at-1210

Risk and reward: Catching the stock market’s big swings p. 14 December 2010 • Volume 11, No. 12

HOW STOCK SPLITS can multiply profits p. 50 ®

UNCOVERED: A critical look at the covered call p. 40 • TRADING STRATEGIES FOR THE FINANCIAL MARKETS •

Welcome to the (SYSTEMS) MATRIX p. 20

TRADING TREND TRANSITIONS: Getting in early p. 28 FINDING THE BEST GAP TRADES p. 34

®

CONTENTS December 2010 • VOLUME 11, NO. 12

14

Trading Strategies Catching longer-term market swings Buying sell-offs in the stock market provides an edge, but it takes money 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

In every issue

6 Contributors

68 Stocks Snapshot Volume, volatility, and momentum

8 Opening Trades Trends and events moving the markets.

64 Trading Calendar

statistics for stocks.

69 ETF Snapshot Volume, volatility, and momentum statistics for exchange-traded funds.

70 Futures Snapshot

Bookshelf lf 73 Trader’s Bookshe

74 Trader’s Marketplace Classified Advertising

76 Upcoming Events 76 Advertising Index 78 Key Concepts

Volume, volatility, and momentum statistics for futures. 2

www.activetradermag.com • December 2010 • ACTIVE TRADER

Contents Contact Active Trader:

Advanced Strategies

44

Editorial inquiries: [email protected]

Not all carry trades are alike

Comments, suggestions: [email protected]

More analysis of the consequences of free

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

money shows that in the battle between Main Street and Wall Street, Wall Street won. By Howard L. Simons

The Business of Trading

60

Trader tax reporting strategies Sort

through the various trader tax forms

and review important strategies for your 2010 return. By Robert A. Green, CPA

Trading System Lab

50

Profiting with stock splits Trading splits can offer a longer-term edge for stock traders. By Robert Sucher Jr.

The Economy

66

U.S. economic briefing Updates on economic numbers and the market’s reaction to them.

The Face of Trading

55

Finding a niche

Trade Diary

This full-time stock trader relies solely on technical analysis. By Active Trader Staff

80

One bad trade is all it takes to ruin a day of careful trading.

Trading Basics

56

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

4


This Month’s ®

CONTRIBUTORS Howard Simons is president of Rosewood Trading Inc. and a strategist for Bianco Research. He writes and speaks frequently on a wide range of economic and financial market issues.

For all subscriber services: Active Trader Magazine P.O. Box Bo x 17015 1701 5 N. Hollywood, CA 91615 • (800) 341-9384 • www.activetradermag.com

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

Editor-in-chief:

Mark Etzkorn [email protected]

Managing editor:

Molly Goad [email protected]

Contributing editor:

Howard L. Simons Contributing writers:

Marc Chandler, Chandler, Keith Schap, Robert A. Green, Chris Peters Editorial assistant and webmaster:

Kesha Green President:

Phil Dorman [email protected]

Publisher, ad a d sales:

Bob Dorman [email protected]

Classified ad sales:

Mark Seger [email protected]

Volume 11, Issue 12 Active Trader is published monthly by TechInfo, Inc., PO Box 487, Lake Zurich, IL 60047-0487. Copyright © 2010 TechInfo, Inc. All rights reserved. Information in this publication may not be stored 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 intended for educational purposes only. It is not meant to recommend, 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 testing to determine the validity of a trading idea. Tradin Trading g and investing carry a high level of risk. Past performance does not guarantee future results.

6

Larry Shover has been a firm and proprietary options trader for more than 25 years and taught courses at a variety of exchanges including the Chicago Mercantile Exchange (CME) for more than 20 years. A large part of his career has been dedicated to developing his own proprietary trading firm, and he has also served as director of education, senior vice president of trading, and director of global trader development at several commodities and options firms. Shover is a member 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 in the Stock Market. They are also the authors of www.VirtueofSelfishInvesting.com. Dave Landry has been actively trading the markets since the early 90s. In 1995 he founded Sentive Trading, LLC (www (www.davelandry .davelandry.com) .com) — a trading and consulting firm. He is author of Dave Landry on Swing Trading (2000), Dave Landry’s 10 Best Swing Trading Patterns & Strategies (2003), and The Layman’s Guide to Trading Stocks (2010). His books have been translated into many languages including Russian, Italian, French, and Chinese (pending 2010). He has made several television appearances, has written articles for several publications including Active Trader and Traders Journal-Singapore. He has been publishing daily web-based commentary on technical trading since 1997. He has spoken at trading conferences both nationally and internationally. He holds a bachelor’s in computer science and has an MBA. 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 the Canary Islands (Spain) where he began actively trading stocks and futures in 1999. In 2002, he started an ongoing journey with Wealth-Lab.com, assisting customers with trading tools and solutions. Robert A. Green, CPA, is CEO of Green & Company (GreenTraderTax.com), a CPA firm focused on traders and investmentmanagement businesses. Green is also founder and CEO of the GreenTraderTax GreenT raderTax Traders Traders Association. He is the author of The Tax Guide for Traders 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 representation; and trade-accounting software. For more information or to participate in free conference calls, visit www.greencompany.com or call (877) 662-2014 or (646) 216-8061. Jim Kharouf is editor of Environmental Markets Newsletter and a freelance reporter who has covered the derivatives markets since 1996.


OPENING Trades

U.S. stocks break out of range Bullish September carries over into mid-October as earnings seasons begins.

In late September and early October, U.S. equities pushed decisively out of the roughly four-month consolidation that followed the early 2010 sell-off. The S&P 500 (SPX) punctured the resistance represented by the June and August highs around 1130 and, having reached 1180 by Oct. 3, had no remaining chart barrier between it and the April high around 1220. The upside breakout was aided by mostly positive earnings announcements as the Q2 reporting period got underway. 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. 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-frequency 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.

Treasuries rocket into October December 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 lows In mid-October the U.S. dollar index (DXY) closed at its lowest level in nearly a year, extending the selloff that began in June and setting itself up for a challenge to last year’s bottom below 75.00.

8


Commodity indices challenge 2010 highs Commodity 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 (DBLCIX) hurdled above its spring highs in October, marking its seventh consecutive 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 dominance over former front-runner wheat (W), while soybeans (S), which had Barclay Trading Group’s managed f ut ut ures ures performance as of Aug. 31 lagged the other two markets much of the year, made a push of their own. Top 10 trader s managing more than th an $10 million The rally in December coffee futures Aug.. Aug 2010 Y TD $ Under (KC) would have been eye-catching ret urn return mgmt. Trading advisor despite their pullback from highs just 1. Clarke Cap’l Mgmt. (Gl. Basic) 24.46% 7.90% 19.0 below 200, but sugar’s continued recovery 2. Clarke Cap’l Mgmt. (Millennium) 17.27% 5.54% 29.3 from the massive sell-off that ended in the 3. DUNN Capital Mgmt. (WMA) 16.96% 21.04% 254.0 spring has grabbed most of the attention 4. Clarke Cap’l Mgmt. (Gl. Magnum) 16.56% 26.65% 17.9 in the softs. As of Oct. 13, December 5. Commodity Fut. Services (IPATS) 16.00% 33.23% 21.4 sugar (SBZ10) had jumped nearly 56 per6. Mulvaney Capital Mg Mgmt. (Gl. Ma Markets) 14.59% -20.12% 115.0 cent from its August low close — and more than 100 percent since June. 7. Global Ag 13.07% 36.50% 42.0 Cotton remained kingly into mid8. Superfund Trading Mgmt (Gold C) 12.30% -12.58% 65.2 October, tricking many traders who bet 9. Quicksilver Trading, Inc. 11.78% 3.38% 211.7 the end-of-September sell-off was the bull 10. Brummer & Partners (Lynx) 10.38% 14.47% 2267.1 move’s death-knell. After dipping below Top 10 traders managing less than $10 million and at least $1 million 100, however, December cotton (CTZ10) leaped above 110, reaching 114 by 1. Clarke Cap’l Mgmt. (Jupiter) 21.26% 11.15% 9.0 Oct. 14 — more than 52 percent above 2. Persistent Cap Mgmt (Perseverance 2X) 19 1 9.46% 1.98% 2.9 July levels.  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 the performance of any single account. PAST RESULTS ARE NOT NECESSARILY NECESSARILY INDICATIVE OF FUTURE PERFOR- MANCE. Source: Barclay Hedge (www.barclayhedge.com) (www.barclayhedge.com)

ACTIVE TRADER • December 2010 • www.activetradermag.com

9

Opening Trades

Gold’s golden run Metal reaches for $1400 in mid-October; $1500 now in sights. 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 closes (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

FIGURE 1: PEDALS TO THE METALS

“

The bubble is in money-printing,

not in gold.

”

— Howard Simons, president of Rosewood Trading

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, 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 selloff 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 contracts 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 warning of a collapse in the market. “The market with the golden Active Trader Trader , February 2010) noted that gold rallied more arm” ( Active than 420 percent on a closing basis from 1974 to the beginning continued on p. 12

TABLE 1: 10 WEEKS OF HIGHER HIGHS Week No. consecutive ending weekly highs

Oct. 15, 2010 Nov. 9, 2007 Feb. 7, 2003 Jan. 25, 1980 July 27, 1979 Gold (top) established another milestone in September, but silver (middle) and copper have outgained it during the recent rally.

10

11 11 10 10 14

Gold futures have put together runs of 10 or more consecutive higher weekly highs just five times over the past three decades.


Opening Trades

CBOE looks to double up Exchange launches new trading centering catering to high-frequency sector. 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 various levels, including the International Securities Exchange (ISE) and the Boston Options Exchange (BOX). The maker-taker pricing model, first introduced in the equity markets, give rebates to those who provide liquidity to the exchange and charges customers 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, exc hange, 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 increasingly 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 percent 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 prorata pricing and maker-taker. “Overall, CBOE is looking at C2 as almost a hedge on makertaker,” 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 feature 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.” 

