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Limit, and on Stop. As can be seen in Table 12-3, the entry model produced by the evolutionary process was profitable across all three order types, both in-sample (as would be expected given the optimization power of GAS) and out-of-sample. In-sample, no return was less than 42% (annualized) for any order. The dollars-per-trade figures were all greater than $14,000, and not one system had less than 60% wins! Out-of-sample, there was more variation. With entry at open and on limit, performance continued to be stellar, with the average trade above $10,000 and the return on account above 60%. With a stop order, performance was not quite as good: The return on account was only 11%, and the average trade yielded $4,246. The only distressing aspect of the results is the small number of trades taken. For example, in-sample, with entry at open, there were only 43 trades taken over a lo-year span on a portfolio of 36 commodities. Out-of-sample, there were only 17 trades over a 5-year period; the trading frequency was roughly constant at approximately 4 trades per year. The rules were apparently detecting unusual (but tradable) market events; the model engaged in what might be termed rare event trading, which is not necessarily a bad thing. An assortment of systems, each trading different rare events, could yield excellent profits. When working with a system such as this, trading a portfolio of systems, as well as a portfolio of commodities, would be suggested. In the current situation, however, few trades would place the statistical reliability of the findings in question. The entire problem can be dealt with by using a somewhat more complex way of handling larger combinations of rules.

Tests 4 through 6: Short-Entry-at-Open Model Tested on Entry at Open, on Limit, and on Stop. In all cases, the performance of the best evolved short entry

Summary

at open model, when tested over the three order types, was poorer on the in-sample data than the long model. Out-of-sample, the results deteriorated significantly and losses occurred. Unlike the long model, this one did not hold up. It should be noted, however, that if both the long and short models had been traded together on out-ofsample data, the profits from the long side would have vastly outweighed the losses from the short side; i.e., the complete system would have been profitable. The pattern of longs trading better than shorts is a theme that has been recurrent throughout the tests in this book. Perhaps the pattern is caused by the presence of a few markets in the standard portfolio that have been in extreme bullish mode for a long time. The way commodities markets respond to excess supply, as contrasted to the way they respond to shortages, may also help explain the tendency.

Communications to customers work. Used correctly, communications will Increase your retention rate and reduce attrition Increase cross sales and up sales

Wood polish, drain cleaners, and pesticides can contain toxic substances, and in some cases, they can cause genetic changes leading to cancer. As a replacement for the very toxic commercial products used for oven cleaning, try this: sprinkle the inside of your oven with water, add baking soda, leave it overnight, and scrub off. There are many other natural cleaning alternatives, such as white vinegar and water, that can be made from products in your kitchen. A catalog of safe cleaning products is available from the American Environmental Health Foundation. (See Resource Guide for contact information.)

Table 12-4 contains the market-by-market results for the best evolved entry-atopen models, both long and short, tested on the optimization and verification samples using each of the three entry orders. Given the small number of trades taken, many of the empty cells in this table simply reflect the absence of trades. The SYM column represents the market being studied. The center and rightmost columns (CO(/NT) contain the number of profitable tests for a given market. The numbers in the first row represent test identifiers: 01, 02, and 03 represent tests with entry at open, on limit, and on stop, respectively, for the long side; 04, 05, and 06 represent corresponding tests for the short side. The last row (COtrAV ) contains the number of markets on which a given model was profitable. The data in this table provides relatively detailed information about which markets are and are not profitable when traded by each of the models: One dash (-) indicates a moderate loss per trade, i.e., $2,000 to $4,000; two dashes (--) represent a large loss per trade, i.e., $4,000 or more; one plus sign (+) means a moderate profit per trade, i.e., $1,000 to $2,ooO; two pluses (+ +) indicate a large gain per trade, i.e., $2,000 or more; and a blank cell means that the loss was between $0 and $1,999 or the profit was between $0 and $1,000 per trade. (For information about the various markets and their symbols, see Table II-1 in the Introduction to Part II.)

Increase referrals Increase pro ts The advent of email has given marketers a tremendously valuable new communication channel that will revolutionize database marketing. The one-to-one communications that we talked about in the 1990s are being carried out in the new century. From now on, anyone who is not using emails will lose out in the competitive race to build customer loyalty.

Tests 1 through 3: Long-Emiy-at-Open Model Tested on Entry at Open, on Limit, and on Stop. Table 12-4 indicates that, in-sample, the model was strongly profitable

Communication with customers through direct mail and email Capturing email names with permission to use them Creating Web sites where customers and fans can register their support of artists and products Online focus groups Real-time reports on the results of email communications Getting customers to ll out personal pro les online Personalizing emails and Web sites Testing emails on small groups to get the right email address and subject