S&c 03-2019.pdf

THE TRADERS’ MAGAZINE SINCE 1982

The 21st-Century Covered Call How to kill two birds with one option

8

Does History Repeat Itself?

Looking for time-based patterns for a trading edge 12

Trading The FakeouT

Keep an eye on these winning patterns

18

Interview

Claudio Demb and the psychology of trading

Breakaway Gaps

34

Testing them for reliability and profitability 38

MARCH 2019

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MARCH 2019

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03-IB19-1167CH1165

Contents 7 Daytrading Pivot Channels The Traders’ MagazineTM EDITORIAL

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by Ken Calhoun Does “intraday swing trading” sound like a contradiction in terms? Well, it may actually not be because it combines the best of both worlds. Find out how this approach can solve some of the trouble spots of both daytrading and swing trading and ultimately boost profit potential.

8 The 21st-Century Covered Call

by Robert J. Seifert Are you tired of writing covered calls only to see your stock get called away as it heads into the stratosphere? Here is a strategy that professional traders use to kill two birds with one option.

FEATURE ARTICLE 12 Does History Repeat Itself In The Markets?

by Alessandro Aldrovandi, Max Malandra, & Fabio Pacchioni Seasonality or repeating patterns in financial data can come in many shapes and sizes and can put the probabilities on your side. Or does general market unpredictability ultimately undermine that edge? Here are some ideas on looking for time-based patterns and an example of how to backtest their reliability.

18 Trading The Fakeout

by Fawad Razaqzada Can you profit from fakeouts? Here’s one trader’s take on how to identify these patterns and trade them profitably.

25 Algo Q&A

by Kevin J. Davey Got a question about system or algo trading?

26 All About OBV

by John Devcic On-balance volume is a classic indicator that can be helpful for indicating trends and for confirming other indicators. You’ve undoubtedly looked at it on your charts, but if you’ve never really known exactly how it works or how it’s most useful, here’s a close-up view.

n Cover: Inga Poslitur n Cover concept: Christine Morrison

MARCH 2019, Volume 37 Number 3 INTERVIEW 34 The Psychology Of Trading With Claudio Demb

by Jayanthi Gopalakrishnan Claudio Demb, MD, is a psychiatrist and has been an individual trader since the 1990s. He is a frequent contributor to this magazine. Along with his professional practice, he has an academic appointment as an instructor in psychiatry at Harvard Medical School. We spoke with him about how he juggles trading and a full-time job, and how he uses his insights into trading psychology to improve his own trading.

38 Trading Breakaway Gaps

by Pawel Kosinski Many traders like to trade breakaway gaps. How effective are they? Here, we look at backtesting results of trading different breakaway gaps to determine if they can be profitable.

41 Futures For You

by Carley Garner Here’s how the futures market really works.

42 Sector-Rotation ETFs Underperform

by Leslie N. Masonson Rotating into leading sectors while switching to a defensive position in downturns is an ideal tactic if it can be implemented successfully, but can it? Are there any ETFs that have succeeded with it? If there are, they could save you the work of implementing the approach yourself, so we’ll investigate.

46 Explore Your Options

by Jay Kaeppel Got a question about options?

60 Trading Perspectives

by Rob Friesen Some perspectives on the equities world.

DEPARTMENTS 6 Letters To S&C 48 Traders’ Tips 57 Advertisers’ Index 57 Editorial Resource Index 58 Futures Liquidity 59 Classified Advertising 59 Traders’ Resource

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4 • March 2019 • Technical Analysis of Stocks & Commodities

Traders take many paths to reach their destination.

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The editors of S&C invite readers to submit their opinions and information on subjects relating to technical analysis and this magazine. This column is our means of communication with our readers. Is there something you would like to know more (or less) about? Tell us about it. Without a source of new ideas and subjects coming from our readers, this magazine would not exist. Email your correspondence to [email protected] or address your correspondence to: Editor, Stocks & Commodities, 4757 California Ave. SW, Seattle, WA 98116-4499. All letters become the property of Technical Analysis, Inc. Letter-writers must include their full name and address for verification. Letters may be edited for length or clarity. The opinions expressed in this column do not necessarily represent those of the magazine.—Editor

LEAVITT CONVOLUTION Editor, I read with interest Jay Leavitt’s article in the January 2019 issue of Technical Analysis of Stocks & Commodities, “Leavitt Convolution.” I then logged in to the S&C archives at Traders.com and read all of his previously published articles in S&C. I am a user of thinkorswim and I see that this is one of the trading platforms that Leavitt uses. The thinkorswim platform has a very nice Hull moving average

indicator; I would be very interested in an indicator that outperforms the HMA. Could you either share or direct me to a place where I might be able to obtain the thinkscript for Leavitt’s convolution indicator? Thank you very much for your time.— David Author Jay Leavitt replies: I just went into the thinkorswim platform and perused the list of studies there, and I was able to find the Leavitt convolution in the list. In any case, here is the code:

script LeavittProjection{ input y = close; input n = 20; rec x = x[1] + 1; def a = (n * sum(x * y, n) - sum(x, n) * sum(y, n) ) / ( n *sum(Sqr(x), n) - Sqr(sum(x, n))); def b = (sum(Sqr(x), n) * sum(y, n) - sum(x, n) * sum(x *y, n) ) / ( n * sum(Sqr(x), n) Sqr(sum(x, n))); plot LeavittProjection= a*x+ b; } script LeavittConvolution { input price = close; input n = 20; def intLength = Floor(Sqrt(n)); plot LeavittConvolution = LeavittProjection (LeavittProjection (price, n), intLength); } def price = Close; input length = 9; def intLength = Floor(Sqrt(length)); plot LeavittConvolution = LeavittProjection (LeavittProjection (price, length), intLength); LeavittConvolution.AssignValueColor(if LeavittConvolution > LeavittConvolution [1] then Color.GREEN else Color.RED);

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Intraday Swing Trading Basics Does “intraday swing trading” sound like a contradiction in terms? Well, it may actually not be because it combines the best of both worlds. Find out how this approach can solve some of the trouble spots of both daytrading and swing trading and ultimately boost profit potential.

O

by Ken Calhoun

ne of the biggest risks active traders struggle with is holding positions overnight that gap against them, causing expensive stop-losses. Classic daytrading can also be challenging, because of the speed and accuracy needed to capitalize on early breakouts that move just $0.30–$0.80 during the opening hour. There is a hybrid trading approach that involves intraday swing trading, in which you enter your position during the first hour of the market open, then close the position at the end of the day. The main benefit to this technique is much higher profit potential compared to classic daytrading, because when stocks run

Winner 16 years in a row!

up all day long you can gain as much as $1 to $5 per share in a single trade.

Enter early, exit late

The best chart pattern to use this technique with is minor gap continuations, as seen in the chart of Canopy Growth Corp. (CGC) in Figure 1. Minor gaps are those in which the opening price gaps up less than 10%, for example, $3 or less for a $30 stock. I use buy stoplimit orders to enter all of my positions. In this example, that would be a buy-stop $31.50 limit $32. I have found that my biggest error in using this approach has been tightening up trailing stops on winners and exiting my trades too soon. If you are a veteran daytrader, you may also experience this, because early profit-taking is a way of life. If I’m up $0.80 per share on a trade I just entered 10 minutes ago, for example, I usually tighten up my stop to lock in a profit. However, the solution that has worked best for me is to scale out, so I may exit half my winning trade with a tight stop at the first sign of a pullback, but let the remaining half of the position

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Step-by-step action plan

Here’s how you can start using this strategy. Scanning: Find a stock with a small gap up of less than 10%, as seen in Figure 1. Step 1: Enter longs $0.50 above the nearest whole number premarket high. We wait this amount to help minimize false breakouts, which often occur during the first $0.20 or so above the opening high day (this would be at $31.50 in Figure 1).

esignal

Step 2: Set your initial stop-loss at $0.50 below the whole number price value ($30.50 in Figure 1). You can also test using a loss of the premarket low as your initial stop value ($30 in Figure 1). FIGURE 1: Intraday Swing Trading (CGC). A large three-point intraday breakout moved up all day. March 2019

Continued on page 32 • Technical Analysis of Stocks & Commodities • 7

Weeklys To The Rescue

Are you tired of writing covered calls only to see your stock get called away as it heads into the stratosphere? Here is a strategy that professional traders use to kill two birds with one option.

W

by Robert J. Seifert

hen I began trading options in 1983, one of the hardest trades to execute was to buy a put to protect against the downside risk inherent in the market. The problem with buying puts then and now is that the price you pay for the protection is high and it’s very hard to overcome the premium loss. An alternative trade was to sell a covered call for each quarterly expiration. Unfortunately, although this offers some protection, it can create a problem in markets that are in a long-term congestion phase. Back then, executing a covered call trade was done by selling a call every quarter against an underlying stock you owned. When the option contract expired, you kept the premium you received when you sold the call, no matter what. If the stock closed above the naked call’s strike price, you would keep the credit, but the stock would be called away from you (exercised). Your upside was limited to the credit while you

8 • March 2019 • Technical Analysis of Stocks & Commodities

had unlimited downside risk. If the stock closed lower than the naked leg strike price but above your cost for the underlying stock, you would have kept the premium, which lowered your average cost for the stock. The problem with this strategy was that when the stock moved against you by more than the credit amount, you were left with unlimited downside risk. Trying to get out of this situation usually resulted in selling lower strikes, which ended with the stock getting called away at a loss. As time moved on, the powers that be decided that if quarterly options were good then perhaps monthly options might even be better, so they added monthly options to the mix. You could write the same call 12 times a year. The advantage of writing the call 12 times a year was that it gave you more downside protection. However, it also gave you four times as much risk in that the premium in the call you sold may not be enough to cover an advance in the stock price and you could then lose the underlying stock. Around 2010, the SEC decided that if monthly options were good, options that expired each week would be great! They started a pilot program using weekly options, which is now the biggest option product in the world. So if selling covered calls four times a year was a good deal, it must be a much better proposition to write them 52 times a year, right? The

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answer is a resounding yes. Over the years, this strategy has evolved and allows traders and investors a much better chance to make money. No approach offers more opportunity than what I call the “21st-century covered call.” Let’s look at how this strategy works.

Initiating the 21st-century covered-call strategy

return and you still own the stock. What about the downside? If Tesla’s stock were to close lower for the week, you would keep the $5.90 spread credit, which would limit your stock loss for the week. The good news is that no matter what happens, if you write the 21stcentury covered call for around a $5.90 credit 52 times a year, it would give you $306.80 ($5.90 × 52= $306.80) of downside protection, which is 95.6% of the purchase price of the stock ($320.87).

First, we must address the number-one problem of writing covered calls: losing the upside potential if the stock takes off. To counter this situation, I sell a weekly call-credit spread How does all this work in real time? instead of simply selling a call. To see how this strategy sets Ideally, you want to do this covered-call strategy with stocks up, let’s look at the option chain in Figure 1 for Tesla Motor that have a good chance of trading higher over the near term and Co. (TSLA) when the stock closed at $320.87. that also have liquid, weekly options. To select stocks that meet With TSLA at $320.87, the at-the-money (ATM) call is the this criteria, you can use a scanner similar to the one we offer 320.0 and it is trading at approximately $11.60. We want to at MarketEdge (www.marketedge.com, a website developed use the ATM strike as the short call leg of the spread since it by Computrade Systems, Inc., which has been in existence as gives you the most premium. Under the old method of selling either a software application or website since 1992). covered calls, you would buy the stock at $320.87 and sell the 320.0 call for $11.60. If the stock settled above $320, you Profit/loss scenarios would lose the stock at $320 but would keep the $11.60 credit for the strategy for a net gain of $10.73 ($11.60 credit - $0.87 stock loss). Not There are six scenarios you can expect each a bad deal, but if the stock went on to $360, you would miss week when employing this strategy: flat, small out on any profit past $10.73. gain, rally, big rally, small loss, and big loss. Meanwhile, under the 21st-century covered-call strategy, Using TSLA as an example, let’s check out you would buy the stock at $320.87, sell the 320.0 call at the possible outcomes. Say you purchase 100 $11.60 and buy another call at a higher strike price with the shares of TSLA at $320 and initiate the 320–335 call credit same expiration date. You can buy any strike price you like spread for $5.90. For illustrative purposes, the following asbut I recommend you buy one that is no more than $15.00 sumes the option positions are closed on the weekly expiration higher than the ATM, which in this case would be the 335.0 date and the stock position remains open. Each week there call (ATM +6). will be a realized gain or loss in your option account and an So you would buy the stock at $320.87 and initiate a 320.0– unrealized gain or loss in your stock account. 335.0 call-credit spread by selling the 320.0 call for $11.60 and buying the 335.0 call for $5.70, resulting in a net credit Initial position: 100 shares (TSLA) @ $320 = $32,000 of $5.90 ($11.60 - $5.70). If the stock settles above $325.90 Options: 320–335 call credit spread @$5.90 = $590 credit ($320.00 + $5.90), let’s say $335.00, the call spread would be a $9.10 loser since you would be called away at $320.00 but you would exercise your 335.0 call for a spread loss of $15.00 less the $5.90 credit. However, the stock portion of the trade would make $14.13 ($335.00 $320.87), so you would net $5.03 ($14.13 stock gain - $9.10 spread loss) for the trade. So while your gain is $5.70 less than what you would have made the oldfashioned way ($10.73 - $5.03), you get a nice FIGURE 1: OPTION CHAIN OF WEEKLYS. See what the at-the-money calls are trading for and then set up your trading strategy. 10 • March 2019 • Technical Analysis of Stocks & Commodities

1) Flat: The stock settles unchanged at $320 This is a no-brainer. You pocket the $590 from the call credit spread. You have no change in your stock value. Continue the next week by initiating another 320.0–335.0 call credit spread for a credit of around $5.90. Total account value: $32,000 + $590 = $32,590 ($590 gain) 2) Small gain: The stock rises by about 1.5% to $325 You pocket the $590 from the call credit spread on Monday, buy back the expiring in-the-money (ITM) short call at $500 on Friday afternoon (before expiration) for a realized gain of $90 on the options, an unrealized gain of $500 on the stock. Continue the next week by initiating a 325.0–340.0 call credit spread for a credit of around $5.90. Total account value: $32,500 + $590 - $500 = $32,590 ($590 gain) 3) Rally: The stock rallies by about 5% ($15) to $335 You pocket the $590 from the call credit spread on Monday, and buy back the expiring in-the-money spread at $1,500 on Friday afternoon (before expiration) for a realized loss of $910 on the options, an unrealized gain of $1,500 on the stock and you keep the stock.

You want to do this coveredcall strategy with stocks that have a good chance of trading higher over the near term and also have liquid, weekly options.

Continue the next week by initiating another 335.0–350.0 call credit spread for a credit of around $5.90. Total account value: $33,500 + $590 - $1,500 = $32,590 ($590 gain) 4) Big rally: The stock rallies by about 9% to $350 You pocket the $590 from the call credit spread on Monday, and buy back the expiring in-the-money spread at $1,500 on Friday afternoon (before expiration) for a realized loss of $910 on the options, an unrealized gain of $3,000 on the stock and Continued on page 33

SINCE

Test-drive it to See All the Signals! THESE RESULTS ARE BASED ON SIMULATED OR HYPOTHETICAL PERFORMANCE RESULTS THAT HAVE CERTAIN INHERENT LIMITATIONS. UNLIKE THE RESULTS SHOWN IN AN ACTUAL PERFORMANCE RECORD, THESE RESULTS DO NOT REPRESENT ACTUAL TRADING. ALSO, BECAUSE THESE TRADES HAVE NOT ACTUALLY BEEN EXECUTED, THESE RESULTS MAY HAVE UNDER-OR OVER-COMPENSATED FOR THE IMPACT, IF ANY, OF CERTAIN MARKET FACTORS, SUCH AS LACK OF LIQUIDITY. SIMULATED OR HYPOTHETICAL TRADING PROGRAMS IN GENERAL ARE ALSO SUBJECT TO THE FACT THAT THEY ARE DESIGNED WITH THE BENEFIT OF HINDSIGHT. NO REPRESENTATION IS BEING MADE THAT ANY ACCOUNT WILL OR IS LIKELY TO ACHIEVE PROFITS OR LOSSES SIMILAR TO THESE BEING SHOWN. THE TESTIMONIAL MAY NOT BE REPRESENTATIVE OF THE EXPERIENCE OF OTHER CLIENTS AND THE TESTIMONIAL IS NO GUARANTEE OF FUTURE PERFORMANCE OR SUCCESS. TECHNICAL ANALYSIS OF STOCKS & COMMODITIES LOGO AND AWARD ARE TRADEMARKS OF TECHNICAL ANALYSIS, INC.

March 2019

• Technical Analysis of Stocks & Commodities • 11

12 • March 2019 • Technical Analysis of Stocks & Commodities

QUANTITATIVE ANALYSIS

Time As A Trading Signal

Does History Repeat Itself In The Markets? Translating it into financial terms, we can easily deduce that Kairos represents market timing, relating to those ex ante, unpredictable times when an operating alert pops up and signals you to open long or short positions due to technical reasons. That reason could be a breakout of a resistance; reaching an overbought or oversold condition on a particular oscillator; the it possible to forecast market moves by using completion of a particular pattern; or so on. Kronos, a “time factor”? Here, we’ll attempt to answer on the other hand, represents the cyclical nature of that question using monthly timeframes. For events that regularly happen in financial markets. this study, we used the forex market and staMarket timing (Kairos) is one of the pillars of tistically analyzed historical monthly performances of technical analysis, while Kronos has not yet been some of the major currency pairs. We hypothesized deeply detected. The question could be: Do we need that the major currencies vs. the US dollar, which is to wait for a technical “event” (such as the break of usually a mean-reverting relationship, could indicate resistance/support, the cross of a moving average, or seasonality in the different months of the year. so on) to have a profitable trade, or do we, perhaps, Then we looked at those monthly performances to need to wait for the “right” day (or month or hour) to try to discover (ex post, or based objectively on past enter a trade? Let’s find out. results) seasonal patterns that we later tested in an in-sample/out-of-sample strategy. Would any seasonal Statistical research effect discovered in exchange rates in past years or While classic technical analysis mostly focuses on dedecades work as well in recent years despite market termining whether price is trending (ignoring whether volatility and central bank quantitative easing policies? and when price trends could happen), technical analysis Or is the market too unpredictable for the monthly based on time focuses on time-based alerts and trends seasonal effect to prevail? observed in past data. In our study, we chose monthly timeframes applied to three well-known and liquid “Timed” technical analysis forex (FX) markets: EUR/USD, GBP/USD, and USD/ Ancient Greeks believed the concept of time was CAD. We followed these two steps: so important that they had two gods dedicated to it: Kronos and Kairos. The former referred to time 1. In-sample analysis. We looked for monthly as “in being,” with the flow of hours, days, months, historical trends (bullish and bearish). From and years, while the latter as “momentum.” Kronos 1998–2013 we calculated monthly returns for connotes time quantitatively as “past,” “present,” and each major pair and the average return for each “future,” while Kairos connotes time in qualitative month over a period of 15 years. When positive, terms as “when something happens.” the trend of that month was defined as “bullish.” Seasonality or repeating patterns in financial data can come in many shapes and sizes and can put the probabilities on your side. Or does general market unpredictability ultimately undermine that edge? Here are some ideas on looking for time-based patterns and an example of how to backtest their reliability.

INGA POSLITUR

Is

by Alessandro Aldrovandi, Max Malandra, & Fabio Pacchioni March 2019

• Technical Analysis of Stocks & Commodities • 13

If the returns were mainly negative for a given month, it was defined as a “bearish” month. The entry point of the “trade” was the open price of the first trading day of the month and the exit point was the closing price of the last trading day of the same month.

2. Out-of-sample analysis. We then used the subsequent four-year period (2014–2017) to test the same signals. Using the same definitions of bullish/bearish months as in the earlier in-sample analysis, we opened long trades in the months that were previously defined as bullish and short trades in the months that were previously defined as bearish. Near-zero-return months weren’t included in this second step, as they were considered trendless.

Year 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Avg

Jan -1.86% -2.78% -4.56% -1.08% -3.39% 2.68% -0.57% -3.81% 2.62% -1.27% 1.88% -8.28% -3.21% 2.62% 1.08% 2.94% -1.06% SHORT

Feb 0.93% -3.25% -0.68% -1.54% 1.21% 0.25% 0.14% 1.31% -1.77% 1.49% 2.13% -0.61% -1.69% 0.82% 1.84% -3.85% -0.20% SHORT

Mar -1.12% -1.97% -0.73% -5.02% 0.29% 1.31% -1.69% -1.91% 1.57% 0.92% 3.81% 5.00% -0.84% 2.55% 0.12% -1.82% 0.03%

Apr 2.37% -1.85% -4.70% 1.25% 3.36% 2.43% -2.63% -0.75% 4.25% 2.19% -1.08% -0.16% -1.60% 4.59% -0.90% 2.79% 0.60% LONG

May 0.07% -1.58% 2.33% -5.06% 3.69% 5.45% 1.82% -4.32% 1.50% -1.45% -0.40% 7.05% -7.69% -2.90% -6.60% -1.28% -0.59% SHORT

Jun -0.38% -0.59% 2.25% 0.53% 6.11% -1.64% -0.05% -1.76% -0.17% 0.67% 1.25% -0.71% -0.54% 0.73% 2.44% 0.12% 0.52% LONG

Jul 1.05% 3.34% -2.69% 3.16% -1.46% -2.51% -1.38% 0.26% -0.20% 1.01% -0.97% 1.60% 6.65% -0.72% -2.92% 2.21% 0.40% LONG

Aug 1.90% -1.29% -4.12% 4.14% 0.43% -2.18% 1.15% 1.78% 0.27% -0.41% -5.97% 0.44% -2.92% 0.10% 2.24% -0.62% -0.31% SHORT

Sep 4.36% 1.08% -1.72% -0.11% 0.43% 6.00% 2.04% -2.25% -1.05% 4.68% -4.12% 2.11% 7.53% -6.85% 2.23% 2.24% 1.04% LONG

Oct 0.86% -1.43% -3.96% -1.16% 0.44% -0.51% 2.88% -0.35% 0.66% 1.57% -9.75% 0.53% 2.27% 3.85% 0.86% 0.41% -0.18% SHORT

Nov -2.15% -4.39% 2.77% -0.51% 0.32% 3.63% 3.61% -1.60% 3.72% 1.02% -0.55% 1.83% -7.03% -2.97% 0.22% 0.05% -0.13% SHORT

Dec 1.05% 0.05% 7.94% -0.70% 5.47% 4.70% 2.06% 0.41% -0.32% -0.42% 10.12% -4.54% 3.09% -3.65% 1.51% 1.14% 1.74% LONG

Figure 1: Average monthly returns of EUR/USD exchange rates, 1998–2013

Note that whenever more than one month in a row presented the same trend (bullish or bearish), the trade wasn’t closed and immediately reopened, but rather kept in place until a month of the opposite sign occurred. Our aim with this two-step analysis was to verify the earlier long-term (15-year) results in a shorter (four-year) time period and test whether the pattern holds up—indicating that history does sometimes tend to repeat—or whether the markets are just too unpredictable for seasonal patterns to be reliably predictive.