GOLD continued from p. 10 of 1980. On an inflation-adjusted basis, it’s still valued well below its 1980 high of $850 — roughly $2,230 in today’s dollars 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 12

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 valueadded, the value value of that that paper is zero,” zero,” he says. says. “Gold is not not rising — paper is falling. This is not — I repeat, not — a bubble so long as money money is being printed. printed. The bubble bubble is in moneymoneyprinting, not in gold.” Which means the question looming l ooming for gold traders is, when will the money presses be switched off? 

www.activetradermag.com

•

December 2010

• ACTIVE TRADER

TRADING Strategies

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

T

he U.S. stock market’s directionless, volatile trajectory in much of the second half of 2010 has been a wash for trend followers and buy-and-hold investors — the market ultimately 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 relatively brisk rallies, scaring many traders as the market tested supported and cheating the hope of bulls as it reached resistance (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 simple 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 experimentation There are many ways to define the price action that preceded this bottom, 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 description, 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 defi nition is extremely e xtremely basic, it i t is also als o objective and specific — a good starting point for the analysis. Not surprisingly, Pattern 1 formed relatively frequently frequently.. From Jan. 1, 2000, through Oct. 4, 2010, there were 213 instances; in continued on p. 16

14


•

December 2010

• ACTIVE TRADER

Trading Strategies

TABLE 1: PA PATTERN TTERN 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

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

56.34%

58.69%

56.34%

53.99%

57.28%

59.62%

60.56%

61.79%

60.95%

57.89%

Total

Win %

Pattern 1's median price moves are positive at all intervals, but the high standard deviations and relatively modest winning percentagess indicate the post-pattern market performance percentage performance is volatile.

many cases, several consecutive days fulfilled the pattern criteria, 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 example, 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

FIGURE 1: WIDE-RANGING SWINGS

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 pattern’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 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 at least 1 percent lower than yesterday’s close.

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.

16

While observation of several patterns pa tterns suggested this might be a distinguishing characteristic of more-successful examples, 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



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

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

52.54%

57.63%

55.37%

54.24%

58.19%

61.02%

61.58%

59.89%

58.19%

58.19%

Total

Win %

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

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-tobar 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 percent 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 market Before 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 dominated 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


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

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

62.00%

61.00%

63.00%

61.00%

62.00%

61.00%

62.00%

65.66%

68.69%

60.61%

Total

Win %

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. percentages.


17

Trading Strategies S trategies

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.

FIGURE 3: PATTERN PERFORMANCE — MEDIAN GAINS

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

18

(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 percentages — 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 shortest and longest intervals. As was the case with the median gains, the winning percentages 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 check The 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 percent. 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 improvement 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


(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 buyand-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 patterns outperformed buy-and-hold by a wide margin, but this is not more important than the risk a trader would have been subjected to: The drawdowns during the 2000-2002 bear market and 20082009 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 variation’s entry signals. Pattern 3 signaled at the February, February, July, and August lows, l ows, plus continued on p. 72

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 l ongest intervals.

FIGURE 5: EQUITY CURVE COMPARISON 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.


19

TRADING Strategies

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.

KC Go to “Key concepts” on p. 78

BY THOMAS STRIDSMAN

for more information about: • Compounded Annual Geometric Return (CAGR) • Stop-and-reverse (SAR)

E

ven 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-following systems A and B usually enter at the same price, but system 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 market 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-following commodity trading advisor (CTA), it would be immensely helpful to be able to compare performance with that of certain benchmark systems. This trend-following system analysis may also help you discover one or two secrets of the professionals, or perhaps provide ideas for your own strategies.

20


The systems To 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-andreverse breakout. System 5: A constant-period average true range (ATR) breakout, with a median stop. continued on p. 22

•

December 2010

• ACTIVE TRADER TRADER

Trading Strategies

FIGURE 1: SYSTEM 2 EQUITY CURVE

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

System 6: A constant-period ATR stop-and-reverse breakout. (For more information about using the center line and median, see “Baseline primer.”) Five versions (short term to long term) of each system will be tracked, for a total of 30 system variations.

trade. This modification approximately triples the trade frequency. Regular rebalancing also shortens the average time in the market per contract traded, and gives the system a shorter-term character.

Test settings System parameters There 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 volatility 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 arbitrarily 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 22

We conducted tests to see how the systems performed in the We recent past, especially this year. The amount of account equity risked per trade for each system in 2010 was based on a backtest on historical data from January 1990 through December 2009. The position size was set in such a way that the back-tested 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 million. 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. www.activetradermag.com • December 2010 • ACTIVE TRADER

Baseline primer

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 volume, while this will only affect the cheaper ones by a few bucks. For all systems the average slippage comes out to three to five ticks per contract traded. Slippage was also deducted 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 performance, 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.

A baseline represents represents the best estimate of the current “equilibrium” “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. (HighestHigh+LowestLow)/2. In most cases, the center-line price will only be implied, and not used as an input in other calculations. 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 average of the middle two. 3. Mode: The mode is the most frequently 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 different baseline calculations.

FIGURE A: SAMPLE BASELINES

The markets The test portfolio contains 49 markets in seven sectors: Currencies: Australian Currencies: Australian dollar, British pound, Canadian dollar, Euro, Japanese yen, Mexican peso, Swiss franc. Interest Inter est rates: 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. Stock indices: S&P 400 Midcap, CAC 40 (France), DAX (Germany), FTSE 100 (Britain), Hang Seng (Hong Kong), Nikkei 225 (Japan), E-Mini Russell 2000. Energies: crude oil, heating oil, Brent crude oil, gas oil, natural gas, gasoline, EUA emission rights. Metals: gold, copper, aluminum (LME forward), nickel (LME forward), palladium, platinum, silver. Grains: wheat (CBOT), soybean oil, corn, wheat (KCBT), rough rice, soybeans, soy-

The average is the most commonly used baseline price, but the median, center, and mode prices all provide additional information about price action and can be used in trading indicators and systems.

Day

Price

Prices (s (sorted low to high) 10 12 12 12 16 20 25 29 33 47 47

1 12 2 10 3 12 4 29 5 47 6 33 7 25 8 16 9 47 10 12 11 20 Average: 23.90 Median: 20 Center: 28.5 Actual mode: 12 Estimated mode: 12.18

continued on p. 24


23


TABLE 1: SYSTEM MATRIX System 1

System 2

System 3

System 4

System 5

2-StD breakout w/ mean stop

1.5-StD breakout reversal

HH/LL breakout w/ center stop

HH/LL breakout reversal

240-day ATR breakout w/ median stop

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

7.6

11.9

5.9

-1.6

-16.6

-9.7

-0.42

14.38

18.2

12.19

11.96

-18.82

-4.01

5.65

60

40

60

40

1,5 ATR

1,5 ATR

0.45

0.5

0.5

1.2

0.25

0.45

0.56

Look-back (days)

Return, YTD Return, 3 months Look-back (days) Risk / trade

System 6 80-day Avg. per-trade ATR breakout return & risk, reversal all systems

-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

Return, 2009

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

-11.7

-17.54

-8.83

-16.91

-12.52

-16.29

-13.97

Return, 3 months

Average of all look-back periods per system Return, 2009 Return, YTD Return, 3 months

-12.36

-6.26

-12.18

-11.32

-3.42

-9

-9.09

-3.74

-5.32

-4.08

-11.34

-10.32

-13.46

-8.04

-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.

bean meal. Meats and softs: softs: feeder cattle, live cattle, lean hogs, coffee, lumber, orange juice, sugar.

Performance analysis Table 1’s system matrix shows the short-term systems have performed 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 te rm 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-

24

cates one of two things: Either the markets have been very trendy, or they have been very choppy. As the negative performance 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 catching 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 performed 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


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.

risk-reward characteristics for the system. Unfortunately,, this Unfortunately thi s isn’t i sn’t always the case, as viewing last year’s returns reveals. The situation was almost the opposite in 2009: Again, most of the th e systems lost money 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 rebalance 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, making 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 longestterm to the shortest-term systems.) So, does two years of lackluster performance for so many different 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 markets, they are in essence designed to lose money in choppy markets. 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 matrix That said, one interesting anomaly is the performance and pertrade 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 percent 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 probably 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 th e systems will wil l 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

25


FIGURE 3: THE IMPACT OF POSITION SIZING

Applying a position-sizing rule to the same system and markets represented represented in Figure 2 produced produced a much more stable equity curve.

Related reading

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

Books and articles by Thomas Stridsman:

Other factors

Trading Systems That Work (2000, McGraw-Hill) Trading Systems and Money Management (2003, McGraw-Hill)

Building a volatilityvolatility-momentum momentum system Octobe oberr 2010 2010 Active Trader , Oct Systematizing volatility and momentum concepts produces compelling test results. “

”

New approaches to volatility September mber 2010 Active Trader , Septe Just as there are alternatives to the moving average when defining trends, there are better ways to measure volatility than the tools you may be used to relying on. “

”

However, keep in mind that CTAs can alter their individual reward-risk profiles by using different proprietary money management (i.e., position sizing) rules, and by incorporating factors such as macroeconomic trends as well as trade and sector-allocation 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’ 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 performance 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.

For information on the author, see p. 6. A baseline trend strategy August ust 201 2010 0 Active Trader , Aug Experimenting with moving medians, modes, and center lines — in addition to moving averages — in a robust trend system. “

26

”

All tests were done with TradingBlox TradingBlox system-testing software software (www.tradingblox.com) using Unfair Advantage data by CSI (www.csidata.com), (www .csidata.com), with the kind and invaluable help of Roger Rines ([email protected] (rdrin [email protected]), t), independent trader and system-develop system-developer er consultant. www.activetradermag.com • December 2010 • ACTIVE TRADER

TRADING Strategies

Trading trend transitions Recognizing a few simple patterns — and trading them correctly — can help you get into new trends early.

