0.05 –756,165843025.4940 80,678 –32,745 6125JO–563,610 –0.11 –0.24 –972,788933236.30–172 53,289 –34,591 5024KC930,0150.14 0.32 –425,906803512.43979 93,178 –31,447 6323LB591,9760.03 0.19 –736,552883833.67212 92,673 –44,172 5522SB65,441 –0.01 0.03 –522,615883654.2615 58,269 –32,013 5522Average 240,8390.05 0.09 –556,802783533.40123 66,112 –34,302 60239,000,0008,000,0007,000,0006,000,0005,000,0004,000,0003,000,0002,000,0001,000,0000–1,000,000–2,000,000
0.550.770.77
© 2002 Lars Kestner – All Rights Reserved
FIGURE
8.27a
Three in a Row Strategy Applied to Futures.
229
Breakdown Statistics (Futures)
8.27b
1990
Net Profit306,767425,295489,8231,526,6581,552,3731,321,623
AverageNet Profit415,694895,259–216,575–555,503702,623225,888204,94566,492
AverageSharpe Ratio0.200.33–0.12–0.270.290.100.100.00
19911992
K-ratio Sharpe Ratio0.090.300.080.310.180.510.301.450.481.280.351.28
1993
Year199619971998199920002001
1994
Year
1995
1996
K-ratio0.15–0.010.48–0.07–0.100.30
Net Profit by Year
Net Profit–73,960334,2701,030,926–315,673101,435503,124
AverageWin64,68166,28463,45346,49974,72274,25564,72373,259
Average Avg. Bars Avg. BarsLossWinLoss6225–30,287–30,2436017–34,6636827–36,98753235923–39,1536324–37,020–33,38960215923–35,326
1997
1998
2000
2001
© 2002 Lars Kestner – All Rights Reserved
1999
Num. ofNumber of Profitable PercentSharpe Ratio Length Windows Windows Profitable14483–0.0557.64%1 Month8962.68%0.293 Months142991.0171.22%6 Months13913398–0.9273.68%12 Months1271000.0978.74%18 Months1211070.4488.43%24 Months
Average Avg. ProfitAverageAverageMax DD Num Trades % Win Per Contract7837345–374,147–306,6594930160–571,1757432–347–915,1908837–1707742323–430,251803760–655,154–409,5638536838633148–746,716
Performance Breakdown by Year
AverageK-ratio0.090.150.01–0.080.160.070.08–0.03
Three in a rowNoneEnter long on three up weeks in a row, enter short on three down weeks in a row1/1/1990 – 12/31/2001Breakdown by Market Sector
Three in a Row Strategy Applied to Futures.
FIGURE
–500,000
0
500,000
1,000,000
1,500,000
2,000,000
Year199019911992199319941995
MarketSectorFXRatesStockMetalsEnergyGrainsMeatsSofts
System Name:Parameters:Description:Run Dates:
Net Profit
PART 2 Harnessing the Power of Quantitative Techniques to Create a Trading Program
230
Trading Strategy Evaluation (Stocks)
Avg. Avg.Bars BarsAvg.Win LossLoss–37,422 56 22–26,996 59 23–44,180 31 66–33,817 59 24–32,826 57 27–32,651 58 23–35,111 73 26–33,512 54 25–34,693 60 25–34,370 69 24–33,602 61 26–34,940 63 25–32,798 54 19–38,907 53 27–32,639 72 25–33,320 51 23–38,129 66 24–34,164 64 24–36,546 65 23–31,082 66 27–35,699 66 24–32,238 72 25–39,076 59 19–37,747 58 26–41,861 60 23–39,396 58 22–32,945 57 26–34,261 62 22–45,735 68 23–36,073 69 25–28,633 63 22–38,014 67 24–34,067 76 25–48,059 61 25–35,750 61 25
Net Profit:Drawdown:K-ratio:
