Another approach that didn’t work was price change forecasting. Even worse, many used these forecasts as the core of a trading approach. The failure is obvious with hindsight. When this approach was good, it was absolutely amazing. When it was bad, it was horrible. These methods were, in effect, gambling, and the gamblers eventually lost.
That’s when many stepped back and started to use these new tools to solve classic trading problems instead of treating them like the Holy Grail of price forecasting. For example, one classic problem with all traditional technical indicators is lag. However, by using a neural network, we could minimize lag and improve the performance of the indicators. One viable approach was using a neural network to predict a moving average crossover two to three bars early in Treasury bond futures. It worked. Similar work was done with moving average convergence-divergence and the ADX.
Despite the ultimate application, all successful implementations of this new philosophy had this in common: They were designed to avoid the worst-case failure rather than go for the home run.
An example of this is using George Taylor’s so-called “book method” to trade the S&P 500. It was covered in Futures in the late 1990s (see, for example, “Born again neural nets,” February 1999). The key to this trading method is the entry trigger: Buying on a limit at the previous day’s low and selling on a limit at the previous day’s high. Two neural networks were used: One to predict the difference between today’s low and yesterday’s low, and one to predict the difference between today’s high and yesterday’s high. Safety measures were integral to the design, so if the neural network failed and the output became random noise, results would simply revert to that of the original system. Any variation from the core, non-optimized strategy would be all in the upside.
Back to the future
We can use these high-level techniques to enhance components of an already profitable strategy. This approach requires domain expertise in classic rule-based market strategies as well as expertise in neural networks, signal processing and cycle processing. This combination is the only way to use these technologies successfully.
Unfortunately, frustration born from an inability to accept this reduced role for neural networks pushed many analysts away from the method. As analysts, we simply had been trying to make neural networks do things they couldn’t do: Predict future price changes.
Now, two key realizations have made neural networks viable again. First was the development of sound, robust trading strategies that these tools could enhance. Second was technology that reduced the expense and time of implementing these resource-intensive techniques. Advances in computing power and software tools such as .NET 4.5 have made it easier to develop multi-core, multi-server implementations.
These two developments couldn’t have come to pass soon enough. Put simply, the markets have become noisier and harder to trade. Realistically, these advanced technologies can improve performance by 20% to 40%, but until recently the time to implement them would have taken two to three times longer than developing the original system. It now makes sense to invest in these technologies to enhance trading strategies.