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CHOOSING THE RIGHT SIMULATOR
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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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SIMULATORS USED IN THIS BOOK
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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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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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I t would be nice to develop trading systems without giving a thought to optimization. Realistically, however, the development of a profitable trading strategy is a trial-and-error activity in which some form of optimization always plays a role. There is always an optimizer around somewhere-if not visible on the table, then lurking in the shadows. An @mizer is simply an entity or algorithm that attempts to find the best possible solution to a problem; optimization is the search process by which that solution is discovered. An optimizer may be a self-contained software component, perhaps implemented as a C++ class, Delphi object, or ActiveX control. Powerful, sophisticated optimizers often take the form of software components designed to be integrated into user-written programs. Less sophisticated optimizers, such as those found in high-end simulation and charting packages, are usually simple algorithms implemented with a few lines of programming code. Since any entity or algorithm that performs optimization is an optimizer, optimizers need not be associated with computers or machines at all; an optimizer may be a person engaged in problem-solving activities. The human brain is one of the most powerful heuristic optimizers on earth!
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WHAT OPTIMIZERS DO Optimizers exist to find the best possible solution to a problem. What is meant by the best possible solution to a problem Before attempting to define that phrase, let us first consider what constitutes a solution. In trading, a solution is a particular set of trading rules and perhaps system parameters,
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All trading systems have at least two mles (an entry rule and an exit rule), and most have one or more parameters. Rules express the logic of the trading system, and generally appear as if-then clauses in whatever language the trading system has been written. Parameters determine the behavior of the logic expressed in the rules; they can include lengths of moving averages, connection weights in neural networks, thresholds used in comparisons, values that determine placements for stops and profit targets, and other similar items. The simple moving-average crossover system, used in the previous chapter to illustrate various trading simulators, had two rules: one for the buy order and one for the sell order. It also had a single parameter, the length of the moving average. Rules and parameters completely define a trading system and determine its performance. To obtain the best performance from a trading system, parameters may need to be adjusted and rules juggled. There is no doubt that some rule and parameter combinations define systems that trade well, just as others specify systems that trade poorly; i.e., solutions differ in their quality. The goodness of a solution or trading model, in terms of how well it performs when measured against some. standard, is often calledjfitness. The converse of fitness, the inadequacy of a solution, is frequently referred to as coot. In practice, fitness is evaluated by a@ness function, a block of programming code that calculates a single number that reflects the relative desirability of any solution. A fitness function can be written to appraise fitness howsoever the trader desires. For example, fitness might be interpreted as net profit penalized for excessive drawdown. A costfuncrion works in exactly the same way, but higher numbers signify worse solutions. The sum of the squared errors, commonly computed when working with linear regression or neural network models, is a cost function. The best possible solution to a problem can now be defined: It is that particular solution that has the greatest fitness or the least cost. Optimizers endeavor to find the best possible solution to a problem by maximizing fitness, as measured by a titness function, or minimizing cost, as computed by a cost function. The best possible solution to a problem may be discovered in any number of ways. Sometimes problems can be solved by simple trial-and-error, especially when guided by human insight into the problem being worked. Alternatively, sophisticated procedures and algorithms may be necessary. For example, simulating the process of evolution (as genetic optimizers do) is a very powerful way to discover or evolve high-quality solutions to complex problems. In some cases, the best problem solver is an analytic (calculus-based) procedure, such as a conjugate gradient. Analytic optimization is an efficient approach for problems with smooth (differentiable) fitness surfaces, such as those encountered in training neural networks, developing multiple linear regression models, or computing simple-structure factor rotations. HOW OPTIMIZERS ARE USED Optimizers are wonderful tools that can be used in a myriad of ways. They help shape the aircraft we fly, design the cars we drive, and even select delivery routes
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