Portfolio Statistics11,194,915Sharpe ratio:–4,518,381Correlation to breakout:0.17Correlation 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: Three in a rowParameters:NoneDescription:Enter long on three up weeks in a row, enter short on three down weeks in a rowRun Dates:1/1/1990 – 12/31/2001Avg.#%ProfitAvg.SharpeofAvg.Market Net Profit K-ratio Ratio Max DD Trades Win hrs (000) Per Con. WinEnergySLB –669,891 –0.09 –0.35 –708,391873815.76–50440,299XOM –273,507 –0.01 –0.16 –463,898843544.67–7441,681MaterialsA1,249,288 0.25 0.62 –289,065767450.6832538,156DD–89,517 0.03 –0.05 –641,212813819.96–6551,168IP–946,175 –0.19 –0.49 –993,565872516.03–67454,251IndustrialsBA–103,014 –0.08 –0.06 –534,635823920.06–6847,497GE525,270 0.05 0.28 –306,206664171.1011771,030MMM –553,306 –0.03 –0.32 –1,017,226 843613.55–55139,410ConsumerDIS30,058 –0.03 0.01 –518,980813535.57165,821337,431 0.03 0.18 –544,930724012.4737962,703Discretionary GMHD409,291 0.07 0.19 –447,493763756.568670,790WMT –267,736 –0.10 –0.14 –946,796793339.79–10458,599ConsumerG–570,872 –0.09 –0.28 –880,420993033.10–19853,816KO–154,159 –0.01 –0.07 –579,604804124.85–7650,832StaplesMO986,028 0.15 0.50 –358,680664225.5557378,825PG–314,944 –0.05 –0.17 –816,805923619.08–16950,566Healthcare AMGN 989,617 0.09 0.41 –289,430744162.1221388,515BMY61,8850.05 0.03 –539,460763832.472857,736JNJ–82,893 –0.05 –0.04 –582,304793539.16–2463,883PFE580,178 0.14 0.27 –265,380704079.1310767,744FinancialsAIG –384,801 –0.05 –0.20 –532,568823040.95–11665,834FNM355,557 0.14 0.19 –351,496713826.3919466,027MER44,5680.01 0.02 –633,089834149.86056,255Information AAPL 1,203,330 0.24 0.72 –140,110675817.4098856,6357842 506.302081,508Technology DELL 817,352 0.19 0.30 –316,438IBM782,142 0.20 0.35 –490,394794320.8839771,421INTC 1,604,063 0.29 0.70 –471,2077939 105.18191 102,227MSFT 816,695 0.13 0.36 –375,034803861.4715782,899SUNW 861,401 0.06 0.37 –493,6336749 228.995773,518TXN830,442 0.17 0.39 –431,974694186.4314283,130TelecomVZ343,562 0.12 0.20 –322,374764219.3120348,685IndicesSPX–81,360 0.02 –0.04 –467,9467833 2241.47073,204NDX819,093 0.12 0.36 –448,5357036 1972.79693,6805 106,931RUT 2,039,840 0.21 0.70 –336,3156949 5283.31Average 329,262 0.06 0.14 –515,7537840 334.484665,15516,000,00014,000,00012,000,00010,000,0008,000,0006,000,0004,000,0002,000,0000–2,000,000
0.550.830.86
© 2002 Lars Kestner – All Rights Reserved
FIGURE
8.28a
Three in a Row Strategy Applied to Stocks.
231
8.28b
19911992
1993
1994
Year
1995
Net Profit by Year
1996
K-ratio0.070.380.36–0.21–0.11–0.05
AverageWin40,99047,85852,64664,47858,51069,47062,70678,76348,68591,272
1997
1998
2000
2001
© 2002 Lars Kestner – All Rights Reserved
1999
Average Avg. Bars Avg. BarsLossWinLoss5722–32,2094939–36,941–33,75862256325–34,401–34,41658236525–34,980–35,67166236224–38,288–28,6336322–40,0476825Profitability WindowsNum. ofNumber of Profitable PercentLength Windows Windows ProfitableSharpe Ratio800.2855.56%1 Month144142721.0450.70%3 Months831.6659.71%6 Months139133850.1663.91%12 Months127850.0766.93%18 Months12186–0.6571.07%24 Months
Average Avg. ProfitAverageAverageMax DD Num Trades % Win Per Contract8636–289–586,1458146–138–641,281–619,3567739–168773691–614,550–658,877843732753981–419,144–505,7187936267444279–388,399–322,374764220372439–417,598
Performance Breakdown by YearYearNet ProfitK-ratio Sharpe Ratio565,07619960.581.272,431,27019970.381.682,306,7131998–0.38–0.62350,420–0.531999–1.26165,0252000–0.51–1.31–1,072,43920011.003.19
AverageAverageK-ratio Sharpe Ratio–0.05–0.260.030.03–0.02–0.03–0.010.060.000.000.060.170.030.000.180.460.120.200.120.34
Three in a Row Strategy Applied to Stocks.