KC Go to “Key concepts” on p. 78 for more information about: • Fibonacci numbers • Weighted and exponential moving averages

BY DAVE LANDRY

FIGURE 1: TRANSITIONAL PATTERNS PATTERNS

Shorts

A

lthough trends don’t last forever forever,, they often last much longer and go much further than most people 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 Fortunately s tock 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 “transitional” patterns, as shown in Figure 1. When you catch a new trend early, early, the payoff can be huge. Unfortunately,, since you are trading what could turn out to be a Unfortunately 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.

Downtrend begins First correction Uptrend Downtrend resumes

Longs

Uptrend continues

Downtrend

First Thrusts Markets 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 Trapped on the wrong side of the market, they find themselves waiting for the market to reverse so they can get

First correction Uptrend begins

Trading trend transitions requires identifying a correction as the market appears to be making a major turn.

continued on p. 30

28


•

December 2010

• ACTIVE TRADER TRADER

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. FIGURE 2: FIRST-THRUST PATTERN

(4) (3)

(2)

(1) 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.

FIGURE 3: LONG FIRST THRUST

The old market participants will soon be forced out at unfavorable 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 pullback bar completes. Notice that the retracement in this example is fairly sharp. This is similar in vein to another transitional pattern, the Gatekeeper.

Gatekeepers

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

30

Markets 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 levels and by shorts taking profits. (Also, the move can be accelerated


FIGURE 4: SHORT FIRST THRUST 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 Fibonacciretracement 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 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 The short trade is triggered only when the stock turns back down after making an will reverse right at the 78.6-perinitial bar with a higher high and higher low. cent retracement level. The advantage of this pattern is FIGURE 5: GATEKEEPER PATTERN that its risk is well-defined (at worst, the trade is stopped out on a move above the old high), high), while the potential reward of cap10-11 days turing the occasional major top or bottom can be great. The pattern is especially helpful for determining when an extended rally (1) 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 (3) 78.6% (1) followed by a sharp sell-off (2). It should then make a move 61.8% back toward the old high but stall somewhere between the 61.8percent 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 (4) 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). (2) 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. The Gatekeeper pattern looks to enter after the It then retraced sharply (3), giving players trapped on the wrong market retraces the move away from a major top (long) side of the market false hope. However, notice price continued on p. 32


or bottom by a certain percentage.

31


stalled just shy of the 78.6-percent retracement. Short entry occurs when the market turned back down at point 4.

FIGURE 6: GATEKEEPER: AFTER THE FLASH CRASH

Bow Ties

A Gatekeeper pattern formed after 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

(3)

(2) 10-day SMA 20-day EMA 30-day EMA 30-day EMA

(1)

20-day EMA 10-day SMA Bow Ties form when the three moving averages reverse their order, signaling a turn in the market.

32

First Thrusts and Gatekeepers are fairly abrupt patterns that form relatively 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 indicators 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 moving average lengths below the slower moving average lengths) to proper uptrend order (the faster moving averages above the slower moving averages). When this happens over a short


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 > 20bar EMA > 30-bar EMA). Ideally, this should happen over a period 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 pullback (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 downtrend 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 market

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. 

For information on the author, see p. 6. 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 magazine (www.currencytradermag.com).

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 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 distance of each other when an entry is triggered, and extends the trade’s default holding period. “

”

Transitional 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 FIGURE 8: BOW-TIE: MOVING AVERA AVERAGE GE INVERSION 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 correction. 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 The reversal of the moving averages that forms the Bow Tie should unfold relatively quickly (just a few bars). traders are trapped on the wrong


33

TRADING Strategies

Trading gaps with the most potential Screening stocks with volume and volatility criteria can help make trading up-gaps less of a guessing game.

KC Go to “Key concepts” on p. 78 BY CHRIS KACHER & GIL MORALES

W

hen investors see a stock gapping to new high ground on huge volume they immediately 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 potentially profitable price-volume signals you will come across. When a stock gaps higher — and exhibits exhi bits certain characteristics — 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 — particularly 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 gaps You can use some simple rules to screen for tradable up-gaps. You 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

34

for more information about: • Simple moving average • True range (average true range)

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 s trong 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 million 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 inves tors 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 management 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 continued on p. 36


•

December 2010

• ACTIVE TRADER

Trading Strategies

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: figures: eSignal

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 typically not high-probability buy points because they are often temporary, 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 36

PCLN above $300 for the first time.

Selling rules Figure 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 (dotted 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 fluctuate 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 position could possibly be held. Also, see if i f the gap day’s low is close to major support, such www.activetradermag.com • December 2010 • ACTIVE TRADER

FIGURE 2: SPARKIN SPARKING G THE MOMENTUM

as the 10-day or 50-day moving average. These moving averages may “catch up” to the price pattern and function as support. The idea is to keep in-line with a maximum stop loss, while avoiding 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 continued 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 position, depending on the stock’s “character “charac ter.” .” Power Powerful ful up-gap up-gapss 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 constitutes 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 average.) This is called the Seven Week W eek Rule. There are three exceptions to this rule:

After this up-gap the stock pushed above $300 for the first t ime. FIGURE 3: STAYING ABOVE THE GAP-DAY’S LOW

After a valid signal, pr ice should not violate the low of the day immediately after the gap.

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: semiconductors, 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 moving 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 average 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


37


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.

38

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 looking 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 upgap 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 moving 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 10day 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 6: USING THE 50-DAY MOVING AVERAGE

highs. In practice, big-volume upgaps, 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 identifying and capitalizing on these trades is critical for success. The approach outlined here is fairly straightforward, and provides 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 equation in your favor. 

For information on the authors, see p. 6.

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:

Gauging gap opportunities Active Trader , January 2007

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

A different look at an “old” pattern offers insights into price behavior in the S&P 500.

“

”

Kacher. Gap trading techniques: Five-article set

“

www.VirtueofSelfishInvesting.com Includes more information from the authors on buyable up-gaps and other technical methods. Other articles: Opening gap locations Active Trader, December 2008 “

”

Historical testing attempts to identify the best setups for fading the opening gap. Double gaps Active Trader , March 2008 “

”

Analysis of the performance of double (and triple) price gaps. Opening gap trader

“

”

A discounted collection of the following five Active Trader articles, published between 2001 and 2004:

Website:

”

A ctive ctive Trader , August 2007 Further analysis points to a new direction for trading an opening-gap signal.


1. “Morning reversal strategy” by Bryan C. Babcock and Arthur Agnelli (May 2003). A strategy that takes its cue from historical tests revealing the tendency of the major stock indices to revert to the previous day’s closing price in the early minutes of the trading session. 2. “Trading the overnight gap” by David Nassar (March 2001). Learn how to spot the early warning signs of opening gaps and how to take advantage of them. 3. “Trading the opening gap” by John Carter (December 2004). Watching pre-market volume is a good way to determine whether to trade or fade the opening move. 4. “Trading System Lab: Gap closer (stocks)” by Dion Kurczek (May 2003). This system test is designed to see if the “all gaps are eventually closed” axiom holds water (tested on a portfolio of stocks). 5. “Trading System Lab: Gap closer (futures)” by Dion Kurczekk (May 2003). The above gap-based system Kurcze tested on a portfolio of futures markets.

39

TRADING Strategies

Uncovering the covered call Market realities make the popular covered call strategy more difficult to pull off than most people think. KC Go to “Key concepts” on p. 78 for more information about:

BY LARRY SHOVER

T

he covered call is the one option strategy people seem to grasp. New brokers with freshly minted Series 7 licenses eagerly take their enhancedincome 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 perspective of both a brokerage house and the investor who is clamoring to generate additional income for his portfolio. Yet for all the history and salesmanship, it’ 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 investing public being duped with a short-sighted method, or is the covered call truly a four-season strategy?

The covered call defined In its most basic form, a covered call position is created when a trader who owns an underlying security sells a near at-themoney or slightly out-of-the-money call. The strategy is “covered” 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 s a “buy-write,” it’s important to recognize they differ in implementation. 40

• At the money • Call option • Implied volatility • In the money • Naked put

• Out of the money • Premium • Skew • Strike price

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 trader’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, receiving 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 www.activetradermag.com • December 2010 • ACTIVE TRADER

TABLE 1: COVERED CALL PROFIT/LOSS AT EXPIRATION

account. The premium received from the options sale ($2 per share) effectively 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 automatically exercise, resulting in the stock being delivered to an exerciser of a long $45 call at $45 per share. The maximum profit in this situation is:

Strike price – purchase price + option premium received, or

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 $45 call Premium Stoc St ock k pr priice Sto tock ck P/ P/L L value received $100 $60,000 ($55,000) $2,000 $75 $35,000 ($30,000) $2,000 $65 $25,000 ($20,000) $2,000 $47 $7,000 ($2,000) $2,000 $40 $0.00 $0.00 $2,000 $38 ($2,000) $0.00 $2,000 $25 ($15,000) $0.00 $2,000 $10 ($30,000) $0.00 $2,000 $0 ($40,000) $0.00 $2,000

Net P/L $7,000 $7,000 $7,000 $7,000 $2,000 $0.00 ($13,000) ($28,000) ($38,000)

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

$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 covered call performance across a range of stock prices at expiration. 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 associated with it is both defined and limited. But keep these risks in mind.

