Enter before ex-date

Net profit: $199,948 $264,735 No. trades: 89 85

Net profit: 200% 265% Win/loss: 85.4% 88.2%

Profit factor: 5.54 7.48 Avg. profit/loss: 43.7% 52.2%

Payoff ratio: 1.31 1.75 Avg. hold time, months: 25.1 24.5

Recovery factor: 2.28 2.33 Avg. winners: 58.9% 64.1%

Exposure: 73% 72.2% Avg. hold time (winners): 23 months 23.4 months

Longest flat period: 28 months 36 months Avg. loss: -44.9% -36.7%

Max. DD: -26.5% -27.5% Avg. hold time (losers): 37.3 months 32.4 months

Commissions: $1,328 $1,272 Max consec. win/loss: 26 / 2 40 / 2

LEGENDNet profit — Profit at end of test period, less commission.

Profit factor — Gross profit divided by gross loss.

Payoff ratio — Average profit of winning trades divided by average loss

of losing trades.

Recovery factor — Net profit divided by maximum drawdown.

Exposure — The area of the equity curve exposed to long or short posi-

tions, as opposed to cash.

Max. drawdown (DD) — Largest percentage decline in equity.

Longest flat period — Longest period, in days, the system is between

two equity highs.

No. trades — Number of trades generated by the system.

Win/loss — The percentage of trades that were profitable.

Avg. profit/loss — The average profit/loss for all trades.

Avg. hold time (bars) — The average holding period for 30-minute

bars.

Avg. winning trade — The average profit for winning trades.

Avg. hold time (winners) — The average holding time for winning

trades.

Avg. losing trade — The average loss for losing trades.

Avg. hold time (losers) — The average holding time for losing trades.

Max. consec. win/loss — The maximum number of consecutive win-

ning and losing trades.

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 prof-

itable periods.

Max. consec. unprofitable — The largest number of consecutive

unprofitable periods.

tsl1210 10/8/10 12:58 PM Page 52

ratio data provided by YCharts.com.

Test period: September 2000 to

September 2010.

Test resultsAlthough the 2000s were a lost decade for

the broader index (see the blue buy-and-

hold equity line of the S&P 500 in Figure 1,

p. 50), the split system’s 12-percent annual-

ized gain suggests the 1996 study still has

teeth more than a decade later — and in a

secular bear market, too. The system did

have two losing years (Figure 2, p. 51), but

the drawdown was capped at only -26.5 per-

cent. (The strategy almost seemed to sense

the impending market debacle by increasing

its cash holdings to nearly 35 percent by the

time September 2008 rolled around.)

The system’s attractive annualized profit

was driven primarily by exploiting a small

number of outsized gains. One of the stocks

in this category that was responsible for a

significant portion of the total net profit is

shown in Figure 3 (p. 51). The extraordinary

trajectory of Titanium Metals (TIE), which

split three additional times after entry during

the course of the holding period, produced

26 percent of the system’s total profit (Figure

4). While it’s easy to write off this trade as a

“white swan” event and exclude it from the

results, it’s not at all uncommon to expect at

least one trade like this in a decade. (In the

previous decade, for example, DELL and

YHOO share prices increased by 25 and 30

times, respectively, in the three years after

their splits in the mid- to late 1990s.)

Regardless, even without TIE, the system’s

net profit was still 135 percent, or 9 percent

annualized.

It should be noted the very high win rate,

85.4 percent, is not truly indicative of how

most of the trades would have ended had

they been held for the entire three-year peri-

od. Just more than half the 89 total trades hit

the 5-percent profit stop after attaining a 25-


FIGURE 5: MONTE CARLO ANALYSIS

Monte Carlo analysis of all trade candidates (excluding the original test’s

largest gainer, TIE) gets robust marks, producing a minimum decade-long

return of approximately 66 percent.

FIGURE 4: PROFIT BY INSTRUMENT

The secret to the strategy’s return is collecting huge profits on a relatively

few trades. A single trade in TIE accounted for more than a quarter of the

system’s profits.

continued on p. 54

tsl1210 10/14/10 12:35 PM Page 53

percent open profit, and thus fell into the

“win” category. As is usually the case, the

stop sacrificed some return (0.2 percent

annualized) in exchange for reducing

risk. Without the stop, the total number

of trades was reduced to 68, of which 49

(72 percent) were winners.

Although it’s impossible to be certain

without a rigorous back-test using a rotat-

ing list of S&P 500 components, it’s

unlikely survivorship bias significantly

influenced the test results. A little more

than one-fifth of the stocks in the S&P

500 split during the test period, and

because stocks that split are generally

trading at or near all-time highs, it’s

improbable (but not impossible) these

stocks would disappear soon after a split.

Certainly, splits can and do mark peaks of

optimism in a stock’s trading history:

Look no further than GLW on Oct. 4,

2000, the test’s worst trade, with a -86.10

percent loss. Nonetheless, system risk is

controlled in large part by the money-

management rule of allocating 4 percent

of account equity per symbol; the per-

symbol risk can easily be further reduced

(likely at the expense of performance) to

3 percent or less, which would also

increase the number of positions.

Monte Carlo (MC) analysis provides a

better picture of the system’s dynamics

and potential using all 243 raw trades.

Excluding the TIE trade from the analysis,

1,000 MC simulations generated profits

ranging from 65.9 percent to 226 percent

and maximum drawdowns of -12.7 per-

cent to -46.2 percent. Figure 5 (p. 53)

shows the net profit distribution for the

MC simulations, which identified an aver-

age return of about 137 percent. As

expected, this is somewhat less than the

original test results, which include TIE’s

big gain.

With respect to holding period, Figure

6’s optimization results unambiguously

demonstrate that time works to the strate-

gy’s advantage, supporting the suggestion

from the Rice University study that stocks

that split tend to outperform their coun-

terparts for up to three years.

SuggestionsCompanies typically announce stock

splits three or more weeks prior to the ex-

date (the day the split actually takes

place). Following the announcement and

the initial price bump that often accom-

panies the announcement, the excitement

around the upcoming event tends to draw

prices even higher. Because the system

buys at the beginning of the month after a

split, the test results do not reflect partici-

pation in the announcement phase.

To simulate entering trades during this

phase, we conducted a second test that

allowed the strategy to “peek” at splits

occurring the following month. The addi-

tional columns in the Strategy Summary

table (p. 52) show the net profit increased

another 65 percent, or an additional 2.5

percent annualized.

Dividend-adjusted data was used to

produce the test results, but it is interest-

ing to note dividends produced more

than 7.5 percent of the net profit, or

$15,000. With the long holding periods

required by the system, it could make

sense to give priority to an income-pro-

ducing stock over one that generates no

dividends at all.

Bottom lineAlthough this might not be a strategy that

makes the blood surge in the veins of a

die-hard active trader, a strategy of

buying stocks that split shows evidence it

can beat the market in both secular bull

and bear markets over a three-year time

horizon.

Trading System Lab



Trading System Lab strategies are testedon a portfolio basis (unless otherwisenoted) using Wealth-Lab Inc.’s testing plat-form. If you have a system you’d like to seetested, please send the trading andmoney-management rules [email protected].

Disclaimer: The Trading System Lab isintended for educational purposes only toprovide a perspective on different marketconcepts. It is not meant to recommend orpromote any trading system or approach.Traders are advised to do their ownresearch and testing to determine thevalidity of a trading idea. Past performancedoes not guarantee future results; histori-cal testing may not reflect a system’sbehavior in real-time trading.

FIGURE 6: RETURNS FOR DIFFERENT HOLDING PERIODS

An optimization of the system’s holding period shows that it’s generally bet-

ter to give positions plenty of time to accumulate profits.

tsl1210 10/8/10 12:58 PM Page 54

ACTIVE TRADER •• December 2010 •• www.activetradermag.com 55

MM any people get into trad-ing after years in anotherbusiness, but MichaelMarroquin got in early.

Using money saved from odd jobs, heopened a custodial trading account whenhe was just 15 years old, reading the WallStreet Journal and always dabbling insmall-scale stock positions.

In college, he studied finance, but did-n’t necessarily envision a trading career. “Ialways had the interest, but I neverthought I could make a career out of itbecause I didn’t know it was possible,”Marroquin says. He remembers havingonly one class on technical analysis. “Theprofessor basically said, ‘Don’t learn anyof this — it’s all hogwash,’” he remem-bers.

Now a full-time stock trader who reliessolely on technical analysis, Marroquinsays “You have to rewire yourself tounlearn everything you learned inschool.”

After college and some time in gradu-ate school, Marroquin worked at a finan-cial planning firm and earned severalNASD, insurance and real estate licenses.He studied for the certified financialplanner (CFP) designation but realized itwas not the career he wanted to pursue.He realized he wanted to be a trader.

In 2006 he began focusing on bothtrading and real estate. “I had a day-trad-ing account and churned away in 2006and 2007,” he says. “I thought I wasgoing to be a millionaire really fast. Isoon realized that wasn’t going to hap-pen. I was scattered, all over the place, acomplete rookie.”

At that time, Marroquin also startedworking as a residential real estate broker,helping clients purchase distressed andinvestment properties.

