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THE TRADERS’ MAGAZINE SINCE 1982
Probability— probably a good thing to know Measuring the probability distribution of indicators
8
The V-Trade Part 8: The basic trading rules
ETFs vs. Mutual Funds: Which Way To Go? Battle of the funds
16
24
One-Day Wonder Trades A strategy for short-term options traders
30
INTERVIEW
Walking forward with Dion Kurczek OCTOBER 2018
34
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OCTOBER 2018
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Contents
OCTOBER 2018, Volume 36 Number 11
7 Inside-Range Gap Breakouts The Traders’ MagazineTM EDITORIAL
[email protected] Editor in Chief Jack K. Hutson Editor Jayanthi Gopalakrishnan Production Manager Karen E. Wasserman Art Director Christine Morrison Graphic Designer Wayne Shaw Webmaster Han J. Kim Contributing Editors John Ehlers, Anthony W. Warren, Ph.D. Contributing Writers Thomas Bulkowski, Martin Pring, Barbara Star, Markos Katsanos
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by Ken Calhoun They may not be your traditional gaps, but they can end up with breakouts that are just as big as or bigger than some of the more popular type of gaps. Discover how to use these gaps in your trading.
FEATURE ARTICLE 8 Probability—Probably A Good Thing To Know
TIPS
by John F. Ehlers The idea of reversion to the mean is one that traders tend to take for granted. Can you confidently assume all indicators subscribe to the normal probability distribution? Here, we measure the probability distribution of a few indicators to determine if they can be used as part of your reversion-to-the-mean trading strategy.
16 The V-Trade, Part 8: The BasicTrading Rules
by Sylvain Vervoort In this eighth part of a multipart series, we look at the V-Trade trading rules.
24 ETFs vs. Mutual Funds: Which Way To Go?
by Cassandra Wang Even short-term traders will have some investments they hold for the longer term. But even those longer-term investments need some monitoring from time to time. Here’s how you can apply a momentum strategy using technical analysis tools to help select mutual funds or ETFs to add to your portfolio.
30 One-Day Wonder Trades
by Robert J. Seifert Is there such a thing as a conservative one-day options trade that gives you an edge? Yes there is, and here is how professional options traders put it into action.
INTERVIEW 34 Walking Forward With Dion Kurczek
by Jayanthi Gopalakrishnan A software developer and individual trader, Dion Kurczek has developed charting and backtesting programs for trading, including the Wealth-Lab platform (acquired by Fidelity Investments in 2004). Recently, he has developed a cloud-based backtesting platform, Quantacula, and a Javascript-based
charting and backtesting site, JuicyCharts.com. We spoke with Kurczek to find out more about why it’s important to him to offer traders a way to develop and test their own trading systems and ideas.
38 Futures For You
by Carley Garner Here’s how the futures market really works.
40 Q&A
by Rob Friesen This professional trader answers a few of your questions.
42 Explore Your Options
by Jay Kaeppel Got a question about options?
44 The Trend Is Your Friend
by Bruce Ross All trends aren’t created equal. How do you know if a trend is truly your friend? Here are a couple of pointers that’ll help you have a long and sustained relationship with the trend.
AT THE CLOSE 60 Safety Is More Than A Dance
by Gabriel Gonzalez Is there such a thing as safety when it comes to investing or trading? Let’s find out.
QUICK-SCAN
28 MarketClub
Web-based trade-selection scanning platform with buy & sell signals for stocks, futures, forex, and mutual funds.
DEPARTMENTS 6 Letters To S&C 46 Trade News & Products 48 Traders’ Tips 57 Advertisers’ Index 57 Editorial Resource Index 58 Futures Liquidity 59 Classified Advertising 59 Traders’ Resource 62 Books For Traders
This article is the basis for TIPS Traders’ Tips this month.
n Cover: Ken Smith n Cover concept: Christine Morrison
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ERRATA: THE V-TRADE, PART 6 Editor, When reading part 6 of Sylvain Vervoort’s series on the V-Trade in the August 2018 issue, I was a little irritated when I read the text on page 17 referring to positive divergence in Figure 2. Is it possible that Figures 2 and 5 were mistakenly exchanged, since the author writes that price is going down but in Figure 2 price is going up? Currently, I am working on a trading system based mainly on Bollinger Bands for the entry (enter long if close>0,5 upper Bollinger Band). My main problem is that there are too many entries, so I need to find another indicator to reduce the bad entries. After testing dozens of different indicators, I am still not satisfied with the system’s reliability. Thus, I was very
interested in Vervoort’s SRSI indicator, which I programmed for the Nanotrader platform. To be honest, I find it difficult to interpret the results of the SRSI, as they are quite different from the normal RSI or STO. Can the author offer any more explanation on why he uses the stochastic on the two SMAs of the RSI differences? In my opinion, one of the greatest influences on the success of a trading system is the timeframe used. Prices are not fractal, and if you use too small of a timeframe, any trading system is going to fail. At least that is my experience. juergen Author Sylvain Vervoort replies: Thank you for writing. You are correct— Figures 2 and 5 were switched. (My fault, I marked these files with the wrong name.)
