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The first test conducted below explores the use of the time-reversed Slow %K network (developed when studying entries) as an additional element to our modified standard exit strategy The net, which generates what might be called a signal exit, cannot be used on its own for exiting because it will not always close out a trade This network was designed to provide entry signals When it generates a signal, the market is likely to behave in some expected manner However, the absence of a signal does not mean that the market will not do something significant When a position is being held, at some point an exit has to be taken, and that action cannot be postponed until a significant event is finally predicted The MSES, in this case, guarantees that all trades have some money management protection and are exited after a given amount of time.



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Right-click the WCFDataServiceDemo.Web project, add a new item, WCF Data Service, and name it to DBService.svc, as shown in Figure 6-25:

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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The neural net, however, can possibly improve the strategy by occasionally triggering an exit when a predicted move against the trade is expected In this way, the net may turn a certain proportion of losing trades into winners..

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Figure 6-25. Adding the WCF Data Services database Now, open the DBService.svc.cs file and change the text for the main entry point class from DataService< /* TODO: put your data source class name here */ > to DataService< ApressBooksEntities>. Next, set the rules of data service operations and entity access in the InitializeService method by adding the two lines, as shown in the following code snippet. config.SetEntitySetAccessRule("*", EntitySetRights.All); config.SetServiceOperationAccessRule("*", ServiceOperationRights.All); In the first line, we are setting access rules to EntitySetRights.All allowing any Read and Write operation for all the entities, specified by the asterisk (*) sign. Here we have Books and BookDetails entities. If you want to specify access rules per entity base, then define the name of the entity rather than using an asterisk (*). Table 6-11 lists possible access rule (EntitySetRights) enumeration values that can be set for the entity.

So our number, 6,783, would be written as follows in base two:

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The second batch of tests (one for the long side, one for the short) involves the use of a genetic algorithm to evolve a set of rules tcr generate a signal exit The rules are used in a manner similar to the way the net is used, ie, to generate additional exits, witbin the context of the MSES, when the market is likely to reverse The rule templates and rule-generating methodology are the same as those used in 12, where evolved rules generated entries In the current tests, rules are evolved to generate additional exits within the context of the MSES The rules are used as signal exits The additional exits will, it is hoped, improve profitability by turning some losses into wins and by killing other trades before they become larger losses More sophisticated exits can be developed using the techniques described above.

From right to left that s one 1, one 2, one 4, one 8, one 16, one 32, one 64, no 128s, no 256s, one 512, no 1,024s, one 1,048s, and one 4,096. Add them all up (1 2 4 8 16 32 64 512 2,048 4,096) and you should get 6,783. That s binary arithmetic. Most PCs today use the seven-bit ASCII character set shown in Table 2-1. The mainframe, however (remember the mainframe ), uses an eight-bit code called EBCDIC. What happens when a seven-bit ASCII PC sends information to an EBCDIC mainframe system that only understands eight-bit characters Clearly, problems would result. Something, therefore, has to take on the responsibility of translating between the two systems so that they can intelligibly transfer data between each other.

Although not explored in this chapter, a neural network could be evolved to produce outputs in the form of placements for stops and limits, as well as for immediate, outright exits Genetically evolved rules could also be employed in this manner When used in the context of entries, neural networks proved to be fairly good forecasters It-sample, incredible profits were produced due to the accurate predictions Out-of-sample, the nets yielded much better than chance results (albeit not very profitable on the whole portfolio) Real predictive ability was demonstrated Using such forecasts to exit trades before the market reverses should improve system performance, even if only by eliminating a small percentage of bad trades The same applies to the genetically evolved rules However, when the rules that produced entry signals for rare event trades are applied to exits, great improvement in exit performance should not be expected.

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