EUR/USD

The last row of the table in Figure 1 shows the average monthly returns of the EUR/USD from 1998 to 2013. Following the specified criteria (positive or negative average monthly returns), we considered April, June, July, September, and December to be bullish. January, February, May, August, October, and November were bearish. Based on the dispersion of the performances, March was considered trendless. Once we defined the bullish and bearish months, we applied the second step of the analysis. The strategy was tested in the 2014–2017 period, going long in the months defined as bullish and short in the ones defined as bearish. The table in Figure 2 displays all trading data (32 total trades—16 long and 16 short, and flat for four months). The chart in Figure 3 shows the equity line for the strategy (3,263 pips earned in total).

GBP/USD

“The cable” is a slang term used by FX traders to refer to the exchange rate between the British pound and the US dollar; this name was coined in the 19th century, when the rate began to be transmitted across the ocean by a submarine communication cable (and since 1866 has been transmitted continuously). In the table in Figure 4 we report the monthly returns of “the cable” for the 1998–2013 period, and in the last row of the table we state the average return for each month. Looking at the data, 14 • March 2019 • Technical Analysis of Stocks & Commodities

N. 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 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

Date Jan 2014 Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan 2015 Feb Mar Apr May Jun Jul Aug Sep

Trade Short Short Long Short Long Long Short Long Short Short Long Short Short Long Short Long Long Short Long

En.Price 1.374 1.349 1.379 1.377 1.387 1.363 1.369 1.339 1.314 1.263 1.252 1.246 1.210 1.128 1.119 1.073 1.122 1.099 1.114 1.097 1.121

Ex.Price 1.349 1.380 1.377 1.387 1.363 1.369 1.339 1.313 1.263 1.252 1.245 1.210 1.129 1.119 1.073 1.122 1.099 1.114 1.099 1.121 1.118

% Chg -1.88% 2.30% -0.11% 0.72% -1.71% 0.43% -2.21% -1.93% -3.85% -0.84% -0.52% -2.87% -6.71% -0.79% -4.08% 4.58% -2.09% 1.36% -1.36% 2.22% -0.29%

Profit 258 -310 0 99 237 58 -303 258 -506 106 65 -358 812 89 0 491 234 149 -151 -243 -33

Cum. Profit 258 -52 -52 47 284 342 39 297 -209 -103 -38 -396 416 505 505 996 1,230 1,379 1,228 985 952

Oct Nov Dec Jan 2016 Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan 2017 Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Short Short Long Short Short Long Short Long Long Short Long Short Short Long Short Short Long Short Long Long Short Long Short Short Long

1.118 1.101 1.057 1.086 1.083 1.087 1.138 1.144 1.113 1.110 1.117 1.116 1.123 1.098 1.059 1.053 1.080 1.057 1.066 1.091 1.124 1.142 1.184 1.191 1.182 1.164 1.190

1.101 1.056 1.086 1.083 1.087 1.138 1.145 1.113 1.110 1.117 1.116 1.124 1.098 1.059 1.051 1.080 1.058 1.065 1.090 1.124 1.142 1.184 1.191 1.181 1.164 1.190 1.200

-1.52% -4.09% 2.78% -0.26% 0.37% 4.67% 0.67% -2.75% -0.23% 0.60% -0.15% 0.74% -2.26% -3.59% -0.71% 2.53% -2.05% 0.72% 2.22% 3.08% 1.63% 3.70% 0.57% -0.81% -1.46% 2.22% 0.79%

170 450 294 28 -40 0 76 315 -26 67 17 83 254 394 -75 -266 221 0 237 -336 183 422 -68 -97 172 -258 94

1,122 1,572 1,866 1,894 1,854 1,854 1,930 2,245 2,219 2,286 2,303 2,386 2,640 3,034 2,959 2,693 2,914 2,914 3,151 2,815 2,998 3,420 3,352 3,255 3,427 3,169 3,263

Figure 2: Long & short trades on EUR/USD in the period 2014–2017

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April, June, July, September, and December can be considered bullish, while January, February, March, May, August, and November are bearish. October has a near-zero average return, so it’s considered trendless and wasn’t included in the out-of-sample test. With the benefit of historical data series analysis, we adopted the same month-based long and short strategy for the subsequent 2014–2017 period, going long (or holding long) in bullish months and short in bearish ones. The table in Figure 5 shows all the trades (32 total trades—16 long and 16 short trades) and the chart in Figure 6 shows the equity line for the strategy (2,474 pips earned on the whole).

Year 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Avg

Jan -0.85% -0.80% -1.32% -1.90% -2.98% 2.32% 2.60% -1.83% 3.13% 0.31% 0.14% -0.78% -1.24% 2.98% 1.61% -2.45% -0.07% SHORT

Feb 0.91% -2.77% -2.11% -1.24% 0.35% -4.29% 2.38% 1.92% -1.38% -0.07% 0.07% -0.82% -4.51% 1.51% 0.98% -4.38% -0.84% SHORT

Mar 1.46% 0.77% 0.77% -2.12% 0.67% 0.46% -1.49% -1.56% -0.96% 0.22% -0.11% 0.37% 0.17% -1.41% 0.58% 0.30% -0.12% SHORT

Apr -0.06% -0.09% -2.51% 0.87% 2.20% 1.11% -3.57% 1.05% 5.12% 1.59% 0.12% 3.26% 0.59% 4.24% 1.51% 2.26% 1.11% LONG

May -2.45% -0.43% -3.43% -0.99% -0.20% 2.42% 3.23% -4.67% 2.64% -0.99% -0.19% 9.45% -4.65% -1.24% -5.07% -2.14% -0.54% SHORT

Jun 2.05% -1.56% 1.11% -0.04% 5.32% 1.65% -0.73% -1.49% -1.19% 1.48% 0.77% 1.71% 2.80% -2.40% 1.93% -0.01% 0.71% LONG

Jul -1.94% 2.77% -1.40% 0.64% 1.99% -2.76% 0.03% -1.84% 1.12% 1.07% -0.40% 1.54% 4.97% 2.29% 0.10% -0.09% 0.51% LONG

Aug 2.91% -1.04% -3.16% 2.03% -0.86% -2.14% -1.05% 2.73% 1.93% -0.70% -8.20% -2.58% -2.35% -1.20% 1.21% 1.98% -0.66% SHORT

Sep 1.13% 2.72% 1.81% 1.50% 1.39% 5.19% 0.51% -1.98% -1.64% 1.48% -1.67% -1.90% 2.41% -4.18% 1.85% 4.34% 0.81% LONG

Oct -1.47% -0.26% -1.79% -1.24% -0.40% 2.11% 1.42% 0.35% 1.77% 1.61% -9.77% 2.92% 2.04% 3.75% 0.01% -0.89% 0.01%

Nov -1.74% -2.80% -1.57% -2.09% -0.42% 1.56% 3.81% -2.20% 3.05% -1.12% -4.57% -0.09% -3.20% -2.39% -0.73% 2.05% -0.78% SHORT

Dec 0.39% 1.37% 4.63% 2.23% 3.43% 3.54% 0.51% -0.36% -0.36% -3.39% -5.01% -1.60% 0.30% -0.99% 1.39% 1.20% 0.46% LONG

Figure 4 Average monthly returns of GBP/USD exchange rates, 1998–2013

USD/CAD

The third major we examined was the exchange rate between the US dollar and the loonie, that is, the Canadian dollar (usually abbreviated as Can$ or C$ to distinguish it from the US dollar). The table in Figure 7 shows all monthly returns of the exchange rate from 1998 to 2013. As usual, the last row of the table shows the average returns for each month. On a historical basis, January, June, July, August, October, and November are bullish months, while February, March, April, May, September, and December are bearish ones. We applied the same out-of-sample strategy to this FX pair. We opened (or continued to hold) long trades in the bullish months and short trades in the bearish ones. The table in Figure 8 shows, in detail, all trades (24 total trades—12 long and 12 short), while the chart in Figure 9 shows the equity line for the strategy (2,124 pips earned in total).

And the results show…

Through time series analysis and concrete application of an operational strategy to test our hypothesis, we attempted to show that for each financial instrument tested, in this case major forex pairs, each month of the year can have a particular bullish or bearish “personality” that lasts over time. Our findings show that in terms of profitability and linearity of the profit curve,

N. 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 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

Figure 3: equity line FOR THE strategy on EUR/USD, 2014–2017

16 • March 2019 • Technical Analysis of Stocks & Commodities

Date Jan 2014 Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan 2015 Feb Mar Apr May Jun Jul Aug Sep

Trade Short Short Short Long Short Long Long Short Long Short Long Short Short Short Long Short Long Long Short Long

En.Price 1.656 1.644 1.675 1.666 1.687 1.676 1.711 1.689 1.659 1.621 1.598 1.565 1.558 1.508 1.544 1.482 1.535 1.529 1.571 1.562 1.534

Ex.Price 1.643 1.674 1.666 1.687 1.675 1.710 1.688 1.660 1.621 1.600 1.565 1.557 1.507 1.543 1.482 1.535 1.529 1.571 1.562 1.534 1.513

% Chg -0.74% 1.82% -0.50% 1.27% -0.73% 2.05% -1.30% -1.71% -2.30% -1.34% -2.06% -0.48% -3.28% 2.31% -4.01% 3.58% -0.40% 2.71% -0.55% -1.80% -1.40%

Oct Nov Dec Jan 2016 Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan 2017 Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Short Long Short Short Short Long Short Long Long Short Long Short Long Short Short Short Long Short Long Long Short Long Short Long

1.513 1.545 1.505 1.474 1.424 1.392 1.436 1.461 1.448 1.331 1.323 1.314 1.294 1.224 1.250 1.234 1.258 1.238 1.254 1.295 1.289 1.301 1.321 1.293 1.339 1.328 1.352

1.543 1.505 1.474 1.425 1.392 1.436 1.461 1.448 1.331 1.323 1.314 1.298 1.224 1.250 1.234 1.258 1.238 1.255 1.295 1.289 1.303 1.321 1.293 1.340 1.328 1.353 1.351

1.99% -2.56% -2.10% -3.33% -2.28% 3.18% 1.75% -0.90% -8.09% -0.60% -0.70% -1.24% -5.38% 2.20% -1.34% 1.89% -1.59% 1.33% 3.25% -0.48% 1.07% 1.55% -2.14% 3.61% -0.78% 1.84% -0.08%

Profit 123 -299 83 211 123 344 -222 288 -381 0 329 -75 511 -348 619 531 61 414 -86 281 -215 0 395 -316 491 324 -442 252 132 -1171 -80 92 -163 0 -269 -168 -233 200 -165 407 62 138 202 283 467 0 -245 -11

Cum. Profit 123 -176 -93 118 241 585 363 651 270 270 599 524 1,035 687 1,306 1,837 1,898 2,312 2,226 2,507 2,292 2,292 2,687 2,371 2,862 3,186 2,744 2,996 3,128 1,957 1,877 1,969 1,806 1,806 1,537 1,369 1,136 1,336 1,171 1,578 1,640 1,778 1,980 2,263 2,730 2,730 2,485 2,474

Figure 5: Long & short trades on GBP/USD in the period 2014–2017

Year 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Avg

For each financial instrument, each month of the year has a particular bullish or bearish “personality” that lasts over time.

Jan 1.66% -1.37% -0.17% 0.21% -0.44% -3.28% 2.26% 3.31% -2.28% 0.96% 0.39% 0.56% 1.65% 0.57% -1.85% 0.47% 0.17% LONG

Feb -2.23% -0.09% 0.20% 2.60% 1.18% -2.41% -0.11% -0.41% -0.25% -0.59% -1.52% 3.81% -1.76% -2.95% -1.27% 3.36% -0.15% SHORT

Mar -0.15% -0.18% -0.11% 2.51% -0.56% -1.05% -1.76% -2.02% 2.77% -1.33% 3.94% -1.18% -3.54% -0.11% 0.84% -1.29% -0.20% SHORT

Apr 0.86% -3.32% 2.19% -2.63% -1.66% -2.50% 4.83% 4.00% -4.38% -3.76% -1.70% -5.36% 0.27% -2.63% -1.13% -1.00% -1.12% SHORT

May 1.84% 1.24% 1.18% -0.52% -2.56% -4.59% -0.87% -0.40% -1.44% -3.70% -1.46% -8.57% 2.68% 2.47% 4.61% 2.99% -0.44% SHORT

Jun 0.71% -0.75% -0.90% -1.42% -1.30% -1.65% -2.14% -2.35% 1.40% -0.32% 2.62% 6.64% 1.87% -0.54% -1.58% 1.47% 0.11% LONG

Jul 3.11% 2.99% 0.51% 1.25% 4.39% 4.42% -0.14% -0.17% 1.31% 0.12% 0.32% -7.30% -3.22% -0.82% -1.35% -2.31% 0.19% LONG

Aug 3.50% -0.93% -0.99% 1.14% -1.60% -1.49% -0.96% -2.89% -2.48% -1.01% 3.84% 1.44% 3.54% 2.42% -1.67% 2.50% 0.27% LONG

Sep -2.28% -1.70% 1.97% 1.86% 1.91% -2.56% -3.89% -1.73% 1.30% -6.04% 0.25% -2.20% -3.42% 7.43% -0.25% -2.14% -0.72% SHORT

Oct 0.78% 0.31% 1.30% 1.03% -1.76% -2.17% -3.53% 1.33% 0.45% -5.10% 13.88% 1.38% -0.97% -4.67% 1.65% 1.16% 0.32% LONG

Nov -0.65% 0.22% 0.82% -1.53% 0.82% -1.53% -2.50% -1.26% 1.62% 5.90% 2.28% -2.59% 0.65% 1.68% -0.52% 1.76% 0.32% LONG

Dec -0.25% -1.63% -2.38% 1.08% 0.51% -0.07% 1.38% -0.33% 2.22% -0.08% -1.25% -0.28% -2.77% 0.36% -0.15% 0.10% -0.22% SHORT

Figure 7: Average monthly returns of USD/CAD exchange rates, 1998–2013

harnessing time in monthly windows has proved to not be meaningless. Using this type of study, you can discover other types of potentially actionable strategies. You could perform similar research by considering different timeframes (weekly data, yearly data, and so on). Once you filter out the background noise that is always present in shorter temporal horizons by always using average returns, you should be able to obtain solid findings on these as well. In addition, in order to test for reliability and persistency, you could easily extend this analysis Continued on page 33

Figure 6: equity line FOR the strategy on GBP/USD, 2014–2017

N. 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 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

Figure 9: strategy equity line on USD/CAD, 2014–2017

Date Jan 2014 Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan 2015 Feb Mar Apr May Jun Jul Aug Sep

Trade Long Short Short Short Short Long Long Long Short Long Long Short Long Short Short Short Short Long Long Long Short

En.Price 1.062 1.113 1.105 1.105 1.096 1.084 1.067 1.090 1.087 1.119 1.126 1.142 1.162 1.273 1.248 1.269 1.208 1.244 1.249 1.309 1.314

Ex.Price 1.113 1.106 1.105 1.096 1.084 1.067 1.090 1.088 1.120 1.127 1.141 1.162 1.273 1.251 1.269 1.208 1.245 1.249 1.309 1.314 1.331

% Chg 4.74% -0.57% -0.05% -0.81% -1.10% -1.62% 2.20% -0.27% 2.96% 0.63% 1.33% 1.74% 9.55% -1.76% 1.67% -4.82% 3.06% 0.42% 4.76% 0.31% 1.32%

Profit 503 63 5 89 121 -176 235 -29 -322 71 150 -199 1110 224 -208 611 -369 52 594 41 -174

Cum. Profit 503 566 571 660 781 605 840 811 489 560 710 511 1,621 1,845 1,637 2,248 1,879 1,931 2,525 2,566 2,392

Oct Nov Dec Jan 2016 Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan 2017 Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Long Long Short Long Short Short Short Short Long Long Long Short Long Long Short Long Short Short Short Short Long Long Long Short Long Long Short

1.331 1.307 1.336 1.384 1.397 1.354 1.300 1.255 1.309 1.292 1.303 1.311 1.313 1.341 1.344 1.344 1.303 1.330 1.330 1.365 1.350 1.296 1.248 1.248 1.247 1.289 1.290

1.308 1.336 1.384 1.397 1.354 1.300 1.255 1.309 1.292 1.303 1.310 1.313 1.341 1.343 1.343 1.303 1.330 1.331 1.365 1.350 1.296 1.248 1.248 1.247 1.288 1.289 1.258

-1.76% 2.20% 3.57% 0.95% -3.10% -3.96% -3.47% 4.34% -1.29% 0.81% 0.60% 0.17% 2.10% 0.21% -0.06% -3.04% 2.06% 0.09% 2.62% -1.13% -3.98% -3.76% 0.02% -0.08% 3.30% 0.05% -2.47%

-234 288 -477 132 433 536 451 -544 -169 105 78 -22 276 28 8 -408 -268 -12 -348 154 -537 -487 3 10 411 7 318

2,158 2,446 1,969 2,101 2,534 3,070 3,521 2,977 2,808 2,913 2,991 2,969 3,245 3,273 3,281 2,873 2,605 2,593 2,245 2,399 1,862 1,375 1,378 1,388 1,799 1,806 2,124

Figure 8: Long & short trades on USD/CAD, 2014–2017 March 2019

• Technical Analysis of Stocks & Commodities • 17

Anticipate, Spot, Trade

Can you profit from fakeouts? Here’s one trader’s take on how to identify these patterns and trade them profitably.

A

by Fawad Razaqzada

false breakout—or fakeout—is by far one of my favorite technical patterns. It is when price momentarily moves above a previous high or below a previous low, but then goes back within the existing range. This happens when there isn’t enough demand above the old high, or enough supply beneath the prior low, to help push price significantly in the direction of the break. When a breakout fails, it suggests the move is exhausted, and the rejection alone could provide significant reward if traded correctly. What’s more, price typically then goes back to areas where the breakout traders, some of whom are now trapped, would be placing their stoploss orders. Those areas now become the ideal profit target zones for fakeout traders.

18 • March 2019 • Technical Analysis of Stocks & Commodities

Remember, price is a function of liquidity: The market goes where orders are resting, that is, from one area of liquidity to the next. So being able to identify such setups may not only lead to better trading opportunities but could potentially help you cut your losing trades quicker if you happen to be one of those breakout traders who got trapped. In this article, I will discuss and share examples of: 1) how to anticipate the emergence of such patterns, so you know exactly what to do when they occur; 2) how to spot these patterns after they are formed; and perhaps most important, 3) how to trade them. I will also discuss various other factors you’ll want to take into account when spotting these patterns, including how price gets to old highs/lows.

Identifying price action

As your experience grows, you will hopefully become more patient and better at reading price action. You will be able to spot the difference between very high-probability setups and normal trades. Professional poker players typically bet

KTASIMAR/SHUTTERSTOCK/COLLAGE:NIKKI MORR

Trading The Fakeout

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TRADING TECHNIQUES

6632.0

anticipate and spot them and take appropriate action when they are formed. To illustrate, I have come up with a few examples on different markets and various timeframes so that you too can potentially spot these patterns.

6628.0 6626.0 6624.0

Selling into support

6660.0

Nasdaq 100 1-Min Chart

6656.0.0 6652.0 6648.0 6644.0 6640.0 6636.0

False breaks can happen on any timeframe. Generally speaking, 6612.0 the higher the timeframe, the bet6608.0 ter the quality of the setup. But 6604.0 even on the one-minute chart, 6600.0 such as in this example of the 6596.0 Nasdaq 100 in Figure 1, the mar6592.0 kets can form false break reversal 6588.0 14:30 15:00 15:30 16:00 16:30 17:00 17:30 18:00 18:30 patterns. The 6590–6595 area Figure 1: false break reversal pattern on a one-minute chart of the nasdaq 100. Notice how the had been a key short-term pivotal index fell to below the 6592 level. This could have been because of a cluster of sell stop orders resting below the 6592 level. zone on the higher timeframes, Some may have been stop-loss orders from those who bought around 6592 earlier in the day (and then tightened their stops to just beneath 6592 as price moved higher, in order to reduce their risk). The rest of these sell stops may have been from so the sharp 65-point jump off breakout traders who, anticipating a breakdown of support, had placed their entry orders just below this low. the 6592 level just after the cash market had opened made perfect technical sense. But when the market came S&P Futures Daily Chart back down again, notice the accelerated downward move just prior to the quick rejection. This is where a lot of newer traders 3. Buy rounded retest of old resistance (now support) get into trouble: selling right into Confirmation: Higher high close 2. Buy the break here support as the fear of missing out High (FOMO) kicks in. The reason the index fell sharply there was Another fakeout — more evidence that sellers are getting into trouble undoubtedly because of the cluster of sell stop orders that were 1. Buy the break here resting below the 6592 level. Some of these may have been the Spikes below old low but closes well above Low stop-loss orders from those who had bought around 6592 earlier RSI in state of negative divergence (higher low relative to S&P, which was making a lower low). This indicates weakening selling pressure in the day (and then tightened their stops to just beneath 6592 FIGURE 2: BUYING OPPORTUNITY? The follow-through that occurred on the day after the fakeout could have been a nice as price moved higher, in order breakout buying opportunity. And if you wanted to wait for extra confirmation, you could have waited for the break above to reduce their risk), while the the old high at 1940. The index formed a false break reversal pattern and went on to make a higher low. rest of these sell stops may have been from breakout traders who, large on premium hands. In trading, putting size on very anticipating a breakdown of the support, had placed their high-probability setups could potentially make your month, entry orders just below this low (which is a typical rookie quarter, or even your year. I am not implying that you should mistake, by the way). be reckless and bet the farm on one trade. Rather, the risk In any case, the point I am making is that the liquidity can remain the same, but the position size could be increased (cluster of sell stop orders) was used here to fill what must have substantially as you become better with precision entry tech- been a large buy order, which explains why the market rallied niques while simultaneously reducing the distance between sharply afterwards, as demand outweighed supply. Once the entry and stop-loss placement, though not so tight that you index started to trade above the 6592 level, the idea here would are shaken out of your position by market noise. have been to go long with a market order and place a tight For me, these types of trades are typically made on the stop-loss beneath the most recent low. The potential reward back of false break setups. Experience has taught me how to from this type of a trade would have been huge. 6620.0

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20 • March 2019 • Technical Analysis of Stocks & Commodities

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What’s the ideal entry point?

