000,000–15,000,000–20,000,000–25,000,000
–0.900.230.10
© 2002 Lars Kestner – All Rights Reserved
FIGURE
7. 3 2 a
MACD Strategy Applied to Stocks.
Breakdown Statistics (stocks)
199119921993
K-ratio–0.330.12–0.29–0.48–0.460.03
1994
Year
1995
Average Avg. Bars Avg. BarsLossWinLoss–19,230197–22,8094413–20,023207207–20,588–20,739218208–20,649–21,659207–22,064228197–20,870–22,984218Profitability Windows
1997
1998
2000
2001
© 2002 Lars Kestner – All Rights Reserved
1999
Num. ofNumber of Profitable PercentSharpe RatioLength Windows Windows Profitable14455–1.7638.19%1 Month14240–0.1828.17%3 Months13927–0.7419.42%6 Months1339–4.476.77%12 Months2–1.891.57%18 Months1270–0.020.00%24 Months1211996
Net Profit by Year
Performance Breakdown by YearNet ProfitK-ratio Sharpe RatioYear–2,494,8320.021996–0.15–297,3951997–0.32–0.10–1,599,9321998–0.42–0.12–2,700,3041999–0.40–1.14–3,375,5652000–0.13–0.71–33,242–0.852001–3.79
MACD Strategy Applied to Stocks.
7. 3 2 b
1990
Net Profit–456,863–282,931–1,696,271–1,211,538–1,131,160–4,602,830
0–500,000–1,000,000–1,500,000–2,000,000–2,500,000–3,000,000–3,500,000–4,000,000–4,500,000–5,000,000
Year199019911992199319941995
FIGURE
Net Profit
System Name:MACDParameters:12 day minus 26 day exponential moving averages; 9 day EMA for signal lineDescription:Buy when MACD crosses above signal line, sell when MACD crosses below signal lineRun Dates:1/1/1990 – 12/31/2001Breakdown by Market SectorAverageAverage Average Avg. ProfitAverageAverageAverageAverageMarketWinSharpe Ratio Max DD Num Trades % Win Per ContractK-ratioNet ProfitSectorEnergy–1,870,428–1,808,698279–0.4229–1.05–27924,286Materials–829,983–434,125182–0.1231–0.23–11539,996Industrials–923,026–539,431259–0.1234–0.27–10933,443Discretionary –663,697263–0.1534–77–0.3332,939–1,048,925Staples–996,479–636,286249–0.1334–0.29–10832,866Healthcare–949,966262–0.1131–85–0.4934,514–1,243,869Financials–1,722,698–1,467,907269–0.2830–0.76–18533,131Info. Tech.–689,491230,8652300.06360.149542,978Telecom–1,625,842270–0.2531–263–0.8626,470–1,722,823Indices134,5862460–0.033444,824–0.01–832,077
CHAPTER 7 Dissecting Strategies Currently Available179
179
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PART 2 Harnessing the Power of Quantitative Techniques to Create a Trading Program
The major worry with these trend-following models is relying on marketsto continue their trending ways. While performance in the early 1990s was verystrong, recent annual profits have been less than average. This is one area for systematic traders to monitor. The results may suggest that exhaustion strategiescould be favored over trend-following strategies during the next 10 to 20 years.We found that stock performance was very mixed. Trend-following strategiesperformed beautifully on specific sectors such as technology and stock indices,but overall, trend following performance was mixed. If we remove the smallbatch of stocks that produced profits, we find that the majority of stocks do notrespond well to trend-following strategies. Removing technology and indices, the14-day Slow %K strategy produced profits in 19 out the 24 remaining markets. Thisphenomenon is grounds for further study.By evaluating today’s popular trading strategies, we have established a baseline to compare new trading strategies that we will create later in the book. Thislevel playing field approach allows traders to see exactly where they stand in theworld of trading success. The performance results of the strategies in this chaptercan be analyzed to gain a better understanding of how markets work.
CHAPTER
8
New Ideas on Entries, Exits, and FiltersEnhancing Trading Performance Using Cutting Edge Techniques
T
here are a number of new trading strategies for use with stocks and futures markets that I will introduce in this section. These ideas are not simply rehashes of currently used techniques; they are unique. Each strategy details the exact rules for buyand sell signals, and presents a price graph that plots sample buy and sell signalsapplied to real market data in addition to performance statistics for a portfolio ofstocks and futures.
A WOLF IN SHEEP’S CLOTHINGThe focus of my trading ideas is adapting indicators to prevailing market conditions. If a market is volatile, we want our entries and exits to be less sensitive toprices changes. We never want to use a fixed dollar stop or other such nonsensewhich is not derived from market statistics. Every idea is based on logic and rigorous statistical principles.I’ve seen a number of “groundbreaking” strategies introduced in magazinessuch as Technical Analysis of Stocks and Commodities or sold as $3000 systems tothe public. Very often, these methods consist of nothing more than the channelbreakout and dual moving average crossover strategies that we have used in thisbook. More times than not, the core strategy will be wrapped with a number of filters that do not enhance strategy performance, and rarely even influence buy andsell signals. An example might be:181
Copyright 2003 by Lars Kestner. Click Here for Terms of Use.
