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A brute force optimizer searches for the best possible solution by systematically testing all potential solutions, i.e., all definable combinations of rules, parameters, or both. Because every possible combination must be tested, brute force optimization can be very slow. Lack of speed becomes a serious issue as the number of combinations to be examined grows. Consequently, brute force optimization is subject to the law of combinatorial explosion. Just how slow is brute force optimization Consider a case where there are four parameters to optimize and where each parameter can take on any of 50 values. Brute force optimization would require that 504 (about 6 million) tests or simulations be conducted before the optimal parameter set could be determined: if one simulation was executed every 1.62 seconds (typical for TradeStation), the optimization process would take about 4 months to complete. This approach is not very practical, especially when many systems need to be tested and optimized, when there are many parameters, when the parameters can take on many values, or when you have a life. Nevertheless, brute force optimization is useful and effective. If properly done, it will always find the best possible solution. Brute force is a good choice for small problems where combinatorial explosion is not an issue and solutions can be found in minutes, rather than days or years. Only a small amount of programming code is needed to implement brute force optimization. Simple loop constructs are commonly employed. Parameters to be optimized are stepped from a start value to a stop value by some increment using a For loop (C, C+ +, Basic, Pascal/Delphi) or a Do loop (FORTRAN). A brute force optimizer for two parameters, when coded in a modem dialect of Basic, might appear as follows:



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Modern switched networks typically fall into one of two major categories: circuit switched, in which the network preestablishes a path for transport of traffic from a source to a destination, as is done in the traditional telephone network; and store-and-forward networks, in which the traffic is handed from one switch to the next as it makes its way across the network fabric. When traffic arrives at a switch in a store-and-forward network, it is stored, examined for errors and destination information, and forwarded to the next hop along the path hence the name, store and forward. Packet switching is one form of store-and-forward technology.

Thus, from some far-away and beleaguered island, where all day long the men have fought a desperate battle from their city walls, the smoke goes up to heaven; but no sooner has the sun gone down than the light from the





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One of the trickiest problems when it comes to working with multiple threads is using shared resources typically, shared memory in the form of objects or primitive types. When it comes to shared data, one potential issue is known as a race condition. Figure 12-4 illustrates two threads attempting to increment a single integer variable named value. However, a simple increment is split into smaller operations behind the scenes: the value of the variable is read, incremented, and stored back into the variable.

Because brute force optimizers are conceptually simple and easy to program, they are often built into the more advanced software packages that arc available for traders. As a practical illustration of bmte force optimization, TradeStation was used to optimize the moving averages in a dual moving-average crossover system. Optimization was for net profit, the only trading system characteristic that TradeStation can optimize without the aid of add-on products, The Easy Language code for the dual moving-average trading model appears below:

Downloaded from Digital Engineering Library @ McGraw-Hill (www.digitalengineeringlibrary.com) Copyright 2004 The McGraw-Hill Companies. All rights reserved. Any use is subject to the Terms of Use as given at the website.

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line of beacons blazes up and shoots into the sky to warn the neighboring islanders and bring them to the rescue in their ships. The Iliad by Homer, circa 700 BC

The system was optimized by stepping the length of the first moving average (LenA) from 2 to 10 in increments of 2 The length of the second moving average (LenB) was advanced from 2 to 50 with the same increments Increments were set greater than 1 so that fewer than 200 combinations would need to be tested (TradeStation can only save data on a maximum of 200 optimization runs) Since not all possible combinations of values for the two parameters were explored, the optimization was less thorough than it could have been; the best solution may have been missed in the search Notwithstanding, the optimization required 125 tests, which took 3 minutes and 24 seconds to complete on 5 years of historical, end-ofday data, using an Intel 486 machine running at 66 megahertz The results generated by the optimization were loaded into an Excel spreadsheet and sorted for net profit.

The first store-and-forward networks were invented and used by the early Greeks and Romans Indeed, Mycenae learned of the fall of Troy because of a line of signal towers between the two cities that used fire in each tower to transmit information from tower to tower An opening on the side of each tower could be alternately opened and blocked, and using a rudimentary signaling code, short messages could be sent between them in a short period of time A message could be conveyed across a large country, such as France, in a matter of hours, as Napoleon discovered and used to his great advantage The earliest modern store-and-forward networks were the telegraph networks.

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