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who believed that this new technology could, with little or no effort on the trader s part, magically provide the needed edge. System developers would train their nets on raw or mildly preprocessed data, hoping the neural networks themselves would discover something useful. This approach was naive; nothing is ever so simple, especially when trading the markets. Not only was this neural newbie approach an ineffective way to use neural networks, but so many people were attempting to use nets that whatever edge was originally gained was nullified by the response of the markets, which was to become more efficient with regard to the technology. The technology itself was blamed and discarded with little consideration to the thought that it was being inappropriately applied. A more sophisticated, reasoned approach was needed if success was going to be achieved. Most attempts to develop neural network forecasting models, whether in a simplistic manner or more elaborately, have focused on individual markets. A serious problem with the use of individual markets, however, is the limited number of data points available on which to train the net. This situation leads to grand opportunities for curve-fitting (the bad kind)--something that can contribute greatly to the likelihood of failure with a neural network, especially with less than ideal data preprocessing and targets. In this chapter, however, neural networks will be trained on a whole portfolio of tradables, resulting in the availability of many tens of thousands of data points (facts), and a reduction in curve-fitting for small to moderatesized networks. Perhaps, in this context, a fairly straightforward attempt to have a neural network predict current or near-future market behavior might be successful. In essence, such a network could be considered a universal market forecaster, in that, trained across an entire portfolio of tradables, it might be able to predict on all markets, in a non-market-specific fashion. FORECASTING WITH NEURAL NETWORKS Neural networks will be developed to predict (1) where the market is in terms of its near-future range and (2) whether tomorrow s open represents a turning point. Consider, first, the goal of predicting where the market is relative to its near-future range. An attempt will be made to build a network to predict a time-reversed Stochastic, specifically the time-reversed Slow %K. This is the usual Stochastic, except that it is computed with time running backward. The time-reversed Slow %K reflects where the current close lies with respect to the price range over the next several bars. If something could predict this, it would be useful to the trader: Knowing that today s close, and probably tomorrow s open, lies near the bottom of the range of the next several days prices would suggest a good buy point; and knowing that today s close, or tomorrow s open, lies near the top of the range would be useful in deciding to sell. Consider, second, the goal of predicting whether tomorrow s open is a top, a bottom, or neither. Two neural networks will be trained. One will predict whether tomorrow s open represents a bottom turning point, i.e.,
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has a price that is lower than the prices on earlier and later bars. The other will predict whether tomorrow s open represents a top turning point, i.e., has a price that is higher than the prices on earlier or later bars. Being able to predict whether a bottom or a top will occur at tomorrow s open is also useful for the trader trying to decide when to enter the market and whether to go long or short. The goal in this study is to achieve such predictions in any market to which the model is applied.
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