False breaks can be traded many different ways and on different timeframes. In the daily chart of the S&P 500 futures in Figure 2, I have highlighted at least three different areas to buy around, after the false break took place. Here, the S&P spiked below the old low at 1805 but the sellers couldn’t hold their ground for too long. To some degree, the fakeout here could have been anticipated given the magnitude of the prior drop and as the relative strength index (RSI), which is a popular momentum indicator, was in a state of positive divergence already as the index retested that low. That is, the RSI was making a higher low as the underlying S&P index tried to break its old low, clearly indicating that the selling momentum was waning. As the false break could have been anticipated, intraday traders could have potentially bought near the low, based on price action on a smaller timeframe chart such as the hourly or even the one-minute as per the Nasdaq example in Figure 1. But you don’t necessarily need to buy near the low. The important point is that once a false break takes place and there is a good reaction away from that level confirming the mismatch between supply and demand, usually—not always—the market goes in the other direction for several days, providing plenty of subsequent tradable opportunities. In this case, the follow-through that occurred on the day after the fakeout could have been a nice breakout buying opportunity, as highlighted on the chart in Figure 2. But if you needed extra confirmation, well, the break above the old high at 1940 was just that: Not only did the index form a false break reversal pattern, it went on to make a higher low. As the bullish trend is starting to emerge, traders can now utilize various entry techniques to get onboard, such as the entry examples I have highlighted on the chart. But it all started with that false break!

The beauty of the false break setup is that sometimes price goes significantly in the opposite direction, hence the saying “from false moves come fast moves in the opposite direction.”

closes lower than the opening price. It clearly shows that the buying pressure has ended abruptly. These types of candlesticks are most effective when they occur at the top of trends, around old resistances or prior swing points, as is the case in this example. Based on this, a potential short trade would have been to simply enter once the hourly candle closed. The stop-loss would have been a tight one, placed just above this shooting star signal candle, and the main target could have been, for example, the liquidity pool below the old low at $1,223 where sell stops would be resting, some of which are used to fill your closing buy order(s).

Downside directional bias

To make it clear that false breaks can literally take place on any timeframe, the chart in Figure 4 shows a weekly chart of WTI crude oil. Although this fakeout has taken place on the weekly, you don’t necessarily need to trade on this timeframe. After a long-term reversal signal such as

Using candlestick

signals When it comes to intraday trading, the same logic applies: The false break will materialize as a temporary move above an old high or below an old low and a quick rejection. In the chart in Figure 3, gold attempted to break above its old high at $1,236, but the bulls’ advance was quickly rejected after a brief break. The resulting price action was an inverted hammer (or shooting star) candlestick pattern on the hourly chart. This particular pattern forms when a security’s price—in this example, the price of gold—advances well above the opening level of that time period (in this case, hourly) but

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FIGURE 3: OLD HIGHS AND OLD LOWS CAN BE HELPFUL. Gold tried to break above its old high at $1236, but was met with rejection. The appearance of the inverted hammer suggests the abrupt end of the buying pressure. March 2019

• Technical Analysis of Stocks & Commodities • 21

Notice of Class Action Settlement If you transacted in Euribor Products1 between June 1, 2005 and March 31, 2011, inclusive (“Class Period”), then your rights will be affected and you may be entitled to a benefit. The purpose of this Notice is to inform you of your rights in connection with the proposed settlement with Settling Defendants Citigroup Inc. and Citibank, N.A. (collectively “Citi”) and JPMorgan Chase & Co. and JPMorgan Chase Bank, N.A. (collectively, “JPMorgan”) in the action titled Sullivan, et al. v. Barclays plc, et al., 13-cv-2811 (PKC) (S.D.N.Y.). The settlement with Citi and JPMorgan (the “Settlement”) is not a settlement with any other Defendant and thus is not dispositive of any of Plaintiffs’ claims against other Defendants. The Settlement has been proposed in a class action lawsuit concerning the alleged manipulation of the Euro Interbank Offered Rate (“Euribor”) and the prices of Euribor Products during the Class Period. The Settlement provides a total of $182.5 million to pay claims from persons who transacted in Euribor Products during the Class Period. If you qualify, you may send in a Proof of Claim and Release form to potentially get benefits, or you can exclude yourself from the Settlement, or object to it. The United States District Court for the Southern District of New York (500 Pearl St., New York, NY 10007-1312) authorized this Notice. Before any money is paid, the Court will hold a Settlement Hearing to decide whether to approve the Settlement.

Who Is Included? You are a “Settlement Class Member” if you purchased, sold, held, traded, or otherwise had any interest in Euribor Products during the Class Period, and during the Class Period were either domiciled in the United States or its territories or, if domiciled outside the United States or its territories, you transacted Euribor Products in the United States or its territories during the Class Period. “Settlement Class Members” include, but are not limited to, all persons who during the Class Period traded CME Euro currency futures contracts, all persons who during the Class Period transacted in NYSE LIFFE Euribor futures and options from a location within the United States, and all persons who during the Class Period traded any other Euribor Product from a location within the United States or its territories. Contact your brokerage firm to see if you purchased, sold, held, or traded or otherwise had any interest in Euribor Products. If you are not sure you are included, you can get more information, including the Settlement Agreement,2 Mailed Notice, Plan of Allocation, Proof of Claim and Release, and other important documents, at www.EuriborSettlement.com (“Settlement Website”) or by calling toll free 800-492-9154.

What Is This Litigation About? Plaintiffs allege that Defendants, during the Class Period, conspired to manipulate and manipulated Euribor and the prices of Euribor Products. Plaintiffs allege that Defendants did so by using several means of manipulation. For example, Plaintiffs allege that panel banks that made daily Euribor submissions to Thomson Reuters, falsely reported banks’ costs of borrowing in order to financially benefit their Euribor Products positions. Plaintiffs also allege that Defendants requested that other

Defendants make false Euribor submissions on their behalf to benefit their Euribor Products positions. Plaintiffs further allege that Defendants continuously conspired to fix the prices of Euribor Products in the over-the-counter market to financially benefit their own Euribor Products positions. In addition to coordinating Euribor submissions and agreeing on where to price Euribor Products, Plaintiffs allege that in order to effectuate their alleged manipulations of Euribor and Euribor Products during the Class Period, Defendants engaged in “pushing cash,” transmitted false bids and offers, used derivative traders as submitters, and rigged bids and offers for Euribor Products. Plaintiffs have asserted legal claims under various theories, including the Sherman Act, the Commodity Exchange Act, the Racketeering Influenced and Corrupt Organizations Act, and common law. Settling Defendants have consistently and vigorously denied Plaintiffs’ allegations.

What Does the Settlement Provide? Under the Settlement, Citi and JPMorgan agreed to pay a total of $182.5 million into the Settlement Fund. If the Court approves the Settlement, potential Settlement Class Members who qualify and send in valid Proof of Claim and Release forms may receive a share of the Settlement Fund after it is reduced by the payment of certain expenses. The Settlement Agreement, available on the Settlement Website, describes all of the details about the proposed Settlement. The exact amount each qualifying Settlement Class Member will receive from the Settlement Fund cannot be calculated until (1) the Court approves the Settlement; (2) certain amounts identified in the full Settlement Agreement are deducted from the Settlement Fund; and (3) the number of participating Class Members and the amount of their claims are determined. In addition, each Settlement Class Member’s share of the Settlement Fund will vary depending on the information the Settlement Class Member provides on their Proof of Claim and Release form. The number of claimants who send in claims varies widely from case to case. If less than 100% of the Settlement Class sends in a Proof of Claim and Release form, you could get more money.

How Do You Ask For a Payment? If you are a Settlement Class Member, you may seek to participate in the Settlement by submitting a Proof of Claim and Release to the Claims Administrator at the address in the Settlement Notice postmarked no later than July 31, 2019. You may obtain a Proof of Claim on the Settlement Website or by calling the toll-free number referenced above. If you are a Settlement Class Member but do not file a Proof of Claim and Release, you will still be bound by the releases set forth in the Settlement Agreement if the Court enters an order approving the Settlement Agreement. (continued on next page)

1 “Euribor Products” means any and all interest rate swaps, forward rate agreements, futures, options, structured products, and any other instrument or transaction related in any way to Euribor, including but not limited to, New York Stock Exchange (“NYSE”) London International Financial Futures and Options Exchange (“LIFFE”) Euribor futures contracts and options, Chicago Mercantile Exchange (“CME”) Euro currency futures contracts and options, Euro currency forward agreements, Euribor-based swaps, Euribor-based forward rate agreements, and/or any other financial instruments that reference Euribor. 2 The “Settlement Agreement” means the agreement between Plaintiffs, Citi and JPMorgan, entered into on November 21, 2018, and filed with the Court in this action.

(continued from previous page)

Notice of Class Action Settlement If you transacted in Euribor Products1 between June 1, 2005 and March 31, 2011, inclusive (“Class Period”), then your rights will be affected and you may be entitled to a benefit. If you timely submitted a Proof of Claim and Release pursuant to the class notice dated November 29, 2017, related to the $94 million settlement with Defendants Barclays plc, Barclays Bank plc, and Barclays Capital Inc. (collectively, “Barclays”); the $45 million settlement with HSBC Holdings plc and HSBC Bank plc (collectively, “HSBC”); and the $170 million settlement with Deutsche Bank AG and DB Group Services (UK) Ltd. (collectively, “Deutsche Bank”) (the “2017 Notice”), you do not have to submit a new Proof of Claim and Release to participate in the Settlement with Citi and JPMorgan. Any member of the Settlement Class who previously submitted a Proof of Claim and Release in connection with the 2017 Notice will be subject to and bound by the releases set forth in the Settlement Agreement with Citi and JPMorgan, unless such member submits a timely and valid request for exclusion, explained below.

What Are Your Other Options? All requests to be excluded from the Settlement must be made in accordance with the instructions set forth in the Settlement Notice and must be postmarked to the Claims Administrator no later than April 12, 2019. All requests for exclusion must comply with the requirements set forth in the Settlement Notice to be honored. The Settlement Notice, available at the Settlement Website, explains how to exclude yourself or object. If you exclude yourself from the Settlement Class, you will not be bound by the Settlement Agreement and can independently pursue claims at your own expense. However, if you exclude yourself, you will not be eligible to share in the Net Settlement Fund or otherwise participate in the Settlement. The Court will hold a Settlement Hearing in this case on May 17, 2019, to consider whether to approve the Settlement and a request by the lawyers representing all Settlement Class Members (Lowey Dannenberg, P.C. and Lovell Stewart Halebian Jacobson LLP) for an award of attorneys’ fees of no more than nineteen percent (19%), or $34,675,000, of the Settlement Fund for investigating the facts, litigating the case, and negotiating the settlement, and for reimbursement of their costs and expenses in the amount of no more than approximately $1,300,000. The Plaintiffs may also request no more than $400,000 from the Settlement Fund as reimbursement of their own expenses and compensation for their time devoted to this litigation. The lawyers for the Settlement Class may also seek additional reimbursement of costs and expenses in connection with services provided after the Settlement Hearing. These payments will also be deducted from the Settlement Fund before any distributions are made to the Settlement Class. You may ask to appear at the Settlement Hearing, but you do not have to. For more information, call toll free 800-492-9154 or visit the website www.EuriborSettlement.com.

The fakeout, if traded correctly, could be a very lucrative setup, requiring very few indicators but a lot of common sense and a ton of patience.

this one is formed, speculators can move down to their own desired timeframes when it comes to looking for entries. The important thing is that price is now in a reversal profile and no longer in a rising trend or sideways profile. Thus, one way to look for potential short entries (in this case) would be to enter as soon as price has formed a trap on the lower timeframes, as we know from the weekly chart that the directional bias is to the downside now. Similarly, fading short-term strength at key old support levels should also provide ideal entry points.

Zoning in …

To illustrate the point of viewing price action on a lower timeframe, I would like to draw your attention to the daily chart of the WTI in Figure 5. We have already seen the fakeout on the higher weekly timeframe (in Figure 4). So any short-term rebounds should now be treated cautiously given that we are now in a potential reversal profile (I say potential here because we don’t know for certain which direction prices would go— obviously this is a hindsight example). Anyway, my point is you don’t need to trade from the weekly chart after a fakeout reversal pattern unfolds on that timeframe. So, looking at the daily chart, I have highlighted five clear entry points using two different entry techniques on this timeframe, all because of that reversal pattern on the weekly. The first sign that the bulls were getting trapped was when price formed a bullish hammer-looking candle (highlighted) but there was no follow-through. So a potential entry on the back of a no-show from bulls here would have been to go short once price broke the support level shown on the chart. Two further similar patterns are also highlighted on this chart. The other entry style would be to sell the rounded retest of broken support levels at $62.60 and $61.26. Notice that once the reversal occurred, the follow-through in selling pressure has been more significant each time prices broke down than was the case when prices rebounded. This is why trading with the trend makes more sense, regardless of your style of trading.

Key takeaway points

In all the examples I have provided in this article, the common denominator behind these false breakout patterns was a sharp move into an old significant high or low. If price drifts toward the level, typically it will break through it. After all, the more distance price must travel to an old swing point, the March 2019

• Technical Analysis of Stocks & Commodities • 23

Swing high

False break

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FIGURE 4: LONG-TERM REVERSAL SIGNAL. After you see a long-term reversal signal on a weekly or higher timeframe, you can move down to a lower timeframe chart to look for entries. Keep in mind that price is now in a reversal profile and no longer in a rising trend or sideways profile.

WTI Crude Oil Daily Chart

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FIGURE 5: FOCUSING IN ON A SHORTER TIMEFRAME. Here you see five clear entry points using two different entry techniques on this timeframe that came about because of that reversal pattern on the weekly.

more likely it will be hit by profit-taking and thereby increasing the likelihood of a rejection. Indeed, there are market participants out there—among them “mean reversioners”—who anticipate breakouts to fail and sometimes, when the conditions meet their requirements, enter heavily as price breaks an old high or low. The beauty of the false break setup is that sometimes price goes significantly in the opposite direction, hence the saying “from false moves come fast moves in the opposite direction.” In addition to the multiple entry potentials after the fakeout is formed, it is also possible for traders to establish long-term position trades from price action on lower timeframe charts such as the hourly, as long as the formation occurs around a higher timeframe technical level. These trades normally have lopsided risk-to-reward profiles, allowing the trader to put on size behind the trade as the stop-loss is typically very tight, especially if the fakeout setup is identified on the lower timeframes relative to, for example, an entry off a daily or weekly chart. These premium types of trades can significantly increase the potential reward, simply because of having a tight stop-loss. 24 • March 2019 • Technical Analysis of Stocks & Commodities

You would rather make $100 from a 10-point move, risking $10 per point, rather than from a 100-point move, risking $1 per point. In both cases, the nominal and percentage r isks a re the same, but the nominal position sizes are significantly different. Therefore, to achieve the same nominal return, price needs to travel a much shorter distance, compared to setups with lower reward-to-risk profiles. The shorter the distance of your profit target, the more likely it is it will be reached, since the more time elapses, the more likely it is for something to go wrong, for example, some unexpected news announcement causing a reversal against your position. So the fakeout, if traded correctly, could be a very lucrative setup, requiring very few indicators but a lot of common sense and a ton of patience.

Fawad Razaqzada is an economist and market analyst who has been involved in the financial markets for almost 10 years. He has worked for several leading brokerages as a market analyst in London. Specializing in forex, commodities, and stock indexes, Razaqzada has expertise in reading price action on the charts. He uses his knowledge of economics together with fundamental analysis to forecast short-term price fluctuations. He has also been trading his personal account for many years. Follow him on Twitter at @Trader_F_R. ‡TradingView

‡See Editorial Resource Index

Algo Q&A ALGORITHMIC TRADING Have a question about system or algo trading? Kevin J. Davey has over 25 years of system trading experience. Davey is a full-time trader, and he also teaches and consults via his Strategy Factory online workshop (http://kjtradingsystems.com). He is the author of several bestselling trading books, including Building Winning Algorithmic Trading Systems and Introduction To Algo Trading. Send your questions or topic suggestions to Kevin Davey at [email protected]. Selected questions will appear in a future issue of S&C. Kevin J. Davey

A DAY IN THE LIFE OF AN algo traders. There is a phrase, “auto- diversification to a trading portfolio, or ALGO TRADER mated trading doesn’t mean unattended just be an improvement over existing As a new algorithmic trader, how should trading,” and that is wise advice. In ad- strategies. This is where many algo I be scheduling my day? I am an expe- dition, there are always “maintenance”- traders spend most of their time. Derienced trader in futures and forex, but type trading activities to be performed, veloping strategies is time-consuming, only as a discretionary trader. such as checking equity runs, conducting but it is critical to keeping your trading It’s good you have trading experience, contract rollovers, adjusting portfolio al- at a high level. since many people try to jump into algo locations, and more. In total, most algo Think of this task as research and trading without knowing the basics. To traders spend part, but not all, of their development. Every successful company know the markets and be able to use your day monitoring and correcting their dedicates some of its budget and mantrading platform are two essentials any live trading. power to R&D. Without it, the company trader should have before starting with Second, good algo traders constantly stagnates and will eventually be overalgorithmic trading. Not understanding develop new strategies. These new taken by competitors. The same holds how to manually enter an emergency strategies can replace broken ones, add true with trading—having a steady supply order, or realizing you must roll of new strategies in development is over futures contracts prior to a way to keep your trading going expiration are just two examples for years and years. No strategy You can’t just automate of the kind of thing you don’t want lasts forever—just ask the formerly an algo and let it run with to have to learn in the heat of the well-known Turtle Traders! no supervision. Checking moment! Finally, algo traders spend a positions throughout the day With the basics under your belt, portion of their time improving what kinds of tasks are involved in their trading. This could include and night is typical for most day-to-day algo trading? Certainly investigating new platforms or algo traders. not what you are used to in discreprogramming languages, learning tionary trading, especially if you different styles of algo trading (for found yourself glued to a screen instance, pair trading or spread Algo Trader — Sample Time Distribution throughout the trading day. Algo trading to complement directional trading is not like that at all. trading), educating themselves on First, since algo traders typically new trends in trading (for example, Monitoring run many automated strategies, machine learning), and researching Live Strategies keeping an eye on these stratenovel and unique approaches to Enhancing Trading gies is your highest-priority task. portfolio trading, position sizing, Skills/Education After all, this is where your money and more. Just as with developis made (or lost!). Luckily, most ing new strategies, the top algo trading platforms are pretty usertraders devote part of their time friendly, and the chances of a tested to enhancing their craft. Reading algorithm going rogue and placing this magazine, for instance, is an Developing New thousands of orders are small. But important endeavor in this area. Strategies odd things can happen, so “set it As an example, for a week I kept and forget it” is definitely not aptrack of my trading-related activipropriate here. ties. During this week, which was You can’t just automate an algo fairly typical, my time was divided and let it run with no supervision. as shown in Figure 1. Checking positions throughout the day and night is typical for most Figure 1: Typical Time Breakdown for Algo Trading Continued on page 62

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March 2019

• Technical Analysis of Stocks & Commodities • 25

The On-Balance Volume

On-balance volume is a classic indicator that can be helpful for indicating trends and for confirming other indicators. You’ve undoubtedly looked at it on your charts, but if you’ve never really known exactly how it works or how it’s most useful, here’s a close-up view.

W

by John Devcic

hile the number of tools available to the trader grows all the time, sometimes the tools and indicators that have been around a long time still serve the trader well. In this article, I’ll examine one of them, the on-balance volume (OBV), and show you how I have used it to trade. Inexperienced traders tend to debate one indicator versus another and which one is the best to use. Experienced traders know there is no one perfect indicator, just as there is no one perfect security or investment. OBV is an indicator that can be used in conjunction with other trading signals for context

26 • March 2019 • Technical Analysis of Stocks & Commodities

and to provide some trend perspective. In order to fully understand OBV, you need to know how it’s formulated. Joseph Granville in 1963 published a book titled Granville’s New Key To Stock Market Profits and in it introduced the on-balance volume calculations. OBV is a tool used to measure current buying and selling pressure levels. This is done by adding to a running total the volume on days with higher closes to give you a measure of buying pressure, and subtracting volume on days with lower closes to measure selling pressure. The formula assumes that on up days, all volume is positive and on down days, all volume is negative. The calculations are simple. Let’s say that today, stock XYZ closed higher, meaning that buying dominated. It would be calculated as: Current OBV + Today’s volume = Today’s OBV Meanwhile, if XYZ closed lower today than the previous close, meaning that selling dominated, it would be calculated as:

XYZ LOGO: FULLRIZQISHUTTERSTOCK/ COLLAGE: CHRISTINE MORRISON

All About OBV

Technical Indicators

Finally, we have to account for sideways moves. This is when today’s close equals yesterday’s closing price: Today’s OBV = Previous OBV I’ll provide a couple of examples to clear up any confusion. Using one week’s worth of trading, here is a simple example to illustrate how to calculate OBV: • On Monday, XYZ closed at 100.

