Using the Z-TrendOscillator for Long-TermBond Market Timing 6 Submitted by Robert T. Zukowski, CMT March4. 1996

Overview Because it reflects mass psychology, the Coppock Curve is labeled by tnost technicians attd traders as a setttimettt This paper examines the concept of modifying the indicator. As a result, the curve siguals market tops and Coppock Curve to better identify major tops attd bottoms bottoms quite well and proves to be a valuable addition to in the bond futures market for lottg-term positioning. The atty trader’s tool kit. Coppock combined art 1 l- and 14 modified versiott of the Coppock Curve is referred to as mouth ROC, smoothed over by a lo-tnottth weighted mov- the Z-Trend Oscillator. Most oscillators are used for trad- ing average, which cat1 be explaitted by the yearly titne ing periods of price consolidation, but the Z-Trend Oscil- cycles frequent in tnost tnarket indices. A buy signal oc- lator is specifically used for trading all market cottditiotts curs when the curve turns up or becotnes positively sloped from accumulation, to trending, to distribution. while below the zero litte. A sell sigttal occurs when the curve turns down or becomes negatively sloped while Introduction to Rate of Change and the above the zero line. Coppock Curve Otte of the older, sitnpler tcchttical ittdicators to un- The Problem - Indicator Consistency derstand is the rate of chattge or ROC for short. ROC catt When the Coppock Curve is applied to the monthly confirm tnarket trends and forewarn of market reversals. continuation chart of U.S. Treasury Bottd future prices The ROC tneasures the pace at which price is chattgittg (UST’s) , traders are confronted with the probletn of indi- for any titne period under study. For example, a lo-da) cator consistency. This tneatts that the cottfidettce level ROC is calculated by subtracting the price today from the for each future buy/sell signal is significantly reduced be- price 10 days ago. The result is thett plotted as a cotttinu- cause the ittdicator’s extremes or overbought/oversold ous series that oscillates above and below att equilibrium levels vary frotn sigttal to signal. Notice how well chattges level that is usually set at 0. The closittg price is getterall! in the curve coincide with each major top attd bottom in used whett calculatittg the ROC. However, the ROC cat1 UST’s (see chart 1). Again, the problem is that the ittdi- be altered to isolate attd other ittdicators such as cator does ttot offer a high level of consistency for future moving averages. Trettdlitte analysis and indicator/price buy/sell signals. In other words, traders are not sure how divergettccs arc other aspects of the ROC that catt be used low or high the indicator will go before a signal is given. to enhance reliability. With this kind of versatility, traders can mattipulatc the ROC itt mattv useful ways. Edwitt S. Coppock, best kttowtt for the developtnettt of the Coppock Curve, used ROC as the basis for his work. First introduced in Barrott’s itt 1962, the Coppock Curve was ettdorsed arouttd the world as a long-term ittdicator used to forecast foreign and domestic equity markets.’ The goal of Coppock’s tnometttum rvork is to smooth a price series in such a way as to make the peaks attd troughs in ROC data significant. Smoothed mometttum (referred to as the Coppock Curve) looks attd acts much like a sitte curve or an overbought/oversold oscillator as it moves from positive (overbought) territory to negative (oversold) territory attd back again. Coppock hypothesized that the market’s emotional state could be determined by addittg up the percetttage price chattges for the time period uu- der study to get a settse of tnarket tnometttutn. The result is a long-term curve that effectively measures tnarket mo- mentum and filters out short-term attd intermediate-term tnarket swings.

‘Dudnrk, Gail S., C,IfT, SbfP Group dnnlyis ,\,Jonthly Briejng (Feb. 1996), p 4.

MTA ~OCRML !Sprin+mmer 1996 49 useful. It maintains a long-term position during a side- ways trend by decelerating as the market’s price ranges become more narrow. (3) The buy/sell equation is writ- ten by combining the slope of the indicator with over- bought/oversold extremes to determine if a profitable trade exists (see table 1 for calculations).