“The two careers work well together,”he notes. “I’m a morning trader and haveto have something else to fill my day. Itactually helps me because if I don’t have

something else to do I will become tiredand less focused,” he explains.

Marroquin turned a net profit in 2008,and recalls he thought he was “invinci-ble” after experiencing his first five-figureweek in January of that year.” Lookingback, he says he now sees “it was toomuch, too quick. I started to realize theimportance of working on myself — thepsychological aspects. That was thebeginning of me realizing what real trad-ing is. It is all a giant self-discoveryprocess.”

Trading method: Marroquin typicallyputs on one to 10 trades in a day. Heusually trades the first two hours of theregular day session, which means he’sdone trading by 8:30 a.m. Pacific Time.“My performance goes down the longerI’m sitting there,” he says.

For the first 30 minutes of the sessionhe puts on scalp trades that last five to 15minutes. For the remainder of his tradingtime, he puts on position trades thatmight last 15 minutes to an hour. Hetrades mostly Nasdaq stocks and moni-tors approximately 25 names using five-minute charts, which is his typical entrytimeframe.

Over the years Marroquin has identi-fied several technical patterns he likes touse, including gaps, opening range break-outs or breakdowns, and trend reversals.“I trade each pattern a specific way andmanage each pattern a specific way,” hesays.

Although he has detailed rules,Marroquin admits for him trading is both“an art and a science” and “a lot is subjec-tive and gut,” especially when it comes tohis exit points.

One setup he trades is to identify atrend on a longer timeframe chart (e.g.,15- or 30-minute, or daily) than look fora pullback on the five-minute timeframe.“I look for the pullback to basicallyexhaust,” he says. “I find that momentwhen that upshot (in a downtrend) is get-ting ready to reverse.”

He uses candlestick “tails” (long wicks)

to enter a trend when a pullback isexhausted. “Tails are your best attempt ata trend reversal,” he says. “I enter on thattail when it is happening.” He also moni-tors eight-period, 20-period, 50-period,and 200-period moving averages. In anuptrend, for example, if he sees a stockthat “dips and touches a rising movingaverage in an uptrend,” he’ll enter on afive-minute chart when a long tailappears. He places a stop-loss at the bot-tom of that tail. When it is clear to himthe tail did, in fact, mark the end of apullback, he’ll add to the long position.

For exits, Marroquin says he sellssurges into resistance areas. He alwaysuses a profit target, which is previoussupport or resistance on the five-minutetime frame.

Became profitable when:Marroquin’s turning point came when hestarted working from a written tradingplan. “I found my niche and wrote atrade-management plan, with all mystop-loss criteria.”

“Typically, I have one losing day amonth and it is always for one reason: Ibreak a rule,” he says. “Why do I break arule? Because I haven’t slept well, or myfocus isn’t in the right place.”

Most important lesson: “It’s a busi-ness,” he says. “You really have to learnyour niche and you have to find yourplace. You have to find something youcan be consistent with.”

Best thing about trading: “Beingyour own boss.”

When not trading: He works out atthe gym five or six times a week. “I workout like it is my job,” he says. “Keepingmyself disciplined both physically andmentally helps keep me more disciplinedin my trading.”

He also spends time with his family onthe beach and brews beer. �

The Face of TRADING Trading setup

Hardware: PC with custom-built dual quad

core extreme processors (4 GB each), 16 GB

RAM, six 22-inch LCD monitors

Software: Lightspeed

Internet: Cable modem

Brokerage: Lightspeed

Name: Michael Marroquin

Age:28

Lives in: San Diego, Calif.

Finding a nicheBY ACTIVE TRADER STAFF

face1210 10/8/10 12:06 PM Page 55

There’s an old and probably apocryphal story that

President Harry Truman once voiced a desire for a one-

armed economist so he would no longer have to hear

the words “On the other hand…”

In the world of theory, nuance and interpretation can be

engaging and even enlightening, but in the markets, with real

money at stake, traders are ill-served by anything less than com-

plete objectivity and specificity. But that is often a rare commod-

ity in market literature, for a variety of reasons.

First, while there may be a profitable discretionary trading

approach that could be reverse engineered and expressed in

quantifiable rules, good luck getting an effective explanation

from the system’s trader who doesn’t think in such terms.

Like musicians who are at the top of their profession but who

are unable to effectively communicate what they do, the more

exceptional the talent, the less likely that talent will translate

into exceptional teaching skills; statistically, superstar athletes

have made sub-par coaches. (Contrast that to Phil Jackson, who

has had far more success as the coach of the Chicago Bulls and

Los Angeles Lakers than he ever had as a player.)

Second, master traders, who are even rarer than superstar

athletes, have no incentive to teach what they do outside of,

perhaps, a select group of employees. While a coaching career is

a logical profession for the athlete who can no longer compete

physically — a way to continue earning a living in the sport

based on one’s knowledge of the game — master traders are

unlikely to be compensated as much from teaching others than

by trading directly for themselves. Hence, Steve Cohen and

George Soros do not conduct trading classes at the local com-

munity college.

Which means aspiring private traders are ultimately on their

own to decipher market action and develop profitable strategies.

It’s a difficult process, and it’s not made easier by subjectivity.

After all, what good is advice from a successful trader if it can’t

be translated into an actionable plan?

Statements such as “Take profits when the move appears to

be losing momentum” or “Where you place your stop is a mat-

ter of personal preference” can mean almost anything — or

nothing. What does “losing momentum” mean? Ten traders

might give 10 different answers. What if your “personal prefer-

ence” is to place your stop at such a level that you risk no more

than $100 on a trade, but the market’s random movement virtu-

ally assures a move of at least $200 over the course of two days?

You will have simply guaranteed you will be stopped out with a

loss of almost every trade you make. In the markets, preferences

and opinions bow to market realities.

In some cases, it’s true, such language may simply be a case

of someone who (understandably) doesn’t want to reveal the

specifics of a good technique, someone who isn’t particularly

good at expressing themselves, or the rare “intuitive” trader who

hasn’t quantified certain aspects of his trading style.

Unfortunately, such language is often used by non-trading

promoters who wish to camouflage their lack of knowledge or

practical trading experience. By using vague language and sub-


TRADING Basics

The subjectivity trap

Vague concepts and ambiguous guidelines are impossible to translate into

real-world trading ideas. Start with market facts and build from there.

BY ACTIVE TRADER STAFF

basics1210 10/8/10 12:13 PM Page 56

jective ideas, they cannot be pinned down and, thus, they can-

not be proven wrong. Such material is typically accompanied by

chart examples that seem to illustrate the approach’s validity, but

which often give an exaggerated impression of its success by

highlighting rare but infrequent best cases while conveniently

ignoring its more common failures.

Consider a claim that the “XYZ pattern is often followed by a

large up move.” First, has the pattern itself been objectively

defined — i.e., could 100 traders read or program the pattern

rules and identify the same price formations, without exception?

Next, how often is “often”? What constitutes “large”? If you were

going to trade this setup, how do you determine when it has

failed and when to get out of the market? These details need to

accompany a trading idea to make it testable and confirmable. If

the pattern can be objectively defined, it’s relatively easy to find

answers to these questions. It might turn out “large up move”

means a 2-percent gain, on average, over the 20 days following

the pattern, and that “often” means 50.5 percent of the time.

These might not be the answers you were looking for, but an

answer that prevents taking a poor trade is better than a non-

answer that puts you in the market without any indication of a

trade’s expectations.

And while every trader or analyst may not be able to provide

answers to those questions, it is in every trader’s best interest to

look for approaches that provide that specificity. Any trader can

use subjective analysis and apply discretion, but having a foun-

dation of objective statistical information — not to mention

years of experience — will make it much more likely for a trader

to operate successfully in the markets. �


basics1210 10/8/10 12:14 PM Page 57

Forms, forms, and more forms. Which form should you

use if you’re a forex trader? Which form is best for secu-

rities traders using the cash method? The different

reporting strategies for the various types of traders make

tax time not so cut-and-dried.

The IRS hasn’t created specialized tax forms for trading busi-

nesses as it has done for just about every other type of business.

For example, other sole-proprietorship businesses report rev-

enues, cost of goods sold, and home-office expenses on

Schedule C. But for traders, only business expenses are reported

on Schedule C. Trading gains and losses are reported on various

forms, depending on the situation (see Table 1).

Securities can be reported on Schedule D (cash method) with

capital losses limited to $3,000 per year; or Form 4797 (Section

475 MTM method) with unlimited business ordinary loss treat-

ment. Futures and forex traders (opting into Section 1256g)

should use Form 6781, unless the futures trader elected Section

475 (in that case, use Form 4797).

In the forex arena, if the trader doesn’t qualify for trader tax

status, by default without an opt-out election he should use line

21 of Form 1040; qualifying traders report on Form 4797. It

can be confusing because the Section 475 MTM and Section

988 elections don’t have tax forms; traders must figure it out on

their own. Don’t forget if you filed a 475 election statement,

existing taxpayers need to follow up with a Form 3115 filing,

too.

With these tax-reporting requirements, the IRS may automati-

cally view a trading business Schedule C as unprofitable even if

it has large net trading gains on other forms; the IRS may audit

sole-proprietorship trading-business tax returns.