• On Tuesday, XYZ closed at 101.10 with a trading volume of 40,000. • On Wednesday, XYZ closed down at 100.10 with volume of 10,000. • On Thursday, XYZ closed up at 102 with volume of 15,000. • On Friday, XYZ closed up again at 102.75 with volume of 10,000. Now let’s put those numbers into the formula. • Tuesday’s close was up, so we have a positive day. The OBV will be 0 + 40,000 = 40,000.

• On Wednesday, XYZ fell, and that’s a negative day. The OBV will be 40,000 – 10,000 = 30,000. • Thursday was an up day, so the new OBV will be 30,000 + 15,000 = 45,000. • Friday saw another positive close, bringing our new OBV to 45,000 + 10,000 = 55,000.

trend—as opposed to diverging from it. Here are two other important things to keep in mind as well: You want to be on the lookout for volume spikes that can cause the OBV indicator to be thrown off; and remember, OBV is using closing prices so you should make it a point to note all support & resistance levels using closing prices.

Divergences can be key

I mentioned that you are looking for whether the OBV trend matches the price trend or diverges from it. There are two main types of divergences to keep a lookout for. A bearish divergence will be seen when price is advancing while the OBV line is declining. Bullish divergence is when price is declining but the OBV is moving higher, signaling that future prices will move higher. For example, when looking at a chart, you want to look for a break in price trend while the OBV remains higher; the higher OBV level signals there is still strength in the price trend, and this minor divergence can allow you to move into a security before it changes direction. Now that you know how to calculate the OBV and understand how it works, let’s look next at using it to trade. I traded a few stocks using the OBV line alone so I could present how it fared. I simply entered a position once an OBV slope was moving upward, and exited when that same OBV line was rapidly moving downward. Here are the stocks I traded, along with the results:

GOOG

Google (GOOG) is my first case study. But first, just to illustrate what OBV looks like on a chart, Figure 1 shows a basic chart of GOOG in 2018 through December 28, 2018. OBV is in the bottom panel, right beneath volume. In Figure 2, the shaded rectangle that I’ve added labeled “1” tells us a few things. On January 2, the stock is moving and closes at 1065, just shy of the high of the day. Volume was not as impressive as the actual point move. That was followed by another new close on January 3 at 1082. This

It’s a simple enough calculation that can also be plotted on a chart, and I’ll provide some chart examples shortly. Calculating OBV and having all these numbers based on volume is nice, but what exactly do we do with them when it comes to trading? A rising OBV signals that the current buying pressure is strong and building and can signal more buying. If the OBV is falling, that can tell us that selling pressure is building and growing stronger and can lead to more down days ahead. According to Granville, you can expect prices to move higher if the OBV is going up and the current price is either sideways or falling slightly. The reverse is also true: If OBV is falling, you should expect prices to move down if current prices are either sideways or rising slightly. Granville explains that the actual OBV number is irrelevant; instead, you want to spot the trend of OBV before you worry about anything else. Once you have figured out the OBV trend, you need to FIGURE 1: ON-BALANCE VOLUME. OBV is shown in the bottom panel on this 2018 price chart of Google see if that trend matches the current price (GOOG). March 2019

• Technical Analysis of Stocks & Commodities • 27

thinkorswim

Current OBV – Today’s volume = Today’s OBV

upward movement continues until the end of the rectangle on January 30 at a close of 1163.69. A good move indeed, 1 but the OBV indicator was not moving upward. This is clearly a divergence and we stayed on the sidelines, although we missed a good move. In Figure 3, the second shaded rectangle tells us a different story. The move down was real and it began on January 31 with a close of 1169.94. Take a look at OBV. It spirals downward fast on that same date. You had a couple of days to get out if you were in. The second thing FIGURE 2: OBV DIVERGENCE. GOOG moves strongly upward in the shaded rectangle area but the OBV that OBV is good at is indicating if a indicator did not, keeping the trader on the sidelines. trend is indeed real. In this case, there is no doubt that the downward selling was on. We hit a bottom on February 8 with a close of 1001. 2 In the shaded rectangle labeled “3” in Figure 4, we see a longer pattern where OBV sent us some very interesting signals. We get one more push upward that stalls on March 12 with a higher close of 1164.50. Volume again is relatively low but OBV signaled some trouble—not enough to sell or get out but enough for me to keep a closer eye on it. The next day was a rough one, with a low of 1133 and a close of 1138. I did not exit nor FIGURE 3: OBV CONFIRMATION. In this case, OBV spirals downward with price, confirming the downward did I see a reason to at this point. This trend and indicating an exit. sets us up for point A, which perfectly corresponds with March 19 and you can see that OBV is now sloping down hard. D 3 E At this point, I exited the position. You’re A not getting back in until May 31 and you would have seen OBV finally moving B upward away from that low. I got back in C on June 1. OBV is now higher and with that next downturn we see that the OBV line does not really move downward as far as the already low points. This works out beautifully and brings us to point D, where we get a high that is tested a couple of times and fails. This is July 27 and now we are getting lower highs. At this point, FIGURE 4: OBV SIGNALS. In the shaded area, OBV is indicating possible trouble ahead. At point A on March volume is stalling but OBV stays strong. 19, 2018, OBV indicated an exit. It didn’t turn upward until May 31, a reentry point. On August 31, OBV signaled We see a divergence between price and another exit. A reentry signal has not yet appeared. the OBV level, which can signal potential opportunity to add to our current position. All of this changes NFLX on August 31 when OBV signals it is time to exit. There was Our next test subject is one of the popular stocks being traded a holiday in between, and your first opportunity to get out is today, that being Netflix (NFLX), shown in Figure 5. Much like Google, it was heading upward right from the on September 4. We can see that for the rest of the year, OBV does not signal another opportunity to get in. This chart ends start of 2018. OBV was trending upward, confirming the on December 28 and so far going into 2019 as I write this, move higher. Our first real signal comes around that point A at January 22. The slope is now clearly heading upward we’ve seen a small rally in Google but no signal to enter. 28 • March 2019 • Technical Analysis of Stocks & Commodities

and an entry here is ideal. Volume backs that up, as we have back-to-back higherthan-average volume bars. The move continues unabated until we get to our first real point where we would even think about the possibility of a trend change on March 27, represented by point B on the chart. Looking at the OBV panel, you can see a corresponding shaded box that shows us that indeed, the current trend is not broken, and while it does move lower, the OBV slope is not rapidly going down, indicating that we should sit tight and do nothing. We are rewarded by this decision, as NFLX continues higher all the way to our first worry point on June 21 (represented by C) when we hit some resistance. If we look to the OBV line we see that it starts moving down but again, as in the previous example, we sit tight. This proves to cost us some profits. Our next worry point, and what eventually turns into the sell point, is point D on July 16. At this point, we have three indications lined up telling us to get out: We have a huge move down in price corresponding to extraordinarily high volume, and an OBV line that slopes down rapidly. Thus, you get out and you stay out for the rest of the year. This is simply because OBV stays in the middle and does not give us any signals that any price move is an entry point or one to consider. Our next level comes on October 17, which is represented by point F on the chart. We have an unexpected move higher with an unusual volume spike, as well as an OBV line that moves up but not high enough to really look at. After this point, NFLX continues moving down and that continues as we head into 2019.

C D

B

F

A

FIGURE 5: IDEAL SIGNALS. OBV trended upward with higher-than-average volume, confirming the move higher in Netflix (NFLX) and signaling an entry near point A on January 22. It didn’t signal an exit until point D on July 16, with the OBV sloping down rapidly and confirmed by high volume and falling price.

A

FIGURE 6: WIDE VIEW. Citigroup (C) is in a channel for most of the year.

A

Citigroup (C)

My next example takes us away from the high-flying tech sector and to the more mature banking sector. The chart in Figure 6 is Citigroup (C). I had an existing position in this stock going into 2018. There are two price levels drawn on the chart. The top one is FIGURE 7: ZOOMING IN. Here in higher resolution, you can see that the first sign of trouble is on October 23 and then the real trouble starts around December 4 with a falling OBV, high volume, and falling price. By December at 76.63 and the lower one was placed at 10 (point A), the OBV line is sloping downward fast and hard, an unmistakable exit signal. 64.26. Between these levels is clearly the area where Citigroup stayed throughout the bulk of the year. the line signaled that the uptrend was intact. From that point On January 29, it hit its high for the year at 80.70. Looking it’s a series of lower highs and just staying in the channel. But at the OBV line, I was confident to stay in at that point, since this chart is far too wide for my taste so I’ll narrow it down March 2019

• Technical Analysis of Stocks & Commodities • 29

If the OBV is falling, that can tell us selling pressure is building and growing stronger and can lead to more down days ahead. a bit to better see what’s going on (Figure 7). The first sign of trouble comes on October 23 when Citigroup touches the lower price level and then closes below it. After that, we have a mini rally but it closes below that level on November 14. At this point OBV is still high and not signaling any real worry. It trades below that level for a few sessions, does a brief rally, and on December 3, it hits a high of 66. December 4 is when OBV starts to slant downward, corresponding to a large drop in the price. Volume at this point is picking up steam and remaining high. The problem is the price is falling. Point A on the chart is on December 10 and this is when there is no mistaking that the OBV line is sloping down fast and hard. This is an exit position straight away. In summary. we had a high of 80 and a low of 48. That’s nearly half the value gone over the course of the year. Currently, there is no buy signal from OBV even though the shares have seen a bit of a pop toward the end of 2018.

ISRG

instead, it remained fairly neutral. We had a minor rally toward the tail end of 2018 but no entry signal was generated.

TWTR

Of course, it’s not all roses when it comes to trading. Here is one example where the OBV was in no way helpful. The stock is Twitter (TWTR), shown in Figure 9. Where do we start? How about right at the beginning of the chart at point A. There is a gap up. The move shows that we should have gotten in since the slope was moving up quickly even though the price was not moving up. This all happened on February 1 when the OBV line moved upward. The gap occurred seven days later. We hit our first high on March 14. This was followed by a move in the other direction. We can see that OBV does not really head down to the lows and instead stays in the middle so I stayed in the trade. I saw another buy signal to add to my current position with a strong upward slope at point B at May 31. This turned out to be an excellent decision, as the price nearly doubled from my original entrance price. OBV remains strong even though after the high is hit on June 15 we are greeted with lower highs until we get to the trouble zone on July 27, represented by point C. Is OBV heading down? Yes, but its slope is not significant enough for me to exit the trade and that’s even followed by a higher OBV line. Here is divergence in action. OBV remains high as the price continues downward. That brings us to point D on October 11 with a close of 27. The OBV line is not even sloped downward but instead remains strong. The price does bounce upward, eventually hitting a high of 37.14 on December 12. The OBV remains high on TWTR heading into 2019. Yes, with hindsight I can quickly point out my mistake of ignoring the lower highs when I saw them occurring. However, you cannot successfully trade with a tool and second-guess it at the same time. Will Twitter move upward in 2019? I don’t know, but currently, I am still in the trade.

Medical stocks are always worth trading and our next example is no different. Intuitive Surgical (ISRG) was a high flyer in 2017 and made some pretty decent moves in 2018 (Figure 8). I was not in this stock at the beginning of 2018 but I decided to enter a position on January 19. Price was moving higher and OBV was sloping upward so I got in. The signal to get out came on February 2, which resulted in a loss. The stock continued to trade slightly upward but I waited until I got that gap up (B) and entered a position on April 18. Since OBV stayed relatively benign I stayed in the position and watched the stock move C up and it did so pretty consistently. The D first sign of trouble came on October 8 (point C) shortly after it hit its high. I A remained in the position for a little while longer and saw that indeed, the OBV line B was sloping downward, and I exited on October 11. I continued to watch the stock the remainder of the year and we got a bump up on November 7 (point D), but OBV remained right in the middle so there was no signal to reenter. Interestingly, even though the stock traded up and down until December 14 8: HIGH FLYER. The gray shaded rectangles highlight strong OBV slopes, which are the signals. and since then it has gone almost straight FIGURE Here, the first trade from January 19 to February 2 ended in a loss while the second trade between April and down, still, the slope of the OBV line was October was profitable. Throughout the ups and downs in ISRG late in the year, the OBV remained neutral, not even close to confirming a downtrend; with no signals generated. 30 • March 2019 • Technical Analysis of Stocks & Commodities

TLRY

One of the hottest sectors of 2018 was the marijuana stocks and there were a couple that were worth playing. The one I traded was the fast-moving Tilray (TLRY), shown in Figure 10. As with most everyone else as well, I didn’t pick up on Tilray until September 5, when OBV slopes upward. I did not get in at that time; instead, I waited and entered on September 13, represented by point A at 119. It ended up being perfect timing, since on September 19, TLRY hit a high of 300 only six days later. The price quickly came right back down and remained in a trading range. On October 2 at point B, an up day is followed by a down day. OBV remained in the upper range, and it stayed in the upper range for the rest of the year. On November 7 at point C, the stock closed at 139.60. That ended the good times, since from that point, price meandered downward almost on a daily basis followed by a declining, almost-dead volume level and an OBV that remained flat but in the upper range.

A B

C D

FIGURE 9: OBV DOESN’T ALWAYS WORK. OBV was less helpful on TWTR in 2018. Two entries signaled by upsloping OBV lines were promising but I didn’t exit my position in 2018 because OBV remained strong despite a falling price. Will 2019 prove OBV correct for TWTR?

A

B

C

Real-world OBV

These examples are based on real trades that were not fitted in any way in order to FIGURE 10: FAST AND FURIOUS STOCKS. One of the hottest sectors of 2018 was marijuana stocks. The fast-moving Tilray (TLRY) stock was one that was worth playing. OBV produced several signals. make the OBV indicator look good or bad. Understandably, you will never want to trade using just OBV, and the last couple of examples demonstrate why. So what’s a better way to use OBV? An easy way is to simply add B another indicator to the same chart. C A Figure 11 is the same chart of TLRY as in Figure 10 but with two simple moving averages (SMAs) added. This is a better real-world example of how OBV should be used. The black line represents a 25day SMA and the red line is a 50-day SMA. Looking at this chart, my exit signal would have been at point C when the 25-day moving average crosses below the 50-day moving average. Why is this FIGURE 11: OBV WITH MOVING AVERAGES. Here’s the same chart as in Figure 10 but with two simple mova more realistic example? Again, it’s not ing averages (SMA) added for signal confirmation. An exit is signaled at point C when the 25-day SMA crosses below the 50-day SMA. It’s best to use OBV in conjunction with other trusted indicators. because it’s hindsight, but rather it highlights why you shouldn’t rely on only one indicator. No indicator, no matter what it measures, should be I’ve demonstrated a couple of examples where OBV worked used in isolation. OBV is a good example of that. Granted, by itself pretty well and resulted in profits. The problem is, March 2019

• Technical Analysis of Stocks & Commodities • 31

we never know which occasions those will be. The second thing that OBV is The other problem is when to get out. Sure, we can easily spot an entry point and say that an OBV sloping upward tells good at is indicating if a trend you the trend is up and you can enter. But as we have seen, we is indeed real. are not given a sell signal the same way, as OBV can diverge from the current trend. OBV signals, or tries to signal, money flowing into and out For this article, I traded with only this one indicator simply of a security. An upward-sloping OBV line indicates money is flowing in and it’s time to enter a position or add to a current to test its effectiveness. Clearly, OBV is a very useful tool position. A downward-sloping OBV line tells us that money when used properly and in conjunction with another indicator that you can trust. is flowing out and it’s time to get out. Pretty simple. You could also use OBV to indicate when a prevailing trend may be losing steam. If the OBV line is sloping downward John Devcic is a market historian and freelance writer. He and price is moving higher, you should be looking for an exit may be reached at [email protected]. as soon as you can. In either case, when used in conjunction with other indica- Further reading tors, OBV can signal spots where money is entering. Granville, Joseph [1963]. Granville’s New Key To Stock In all the examples in this article, you can see when accuMarket Profits. mulation happens. Use OBV along with other reliable indica- ‡thinkorswim (TD Ameritrade) tors, as was shown in my final example where I simply added ‡See Editorial Resource Index two simple moving averages that would have told me to exit. Other indicators might have signaled an earlier exit. TrAdinG On MOMenTUM

terns. I will often use this technique with three to fi ve trades per day. You should always diversify, and trade several stocks simultaneously, to minimize your overall risk. Successful trading is all about Step 3: Don’t watch the stock chart minimizing upfront risk, and maximizall day long; instead, simply come ing overall profi t potential. This intraday back during the last 30 minutes or so swing trading approach accomplishes and close the entire position around both for you. My personal trading goal 3:50 pm ET. is to make the majority of my trades with this technique (it requires patience!). As insights: Why this techniQue WOrks a reminder, on down days in which there This strategy combines the best of both are few upside gaps, I use this technique worlds from intraday and longer-term with inverse ETFs and ETNs like TVIX, swing trading. You get the benefi t of a SQQQ, and LABD. On up/green days multipoint move, without the risk of hold- I use this strategy with long ETFs like ing overnight. I believe this is the single TQQQ, SPXL, and LABU. best approach for active traders, because you can use a smaller 100- to 300-share traDe ManageMent tips size initial position and still have solid In doing over 2,280 real-money trades profi t potential. It also avoids the foolish during the last several months of 2018, upfront risk that inexperienced, young, small-cap daytraders advocate, trying This strategy combines to trade thousands of shares of cheap, under-$10 stocks for quick scalps, which the best of both usually go against the trader and cause worlds—intraday and massive losses. longer-term swing By trading strong initial gap charts for trading. all-day-long trades, you can often get big moves out of these uptrending chart pat-

CALHOUN

Continued from page 7

32 • March 2019 • Technical Analysis of Stocks & Commodities

it was my experience that many of my biggest winning trades used a “set it and forget it” intraday swing trading approach. By this, I simply mean that I initiated the trades between 9:30 and 10:30 am, entered my stop-loss, then came back during the last hour of the market to close the trades. This helped keep me from overtrading or getting out too soon during intraday minor reversals. The two critical risk management variables are the initial share size and initial stop-loss price. You may want to experiment with these to fi nd the best combination for your personal trading style. You can also consider adding to a winning trade sometime in the middle of the day to scale in; if you do this, then be sure to tighten up your trailing stop to breakeven on the entire position. Ken Calhoun is a producer of trading courses, a live trading room, and videobased training systems for active traders. He is the founder of TradeMastery.com, an educational resource site for active traders and is a UCLA alumnus.

Seifert/21st Century covered call Continued from page 11

you keep the stock instead of having it called away at $320. Continue the next week by initiating a 350.0–365.0 call credit spread for a credit of around $5.90. Total account value: $35,000 + $590 - $1,500 = $34,090 ($2,090 gain) 5) Small loss: Stock drops by about 1.5% to $315 You pocket the $590 from the call credit spread. You have an unrealized loss of $500 in the stock. Continue the next week by initiating a 315.0–330.0 call credit spread for a credit of around $5.90. Total account value: $31,500 + $590 = $32,090 ($90 gain) 6) Big loss: The stock drops by about 5% ($15) to $305 You pocket the $590 from the call credit spread. You have an unrealized loss of $1,500 in the stock. Continue the next week by initiating a 305.0–320.0 call credit spread for a credit of around $5.90. Total account value: $30,500 + $590 = $31,090 ($910 loss)

Manage the weeklys

As you can see, the advantage of this strategy is that it can be used either long or short term. If the stock goes higher, you may give up some of your potential gain, but you are guaranteed

aldrovandi, Malandra, & PaCCHioni/HiStory Continued from page 17

to different types of financial instruments. You could also add a money management strategy, since we did not incorporate that in this study. Alessandro Aldrovandi has worked as a futures trader and portfolio manager. He has a degree in economics and is a member of the Italian Society of Technical Analysis and the Italian Financial Analysts Association. He founded the proprietary trading firm STRATEGIEDITRADING.IT, which uses both discretionary and quantitative strategies. He has authored five books on trading techniques. He may be reached at info@

a profit, and the stock can’t be called away. If the stock goes slightly lower, you may win money. If the stock tanks week after week, you lower your average price and hopefully when it recovers you have lowered your average price so much that you are in a position to make money on the rebound. The trade requires just a few minutes a week to manage and removes almost all of the problems of the covered-call strategy! Robert J. Seifert is president and CEO of The Optionomics Group LLC, www.optionomicsgroup.com. He is a 35-year veteran options trader and was an options market maker for almost 20 years at the CME, CBOT, and CBOE. In the early 1990s, he was appointed by the CME to the position of ViceChairman of International Monetary Markets (IMM), which was the highest-ranking options floor position at the CME. Until his retirement in 2018, he was an adjunct instructor at UNLV where he taught Finance 485, an advanced options strategy course. He is the author of Profiting From Weekly Options and Trading Options My Way. He may be reached at [email protected].

Further reading

Seifert, Robert J. [2015]. Profiting From Weekly Options: How To Earn Consistent Income Trading Weekly Option Serials, Wiley Trading. [2018]. “One-Day Wonder Trades,” Technical Analysis of Stocks & Commodities, Volume 36: October. Trading Options My Way, digital booklet, www.optionomicsgroup.com. ‡Optionomics Group LLC

‡See Editorial Resource Index †See Traders’ Glossary for definition

strategieditrading.it or www.strategieditrading.it. Max Malandra is a financial analyst and journalist with a degree in economics. He cofounded the financial website www.finanzaoperativa.com, which was later sold. He recently authored a book on the markets (published in Italian). He may be reached at [email protected] or www. finanzaoperativa.com. Fabio Pacchioni is a blockchain developer and trading system designer with a master’s degree in electronic engineering. He is also a software engineer using various commercial trading platforms to test and validate trading systems. He may be reached at [email protected].