FIGURE
1990
Net Profit2,064,4103,427,289–876,591–1,753,084–1,034,5384,751,283
6,000,0005,000,0004,000,0003,000,0002,000,0001,000,0000–1,000,000–2,000,000–3,000,000
Year199019911992199319941995
Breakdown Statistics (Stocks)
Three in a rowNoneEnter long on three up weeks in a row, enter short on three down weeks in a row1/1/1990 – 12/31/2001Breakdown by Market Sector
AverageMarketNet ProfitSectorEnergy–471,699Materials71,199Industrials–43,683Discretionary 127,261Staples–13,487Healthcare387,197Financials5,108Info. Tech.987,918Telecom343,562Indices925,857
System Name:Parameters:Description:Run Dates:
Net Profit
PART 2 Harnessing the Power of Quantitative Techniques to Create a Trading Program
232
its foundation. Whereas the 1980s saw a plethora of unsuccessful academic ideas,more recent research has uncovered potentially valuable strategies. One suchexample is the work done by Michael Cooper, a professor of finance at PurdueUniversity. Cooper studied stock returns from 1962 to 1993. His research foundthat one-week returns accompanied by a decrease in trading volume tend toreverse the following week.Using this idea, we test the theory on stock data. For entries, we require thatthe five-day absolute price change be greater than the 100-day standard deviationof price changes and the five-day average volume be less than 75 percent of thefive-day average volume beginning 10 days prior. These conditions identify significant price movement accompanied with a decrease in trading volume. We enterlong if the most recent five-day price change is negative while we enter short if thefive-day price change is positive. All entries are exited on the fifth day of the trade.We apply the volume reversal strategy to Johnson & Johnson (JNJ) in thechart below (Figure 8.29). Based on these signals, it appears that the volumereversal strategy does well to pick short-term tops and bottoms. This is the firstand only strategy within this text to use data other than price to generate tradingsignals.
FIGURE
8.29
Volume Reversal Strategy Applied to Johnson & Johnson.
CHAPTER 8 New Ideas on Entries, Exits, and Filters
233
Results from the volume reversal strategy are spectacular (Figures 8.30a and8.30b). Stocks produce a Sharpe ratio of 1.15 and a K-ratio of 0.49. The strategyis profitable in 9 out of 12 years. Best performing sectors are energy, industrials,and financials.Saitta’s Support and Resistance StrategyMy favorite technical analyst on Wall Street, Alex Saitta, may not be the best known,but I believe he is one of only a handful that puts his predictions on the line day inand day out without the typical hedging jargon associated with this type of analysis.Saitta is known for writing a daily piece at Salomon Smith Barney during the 1990swhich focused on short-term market trends in the stock and bond markets. Contraryto the typical technical analyst at an investment bank, Saitta was very involved inresearch and has created a number of quantitative techniques for trading. One of myfavorite techniques is his method of defining market tops and bottoms.Saitta begins with a 20-day simple moving average of highs and lows. Whenprices rise above the average of highs, the market has ended a negative phase andentered into a positive phase. When the market falls below the average of lows, apositive trend has ended and a negative trend has started. The beauty here is not oftrend definition; rather, it is the method to determine previous important tops andbottoms. At the time a new uptrend has started, Saitta looks for the lowest