Transfer trading gains to Schedule C The most important tax strategy for sole proprietorship business

traders is to transfer some trading gains, if possible, to Schedule

C to zero the income out, but not show a net profit. Showing a

profit could cause the IRS to inquire about a self-employment

(SE) tax, which is otherwise not due for traders who aren’t


THE BUSINESS of Trading

Trader tax reporting strategies

As 2010 comes to a close, it’s high time to begin thinking about your year-end tax return.

BY ROBERT A. GREEN, CPA

bot1210 10/8/10 12:22 PM Page 60

members of a futures or options

exchange.

This special income-transfer strategy

also unlocks the home-office deduction

and Section 179 (100-percent) deprecia-

tion deduction, both of which require

income. This strategy isn’t included on

tax forms or form instructions. It’s an

industry-accepted practice to date

designed to deal with insufficient tax

forms for sole-proprietorship trading

businesses, and it must be carefully

explained in footnotes — another impor-

tant strategy for business traders.

Include footnotes Always include well-written tax-return

footnotes. They should explain trader tax

law, why and how the taxpayer qualifies

for trader tax status, whether he or she

elected Section 475 MTM and other trad-

er-tax reporting treatment, such as the

income-transfer strategy. Part-time traders

use footnotes to explain how they allocate

their time between other activities and

trading.

Separate entities can deflect IRS questions The IRS has been challenging trader tax

status more frequently lately, so it’s wise

to consider establishing a separate entity

— such as an LLC, general partnership,

or S-corporation — for your trading busi-


continued on p. 62

TABLE 1: IN GOOD FORM

Tax form Who should use it

Schedule D Securities traders using the cash method

Form 1040Forex traders using the Section 988 method who

don’t qualify for trader tax status

Form 4797

Securities and futures traders electing section 475

MTM; forex traders who use the Section 988 method

and qualify for trader tax status

Form 6781Futures traders who did not elect Section 475;

forex traders opting into Section 1256

bot1210 10/8/10 12:24 PM Page 61

The Business of Trading


ness. Sole-proprietor business returns

(Schedule C) are very useful after the fact

(meaning after year-end), but forming a

separate legal entity during the year will

make your case stronger. Entities have

several benefits over sole-proprietor

schedule Cs, including the “red-flag” fac-

tor. A partnership tax return Form 1065

shows trading gains, losses, and expenses

on one set of forms, plus the IRS won’t

see the taxpayer’s other activities.

A Form 1065 partnership tax return is

filed for a general partnership or multi-

member LLC choosing to be taxed as a

partnership. Form 1120S is filed for an S-

corporation and a single-member LLC

electing to be taxed as an S-corp. Forms

1065 and 1120S issue Schedule K-1s to

the owners, so taxes are paid at the owner

level rather than at entity level, thereby

avoiding double taxation. Ordinary

income or loss (mostly business expenses)

is summarized on Form 1040 Schedule E

rather than in detail on Schedule C

(hence less IRS attention). Section 179 is

broken out separately on Schedule E, along

with unreimbursed partnership expenses

(UPE) including home-office expenses.

Under the “trading rule,” these are con-

sidered “active” rather than “passive-loss”

activities, so losses are allowed in full

without restriction. Portfolio income is

passed through to Schedule B. Capital

gains and losses are passed through to

Schedule D in summary form, whereas

sole proprietorships must list portfolio

income line by line on the individual tax

return. Pass-through entities draw less IRS

attention than a detailed Schedule C fil-

ing. Net taxes don’t change; they’re still

paid on the individual level.

For more on this topic, see “An in-

depth look at trading entities” (Active

Trader, May 2010).

Don’t botch Schedule D Reporting trading gains and losses prop-

erly can also be a challenge for securities

traders. Failing to follow the tax rules can

lead to IRS questions, jeopardy assess-

ments, and exams.

In 2005, the IRS made a well-publi-

cized effort to clarify Schedule D and D-1

instructions. It reminded taxpayers that

they must list all securities trades line by

line and they could no longer follow prior

industry-accepted summary reporting and

use language including “details available

on request.” The IRS is rightfully con-

cerned that many traders are botching

their tax reporting and sometimes fudging

cost-basis information.

Form 4797 instructions for Section 475

also require line-by-line reporting, but the

IRS didn’t go out of its way to clarify

those rules. With MTM reporting, some

believe summary reporting may still be

acceptable, but play it safe and use line-

by-line reporting if you can. (The best

solution is to use up-to-date software.)

New IRS rules In 2008, the IRS passed a “close the tax

gap” initiative requiring brokerage firms

to significantly improve 1099-B tax infor-

mation reporting for securities transac-

tions starting in 2011. The IRS has been

having problems with securities traders

because many online and direct-access

brokerage firms report minimal required

tax information on 1099-Bs. They only

report proceeds on sales of securities,

ignoring cost basis, short-term vs. long-

term gain or loss, wash sales, and stock-

option sales and purchases.

Some online brokerage firms have been

issuing more complete supplemental

information and tax information reports

(which aren’t sent to the IRS), but often

this information isn’t entirely accurate or

useful for tax-reporting purposes. The

new IRS rules require 1099-Bs to include

adjusted cost basis and short-term vs.

long-term holding periods. Although this

is a big step forward, it doesn’t contain all

the tax information a trader needs. (It still

Related Reading

““Trader tax treatment options””Active Trader, September 2010

It’s not always clear how the IRS

treats the growing number of

instruments traded today. This

review can help.

““Trader tax scams””Active Trader, June 2010

“Dual-entity” trading business

setups might sound attractive, but

these expensive arrangements are

likely to land you in hot water with

the IRS.

““An in-depth look at trading entities””Active Trader, May 2010

When it comes to business entities

for traders, one size doesn’t fit all.

““Are you a trader?””Active Trader, March 2007

Qualifying for trader tax status can

save you money, but IRS rules

regarding it are vague and most

traders miss out on its potential

benefits. Learn how to build a

winning tax position in the eyes

of the IRS.

““Trading business expenses””Active Trader, April 2010

Learn which trading business

expenses are tax deductible,

and which ones aren’t.

Green’s 2010 Trader TaxGuideGreen & Company, Inc.,

January 2010.

This PDF guide includes strategies,

tips, and advice for preparing your

2009 tax return and planning

ahead for the 2010 tax season.

bot1210 10/8/10 12:24 PM Page 62

omits options and also wash sales across

all accounts, for example.)

Futures traders use summary 1099-B

reporting of net (Section 1256 MTM) gain

or loss, and it’s very easy to enter that one

summary number on Form 6781. Forex

is similar only the brokerage should not

issue a 1099.

Claiming trader tax status and preparing returns If you qualify for trader tax status and

haven’t formed a separate legal entity,

you’re classified as a “sole proprietor” or

“unincorporated business.” Report your

trading business expenses on Form 1040

Schedule C (Profit or Loss from Business).

Home-office deductions are reported on

Form 8829. Depreciation and amortiza-

tion are reported on Form 4562. Both

forms require transferring deductions to

Schedule C; income is required for home-

office deductions and Section 179 (100

percent) depreciation. You can use the

transfer strategy mentioned earlier.

Reporting large trading losses on Form 8886 If you have a large trading loss, you may

have to file a Form 8886 (Reportable

Transaction Disclosure Statement). The

instructions mention losses of $2 million

in any single tax year ($50,000 if the loss-

es are from certain foreign currency trans-

actions) or $4 million in any combination

of tax years. If your forex loss is ordinary

under Section 988, the $50,000 rule

applies; however, if your forex transac-

tions have capital gains and loss treat-

ment, the $2 million limitation may

apply.

Tax-preparation programs I recommend using good trading software

to download, match, and properly

account for your active trading in securi-

ties. Some consumer tax-preparation pro-

grams offer trade-import capability, but

many aren’t robust enough for hyperac-

tive traders and some have glitches with

short sales and other trade complications.

Shop carefully for a software program that

will meet your needs as an active trader.

The best trade-accounting programs don’t

handle tax preparation; they only handle

the Schedule D or Form 4797 tax sched-

ules. It’s best to use two different software

programs — one for trade accounting and

one for tax preparation.

It’s also wise to have a trader tax expert

review the results and help reconcile tax

matters with 1099-Bs and more.�This is adapted and updated from Green’s

2010 Trader Tax Guide, available at

www.greencompany.com. For information on

the author, see p. 6.

bot1210 10/11/10 4:05 PM Page 63


1 October construction spending

November ISM manufacturing report

FDD: December crude oil, natural gas, gold, silver, copper, plat-

inum, palladium, corn, wheat, soybean products, and oat futures

(CME); December coffee, cocoa, and cotton futures (ICE)

2 FND: December heating oil and RBOB gasoline futures (CME)

3 October factory orders

November employment report ad ISM non-manufacturing report

LTD: January cocoa and December U.S. dollar index options (ICE)

4

5

6 FND: December live cattle futures (ICE)

7 October consumer credit

8 LTD: December cotton futures (ICE)

9 October wholesale inventories

FDD: December live cattle futures (CME)

10 October trade balance

November federal budget

December University of Michigan consumer sentiment

LTD: January coffee options (ICE)

11

12

13 LTD: December forex futures; December U.S. dollar index futures

(ICE)

14 October business inventories

November PPI and retail sales

FND: December U.S. dollar index futures (ICE)

LTD: December corn, wheat, soybean products, and oat futures

(CME); January sugar options (ICE)

15 September production and capacity utilization

November CPI

FDD: December forex futures; December U.S. dollar index futures

(ICE)

LTD: December cocoa futures (ICE); January crude oil and plat-

inum options (CME)

LEGEND

CME: Chicago Mercantile Exchange

CPI: Consumer price index

ECI: Employment cost index

FDD (first delivery day): The first

day on which delivery of a commodity

in fulfillment of a futures contract can

take place.