March 2019

• Technical Analysis of Stocks & Commodities • 33

INTERVIEW

Getting Your Game On

The Psychology Of Trading With Claudio Demb Claudio Demb, MD, is a psychiatrist and has been an individual trader and investor since the 1990s. He is a frequent contributor to this magazine on the topic of trading psychology. Along with his professional practice, he has an academic appointment as an instructor in psychiatry at Harvard Medical School. He is the author of the recently published Trading Your A Game: How Feelings & Emotions Influence Trading & Investing Decisions. His blog about trading and the psychological aspects of trading can be found at claudiodemb.com. Stocks & Commodities Editor Jayanthi Gopalakrishnan spoke with Claudio Demb on January 15, 2019 about how he juggles trading and a full-time job, and how he uses his insights into trading psychology to improve his own trading. Claudio, tell us a little bit about your background. What led to your interest in trading? I grew up in Argentina. My grandparents had escaped from Russia. The reason it matters is because they came to Argentina without any money and died poor. Thus, my parents wanted to do well and wanted to do better than my grandparents did. So money matters were important in my house as a way to succeed, and we were mindful or aware of the power of trying to live well given how poor my grandparents were. And as a boy, I was curious about finance and wanted to learn about it. In school, there was a government program that encouraged kids to save money, and that’s what I did. You could buy government stamps that could be redeemed for money. But something happened in Argentina when I was young—I lived through hyperinflation. That was a lesson that left a mark on me forever. People born and raised in the US may be able to understand that conceptually, but to live through it is something else. Imagine what happens when a country’s currency loses its value extremely rapidly. If you saved, say, $100 today, at the end of the month it could be worth only $1. So all that effort to painstakingly save money could mean

nothing after a few years. I couldn’t believe I could lose all that. As a kid, that was a hard lesson. I was always conscientious with money; I was conservative and was always saving. So I started to buy foreign currency, mostly US dollars and Swiss francs. When I was a teenager, I would take whatever savings I had and I would sneak into a dealer bank and buy foreign currency. At that time, I had no idea about stocks or bonds. I had no clue whatsoever because that was not part of the culture. It was not available to me. But buying foreign currency was how the seeds got planted. Pursuing a graduate-level education was highly encouraged in my family. I had an interest in people and biology, so I chose medicine. It was a well-received choice. And I dedicated many years to attending medical school. My stint living in Argentina was traumatic, not just in terms of finances, but also politically. During my medical school years, we lived through a government led by military rule. Then came the Falkland Wars. I was disenchanted by all that, so I wanted to emigrate. I came to the US. I did my post-medical school training in the US, and all my education in psychiatry was here as well. I’ve been here ever since then. That was in 1990.

34 • March 2019 • Technical Analysis of Stocks & Commodities

Short-term trading doesn’t match up with my personality. I need to make sure I am comfortable with my trading style. When I was finishing up my medical residency in New York City, I started to think about saving money again, since until then, I had no money. When I started to earn some money, it gave me the opportunity for the first time as a young adult to save. I would go to a Barnes & Noble bookstore and sit on the floor and read all the books there were about finance. That’s how I learned about stocks, bonds, and mutual funds. I started to buy mutual funds. I wasn’t thinking at that point about trading; it was more for long-term investment. In 1997 I bought my first stock ever, which was Dell. At the time, I had no clue that we were in the midst of a huge bull market. I bought 100 shares of Dell for $99. I remember how it quadrupled in price—a 400%

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know about risk management. That large drawdown I found that I manage my sent me into a panic, and emotions much better if I suddenly I felt that I knew divide my position in thirds. nothing. I had an emotional meltdown and could hardly I scale out by one third, recognize myself because up then take another third. until that point I was living on a cloud. But the market handed me a very sobering return in less than three years. dose of reality, which brought me down Of course, I thought I knew everything to earth. That’s when my life as a serious about the stock market, and I was hooked. trader began. I started to get serious about it and read more books on it. I thought I could in- What did you do? What changes did vest in stocks as a sort of part-time job you make? while being a psychiatrist. What hooked I started to look for a mentorship me was not just the thrill of it but also through a technical analysis group in the emotional aspects of it. Since I was the Boston area. I would go to every a psychiatrist, I thought this was the meeting of the group, which took place ultimate study in human behavior. on the MIT campus. Through those A lot of emotions are involved in buy- meetings, I met David Weis. He mentored ing and selling, or in the reactions to the a lot of traders and wrote a newsletter market. I had an instinctual attraction to about technical analysis of the indexes. studying the market and market behavior, He was my first coach/mentor. I studto understand why things rise so fast and ied basic chart patterns, chart analysis, why they drop so fast. doing charts by hand, and recognizing areas of interaction. He’s a Wyckoffian What led you to technical analysis? and he teaches the Wyckoff method of I was attracted to the behavioral technical analysis, so I learned all the aspects of the market, which technical Wyckoff language of the market. That analysis is basically all about. It was love approach was close to my heart, and at first sight. I realized that fundamentals that’s how I started to get serious about didn’t make much sense, but reading market analysis. I worked with him for a and studying charts, and the behavioral while, learning about stocks and futures, patterns that showed up in charts over and I even dabbled a bit in options and time, spoke to me like a playbook. And shorting. But eventually, I settled on inthat’s how I began my parallel career in dividual stocks, which is what I do today. investing and trading. After David, I had another mentor for a The problem was that I started out in while who is also well-known to your a bull market and I did very well, which magazine, Dr. Alexander Elder. made me think investing was easy. I knew about long-term charts and I would How do you juggle trading with your focus on them, and I started to learn to full-time work? How do you make that recognize patterns of euphoria or manic work for you? behavior. Somehow, even though I was a In my trading, I manage two accounts: young investor, I sold most of my holdings a long-term account and a short-term before the 2000 technology collapse. So account, mostly on the long side. Over for me at the time, I made a significant 90% of my trades are on the long side, amount of money. So naturally, I believed I knew what I was doing—that is, until the 2001–2002 bear market came along. I ended up with a huge drawdown, in fact, a drawdown of 79% of my entire capital that I had allocated to trading and investing. I didn’t 36 • March 2019 • Technical Analysis of Stocks & Commodities

partly because I work as a psychiatrist full-time and can’t also be a trader fulltime. So I had to adapt my trading not just to my personality but also to the hours and amount of time I had available in my life. If you want to be a full-time trader, it’s a full-time job. You can’t be a fulltime trader and also have another line of work. But you can be a part-time trader, as long as you have realistic expectations and as long as you can adapt your trading to what you’re comfortable with. I’m drawn to the Wyckoff aspects of the market: supply/demand and technical analysis, because to me, it’s a reflection of the people in the market, and that’s what my life’s work is all about. For me, it’s a parallel or linked universe. What led you to begin writing about trading? I’ve been trading and investing for more than two decades. I keep it very simple now, but that has been after years and years of doing it. You become good at it and that’s when you can simplify it and know what you’re doing. I would say I’m a classic or typical type of investor or trader. And because I’m passionate about it, I started to write about it from what I know, that is, about the psychological aspects: the feelings and emotions that are behind trading and financial decisions. Most of the investing mistakes that are made are due to human folly or due to making decisions emotionally. It happens with institutions as well. As we all know, there have been situations where the Fed had to come to the rescue. Why do you think these types of situations happen? I think there is a complete disregard for the possibility of a black swan event that can wipe you out. I find these types of things very interesting. I’m naturally inclined towards working with human struggles. A number of times, traders have asked me questions or asked for advice, and from them I know the struggle is not about intelligence or IQ, it has to do with individual psychological makeup. Someone may have a great system, but then something happens. The problem is not with the system; it’s with

the trader. If a trader suddenly enters into an impulsive trade—a trade they shouldn’t have put on—then it means they didn’t honor the process. A calculated loss that was supposed to be 1% of the equity account becomes 10%. They blow up the account, which leads to negative consequences, and everything can get messy. But it may have had nothing to do with the method itself. That’s why I started to write about trading and psychology. You can find some of my writings on my blog, as well as in this magazine, and the book I recently published is Trading Your A Game, and it’s my story. It’s very real, and I’m very open with it. In it I discuss the psychological aspects of trading and share my actual trades in both my shortterm swing trading account and in my long-term account. I also published in my book my track record of managing my long-term account for the last 20 years. I did that to tell my story but also to be receptive. In fact, part of the purpose of launching my blog was to build up a network of traders who are interested in the same aspects of trading as I am, because otherwise, trading can be lonely. I understand the importance of connecting with others who are doing similar things or who have similar interests, especially if they struggle with something. It gives them somewhere or someone to touch base with. You said that you have both long-term and short-term accounts. Are the nature of your investments in these accounts different in terms of how you select stocks? In my long-term account, I buy discounted stocks and hold them when they’re not popular in sectors that have been out of favor and they’re selling at a heavy discount. They’re very cheap. And you may need to hold onto stocks like these for as long as a couple of years. I may build a significant position there and wait it out, which I don’t do in the shortterm account. There’s a chunk of my net worth in my long-term account, but that’s a no-no in my trading account. So yes, the nature of my investments in each account is different. In the long-term

account, I use a strategy that I don’t use in the short-term account as far as the type If you go for all the best of investment I am seeking, setups—or A setups—your which is value stocks. And returns are much better. in the short-term account, I may use stock selections that I normally wouldn’t use in my long-term account. In my trading works for me. account, I will pick a growth stock even I may trade some ETFs—my watchif it’s expensive, provided there is a good list includes both ETFs and individual setup, which is something I probably stocks. Someone may send me a stock wouldn’t do in the long-term account. trading idea and I may check it out to For my trading account, I tend to look see if it meets my criteria and I’ll put for small caps because there is more bang it on my watchlist. If I have the right for the money, so better returns. And I setup, I may go with it. I would need the look for stocks with good volume. market to be in a bullish mode before I In my short-term account, I swing go long. If I’m not seeing that, I’ll be trade, and I will scale out. I could be a sitting on cash. pure trend follower who gets all in and all I look at the market in the context of out. But what happens is, in my mind, I whether it’s trending. I need a setup and can do it, but then in the actual trade, I’m the right context. To go long, I need the not so good. That’s because if the trade market to be either in a trading range or starts to move in my favor, and it starts bullish. If the market is in a downtrend to get good and there’s a lot of money in or a bear market, I get a red light. I can’t it, I find it very difficult to manage when go long in my short-term account in that a big drawdown starts to occur or when case. But even if the US markets are the trade turns against me. I found that in a downtrend, there may be another I manage my emotions much better if I market that’s in an uptrend that I can divide my position in thirds. So I get in, still trade. all in, but then I have predefined targets. I scale out by one third, then take another When you wait for the setup and the third, and then I can usually follow the right market context, do you use an rest without any problem. automated trading system or are you With regard to that, it’s not that dif- a discretionary trader? ferent from what I do in the long-term I’m a discretionary trader, but the account. system is well-defined. It’s not based on a whim. I use TradeStation and I have So in your short-term account, your a well-defined system I developed with time horizon is all swing trades? my latest coach. I need to see a clear My short-term account is mostly all formation. I don’t have an automated swing trading. I may do some shorting trading system; I initiate the trades, I put here and there, but then I need to be very on a trade, and then I exit a trade. It’s all attentive and need to move fast. But very visual. I use the same strategy over and short-term trading doesn’t match up well over and over but it’s not necessarily the with my personality. I need to make sure same setup each time. It’s the repetition that I am comfortable with my trading that makes it easier to identify the formastyle. If I’m not comfortable with it, I tion and the opportunity. can’t do the task well. Swing trading You said that you generally don’t trade short, despite the fact that you have a well-defined system? That’s correct. I don’t often go short not only because my personality is not Continued on page 56 March 2019

• Technical Analysis of Stocks & Commodities • 37

BULL LOGO: NIFFHAN/GOLD DUST: MILA_LS/SHUTTERSTOCK/ COLLAGE: JOAN BARRETT

Look For That Extra Thrust

Trading Breakaway Gaps

G

by Pawel Kosinski

aps occur on a price chart because of some impetus that made the price move higher or lower than the previous day’s closing price. Several types of gaps are possible and in this article I’ll focus on upward breakaway gaps, which occur when price suddenly jumps from a congestion zone and moves up. It is considered to be a bullish action and something traders are familiar with. As tempting as it may be to simply jump on a breakaway gap when you see one appear on a chart, I prefer backtesting a trading strategy before applying it. For this reason, I created a computer program that recognizes breakaway gaps.

It’s breaking away

You can see an example of a typical breakaway gap in Figure 1. Initially, there was equilibrium between the bulls and bears. Price oscillated around some average value. Suddenly, a breakout in the upward direction, together with a gap, initiated bullish price action. 38 • March 2019 • Technical Analysis of Stocks & Commodities

In my backtesting, I used stocks from the Russell 3000 index. I ran the backtests from January 2000–June 2018. During this period there were two bear markets, included so that your real trading could lead to better performance than what statistical results might otherwise suggest. I analyzed the backtesting results by focusing on the following parameters:

Entry

Gap

A FIGURE 1: A BREAKAWAY GAP IN NIKE IN 2013. A breakaway gap originates from a congestion zone, which is when price moves within a trading range. A sudden gap in the upward direction may initiate bullish action. Point A shows a local minimum in price that occurred shortly before the breakout.

TRADINGVIEW

Many traders like to trade breakaway gaps. How effective are they? Here, we look at backtesting results of trading different breakaway gaps to determine if they can be profitable.

charting

• Profit factor (the higher the better)

• Maximum drawdown (I want its absolute value to be as low as possible)

• Number of trades (the more successful trades there are, the more you will earn, and statistical results are more reliable) • Percentage of profitable trades • Average trade

• Win/loss ratio

• Kelly ratio (discussed in my September 2018 S&C article “Double Bottoms Revisited”) In my initial backtesting, I set the gap size be at least 1% of the stock price. Most traders say that the breakout day should be accompanied by high volume, so I state that it has to be at least twice the average trading volume of the past 14 days. My final assumption was that the breakout price was the highest of the past 60 days, but I didn’t try to optimize or change this value in my backtesting.

How long should you hold?

I enter a trade at the open on the next day after the breakout (see Figure 1), that is, after I know for sure the breakout has happened. I investigated four simple cases: exiting the trade after 5, 10, 15, or 20 days. I don’t specify any stop-loss or take-profit levels, so it isn’t a complete trading strategy. My objective was to investigate the market direction after the gap. The results can be seen in Figure 2. The shortest holding period (five days) is also the safest because maximum drawdown is the lowest, as expected. But the average trade percent is also the lowest so it may be better if you hold onto a stock longer. Keep in mind that if you earn 0.33% per trade you’ll have to consider how much you have to pay for commissions. Note that the Kelly criterion is the highest for the longest holding period, indicating that the breakaway gap can identify the start of a longer rally and not just a short-term event. In the subsequent testing, I use 20 days as the number of days in a trade.

How large should the gap be?

In the backtesting shown in the previous section, I used a gap size of 1.0%. What happens if I increase the gap size to 2%, 3%, 4%, and finally to 5%? The results are shown in Figure 3 and the results can be compared with the last column of Figure 2. Generally, the larger the gap, the better your trading results. Most parameters increase: profit factor, average trade, win/ loss ratio, and especially the Kelly ratio. On the other hand, for the gap size of 5% there is no significant improvement in performance. But the number of trades is lower so it may be difficult to say whether such large gaps should be avoided or whether the number of trades isn’t enough to confirm the statistical results. Such large gaps may also bring up the ques-

Generally, the larger the gap, the better your trading results. tion of stop-loss placement. If you place a stop-loss under the gap, your potential loss could be high. But I’ll discuss this further later in this article.

Then there’s volume

I assumed trading volume on the breakout day was twice the average volume of the past 14 days. In other words, the “multiplication factor” was 2.0. Now I will consider other cases: a multiplication factor of less than 1.0, greater than 3.0, and greater than 4.0. You can see the backtesting results in Figure 4. The results are quite clear: The larger the trading volume on the breakout day, the better. This, I observed when looking at all the studied parameters. Of course, the number of trades decreases when you look for stocks with high trading 5 days 10 days 15 days 20 days Profit factor 1.43 1.24 1.31 1.39 Maximum drawdown -2.36% -3.16% -3.63% -3.97% Number of trades 1208 1208 1208 1208 Profitable trades [%] 53.81% 54.39% 55.71% 58.44% Average trade [%] 0.33% 0.49% 0.76% 0.97% Win/loss 1.07 1.04 1.05 1.01 Kelly ratio 0.106 0.105 0.135 0.173 FIGURE 2: HOW LONG SHOULD YOU HOLD THE SHARES? According to the backtesting results, the longer the holding time, the better for your account. Nevertheless, the maximum drawdown (risk) increases gradually with time. 2% 3% 4% 5% Profit factor 1.43 1.46 1.59 1.58 Maximum drawdown -3.69% -3.53% -3.19% -3.07% Number of trades 912 669 513 368 Profitable trades [%] 58.00% 57.10% 57.31% 57.07% Average trade [%] 1.04% 1.13% 1.41% 1.49% Win/loss 1.05 1.12 1.21 1.22 Kelly ratio 0.180 0.188 0.220 0.219 FIGURE 3: INFLUENCE OF GAP SIZE. The larger gap results in better performance but note the number of trades decreases. Less Greater Greater than 2 than 3 than 4 Profit factor 1.26 1.46 1.69 Maximum drawdown -4.11% -3.38% -2.62% Number of trades 912 623 250 Profitable trades [%] 54.80% 57.95% 60.80% Average trade [%] 0.78% 1.18% 1.69% Win/loss 1.05 1.08 1.10 Kelly ratio 0.180 0.188 0.219 FIGURE 4: VOLUME’S INFLUENCE. When trading volume was more than four times the average volume of the previous 14 days, the strategy outperformed in spite of having the lowest number of trades. March 2019

• Technical Analysis of Stocks & Commodities • 39

volume, but you shouldn’t ignore the importance of volume when trading breakaway gaps. Note that if the multiplication factor is more than 4.0, the Kelly criterion increases to about 0.25. This is quite an outstanding result.

Candle type

Let’s look at the candle bars of the breakout day. The bars can be of different shapes and sizes, so I looked at two cases that are considered bullish by most traders: 1. The body of the candle was the longest of the past four trading days (as it appears in Figure 1)

2. The upper shadow/wick of the candle is relatively short (shorter than the candle body itself). You can see the results I collected in the table in Figure 5. The long body on the breakout day results in a significant improvement in results. Here again, all parameters in the table are better than for the standard case. The number of trades is less, but the odds increase greatly if the breakout candle is bullish. What about the second option, the short upper wick? The number of entries is reduced again and the strategy outperformed the standard one. Nevertheless, the performance isn’t as remarkable as for the long bullish candle. Of course, these are two different scenarios and it’s like comparing apples and oranges.

A full trading strategy

Is it possible to develop a full trading strategy with the information we have? For starters, I’ll need to specify a stop-loss and a take-profit level. Let us consider the four cases. In the first case (case 1), I place a stop-loss a few cents Short upper wick Profit factor 1.51 1.41 Maximum drawdown -3.24% -3.12% Number of trades 654 756 Profitable trades [%] 59.79% 58.60% Average trade [%] 1.22% 1.01% Win/loss 1.02 1.00 Kelly ratio 0.180 0.219 FIGURE 5: LONG BODY OR SHORT WICK? Here you see that the long body of the breakout candle outperforms. The short upper wick improves results but not as much. Long body

Case 1 Case 2 Case 3 Case 4 Profit factor 1.32 1.36 1.38 1.43 Maximum drawdown -3.47% -3.88% -6.21% -8.70% Number of trades 1208 1208 1202 1190 Profitable trades [%] 61.09% 57.95% 69.47% 63.03% Average trade [%] 0.71% 0.87% 1.44% 2.23% Win/loss 0.86 1.01 0.61 0.85 Kelly ratio 0.159 0.163 0.194 0.195 FIGURE 6: COMPARING FOUR TRADING STRATEGIES. Cases 3 and 4 show greater profitability but their maximum drawdown is high.

40 • March 2019 • Technical Analysis of Stocks & Commodities

The larger the trading volume on the breakout day, the better. before the recent lowest price (shown as point A in Figure 1). Then I calculate the distance: closing price of the breakout day minus this lowest price. I add this distance to the closing price of the breakout day. This means a potential reward/risk ratio of about 1.0. Nevertheless, I still limit the maximum number of days in the trade to 20, that is, I exit the trade after 20 days if neither the stop-loss nor the take-profit level has been reached. In the second case (case 2), the take-profit level will be higher: the reward/risk ratio is equal to 1.5. The other conditions are the same as for case 1. The third case (case 3) is similar to case 1, but I don’t limit the number of days in trade to be less than 20. In other words, I wait until the stop-loss or take-profit levels are reached. The fourth case (case 4) is similar to case 2, but I remove the requirement of the maximum 20 days in trade (like in case 3). All these four cases refer to the standard setup discussed previously: Trading volume on the breakout day was at least twice the average of the past 14 days and the gap size was at least 1% of the closing price of the stock. I didn’t analyze the candle type on the breakout day here. All the results are displayed in Figure 6. The most important observation is that all the cases are profitable and the performance is acceptable. Most of the parameters indicate that cases 3 and 4 outperform, that is, it is profitable to hold the stock longer and not exit too early. This could increase your risk but the final result may be superior. The most profitable is case 4, where the take-profit level is located higher but the percentage profitable is lower, as expected, and the maximum drawdown is less attractive.

Size, volume, and direction

Even though I tried different modifications such as changing the gap size, trading volume, and so on, the results were always positive. This confirms that trading breakaway gaps can lead to profitable results. Of course, the odds can be further increased by some bullish action even though this could reduce the number of successful trades. In real trading, if the trade continues to move upward, you can adjust the stop-loss and take-profit levels by trailing the stop. Sometimes it may be less risky to wait until the price retraces (or pulls back), and then enter. This may reduce the risk, but you could always lose some profitable trading opportunities. After all, the price can continue north without waiting for you (and such trades may turn out to be even more profitable). Nevertheless, I didn’t test these issues. Continued on page 56

FUTURES FOR YOU INSIDE THE FUTURES WORLD Want to find out how the futures markets really work? Carley Garner is the senior strategist for DeCarley Trading, a division of Zaner, where she also works as a broker. She has written four books on futures and options trading, with the latest being a new edition of her book A Trader’s First Book On Commodities (third edition, October 2017) as well as Higher Probability Commodity Trading (July 2016). Garner also authors widely distributed e-newsletters; for a free subscription, visit www.DeCarleyTrading.com. To submit a question, email her at info@ carleygarnertrading.com or via www.DeCarleyTrading.com. Selected questions will appear in a future issue of S&C.