closingprice (or low) over the prior downtrend. This point defines a market bottom. At thetime a new downtrend has started, the highest close of the previous uptrenddenotes a market top.My strategy uses Saitta’s tops and bottoms, defined from the method above,to define support and resistance points.The old saying in market lore is that old resistance becomes new support andold support becomes new resistance. The idea is that once a previous top has beenformed, investors and traders will likely clump sell orders near the previous top.After all, market participants who did not sell at the previous high will not want tomake the same mistake twice. Once prices punch through the old top, sellers haveexhausted their ammunition and buyers will typically take the market higher.The same principles apply to market bottoms. Buyers who missed buying thelast bottom will clump orders near old lows. If enough sellers are able to push pricesthrough this band of support, buyers will be sparse and lower prices are likely.We will use the Saitta support and resistance points to determine where theseclumps of buy and sell orders are located in the market. Long positions for theSaitta support and resistance strategy are entered when prices rise above a previous market top, while short positions are entered when prices fall below a previous market bottom.The chart below (Figure 8.31) applies signals to the Japanese yen. The solidlines represent previous Saitta tops and bottoms. Because tops (bottoms) reset
PART 2 Harnessing the Power of Quantitative Techniques to Create a Trading Program
234
Trading Strategy Evaluation (Stocks)
Net Profit:Drawdown:K-ratio:
Portfolio Statistics9,711,474Sharpe ratio:–948,845Correlation to breakout:0.49Correlation 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: Volume reversal strategyParameters:5 day returns, 5 day average volumeDescription:Enter long on declines with lighter volume, enter short on rallies with lighter volumeRun Dates:1/1/1990 – 12/31/2001Avg. Avg.Avg.#Sharpe%Avg.Avg.Avg. Bars BarsProfitofMarket Net Profit K-ratio Ratio Max DD Trades Win hrs (000) Per Con. WinLoss Win LossEnergySLB591,684 0.40 0.54 –115,357 1735817.61194 19,112 –18,592 45XOM492,855 0.20 0.57 –144,794 1266048.4381 16,905 –15,197 45MaterialsAA –873,770 –0.11 –0.42 –889,270543149.35–316 54,584 –47,856 8443DD–28,541 –0.01 –0.03 –327,348 1445120.79-10 18,094 –19,006 45IP107,617 0.08 0.11 –257,189 1795516.4836 16,932 –19,608 45IndustrialsBA211,197 0.03 0.24 –311,235 1675120.6561 17,590 –15,658 45GE172,165 0.07 0.16 –243,585 1175883.1418 17,729 –21,089 55MMM 674,216 0.29 0.75 –107,818 1656213.80296 15,662 –15,144 55ConsumerDIS705,644 0.16 0.68 –222,881 1686335.97117 17,451 –17,885 45164,242 0.12 0.17 –159,139 1585713.8475 16,748 –19,751 45Discretionary GMHD368,197 0.13 0.33 –204,918 1815666.9030 19,100 –20,000 55WMT205,227 0.08 0.18 –293,609 1595744.4029 18,277 –20,865 45ConsumerG98,022 –0.02 0.10 –444,604 1615634.7418 16,969 –20,129 55KO360,798 0.17 0.32 –271,710 1455927.8989 18,060 –20,209 45StaplesMO287,894 0.16 0.29 –171,992 1576022.6781 15,776 –18,969 45PG620,935 0.25 0.62 –175,243 1506120.42206 16,765 –15,155 45HealthcareAMGN 283,414 0.07 0.25 –465,411 2166063.9821 15,409 –19,916 45BMY413,994 0.12 0.44 –164,105 1375735.2484 18,487 –17,576 45JNJ –232,518 –0.06 –0.24 –328,762 1415243.99–37 17,115 –21,793 55PFE442,671 0.11 0.39 –193,344 1535788.7533 20,515 –20,167 45FinancialsAIG444,376 0.18 0.39 –211,829 1526345.4864 17,454 –21,295 45FNM925,626 0.27 0.95 –137,013 1536126.81226 19,769 –15,808 