182
PART 2 Harnessing the Power of Quantitative Techniques to Create a Trading Program
Setup 1: An inside day, a pattern where both today’s high is less than yesterday’shigh and today’s low is greater than yesterday’s low, occurred within thelast 10 trading days.Setup 2: An outside day, a pattern where both today’s high is greater than yesterday’s high and today’s low is less than yesterday’s low, occurred within thelast 10 trading days.Setup 3: Today’s close is the highest close of the past 40 days.Setup 4: Today’s close is the lowest close of the past 40 days.If Setup 1, 2, and 3 are fulfilled, then buy the market.If Setup 1, 2, and 4 are fulfilled, then sell the market.You might see this strategy dubbed as the “Wiggle-Waggle EntryMechanism,” or some other catchy title. The wiggle and waggle (the inside andoutside day filter) likely have nothing to do with the performance of the system. I see no reason why having an inside and outside day would affect the performance of a channel breakout system. It is a nonsensical trend filter. Despitethe irrelevance of Setup 1 and Setup 2, this strategy is profitable. Why?Because the 40-day channel breakout that drives the performance of this strategy is inherently profitable. Although performance may actually be better without the wiggle and waggle setups, chances are the strategy creator will not tellyou that.One method to gauge the similarity of strategies is to calculate a correlationmatrix of returns. Remember, systems with low correlation can be combined toutilize benefits of diversification, while highly correlated systems (such as thewiggle-waggle example above, and the 40-day/20-day channel breakout) do notprovide any benefit to the trader. Keep an eye on the correlation statistics featuredat the bottom of my performance templates. These numbers are an easy way todetect this high correlation to popular trend-following strategies.
THE SONG REMAINS THE SAME: SIMILARITIES OF OSCILLATORSTrading strategies are not the only piece of the trading puzzle that suffers fromsevere correlation. Indicators—specifically overbought/oversold price oscillatorssuch as the Relative Strength Index and %K stochastics—face similar problemsdue to the similarities in their calculation. As a result, their movements track eachother very closely (Figure 8.1), so using one particular oscillator versus anothermay not be terribly important in trading decisions.
11 NEW TRADING TECHNIQUESWhy do I point out the similarities in oscillators and overspecified trading strategies? In the following pages, I will present 11 new ideas that are not a rehash of
CHAPTER 8 New Ideas on Entries, Exits, and Filters
183
Price, RSI, and Stochastics130
1200Price
1000
110
Price
7060050
RSI
Oscillators
90800
40030Stochastics
200
10–100
30
60
90
Date
FIGURE
8.1
Price, RSI, and Stochastics. Moves in a 14-day RSI and 14-day Slow %K stochastics are very similar.
the old and overused. Nor are these techniques copycats of current trading logic.While many are variations of current methodologies, they’re applied in a way thatwill generate entirely new and improved trading signals. The 11 are:Kestner’s Moving Average systemSecond Order Breakout■ MACD histogram replacement■ Divergence Index■ Moving Average Confluence method■ Normalized Envelope Indicator■ Multiple Entry Oscillator system■ Adjusted stochastic■ Three in a row■ Volume Reversal strategy■ Saitta’s Support and Resistance strategy■■
Kestner’s Moving Average SystemThe first system I ever designed involved improving an already popular moving averagecrossover system. In the traditional single moving average crossover, buy signals are gen-
184
PART 2 Harnessing the Power of Quantitative Techniques to Create a Trading Program
erated when prices close above the moving average, while sell signals are generated whenprices close below the moving average. The most problematic feature of the single moving average crossover is the whipsaws caused by frequent crossings of price and themoving average. As explained in Chapter 4, if a market oscillates between strength andweakness with little follow-through, the typical moving average strategy can be whipsawed as it buys after periods of strength and sells after periods of weakness. This usually is the main driver to losses or failure of a moving average strategy’s performance.One way to alleviate these whipsaws is to create a channel using two separateaverages. I accomplish this by calculating two averages: one using daily highs andanother using daily lows. I require prices