OPTION SELLING: RISKS AND REMEDIES (Part 2 of 2) In late 2018, energy market option sellers paid a price for trading naked. How could this have been avoided? As I discussed in this column last month, the practice of option selling is a relatively high-probability trading strategy in that it often comes with unusually high win percentages. Yet we also know that it comes with unlimited risk, and the small percentage of losing trades can wreak havoc on trading accounts. We witnessed several examples of this in the energy complex in late 2018, where at least one large hedge fund (and likely others we aren’t aware of) were wiped out. While I am a proponent of optionselling strategies, years of trial & error and learning about the difficulties of option selling firsthand lead me to believe it is always in the best interest of the trader to have some sort of catastrophic insurance in place to prevent the unthinkable from happening. This is in line with the thinking of a typical vertical credit spread in which a trader sells an option and then purchases an option with the same expiration date but with a more distant strike price to limit risk exposure. In my view, traders shouldn’t confine themselves to buying protection in the same expiration month of the primary short option. Instead, traders should look to various expiration dates in search for the “option” (no pun intended) that offers the best protection, for the lowest cost, for the appropriate timeframe. In other words, directional option sellers might be speculating on a move anticipated to occur in the coming weeks, thus there isn’t necessarily a need to pay for months

of protection. Accordingly, looking to options with expiration dates prior to that of the primary short option might make sense. For example, a trader speculating on an imminent rally in crude oil while the market is valued at $44 per barrel might look to sell a March $38 put option with 60 days to expiration for $750, and then purchase a February $36 put with 30 days to expiration for $150 to create a net credit of $600. Because the trader believes the price reversal will occur within 30 days, there is no need to purchase a March put as protection which is more expensive (time is money when it comes to option

It is always in the best interest of the trader to have some sort of catastrophic insurance in place to prevent the unthinkable from happening. pricing). Further, the lack of time in the February options and cheaper pricing structure because of the time difference enables traders to purchase protection with a more proximal strike price. In this case, the trader has “naked” risk from $38 to $36, a gap worth $2,000 of risk, but is covered beneath $36. Had the trader constructed a similar spread using a March put option as protection, it would have been necessary to either spend more money for the protection, which cuts into profit potential, or purchase a deep outof-the-money put option as protection, which weakens the risk management. To March 2019

Carley Garner

illustrate, the trader would have needed to purchase a March put with a strike price of $30 to keep the premium spent comparable, near $150. Of course, this creates a spread with a much deeper risk of roughly $8,000 before considering the premium collected. This is obviously much more concerning than the $2,000 of exposure the previous version of the trade offers (prior to considering the credit collected). While this column isn’t dedicated to teaching the mechanics of option spreads, I am compelled to clarify that, when trading options with differing expirations, it is impossible to quantify the maximum risk. This is because the profit and loss of such a position will depend on time and volatility, whereas an option spread utilizing securities with the same expiration date can always be mathematically defined using an “at expiration” equation. In the case of a diagonal spread, such as selling the March $38 and buying the February $36, we have already noted the intrinsic risk between $38 and $36, which equates to $2,000 for a trader. But we must note the risk is reduced by the premium collected, in this case, $600. We can use $1,400 ($2,000 - $600) as a starting point for a guess as to what the worst-case scenario might be; to figure the upper end of the range we will use the intrinsic risk of $2,000. Thus, when deciding whether the risk and the reward make sense, the trader should work on the premise that he is collecting $600, which represents the maximum profit potential, and risking $1,400 to $2,000 to do it (again, these are estimates not hard numbers, depending on timing and Continued on page 62

• Technical Analysis of Stocks & Commodities • 41

Following The Money

Sector-Rotation ETFs Underperform

S

by Leslie N. Masonson

ector investing has been popular since Fidelity introduced its sector mutual funds in the mid1980s, and even more so with the offering of nine popular SPDR sector ETFs in 1999. Since then many more have been made available from multiple ETF providers. Investors and traders looking to achieve a performance advantage are embracing a sector-rotation approach to select the best-performing ETFs based on fundamental and/or technical factors, and riding them until their trend changes. In reality, that objective is much more difficult to achieve than it appears, as shown by the disappearance of sector fund newsletters and the closure of two sector-rotation ETFs.

Sector investing popular in past decades

For example, in the 1990s and early 2000s, I noticed a proliferation of ads from newsletters offering sector mutual fund investing/timing services for conservative to aggressive investors. Their backtest results, when provided, were always excellent, and a few had outperformance with real-time records, but overall, their expected results did not live up to 42 • March 2019 • Technical Analysis of Stocks & Commodities

the hype, especially in bull markets where moving between sector funds too often resulted in missed opportunities and subpar performance. Today, all of those newsletters have disappeared from the marketplace. There is probably a handful or so of active sector fund ETF managers/timers who offer this type of investment service online. Some may be tracked by TimerTrac.com, a market timing service tracking professional timers; however, in some instances you have to pay for this service if you want to check them out any further. A quarterly trial is $74.95 to view all the timing strategies compiled on 570 timing services. Alternatively, you can search the web for that type of manager and request verified past results from those offering the service. And in some cases they will provide their TimerTrac results for their performance, if they are tracked. Because of the constant ebb and flow of the markets, and particularly the performance and volatility of individual market sectors, successful investing with a sector rotation focus has been fleeting compared to a simple buy & hold approach even with its inherent bear market risk and losses along the way.

Sector rotation ETFs reviewed

I have covered varying aspects of ETF sector investing in three previous articles in this magazine, which are listed in the “Further reading” section at the end of this article. Those articles covered seven sector fund families, momentum sectors, and a focus on three technology ETFs, respectively. This article provides insights into investing in ETFs that offer actual sector-rotation strategies, where the sector mix is based on

IMAGENTLE/SHUTTERSTOCK

Rotating into leading sectors while switching to a defensive position in downturns is an ideal tactic if it can be implemented successfully, but can it? Are there any ETFs that have succeeded with it? If there are, they could save you the work of implementing the approach yourself, so we’ll investigate.

WHY TRADE ETFS?

• First Trust Dorsey Wright Focus 5 ETF (FV) • First Trust Dorsey Wright Dynamic Focus 5 ETF (FVC) • Global X JPMorgan US Sector Rotation Index ETF (SCTO) • Invesco DWA Tactical Sector Rotation ETF (DWTR) • Main Sector Rotation ETF (SECT)

similar to FVC but below SCTO (1.6%) and SECT (1.02%) The one-year performance through December 26, 2018 was -7.77%, which was better than the others but lagging the SPY (S&P 500) at -3.65%. However, over three years, FV advanced only 13.30% compared to 34.53% for the SPY, not a favorable outcome for an actively managed rule-based portfolio. This ETF had 48% of large-cap exposure, 37% of mid-cap exposure, and 10% small- and micro-cap exposure with a 75% style exposure to growth. First Trust Dorsey Wright Dynamic Focus 5 ETF (FVC) FVC, the second five-sector rotation ETF from First Trust Advisors, LP, follows the same investment logic and methodology as FV with two differences. First, it was introduced two years later on March 17, 2016. Second, when market conditions change, investment exposure to a one- to three-month US Treasury bill component can be put in place. This comes into play when the relative strength of more than one-third of the universe of First Trust ETFs declines relative to the cash index that is evaluated twice monthly. The cash index can vary from 0% to 95% of the Dorsey Wright benchmark index, but is limited to no more than 33% per evaluation. As of December 26, 2018, its current holdings were: FXL, FDN, QTEC, FXH, and FBT—the same as that of FV! Since the September 2018 highs and subsequent collapse into yearend, it is surprising that this portfolio remained in the nondefensive sectors—technology, Internet, biotech, and (somewhat defensive) healthcare—rather than holding any cash position at all. If a 15% to 20% market drop doesn’t effect a portfolio change, then that is something investors should be concerned about, as the portfolio is evaluated semimonthly. Both FV and FVC have an annual expense ratio of 0.89%, higher than that of the others reviewed here, which ranged between 0.75% and 0.83%. FVC’s one-year performance was -7.90% compared to -3.65% for SPY, and was slightly worse than FV’s by 23 basis points. There is no XTF rating for this ETF, probably because of its short track record and perhaps its underwhelming performance.

First Trust Dorsey Wright Focus 5 ETF (FV) This ETF was the earliest entrant among the five reviewed, with an inception date of March 5, 2014. The fund was developed to provide specific exposure to only five of First Trust’s many sector and industry ETF offerings. Dorsey Wright & Associates (DWA), the fund’s manager, uses its proprietary relative strength approach to select the strongest-performing ETFs, which are ranked from highest to lowest. Also included in the selection criteria are minimum daily trading volume and sufficient liquidity. Initially, the ETFs with the five highest-ranking scores were placed in the portfolio. Thereafter, twice a month, DWA performs its review by replacing ETFs falling in ranking to a predetermined level. The remaining ETFs are then rebalanced, so that there are five nearly equally weighted positions. The current portfolio holdings, as of the previous night, can easily be found on its website by keying in the ticker symbol in the site’s search box. In this way, you can look for any changes, although they aren’t made often, sometimes for months. Its holdings as of December 26, 2018, each with about 20% of the assets, we re: FXL, FDN, QTEC, FXH, and FBT. The market took a big hit during the last few months of 2018. More than 50% of S&P 500 stocks were down more than 20% for the year since the September 2018 highs and this portfolio remained in the nondefensive sectors of FV FVC SCTO DWTR SECT SPY technology, Internet, biotech, and health- ETF care (somewhat defensive). XTF Rating 4.3 1.7 9.7 XTF.com rates this ETF with an XTF Expense Ratio 0.89% 0.89% 0.83% 0.75% 0.88% 0.10% rating of 4.3 out of 10. Its rating system Market Cap $2B $496M $3M $58M $375M $245B methodology is provided on its website, Avg. Daily Volume 286,493 98,964 933 17,405 63,632 87,752,464 and is based on a statistical analysis Annual Yield 0.64% 0.65% 1.6% 1.02% 1.44% of structural integrity and investment Inception Date 03/05/2014 03/17/2016 10/22/2014 10/09/2015 09/05/2017 01/22/1993 metrics. Global US US US US US As Figure 1 shows, FV is the heavy- Geography CustomEqualCustomEqualCapCapweight as far as asset accumulation with Index Composition Weighted Weighted Weighted Weighted Weighted Weighted $2 billion—far outpacing FVC ($496 5 5 5 4 11 505 million) and SECT ($375 million). It Avg. # of Components 26% 33% 67% also has the highest daily trading volume Investment Metric rank of 286,493 shares, with FVC second at Perf. - 1 Year -7.77% -7.90% -9.59% -12.27% -8.01% -3.65% 98,964. It is the only one with options Perf. - 3 Years 13.30% 11.25% 1.32% 34.53% available for the more venturesome trad- FIGURE 1: SECTOR ROTATION ETF COMPARISON. The SPY has outperformed these ETFs with a much ers. It offers an annual yield of 0.64%, lower annual expense ratio. March 2019

• Technical Analysis of Stocks & Commodities • 43

DATA SOURCE: XTF.com

predetermined criteria. The five existing ETFs, all open-end investment companies, are shown in Figure 1. Their names and ticker symbols are as follows:

This ETF had 48% of large-cap exposure, 37% mid-cap exposure, and 10% small- and micro-cap exposure with a 51% style exposure to growth, which is getting pummeled as this is written in late December 2018. Global X JPMorgan US Sector Rotation Index ETF (SCTO) The current issuer of this ETF is Mirae Asset Global Investments Co., Ltd. SCTO was introduced seven months after FV in October 2014, but it has been unable to match FV’s massive asset-gathering success, with only $3 million in assets—a very low number by anyone’s measure—and a minuscule daily trading volume of 933 shares. Its annual expense ratio at 0.83% is lower than that of First Trust’s products, and its 1.6% yield is 100 basis points better, as well as 16 basis points above the SPY. However, its one-year performance of -9.59% is the second worst of the group. This ETF’s goal is to invest in the best-performing three to six sectors (out of the 10 available) while limiting exposure during market setbacks or periods of high volatility. The portfolio as of December 26, 2018 consisted of: RWR (28.3%), XLV (19.5%), XLI (17.6%), XLF (17.5%), and XLB (16.2%). This ETF had 75% large-cap exposure, 19% mid-cap exposure, and 6% small- and micro-cap exposure, with a 50% style exposure to growth. Since the market’s large decline from the September 2018 highs to late December 2018, this portfolio had no cash component but does have the SPDR Dow Jones REIT ETF (RWR), which is somewhat defensive. On September 30, 2018, the portfolio contained XLV (30.4%), XLY (18.4%), XLK (17.8%), XLY (17.0%), and RWR (16.4%). So you can see that since the end of September through late December, the portfolio increased its exposure to XLV by just over 10 percentage points, eliminated XLY and XLI, reduced RWR by 12 percentage points, and added new positions in XLF and XLB. Thus, the portfolio composition was shifted to include XLF and XLB, which don’t offer the expected defensive posture. Invesco DWA Tactical Sector Rotation ETF (DWTR) This Invesco Capital Management LLC ETF came into existence on October 9, 2015. It is based on the Dorsey Wright Sector 4 Index. At least 90% of assets are invested in securities in this index. A relative strength concept is used, as is the case with all the DWA-managed ETFs. The universe available for this ETF is the nine PowerShares ETFs. This ETF may hold up to 100% cash using one- and three- month T-bills when stocks are out of favor, although as of late December 2018 with the market in correction mode or worse, this ETF had no cash holdings. It has the second-lowest asset base at $58 million and the lowest annual expense ratio at 0.75%. Its one-year performance is the worst at -12.27%. And its three-year performance of 1.32% is poor compared to 34.53% for the SPY and 23.4% for its Russell 3000 benchmark for that period. A daily trading volume of 17,405 is the second-lowest of the group. The portfolio as of December 26, 2018 was as follows: 44 • March 2019 • Technical Analysis of Stocks & Commodities

These specialized, highly concentrated ETFs have had a rough time meeting their goals or keeping up with their benchmarks or the S&P 500. PRN (30.5%), PTF (28.0%), PEZ (23.2%), and PTH (18.3%). These sector funds represent, in order: industrials, technology, consumer cyclicals, and healthcare. Its September 30, 2018 holdings were the same within less than a percentage difference. So there was no portfolio change in that time span although the market took a 15% to 20% hit during that period. The healthcare ETF is somewhat defensive, but the fact that there was no cash at all is something that both potential and existing investors in this ETF should be concerned about, since the portfolio is evaluated semimonthly and a defensive posture could have been put into place to limit potential losses as the market decayed. The ETF and benchmark index are analyzed monthly and rebalanced and reconstituted as necessary. There is no XTF rating. This ETF had 31% of large-cap exposure, 31% of midcap exposure, and 24% small- and 14% micro-cap exposure with a huge 83% style exposure to growth. Its sector exposure is 30% technology, 21% industrials, 20% healthcare, and 18% consumer cyclicals. Main Sector Rotation ETF (SECT) This is an actively managed cap-weighted ETF introduced by Main Management LLC and is now known as Northern Lights Fund Trust IV Main Sector Rotation ETF. It seeks to outperform the S&P 500 index in advancing markets, while limiting losses during market declines by using dynamic sector rotation based on fundamental analysis (for example, economic forecasts, inflation data, macroeconomic and capital markets input). SECT was born on September 5, 2017 and has amassed a respectable $375 million in assets in just over 14 months. Daily trading volume is 63,632. Its one-year performance is -8.01%, slightly worse than FV and FVC. Note the portfolio consists of 505 positions, a great variance from the other ETFs reviewed here. The portfolio holds 10 ETFs and a 3% component of cash. The fund has a low XTF rating of 1.5. Its annual expense ratio is 0.88% and annual yield is 1.02%, the second-best in the group. Up to 20% of the portfolio can hold any market cap or country-denominated security in any currency. This ETF had 63% of large-cap exposure, 11% of mid-cap exposure, 10% small-cap, and 10% emerging market exposure with a 48% style exposure to growth. Its sector exposure when I checked it was 20% technology, 15% financials, 17% healthcare, and 9.7% banking, with another 34% spread among six other sectors.

Performance during year-end meltdown

How did these five funds perform during the September 21 to December 26 market collapse? Here are the numbers: FV FVC SCTO DWTR SECT SPY

-20.78% -20.79% -15.71% -22.60% -17.45% -15.20%

Two rotation ETFs already closed down Based on the arduous task of beating its benchmark and garnering sufficient assets, it wasn’t shocking that two ETFs in this category have already closed their doors. The most recent closure was the ALPS/Dorsey Wright Sector Momentum ETF (SWIN). It had a short life, as it was introduced on January 10, 2017 and was liquidated on October 22, 2018 with about $11.6 million in assets, and an annual expense ratio of 0.40%. SWIN used a rules-based approach to select, from a 10-sector universe, a total of 50 stocks with the highest relative strength (excluding real estate) in the NASDAQ US Large Cap and Midcap

A rough road

Clearly, these specialized, highly concentrated ETFs have had a rough time meeting their goals or keeping up with their benchmarks or the S&P 500. Most Continued on page 55

Stockcharts.com

Clearly, none of the funds offered any benefit in performance compared to the S&P 500 index, even though they all profess to be able to assess the situation and move to more defensive sectors or cash during a market downturn to minimize principal losses. That goal was not met in any of these ETFs. Let’s go to a longer time period to see if the results are any better. First, let’s review these ETFs with their earliest common date of September 26, 2017. Figure 2 shows the bar chart data. The S&P 500 outperformed these ETFs showing a return (based on price, not total return) of 2.67% through December 26, 2018 compared to losses ranging from -1.2% to -6.26%, again not a favorable outcome. Finally, Figure 3 shows the return, excluding the more recent SECT, to examine an earlier common date of March 18, 2016. Again, the S&P 500 beat all these funds over this 30-month period by a minimum of 9.5 percentage points to a maximum of 23.55 percentage points. Thus, over the three time periods reviewed here, these unique ETFs have consistently underperformed.

Indexes. The top three sectors with the best momentum (for example, relative strength) received 20% of the cash, with 10 stocks in each of those sectors each being allocated 2% of the cash. The next four sectors with the highest relative strength received 10% of the cash, with five stocks in each sector receiving 2% of the cash. This ETF used the Dorsey Wright proprietary Point & Figure Relative Strength charts to select the 50-stock portfolio. A quarterly ranking of sectors was used and ranking changes were made as necessary. During its short lifespan, SWIN returned a respectable 22%, which compared favorably to the SPY, which rose 22% as well. A second ETF that folded was the Guggenheim Sector Rotation ETF (XRO). It was launched on September 21, 2006 and closed on March 23, 2012. During that nearly six-year period it returned 10.7% compared to 18.3% for the SPY. The ETF used a proprietary quantitative methodology that focused on sectors with superior risk–return characteristics. The portfolio held 100 stocks that were selected from a universe of the 1,000 largest market-cap listed equities. It had an annual expense ratio of 0.60%. Formerly, this ETF was known as the Claymore/Zacks Sector Rotation ETF.

FIGURE 2: ETF PERFORMANCE SINCE SEPTEMBER 26, 2017. During this rapid decline, these ETFs failed to provide the defensive shift in the portfolio that was expected based on their objectives. The SPY did much better.

FIGURE 3: ETF PERFORMANCE SINCE MARCH 18, 2016. Once again, with a longer time horizon, these ETFs failed to deliver. The SPY outpaced them by at least 9.5 percentage points. March 2019

• Technical Analysis of Stocks & Commodities • 45

Explore Your Options Got a question about options? Jay Kaeppel has over three decades of experience in the options markets. He was a head trader for a CTA firm, an options trading software developer, and is a portfolio manager for an investment management firm. He also spent several years writing a weekly column titled “Kaeppel’s Corner” and now publishes a blog, “Jay On The Markets” (http:// jayonthemarkets.com). He is the author of several books, including The Four Biggest Mistakes In Option Trading; The Option Trader’s Guide To Probability, Volatility, And Timing; and Seasonal Stock Market Trends. Send your questions or topic suggestions to Jay Kaeppel at [email protected]. Selected questions will appear in a future issue of S&C.

Crude oil Crude oil has displayed a negative seasonal bias during the months of October and November. Let’s assume a trader

wanted to play this bias in a limited risk fashion. One possibility would be to buy a deep in-the-money (ITM) January 2019 put option on United States Oil ETF (USO) which ostensibly tracks the price of crude oil. Say USO is trading at $15.52 a share and a trader bought 10 January 2019 17 puts at $1.82 apiece for a total cost of $1,820. As shown in Figure 1, this represents the total risk on the trade and the breakeven price at January option expiration is $15.18 a share. By October 23, 2018 USO had fallen -8% and the put option was showing a gain of +$1,170 (+64%). At this point

our trader may have considered several choices. The simplest thing to do is let the position ride and hope the decline continues and that profits continue to grow. The problem here is that the trade now has essentially $2,990 of risk if USO reverses and rallies (the original $1,280 cost plus the $1,170 open profit). If the trader decides to adjust at this point there are many choices. What follows is a discussion of just a handful of possibilities, not a definitive “best choice.” 1. Sell enough to lock in a profit: In this example, to lock in a profit by sell-

www.OptionsAnalysis.com

ADJUSTING POSITIONS USING OPTIONS I hear traders refer to “adjusting” an option position. What does that mean, and is it worth knowing about? Learning to adjust an option position is absolutely worth learning about and is one of the key advantages to trading options versus other vehicles such as stocks, exchange traded funds (ETFs), and commodity futures. To “adjust” an option position means buying or selling options to change the nature of an existing position. The good news is that option adjustments can often be used to improve the reward-to-risk profile of the trade in question. The tradeoff—in most cases you give up something in order to get something else. This idea will become clearer in the examples that follow. Trade “adjustments” are frequently used to try to “lock in” a profit and then let the remaining adjusted position “ride.” One old adage in option trading is to “sell half after a double.” In other words, if you buy a call or put option and it doubles in price, you can essentially lock in at least a breakeven situation if you sell half of the options you bought. While this isn’t bad advice, the reality is that this approach first requires you to buy a call or put and have it double in price. Anyone who has traded for any length of time will know this is no simple feat (and doesn’t happen nearly as frequently as most of us would like). So let’s look at some less-rigid examples.

Jay Kaeppel

Figure 1: Playing seasonal weakness in crude oil with USO put options. If you bought 10 January 2019 17 puts at $1.82 apiece for a total cost of $1,820 that would be the total risk on the trade. The breakeven price at January option expiration is $15.18 a share.