FIGURE 1: SRSI. Ideally, the SRSI indicates buy & sell signals when turning up or down from the low or upper side (see vertical dotted lines) while at the same time showing the type of reversal based on the convergent and divergent moves.
6 • October 2018 • Technical Analysis of Stocks & Commodities
The main purpose of the SRSI is to confirm corrections and reversals based on the convergent or divergent moves. The SMAs applied to the RSI differences are there to smooth the signal. The ideal SRSI should indicate buy and sell signals when turning up or down from the low or upper side (see the vertical dotted lines in the chart in Figure 1 here) while at the same time showing the type of reversal based on the convergent and divergent moves. Personally, I apply all the techniques explained in my V-Trade article series. That works fine but it is not easy. You need a lot of practice. I find that the trader needs to apply a series of different techniques to remain consistently profitable. In addition, you should not use, or use as little as possible, any technique that uses optimization. Weekly & Daily Stochastic Editor, First and foremost, thank you for Vitali Apirine’s September 2018 article in S&C, “Weekly & Daily Stochastics,” which even a novice technical analyst like me can truly understand. My investing timeframe is longer than that of most traders/investors and thus, I find the weekly/monthly charts to be very important in my work. In Apirine’s article, he uses settings of 14 (daily) and 70 (weekly). I would like to construct a weekly and monthly dual stochastic on a single chart. Therefore, would the author be so kind as to tell me what monthly input setting he would use for the monthly, assuming that 70 would still be used for the weekly input? Thank you very much. Nancy West
Author Vitali Apirine replies: Thank you for your kind words about Continued on page 33
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Inside-Range Gap Breakouts They may not be your traditional gaps, but they can end up with breakouts that are just as big as or bigger than some of the more popular type of gaps. Discover how to use these gaps in your trading.
T
by Ken Calhoun
Trading inside gaps Whenever a stock opens at a price higher than the previous day’s close, this small gap is likely to attract additional buying. This is because it will show up on various scanning programs that identify percentage gainers each morning for active traders, who then buy shares. You can see this pattern in the chart of Noble Energy Inc. in Figure 1, when, on July 24, 2018, the inside-range price gapped up from the previous day’s close of $34.55 up to $35/share. It ran all the way up to $35.80 before pivoting.
Step-by-step action plan
Here’s how you can start to use the insiderange gap breakout strategy: Step 1: Find charts with a small gap up that is above the prior day’s close and below the prior day’s high, as seen in Figure 1 on July 24, 2018. Step 2: Use an entry price of $0.20 above the low of the gap up ($34.80 plus $0.20 equals $35 in this example). Step 3: Use an initial stop of the prior day’s low ($34.50 in this example). Step 4: Anticipate reversals right under whole numbers and close out all daytrades once they reach a price level of 0.80 or above ($35.80 in this example). If you’re swing trading, use the prior day’s high as your initial stop level.
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www.NeuroShell.com 301.662.7950 Insights: Why this
technique works Gaps occur when traders are willing to pay a premium price above the previous Continued on page 46
esignal
rading gaps is a long-time favorite strategy for active day and swing traders. In this month’s column, I will show you a new strategy for trading gaps that begin inside the previous day’s trading range. This is different from traditional gap-trading strategies, which rely primarily on multipoint gaps above prior resistance levels. These smaller inside gaps often lead to big breakouts once they continue upward above the previous day’s high. You can see these visually by using a two-day chart similar to the one featured here in Figure 1.