FND (first notice day): Also known

as first intent day, this is the first day

on which a clearinghouse can give

notice to a buyer of a futures contract

that it intends to deliver a commodity

in fulfillment of a futures contract. The

clearinghouse also informs the seller.

FOMC: Federal Open Market

Committee

GDP: Gross domestic product

ISM: Institute for Supply Management

LTD (last trading day): The final

day trading can take place in a futures

or options contract.

PMI: Purchasing managers index

PPI: Producer price index

Quadruple witching Friday: A day

where equity options, equity futures,

index options, and index futures all

expire.

TRADING CalendarDecember 2010

S M T W T F S

28 29 30 1 2 3 4

5 6 7 8 9 10 11

12 13 14 15 16 17 18

19 20 21 22 23 24 25

26 27 28 29 30 31 1

calendar1210 10/8/10 12:09 PM Page 64

16 November housing starts

December Philadelphia fed survey

17 November leading indicators

LTD: December index futures; December single stock futures (OC);

January orange juice and cotton options (ICE); January index and

equity options

18

19

20 November Chicago fed national activity index

LTD: January crude oil futures (CME); December coffee futures

(ICE)

21

22 Q3 GDP (third estimate)

November existing home sales

FND: January crude oil futures (CME)

23 November personal income, durable goods, and new home sales

LTD: January soybean and soybean product futures (CME)

24 Markets closed — Christmas holiday

25

26

27

28 December consumer confidence

LTD: January natural gas futures (CME); January heating oil, RBOB

gasoline, gold, silver, and copper options (CME)

29 FND: January natural gas futures (CME)

LTD: December gold, silver, copper, platinum, and palladium

futures (CME)

30 December Chicago PMI

31 FND: January gold, silver, copper, platinum, palladium, and soy-

bean futures (CME)

LTD: January heating oil, RBOB gasoline, and December live cattle

futures (CME)


Report times

Economic Release release time (ET)

GDP 8:30 a.m.

CPI 8:30 a.m.

ECI 8:30 a.m.

PPI 8:30 a.m.

Productivity and costs 8:30 a.m.

Employment 8:30 a.m.

Personal income 8:30 a.m.

Business inventories 8:30 a.m.

Durable goods 8:30 a.m.

Retail sales 8:30 a.m.

Trade balance 8:30 a.m.

Housing starts 8:30 a.m.

Chicago Fed

national activity index 8:30 a.m.

Production

& capacity utilization 9:15 a.m.

Leading indicators 10 a.m.

Consumer confidence 10 a.m.

University of Michigan

consumer sentiment 10 a.m.

Wholesale inventories 10 a.m.

Philadelphia Fed survey 10 a.m.

Existing home sales 10 a.m.

Construction spending 10 a.m.

Chicago PMI report 10 a.m.

ISM report on business 10 a.m.

ISM non-manufacturing report

on business 10 a.m.

New home sales 10 a.m.

Factory orders 10 a.m.

Federal budget 2 p.m.

Consumer credit 3 p.m.

The information on this page is subject to

change. Active Trader is not responsible

for the accuracy of calendar dates beyond

press time.

calendar1210 10/14/10 2:28 PM Page 65

FIGURE 2: PAYROLLS VS. UNEMPLOYMENT RATE

Non-farm payrolls declined in September, but to a lesser extent than inAugust.

Source: Bureau of Labor Statistics Seasonally adjusted

U.S. economic briefing

FIGURE 1: QUARTERLY GDP PERFORMANCE

The third estimate of Q2 GDP extended the previous quarter's contraction.

Source: Bureau of Economic Analysis

FAMILIAR ILLS CONTINUE TO PLAGUE RECOVERY

Meeting: Federal Open Market Committee

Date and time: Sept. 21 at 2:15 p.m.

Summary: The FOMC left key lending rates

unchanged, leaving the target range for the

federal funds rate untouched at 0 to 0.25 per-

cent. Unemployment continues to drag on the

economy, the committee wrote in its release:

“Household spending is increasing gradually,

but remains constrained by high unemploy-

ment, modest income growth, lower housing

wealth, and tight credit.”

The following tables compare the S&P

500’s daily and weekly responses to economic

releases, as well as historical post-announce-

ment behavior since 1997 (or earlier). The

S&P fell 0.3 percent on the date of the com-

mittee’s announcement. Historically, the S&P

has risen nearly 4 percent on FOMC rate

announcement.


FIGURE 3: OVERALL VS. “CORE” INFLATION

Price levels stabilized more during the summer months.

Source: Bureau of Labor Statistics Not seasonally adjusted

THE Economy

REVISED SLIGHTLY HIGHER

ReportGross domestic product for

Q2 2010 (third estimate)

Date/time Sept. 30 at 8:30 a.m.

Actual 1.7%

Previous 1.6%

Consensus 1.6%

S&P 500 reaction

Historical moves since ‘94

Report day -0.31% 0.04%

Five dayslater

1.33% 0.33%

RATE CHANGES

S&P 500 reaction


Report day -0.26% 0.35%

Five dayslater

-0.05% 0.43%

snapshots-1210 10/13/10 10:01 AM Page 66

The S&P rose

throughout

September, but was

somewhat subdued

on release dates.

Source: eSignal

FIGURE 6: MARKET REACTION TO ECONOMIC REPORTS

The S&P remained fairly stable on economic release dates in September andearly October.


FIGURE 4: ISM MANUFACTURING INDEX

Manufacturing sentiment fell slightly in September but remained positive(above 50).

Source: Institute of Supply Management Seasonally adjusted

FIGURE 5: S&P 500

CONSUMER PRICES INCREASE IN AUGUSTReport Consumer Price Index (CPI)


Actual 0.3% (core 0.0%)

Previous 0.3% (core 0.1%)

Consensus 0.2% (core 0.1%)

S&P 500 reaction


Report day 0.08% 0.08%

Five dayslater

0.02% 0.14%

Report Producer Price Index (PPI)


Actual 0.4% (core 0.1%)

Previous 0.2% (core 0.3%)

Consensus 0.3% (core 0.1%)

S&P 500 reaction


Report day -0.04% 0.06%

Five dayslater

0.82% 0.31%

PAYROLLS TAKE A HITReport Employment

Date/time Oct. 8 at 8:30 a.m.

Non-farm payrolls

Actual -94K

Previous -57K

Consensus 0K

Unemployment rate

Actual 9.6%

Previous 9.6%

Consensus 9.7%

S&P 500 reaction



Five dayslater

0.95% -0.09%

ISM FALLS TO 10-MONTH LOWReport ISM manufacturing index

Date/time Oct. 1 at 10 a.m.

Actual 54.4

Previous 56.3

Consensus 54.8

S&P 500 reaction



Five dayslater

1.48% 0.22%

snapshots-1210 10/18/10 9:11 AM Page 67


STOCKS Snapshot as of Oct. 6

1-year 10-day 20-day 60-day Volatility Stock Symbol Volume return move/rank move/rank move/rank ratio/rankPositive one-year performanceLas Vegas Sands LVS 27.04 M 97.48% 9.23% / 40% 14.31% / 45% 50.60% / 86% .34 / 40%

Ford Motor F 57.45 M 84.40% 6.95% / 94% 12.20% / 68% 13.55% / 64% .43 / 80%

SanDisk SNDK 12.97 M 75.71% 4.39% / 33% -0.51% / 3% -18.35% / 66% .20 / 8%

Apple AAPL 19.42 M 52.79% 0.50% / 0% 9.99% / 64% 14.85% / 54% .30 / 28%

Xerox XRX 12.68 M 41.13% 5.29% / 20% 20.00% / 77% 24.45% / 74% .25 / 20%

Altria Group MO 15.28 M 36.86% 2.35% / 37% 4.18% / 33% 14.48% / 80% .17 / 23%

Oracle ORCL 44.88 M 32.60% 1.40% / 0% 14.25% / 71% 16.32% / 81% .13 / 5%

Vale VALE 20.57 M 30.40% 12.66% / 82% 18.70% / 100% 27.94% / 98% .57 / 88%

Texas Instruments TXN 14.80 M 25.65% 11.28% / 94% 19.55% / 97% 11.50% / 67% .69 / 70%

Bristol Myers Squibb BMY 12.15 M 21.71% -2.26% / 33% 1.64% / 27% 7.43% / 54% .25 / 50%

Fifth Third Bancorp FITB 11.22 M 21.22% 1.99% / 15% 5.86% / 56% -10.30% / 56% .30 / 25%

