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Building a data warehouse for 100 percent of their customers and those customers data has proved to be very expensive for many companies. Most large companies that have built such a warehouse have spent at least $20 million on the project. Why is it so expensive Because a data warehouse is designed by a committee. It has to include all the data that an organization ever conceivably uses: data on customers, prospects, leads, transactions, promotions, responses, products, prices, models, and employees. Since the accountants are involved, the data in the warehouse have to balance to the penny. Since IT is involved, the warehouse is built by inhouse programmers who have never built a warehouse before; they are very excited by the idea, but they have no experience at all. As most people who have studied data warehouses will tell you, these warehouses almost never pay for themselves in terms of increased profits. Why not Because the bene ts of using a warehouse are incremental. The warehouse will enable you to do better targeting of your communications. Instead of getting a 2 percent response rate, you will get a 2.1 percent response rate. The question is, will the pro ts from the 5 percent increase in the response rate pay for the cost of the warehouse

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If you are serious about developing sophisticated trading systems, need to work with large portfolios, or wish to perform tests using individual contracts or options, then buckle down, climb the learning curve, and go for an advanced simulator that employs a generic programming language such as the C++ or Object Pascal. Such a simulator will have an open architecture that provides access to an incredible selection of add-ons and libraries: technical analysis libraries, such as those from PM Labs (609-261-7357) and Scientific Consultant Services (516-6963333); and general numerical algorithm libraries, such as Numerical Recipes (800.872-7423), Numerical Algorithms Group (NAG) (44-1865.511.245), and International Mathematics and Statistics Library (IMSL), which cover statistics, linear algebra, spectral analysis, differential equations, and other mathematics. Even neural network and genetic algorithm libraries are readily available. Advanced simulators that employ generic programming languages also open up a world of third-party components and graphical controls which cover everything from sophisticated charting and data display to advanced database management, and which are compatible with Borland s C++ Builder and Delphi, as well as with Microsoft s Visual Basic and Visual C+ +. If your needs are somewhat less stringent, choose a complete, integrated solution Make sure the simulation language permits procedures residing in DLLs to be called when necessary. Be wary of products that are primarily charting tools with limited programming capabilities if your intention is to develop, back-test, and trade mechanical systems that go significantly beyond traditional or canned indicators.

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In most cases, they won t, and you can prove it to yourself with a simple Excel spreadsheet before you begin to build your warehouse. Suppose your company is taking in $156 million per year in direct sales to customers. The bene ts of a warehouse are shown in Table 101. That s very nice. Your warehouse has brought in 30,000 more sales, totaling $7.8 million more per year. But let s look at the incremental pro ts. Suppose you are making a 30 percent pro t on direct sales. Table 10-2 shows what would happen. Your warehouse will lose you about $1.1 million per year. So what should you do Use the 80 percent rule. Don t build a data warehouse that will retain all the data you could ever possibly need. Build a simple data mart that meets 80 percent of your marketing needs. Even the largest corporation in America can build such a data mart for less than $1.5 million total, with an annual cost of about $800,000. Table 10-3 Table 10-1 Warehouse Costs

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We personally prefer simulators built using modern, object-oriented programming practices. One reason for our choice is that an object-oriented simulator makes it easy to create as many simulation instances or simulated accounts as might be desired. This is especially useful when simulating the behavior of a trading system on an entire portfolio of tradables (as is done in most tests in this book), rather than on a single instrument. An object-oriented simulator also comes in handy when building adaptive, self-optimizing systems where it is sometimes necessary to implement internal simulations. In addition, such software makes the construction of merasysrems (systems that trade in or out of the equity curves of other systems)

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Current Number of offers Response rate Number of responses Sales conversion Number of sales Average sale Sales 100,000,000 2.0% 2,000,000 30% 600,000 $260 $156,000,000 Warehouse 100,000,000 2.1% 2,100,000 30% 630,000 $260 $163,800,000 Increase

a simple matter. Asset allocation models, for instance, may be treated as metasysterns that dynamically allocate capital to individual trading systems or accounts. A good object-oriented simulator can generate the portfolio equity curves and other information needed to create and back-test asset allocation models operating on top of multiple trading systems. For these reasons, and such others as familiarity, most tests carried out in this book have been performed using the C-Trader toolkit. Do not be alarmed. It is not necessary to have any expertise in C+ + or modem software practices to benefit from this book. The logic of every system or system element examined will be explained in great detail in the text.

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