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Figure 1: Inside-Range Gap Breakout (NBL). In this case, the small inside gap continued upward for a full point. October 2018
• Technical Analysis of Stocks & Commodities • 7
8 • October 2018 • Technical Analysis of Stocks & Commodities
TRADING SYSTEMS
Back To Normal
Probability—Probably A Good Thing To Know accumulate the bin counts across the data history. Then, on the last bar on the chart, I export all the bin values to a text file. I can then import this text file into Excel and plot the occurrences in each of the bins as a bar chart. The result is the probability density of the indicator. In the sidebar “EasyLanguage Code For Measuring Probability Distribution,” I provide a listing titled wing trading, or reversion to the mean, is a “Code fragment to measure indicator probability popular trading style mainly because it is distribution” that shows how to accumulate the bin a strategy that typically yields a relatively counts and export them to a text file. high percent of winning trades. The idea In my May 2018 Stocks & Commodities article behind it is that if prices have swung far enough from “RocketRSI,” I described an RSI indicator that swings their mean price, there is high probability that price from -1 to +1. I’ll use that indicator to measure the will swing back to the mean value. Two of the most probability distribution of an RSI. You can find the inelegant technical analysis terms—overbought and complete code listing for doing this in “MyRSI With oversold—imply probability considerations. There Probability Distribution Measurement” in the sidebar are a number of strategies based on overbought and to this article. Since this RSI only swings from -1 to oversold conditions. For example, a commonly pro- +1, I won’t be using all the bins, but it will still provide moted rule is to buy when an RSI is crossing over 20. a good handle on the probability distribution itself. The assumption is that when RSI is below 20, it is a The measured probability density of the MyRSI low-probability event and prices could recover back indicator, measured with default settings and applied toward the mean. Similarly, the complimentary rule to daily data of SPY for the 10-year period from is to sell short when the RSI is crossing under 80. January 1, 2008 to December 31, 2017, can be seen in Figure 1. The probability distribution is certainly Let’s challenge those assumptions It turns out that measuring the probability distribution of an oscillator-type indicator is relatively easy. First of all, you can assume the oscillator has a zero mean. If it doesn’t have a zero mean, it will be apparent in the probability distribution measurement itself. Next, assume the oscillator swings between -3 and +3. (I will revisit the oscillator with scaling to make this a good assumption.) I will provide 30 bins be- Figure 1: RSI MEASURED WITH DEFAULT SETTINGS ON SPY FROM JANUARY 2008 TO DECEMBER 31, 2017. Here you see the RSI probability distribution is low zero and 30 bins above zero in which to place 1, nearly uniform with an upside bias because of the uptrend in data over the time period the indicator value on each bar through history, and analyzed. The idea of reversion to the mean is one that traders tend to take for granted. Can you confidently assume all indicators subscribe to the normal probability distribution? Here, we measure the probability distribution of a few indicators to determine if they can be used as part of your reversion-to-the-mean trading strategy.
KEN SMITH
by John F. Ehlers October 2018
• Technical Analysis of Stocks & Commodities • 9
microsoft excel
S
Figure 2: rocket rsi indicator’s effect on probability distribution. The RocketRSI has a bell-shaped Gaussian probability distribution, which suggests it’s an indicator that could be used in a mean-reversion strategy.
not the bell-shaped probability distribution that is commonly assumed. I would characterize it as having a nearly uniform probability distribution with a bias toward the upside due to the uptrend in the data over the 10-year period. Certainly, this probability distribution shows the RSI should not be your indicator of choice to swing trade the SPY. The probability of being “overbought” is more than twice the probability at the mean. The upside bias due to the general trend up is also apparent in Figure 1.
But there is a way around it
All is not lost if you really want to use reversion to the mean as your trading strategy. A characteristic of the nonlinear Fisher transform is to convert virtually any waveform into a waveform having a nearly bell-shaped Gaussian probability distribution. I used this characteristic of the Fisher transform when I described the RocketRSI. The RocketRSI swings are limited to plus and minus three standard deviations, which is the reason I scaled the bins to measure probabilities to range between -3 and +3. The code for the RocketRSI is repeated here from my earlier article and can be found in the code listing “RocketRSI with probability distribution measurement” in the sidebar to this article, with the code to measure the probability distribution appended. When you apply the RocketRSI indicator to the same 10-year span of SPY data, it results in the beautiful bell-shaped probability distribution you see in Figure 2. The nearly Gaussian probability distribution means we have an indicator that we can use in a reversion-to-the-mean strategy with confidence. For example, if the indicator crosses above -2, this means it is departing a region that has only a 2.5% probability of occurring. In other words, there is a high probability of reversion to the mean, and that is a good opportunity to buy. Alternatively, you could anticipate the turning point by buying when the RocketRSI crosses under the -2 level. You might be a little early in your entry if you use this strategy, but you have also mitigated some of the lag created by calculating the indicator itself.
