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V.15:11 (497-504): Adding The Human Element To Neural Nets by Jeffrey Owen Katz, Ph.D. with Donna L. McCormick
V.15:11 (497-504): Adding The Human Element To Neural Nets by Jeffrey Owen Katz, Ph.D. with Donna L. McCormick
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Adding The Human Element
To Neural Nets by Jeffrey Owen Katz, Ph.D., with Donna L. McCormick
Is it possible to train a neural network to "see" the way you and I do? These Contributing Writers walk you through their research in selecting chart-based trading points by hand and then training the neural network to repeat the process.
In the November 1996 article "On developing trading systems," I demonstrated that neural network technology no longer works well when used in a manner employing brute force. To demonstrate, I trained a neural network to directly predict price change over the next several bars and achieved poor results, as expected. I speculated that the reason neural network technology no longer works well when applied in a crude fashion is because too many traders took the neural net approach and made the markets efficient relative to such techniques. However, if applied in a manner consistent with its capabilities and targeted at unique problems, neural network technology can be highly effective. Let us, then, explore a different approach to training a neural network. This time, I will train neural networks to emulate my subjective recognition of chart patterns.
When looking at charts, I often feel that I can intuitively identify good entry points based on visually recognizable patterns. One pattern I look for is a pullback in a trend, in which the market is clearly moving in a given direction before it undergoes a small correction - that is, a pullback - before continuing. Could a neural net be trained to automatically perform the same process of recognition that I believe I can accomplish by eye? Such an application of the technology might be a more appropriate use of neural networks than trying to train a neural net to directly predict the market.
Is it possible to train a neural network to "see" the way you and I do? These Contributing Writers walk you through their research in selecting chart-based trading points by hand and then training the neural network to repeat the process.
In the November 1996 article "On developing trading systems," I demonstrated that neural network technology no longer works well when used in a manner employing brute force. To demonstrate, I trained a neural network to directly predict price change over the next several bars and achieved poor results, as expected. I speculated that the reason neural network technology no longer works well when applied in a crude fashion is because too many traders took the neural net approach and made the markets efficient relative to such techniques. However, if applied in a manner consistent with its capabilities and targeted at unique problems, neural network technology can be highly effective. Let us, then, explore a different approach to training a neural network. This time, I will train neural networks to emulate my subjective recognition of chart patterns.
When looking at charts, I often feel that I can intuitively identify good entry points based on visually recognizable patterns. One pattern I look for is a pullback in a trend, in which the market is clearly moving in a given direction before it undergoes a small correction - that is, a pullback - before continuing. Could a neural net be trained to automatically perform the same process of recognition that I believe I can accomplish by eye? Such an application of the technology might be a more appropriate use of neural networks than trying to train a neural net to directly predict the market.
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