Summary
Neural networks are a constantly evolving analysis tool. This second part of a series shows the first steps in implementing this technology in real-world trading applications. Instead of building a system around a neural network, they can be used to enhance an existing strategy. Using a neural network, lag can be cut to about zero and greatly improve the performance of the system. Pattern recognition is another area where the strengths of neural networks can offer some benefits. Neural networks also can be useful in predicting trend and volatility. Predicting price change and price itself is the third rail of neural networks. As noted, it is best to start with a profitable trading strategy. The simplest example would be to replace a standard indicator with its neural network predicted counterpart. Another example could be built around a so-called "Taylor-based" trading system, which buys on a limit order at today's low minus one tick.
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Neural Nets for the Real World
In the first part of this series, we looked at the history of neural networks and their first-generation use in trading applications. We discussed how the large institutional traders who tried to use this technology failed to increase trading profits despite spending millions of dollars on research and development. They failed because they tried to make the neural networks do too much and treated trading as a pure signal-generation process.
The nets were used so that if the net failed to be predictive out-of-sample, the entire system failed. Often, the net was used as the engine of the system. For example, the system signaled a buy if the neural net predicted prices would rise throughout the next five days and si...See the full content of this document
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