Beyond RSI
The use of indicators for swing trading or reversion-to-the-mean strategies isn’t limited to the RSI. For example, I created a simple oscillator indicator in my July 2018 Stocks & Commodities 10 • October 2018 • Technical Analysis of Stocks & Commodities
article “Deviation-Scaled Moving Average.” The code for this oscillator is shown in the code listing titled “Deviation-scaled oscillator” in the sidebar to this article, with the code to measure the probability distribution appended. That indicator starts with an oscillator called zeros that is a simple two-bar difference of prices. This oscillator is important because of two characteristics in its transfer response, which I’ll describe next. First, when the cycle periods are long, and at the limit there is no change in price, the transfer response is zero. It is this characteristic that provides the nominal zero mean in the oscillator output. Further, its filter rolloff from shorter-cycle periods is -6 dB per octave. Market data is fractal, meaning the cycle amplitudes in its spectrum increase in direct proportion to their cycle periods. That means the data cycle amplitudes increase statistically at the rate of 6 dB per octave. Since the oscillator rolloff is -6 dB per octave and spectrum amplitudes are statistically increasing at the rate of +6 dB per octave, the result is that the zeros oscillator whitens the price spectrum. This is a good thing. Second, when the cycle period is exactly at twice the sampling rate, the samples are exactly one cycle period apart. This is called the Nyquist frequency period and is the shortest possible period in sampled data. In the zeros oscillator, the transfer response is zero at the Nyquist period because the samples are exactly one period apart for that spectral component. Having a zero in the transfer response at the Nyquist period eliminates the 6 dB increase in noise produced by a simple one-bar difference. Having a zero in the transfer response at the Nyquist period also reduces the impact of aliased data in the oscillator output. The zeros oscillator output is smoothed in my two-pole SuperSmoother filter (for more on this, see my January 2014 S&C article “Predictive And Successful Indicators”). The critical period of the SuperSmoother filter is the “half the input” period to retain the oscillator’s responsiveness, and the filter coefficients are calculated only on the first bar of data for computational efficiency. Since the zeros oscillator has a nominally zero mean, the SuperSmoother filter output also has a nominally zero mean. Therefore, the standard deviation can be calculated as the square root of the average sum of the squares of the smoothed filter waveform over the input period. This is commonly called the root mean square (RMS).
Not all oscillators are suitable for swing trading because their probability distributions don’t necessarily have low-probability events at the extreme swings of the indicator.
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A characteristic of the nonlinear Fisher transform is to convert virtually any waveform into a waveform having a nearly bellshaped Gaussian probability distribution. FIGURE 3: PROBABILITY DISTRIBUTION FOR DEVIATION-SCALED OSCILLATOR. The oscillator is suitable for swing trading but there is a trend bias and the distribution has “fat tails.”
Dividing the RMS into the smoothed filter waveform scales the waveform in terms of standard deviations, and you can evaluate the indicator by measuring its probability distribution. I did that using the same data as before, and Figure 3 shows the oscillator is suitable for swing trading, but the trend bias is obvious and the distribution has “fat tails.” That is, the probability of being further from the mean is higher than a Gaussian probability distribution would have at the same deviation. This necessarily reduces the efficacy of the indicator for swing trading. It is possible to improve the probability distribution by using a trick associated with the Fisher transform. The indicator is already scaled in terms of standard deviations. You don’t care much if the deviation exceeds an absolute value of 2. So you simply apply the Fisher transform to absolute values of the indicator that are less than 2, and divide the input to the Fisher transform by 2 to avoid having the transform blow up. Fisher transform inputs must be limited to absolute values less than unity. In the code listing “Deviation-scaled oscillator with Fisher transform” in this article’s sidebar, you see the same code as for the deviation-scaled oscillator but with the Fisher transform trick included. When the Fisher transform is introduced with this trick and added to the deviation-scaled oscillator, and the oscillator is applied to the same data as before, it results in the nearly perfect bell-shaped Gaussian probability distribution. This is demonstrated in Figure 4.
Low or high probability?
Not all oscillators are suitable for swing trading because their probability distributions don’t necessarily have low-probability events at the extreme swings of the indicator. With this article, I have provided a code fragment that can be appended to any properly scaled oscillator and modified to produce the probability distribution of that oscillator on any data symbol of your choice. If the oscillator is not scaled, you can apply the RMS scaling without distorting the oscillator response or introducing any computational lag. Almost any oscillator-type indicator can have its probability distribution improved for swing trading by applying the Fisher transform using the technique I’ve described here and that is shown in the code listing “Deviation-scaled oscillator with Fisher transform” in the sidebar to this article. Good swing trading! 12 • October 2018 • Technical Analysis of Stocks & Commodities
FIGURE 4: adding THE FISHER TRANSFORM TO THE DEVIaTiON-SCALED OSCILLATOR. This improved the probability distribution, as can be seen by the nearly perfect bell-shaped Gaussian probability distribution. This makes it a suitable indicator for swing trading.
Stocks & Commodities Contributing Editor John Ehlers is a pioneer in the use of cycles and DSP technical analysis. He is president of MESA Software and cofounder of StockSpotter. com and BeYourOwnHedgeFund.com, which is a new site that provides portfolios based on his algorithmic strategies.
