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Test 12: Volatility Breakout with Limit Entry and Trend Filter. The same model from Tests 10 and 11 is used; instead of restriction to long positions or currencies, the signals are filtered for trending conditions using the Average Directional Movement index (or ADX; Wilder, 1978). By not entering trendless markets, whipsaws and languishing trades, and the resultant erosion of capital, can hopefully be reduced. The ADX was implemented to filter breakouts as suggested by White (1993). Trending conditions exist as long as the 18-bar ADX makes a new 6-bar high, and entries are taken only when trends exist.

Amounts Requests sent Cost per request Cost Response rate Responses Return postage Data entry cost Response cost Total cost Cost per survey 200,000 $0.60 $120,000 7.0% 14,000 $0.80 $0.35 $16,100 $136,100 $9.72

As in previous tests, a genetic algorithm optimized the parameters. All 100 parameter combinations except one produced positive returns in-sample; 88 returned greater than 20%, demonstrating the model s tolerance of parameter variation. The best parameters were: bandwidth, 2.6; moving average length, 8; and average true range period, 34. With these parameters the in-sample return was 68.3%; the probability that such a high return would result from chance was less than one in two-thousand, or about one in twenty-nine instances when corrected for optimization. There were 872 trades and 47% wins. The average trade generated about $4,500 in profit. Out-of-sample the average trade lost $2,415 and only 36% of all trades taken (373) were winners. The return was -20.9%, one of the worst out-of-sample performances. The ADX appears to have helped more in the past than in current times.

Most currencies, Heating Oil, Coffee, Lumber, and lo-Year Notes were profitable out-of-sample. The S&PSOO, Kansas Wheat, and Comex Gold were profitable out-of-sample, but lost money it-sample. The pattern is typical of what has been observed with breakout systems, i.e., the currencies, oils, and Coffee tend to be consistently profitable.

Amounts Requests sent Cost per email Cost Response rate Responses Data entry Data entry cost Total cost Cost per survey 200,000 $0.04 $8,000 7.0% 14,000 $0.00 $0 $8,000 $0.57

Table 5-3 summarizes breakout results broken down by model, sample, and order type. ARRR = the annualized risk-to-reward ratio, ROA = the annualized return on account, and AVTR = the average trade s profit or loss.

There is no data entry cost. A Web response customer pro le will cost you about $0.57, as shown in Table 14-3. Why would customers want to give you this information You have to come up with some good reasons, or they won t do it. One way to get them to do it is to call these surveys preference pro les instead. The bene t to the customer is that you will use these preference pro les to provide customized service just for her. Figure 14-5 gives an example of a preference pro le. Figure 14-6 shows how Macy s asks for pro les. Why should you register with Macy s Figure 14-7 tells you why. While visitors to your Web site are lling out these surveys, you can ask them their age, income, marital status, and so on. The information you get will be far more accurate than appended data. Figure 14-5 Disney Survey

(continued)

In the optimization sample (1985 to 1995). volatility breakouts worked best, the highest-high/lowest-low breakout fell in-between, and the close-only breakout did worst: this pattern was consistent across all three order types. In the verification period (1995 through 1998), the highest-higMowest-low continued to do slightly better than the close-only, but the volatility model performed much worse. For reasons discussed earlier, optimization cannot account for the relatively dramatic deterioration of the volatility breakout in recent years. Perhaps the volatility break out deteriorated more because of its early popularity. Even the best breakout models, however, do poorly in recent years. When broken down by model, three distinct periods were observed in the averaged equity curves. From August 1985 through June 1988, all models were about equally profitable. From June 1988 to July 1994, the HHLL and close-only models were flat and choppy. The volatility model showed a substantial gain from August 1992 through July 1994. From July 1994 until December 1998, the HHLL and close-only breakouts were choppy and slightly down, with the HHLL model somewhat less down than the close-only model; equity for the volatility model declined significantly.

Figure 14-6

Both in- and out-of-sample, and across all models, the limit order provided the greatest edge; the stop and market-at-open orders did poorly. The benefit of the limit order for entering the market undoubtedly stemmed from its ability to obtain entries at more favorable prices, The dramatic impact of transaction costs and unfavorable entry prices is evident in Tests 1 and 2. Surprisingly, the limit order even worked with a trend-following methodology like breakouts. It might be expected that too many good trends would be missed while waiting to enter on a limit; however, the market pulls back (even after valid breakouts) with