Corning GLW 14.83 M 18.60% 6.99% / 70% 9.94% / 57% 3.55% / 41% .38 / 37%

The Home Depot HD 11.40 M 18.04% 2.78% / 15% 7.78% / 66% 10.82% / 33% .13 / 17%

Verizon Communications VZ 17.75 M 14.40% 2.99% / 5% 9.52% / 55% 24.06% / 98% .21 / 15%

Merck MRK 12.09 M 14.12% 0.14% / 0% 3.35% / 58% 1.54% / 22% .21 / 23%

Comcast CMCSA 22.47 M 13.48% -1.06% / 57% -0.89% / 7% -6.62% / 95% .43 / 25%

News Corp. NWSA 17.83 M 11.68% -0.15% / 0% 2.28% / 14% 1.79% / 20% .50 / 97%

EMC EMC 25.44 M 10.67% -5.11% / 100% -1.94% / 10% -1.45% / 32% .61 / 63%

AT&T T 25.84 M 10.33% 0.10% / 0% 4.49% / 36% 14.71% / 86% .16 / 5%

Marvell Technology Group MRVL 16.11 M 10.20% -4.65% / 100% -5.58% / 19% -5.36% / 14% .29 / 78%

Lowe's LOW 11.58 M 9.10% 4.18% / 53% 5.25% / 56% 7.14% / 33% .21 / 68%

American Express AXP 12.87 M 8.69% -11.15% / 100% -5.12% / 52% -13.92% / 100% 1.13 / 100%

QUALCOMM QCOM 18.19 M 7.72% 3.12% / 10% 9.12% / 52% 24.20% / 80% .12 / 3%

General Electric GE 57.27 M 4.19% 2.42% / 33% 7.64% / 46% 11.11% / 53% .30 / 73%

Taiwan Semiconductor TSM 14.53 M 3.83% 5.10% / 79% 8.19% / 90% 1.58% / 38% .75 / 98%

Pfizer PFE 42.33 M 3.35% 0.23% / 5% 4.23% / 15% 16.70% / 77% .17 / 35%

Negative one-year performance

Research In Motion RIMM 19.77 M -30.08% 0.80% / 0% 7.48% / 57% -13.64% / 21% .16 / 32%

Nokia NOK 24.79 M -27.29% 5.33% / 29% 7.94% / 26% 22.31% / 92% .39 / 93%

Petroleo Brasileiro SA PBR 23.12 M -26.03% 1.09% / 15% -3.97% / 23% -1.43% / 3% .41 / 87%

The Charles Schwab SCHW 12.08 M -25.28% 4.55% / 80% 3.79% / 33% -2.33% / 9% .23 / 80%

Adobe Systems ADBE 15.94 M -25.05% -3.52% / 0% -12.21% / 79% -8.47% / 37% .18 / 33%

NVIDIA NVDA 20.92 M -22.94% -5.36% / 100% 4.46% / 18% -1.28% / 4% .37 / 92%

Bank of America BAC 145.49 M -22.74% -0.22% / 0% 0.15% / 5% -14.55% / 53% .16 / 32%

Seagate Technology STX 14.09 M -22.47% 3.51% / 30% 4.08% / 38% -20.54% / 28% .13 / 53%

Morgan Stanley MS 13.99 M -20.54% 1.72% / 20% -2.27% / 32% -1.09% / 11% .28 / 32%

Yahoo YHOO 23.51 M -17.41% 3.42% / 58% 5.60% / 53% -6.44% / 18% .19 / 45%

Dell DELL 25.61 M -16.43% 6.79% / 94% 5.42% / 48% 0.15% / 0% .45 / 100%

Alcoa AA 25.26 M -13.80% 5.73% / 40% 11.74% / 69% 12.45% / 70% .29 / 67%

Hewlett-Packard HPQ 27.32 M -12.31% 3.01% / 50% 4.97% / 50% -12.89% / 57% .21 / 50%

JPMorgan Chase JPM 34.49 M -11.92% -0.10% / 11% 1.99% / 19% -1.43% / 11% .39 / 50%

Applied Materials AMAT 22.69 M -10.32% 5.11% / 47% 10.87% / 87% -6.31% / 22% .19 / 20%

Wells Fargo WFC 36.44 M -9.47% 1.90% / 0% 4.28% / 37% -5.87% / 17% .26 / 42%

Symantec SYMC 13.82 M -9.18% 0.13% / 0% 2.40% / 3% -0.33% / 2% .28 / 57%

Exxon Mobil XOM 20.87 M -7.40% 4.05% / 95% 5.25% / 74% 7.61% / 88% .50 / 100%

Cisco Systems CSCO 57.11 M -5.79% 2.91% / 38% 8.04% / 70% -3.42% / 9% .18 / 23%

Microsoft MSFT 61.06 M -4.83% -0.73% / 22% 2.09% / 17% -2.79% / 9% .21 / 63%

Intel INTC 67.49 M -2.87% 1.58% / 19% 7.88% / 83% -8.09% / 45% .15 / 5%

eBay EBAY 14.32 M -1.45% 0.45% / 0% -0.49% / 11% 16.37% / 75% .18 / 40%

US Bancorp USB 13.09 M -0.84% -0.49% / 0% 0.40% / 0% -8.27% / 45% .33 / 58%

United States Steel X 12.93 M -0.09% 1.36% / 0% -8.34% / 90% 3.50% / 13% .26 / 52%

Active Trader’s Snapshot tables summarize the trading activity in the most actively traded stocks, ETFs, and futures. The information does NOT con-stitute trade signals. It is intended only to provide a synopsis of each market’s liquidity, direction, and levels of momentum and volatility.

snapshots-1210 10/8/10 4:07 PM Page 68

Leverage: “2x” = double leverage; “3x” =triple leverage.

Volume: 30-day average daily volume.

1-year return: The percentage price movefrom the close one year ago (250 trading days)to today’s close.

10-day move: The percentage price movefrom the close 10 days ago to today’s close.



The “Rank” fields for each time window (10-daymoves, 20-day moves, etc.) show the percentilerank of the most recent move to a certain num-ber of the previous moves of the same size andin the same direction. For example, the “Rank”for 10-day move shows how the most recent10-day move compares to the past twenty 10-day moves; for the 20-day move, the “Rank”field shows how the most recent 20-day movecompares to the past sixty 20-day moves; for the60-day move, the “Rank” field shows how themost recent 60-day move compares to the pastone-hundred-twenty 60-day moves. A reading

of 100 percent means the current reading islarger than all the past readings, while a readingof 0 percent means the current reading is small-er than all previous readings. These figures pro-vide perspective for determining how relativelylarge or small the most recent price move is com-pared to past price moves.

Volatility ratio/rank: The ratio is the short-term volatility (10-day standard deviation ofprices) divided by the long-term volatility (100-day standard deviation of prices). The rank isthe percentile rank of the volatility ratio overthe past 60 days.

1-year 10-day 20-day 60-day Volatility

ETF Symbol Leverage Inverse Volume return move/rank move/rank move/rank ratio/rank

Positive one-year performanceUltra QQQ ProShares QLD 2x 6.48 M 31.13% 2.21% / 0% 13.68% / 53% 17.57% / 52% .23 / 15%

iShares DJ US Real Est. Index Trust IYR 10.86 M 29.67% 0.65% / 0% 1.69% / 14% 8.54% / 52% .28 / 12%

iShares Silver Trust SLV 11.61 M 29.51% 9.55% / 100% 16.36% / 98% 27.33% / 100% .46 / 50%

SPDR Gold Trust GLD 12.56 M 27.18% 4.45% / 100% 7.42% / 100% 11.36% / 87% .46 / 92%

S&P Select Cons. Disc. SPDR Fund XLY 7.20 M 21.72% 2.51% / 25% 5.81% / 45% 8.46% / 44% .27 / 32%

S&P Select Industrial SPDR Fund XLI 17.05 M 20.85% 3.03% / 42% 6.10% / 49% 10.38% / 56% .32 / 43%

Market Vectors Gold Miners ETF GDX 9.28 M 19.37% 3.10% / 42% 8.29% / 83% 15.34% / 77% .31 / 33%

S&P Select Retail SPDR Fund XRT 10.51 M 19.24% 3.50% / 5% 8.89% / 68% 11.84% / 41% .24 / 35%

iShares MSCI Hong Kong Index EWH 6.68 M 18.38% 4.80% / 30% 11.21% / 86% 19.99% / 92% .27 / 38%

Vanguard Emer. Markets Stock ETF VWO 12.85 M 18.37% 5.77% / 80% 10.35% / 89% 15.38% / 91% .36 / 62%

PowerShares QQQ Trust QQQQ 77.13 M 16.55% 1.11% / 0% 6.44% / 53% 8.60% / 55% .21 / 7%

ProShares Ultra S&P 500 SSO 2x 16.05 M 16.46% 4.44% / 37% 11.15% / 57% 11.70% / 49% .33 / 67%

iShares MSCI Emerging Market EEM 55.20 M 15.75% 5.71% / 75% 10.08% / 84% 14.91% / 90% .38 / 68%

iShares Russell 2000 Index Trust IWM 59.89 M 13.01% 4.21% / 58% 8.08% / 69% 6.87% / 34% .43 / 78%