Further reading
Ehlers, John F. [2018]. “RocketRSI—A Solid Propellant For Your Rocket Science Trading,” Technical Analysis of Stocks & Commodities, Volume 36: May. [2018]. “The Deviation-Scaled Moving Average,” Technical Analysis of Stocks & Commodities, Volume 36: July. [2014]. “Predictive And Successful Indicators,” Technical Analysis of Stocks & Commodities, Volume 32: January. Ehlers, John F. [2013]. Cycle Analytics For Traders, Wiley. ‡TradeStation
‡See Editorial Resource Index
The code given in this article is available in the Article Code section of our website, Traders.com. See our Traders’ Tips section beginning on page 48 for commentary and implementation of John Ehlers’ technique in various technical analysis programs. Accompanying program code can be found in the Traders’ Tips area at Traders.com.
EasyLanguage Code For MEASURING Probability Distribution Code fragment to measure indicator probability distribution For I = 1 to 60 Begin J = (I - 31) / 10; K = (I - 30) / 10; If Indicator > J and Indicator 0 Then CU = CU + Filt[count] Filt[count + 1]; If Filt[count] - Filt[count + 1] < 0 Then CD = CD + Filt[count + 1] Filt[count]; End; If CU + CD 0 Then MyRSI = (CU - CD) / (CU + CD); Plot1(MyRSI); Plot2(0); //Bin the indicator values in Bins from -3 to +3 For I = 1 to 60 Begin J = (I - 31) / 10; K = (I - 30) / 10; If MyRSI > J and MyRSI 0 Then CU = CU + Filt[count] Filt[count + 1]; If Filt[count] - Filt[count + 1] < 0 Then CD = CD + Filt[count + 1] Filt[count]; End; If CU + CD 0 Then MyRSI = (CU - CD) / (CU + CD); //Limit RocketRSI output to +/- 3 Standard Deviations IF MyRSI > .999 Then MyRSI = .999; If MyRSI < -.999 Then MyRSI = -.999; //Apply Fisher Transform to establish Gaussian Probability Distribution RocketRSI = .5*Log((1 + MyRSI) / (1 - MyRSI)); Plot1(RocketRSI); Plot2(0); //Bin the indicator values in Bins from -3 to +3 For I = 1 to 60 Begin J = (I - 31) / 10; K = (I - 30) / 10; October 2018
• Technical Analysis of Stocks & Commodities • 13
If RocketRSI > J and RocketRSI J and FisherFilt J and ScaledFilt J and FisherFilt "Add a reference" //2. In "C:\Windows\Microsoft.NET\Framework64\v4.0.30319\" (or "Framework" on 32-bit systems), //choose and okay "System.Windows.Forms.DataVisualization.dll" namespace WealthLab.Strategies { public class MyStrategy : WealthScript { protected override void Execute() { var rrsi = RocketRSI.Series(Close,8,10); ChartPane paneRocketRSI = CreatePane(40,true,true); PlotSeries(paneRocketRSI,rrsi,Color.DarkBlue,LineStyle. Solid,2);
double[] bin = new double[61]; for(int bar = GetTradingLoopStartBar(10 * 3); bar < Bars.Count; bar++) { for(int i = 1; i j && rrsi[bar] J And RocketRSI J And FisherFilt >> 12.1 10-Year T-Note (Sep ’18) CBOT 1 6 ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••> 8.4 Russell 2000 E-Mini (Sep ’18) GBLX 2.3 1 ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••> 5.1 5-Year T-Note (Sep ’18) CBOT 0.7 9 ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••> 8.2 Ultra T-Bond (Sep ’18) CBOT 2.3 2 •••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 10.7 T-Bond (Sep ’18) CBOT 1.7 3 •••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 8 Crude Oil WTI (Oct ’18) NYMEX 5.6 2 •••••••••••••••••••••••••••••••••••••••• 9.1 Nasdaq 100 E-Mini (Sep ’18) GBLX 4.3 1 ••••••••••••••••••••••••••••••••••••••• 8.9 2-Year T-Note (Sep ’18) CBOT 0.2 10 ••••••••••••••••••••••••••••••••• 6.3 Soybeans (Nov ’18) CBOT 6.1 5 ••••••••••••••••••••••••••••••• 14.6 Sugar #11 (Oct ’18) ICEUS 9.2 5 ••••••••••••••••••••••••••• 6.8 Ultra 10-Year T-Note (Sep ’18) CBOT 1.2 4 ••••••••••••••••••••••••• 7.7 Eurodollar (Dec ’18) CME 0.1 14 •••••••••••••••••••••• 2.9 Corn (Dec ’18) CBOT 4.9 21 •••••••••••••••••• 22.5 Euro FX (Sep ’18) CME 1.8 5 ••••••••••••••••• 17 Gold (Dec ’18) COMEX 2.9 4 ••••••••••••••••• 16.9 S&P Midcap E-Mini (Sep ’18) GBLX 4.1 1 •••••••••••••••• 10.1 Coffee (Dec ’18) ICEUS 6.1 3 ••••••••••••••• 8.3 Gasoline RBOB (Oct ’18) NYMEX 5.9 2 ••••••••••••••• 10.9 Dow Indu 30 E-Mini (Sep ’18) CBOTM 4.2 2 •••••••••••• 10.5 ULSD NY Harbor (Oct ’18) NYMEX 5 1 •••••••••••• 8.2 