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I n this chapter, the modified standard exit strategy (MSES) is explored with the
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addition of elements based on neural networks and genetics. In 11, neural network forecasters were developed for use in generating entries. One of the particular neural forecasters (the time-reversed Slow %K net) attempted to predict whether tomorrow s prices would be near the low or high end of the range of prices that would occur over the next several days. This network can be added to an exit strategy: If the net suggests that the market is near the top of its near-future range and a long position is being held, it would probably he a good idea to exit before the market begins to move down. Likewise, if the net forecasts a rising market while a short position is being held, the trade should be exited before the market begins to rise. 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. 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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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, i.e., 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. 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. Given the nature of the rules, only a small number of signals are likely to be generated, which means that only small numbers of trades will be affected. If only a few of the large number of trades that will be taken are improved, only a small overall benefit will be evidenced. Since the rules are being reevolved for the tests below, more inslances of exit opportunities may be found than were discovered for entry opportunities. TEST METHODOLOGY FOR THE NEURAL EXIT COMPONENT The larger of the two best neural networks, trained to predict the time-reversed Slow %K, are used. The preprocessing and forecast-generating logic are identical to those discussed in 11. A series of predictions are generated using the 18-14-4-1 net (18 first-layer neurons, 14 neurons in first middle layer, 4 in the second middle layer, and 1 output). The MSES is also used. Along with the exits provided by the MSES, an additional exit condition is being added: If the predicted reverse Slow %K is greater than some threshold, indicating that the market is high relative to its near-future price range, any long position is exited. Likewise, if the net s prediction indicates that the market is near the low of its neat-future price range, by being below 100 minus the previous threshold, any short position is exited. Exits triggered by the neural net forecasts are taken at the close of the bar.
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exit trades using the modified standard exit signal exit z 0) ( I entryprice = entryprice + pt1im - mstp * exitatrLcb1; * exitatr[cbl ;
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