TABLE 1 Calculations: Z-trend Oscillator in Computrac Snap version 4.2 - coef: coef 6. study: rt-chg rt-chg(close, coed)-100 [see below] L coef: coef2 8. [z-tnagsc au-aug study: I-t-chg2 rt-chg(close, coef2)-100 [see below] user: sum-rot rt-chg t rt-chg 2 coef: coef3 10. study: wtd-ma \vtd-ma (sum-rot, coef3) studp: rsi rsi(wtd-ma, 10) user: z-trend-osc (2 * rsi)-100 studv: mov-avg rnov-avg(z-trend-osc, 5) user: buy (z-trend-osc > z-trend-osc [l] & That could lead to the wrong position in terms of timing, z-trend-osc < -40) (see chart 2). 111chart 2, two examples of false signals user: sell (z-trend-osc < z-trend-osc [l] SC that resulted in big lossescan be seen in June 1980 and -trend-osc > 50) January 1992. Basically, the curve failed to keep traders user: sold sell * .5 in a long-term position during these periods of price ac- trade: trade trade(buy, sell, sell, buy) tion. trade: open-p1 open-pl(trade, close, j/32, 2/32) trade: trad-pl trad-pl(trade, close, 5/32, 2/32) The Solution - Modifying the Coppock Curve trade: clos-pl clos-pl(trade, close, 0, 0) By modifying the Coppock Curve, traders can isolate user: equit) open-p1 t clos-pl overbought/oversold conditions and buy/sell signalsmore [Note] - 1st step selert and add the rt-rhg study in snup. 2nd step: effectively. The added value is a high performance mo- manually edit the rt-rhg stud) 6~ tJjb]g in -100 mentum oscillator with fixed buy/sell zones. The Z-Trend There are four advantages to using the Z-Trend Oscil- Oscillator uses the same basic calculation as the Coppock lator over the Coppock Curve: (1) It is smoother and less (;urve. but has a few added dimensions. (1) The indica- volatile, (see chart 3 for a comparison). (2) The ampli- tor is optimized wily OI~C time and then back tested to tude is controlled through the modified version of the lind optimum KOC and smoothing periods. Interestingly, CHART 3 us Boms-mmi C3rr:mT:oH the ROC part of the indicator when optimized coincided \\ith the 2 l-week cycle, and the weighted portion coincided with the 40-week cycle, much like the yearly cycles Coppock found in most equity markets. Both the 21-week and 40-week cycles were popularized by Jim E. Tillman, CMT, of Interstate/Johnson Lane, and are fre- quently used in forecasting turning points in UST’s. (2) The indicator uses the concept behind J. Welles Wilder’s Index (RSI) to identify overbought/over- sold conditions. III other words, the Z-trend Oscillator is a11 RSI study of the Coppock Curve. First, the raw num- bers of the Coppock Curve are substituted for the usual closing price within the RSI calculation to normalize it on a scale of 0 to 100. Second, this modified version of the coppocx RSI is multiplied bv itself and then subtracted from 100 to make it oscillate above and below 0 and between defined Lanes. A&l example of defined zones would be between -70 and 70. This will identifv proxies of overbought/over- sold. Once that is accompli&ed, buy/sell signalsbecome more visible. This is where the Z-Trend Oscillator becomes

50 MTA ~OUIWWSptin~-Summer 1996 RSI formula. (3) Major buy/sell signals become more visible. (4) Overbought/oversold zones are identified. The only disadvantage noticed is: (1) It is iueffective when used over shorter time periods such as weekly, daily arid intra-da):

Using and Customizing the Z-Trend Oscillator A buy signal occurs when the Z-Trend oscillator (this month) is greater than it was (last month) and is less than -40. A sell signal occurs when the Z-Trend Oscillator (this month) is less thau it was (last month) arid is greater than .iO. Lead time is significantly increased over using CHART4 3s BONDS-ncNTHLYc3h7m*~:~?l I Ius -, -n hue Iz-tnd-osc lou-aug