British Pound (Sep ’18) CME 2.2 5 •••••••••• 10 Soybean Meal (Dec ’18) CBOT 5.7 7 ••••••••• 15.6 Wheat (Dec ’18) CBOT 6.3 9 •••••••• 18.7 30-Day Fed Funds (Oct ’18) CBOT 0 10 ••••••• 2.1 Natural Gas (Oct ’18) NYMEX 4.5 6 ••••••• 9.8 Silver (Sep ’18) COMEX 5.5 2 ••••••• 12 Japanese Yen (Sep ’18) CME 1.1 6 •••••• 8.9 Cotton #2 (Dec ’18) ICEUS 7.2 6 ••••• 22.6 Lean Hogs (Oct ’18) CME 6.4 5 ••••• 7.9 Live Cattle (Oct ’18) CME 3.8 6 ••••• 11.5 CBOT Chicago Board of Trade, Division of CME Soybean Oil (Dec ’18) CBOT 3.9 14 ••••• 11.3 CFE CBOE Futures Exchange Crude Oil Brent (F) (Dec ’18) NYMEX 5.7 2 •••• 8.9 CME Chicago Mercantile Exchange Hard Red Wheat (Dec ’18) KCBT 6.4 9 •••• 19.1 COMEX Commodity Exchange, Inc. CME Group Platinum (Oct ’18) NYMEX 4.8 4 •••• 8.8 GBLX Chicago Mercantile Exchange - Globex Australian Dollar (Sep ’18) CME 1.9 9 ••• 15.5 ICE-EU Intercontinental Exchange-Futures - Europe Canadian Dollar (Sep ’18) CME 2.2 10 ••• 19.8 ICE-US Intercontinental Exchange-Futures - US Cocoa (Dec ’18) ICEUS 8.9 8 ••• 19.3 KCBT Kansas City Board of Trade High Grade Copper (Sep ’18) COMEX 4.7 5 ••• 17.4 MGEX Minneapolis Grain Exchange Mexican Peso (Sep ’18) CME 5 19 •• 30.7 NYMEX New York Mercantile Exchange S&P GSCI (Sep ’18) CME 4 2 •• 9.7 Swiss Franc (Sep ’18) CME 2.3 7 •• 25.3 Brazilian Real (Sep ’18) CME 6.1 9 • 17.1 Feeder Cattle (Oct ’18) CME 4.1 3 • 11 1810 New Zealand Dollar (Sep ’18) CME 2 14.3 9 • 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 • October 2018 • Technical Analysis of Stocks & Commodities
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October 2018
• Technical Analysis of Stocks & Commodities • 59
Safety-Based Investing
Safety Is More Than A Dance
F
by Gabriel Gonzalez
or fixed-income earners, safety-based investments are ideal and tend to be the most stable in terms of payout. But they also tend to pay back the least. When you think of safety-based investment vehicles, you think treasury bills, notes, government bonds, certificates of deposit, commercial paper, and bankers’ acceptance slips. These are commonly known as debt securities. Money markets and bond markets are where most of the safety-based investment vehicles are exchanged. Stable, established business entities and governments issue many of the safest investment vehicles. Last time, in my August 2018 article, I established three big objectives you could use when determining what investment strategy to implement with your hard-earned money. I also suggested you can’t typically excel in one without compromising the other two. That said, I will continue building upon the foundation of general investing knowledge and give each objective its due diligence, starting with safety.
60 • October 2018 • Technical Analysis of Stocks & Commodities
What, again, does “safety” mean?
When something is considered safe, you typically think of it as being, at the very least, reliable and secure. In the context of investing, and this applies particularly to rookie investors or those with fixed incomes, you want your investment vehicle to be reliable in fulfilling its purpose and secure against outside change that could compromise that reliability. Safety-based investments meet these criteria, typically because the institutions involved are usually established and stable, such as governments or big corporations. I should add that it cannot be emphasized enough that there is ultimately no such thing as a truly safe investment. Everything mentioned here will always carry some kind of risk. That risk is just the lowest relative to other investment vehicles and markets. Get it? Securities At the foundation of safe investing are debt securities, which are defined by parameters such as notional amount (amount borrowed), interest rate, maturity, and renewal date (where applicable). We all know what a debt is, but what is a security? A security is an official document, verifying a specific fact—usually, credit or ownership of stocks and/or bonds. In
VICTORIA KURPOS/SHUTTERSTOCK
Is there such a thing as safety when it comes to investing or trading? Let’s find out.