The four realities of the covered call strategy First, don’t assume you can consistently pick stocks (or futures) that have both a high amount of option premium and a stable ACTIVE TRADER • December 2010 • www.activetradermag.com

price. In fact, the opposite is generally true. Only volatile instruments 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 continued on p. 42

41

Trading Strategies

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

Naked put trade:

Stock price = $30 2-month $30 call = $1 2-month $30 put = $1

Stock price

Stock P/L

$80 $70 $60 $50 $40 $30 $20 $10 $0

N/A N/A N/A N/A N/A N/A N/A N/A N/A

Stock price

Stock P/L

$80 $70 $60 $50 $40 $30 $20 $10 $0

$50,000 $40,000 $30,000 $20,000 $10,000 $0.00 ($10,000) ($20,000) ($30,000)

Sell 10 two-month $30 puts at $1 Naked put P/L at expiration 2-month $30 put Put premium value received $0.00 $1,000 $0.00 $1,000 $0.00 $1,000 $0.00 $1,000 $0.00 $1,000 $0.00 $1,000 ($10,000) $1,000 ($20,000) $1,000 ($30,000) $1,000 Covered call P/L at expiration 2-month $30 Call premium call value received ($50,000) $1,000 ($40,000) $1,000 ($30,000) $1,000 ($20,000) $1,000 ($10,000) $1,000 $0.00 $1,000 $0.00 $1,000 $0.00 $1,000 $0.00 $1,000

Covered call trade: Buy 1,000 shares at $30,000 Sell 10 two-month $30 calls $1.00

Net P/L $1,000 $1,000 $1,000 $1,000 $1,000 $1,000 ($9,000) ($19,000) ($29,000)

Net P/L $1,000 $1,000 $1,000 $1,000 $1,000 $1,000 ($9,000) ($19,000) ($29,000)

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

trend) begins, it takes time before it clearly is considered a bull could potentially violate the fundamentals of conservative market. Who exactly decides a bull market is a bull market, any- options trading, the primary objectives of which are maximizing way? It can take a lifetime for everyone to agree a long-term income while using leverage to limit portfolio risk. There are market trend has developed, and by the time unanimity is stocks and various circumstances for which the covered call achieved, the up move has ended. Second, there will always be makes sense, but you must apply the strategy correctly and be stocks that underperform in a rising market and vice versa. To fully aware of its risks. base a covered call strategy solely on broad market assumptions is nothing short of living by faith. Three reasons to reconsider the covered call strategy Finally, covered call writing is not as simple as it appears. The Would Would you sell a put option naked? A covered call’ call’ss risk profile complexity is not so much in the strategy itself but rather in looks a lot like selling a naked put. The only difference is that addressing the two primary challenges the strategy presents: the underlying will not expire. As a result, as the underlying price begins to fall, agony is prolonged, and losses are increased. downside risk is reduced but not eliminated, and potential profit is Table 2 compares covered call and naked put positions. capped. Given those challenges, it would appear this strategy 42


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

SPX = $1,064.00 Interest rate = 1.75% Stri St rike ke pr pric ice e Ca Call ll va vallue Put Put va valu lue e $0.80 $106.50 $1,170 $1,150 $1.75 $87.50 $1,130 $3.60 $69.40 $1,110 $7.25 $53.00 $12.95 $39.00 $1,090 $21.80 $27.80 $1,070 $33.35 $19.35 $1,050 $47.00 $13.05 $1,030 $63.00 $8.85 $1,010 $80.00 $6.00 $990 $970 $98.00 $4.00

Str trik ike e vo vola lati tili litty 18.50% 19.00% 19.50% 20.00% 20.50% 21.50% 23.00% 24.50% 26.00% 27.50% 29.00%

Why cap the upside? All active traders will at some s ome 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 Because of negative skew, the out-of-the-money equity call Ph.D.s to build computer models, and many traders like to option cost less than the equally out-of-the-money put option. think of themselves as brilliant. But this is not the case. The beautiful notion of randomness means that much what goes on in the world of choosing Effective call writing stocks is nothing more than luck. The reality is we are very poor V Volatile olatile stocks with high option opti on premiums are needed ne eded to get the decision makers, and to think we can consistently pick successkind of returns covered call investors are looking for. But that’s ful stocks is foolhardy at best. To be consistent, traders need to the problem. A low-priced, highly volatile stock is needed to ride winners and cut losses. The problem with the covered call make this strategy work from a cash-flow perspective. The share is there will always be the one stock that got away. prices for these stocks, however, tend to go up and down, some Why sell an option into the hole? “Skew” is the contour, or times in stunning fashion. When the share price rises, traders the unevenness, in a distribution of values. The negative skew miss out on profiting from that increase by putting a cap on the seen in equity and index options reflects the reality that the strike price for the options they sold. prospect of losing money, maybe a great deal of money, is much In the end, why buy a stock and then cap its upside potenmore likely than taking home large gains (Table 3). In a theoreti- tial? When looking at a long-term investment, if a stock’s price cally precise, ideal, normal distribution, the probability of enjoy- isn’t likely to go up soon, why tie up cash to buy it now? ing strong gains or suffering large losses is the same. Equity Keep in mind human frailty. Trading is all about possible loss options, however, typically have a built-in negative skew. Outthat can’t be predicted and controlled. It’s the result of living in of-the-money call options cost less than equally out-of-thean imperfect world with imperfect people who become greedy money put options, and more often than not, at-the-money and short-sighted, who panic, who make blunders and then try options have implied volatility somewhere in between, for two to hide them, who try to protect their jobs, who have more conreasons. First, the market always insists a trader is more likely to fidence than experience, or who have too much experience and lose money with any strategy or position. More important, the grow complacent. Trading rises from an unpredictable world investing public joins the investment distress. People panic, or at with too many human factors to count. And nothing is more least get uneasy — even financial analysts with Ph.D.s in unpredictable in markets and trading than the humans who are physics. That means that the public normally sells out-of-thebehind it all.  money calls and buys of out-of-the-money puts to protect against potential losses. For information on the author, see p. 6. ACTIVE TRADER • December 2010 • www.activetradermag.com

43

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

A

ll professions develop a shorthand sooner or later. Not only does it facilitate communication amongst those with a shared knowledge (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 twoyear 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 market strip, and is thus linked to the cost of rolling a one-year money market strip forward for another year. The global drive toward zero percent interest rates in 2008-2010 compressed 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

FIGURE 1: TWO DIFFERENT YIELD CURVES

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

44

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 threemonth OIS. Wee can compare the two yield W 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 FRR 2,10. The period-


ic outbursts of fear regarding short-term rates tend to make the highly expectational two-year note yield jump around violently, while the OIS rate stayed anchored by the Federal Reserve.

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

Stock market impact The federal government made the financial sector its special project during and after the financial crisis of 2008. The low borrowing costs it created allowed banks and other financial institutions to rebuild their balance sheets by borrowing low at the short end and lending high at the long end. Indeed, this engineered carry trade allowed the Federal Reserve to monetize Treasury debt by letting 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 purchases financed out of thin air. Not that this free money led to stock market

Of the 12 S&P 1500 financial sector industry groups, the favored favored groups of the banking sector underperformed, underperfor med, and the mortgage-finance group 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.

continued on p. 46


45

Advanced Concepts

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.

46

outperformance. If we go back to the day in November 2008 when Timothy Geithner (present at the creation during his stay as president of the Federal Reserve Bank of New York) was appointed to be Secretary of the Treasury and Citigroup got backstopped once again by the Paulson Treasury, and compare the total returns for the 12 industry 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, carry, the OISbased or the two-year note-based, was the better 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 6: RELATIVE PERFORMANCE & TWO CARRY TRADES: CONSUMER FINANCE

of the industry was so dominated by external factors, such as the question of nationalization and the repayment of TARP funds, the actual answer to which carry trade was more important must be, for most of 2010, the OIS carry 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 function of either carry trade. What about the investment bank and brokerage group (Figure 5)? Here the answer lies in between. The OIS carry clearly produces the better 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 happened to the investment

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

continued on p. 48


47

Advanced Concepts

Related reading Other Howard Simons articles: 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 policies. “

”

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 deconstruct ed 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 energy 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. “

48

”

banks was their post-2008 status as commercial 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 custodial 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 performance 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 reasonable 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 circumstances, such as free money and government 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, capital raised, assets written off, government capital infusions, all manner of extraordinary items, the elimination of FAS 157 mark-to-market accounting, etc. A stock price, in contrast, is observable and more or less beyond dispute. Wee can infer from the observations W observati ons 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 traditional yield curve spread between the two and 10-year note. Recent evidence suggests the one-month OIS rate has become more important now than the threemonth 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 everything?

A stock stock can can rise rise in the face of poor earnings if there is a reasonable belief business will improve or the firm will be rescued. We can also infer much of the free We 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? 

For information on the author, see p. 6.


TRADING System Lab

Profiting with stock splits KC Go to “Key concepts” on p. 78 for more information about:

BY ROBERT SUCHER JR.

• Current ratio

T

his 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 resulting 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 (multiples 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 generFIGURE 1: EQUITY CURVE ally accompanied by tighter bidask spreads. On the other hand, if price is too low, the shares will not attract institutional investment. In short, for our purposes it’s interesting to consider that a company can essentially manage (perhaps even optimize) the range of its stock price to make it attractive to the maximum number of market 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 As the worst of t he financial crisis approached, the strategy had already increased its list of symbols that have split with cash position, which served to cushion the blow. The S&P 500 index, by compari- a ratio 1.5 (a 3:2 split) or higher. son, declined approximately 58 percent. The list is ranked by current ratio Source for all figures: Fidelity Wealth-Lab Developer 6.0

50


and 4 percent of the account equity is allocated to buy the top two stocks each month. Any viable ranking strategy could be used; current ratio was selected for this test to give priority to companies with stronger capital positions. The maximum holding period is three years, but once a position has attained a 25-percent gain on an intraday basis, a 5-percent “profit stop” is placed below the market (i.e., 5-percent above the entry price). While the strategy doesn’t use stops after opening 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 investment 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 phasein 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: Enter er lo long ng on the first trading day 1. Ent of the month following a split with a ratio of 1.5 (3:2 split) or higher. 2. When multiple candidates exist

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.

continued on p. 52


51

Trading System Lab

(the usual case), priority is given to the two stocks with the highest current ratio. System exit rules: 1. Sell after three years or, 2. When the stock achieves a 25-percent gain, set a stop 5 percent above the entry price.