Diamonds Trust DIA 6.46 M 12.11% 2.21% / 20% 5.57% / 64% 5.82% / 56% .28 / 25%

iShares MSCI Taiwan Index EWT 10.50 M 11.35% 3.94% / 45% 9.40% / 84% 14.04% / 92% .21 / 27%

Semiconductor HOLDRS SMH 14.38 M 11.12% 6.05% / 78% 10.37% / 83% -1.10% / 10% .40 / 52%

S&P Select Technology SPDR Fund XLK 10.54 M 11.02% 2.07% / 5% 6.97% / 54% 6.33% / 53% .24 / 12%

iShares MSCI Brazil Index Fund EWZ 17.49 M 9.69% 7.14% / 75% 11.56% / 84% 17.50% / 89% .44 / 90%

S&P Select Consumer Staples SPDR XLP 6.95 M 9.16% 1.36% / 26% 3.41% / 58% 5.30% / 66% .24 / 30%

S&P Select Utilities SPDR Fund XLU 6.69 M 9.01% 1.18% / 43% 1.21% / 20% 5.63% / 58% .19 / 13%

S&P Depository Receipts SPY 199.01 M 8.84% 2.30% / 37% 5.09% / 55% 5.81% / 51% .32 / 58%

S&P Select Materials SPDR Fund XLB 8.91 M 7.79% 3.19% / 53% 3.86% / 33% 10.13% / 73% .29 / 50%

iShares Barclays 20+ Year T-Bond TLT 8.53 M 7.55% 0.91% / 11% 0.67% / 8% 7.35% / 53% .21 / 17%

Small Cap Bull 3x Shares TNA 3x 13.70 M 7.50% 13.38% / 58% 25.76% / 70% 16.93% / 24% .42 / 100%

S&P Select Health Care SPDR Fund XLV 6.74 M 7.06% 0.72% / 0% 5.04% / 69% 4.90% / 64% .25 / 30%

iShares FTSE/Xinhua China 25 FXI 17.18 M 3.53% 4.81% / 80% 8.64% / 92% 8.27% / 84% .49 / 72%

S&P Select Energy SPDR Fund XLE 15.16 M 3.06% 6.41% / 85% 7.58% / 77% 9.08% / 80% .61 / 100%

iShares MSCI EAFE Index Trust EFA 19.46 M 2.50% 4.08% / 50% 8.68% / 73% 11.62% / 85% .29 / 53%

iShares MSCI Japan Index Fund EWJ 18.52 M 1.91% 3.68% / 79% 4.54% / 82% 5.63% / 88% .60 / 100%

Negative one-year performanceSmall Cap Bear 3X Shares TZA 3x yes 25.13 M -58.00% -13.89% / 60% -23.64% / 67% -28.06% / 60% .31 / 12%

United States Natural Gas Fund UNG 22.52 M -48.55% -5.80% / 55% -3.14% / 14% -16.04% / 26% .27 / 33%

Large Cap Bear 3x Shares BGZ 3x yes 7.04 M -43.00% -7.45% / 32% -16.48% / 59% -21.63% / 62% .22 / 7%

ProShares UltraPro Short S&P500 SPXU 3x yes 7.97 M -41.38% -7.26% / 32% -16.14% / 59% -20.87% / 60% .22 / 7%

UltraShort Russell 2000 ProShares TWM 2x yes 7.97 M -39.71% -9.33% / 60% -16.17% / 68% -18.12% / 54% .35 / 20%

Financial Bear 3x Shares FAZ 3x yes 47.89 M -36.80% -5.87% / 21% -11.47% / 48% -9.02% / 27% .29 / 23%

UltraShort QQQ ProShares QID 2x yes 13.64 M -36.74% -2.88% / 0% -13.02% / 56% -18.26% / 68% .16 / 2%

UltraShort 20+ Year Tr. ProShares TBT 2x yes 10.62 M -29.66% -2.88% / 30% -2.92% / 15% -17.76% / 58% .14 / 20%

UltraShort S&P 500 ProShares SDS 2x yes 33.11 M -27.71% -4.83% / 32% -11.00% / 59% -13.90% / 60% .24 / 8%

UltraShort Financials ProShares SKF 2x yes 6.56 M -21.84% -4.09% / 29% -7.80% / 46% -4.39% / 21% .32 / 27%

Financial Bull 3x Shares FAS 3x 36.01 M -19.10% 4.13% / 8% 9.45% / 38% -3.73% / 7% .31 / 92%

ProShares Ultra DJ-UBS Crude Oil UCO 2x 6.40 M -5.19% 23.56% / 100% 18.61% / 86% 10.04% / 55% 1.10 / 100%

S&P Select Financials SPDR Fund XLF 79.97 M -2.58% 1.45% / 17% 2.72% / 28% -1.14% / 8% .37 / 70%

United States Oil Fund USO 9.67 M -1.09% 11.36% / 100% 9.47% / 85% 4.43% / 53% 1.04 / 100%


ETF Snapshot as of Oct. 6



Open 10-day 20-day 60-day Volatility

Market Symbol Exchange Volume interest move/rank move/rank move/rank ratio/rank

E-Mini S&P 500 ES CME 1.96 M 2.44 M 2.30% / 32% 5.14% / 58% 6.06% / 52% .31 / 57%

10-yr. T-note TY CME 1.26 M 1.57 M 1.48% / 33% 2.33% / 65% 4.80% / 77% .24 / 60%

5-yr. T-note FV CME 469.4 866.3 0.80% / 33% 1.57% / 73% 2.63% / 65% .21 / 50%

Crude oil CL CME 350.6 283.4 11.40% / 100% 11.46% / 91% 7.88% / 46% .89 / 100%

EUR/USD EC CME 318.3 190.1 4.07% / 45% 9.50% / 100% 9.66% / 96% .39 / 73%

E-Mini Nasdaq 100 NQ CME 310.6 330.8 1.25% / 0% 6.75% / 57% 8.78% / 56% .19 / 0%

30-yr. T-bond US CME 347.1 622.7 1.54% / 30% 1.66% / 30% 7.28% / 64% .20 / 33%

2-yr. T-note TU CME 204.4 660.9 0.05% / 40% 0.11% / 96% 0.15% / 39% .22 / 72%

Eurodollar* ED CME 210.8 810.0 0.09% / 38% 0.23% / 60% 0.66% / 43% .07 / 35%

Mini Dow YM CME 129.6 75.3 2.18% / 20% 4.95% / 62% 6.01% / 61% .27 / 27%

E-Mini Russell 2000 TF CME 141.1 362.8 4.45% / 65% 8.27% / 73% 7.17% / 27% .45 / 78%

JPY/USD JY CME 133.2 122.5 1.93% / 30% 1.31% / 26% 6.77% / 58% .19 / 37%

Gold 100 oz. GC CME 110.5 399.3 4.30% / 95% 7.17% / 100% 11.06% / 84% .42 / 92%

Corn C CME 185.9 694.2 -3.29% / 50% 5.62% / 20% 30.17% / 73% .30 / 20%

GBP/USD BP CME 108.5 95.6 1.44% / 47% 2.63% / 47% 4.82% / 41% .14 / 5%

AUD/USD AD CME 86.2 117.9 2.34% / 25% 5.62% / 69% 10.84% / 87% .18 / 8%

Natural gas NG CME 106.9 154.5 -2.55% / 36% 1.34% / 20% -11.23% / 17% .23 / 28%

CAD/USD CD CME 85.1 89.7 2.09% / 56% 2.51% / 63% 2.15% / 47% .55 / 75%

Soybeans S CME 73.5 260.9 -2.43% / 67% 1.28% / 16% 6.73% / 48% .52 / 80%

Sugar SB ICE 61.8 263.9 1.51% / 11% 10.10% / 37% 37.10% / 61% .24 / 32%

Wheat W CME 44.7 232.6 -8.53% / 82% -7.43% / 50% 19.85% / 33% .20 / 30%

CHF/USD SF CME 40.8 52.8 2.71% / 60% 5.43% / 89% 9.91% / 66% .17 / 33%

Soybean oil BO CME 20.4 54.7 2.12% / 5% 6.06% / 46% 15.15% / 85% .21 / 5%

Heating oil HO CME 46.4 60.5 9.53% / 100% 10.86% / 77% 12.72% / 77% .89 / 98%

RBOB gasoline RB CME 43.9 63.3 13.38% / 100% 11.16% / 96% 3.54% / 28% .95 / 100%

Silver 5,000 oz. SI CME 35.1 90.1 9.44% / 95% 15.16% / 98% 26.21% / 98% .42 / 35%

S&P 500 index SP CME 26.6 267.8 2.29% / 32% 5.13% / 58% 6.06% / 52% .31 / 57%

E-Mini S&P MidCap 400 ME CME 30.3 90.1 2.93% / 40% 6.14% / 57% 7.24% / 44% .35 / 50%

Copper HG CME 25.9 86.3 5.27% / 75% 7.21% / 63% 24.37% / 95% .20 / 35%

Soybean meal SM CME 17.0 38.4 -3.40% / 38% -2.09% / 60% -0.40% / 10% .68 / 98%

MXN/USD MP CME 29.9 91.3 1.59% / 32% 3.91% / 88% 1.98% / 27% .33 / 58%

U.S. dollar index DX ICE 20.7 25.0 -3.05% / 50% -6.36% / 100% -7.15% / 96% .29 / 64%