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5). From September 1982 to Jlarch 1983, the UST mar- ket was considered extremely overbought, which was eli- dent by an indicator reading of greater than 70. That was a waruulg sign suggesting traders should start looking for a new sell signal. In fact, the signal was giveu in April 1983 when the indicator began to decelerate while still above the trigger level set at 50. The result wasa 1Gmonth trade Ccldiug 12.69 points. From September 1987 to So- vemb& 1987, the Z-Treud Oscillator rcachcd a rcadiug of lessthau -70, which warned of a potential market reversal from down to up. This oversold rcadiug bcgau to unwind Coppock’s original bu~/sell strategy (see chart 4). X more in December 1987 when the indicator touched the bul couscrvative buy/ sell approach would be to wait until the trigger level set at -40. The result \va?sa 24-mouth trade indicator crossed above or below the 0 line. However, the for 10.25 points. Lero lille trade reduces profit and iucreases risk because Other examples of the Z-Trend Oscillator iI1 action ca11 siguals occur well after a top or bottom has beeri com- be sew during 1983, 198.5, 1988, 1991 alld 1994. These plete. periods were major cougestiou zones, but uoticc that the A histogram is used to display the indicator for clarity indicator kept each position active by not getting too over- but is just as accurate ~vherl displayed as a liue chart. A 5- bought or too oversold during each of these periods. 111 moutil simple moving average of the indicator cau be used fact, the indicator hovered closer to the zero liue \\heu to help cuulirm market direction. Since this moving a~- price rauges became more uarrolv. 01lce price ranges cragc is ouly used as a coufirmation tool and not part of begau to widen and the market resumed its original di- the buviscll equation, it was uot optimized. If the indica- rectiou. the iudicator accelerated. This is an important tor is above lhc 5-month simple moviug average, there is aspect \vhcu dealing rvith long-term tiuiiug iudicators be- buviug coulirmatiou. 011 the other hand, if the indicator cause false buy/sell signals tend to occur during a uon- is below the 5-mouth simple moving average, there is scll- trending (consolidation) period. [Note: During a sidc- iug co1lfirmatioIl. AII expolleutial moving average could wavs price trend, the Z-Trend Oscillator’s maximum draw also be used in place of the simple moving average. Trad- dowl (licgative open profit/loss) cau kcomc greater. ers are eucouragcd to expel-iment with other moving a\- This is caused by slippage/ commission aud price \olatil- et-ages and time periods because trading styles vdrv. ity Hoivever. these lossesare kept to a miuinlum and arc Iiltcrcd out ouce the price trend resumes iI1 its original Applying the Z-Trend Oscillator to Market direction.] Conditions Testing The Z-Trend Oscillator From late 1977 to early 1996, the Z-Treud Oscillator generated a total of 0 ollt of 9 wimling trades. (see chart Since UST futures oulv started trading in late-19Ti. the best way to show that the Z-Trend Oscillator is uot a “b! Buy and sell signalswere also modified, (see chart 7 for chance” indicator is to conduct a number of tests (see chart the indicator and table 3 for the results). Conclusion: 6 for the indicator and table 2 for the results). In the first The results were a little better, but false signalswere still test, the Coppock Curve was applied to UST’s using present. Therefore, the onlv solution was to optimize Coppock’s ll- and 14month ROC, smoothed over by a the ROC and smoothing peiiods. lo-month weighted moving average. Also used rvas Coppock’s buy/sell strategy in an attempt to show that this indicator needs to be modified in order to work prop- erly when applied to UST’s. Conclusion: Due to the num- ber of false signals and poor results, the only solution was to modifv the curve. In the second test, the Z-Trend Os- cillator was used, but the ll-, 14 and lo-month param- eters were maintained.