to close above the moving average ofhighs to generate buy signals, while prices must close below the moving average oflows to generate sell signals. In addition, I filter these signals by only taking buyswhen today’s moving average is greater than the moving average x days ago, andonly taking sell signals when today’s moving average is less than the moving average x days ago. Typically, the x parameter used in the filtering process is equal tothe length of the moving average.The graph in Figure 8.2, shows the Kestner Moving Average System using a20-day moving average when applied to General Electric (GE). In early June, bothconditions are met to generate a long entry: prices rally above the 20-day movingaverage of highs, and the 20-day moving average is greater than the prior day’svalue. In late July, prices begin to fall leading to a decline below the 20-day moving average of lows and a falling 20-day moving average of closes. At this pointwe exit our long positions and enter short.I generate trading signals by combining two separate systems—using a 20day moving average and an 80-day moving average. Positions are taken when bothsystems are in agreement. That is, if the new moving average strategy using a 20day moving average is long and the new strategy using an 80-day moving averageis also long, we will enter a long position in the market tested. If the new movingaverage strategy using a 20-day moving average is short and the new strategy usingan 80-day moving average is also short, we will enter a short position in the market tested. Long positions are exited if today’s close falls below the 80-day simplemoving average of lows. Short positions are exited if today’s close rises above the80-day simple moving average of highs.Kestner’s Moving Average system performs very well on our stable of futuresmarkets (Figures 8.3a and 8.3b). A Sharpe ratio of 0.95 and K-ratio of 0.43 are twoof the highest we will see. This strategy is profitable in 11 out of 12 years tested.Performance on stocks is not as impressive, but still generates profits in seven of12 years (Figures 8.4a and 8.4b). The trading logic yields high correlation to standard trend-following strategies for both stocks and futures.Second Order BreakoutThis strategy was designed to distinguish between the strength of varying lengthchannel breakouts. In a typical 20-day channel breakout, a long position is established
CHAPTER 8 New Ideas on Entries, Exits, and Filters
FIGURE
185
8.2
Kestner’s Moving Average System Applied to General Electric.
when the market makes a 20-day high. By definition, a 40-day high is also a 20-dayhigh, since a market making a longer-term high is always making a shorter-term high.As such, the 40-day high would be considered a buy signal in our 20-day channelbreakout strategy. Perhaps we need to distinguish the two by saying that a 40-day highis more significant than a 20-day high. Likewise, an 80-day high is more significantthan a 40-day high. I developed the second order breakout to determine the significance of each new high and low.Each day, we determine the significance of that day’s close compared to theclose of the past 80 days. By comparing today’s close to the past 80, we determinethe degree of breakout that is occurring today. If today’s close is the highest of thepast five days’ closes, then today is a five-day high. If today’s close is the highestof the past 20 days’ closes, then today is a 20-day high. We search up to 80 daysprior to determine the extent of today’s breakout. The number of days high or lowis recorded as the Second Order Breakout statistic. Highs are recorded as positivevalues and lows are recorded as negative values. For example, if today were thehighest close of the past 40 days, then today’s Second Order Breakout statisticwould have a value of 40. If today were the lowest close of the past 35 days, thentoday’s Second Order Breakout statistic would have a value of –35. We generatetrading signals using the typical channel breakout methodology instead of enter-
PART 2 Harnessing the Power of Quantitative Techniques to Create a Trading Program
186
Trading Strategy Evaluation (Futures)
Avg. Avg.Bars BarsWin Loss9123112189820109209617952311814110191051610020115219020962111019101281111994247517101181262511319962710720111231092095191062310520942010020
Net Profit:Drawdown:K-ratio:
Portfolio Statistics14, 658,917Sharpe ratio:–1,440,842Correlation to breakout:0.43Correlation to 10-40 MA:
Dec-01
Jan-01
Jan-00
Jan-99
Jan-98
Jan-97
Jan-96
Jan-95
Jan-94
Jan-93