Starting equity: $100,000. Deduct $8 per trade in commissions. Test data: The system was tested on all S&P 500 component stocks that were in the index as of Sept. 8, 2010. Dividendadjusted price data provided by Yahoo.com (an important manual correction to FHM’s split on 9/10/08 is required). Current

STRATEGY SUMMARY Profitability

Original

Enter before ex ex-date

Net profit:

$199,948

$264,735

Net profit:

200%

265%

Profit factor:

5.54

Payoff ratio:

Original

Enter be before ex-date

89

85

Win/loss:

85.4%

88.2%

7.48

Avg. profit/loss:

43.7%

52.2%

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%

23 months

23.4 months

28 months

36 months

-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

Longest flat period:

Trade statistics

No. trades:

Avg. hold time (winners): Avg. loss:

PERIODIC RETURNS Avg. return

Sharpe ratio

Best return

Worst return

% profitable periods

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

12.02%

0.67

40.91%

-20.69%

72.7

5

1

Annually

LEGEND Net profit — Profit at end of test period, less commission. Profit factor — Gross profit divided by gross loss. profit of winning trades divided by average loss Payoff ratio — Average profit

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.

52

Max. consec. Max. consec. profitable unprofitable

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. divided by standard deviation of returns returns Sharpe ratio — Average return divided

(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.


ratio data provided by YCharts.com.

FIGURE 4: PROFIT BY INSTRUMENT

Test period: September 2000 to

September 2010. Test results

Although the 2000s were a lost decade for the broader index (see the blue buy-andhold equity equity line of the S&P 500 in Figure Figure 1, p. 50), the split system’s 12-percent annualized 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 percent. (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 period. Just more than half the 89 total trades hit the 5-percent profit stop after attaining a 25-

The secret to the strategy’s return is collecting huge profits on a relatively few trades. A single trade i n TIE accounted for more than a quarter of the system’s profits.

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.

continued on p. 54


53

Trading System Lab

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 certa in without a rigorous back-test using a rotating 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 moneymanagement rule of allocating 4 percent of account equity per symbol; the persymbol 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 percent to -46.2 percent. Figure 5 (p. 53) shows the net profit distribution for the MC simulations, which identified an average 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 F igure 6’s optimization results unambiguously demonstrate that time works to the strategy’s advantage, supporting the suggestion from the Rice University study that stocks that split tend to outperform their counterparts for up to three years.

prices even higher. Because the system buys at the beginning of the month after a split, the test results do not reflect participation 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 additional 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 interesting 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-producing stock over one that generates no dividends at all.

Suggestions

Bottom line

Companies typically announce stock Although this might not be a strategy that splits three or more weeks prior to the ex- makes the blood surge in the veins of a date (the day the split actually takes die-hard active trader, a strategy of place). Following the announcement and buying stocks that split shows evidence it the initial price bump that often accomcan beat the market in both secular bull panies the announcement, the excitement and bear markets over a three-year time around the upcoming event tends to draw horizon.

FIGURE 6: RETURNS FOR DIFFERENT HOLDING PERIODS

For information on the author, see p. 6.

Trading System Lab strategies are tested on a portfolio basis (unless otherwise noted) using Wealth-Lab Inc.’s testing plat- form. If you have a system you’d like to see tested, please send the trading and money-management rules to [email protected].

An optimization of the system’s system’s holding period shows that it’s generally better to give positions plenty of time to accumulate profits.

54

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


Trading setup The Face of TRADING with th custom-built dual quad Hardware: PC wi

core extreme processors (4 GB each), 16 GB

Finding a niche

RAM, six 22-inch LCD monitors


Internet: Cable

Software: Lightspeed modem

Brokerage: Lightspeed

Name: Michael Marroquin Age: 28 Lives in: San Diego, Calif.

M

any people get into trading after years in another business, but Michael Marroquin got in early early.. Using money saved from odd jobs, he opened a custodial trading account when he was just 15 years old, reading the Wall Street Journal and always dabbling in small-scale stock positions. In college, he studied finance, but didn’t necessarily envision a trading career. “I always had the interest, but I never thought I could make a career out of it because I didn’t know it was possible,” Marroquin says. He remembers having only one class on technical analysis. “The professor basically said, ‘Don’t learn any of this — it’s all hogwash,’” he remembers. Now a full-time stock trader who relies solely on technical analysis, Marroquin says “You have to rewire yourself to unlearn everything you learned in school.” After college and some time in graduate school, Marroquin worked at a financial planning firm and earned several NASD, insurance and real estate licenses. He studied for the certified financial planner (CFP) designation but realized it was not the career he wanted to pursue. He realized he wanted to be a trader. In 2006 he began focusing on both trading and real estate. “I had a day-trading account and churned away in 2006 and 2007,” he says. “I thought I was going to be a millionaire really fast. I soon realized that wasn’t going to happen. I was scattered, all over the place, a complete rookie.” At that time, Marroquin also started working as a residential real estate broker, helping clients purchase distressed and investment properties. “The two careers work well together,” he notes. “I’m a morning trader and have to have something else to fill my day. It actually helps me because if I don’t have

something else to do I will become tired and less focused,” he explains. Marroquin turned a net profit in 2008, and recalls he thought he was “invincible” after experiencing his first five-figure week in January of that year.” Looking back, he says he now sees “it was too much, too quick. I started to realize the importance of working on myself — the psychological aspects. That was the beginning of me realizing what real trading is. It is all a giant self-discovery process.” Trading method: Marroquin typically puts on one to 10 trades in a day. He usually trades the first two hours of the regular day session, which means he’s done trading by 8:30 a.m. Pacific Time. “My performance goes down the longer I’m sitting there,” he says. For the first 30 minutes of the session he puts on scalp trades that last five to 15 minutes. For the remainder of his trading time, he puts on position trades that might last 15 minutes to an hour. He trades mostly Nasdaq stocks and monitors approximately 25 names using fiveminute charts, which is his typical entry timeframe. Over the years Marroquin has identified several technical patterns he likes to use, including gaps, opening range breakouts or breakdowns, and trend reversals. “I trade each pattern a specific way and manage each pattern a specific way,” he says. Although he has detailed rules, Marroquin admits for him trading is both “an art and a science” and “a lot is subjective and gut,” especially when it comes to his exit points. One setup he trades is to identify a trend on a longer timeframe chart (e.g., 15- or 30-minute, or daily) than look for a pullback on the five-minute timeframe. “I look for the pullback to basically exhaust,” he says. “I find that moment when that upshot (in a downtrend) is getting ready to reverse.” He uses candlestick “tails” (long wicks)


to enter a trend when a pullback is exhausted. “Tails are your best attempt at a trend reversal,” he says. “I enter on that tail when it is happening.” He also monitors eight-period, 20-period, 50-period, and 200-period moving averages. In an uptrend, for example, if he sees a stock that “dips and touches a rising moving average in an uptrend,” he’ll enter on a five-minute chart when a long tail appears. He places a stop-loss at the bottom of that tail. When it is clear to him the tail did, in fact, mark the end of a pullback, he’ll add to the long position. For exits, Marroquin says he sells surges into resistance areas. He always uses a profit target, which is previous support or resistance on the five-minute time frame. Became profitable when: Marroquin’s turning point came when he started working from a written trading plan. “I found my niche and wrote a trade-management plan, with all my stop-loss criteria.” “Typically, I have one losing day a month and it is always for one reason: I break a rule,” he says. “Why do I break a rule? Because I haven’t slept well, or my focus isn’t in the right place.” Most important lesson: “It’s a business,” he says. “You really have to learn your niche and you have to find your place. You have to find something you can be consistent with.” Best thing about trading: “Being your own boss.” When not trading: He works out at the gym five or six times a week. “I work out like it is my job,” he says. “Keeping myself disciplined both physically and mentally helps keep me more disciplined in my trading.” He also spends time with his family on the beach and brews beer. 

55

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.


T

here’s an old and probably apocryphal story that President Harry Truman once voiced a desire for a onearmed 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 complete objectivity and specificity. But that is often a rare commodity 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 56

by trading directly for themselves. Hence, Steve Cohen and George Soros do not conduct trading classes at the local community college. Which means aspiring private traders are ultimately ulti mately 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 wh at 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 matter 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 preference” 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 virtually assures a move of at least $200 over the course of two days? You will have simply guaranteed you will wi ll be stopped s topped 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 subwww.activetradermag.com

•

December 2010

• ACTIVE TRADER

jective ideas, they cannot be pinned down and, and , thus, they th ey cannot 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

ACTIVE TRADER •

December 2010

•


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 nonanswer that puts you in the market without any indication of a trade’ss expectations. trade’ expecta tions. 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 foundation 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. 

57

THE BUSINESS of Trading

Trader tax reporting strategies As 2010 2010 comes comes to a close close,, it’s high high time time to begin begin think thinking ing about about your your year year-end -end tax tax retur return. n.

BY ROBERT A. GREEN, CPA

F

orms, forms, and more forms. Which form should you use if you’re a forex trader? Which form is best for securities 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 businesses as it has done for just about every other type of business. For example, other sole-proprietorship businesses report revenues, 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 treatment. 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. 60

With these tax-reporting requirements, the IRS may automatically 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 www.activetradermag.com

•

December 2010

• ACTIVE TRADER

members of a futures or options exchange. This special income-transfer strategy also unlocks the home-office deduction and Section 179 (100-percent) depreciation 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 important strategy for business traders.

TABLE 1: IN GOOD FORM

Tax form

Who should use it

Schedule D

Securities traders using the cash method

Form 1040

Forex 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 6781

Futures traders who did not elect Section 475; forex traders opting into Section 1256


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 trader-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 busicontinued on p. 62

61

The Business of Trading

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 Tax Guide

Green & Company, Inc., January 2010. This PDF guide includes includes strategies, strategies, tips, and advice for preparing your 2009 tax return and planning ahead for the 2010 tax season.