AT THE CLOSE
this case, we are referring to debt; more specifically, debt securities of the government-issued variety. When you purchase a government-issued security, you are basically entering into a financial agreement with that government by lending it money. The type of security you purchase determines the terms of executing that agreement. One of the safest forms of government security is the treasury bill, also known as a T-bill, which is basically a governmentissued “IOU.” These are considered short-term loans with their maturities—or rates of duration—ranging from a few days up to a year (rarely exceeding this), and are typically arranged in one-, three-, and six-month intervals. During these monetary gestation periods, interest is accrued. The longer the maturity, the higher the interest rate goes. When the end of maturation is reached, the debt is repaid along with whatever interest is acquired. Notes & bonds Now we move on to notes and bonds, the next-tier government securities. They function similarly to T-bills, but the biggest differences are the maturity rates and interest payment methods. Notes and bonds are long-term loans, where notes are offered at one-, three-, five-, seven-, and 10-year maturities, while bonds are offered at even greater intervals, typically 20 and 30 years. Though T-bills are paid back at the end of the contract, notes and bonds pay the lender interest payments, called coupons, in six-month intervals. Certificates of deposit Edging toward the deep end of the relatively shallow risk pool are certificates of deposit, or CDs. Like T-bills, notes, and bonds, CDs have a maturity and interest rate. Unlike Tbills, notes, and bonds, they are promissory notes issued by banks that establish a time deposit, restricting the owner from withdrawing funds on demand. Despite this, their value is covered by the government, specifically the Federal Insurance Deposit Corporation, or FDIC, which will cover the costs in the event of the issuing bank’s catastrophic failure. CDs tend to be favorable as they are more autonomous, meaning they are usually issued electronically, and can be set to automatically renew themselves upon reaching maturation. Bankers’ acceptance slips and commercial papers Finally, the riskiest of the safety-oriented vehicles are bankers’ acceptance slips and commercial papers. The former are short-term debt instruments issued by a company with the backing of a commercial bank. Each of the mentioned institutions is liable for the amount due when the investment matures, making it another relatively safe investment. On the other hand, the latter is known as an unsecured debt, meaning it has no form of collateral to back it up in the event the borrowing entity can’t repay the debt. But to reiterate the recurring theme with most of these vehicles, the short-term maturation of these loans makes them relatively safe from detrimental outside influence.
Financial markets are places where safety-centered vehicles along with many other, riskier vehicles are traded. A Brief intro to financial markets
Financial markets are places where safety-centered vehicles along with many other, riskier vehicles are traded. They come in two main categories: money markets and capital markets. Though there is some overlap in form and function, for the purpose of explaining safety, suffice it to say for now that most of the vehicles mentioned here reside in the money market, where most short-term vehicles are traded, though the long-term vehicles are usually found in the capital market. Stability— and this includes market stability—wherever it can be found, will only help these vehicles. In short, markets are considered stable when they can handle large volumes of trades without causing large shifts in prices. All things said, dealing with organizations that have so many digits on their ledgers usually means they work in large denominations of investment vehicles. For most individual investors, this simply isn’t an option. These numbers are too big to keep up. But this is no reason to tap out just yet. Groups of investors can form what are called investment funds, pooling their resources together to make these gargantuan bulk purchases.
The takeaway
Debt securities tend to be a good, safe start for the novice or fixed-income investor. Short maturities, more often than not, result in safer investments because there is less time for outside influences to affect their interest rate. Longer maturities are often made safer if they are issued and backed up by large, reputable financial entities. Ultimately, for the most reliable, albeit modest, return, going with stable markets and purchasing debt from large borrowing bodies that can weather uncertainty and the occasional crisis are the way to go. Gabriel Gonzalez is a freelance author and software designer with a bachelor’s degree in computer science. His latest book, a work of fiction, is Althea: An Oneiric’s Tale.
Further reading
Gonzalez, Gabriel [2018]. “Investing: An Objective-Based Primer,” Technical Analysis of Stocks & Commodities, Volume 36: August. Kenny, Thomas [2018]. “Differences Between Treasury Bills, Notes, and Bonds,” The Balance, April 18, retrieved online 7/9/2018. Siegel, David [2017]. “Basic Investment Objectives.” Investopedia, December 22, retrieved online 6/25/2018. Zucchi, Kristina [2018]. “Financial Markets: Capital Vs. Money Markets,” Investopedia, January 2, retrieved online 7/2/2018. October 2018
• Technical Analysis of Stocks & Commodities • 61
The following selection of book descriptions represents a sampling of recent book releases in the investing field. Books described here may be from some of the major book publishers as well as some independent book publishers. These are not critical reviews or editorial evaluations, but rather a brief look at the book marketplace to help keep readers up to date on new or recent book offerings.