TABLE 3 Trading Results: Risk management overlay for UST futures using Z-Trend Oscillator from 1977-1996 [ll- and 14month ROC with a lo-month smoothing] net profits 69.41 points 5%wins 71% wins 5 losses 2 TABLE 2 long wins 2 long losses I Trading Results: Risk management overlay for UST long win 57 67% futures using Coppock Curve from 1977-1996 short wins 3 (1 l- and 14month ROC with a lo-month smoothing) short losses 1 net profits 34.00 points short win L/;.# i5% 7; wins 55% max. cons. wins 3 wins max. cons. losses 2 losses i largest win 25.91 points long wins 3 largest loss 1.91 points long losses 1 average win 13.67 points long win ‘;I; 75% average loss 1 .OO point short wins 2 average w/I 9.92 points short losses 3 max. draw down 9.44 points short win Y’i 25% longest draw down 15 months max. cons. wins 4 slippageicomm. T/32 max. cons. losses 2 months winning 102 months largest win zS,Ljl points months losing 36 months largest 10s~ i.13 points averagewin 10.25 points In the third test, the %-Trwd Oscillator was optimized werage loss -1. mrI L) points ;wrage wi I 3.78 points IO liud optimum ROC alld smoothing periods (see chart max. d2-XV down 19.09 points 8 for the indicator and table 4 for the rcsultsj. The opti- longest draw down 34 months mization process resulted iu a fi- and H-month ROC. slippage; comm. 7/32 smoothed over bv a lOmonth lveightcd moving average. months winning 74 months Conclusion: The’optimizatioll process enhanced the e6 months losing 92 months

52 MTA ~CILK\.U Spriny-Summer ICM fectlveness of the Z-Trend Oscillator, which is reflected in the results. However, to show that the 6, S-and lo-month parameters were valid, the Dow Jones-20 Bond Average (DJ-20 Bond Avg), a proxy of LIST’s going back to 1915, was tested.

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TABLE 5 Trading Results: Risk management overlay for DJ-20 Bond Avg using Z-Trend Oscillator from 1915-1946 [ 6 and &month ROC with a 1O-month smoothing] net profits 59.97 points % wins 100% TABLE 4 wins Trading Results: Risk management overlay for UST losses t futures using Z-Trend Oscillator from 1977-1996 long wins 2 (6- and S-month ROC with a lo-month smoothing) long losses 0 long win c/c 100% net profits 156.81points short wins 2 c/cwins 100% short losses 0 wins 9 short win % 100% losses 0 max. cons. wins 4 long wins max. cons. losses 0 long losses R largestwin 20.94 points long win 7 100% largest loss 0 points short wins 4 averagewin 14.99points short losses 0 averageloss 0 points short win ‘;% 100% averagew/l 14.99points max. cons.wins 9 max. draw down 5.94 points max. cons. losses 0 longestdraw down 7 months largestwin 31.56 points slippage/comm. 0 largestloss 0 points months winning 316 months averagewin 17.42 points months losing 21 months wcrage loss 0 points averagew i I li.42 points max. draw down 5.03 points longestdraw down 6 months slippage,‘comm. 7/32 months winning 150 months months losing 21 months

To make it a thorough aud fait- test, 3 separate tests from 1913 to 1946, from 1947 lo 1977 a11d from 1978 to 1996 were conducted, (see charts 9, 10 and 11 for tbc iu- dicators and tables .i, 6 aud 7 for the results).

MTA lOURUAL./Sprin~Summer1996 53 I pct19 lFt?bsZUun84 Pm leb89Nun91 KM93 WI6 I

TXBLE 6 TABLE 7 Trading Results: Risk management overlay for DJ-20 Trading Results: Risk management overlay for DJ-20 Bond Avg using Z-Trend Oscillator from 1947-1977 Bond Avg using Z-Trend Oscillator from 1978-1996 [6- and S-month ROC with a IO-month smoothing] [6- and S-month ROC with a lo-month smoothing1 u- net profits 52.67 points net profits 73.40 points 5%wins 71% % wins 78% wins 12 wins 7 losses 5 losses 2 long wins 4 long wins 4 long losses 4 long losses 0 long win R 50% long win % 100% short wins 8 short wins 3 short losses short losses short win ?‘I AS% short win % iO% max. cons. wins 3 max. cons. wins 3 max. cons. losses 1 max. cons. losses 1 largest win 13.21 points largest win 19.35 points largest loss 2.21 points largest loss 5.26 points average win 4.tiO points average win 10.39 points average loss 1.31 points average loss 3.17 points averagc IV: I 3.29 points average w/l 8.15 points max. draw down 3.70 points max. draw down 4.22 points longest draw down 21 months longest draw down 9 months slippage/ comm. 0 slippage/comm. 0 months winning 294 months months winning 150 months months losing 75 months months losing 18 months