Coffee KC ICE 12.1 89.9 -2.45% / 0% -9.77% / 100% 5.98% / 24% .26 / 60%

Crude oil e-miNY QM CME 12.7 4.9 11.41% / 100% 11.45% / 91% 7.87% / 49% .93 / 100%

Nikkei 225 index NK CME 10.5 29.8 2.48% / 19% 6.53% / 89% 0.57% / 11% .31 / 85%

Live cattle LC CME 19.4 87.1 -1.38% / 47% -2.47% / 58% 4.29% / 14% .23 / 43%

Lean hogs LH CME 14.1 55.2 -4.35% / 86% -1.34% / 21% -5.14% / 54% .51 / 85%

Cocoa CC ICE 8.6 65.5 -0.65% / 11% 0.29% / 0% -9.89% / 82% .35 / 38%

NZD/USD NE CME 8.1 23.2 2.20% / 65% 3.61% / 67% 4.62% / 73% .32 / 47%

Mini-sized gold YG CME 3.4 4.9 4.45% / 100% 7.47% / 100% 11.39% / 87% .45 / 92%

E-Mini EUR/USD ZE CME 4.3 3.5 4.07% / 45% 9.50% / 100% 9.66% / 96% .39 / 73%

Fed Funds** FF CME 3.0 61.0 0.02% / 90% 0.02% / 22% 0.07% / 3% .08 / 85%

Mini-sized silver YI CME 2.0 2.6 9.56% / 100% 16.33% / 98% 26.98% / 99% .45 / 46%

Feeder cattle FC CME 0.9 5.0 -0.05% / 5% -2.59% / 74% -3.89% / 95% .42 / 53%

Natural gas e-miNY QG CME 1.8 2.8 -2.52% / 45% 1.31% / 27% -11.25% / 22% .23 / 25%

Nasdaq 100 ND CME 1.5 13.7 1.25% / 0% 6.75% / 54% 8.78% / 56% .19 / 0%

Dow Jones Ind. Avg. DJ CME 0.6 5.1 2.18% / 20% 5.62% / 72% 6.01% / 61% .27 / 27%

Note: Average volume and open-interest data includes both pit and side-by-side electronic contracts (where applicable). Price activity for CME

futures is based on pit-traded contracts. Volume figures are for the most-active contract month in a particular market and may not reflect total

volume for all contract months. *Average volume and open interest based on highest-volume contract (September 2011). **Average volume

and open interest based on highest-volume contract (February 2011).

This information is for educational purposes only. Active Trader provides this data in good faith, but it cannot guarantee its accuracy or timeliness.

Active Trader assumes no responsibility for the use of this information. Active Trader does not recommend buying or selling any market, nor does it solicit orders to buy or

sell any market. There is a high level of risk in trading, especially for traders who use leverage. The reader assumes all responsibility for his or her actions in the market.

FUTURES Snapshot as of Oct. 6


Trading Strategies continued from p. 19


one early signal on Jan. 27. Patterns 1 and 2 issues repeated

early signals — and, it must be noted, triggered on May 5, the

day before the flash crash. Although on a closing basis, the loss-

es would not have been disastrous, these trades would have

experienced massive intraday drawdowns on May 6.

Risk control and money managementIt is important that no attempt was made to optimize the pat-

tern, and no stop-losses or other risk controls were introduced

in testing. It is safe to assume that adding risk controls would

have reduced the patterns’ drawdowns, while also curbing their

winning percentages and total profits.

Also, the money management approach used in the test — a

fixed-dollar trade size — will exacerbate the drawdowns during

prolonged and severe market

declines. As price drops dra-

matically — as it did in late

2008, for example — more

shares are purchased per sig-

nal because of the lower

stock price. During a brief

and not-too-severe decline,

this will not present too much

of a problem, but when an

initial decline is followed by

an even greater decline, trig-

gering repeated signals and

ever-larger trades, the losses

will mount with almost geo-

metric speed. This drawback

could be countered by using a

different approach, such as

adjusting position size according to account equity: As the

account equity declines, the number of shares purchased will

decrease. However, this will also likely mitigate the patterns’

obvious tendency to bounce back quickly from losses because

trade sizes will be smaller when the market is rebounding.

The lesson of the pattern: Over time, sharp sell-offs such as

those identified by this price model are buying opportunities in

the S&P. Taking advantage of them, however, requires both

financial wherewithal and psychological fortitude. Pattern 3 had

the smallest drawdowns because it entered much more selective-

ly than the other pattern variations — it tended to avoid enter-

ing too early in most down moves, and when it did, it was less

likely to enter repeatedly, which resulted in relatively smaller

trade sizes for losing trades. Although it ended the analysis peri-

od with the lowest total equity, its return was far superior on a

risk-adjusted basis.

Pattern 3’s greater stability and relative outperformance also

hint at the benefits of incorporating comparisons of non-consec-

utive price bars. Finally, traders who don’t want to sit through

huge losses during market down swings must incorporate stop-

losses and appropriate money management. �

FIGURE 6: SIGNAL COMPARISON

Pattern 3’s superior performance can be attributed to its more selective entries. The first

two pattern variations often entered repeatedly as the market continued to decline,

resulting in exceptionally large drawdowns.

KC Go to “Key concepts” on

p. 78 for more information about:

• Average and median

• Variance and standard

deviation


The Evolution of

Technical Analysis:

Financial Prediction

from Babylonian

Tablets to

Bloomberg

Terminals

By Andrew W. Lo

and Jasmina

Hasanhodzic

Hardcover, 212 pages

Bloomberg Press/Wiley

A history of the evolution of technical

analysis from ancient times (such as com-

modity pricing methods in Babylon) to

the Internet age. The authors explore the

history of technical analysis, including:

the origins of the field, Eastern practices

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In this eBook,

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Manager Profiles

(3rd Edition)

By Daniel A.

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Paperback, 208 pages

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John Wiley & Sons

This is the latest edition of the book on

hedge fund basics, updated to reflect

today’s “post-crisis industry.” In the wake

of Ponzi schemes and insider trading

scandals — as well as the loss of billions

of dollars in assets under management

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agers and investors. It also profiles 10

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The Universal

Principles of

Successful Trading:

Essential Knowledge

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Markets

By Brent Penfold

Hardcover

256 pages

Wiley

The book’s goal is to clearly articulate

“trading principles that distinguish the

winners from the losers.” In reviewing the

commonalities of consistently profitable

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piece of advice that emphasizes an essen-

tial element of the universal principles.�


Trader’s Bookshelf is a forum to announce

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from the Active Trader Magazine Group. E-

mail press releases to editorial@activetrader-

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At the money (ATM): An option whose strike price is identical (orvery close) to the current underlying stock (or futures) price.

Average and median: The mean (or average) of a set of values isthe sum of the values divided by the number of values in theset. If a set consists of 10 numbers, add them and divide by 10to get the mean.A statistical weakness of the mean is that it can be distorted byexceptionally large or small values. For example, the mean of 1,2, 3, 4, 5, 6, 7, and 200 is 28.5 (228/8). Take away 200, and themean of the remaining seven numbers is 4, which is much morerepresentative of the numbers in this set than 28.5.

The median can help gauge how representative a mean reallyis. The median of a data set is its middle value (when the set hasan odd number of elements) or the mean of the middle two ele-ments (when the set has an even number of elements). Themedian is less susceptible than the mean to distortion fromextreme, non-representative values. The median of 1, 2, 3, 4, 5,6, 7, and 200 is 4.5 ((4+5)/2), which is much more in line withthe majority of numbers in the set.

Call option: An option that gives the owner the right, but not theobligation, to buy a stock (or futures contract) at a fixed price.

Compound annual growth rate (CAGR): The annualized gain repre-sented by an investment’s total return over a certain period.Unlike the average return, CAGR represents the annual gain ofan investment if it had increased at a steady rate during the timeperiod.

Suppose you bought $1,000 in stock four years ago, and theinvestment rose to $1,100 in the first year, $1,150 in the secondyear, $1,275 in the third year, and $1,425 in the fourth year.

The CAGR formula is:

(Ending value / beginning value)(1/no. of years) – 1

In this case:

($1,425 / $1,000)(1/4) – 1= 1.425(1/4) – 1= 1.0926 – 1 = .0926, or 9.26%

Current ratio: A company’s current assets divided by its currentliabilities. It is used as a rough measure of a company’s financialhealth by determining its ability to pay off short-term liabilitieswith short-term assets, such as cash and inventories. A currentratio below 1.00 means the company’s liabilities outweigh itsassets.

Exponential moving average (EMA): A type of weighted moving aver-age that uses the following formula:

EMA = SC * price + (1 - SC) * EMA(yesterday)where:SC is a “smoothing constant” between 0 and 1, andEMA(yesterday) is the previous day’s EMA value.

You can approximate a particular SMA length for an EMA by

using this formula to calculate the equivalent smoothing con-stant:

SC = 2/(n + 1)where:n = the number of days in a simple moving average ofapproximately equivalent length.