46 • March 2019 • Technical Analysis of Stocks & Commodities

Explore Your Options To “adjust” an option position means buying or selling options to change the nature of an existing position. ing part of his position, the trader would have to sell seven of his 10 puts. The risk curves for this adjustment appear in Figure 2. The good news is that the trade has a locked-in profit of +$273. The bad news is that a lot of profit potential is removed. Before the adjustment, the open trade had a delta of roughly -900. This implied that for each $1 USO shares declined in price, this position would add an additional $900 in profit. After the adjustment, the delta is down to -270. So if USO continues to decline, the trader will make significantly less additional profit than if they “let it ride.” 2. Roll down the strike price: In this example, our trader simply sells the original position and simultaneously enters a new one, this time at a lower strike price. For this example, the trader sells 10 January 2019 17 puts at $2.99 a piece and buys 10 January 2019 14 puts at $72 apiece. As you can see in Figure 3, by doing this the trader has locked in a profit of $450 and has more profit potential than the trader in example #1 because this new position has a delta of -448. 3. Playing both sides: Now let’s go off the beaten path a bit. In this example, let’s say that our trader has decided that an explosive move in crude oil will occur within the next six months, but they aren’t quite willing to fully buck the bearish trend just yet. The trader in this example might choose to use his or her open profit to finance a trade to play their new projected scenario. In his example, the trader sells 10 January 2019 16 puts at $2.99 apiece and buys four April 2019 14 calls and four April 2019 14 puts at $1.12 and $1.01 apiece, respectively. The risk curves for this new adjusted position appear in Figure 4. Continued on page 62

Figure 2: Selling enough to lock in a profit. To lock in a profit by selling part of the position, the trader would have to sell seven of the 10 puts. The trade has a locked-in profit of +$273 but a lot of profit potential is removed.

Figure 3: Rolling down by selling the 17 strike price puts and buying the 14 strike price puts. You could sell the original position and simultaneously enter a new one, this time at a lower strike price. March 2019

• Technical Analysis of Stocks & Commodities • 47

For this month’s Traders’ Tips, the focus is Sylvain Vervoort’s article in the September 2018 issue, “The V-Trade, Part 7: Technical Analysis—V-Wave Count.” Here, we present the March 2019 Traders’ Tips code with possible implementations in various software. The code for the following Traders’ Tips selections is posted here: • Traders.com  S&C Magazine  Traders’ Tips

F TRADESTATION: MARCH 2019 TRADERS’ TIPS CODE In “The V-Trade, Part 7: Technical Analysis-V-Wave Count,” which appeared in the September 2018 issue of Technical Analysis of Stocks & Commodities, author Sylvain Vervoort introduces a tool to assist in counting waves based on Elliott wave theory. His SVE zigzag ticks indicator automatically draws trendlines highlighting the waves using a specified number of points to detect the pullbacks. A sample chart is shown in Figure 1. Here is the TradeStation EasyLanguage code for a zigzag indicator based on the author’s description. Indicator: SVE ZigZag Ticks // SVE ZigZag Ticks // TASC Mar 2019 // Sylvain Vervoort

At Traders.com you can also right-click on any chart to open it in a new tab or window and view the chart at a much larger size. The Traders’ Tips section is provided to help readers implement a selected technique from an article in this issue or another recent issue. The entries here are contributed by software developers or programmers for software that is capable of customization.

Point( NULL ), double NewSwingPrice( 0 ), double SwingPrice( Close ), int TLDir( 0 ), bool SaveSwing( false ), bool AddTL( false ), bool UpdateTL( false ); method TrendLine CreateNewTrendline() variables: TrendLine tempTL; begin if UseBNPoint then tempTL = TrendLine.Create( LastSwingBNPoint, SwingBNPoint ) else { use DTPoint } tempTL = TrendLine.Create( LastSwingDTPoint, SwingDTPoint );

using elsystem; using elsystem.drawing; using elsystem.drawingobjects; inputs: double HighPivotPrice( High ), double LowPivotPrice( Low ), double RetracePnts( 5 ), int LineColor( Yellow ), int LineWidth( 1 ) ; variables: intrabarpersist bool UseBNPoint( false ), int DrawingObjectBarNumber( 0 ), TrendLine ZigZagTrendline( NULL ), DTPoint SwingDTPoint( NULL ), DTPoint LastSwingDTPoint( NULL ), BNPoint SwingBNPoint( NULL ), BNPoint LastSwingBN-

Figure 1: TRADESTATION. This sample TradeStation mean renko chart of the FDAX index shows the SVE zigzag ticks indicator.

48 • March 2019 • Technical Analysis of Stocks & Commodities

tempTL.Persist = false; tempTL.Lock = true; tempTL.Color = GetColorFromInteger( 255, LineColor ); tempTL.ExtLeft = false; tempTL.ExtRight = false; tempTL.Weight = LineWidth; if DrawingObjects <> NULL then DrawingObjects.Add( tempTL ); return tempTL; end; method Color GetColorFromInteger( int Alpha, int ColorInteger ) begin return Color.FromARGB( Alpha, GetRValue( ColorInteger ), GetGValue( ColorInteger ), GetBValue( ColorInteger ) ); end; once begin UseBNPoint = BarType = 0 or ( BarType > 4 and BarType <> 14 ); if UseBNPoint then SwingBNPoint = BNPoint.Create( CurrentBar + MaxBarsBack - 1, Close ) else SwingDTPoint = DTPoint.Create( BarDateTime, Close ); end; if UseBNPoint then DrawingObjectBarNumber = CurrentBar + MaxBarsBack - 1; NewSwingPrice = SwingHigh( 1, HighPivotPrice, 1, 2 ); if NewSwingPrice <> -1 then begin if TLDir <= 0 and NewSwingPrice >= SwingPrice + RetracePnts then begin SaveSwing = true; AddTL = true; TLDir = 1; end else if TLDir = 1 and NewSwingPrice >= SwingPrice then begin SaveSwing = true; UpdateTL = true; end; end else begin NewSwingPrice = SwingLow( 1, LowPivotPrice, 1, 2 ); if NewSwingPrice <> -1 then begin if TLDir >= 0 and NewSwingPrice <= SwingPrice - RetracePnts then begin SaveSwing = true;

AddTL = true; TLDir = -1; end else if TLDir = -1 and NewSwingPrice <= SwingPrice then begin SaveSwing = true; UpdateTL = true; end; end; end; if SaveSwing then begin if UseBNPoint then begin LastSwingBNPoint = SwingBNPoint; SwingBNPoint = BNPoint.Create( DrawingObjectBarNumber[1], NewSwingPrice ); end else begin LastSwingDTPoint = SwingDTPoint; SwingDTPoint = DTPoint.Create( BarDateTime[1], NewSwingPrice ); end; SwingPrice = NewSwingPrice; SaveSwing = false; end; if AddTL then begin ZigZagTrendline = CreateNewTrendline(); AddTL = false; end else if UpdateTL then begin if ZigZagTrendline <> NULL then begin if UseBNPoint then ZigZagTrendline.SetEndPoint( SwingBNPoint ) else ZigZagTrendline.SetEndPoint( SwingDTPoint ); end; UpdateTL = false; end;

To download the EasyLanguage code, please visit our TradeStation and EasyLanguage support forum. The files for this article can be found here: https://community.tradestation.com/Discussions/Topic.aspx?Topic_ID=156727. The filename is “TASC_MAR2019.ZIP.” For more information about EasyLanguage in general, please visit TradeStation. com. This article is for informational purposes. No type of trading or investment recommendation, advice, or strategy is being made, given, or in any manner provided by Trade­ Station Securities or its affiliates. —Doug McCrary TradeStation Securities, Inc. www.TradeStation.com

March 2019

• Technical Analysis of Stocks & Commodities • 49

release. Description: The V-Trade. Part 7: Technical Analysis—VWave Count by Sylvain Vervoort Version: 01/15/2019

1.00

Formula Parameters: Default: NumberOfTicks 200 Notes: The related article is copyrighted material. If you are not a subscriber of Stocks & Commodities, please visit www.traders. com. Figure 2: eSIGNAL. Here is an example of the study plotted on a 60-minute chart of SPY.

***************************** *****/

var fpArray = new Array();

F eSIGNAL: MARCH 2019 TRADERS’ TIPS CODE For this month’s Traders’ Tip, we’ve provided the study SveHLZigZagTicks.efs based on the article by Sylvain Vervoort, “The V-Trade, Part 7: Technical Analysis-V-Wave Count,” from the September 2018 issue of Technical Analysis of Stocks & Commodities. The study displays the waves (swings) on the price chart. A sample chart is shown in Figure 2. The study contains formula parameters that may be configured through the edit chart window (right-click on the chart and select “edit chart”). To discuss this study or download a complete copy of the formula code, please visit the EFS library discussion board forum under the forums link from the support menu at www. esignal.com or visit our EFS KnowledgeBase at http://www. esignal.com/support/kb/efs/. The eSignal formula script (EFS) is shown below and is also available for copying & pasting from the Stocks & Commodities website at Traders.com from the Traders’ Tips menu. /********************************* Provided By: eSignal (Copyright c eSignal), a division of Interactive Data Corporation. 2016. All rights reserved. This sample eSignal Formula Script (EFS) is for educational purposes only and may be modified and saved under a new file name. eSignal is not responsible for the functionality once modified. eSignal reserves the right to modify and overwrite this EFS file with each new 50 • March 2019 • Technical Analysis of Stocks & Commodities

function preMain(){ setPriceStudy(true); setStudyTitle("SveHLZigZagTicks"); var x = 0; fpArray[x] = new FunctionParameter("NumberOfTicks", FunctionParameter.NUMBER); with(fpArray[x++]){ setName("Reversal Number of Ticks"); setLowerLimit(2); setDefault(200); } fpArray[x] = new FunctionParameter("LineColor", FunctionParameter.COLOR); with(fpArray[x++]){ setName("Line Color"); setDefault(Color.RGB(0,148,255)); } } var bInit = false; var bVersion = null; var vFlat = null; var vCurrentTrend = null; var xOpen = null; var xHigh = null; var xLow = null; var xClose = null; var vLastHigh = null; var vLastHigh_1 = null; var vLastLow = null; var vLastLow_1 = null; var vZZTemp = []; var vZZfinal;

else { vLastHigh = vLastHigh_1; if (!fNewBar) vZZTemp.shift(); vZZTemp.unshift(vLastHigh); }

var point = null; var fNewBar = null; var vLastSwingH = null; var Count = null; var x1a = null; var x1b = null; var y1a = null; var y1b = null; var cntr = null; var sType; function main(NumberOfTicks, LineColor){ if (bVersion == null) bVersion = verify(); if (bVersion == false) return; if (getBarState() == BARSTATE_ALLBARS){ bInit = false; } if (!bInit){ xClose = close(); xHigh = high(); xLow = low(); xOpen = open(); point = getMinTick(); vCurrentTrend = 1; Count = null;

} else { if (xLow.getValue(0) <= vLastLow_1){ vLastLow = xLow.getValue(0); if (!fNewBar) vZZTemp.shift(); vZZTemp.unshift(xLow.getValue(0)); } else if (xHigh.getValue(0) >= (vLastLow_1 + (point * NumberOfTicks))) { vLastHigh = xHigh.getValue(0); vLastLow = xHigh.getValue(0); if (!fNewBar) vZZTemp.shift(); vZZTemp.unshift(xHigh.getValue(0)); vCurrentTrend = 1; } else { vLastLow = vLastLow_1; if (!fNewBar) vZZTemp.shift(); vZZTemp.unshift(vLastLow); } } if (vZZTemp[vFlat] < vZZTemp[vFlat+1] && vZZTemp[0] >= vZZTemp[1]) { if (vLastSwingH == false ){ removeLine(sType+cntr) cntr -= 1; } else { x1a = x1b; y1a = y1b; }

x1b = null; y1b = null; x1a = null; y1a = null; cntr = null; bInit = true;

}

x1b = -vFlat; y1b = xLow.getValue(-vFlat)

fNewBar = false; if (getBarState() == BARSTATE_NEWBAR){ vLastHigh_1 = vLastHigh; vLastLow_1 = vLastLow; fNewBar = true; Count = Count+1;

}

if (x1a != null) x1a -= 1; if (x1b != null) x1b -= 1;

doLine("con",LineColor); vLastSwingH = false;

} else if (vZZTemp[vFlat] > vZZTemp[vFlat+1] && vZZTemp[0] <= vZZTemp[1]) { if (vLastSwingH == true ){ removeLine(sType+cntr) cntr -= 1; } else { x1a = x1b; y1a = y1b; } x1b = -vFlat; y1b = xHigh.getValue(-vFlat)

vFlat = 1; if (vCurrentTrend > 0) { if (xHigh.getValue(0) >= vLastHigh_1) { vLastHigh = xHigh.getValue(0); if (!fNewBar) vZZTemp.shift(); vZZTemp.unshift(xHigh.getValue(0)); } else if (xLow.getValue(0) <= (vLastHigh_1 - (point * NumberOfTicks))){ vLastLow = xLow.getValue(0); vLastHigh = xLow.getValue(0); if (!fNewBar) vZZTemp.shift(); vZZTemp.unshift(xLow.getValue(0)); vCurrentTrend = -1; }

}

doLine("con",LineColor); vLastSwingH = true;

if (vZZTemp[0] == vZZTemp[1]) vFlat = vFlat + 1; else vFlat = 1; return; March 2019

• Technical Analysis of Stocks & Commodities • 51

Figure 3: WEALTH-LAB. This sample chart demonstrates application of the indicator to a one-minute chart of TSLA (data provided by AlphaVantage).

} function doLine(sType, LineColor) { cntr += 1; if (x1a != null && x1b != null){ drawLineRelative(x1a, y1a, x1b, y1b, PS_SOLID, 3, LineColor, sType+cntr); } return; } function verify(){ var b = false; if (getBuildNumber() < 779){ drawTextAbsolute(5, 35, "This study requires version 10.6 or later.", Color.white, Color.blue, Text. RELATIVETOBOTTOM|Text.RELATIVETOLEFT|Text. BOLD|Text.LEFT, null, 13, "error"); drawTextAbsolute(5, 20, "Click HERE to upgrade.@ URL=http://www.esignal.com/download/default.asp", Color.white, Color.blue, Text. RELATIVETOBOTTOM|Text.RELATIVETOLEFT|Text. BOLD|Text.LEFT, null, 13, "upgrade"); return b; } else b = true; return b; } —Eric Lippert eSignal, an Interactive Data company 800 779-6555, www.eSignal.com 52 • March 2019 • Technical Analysis of Stocks & Commodities

F WEALTH-LAB: MARCH 2019 TRADERS’ TIPS CODE The SVEHLZigZagTicks indicator, introduced by Sylvain Vervoort in his September 2018 S&C article, “The V-Trade, Part 7: Technical Analysis-V-Wave Count,” is now part of our TASCIndicators library. We coded it as a trailing reverse indicator, which marks the reverse of a trend-following move of a certain number of ticks off the last price extreme. It can be applied to any bar scale, not just tick-based charts. Crossovers and crossunders of the indicator line can be used to trigger trend trades. A sample chart is shown in Figure 3. In addition, we’ve extended the already-published SVEHLZZperc indicator to accept “points” (that is, ticks) as input in addition to the other types of price movement (percent, ATR, and combined). After updating the TASCIndicators library to its latest version, the SVEHLZigZagTicks indicator can be found under the TASC Magazine Indicators group. Please install (or update if you haven’t done so already) the library from the wealth-lab.com site to its latest version. Then you can plot it on a chart or use it as an entry or exit condition in a rule-based strategy without having to program any code yourself.

—Gene (Eugene) Geren, Wealth-Lab team MS123, LLC www.wealth-lab.com

Figure 4: NEUROSHELL TRADER. This NeuroShell Trader chart displays the EUR/USD 200-tick SveHLZigZagTicks indicator.

F NEUROSHELL TRADER: MARCH 2019 TRADERS’ TIPS CODE The SveHLZigZagTicks indicator that was introduced in Sylvain Vervoort’s September 2018 article in Stocks & Commodities, “The V-Trade, Part 7: Technical Analysis-V-Wave Count,” can be implemented in NeuroShell Trader using NeuroShell Trader’s ability to call external dynamic linked libraries. Dynamic linked libraries can be written in C, C++, Power Basic, or Delphi. After coding the indicator in your preferred compiler and creating a DLL, you can insert the resulting SveHLZigZagTicks indicator as follows: 1. Select “new indicator” from the insert menu 2. Choose the “external program & library calls” category 3. Select the appropriate external DLL call indicator 4. Set up the parameters to match your DLL 5. Select the finished button. An alternative method to analyzing wave counts in NeuroShell Trader is to use the Turning Points add-on, which not only plots lines between peaks and valleys, but also plots peak/valley support & resistance lines, the support/resistance price oscillator, Fibonacci retracement lines, price, time and slope statistical measures, and the probability that

the current price is a new peak or valley. Users of NeuroShell Trader can go to the Stocks & Commodities section of the NeuroShell Trader free technical support website to download a copy of this or any previous Traders’ Tips. A sample chart is shown in Figure 4.

—Marge Sherald, Ward Systems Group, Inc. 301 662-7950, [email protected] www.neuroshell.com

F Quantacula Studio: MARCH 2019 TRADERS’ TIPS CODE In “The V-Trade, Part 7: Technical Analysis-V-Wave Count” in the September 2018 issue of Technical Analysis of Stocks & Commodities, author Sylvain Vervoort uses several customized zigzags as part of his trading methodology. The Quantacula platform contains two built-in indicators to let you easily add zigzags to your charts or to your trading models. • Our ZigZag indicator takes as input a single data series, generating zigzags based on a specified reversal amount, March 2019

• Technical Analysis of Stocks & Commodities • 53

troughs of the price moves. This is your typical zigzag and is plotted in red in Figure 5. Notice how the black zigzag is always delayed by one or more bars. The red zigzag can be useful when scanning for historical chart patterns, but it is not safe to use when backtesting, since it relies on future information when establishing its values. Quantacula defaults to the “safe” Figure 5: quantacula. The standard zigzag is plotted in red, while the zigzag that is “safe” for backtesting is plotted in black. zigzag option, which can be used in a expressed either as a percentage or a flat value. backtest. But it also provides the classic zigzag for charting • Our ZigZagHL indicator uses the historical highs and and pattern-detection purposes. More importantly, it clearly lows to create a zigzag, letting you specify the reversal shows you the delay in the standard zigzag, so you can make amount to use, either as a percentage or a flat value. more informed decisions when using zigzag in a trading Both indicators have an important additional parameter strategy.

called assign when confirmed. When this is set to true (default), the zigzag points are anchored to the bars at which they were detected, that is, the point in time that the source data moved by at least the specified reversal amount. In the chart shown in Figure 5, this zigzag is colored in black. This indicator is safe to use in a backtest. When assign when confirmed is false, the zigzag is anchored to the points on the chart that contain the peaks and

—Dion Kurczek, Quantacula LLC [email protected] www.quantacula.com

F AIQ: MARCH 2019 TRADERS’ TIPS CODE The AIQ program has a built-in zigzag indicator that is similar to the one discussed in Sylvain Vervoort September 2018 S&C article, “The V-Trade, Part 7: Technical AnalysisV-Wave Count.” This indicator is demonstrated on an AIQ chart in Figure 6. —Richard Denning info@TradersEdge­ Systems.com for AIQ Systems

Figure 6: AIQ. This demonstrates the built-in zigzag indicator on an AIQ chart of SPY.

54 • March 2019 • Technical Analysis of Stocks & Commodities

Once the file is downloaded, you can import the indicator into NinjaTader 8 from within the Control Center by selecting Tools → Import → NinjaScript Add-On and then selecting the downloaded file for NinjaTrader 8. To import into NinjaTrader 7, from within the Control Center window, select the menu File → Utilities → Import NinjaScript and select the downloaded file. You can review the indicator’s source code in NinjaTrader 8 by selecting the Figure 7: NINJATRADER. The SveHLZigZagTicks indicator is displayed on a one-minute EURUSD chart during January menu New → NinjaScript 2019. Editor → Indicators from within the Control Center window and selecting the SveHLZigZagTicks file. You can review the indicator’s source code in NinjaTrader 7 by selecting the menu Tools → Edit F NINJATRADER: MARCH 2019 TRADERS’ TIPS CODE NinjaScript → Indicator from within the Control Center The SveHLZigZagTicks indicator, as discussed in the Septemwindow and selecting the SveHLZigZagTicks file. ber 2018 S&C article titled “The V-Trade, Part 7: Technical NinjaScript uses compiled DLLs that run native, not inAnalysis-V-Wave Count” by Sylvain Vervoort, is available terpreted, which provides you with the highest performance for download at the following links for NinjaTrader 8 and possible. for NinjaTrader 7: A sample chart implementing the indicator is shown in Figure 7. NinjaTrader 8: www.ninjatrader.com/SC/ —Raymond Deux & Jim Dooms March2019SCNT8.zip NinjaTrader, LLC NinjaTrader 7: www.ninjatrader.com/SC/ www.ninjatrader.com March2019SCNT7.zip

MASONSON/ETFS

Continued from page 45

of the period during which these ETFs existed, except for XRO, was bullish, so it is surprising the returns weren’t a good deal better. Based on the analysis provided in this article, you could conclude that these ETFs don’t offer advantages over buying and holding SPY with its low annual expense ratio of 0.10%. However, the lifespan of these ETFs is rather short; perhaps a performance review after at least 10 years will show improved relative performance. In the meantime, I recommend investors look elsewhere for more practical, viable, and less-expensive ETFs. Are you interested in learning more about using exchange traded funds (ETFs) in your trading? Leslie N. Masonson, an active ETF trader, is president of Cash Management Resources, a financial consulting firm that focuses on ETF

strategies. He is the author of Buy—Don’t Hold: Investing With ETFs Using Relative Strength To Increase Returns With Less Risk; and All About Market Timing, as well as Day Trading On The Edge. His website is www.buydonthold.com, where he writes a weekly blog. To submit topics for future columns, reach him at [email protected].