The first test from 1915 to 1946 was a partial success. The &, S-and lO-month parameters worked extremely well. Howewr. the indicator failed to react to the different market conditions. The second test from 1947 to 1977 leas also a partial success. The 6-. 8- aud lOmouth param- eters generated excellent results, but the indicator failed to react as it should have. given the different market con- ditions. The third test from 19% to 1996 was a complete success. The 6, 8- and lOmonth parameters worked ex- tremely ~vell, aud the indicator operated properly given the different market conditions. Conclusion: Though the Z-Trend Oscillator seems to work better on UST’s than on the DJ-20 Bond Avg, the Bibliography results are very encouraging. Since the Z-Trend Oscilla- tor managed to signal every major top and bottom over Colby, Robert W. & Meyers, Thomas A., The Encvclonedia of Technical Market Indicators , 1988, Business One, an 80 year period in the DJ-20 Bond Avg, the results re- Irwin, p. 414. vealed that buy/sell signals did not occur “by chance.” In an attempt to show that the Z-Trend Oscillator can sig- Faber, Bruce R., “The Rate of Change Indicator,” Techni- nificantly increase profit potential, one last test was con- cal Analvsis of Stocks & Commodities, Volume 12; October 1992, p. 13. ducted that revealed using the Z-Trend Oscillator is more profitable than using a simple buy-and-hold strategy, (see Hayes, Tim, “The Coppock Guide,” Technical Analvsis of table 8 for the results). Stocks & Commodities, Volume 11; March 1993, p. 50. Kemplin, Raymond, “The Coppock Curve: A Famous TABLE 8 Indicator Flashes a Long-Term Buy Signal,” Barron’s, Comparison: Z-Trend Oscillator versus buy-and- November 22, 1982, p. 10. hold strategy Middleton, Elliott, “The Coppock Curve,” Technical Market Z-Trend Oscillator Buy-and-Hold Analvsis of Stocks & Commodities, Volume 12; November 1994, p. 59. DJ-20Bond Avg (1915- 1946) 59.97 points 12.19points DJ-20Bond Avg (1947- 1977) 52.67points -36.50points Pring, MartinJ., Martin Prine on Market , DJ-20Bond Avg(1978 - 1996) 73.40points 9.34 points 1993, Probus Publishing, p. 52. UST futures (1977- 1996) 156.81points 25.13 points Wilder, Welles J., New Concerts in Technical Trading Systems , 1978 , Trend Research, p, 112. Conclusions Based on the results of all tests, it was well worth the time and effort to construct, customize, optimize, back test, and update the Z-Trend Oscillator. The tests show that a complete and effective indicator can be used to sig- nal every major top and bottom in UST’s. Traders now have a long-term indicator within their technical arsenal that can (1) Consistently identify an overbought/oversold condition within the market; (2) Locate buy/sell signals with a much faster lead time; (3) Identify the long-term trend of the market. Traders who follow other markets could try this indicator on those markets. Note: The op- timized parameters would most likely be different in other markets and overbought/oversold conditions could also vary. For example, instead of being overbought at 70 and oversold at -70; a market could become overbought at 50 and oversold at -50. Traders should consider isolating dominant time cycles within the market and using those cycles in place of the ROC and smoothing periods. Long-term trend analysis is a very important aspect of , and if done correctly, there is no rea- son why traders shouldn’t be on the right side of the trend. During the research, a few areas that warrant further at- tention were discovered: (1) Using indicator and moving average crossovers as the basis for the buy/sell equation. (2) Fitting the indicator to weekly, daily and intraday time periods. (3) Applying the indicator to commodity mar- kets, currency markets and mutual funds. Despite these minor troubling aspects, the Z-Trend Robert T. Zukowski, CMT Oscillator can do what was once thought impractical: con- sistently signal major tops and bottoms, and identify the Robert T. Zukowski, CMT, is a Senior Technical trend for long-term positioning. Analyst at MCM MoneyWatch, a financial advisory firm located in New York. He is also a board mem- ber of the professional Market Technicians Associa- tion in charge of public relations.

MTA JOURNAL/Spring-Summer 1996