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Large Parameter Sets An excessive number of free parameters or rules will impact an optimization effort in a manner similar to an insufficient number of data points As the number of elements undergoing optimization rises, a model s ability to capitalize on idiosyncrasies in the development sample increases along with the proportion of the model s fitness that can be attributed to mathematical artifact The result of optimizing a large number of variables-whether rules, parameters, or both-will be a model that performs well on the development data, but poorly on out-of-sample test data and in actual trading It is not the absolute number of free parameters that should be of concern, but the number of parameters relative to the number of data points.

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Setting up a Web site for customer access without talking to your customers rst to determine whether they would use it. Setting up such a system without rst working out the LTV charts that show what pro t you could make from the system. Many, if not most, such systems will not be pro table. Spending too much on a software system without rst testing the concepts on a small scale.

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The shrinkage formula discussed in the context of small samples is also heuristically relevant here: It illustrates how the relationship between the number of data points and the number of parameters affects the outcome When there are too many parameters, given the number of data points, mathematical artifacts and capitalization on chance (curve-fitting, in the bad sense) become reasons for failure No Verification One of the better ways to get into trouble is by failing to verify model performance using out-of-sample tests or inferential statistics Without such tests, the spurious solutions resulting from small samples and large parameter sets, not to mention other less obvious causes, will go undetected The trading system that appears to be ideal on the development sample will be put on-line, and devastating losses will follow.

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Developing systems without subjecting them to out-of-sample and statistical tests is like flying blind, without a safety belt, in an uninspected aircraft HOW TO SUCCEED WITH OPTIMIZATION Four steps can be taken to avoid failure and increase the odds of achieving successful optimization As a first step, optimize on the largest possible representative sample and make sure many simulated trades are available for analysis The second step is to keep the number of free parameters or rules small, especially in relation to sample size A third step involves running tests on out-of-sample data, that is, data not used or even seen during the optimization process As a fourth and final step, it may be worthwhile to statistically assess the results Large, Representative Samples As suggested earlier, failure is often a consequence of presenting an optimizer with the wrong problem to solve Conversely, success is likely when the optimizer is.

1. What was the point of the Tupperware story a. Befuddled salesmen are the best. b. Customers like to participate in the selling process. c. Tupperware is no longer successful with parties. d. You should run parties to sell products. e. None of the above. 2. Why provide customer service over the Web a. It saves millions of dollars. b. Customers never get put on hold. c. You can provide data and pictures that are unavailable in any other way. d. You can build loyalty. e. All of the above. 3. Which of the following is not one of the rules for successful customer service a. Provide good information. b. Empower the CSR agents. c. Set up test and control groups.

presented with the right problem The conclusion is that trading models should be optimized on data from the near future, the data that will actually be traded; do that and watch the profits roll in The catch is where to find tomorrow s data today Since the future has not yet happened, it is impossible to present the optimizer with precisely the problem that needs to be solved Consequently, it is necessary to attempt the next-best alternative: to present the optimizer with a broader problem, the solution to which should be as applicable as possible to the actual, but impossible-to-solve, problem One way to accomplish this is with a data sample that, even though not drawn from the future, embodies many characteristics that might appear in future samples Such a data sample should include bull and bear markets, trending and nontrending periods, and even crashes.

Year 1 Retention rate Customers Visits per year Spending per visit Revenue Cost percentage Costs Acquisition cost ($32) Total costs Pro t Discount rate NPV of pro t Cumulative NPV of pro t Lifetime value 40% 200,000 1.4 $50 $14,000,000 50% $ 7,000,000 $ 6,400,000 $13,400,000 $600,000 1 $600,000 $600,000 $3.00 Year 2 45% 80,000 1.6 $60 $7,680,000 49% $3,763,200 $3,763,200 $3,916,800 1.12 $3,497,143 $4,097,143 $20.49 Year 3 50% 36,000 1.8 $70 $4,536,000 48% $2,177,280 $2,177,280 $2,358,720 1.32 $1,786,909 $5,884,052 $29.42