FURTHER READING

Masonson, Leslie N. [2017]. “ETF Sector Funds,” Techni­ cal Analysis of StockS & commoditieS, Volume 35: September. [2017]. “ETF Sector Investing,” Technical Analysis of StockS & commoditieS, Volume 35: January. [2016]. “ETF Sector Investing,” Technical Analysis of StockS & commoditieS, Volume 34: November. • www.ftportfolios.com • www.invesco.com • www.globalxfunds.com • www.mainmgt.com

March 2019

• Technical Analysis of Stocks & Commodities • 55

Interview/Claudio Demb Continued from page 37

well-suited for shorting, but in addition, I’m no longer in front of the screen when the market is open because of my professional life outside of trading. The market moves too fast when it is going down. I need clarity. So I put all my trades on when the market is closed and with conditional orders. I didn’t used to do that. I used to carve out one day a week when I would be in front of the screen. I can no longer do that. And that forces me to be more patient. It’s not necessarily a drawback. No, it’s not at all. Earlier you said something interesting in that if you’re not comfortable with something, you won’t do it well. That’s something people don’t give much importance to. They think that if something is moving fast, they will need to get in there and make money quickly. I think a lot of people fall into that trap. Do you often see that with traders? Absolutely! People underestimate, by a wide percentage, what it takes to be successful in trading. Trading is an extremely difficult activity and is highly seductive. You get attracted to it, and you think that you can do it; you see movement up and down and you think “this will be easy.” It calls a part of our brain that responds to this, but it’s an optical illusion. It’s very difficult to be a successful trader and it takes a tremendous amount of work. Many people will start trading but they’ll lack the knowledge and know-how and before they know it,

they realize they’re wiped out. A lot of accounts get blown up this way. It takes years to develop something that you feel you know and you’re on a solid foundation with and can make money from it. But yes, it’s extremely difficult and many people downplay the difficulty. They think it’s a lot easier than it is.

But over time, I’ve learned what the A game looks like. I know what my A trades or best-quality setups look like. Once one appears, you need to trade it, even though you may feel it’s not good. You have to go for it because if you don’t trade those, then what are you going to trade? That’s what an “A game” means.

Your book is called Trading Your A Game. How did you come up with the title? My most recent coach, Kerry Lovvorn, always spoke about finding your A trade. He would ask, “What’s your best setup that leads to your best trade?” So I learned to categorize or rate my trades into A, B, C, or F as in failure. I did a small study—I don’t know how statistically significant it was—on the returns I made by doing only my A trades. The difference was almost 100% more earned during the period I studied. That’s what the book is about: your “A trade” or “A game.” The book is about trading your best-quality setups. I replicated the study with the trades made by a friend of mine who is a daytrader, and we saw that if you go for all the best setups, or A setups, your returns are much better. The problem is, you don’t always have the best-quality setups. If you do trade only your A trades, then you’ll have to be prepared to sit on your hands for a while because these setups don’t come often. And then that poses another problem: You can’t sit on your hands for a long period of time and then pretend that you’re going to play your A game when you haven’t been playing for a while.

Thank you for sharing your thoughts with us, Claudio.

KoSInSKI/breaKawaY GaPS Continued from page 40

Pawel Kosinski, PhD, MEng, is a professor in process technology at University of Bergen in Norway. His research interests involve mathematical modeling of various physical phenomena, and he uses this experience for researching the financial markets. He was the principal founder of the site lookintotrade.com, which offers backtesting of various strategies. He may be reached at [email protected]. 56 • March 2019 • Technical Analysis of Stocks & Commodities

Further reading

Demb, Claudio [2018]. Trading Your A Game: Are Feelings Expensive? [2019]. “How Does Impulsivity Affect Your Trading Results?” Technical Analysis of Stocks & Commodities, Volume 37: January. [2018]. “Decision-Making: Why Is It So Difficult?” Technical Analysis of Stocks & Commodities, Volume 36: November. [2017]. “Unrealistic Expectations,” Technical Analysis of Stocks & Commodities, Volume 35: August. [2017]. “The Pernicious Effect Of The Loss Of Opportunity,” Technical Analysis of Stocks & Commodities, Volume 35: May. [2017]. “Successful Trader Must-Haves,” Technical Analysis of Stocks & Commodities, Volume 35: March. [2017]. “How Feelings Influence Your Trading,” Technical Analysis of Stocks & Commodities, Volume 35, January. • claudiodemb.com

trading breakaway gaps can lead to profitable results. Further reading

Kosinski, Pawel [2018]. “Double Bottoms Revisited,” Technical Analysis of StockS & commoditieS, Volume 36: September.

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March 2019

• Technical Analysis of Stocks & Commodities • 57

FUTURES LIQUIDITY

T

rading liquidity is often overlooked as a key technical measurement in the analysis and selection of commodity futures. The following explains how to read the futures liquidity chart published by Technical Analysis of Stocks & Commodities every month.

very high volumes. The greatest number of dots indicates the greatest activity; futures with one or no dots show little activity and are therefore less desirable for speculators. Courtesy of CBOT

Commodity futures

The futures liquidity chart shown below is intended to rank publicly traded futures contracts in order of liquidity. Relative contract liquidity is indicated by the number of dots on the right-hand side of the chart. This liquidity ranking is produced by multiplying contract point value times the maximum conceivable price motion (based on the past three years’ historical data) times the contract’s open interest times a factor (usually 1 to 4) for low or

three-year period. Thus, all numbers in this column have an equal dollar value. Columns indicating percent margin and effective percent margin provide a helpful comparison for traders who wish to place their margin money efficiently. The effective percent margin is determined by dividing the margin value ($) by the three-year price range of contract dollar value, and then multiplying by one hundred.

Stocks

All futures listed are weighted equally under “contracts to trade for equal dollar profit.” This is done by multiplying contract value times the maximum possible change in price observed in the last

Trading liquidity has a significant effect on the change in price of a security. Theoretically, trading activity can serve as a proxy for trading liquidity and equals the total volume for a given period expressed as a percentage of the total number of shares outstanding. This value can be thought of as the turnover rate of a firm’s shares outstanding.

Trading Liquidity: Futures

Contracts to Trade for Equal Relative Contract Liquidity Dollar Profit S&P 500 E-Mini (Mar ’19) CME 5 15.4 2 ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••>>>> 10-Year T-Note (Mar ’19) CBOT 1 9.8 7 ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••>> 5-Year T-Note (Mar ’19) CBOT 0.7 9.4 10 ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••> T-Bond (Mar ’19) CBOT 1.9 8.6 3 ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• Ultra T-Bond (Mar ’19) CBOT 2.5 11.4 2 ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• Crude Oil WTI (Mar ’19) NYMEX 8.7 16.9 3 •••••••••••••••••••••••••••••••••••••••••••••••••••••• 2-Year T-Note (Mar ’19) CBOT 0.3 7.3 11 ••••••••••••••••••••••••••••••••••••••••••••• Russell 2000 E-Mini (Mar ’19) CME 1.7 4.5 2 ••••••••••••••••••••••••••••••••••••••••••• Nasdaq 100 E-Mini (Mar ’19) CME 6.2 14.5 1 •••••••••••••••••••••••••••••••••••• Euro FX (Mar ’19) CME 1.8 13.4 5 ••••••••••••••••••••••••• Ultra 10-Year T-Note (Mar ’19) CBOT 1.3 9.1 5 ••••••••••••••••••••••• Eurodollar (Dec ’19) CME 0.1 3.6 16 •••••••••••••••••••• Soybeans (Mar ’19) CBOT 4.2 13.2 6 •••••••••••••• Gold (Feb ’19) COMEX 2.9 20.6 5 •••••••••••• Sugar #11 (Mar ’19) ICE/US 7.2 8.6 7 ••••••••••• Corn (Mar ’19) CBOT 4 19.1 21 •••••••••• Dow Indu 30 E-Mini (Mar ’19) CBOT 5.3 14.2 2 ••••••••• Natural Gas (Mar ’19) NYMEX 20.2 34.3 5 ••••••••• Silver (Mar ’19) COMEX 5.2 13.6 3 ••••••••• Gasoline RBOB (Mar ’19) NYMEX 8.3 14.5 2 •••••••• British Pound (Mar ’19) CME 3.3 20.3 7 ••••••• Coffee (Mar ’19) ICE/US 7.5 11.1 3 ••••••• Japanese Yen (Mar ’19) CME 1.7 16.3 7 •••••• S&P Midcap E-Mini (Mar ’19) CME 5 15.4 1 •••••• Soybean Meal (Mar ’19) CBOT 4.2 11.2 7 •••••• ULSD NY Harbor (Mar ’19) NYMEX 6.2 11.7 2 •••••• High Grade Copper (Mar ’19) COMEX 5.1 19.6 5 ••••• 30-Day Fed Funds (Apr ’19) CBOT 0 2 10 •••• Live Cattle (Apr ’19) CME 3.2 12.5 6 •••• Wheat (Mar ’19) CBOT 5.3 17.4 11 •••• Australian Dollar (Mar ’19) CME 1.9 14.1 9 ••• Hard Red Wheat (Mar ’19) KCBT 5.7 20.6 12 ••• Palladium (Mar ’19) NYMEX 5.4 8.6 1 ••• Platinum (Apr ’19) NYMEX 4.7 9.3 4 ••• CBOT Chicago Board of Trade, Division of CME Soybean Oil (Mar ’19) CBOT 2.8 9.1 16 ••• CFE CBOE Futures Exchange Canadian Dollar (Mar ’19) CME 1.7 16.5 11 •• CME Chicago Mercantile Exchange Cotton #2 (Mar ’19) ICE/US 7.9 25.8 8 •• COMEX Commodity Exchange, Inc. CME Group Crude Oil Brent (F) (Mar ’19) NYMEX 8.1 15.3 3 •• ICE-EU Intercontinental Exchange-Futures - Europe Lean Hogs (Apr ’19) CME 5.6 14 8 •• ICE-US Intercontinental Exchange-Futures - US Cocoa (Mar ’19) ICE/US 9.1 22.5 9 • KCBT Kansas City Board of Trade Feeder Cattle (Mar ’19) CME 4.3 25.7 7 • MGEX Minneapolis Grain Exchange Mexican Peso (Mar ’19) CME 4.3 32.3 25 • NYMEX New York Mercantile Exchange S&P GSCI (Feb ’19) CME 5.3 15.7 2 • Spring Wheat (Mar ’19) MGEX 5.4 12.9 7 • 1903 Swiss Franc (Mar ’19) CME 2.3 19.9 6 • Trading Liquidity: Futures is a reference chart for speculators. It compares markets “Relative Contract Liquidity” places commodities in descending order according to according to their per-contract potential for profit and how easily contracts can be bought how easily all of their contracts can be traded. Commodities at the top of the list are easior sold (i.e., trading liquidity). Each is a proportional measure and is meaningful only est to buy and sell; commodities at the bottom of the list are the most difficult. “Relative Contract Liquidity” is the number of contracts to trade times total open interest times a when compared to others in the same column. The number in the “Contracts to Trade for Equal Dollar Profit” column shows how volume factor, which is the greater of: many contracts of one commodity must be traded to obtain the same potential return In volume 1 or exp –2 as another commodity. Contracts to Trade = (Tick $ value) x (3-year Maximum Price In 5000 Excursion). Commodity Futures

Exchange

% Margin

Effective % Margin

58 • March 2019 • Technical Analysis of Stocks & Commodities

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The information in Traders’ Resource is the most accurate at the time of posting and is subject to change. Because the vendors posting to Traders’ Resource are responsible for their own listing, Technical Analysis, Inc. declines any and all liability for any representations made by the businesses and individuals listed. Nor can Technical Analysis, Inc. endorse any business or individual listed on Traders’ Resource. Technical Analysis, Inc. makes no warranties, express or implied, as to the accuracy and reliability of claims herein. You agree to release Technical Analysis, Inc., together with its respective employees, agents, officers, directors and shareholders, from any and all liability and obligations whatsoever in connection with or arising from your use of Traders’ Resource. If at any time you are not happy with the information posted to Traders’ Resource or object to any material within Traders’ Resource, your sole remedy is to cease using it. This list is updated frequently. If you are aware of a business that should be listed, please email us at [email protected].

March 2019

• Technical Analysis of Stocks & Commodities • 59

Trading Perspectives SOME PERSPECTIVES ON THE EQUITIES WORLD Rob Friesen is a professional trader and president & COO of Bright Trading (www. stocktrading.com), a proprietary trading firm hosting independent trader/members, an online trading school, and utilizing the StockOdds database (www.stockodds. net). This column shares his thoughts and outlooks on trading, locating opportunity, probabilistic outcome, and maintaining perspective throughout industry changes. He can be reached at [email protected] or via www.stocktrading.com. Rob Friesen

PAIR TRADES: TO BUILD OR NOT TO BUILD Continuing with the theme of pair trading that I set out to focus on in this column for 2019, I will discuss some of the areas that offer opportunities to handle things in one of more ways, or not at all. Tips on position building in pair trading In my opinion, it is more important to be a student of your own trading than a student of the markets. Through this sort of self-analysis, you may discover that you are accurate on your initial trades, resulting in “one-off” trades and without the need to build larger positions through averaging in additional pair layers. Others may find themselves usually wrong on the first pair trade, but able to perform better with the second and third layer using that first layer for information-gathering. There is a difference between establishing a position through averaging layers (that is, having an average spread price) and trading the position according to that average price. For stocks, it may be suitable to trade the average, but for pairs I never recommend trading the average of a position. My personal experience, along with my numerous observations in working with other traders during my career, is that the mathematics of successful pair trading doesn’t support trading the average spread price. It is better to trade each layer or unit independently so that the trader focuses on the production of some or all the units. Here are some examples: Trader A: • Has a long bias for a pair • Wants to establish the first unit of capital or layer at a spread price of -1.

In total, Trader A (TA) adds three layers at spread prices -1, -2, -4, so the average is -2.33. Say the pair retraces from -4 back to the average at -2.33. At this point, TA feels happy because the pair is back to the breakeven price. TA takes no action, watching closely. What is most likely going through TA’s mind is the thought that the pair will continue to move higher from this point. (Like most people, TA would be inclined to value the recent move from -4 to -2.33 heavily, when what should be consid-

When pair trading, it is better to trade each layer or unit independently so that the trader focuses on the production of some or all the units. ered is overall context—what catalyst caused the move up, the pair’s average daily range (ADR) or average true range (ATR) and percentage move of that, and readouts from other suitable indicators). Later, the pair retraced back to -4 and unfortunately, nothing was accomplished as Trader A sat through the volatility. Most likely, risk has increased with the spread action, and TA is fully loaded and exposed. Here might be a better approach for Trader A: If TA in the same situation acted at the -2.33 by taking the -4 layer off, TA would have made 1.67 times the position size of that last layer. This would have reduced the total capital needed. In addition, when the spread sold back down to -4, TA could have redeployed that unit of capital. Or TA may have decided the risks were increasing and

60 • March 2019 • Technical Analysis of Stocks & Commodities

therefore didn’t reestablish. Regardless of TA’s decision to add or not, production in the pair would have occurred and closed profits would have been made, offsetting the open losses from the first two layers. Note: Traders may differ from investors in tax considerations, so be sure to know what is best for your specific tax situation. Don’t mix styles on the fly or outside your plan. In the heat of the battle or the fog of war, traders can abandon disciplines regarding rules, trading plans for the day, or plans for the specific pair. Trying to hit homeruns, get your money back, or get even will all work to hurt you. I recommend readers to take a moment to reflect on the value of managing positions, trading around a core position, and closing profits through the “production” of multiple slices of capital into a pair. Can this work with trading a stock naked? Certainly, but naked traders may have market exposure. When layering, logarithmic distribution may provide an advantage over fixed-distance scaling. Varying bets at key levels or inflection points may also increase the benefits. Be mindful of my previous recommendations in this column to classify all pairs into two distinct categories: those that are trendy by nature (signal) versus those that are range-bound by nature (noise). Classifying pairs accordingly can assist you in allocating capital correctly. Noise trading suggests scaling, while signal trading suggests “one-off” trades may be better for harvesting opportunity while managing risk. To stop-loss or not to: Tips on the mechanics and psychology of stoplosses How should stop-loss considerations be incorporated into our trading plans?

Trading Perspectives We all know that many traders bleed accounts out through the repetitive actions of stopping out trades that are losing money. So perhaps the answer is in the initial construction of the trade. A benefit of trading pairs is the ability to reduce direct market exposure and macro exposure in some cases. Pair traders tend to be indifferent to or insulated from the types of problems naked traders can have. Let’s say a strong setup or signal occurs and through point & click or through automation, the signal is acted on opening a long position. Through no fault of their own, the market has a significant selloff and renders the trade as a loser. The pair trader may have seen no real change to his P/L during this market event, as both stocks went down together. Next, it’s about the position size. If you trade smaller per idea, there may be less need to stop out with the minor fluctuations that are hard to sit through with size. This applies to both the naked trader and the pair trader. In my July 2018 column I wrote regarding stop-losses: “One of the ways to position size is by how much you are willing to lose on any given bet.” I discussed that this is a noble ambition with no guarantees, especially if you take positions overnight. Regardless, position sizing is crucial and should be specific to several factors. Position size should be based on risks such as news catalysts, fundamentals, price, liquidity, volatility, and your specific holding timeframes. Perhaps it’s better to go wide rather than deep into one idea or one strategy. If you could touch 10 symbols with qualified probabilities instead of just one, you would reduce your need to be perfectly right on the single selection, and your advantage could come from being right as an average from all your bets. Remember, there is only a one in 10 chance of selecting the right (profitable) symbol out of 10 symbols, but if you have a positive expectation for each symbol, then the outcome might be that four of the 10 give you significant profits, one a slight profit, three breakeven, and two have a loss. As long as the magnitude of losses doesn’t outweigh the gains, stop-

loss procedures could be introduced that are parameters based on any of the 10 symbols or they could remain symbolspecific. The overall theme, though, is that going wide and reducing the size into any one idea, you reduce risk and therefore reduce the stopping out of large losses.

I view stop-losses not as stopping losses but as responding to the validity of a trade. I view stop-losses not as stopping losses but as responding to the validity of a trade. We need a valid reason to enter a position, build a position, remain in the position, and finally, a valid reason to exit. If the pair you have chosen is no longer “business as usual,” then it may be best to exit the position, whether profitable, breakeven, or a loss. Just as there is usually a fundamental reason behind a chart pattern, there could be a fundamental reason to enter, remain in, or exit a single pair trade or an averagedin position. Stop-loss parameters are often introduced by drawing on the common and familiar considerations. This is problematic for a naked trader more so than for the pair trader. When a pair trader constructs a unique opportunity not widely known, or adopted, then that trader is insulated from the traps and predatory, informed order flow. If the pair is widely traded, the trader is more vulnerable to many who are watching the same support, resistance, and key price points or ranges. To trade or not to trade: timing the market From speaking to industry colleagues, it appears the message that needs to be communicated to market participants is “you can time the market.” This has been a debated subject and of course, the trading industry wants activity in the form of volume and purchases of tools and services related to the industry. Many, through failure, disillusionment, increased fees, and lifestyle choices, have March 2019

abandoned self-management and any attempt to produce any alpha. Some buy ETFs to somewhat self-manage, but don’t want to be stock pickers. Others hand their money over to mutual funds, personal advisors, robo-advisors, or hedge funds. I understand the sentiment and reasons for these decisions, but I will communicate my views here. I believe that arming yourself with information and the education on how to apply that information in a focused manner can be a game changer. There are still two distinct communities in equities markets: the herd and the informed. The herd is rather large, and the informed community is smaller, considering the information age. It is a challenge and it takes effort to create alpha, but through using analytical data and tools, trading with a positive expectation, and staying focused on relative performance, it is my view that it can be achieved.

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• Technical Analysis of Stocks & Commodities • 61

Algo Q&A Explore Your Options Davey

Continued from page 25

As you can see, I spend the bulk of my time developing strategies. This is also the hardest task, since many potential strategies get thrown away before an

acceptable one is found. So this task is frequently frustrating, which may be one reason people do not spend enough time on it. Meanwhile, I still spend significant time on the other tasks, which helps keep every trading day interesting. While every algo trader’s time alloca-

tion will be different, hopefully you now have an idea of what to expect in the life of an algo trader. I’m sure you’d agree it is much different than for discretionary trading.

outnumber the losers. I should point out that because the protection expires prior to the short option, it will eventually be necessary for the trader to either purchase fresh insurance for the remainder of the trade, exit the short option upon expiration of the protective put, or face theoretically unlimited risk beyond expiration of the February option. In other words, unlike vertical credit spreads, diagonal spreads require moderately more attention and ongoing management. The strategy of purchasing catastroph-

ic insurance outlined here should not be confused with a strategy that offers absolutely limited risk, nor should it be assumed that normal ebb and flow of market pricing won’t impact this trade. What I’ve outlined here is simply a way for option sellers to continue with premium-collection efforts without exposing themselves to the tail risk and account blowups that can occur on rare occasions.

FUTURES FOR YOU

GaRNeR

Continued from page 41

volatility the risk might prove to be much less or moderately more). Those accustomed to buying options or trading futures might look at this risk and reward profile as unattractive, but that assumption is overlooking the primary benefit of option selling; options are eroding assets that generally expire worthless. Thus, risking more than the expected payout makes sense if it is expected that the winners will considerably

KaePPeL

Continued from page 47

As you can see in Figure 4, the trader can make money if crude oil rallies sharply or falls sharply but will suffer time decay if USO remains relatively unchanged. Still, the worst-case scenario for this position is an open profit of +$318. The benefi ts of adjusting Everything in trading is a tradeoff. This is especially true when adjusting an option position. In most cases, you give up something (in these examples, primarily a certain amount of profit potential) in order to get something (in these examples, the elimination of this risk of loss). Often adjusting an option position can result in significant psychological benefits by virtue locking in a profit and “playing with the house money”, as well as freeing up a significant amount of trading capital that can be deployed elsewhere.

FIGURe 4: aDJUSTING INTO a LONG STRaDDLe. By adjusting the position, you can make money if crude oil rallies sharply or falls sharply. However, your position will suffer time decay if USO remains relatively unchanged. Still, the worst-case scenario for this position is an open profit of +$318.

62 • March 2019 • Technical Analysis of Stocks tockS & C commodities ommoditieS

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