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These results were neither very good nor very bad: There is better than an 80% chance that the system is capitalizing on some real (non-chance) market inefficiency The serial correlation in the test sample, however, was quite a bit higher than in the optimization sample and was significant, with a probability of 01572; ie, as large a serial correlation as this would only be expected about 16% of the time by chance alone, if no true (population) serial correlation was present Consequently, the t-test on the profit/loss figures has likely.

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overstated the statistical significance to some degree (maybe between 20 and 30%). If the sample size was adjusted downward the right amount, the t-test probability would most likely be around 0.18, instead of the 0.1392 that was calculated. The confidence interval for the percentage of wins in the population ranged from about 17% to about 53%. Overall, the assessment is that the system is probably going to hold up in the future, but not with a high degree of certainty. Considering there were two independent tests--one showing about a 31% probability (corrected for optimization) that the profits were due to chance, the other showing a statistical significance of approximately 14% (corrected to 18% due to the serial correlation), there is a good chance that the average population trade is profitable and, consequently, that the system will remain profitable in the future.

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Conservative New mailing Response rate New customers Average order Revenue Name rental ($100/M) Catalogs @ $1 Ful llment @ 10% Database One-time DB costs Cost of goods (47%) Total costs Net pro t Investment Return on investment 600,000 2.20% 13,200 $250 $3,300,000 $60,000 $600,000 $330,000 $200,000 $140,000 $1,551,000 $2,881,000 $419,000 $1,000,000 1.42 Bold 1,000,000 2.00% 20,000 $250 $5,000,000 $100,000 $1,000,000 $500,000 $200,000 $140,000 $2,350,000 $4,290,000 $710,000 $1,440,000 1.49 Aggressive 1,600,000 1.80% 28,800 $250 $7,200,000 $160,000 $1,600,000 $720,000 $200,000 $140,000 $3,384,000 $6,204,000 $996,000 $2,100,000 1.47

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The following section is intended only to acquaint the reader with some other statistical techniques that are available. We strongly suggest that a more thorough study be undertaken by those serious about developing and evaluating trading systems.

money in their rst year of database marketing and recoup it later. This study shows that for this company, it was not necessary to predict a loss. Furthermore, this chart includes only new sales. Sejal assumed that the cataloger would also continue to get the existing annual sales of about $14 million.

We develop many systems using genetic algorithms. A popular$fimessfunction (criterion used to determine whether a model is producing the desired outcome) is the total net profit of the system. However, net profit is not the best measure of system quality! A system that only trades the major crashes on the S&P 500 will yield a very high total net profit with a very high percentage of winning trades. But who knows if such a system would hold up Intuitively, if the system only took two or three trades in 10 years, the probability seems very low that it would continue to perform well in the future or even take any more trades. Part of the problem is that net profit does not consider the number of trades taken or their variability. An alternative fitness function that avoids some of the problems associated with net profit is the t-statistic or its associated probability. When using the t-statistic as a fitness function, instead of merely trying to evolve the most profitable systems, the intention is to genetically evolve systems that have the greatest likelihood of being profitable in the future or, equivalently, that have the least likelihood of being profitable merely due to chance or curve-fitting. This approach works fairly well. The t-statistic factors in profitability, sample size, and number of trades taken. All things being equal, the greater the number of trades a system takes, the greater the t-statistic and the more likely it will hold up in the future. Likewise, systems that produce more consistently profitable trades with less variation are more desirable than systems that produce wildly varying trades and will

In addition to the annual database costs, there were one-time setup costs, shown in Table 12-6.

yield higher t-statistic values. The t-statistic incorporates many of the features that define the quality of a trading model into one number that can be maximized by a genetic algorithm.

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