For example, a smoothing constant of 0.095 creates an expo-nential moving average equivalent to a 20-day SMA (2/(20 + 1)= 0.095). The larger n is, the smaller the constant, and thesmaller the constant, the less impact the most recent price actionwill have on the EMA. In practice, most software programs allowyou to simply choose how many days you want in your movingaverage and select either simple, weighted, or exponential calcu-lations.

Fibonacci series: A number progression in which each successivenumber is the sum of the two immediately preceding it: 1, 2, 3,5, 8, 13, 21, 34, 55, and so on. As the series progresses, theratio of a number in the series divided by the immediately pre-ceding number approaches 1.618.

A basic application is to calculate likely price targets. Forexample, if a stock broke out of a trading range and rallied from25 to 55, potential retracement levels could be calculated bymultiplying the distance of the move (30 points) by Fibonacciratios –– say, 0.382, 0.50, and 0.618 –– and then subtractingthe results from the high of the price move. In this case, retrace-ment levels of 43.60 [55 - (30 * 0.38)], 40 [55 - (30 * 0.50)],and 36.40 [55 - (30 * 0.62)] would result. The most commonlyused ratios are 0.382, 0.50, 0.618, 0.786, 1.00, 1.382, and1.618. Depending on circumstances, other ratios, such as 0.236and 2.618, are used.

While Fibonacci retracements are used to calculate the possi-ble partial correction levels of a previous price move (i.e., areversal of up to 100 percent of a previous price swing),Fibonacci extension levels are used to extrapolate moves in thesame direction as a previous price swing — for example, pro-jecting a target for a new upswing that represents a 161.8-per-cent gain from a certain price level based on the size of the pre-vious upswing.

In the money (ITM): A call option with a strike price below theunderlying instrument’s current price, or a put option with astrike price above the underlying instrument’s current price.

Naked (uncovered) puts: Selling put options to collect premiumthat contains risk. If the market drops below the short put’sstrike price, the holder may exercise it, requiring you to buystock at the strike price (i.e., above the market).

Out of the money (OTM): A call option with a strike price above theprice of the underlying instrument, or a put option with a strikeprice below the underlying instrument’s price.

Premium: The price of an option.

Stop-and-reverse (SAR): A trading system that is always in themarket, liquidating long trades and going short when a sell sig-

Key CONCEPTS



nal occurs and covering shorts and going long when a buy sig-nal occurs.

Strike (“exercise”) price: The price at which an underlying instru-ment is exchanged upon exercise of an option.

True range: A measure of price movement or volatility thataccounts for the gaps that occur between price bars. This calcu-lation provides a more accurate reflection of the size of a pricemove over a given period than the standard range calculation,which is simply the high of a price bar minus the low of a pricebar. The true range calculation was developed by Welles Wilderand discussed in his book New Concepts in Technical TradingSystems (Trend Research, 1978).

True range can be calculated on any time frame or price bar— five-minute, hourly, daily, weekly, etc. Using daily price barsas an example, true range is the greatest (absolute) distance ofthe following:

1. Today’s high and today’s low.2. Today’s high and yesterday’s close.3. Today’s low and yesterday’s close.

Average true range (ATR) is simply a moving average of the truerange over a certain time period. For example, the five-day ATRwould be the average of the true range calculations over the lastfive days.

Variance and standard deviation: Variance measures how spread outa group of values are — in other words, how much they vary.Mathematically, variance is the average squared “deviation” (ordifference) of each number in the group from the group’s meanvalue, divided by the number of elements in the group. Forexample, for the numbers 8, 9, and 10, the mean is 9 and thevariance is:

{(8-9)2 + (9-9)2 + (10-9)2}/3 = (1 + 0 + 1)/3 = 0.667

Now look at the variance of a more widely distributed set ofnumbers: 2, 9, and 16:

{(2-9)2 + (9-9)2 + (16-9)2}/3 = (49 + 0 + 49)/3 = 32.67

The more varied the prices, the higher their variance — themore widely distributed they will be. The more varied a market’sprice changes from day to day (or week to week, etc.), the morevolatile that market is.

A common application of variance in trading is standard devi-ation, which is the square root of variance. The standard devia-tion of 8, 9, and 10 is: sq. rt. 0.667 = .82; the standard deviationof 2, 9, and 16 is: sq. rt. 32.67 = 5.72

Volatility: The level of price movement in a market. Historical(“statistical”) volatility measures the price fluctuations (usuallycalculated as the standard deviation of closing prices) over a cer-tain time period — e.g., the past 20 days. Implied volatility isthe current market estimate of future volatility as reflected in thelevel of option premiums. The higher the implied volatility, thehigher the option premium.

Volatility skew (“smile”): The tendency of implied option volatilityto vary by strike price. Although, it might seem logical that alloptions on the same underlying instrument with the same expi-ration would have identical (or nearly identical) implied volatili-ties. For example, deeper in-the-money and out-of-the-moneyoptions often have higher volatilities than at-the-money options.This type of skew is often referred to as the “volatility smile”because a chart of these implied volatilities would resemble aline curving upward at both ends. Volatility skews can take otherforms than the volatility smile, though.

Weighted moving average: A simple moving average (SMA) is theaverage price of a stock, future, or other market over a certaintime period. A five-day SMA is the sum of the five most recentclosing prices divided by five, which means each day’s price isequally weighted in the calculation.

The weighted moving average (WMA) — as well as the expo-nential moving average (EMA) — puts greater emphasis onrecent prices under the assumption current market activity ismore important than more distant activity, which makes theaverage more responsive to price changes.

A WMA multiplies each day’s closing price by a “weightingfactor,” with the most recent close receiving the heaviest weight-ing and the greatest impact on the moving average value. Theweighting factors are based on the number of days in the aver-age. The sum of the weighted closes is then divided by the sumof the weighting factors over the desired period to derive theweighted moving average value.

The following table shows how a basic five-day weightedmoving average would be calculated:

The most recent day is given a weight of 5, the next mostrecent day a weight of 4, and so on. The most recent day in a20-day WMA would be weighted by 20, and so on. The closesare multiplied by their respective weighting factors. Theseresults are added together (216.25) and then divided by the sumof the weighting factors (in this case, 15). The result is a five-dayweighted average value of 14.42, compared to a simple averagevalue of 13. �

Closingprice

Weightingfactor

Weighted closing price

(closing price timesweighting factor)

Day 1 10 1 10

Day 2 10.5 2 21

Day 3 11.25 3 33.75

Day 4 14.75 4 59

Day 5 (mostrecent day) 18.5 5 92.5

Sum: 15 216.25

5-day SMA (avg. of closing prices): 13

5-day WMA (sum ofweighted closing pricesdivided by sum ofweighting factors): 14.42


TRADE

Date: Monday, Sept. 20, 2010.

Entry: Short and long the December E-

Mini Dow futures (YMZ10).

Reason for trade: After a strong early

session rally, we decided to take intraday

swing positions based on the idea the

market was unlikely (based on daily

price-action analysis) to expand its

range significantly for the remainder of

the day. The preference for short-side

trades was also predicated on the fact

that, after a blistering rally off the late-

August low, the market was challenging

resistance (the previous day’s high, which

was around the early August high) just

above the whole-number price level of

10,600.

The first short position was established

after the market had retreated from

the morning high of 10,656.

Initial risk: 10,669, 13 points above the

intraday high.

Initial target: The goal was to look for a move back toward the

previous day’s low (around 10,500), potentially holding the

position overnight if the market closed weakly.

TRADE SUMMARY

Profit/loss: -36 points.

Outcome: The day’s trading would not have been worth men-

tioning if not for the discipline mistake on the final short trade.

Frustration built throughout the day as the market mostly wig-

gled sideways. After the market failed to follow through to the

downside after the first short trade, we flip-flopped on the mar-

ket, going long but eventually scratching the position after a lit-

tle more than a half hour. Another short trade was essentially

scratched in the next 20 minutes.

It was at this juncture — with the irritation level at its

highest — that the final short position was established at

10,644. Having gotten shaken out of the previous trades (and

not even being able to take advantage of the modest profits that

were available with each of these swings), we dug in our heels,

unwilling to believe the anticipated sell-off would not material-

ize. We held on to the trade well past the stop level, but got

bailed out by a late down swing that let us get out only 15 ticks

worse than the original stop level.

Luckily, the mistake was on a small scale, but giving in to

impatience and then compounding the problem by getting mar-

ried to a position is a certain recipe for losses. �


TRADE Diary

One bad trade is all it

takes to ruin a day of

careful trading.

Source: TradeStation

Note: Initial targets for trades are typically based on things such as the historical per-formance of a price pattern or trading system signal. However, individual trades are afunction of immediate market behavior; initial price targets are flexible and are mostoften used as points at which a portion of the trade is liquidated to reduce the posi-tion’s open risk. As a result, the initial (pre-trade) reward-risk ratios are conjecturalby nature.

Trade SummaryTime (CT) Long/Short Trade price Point P/L % P/L10:59 a.m. Short 10641

2 0.02%11:12 a.m. Cover 10639

11:12 a.m. Long 106380 0.00%

11:58 a.m. Sell 10638

12:04 p.m. Short 106422 0.02%

12:18 p.m. Cover 10640

12:30 p.m. Short 10644-40 -0.38%

3:01 p.m. Cover 10684

diary80-1210 10/12/10 2:52 PM Page 80

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