Silicon Valley Babble On (440 pages, $39.95 softcover, $79.95 full-color version, $9.99 ebook, August 2018) by Tim Knight. Tim Knight started the SlopeOfHope.com website before the 2007 financial crisis began, with the site named after the maxim “markets climb a wall of worry and slide down a slope of hope.” It attracts a community of traders who use charts, economic cycles, and political discourse to shape their investment decisions. Over a dozen years, Knight posted over 20,000 blog entries on the Slope Of Hope site, and for this new book, he gathered up what he considers the best and most illuminating of them. Topics covered include startups and culture in Silicon Valley; his own experiences building and selling high-tech businesses; lessons from the fast-paced world of equity trading; and life lessons drawn from personal experience. Some of the wide-ranging questions considered in the collected writings include: What do the words “color” and “clinkle” have to do with one another? How many successful companies have launched from The Startup Castle? Where do Silicon Valley parents send their kids instead of normal summer camps? How did Marissa Mayer make millions while at the same time bungling Yahoo? How did Knight get into the financial data business by way of a convicted speed dealer? What cofounders pocketed $6 million in investor cash and got away with it? What company raised a billion dollars and got nothing for it except a federal indictment? What is China’s social credit score and what will it mean? Will the concept of universal basic income save or ruin us? What is going to be the most meaningful technological shift in the next 20 years? SlopeOfHope.com
The WealthTech Book (336 pages, $39.95 softcover, $25.99 ebook, April 2018, ISBN 978-1-119-36222-7) by Susanne Chishti and Thomas Puschmann, published by Wiley. This crowdsourced guide aims to show the disruption, innovation, and opportunity that technology is bringing to the investment management sector. It seeks to provide information for entrepreneurs, innovators, investors, insurers, analysts, and consultants working in or interested in investing in this space. The digital evolution is enabling the creation of sophisticated software solutions that make money management more accessible, affordable, and eponymous. Full automation is attractive to investors at an early stage of wealth accumulation, but hybrid models are of interest to investors who control larger amounts of wealth, particularly those who have enough wealth to be able to efficiently diversify their holdings. The book explains how 62 • October 2018 • Technical Analysis of Stocks & Commodities
the wealth management sector is being affected by competition from low-cost robo-advisors; it explores how to keep customers happy while managing their assets; and offers inspirational success stories. The book is intended for investment and fund managers, asset allocators, family offices, and entrepreneurs. www.wiley.com
Review Of Financial Economics (quarterly journal, $676, print ISSN: 1058-3300, online ISSN 1873-5924), edited by Tarun Mukherjee and Gerald Whitney, published by Wiley Periodicals. The Review Of Financial Economics (RFE) publishes original research in various areas of financial economics (for example, corporate finance, investments, financial institutions, international finance, and the relationship between macroeconomic factors and corporate financial decisions). The RFE’s scope is to consider empirical research rather than theoretical work. www.wiley.com
High Frequency (bimonthly journal, online ISSN 2470-6981), edited by Ionut Florescu and Frederi Viens, published by Wiley Periodicals. High Frequency is a interdisciplinary journal on applied and theoretical topics devoted to high-frequency data questions. The intent is for the journal to be a focal point around which the emerging high-frequency community of modelers, quantitative researchers, empirical analysts, and data scientists can coalesce without regard to disciplinary boundaries, to help enhance and produce impactful applications in all areas of science and engineering. www.wiley.com
Engines That Move Markets: Technology Investing From Railroads To The Internet And Beyond, 2d. ed. (570 pages, £49.99 hardcover with free ebook version, August 2018, ISBNs 9780857195999 / 9780857196002) by Alasdair Nairn, published by Harriman House. This book’s premise is that some of the biggest technological innovations in the world have followed similar market and social patterns: scepticism is replaced by enthusiasm; venture capital is supplied; many companies are started and their stocks rise. But as the technology is developed and financial reality sets in, companies disappear, stocks collapse, and naive investors lose money. Nairn examines the impact that some of the greatest technological inventions of the past 200 years have had on financial markets and investors’ fortunes. Each chapter explores a different technological innovation and offers insights on how to apply these lessons to appraise the “new technology” companies of the future. Some of the key historical episodes examined include electric light and its commercial exploitation; the railway boom; development of the automobile industry; early development of the crude oil industry; the rise of the personal computer; the rise of wireless communication; the Internet and dotcom bubble. The books seeks to teach the reader how to recognize these patterns unfolding in the economy to profit from market-shaping events. www.harriman-house.com
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