62

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” factor. 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 partn ership tax return is filed for a general partnership or multimember LLC choosing to be taxed as a partnership. Form 1120S is filed for an Scorporation 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). Secti Section on 179 is is broken out separately on Schedule E, along with unreimbursed partnership expenses (UPE) including home-office expenses. Under the “trading rule,” these are considered “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 filing. Net taxes don’t change; they’re still paid on the individual level. For more on this topic, see “An indepth look at trading entities” ( Active Trader , May 2010).

Don’t botch Schedule D Reporting trading gains and losses properly can also be a challenge for securities

traders. Failing to follow the tax rules can lead to IRS questions, jeopardy assessments, and exams. In 2005, the IRS made a well-publicized 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 concerned 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 lineby-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 information reporting for securities transactions 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. longterm gain or loss, wash sales, and stockoption 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


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 amortization 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 losses are from certain foreign currency transactions) 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 transactions have capital gains and loss treatment, 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 securities. Some consumer tax-preparation programs offer trade-import capability capability,, but many aren’t robust enough for hyperactive 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 schedules. 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.

December 2010

TRADING Calendar

LEGEND CME: Chicago Mercantile Exchange

1

October construction spending November ISM manufacturing report FDD: December crude oil, natural gas, gold, silver, copper, platinum, 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)

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 contract can take place. FND (first notice day): Also 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. 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.

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

Purchasing asing managers index PMI: Purch

11

PPI: Produce Producerr price index Quadruple witching Friday: A Friday: A day where equity options, equity futures, index options, and index futures all expire.

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

64

October trade balance November federal budget December University of Michigan consumer sentiment LTD: January coffee options (ICE)

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 platinum options (CME) www.activetradermag.com • December 2010 • ACTIVE TRADER

Report times

16 17

Economic release November housing starts December Philadelphia fed survey 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 futures LTD: January crude oil futures (CME); December coffee futures (ICE)

21 22 23 24

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

November personal income, durable goods, and new home sales LTD: January soybean and soybean product futures futures (CME)

& capacity utilization Leading indicators

10 a.m.

Markets closed — Christmas holiday

Consumer confidence

10 a.m.

national activity index

8:30 a.m.

Production 9:15 a.m.

University of Michigan

26 27

29

GDP

Q3 GDP (third estimate) November existing home sales FND: January crude oil futures (CME)

25

28

Release time (ET)

December consumer confidence LTD: January natural gas futures (CME); January heating oil, RBOB gasoline, gold, silver, and copper options (CME) 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, silver, copper, copper, platinum, palladium, and soybean futures (CME) LTD: January heating oil, RBOB gasoline, and December live cattle futures (CME)


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 re 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. 65

THE Economy

U.S. economic briefing FAMILIAR ILLS CONTINUE TO PLAGUE RECOVERY

FIGURE 1: QUARTERL QUARTERLY Y GDP PERFORMANCE

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 percent. Unemployment continues to drag on the economy, the committee wrote in its release : “Household spending is increasing gradually gradually,,

The third estimate of Q2 GDP extended the previous quarter's contraction.

but remains constrained by high unemployu nemploy-

Source: Bureau of Economic Analysis

ment, modest income growth, lower housing FIGURE 2: PAYROLLS VS. UNEMPLOYMENT RATE

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-announcement behavior since 1997 (or earlier). The S&P fell 0.3 percent on the date of the committee’s announcement. Historically, the S&P has risen nearly 4 percent on FOMC rate announcement.

RATE RA TE CHANGE CHANGES S S&P 500 Historical reaction moves since ‘94 Report day -0.26%

0.35%

Five days later

0.43%

-0.05%

Non-farm payrolls declined in September, but to a lesser extent than in August. Source: Bureau of Labor Statisti Statistics cs

Seasonally adjusted

FIGURE 3: OVERALL VS. “CORE” INFLATION

REVISED SLIGHTLY HIGHER Report

Gross 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 Historical reaction moves since ‘94

Report day -0.31%

0.04%

Five days later

0.33%

66

1.33%

Price levels stabilized more during the summer months. Source: Bureau of Labor Statist Statistics ics

Not seasonally adjusted


CONSUMER PRICES INCREASE IN AUGUST Report

Consumer Price Index (CPI)

Date/time

Sept. 17 at 8:30 a.m.

FIGURE 4: ISM MANUFACTU MANUFACTURING RING INDEX

0.3% (core 0.0%)

Actual Previous

0.3% (core 0.1%)

Consensus

0.2% (core 0.1%) S&P 500 Historical reaction moves since ‘80

Report day

0.08%

0.08%

Five days later

0.02%

0.14%

Report

Producer Price Index (PPI)

Date/time

Sept. 16 at 8:30 a.m.

Actual

0.4% (core 0.1%)

Previous

0.2% (core 0.3%)

Consensus

0.3% (core 0.1%) S&P 500 Historical reaction moves since ‘94

Report day -0.04%

0.06%

Five days later

0.31%

0.82%

Manufacturing sentiment fell slightly in September but remained positive (above 50). Source: Institute of Supply Management

Seasonally adjusted

FIGURE 5: S&P 500

The S&P rose throughout September, but was somewhat subdued on release dates. Source: eSignal

ISM FALLS TO 10-MONTH LOW Report

ISM manufacturing index

Date/time

Oct. 1 at 10 a.m. 54.4

Actual Previous

56.3

Consensus

54.8 S&P 500 Historical reaction moves since ‘97

Report day

0.44%

0.27%

Five days later

1.48%

0.22% FIGURE 6: MARKET MARKET REACTION TO ECONOMIC REPORTS

PAYROLLS TAKE A HIT Report

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 Historical reaction moves since ‘94

Report day

0.61%

0.12%

Five days later

0.95%

-0.09%

The S&P remained fairly stable on economic release dates in September and early October.


67

STOCKS Snapshot

as of Oct. 6

Active Trader’ Trader’s Snapshot Snapshot tables summarize summarize the trading activity activity in the most actively traded traded stocks, ETFs, and futures. The The information information does NOT con- con- stitute trade signals. It is intended only to provide a synopsis of each market’s liquidity, direction, and levels of momentum and volatility.

Stock Positive one-year performance Las Vegas Sands Ford Motor SanDisk

Symbol

Volume

1-year return

10-day move/rank

20-day move/rank

60-day move/rank

Volatility ratio/rank

LVS

27.04 M

97.48%

9.23% / 40%

14.31% / 45%

50.60% / 86%

.34 / 40%

F

57.45 M

84.40%

6.95% / 94%

12.20% / 68%

13.55% / 64%

.43 / 80%

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% / 7 7%

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%

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%

.13 / 5%

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%

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%

Merck

E MC

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%

AXP

12.87 M

8.69%

QCOM 18.19 M

7.72%

3.12% / 10%

9.12% / 52%

24.20% / 80%

.12 / 3%

American Express QUALCOMM

-11.15% / 1 00% -5.12% / 52% -13.92% / 100 % 1.13 / 100%

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

PF E

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

N OK

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%

SCHW 12.08 M

-25.28%

4.55% / 80%

3.79% / 33%

-2.33% / 9%

.23 / 80%

ADBE

15.94 M

-25.05%

-3.52% / 0 %

-12.21% / 79%

-8.47% / 37%

.18 / 33%

NVDA 20.92 M

-22.94%

-5.36% / 100%

4.46% / 18%

-1.28% / 4%

.37 / 92%

The Charles Schwab Adobe Systems NVIDIA 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%

MS

13.99 M

-20.54%

1.72% / 20%

-2.27% / 32%

-1.09% / 11%

.28 / 32%

YHOO 23.51 M

-17.41%

3.42% / 58%

5.60% / 53%

-6.44% / 18%

.19 / 45%

Morgan Stanley Yahoo De ll

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% / 6 9%

12.45% / 70 %

.29 / 67%

Hewlett-Packard

H PQ

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% / 1 1%

1.99% / 19%

-1.43% / 11%

.39 / 50%

AMAT 22.69 M

-10.32%

5.11% / 47%

10.87% / 87%

-6.31% / 22%

.19 / 20%

1.90% / 0%

4.28% / 37%

-5.87% / 17%

.26 / 42%

Applied Materials Wells Fargo

WFC

36.44 M

-9.47%

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%

X

12.93 M

-0.09%

1.36% / 0%

-8.34% / 90%

3.50% / 13%

.26 / 52%

United States Steel

68


ETF Snapshot

as of Oct. 6

10-day move/rank

20-day move/rank

60-day move/rank


6.48 M 10.86 M 11.61 M 12.56 M 7.20 M 17.05 M 9.28 M 10.51 M 6.68 M 12.85 M 77.13 M 16.05 M 55.20 M 59.89 M 6.46 M 10.50 M 14.38 M 10.54 M 17.49 M 6.95 M 6.69 M 199.01 M 8.91 M 8.53 M 13.70 M 6.74 M 17.18 M 15.16 M 19.46 M 18.52 M

31.13% 2.21% / 0% 29.67% 0.65% / 0% 29.51% 9.55% / 100% 27.18% 4.45% / 100% 21.72% 2.51% / 25% 20.85% 3.03% / 42% 19.37% 3.10% / 42% 19.24% 3.50% / 5% 18.38% 4.80% / 30% 18.37% 5.77% / 80% 16.55% 1.11% / 0% 16.46% 4.44% / 37% 15.75% 5.71% / 75% 13.01% 4.21% / 58% 12.11% 2.21% / 20% 11.35% 3.94% / 45% 11.12% 6.05% / 78% 11.02% 2.07% / 5% 9.69% 7.14% / 75% 9.16% 1.36% / 26% 9.01% 1.18% / 43% 8.84% 2.30% / 37% 7.79% 3.19% / 53% 7.55% 0.91% / 11% 7.50% 13.38% / 58% 7.06% 0.72% / 0% 3.53% 4.81% / 80% 3.06% 6.41% / 85% 2.50% 4.08% / 50% 1.91% 3.68% / 79%

