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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.

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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, ie,.

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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.

Figure 5-2

Three nets will be trained, yielding three entry models no models will be constmcted for turning points One model will be designed to detect bottoms, the other model to detect tops For the bottom detection model, if the neural net indicates that the probability that tomorrow s open will be a bottom is greater than some threshold, then a buy order will be posted For the top detection model, if the neural net indicates that the probability that tomorrow s open will be a top is greater than some other threshold, then a sell order will be posted Neither model will post an order under any other cicumstances These rules amount to nothing mom than a simple strategy of selling predicted tops and buying predicted bottoms If, with better than chance accuracy, the locations of bottoms and tops can be detected in time to trade them, trading should be profitable.

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The detection system does not have to be perfect, just sufficiently better than chance so as to overcome transaction costs For the model that predicts the time-reversed Slow %K, a similar strategy will be used If the prediction indicates that the time-reversed Slow %K is likely to be less than some lower threshold, a buy will be posted; the market is near the bottom of its near-future range and so a profit should quickly develop Likewise, if the predicted reverse Slow %K is high, above an upper threshold, a sell will be posted These entries share the characteristics of other entries based on predictive, rather than responsive, analysis The entries lend themselves to countertrend trading and, if the predictions are accurate, can dramatically limit transaction costs in the form of slippage, and provide good fills since the trader will be buying when others are selling and vice versa.