Jan-92
Jan-91
Jan-90
Equity
Strategy Name: Kestner’s moving average systemParameters:20 day averages, 80 day averagesDescription:Buy when 20 day and 80 day in agreement; exit on break below opposite 20 dayRun Dates:1/1/1990 – 12/31/2001#Avg.Sharpe%Avg.Avg.Avg.ofProfitMarket Net Profit K-ratio RatioMax DD Trades Win Contracts Per Con. WinLossFXAD–137,730 0.05 –0.08 –271,290642725.29–77 70,164 –28,033BP–333,850 –0.11 –0.18 –1,002,013 751717.75–257 101,761 –26,849CD–124,230 0.00 –0.06 –637,370632748.52–34 76,660 –30,608JY1,567,200 0.48 0.80 –119,725444513.312,476 102,410 –24,908SF759,6500.16 0.40 –143,275573515.62881 82,489 –23,384RatesED2,585,325 0.24 1.10 –130,200404887.00401 101,576 –25,481TY1,064,641 0.33 0.52 –149,875523528.60728 114,679 –28,875US945,2190.27 0.51 –143,875503620.00944 98,139 –25,690StockSP–422,300 –0.03 –0.22 –610,01375238.39–657 70,799 –27,886MetalsGC77,6300.05 0.04 –328,840602842.2839 74,997 –27,341HG518,3630.13 0.31 –347,050533031.85345 95,070 –25,383PL–261,735 –0.09 –0.14 –738,325672749.47–89 65,387 –30,059SL–261,965 –0.08 –0.13 –462,110662631.15–149 51,990 –24,305EnergyCL1,880,420 0.37 0.78 –250,510454229.091,367 131,153 –27,001HO1,275,162 0.16 0.49 –199,538483824.37962 117,928 –33,247HU915,3780.17 0.37 –285,298533223.07717 114,138 –29,541GrainsC1,149,138 0.20 0.50 –246,025474578.77297 89,036 –29,640S–572,125 –0.10 –0.30 –924,663713031.63–276 47,118 –32,184W512,7880.11 0.24 –332,363603050.18170 99,026 –30,230MeatsFC5,3100.00 0.00 –644,975621839.082 132,730 –28,524LC510,1240.10 0.28 –281,028543049.89129 82,301 –25,525LH265,2840.11 0.14 –284,236523132.57150 80,019 –28,485PB–501,848 –0.11 –0.23 –710,852731819.88–358 93,657 –28,957SoftsCC–135,320 –0.07 –0.07 –575,000602349.03–93 67,894 –26,642CT780,4550.10 0.41 –386,230602724.11539 116,606 –24,665JO–505,253 –0.10 –0.22 –848,558702337.52–194 70,970 –30,448KC1,534,935 0.18 0.51 –255,056513311.622,315 130,088 –24,708LB1,199,056 0.14 0.40 –265,664503832.12747 114,297 –31,374SB369,1970.12 0.21 –274,154573553.02122 68,584 –27,094Average 488,6310.09 0.21 –394,937563033.51372 88,722 –26,90218,000,00016,000,00014,000,00012,000,00010,000,0008,000,0006,000,0004,000,0002,000,0000-2,000,000
0.950.850.81
© 2002 Lars Kestner – All Rights Reserved
FIGURE
8.3a
Kestner’s Moving Average System Applied to Futures.
187
Net Profit2,896,3221,015,3451,172,6791,296,0091,941,8941,167,284
Year199019911992199319941995
8.3b
AverageSharpe Ratio0.180.53–0.220.020.550.150.050.20
1991
K-ratio0.800.110.240.190.610.25
1992
Sharpe Ratio2.200.771.021.131.521.42
1993
Net Profit1,846,725946,459619,292–149,3731,771,296104,071
1994
1996
K-ratio0.410.150.23–0.100.480.09
1995Year
Net Profit by Year
Year199619971998199920002001
AverageWin86,69778,59970,79971,861121,07378,39397,17694,740
Average Avg. Bars Avg. BarsLossWinLoss10120–26,756–20,0128114–27,8861051610021–26,77210722–29,9309020–30,68511023–27,873–27,48810321
1997
1998
2000
2001© 2002 Lars Kestner – All Rights Reserved
1999
Num. ofNumber of Profitable PercentSharpe RatioLength Windows Windows Profitable144821.0356.94%1 Month142951.0566.90%3 Months1391120.4480.58%6 Months133121–0.0890.98%12 Months1271231.8396.85%18 Months1211200.0699.17%24 Months
Average Avg. ProfitAverageAverageMax DD Num Trades % Win Per Contract6130598–434,735–105,9883630518–610,0137523–657622836–469,08149371,016–245,115593564–501,0176024–19–480,273–434,1105830573
Performance Breakdown by Year
AverageK-ratio0.110.21–0.030.000.230.070.030.06
Kestner’s Moving Average System Applied to Futures.
FIGURE
–500,000
0
500,000
1,000,000
1,500,000
2,000,000
2,500,000
3,000,000
1990
AverageNet Profit346,2081,148,796–422,30018,0731,356,987363,26769,718540,512
3,500,000
Breakdown Statistics (Futures)Kestner’s moving average system20 day averages, 80 day averagesBuy when 20 day and 80 day in agreement; exit on break below opposite 20 day1/1/1990 – 12/31/2001Breakdown by Market Sector
MarketSectorFXRatesStockMetalsEnergyGrainsMeatsSofts
System Name:Parameters:Description:Run Dates:
Net Profit
PART 2 Harnessing the Power of Quantitative Techniques to Create a Trading Program
188
Trading Strategy Evaluation (Stocks)
Strategy Name: Kestner’s moving average systemParameters:20 day averages, 80 day averagesDescription:Buy when 20 day and 80 day in agreement; exit on break below opposite 20 dayRun Dates:1/1/1990 – 12/31/2001#Avg.Avg. Avg.Sharpeof%Avg.ProfitAvg.Avg. Bars BarsMarket Net Profit K-ratio Ratio Max DD Trades Win hrs (000) Per Con. WinLoss Win LossEnergy12,2140.02 0.01 –366,4036727 15.88–368,403 –25,185 96 18SLB7625