13.68% / 53% 1.69% / 14% 16.36% / 98% 7.42% / 100% 5.81% / 45% 6.10% / 49% 8.29% / 83% 8.89% / 68% 11.21% / 86% 10.35% / 89% 6.44% / 53% 11.15% / 57% 10.08% / 84% 8.08% / 69% 5.57% / 64% 9.40% / 84% 10.37% / 83% 6.97% / 54% 11.56% / 84% 3.41% / 58% 1.21% / 20% 5.09% / 55% 3.86% / 33% 0.67% / 8% 25.76% / 70% 5.04% / 69% 8.64% / 92% 7.58% / 77% 8.68% / 73% 4.54% / 82%

17.57% / 52% .23 / 15% 8.54% / 52% .28 / 12% 27.33% / 100% .46 / 50% 11.36% / 87% .46 / 92% 8.46% / 44% .27 / 32% 10.38% / 56% .32 / 43% 15.34% / 77% .31 / 33% 11.84% / 41% .24 / 35% 19.99% / 92% .27 / 38% 15.38% / 91% .36 / 62% 8.60% / 55% .21 / 7% 11.70% / 49% .33 / 67% 14.91% / 90% .38 / 68% 6.87% / 34% .43 / 78% 5.82% / 56% .28 / 25% 14.04% / 92% .21 / 27% -1.10% / 10% .40 / 52% 6.33% / 53% .24 / 12% 17.50% / 89% .44 / 90% 5.30% / 66% .24 / 30% 5.63% / 58% .19 / 13% 5.81% / 51% .32 / 58% 10.13% / 73% .29 / 50% 7.35% / 53% .21 / 17% 16.93% / 24% .42 / 100% 4.90% / 64% .25 / 30% 8.27% / 84% .49 / 72% 9.08% / 80% .61 / 100% 11.62% / 85% .29 / 53% 5.63% / 88% .60 / 100%

Negative one-year performance Small Cap Bear 3X Shares United States Natural Gas Fund Large Cap Bear 3x Shares ProShares UltraPro Short S&P500 UltraShort Russell 2000 ProShares Financial Bear 3x Shares UltraShort QQQ ProShares UltraShort 20+ Year Tr. ProShares UltraShort S&P 500 ProShares UltraShort Financials ProShares Financial Bull 3x Shares ProShares Ultra DJ-UBS Crude Oil S&P Select Financials SPDR Fund United States Oil Fund

25.13 M 22.52 M 7.04 M 7.97 M 7.97 M 47.89 M 13.64 M 10.62 M 33.11 M 6.56 M 36.01 M 6.40 M 79.97 M 9.67 M

-58.00% -13.89% / 60% -48.55% -5.80% / 55% -43.00% -7.45% / 32% -41.38% -7.26% / 32% -39.71% -9.33% / 60% -36.80% -5.87% / 21% -36.74% -2.88% / 0% -29.66% -2.88% / 30% -27.71% -4.83% / 32% -21.84% -4.09% / 29% -19.10% 4.13% / 8% -5.19% 23.56% / 100% -2.58% 1.45% / 17% -1.09% 11.36% / 100%

-23.64% / 67% -3.14% / 14% -16.48% / 59% -16.14% / 59% -16.17% / 68% -11.47% / 48% -13.02% / 56% -2.92% / 15% -11.00% / 59% -7.80% / 46% 9.45% / 38% 18.61% / 86% 2.72% / 28% 9.47% / 85%

-28.06% / 60% .31 / 12% -16.04% / 26% .27 / 33% -21.63% / 62% .22 / 7% -20.87% / 60% .22 / 7% -18.12% / 54% .35 / 20% -9.02% / 27% .29 / 23% -18.26% / 68% .16 / 2% -17.76% / 58% .14 / 20% -13.90% / 60% .24 / 8% -4.39% / 21% .32 / 27% -3.73% / 7% .31 / 92% 10.04% / 55% 1.10 / 100% -1.14% / 8% .37 / 70% 4.43% / 53% 1.04 / 100%

TZ A UN G BGZ SPXU TWM FAZ QID TBT SDS SKF FAS UCO XLF USO

Leverage: “2x” = double leverage; “3x” = triple leverage. Volume: 30-day average daily volume. 1-year return: The percentage price move from the close one year ago (250 trading days) to today’s close. 10-day move: The percentage price move from the close 10 days ago to today’s close. 20-day move: The percentage price move from the close 20 days ago to today’s close. 60-day move: The percentage price move from the close 60 days ago to today’s close.

3x

yes

3x 3x 2x 3x 2x 2x 2x 2x 3x 2x

yes y es y es yes yes yes y es y es

Volume

1-year rreeturn

ETF Symbol Leverage Inverse Positive one-year performance Ultra QQQ ProShares QLD 2x iShares DJ US Real Est. Index Trust IYR iShares Silver Trust SLV SPDR Gold Trust GLD S&P Select Cons. Disc. SPDR Fund XLY S&P Select Industrial SPDR Fund XLI Market Vectors Gold Miners ETF GDX S&P Select Retail SPDR Fund XRT iShares MSCI Hong Kong Index EWH Vanguard Emer. Markets Stock ETF VWO PowerShares QQQ Trust QQQQ ProShares Ultra S&P 500 SSO 2x iShares MSCI Emerging Market EEM iShares Russell 2000 Index Trust IWM Diamonds Trust DIA iShares MSCI Taiwan Index EWT Semiconductor HOLDRS SMH S&P Select Technology SPDR Fund XLK iShares MSCI Brazil Index Fund EWZ S&P Select Consumer Staples SPDR XLP S&P Select Utilities SPDR Fund XLU S&P Depository Receipts SPY S&P Select Materials SPDR Fund XLB iShares Barclays 20+ Year T-Bond TLT Small Cap Bull 3x Shares TN A 3x S&P Select Health Care SPDR Fund XLV iShares FTSE/Xinhua China 25 FXI S&P Select Energy SPDR Fund XLE iShares MSCI EAFE Index Trust EFA iShares MSCI Japan Index Fund EWJ

The “Rank” fields for each time window (10-day moves, 20-day moves, etc.) show the percentile rank of the most recent move to a certain number of the previous moves of the same size and in the same direction. For example, the “Rank” for 10-day move shows how the most recent 10-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 move compares to the past sixty 20-day moves; for the 60-day move, the “Rank” field shows how the most recent 60-day move compares to the past one-hundred-twenty 60-day moves. A reading


of 100 percent means the current reading is larger than all the past readings, while a reading of 0 percent means the current reading is smaller than all previous readings. These figures provide perspective for determining how relatively large or small the most recent price move is compared to past price moves. Volatility ratio/rank: The ratio is the short- term volatility (10-day standard deviation of prices) divided by the long-term volatility (100- day standard deviation of prices). The rank is the percentile rank of the volatility ratio over the past 60 days.

69

FUTURES Snapshot Market

E-Mini S&P 500

as of Oct. 6

Symbol

Exchange

Volume

Open interest

ES

CME

1.96 M

2.44 M

10-day move/rank

2.30% / 32%

20-day move/rank

5.14% / 58%

60-day move/rank

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

7.88% / 46%

.89 / 100%

EUR/USD

EC

CME

318.3

190.1

.39 / 73%

E-Mini Nasdaq 100

NQ

CME

310.6

30-yr. T-bond

US

CME

347.1

2-yr. T-note

TU

CME

Eurodollar*

ED

Mini Dow

YM

E-Mini Russell 2000 JPY/USD Gold 100 oz.

11.40% / 100% 11.46% / 91% 4.07% / 45%

9.50% / 100%

9.66% / 96%

330.8

1.25% / 0%

6.75% / 57%

8.78% / 56%

.19 / 0%

622.7

1.54% / 30%

1.66% / 30%

7.28% / 64%

.20 / 33%

204.4

660.9

0.05% / 40%

0.11% / 96%

0.15% / 39%

.22 / 72%

CME

210.8

810.0

0.09% / 38%

0.23% / 60%

0.66% / 43%

.07 / 35%

CME

129.6

75.3

2.18% / 20%

4.95% / 62%

6.01% / 61%

.27 / 27%

TF

CME

141.1

362.8

4.45% / 65%

8.27% / 73%

7.17% / 27%

.45 / 78%

JY

CME

133.2

122.5

1.93% / 30%

1.31% / 26%

6.77% / 58 %

.19 / 37%

GC

CME

110.5

399.3

4.30% / 95%

7.17% / 100%

11.06% / 84%

.42 / 92%

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%

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

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%

Corn

Sugar

13.38% / 100% 11.16% / 96%

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 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 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. no responsibility for the use of this information. Active Trader Trader does not recommend buying or selling any market, nor does i t 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.

Active Tr Trader ader assumes

70



continued from p. 19

FIGURE 6: SIGNAL COMP COMPARISON ARISON

this will not present too much of a problem, but when an initial decline is followed by an even greater decline, triggering repeated signals and ever-larger trades, the losses will mount with almost geoPattern 3’s superior performance can be attributed to its more selective entries. The first metric speed. This drawback two pattern variations often entered repeatedly as the market continued to decline, resulting in exceptionally large drawdowns. could be countered by using a different approach, such as one early signal on Jan. 27. Patterns 1 and 2 issues repeated adjusting position size according to account equity: As the early signals — and, it must be noted, triggered on May 5, the account equity declines, the number of shares purchased will day before the flash crash. Although on a closing basis, the lossdecrease. However, this will also likely mitigate the patterns’ es would not have been disastrous, these trades would have obvious tendency to bounce back quickly from losses because experienced massive intraday drawdowns on May 6. trade sizes will be smaller when the market is rebounding. The lesson of the pattern: Over time, sharp sell-offs such as Risk control and money management those identified by this price model are buying opportunities in It is important that no attempt was made to optimize the patthe S&P. S&P. Taking advantage of them, however, requires both tern, and no stop-losses or other risk controls were introduced financial wherewithal and psychological fortitude. Pattern 3 had in testing. It is safe to assume that adding risk controls would the smallest drawdowns because it entered much more selectivehave reduced the patterns’ drawdowns, while also curbing their ly than the other pattern variations — it tended to avoid enterwinning percentages and total profits. ing too early in most down moves, and when it did, it was less Also, the money management approach used in the test — a likely to enter repeatedly, which resulted in relatively smaller fixed-dollar trade size — will exacerbate the drawdowns during trade sizes for losing trades. Although it ended the analysis periprolonged and severe market od with the lowest total equity, its return was far superior on a declines. As price drops drarisk-adjusted basis. matically — as it did in late KC Go to “Key concepts” on Pattern 3’s greater stability and relative outperformance also 2008, for example — more p. 78 for more information about: hint at the benefits of incorporating comparisons of non-consecshares are purchased per sig- utive price bars. Finally, traders who don’t want to sit through • Average and median nal because of the lower • Variance and standard huge losses during market down swings must incorporate stopstock price. During a brief deviation losses and appropriate money management.  and not-too-severe decline, 72


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Wealth Lab

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www.wealth-lab.com Wiley

47

www.wiley.com X-view

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www.xview.com


Key CONCEPTS At the mone y (ATM): An option whose strike price is identical (or

very close) to the current underlying stock (or futures) price. Average and median: The mean (or average) of a set of values is

the sum of the values divided by the number of values in the set. If a set consists of 10 numbers, add them and divide by 10 to get the mean. A statistical weakness of the mean is that it can ca n be distorted by exceptionally 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 the mean of the remaining seven numbers is 4, which is much more representative of the numbers in this set than 28.5. The median can help gauge how representative a mean really is. The median of a data set is its middle value (when the set has an odd number of elements) or the mean of the middle two elements (when the set has an even number of elements). The median is less susceptible than the mean to distortion from extreme, 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 with the majority of numbers in the set. Call option: An option that gives the owner the right, but not the

obligation, 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 of an investment if it had increased at a steady rate during the time period. Suppose you bought $1,000 in stock four years ago, and the investment rose to $1,100 in the first year, $1,150 in the second year, $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%

using this formula to calculate the equivalent smoothing constant:

SC = 2/(n + 1) where: n = the number of days in a simple moving average of approximately approx imately equivalent length. For example, a smoothing constant of 0.095 creates an exponential moving average equivalent to a 20-day SMA (2/(20 + 1) = 0.095). The larger n is, the smaller the constant, and the smaller the constant, the less impact the most recent price action will have on the EMA. In practice, most software programs allow you to simply choose how many days you want in your moving average and select either simple, weighted, or exponential calculations. Fibonacci series: A number progression in which each successive

number 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, the ratio of a number in the series divided by the immediately preceding number approaches 1.618. A basic application appl ication is to calculate likely price targets. For example, if a stock broke out of a trading range and rallied from 25 to 55, potential retracement levels could be calculated by multiplying the distance of the move (30 points) by Fibonacci ratios –– say, say, 0.382, 0.50, and 0.618 –– and then subtracting the results from the high of the price move. In this case, retracement levels of 43.60 [55 - (30 * 0.38)], 40 [55 - (30 * 0.50)], and 36.40 [55 - (30 * 0.62)] would result. The most commonly used ratios are 0.382, 0.50, 0.618, 0.786, 1.00, 1.382, and 1.618. Depending on circumstances, other ratios, such as 0.236 and 2.618, are used. While Fibonacci retracements are used to calculate the possible partial correction levels of a previous price move (i.e., a reversal of up to 100 percent of a previous price swing), Fibonacci extension levels are used to extrapolate moves in the same direction as a previous price swing — for example, pro jecting a target for a new ne w upswing that represents a 161.8-percent gain from a certain price level based on the size of the previous upswing. In the money (ITM): A call option with a strike s trike price below the

Current ratio: A company’s company’s current assets asse ts divided by its current

liabilities. It is used as a rough measure of a company’s financial health by determining its ability to pay off short-term liabilities with short-term assets, such as cash and inventories. A current ratio below 1.00 means the company’s liabilities outweigh its assets.

underlying instrument’s current price, or a put option with a strike price above the underlying instrument’s current price. Naked (uncovered) puts: Selling put options to collect premium

that contains risk. If the market drops below the short put’s strike price, the holder may exercise it, requiring you to buy stock at the strike price (i.e., above the market).

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, and EMA(yesterday) is the previous day’s EMA value.

Out of the money (OTM): A call option with a strike stri ke price above a bove the

price of the underlying instrument, or a put option with a strike price below the underlying instrument’s price. Premium: The price of an option. Stop-and-reverse (SAR): A trading system syste m that is always in the

You can approximate a particular particul ar SMA length for an EMA by 78

market, liquidating long trades and going short when a sell sigwww.activetradermag.com

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December 2010

• ACTIVE TRADER

nal occurs and covering shorts and going long when a buy signal occurs. Strike (“exercise”) price: The price at which an underlying instru-

ment is exchanged upon exercise of an option. True range: r ange: A measure of price pric e movement or volatility volati lity that

accounts for the gaps that occur between price bars. This calculation provides a more accurate reflection of the size of a price move over a given period than the standard range calculation, which is simply the high of a price bar minus the low of a price bar. The true range calculation was developed by Welles Wilder and discussed in his book New Concepts in Technical Trading Systems (T (Trend rend Research, 1978). True range can be calculated on any time frame or price bar — five-minute, hourly hourly,, daily, weekly, weekly, etc. Using daily price bars as an example, true range is the greatest (absolute) distance of the 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 true Average range over a certain time period. For example, the five-day ATR would be the average of the true range calculations over the last five days. Variance and standard deviation: V Variance ariance measures how spread out

a group of values are — in other words, how much they vary. Mathematically, variance is the average squared “deviation” (or difference) of each number in the group from the group’s mean value, divided by the number of elements in the group. For example, for the numbers 8, 9, and 10, the mean is 9 and the variance 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 of numbers: 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 — the more widely distributed they will be. The more varied a market’s price changes from day to day (or week to week, etc.), the more volatile that market is. A common application of variance in i n trading is standard deviation, which is the square root of variance. The standard deviation of 8, 9, and 10 is: sq. rt. 0.667 = .82; the standard deviation of 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 (usually calculated as the standard deviation of closing prices) over a certain time period — e.g., the past 20 days. Implied volatility is the current market estimate of future volatility as reflected in the level of option premiums. The higher the implied volatility, the higher the option premium.

ACTIVE TRADER •

December 2010

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Volatility skew (“smile”): The tendency of implied option volatility

to vary by strike price. Although, it might seem logical that all options on the same underlying instrument with the same expiration would have identical (or nearly identical) implied volatilities. For example, deeper in-the-money and out-of-the-money options 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 a line curving upward at both ends. Volatility skews can take other forms than the volatility smile, though. Weighted moving average: A simple moving average (SMA) is the

average price of a stock, future, or other market over a certain time period. A five-day SMA is the sum of the five most recent closing prices divided by five, which means each day’s price is equally weighted in the calculation. The weighted moving average (WMA) — as well as the exponential moving average (EMA) — puts greater emphasis on recent prices under the assumption current market activity is more important than more distant activity, which makes the average more responsive to price changes. A WMA multiplies each day’s closing price by a “weighting factor,” with the most recent close receiving the heaviest weighting and the greatest impact on the moving average value. The weighting factors are based on the number of days in the average. The sum of the weighted closes is then divided by the sum of the weighting factors over the desired period to derive the weighted moving average value. The following table shows how a basic five-day weighted moving average would be calculated:

Weighted closing price

Closing Weighting price factor (closing price times weighting factor)

Day 1 Day 2 Day 3 Day 4 Day 5 (most recent day)

10 10.5 11.25 14.75

1 2 3 4

10 21 33.75 59

18.5

5

92.5

15

216.25

Sum: 5-day SMA (avg. of closing prices): 5-day WMA (sum of weighted closing prices divided by sum of weighting factors):

13

14.42

The most recent day is given a weight of 5, the next most recent day a weight of 4, and so on. The most recent day in a 20-day WMA would be weighted by 20, and so on. The closes are multiplied by their respective weighting factors. These results are added together (216.25) and then divided by the sum of the weighting factors (in this case, 15). The result is a five-day weighted average value of 14.42, compared to a simple average value of 13.  79

TRADE Diary One bad trade is all it takes to ruin a day of careful trading.

TRADE Date: Monday, Sept. 20, 2010. Entry: Short and long the December EMini 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, 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.

Source: TradeStation

Trade Summary Time (CT)

Long/Short

Trade price

10:59 a.m.

Short

10641

11:12 a.m.

Cover

10639

11:12 a.m.

Long

10638

11:58 a.m.

Sell

10638

12:04 p.m.

Short

10642

12:18 p.m.

Cover

10640

12:30 p.m.

Short

10644

3:01 p.m.

Cover

10684

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. weakly.

TRADE SUMMARY Profit/loss: -36 points. Outcome: The day’s trading would not have been worth mentioning if not for the discipline mistake on the final short trade. Frustration built throughout the day as the market mostly wiggled sideways. After the market failed to follow through to the downside after the first short trade, we flip-flopped on the market, going long but eventually scratching the position after a little 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 80

Point P/L

% P /L

2

0.02%

0

0.00%

2

0.02%

-40

-0.38%

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 materialize. We 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 married to a position is a certain recipe for losses. 

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, However, individual trades are a function of immediate market behavior; initial price targets are flexible and are most often used as points at which a portion of the trade is liquidated to reduce the position’ss open risk. As a result, the initial ( pre-trade tion’ pre-trade)) reward-risk ratios are conjectural by nature.


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