Developing trading systems
The key to developing reliable and intelligent trading strategies using neural network or kernel regression is to integrate these technologies with already profitable trading strategies, rather than to expect the neural or kernel model to be everything.
Efforts from the 1990s use dual- or triple-moving-average crossovers. We now can use intelligent neural or kernel technology to predict such crossovers early. It’s often possible to decrease lag by predicting up to 30% of the period of the shorter moving average. We want to find robust moving average parameters with only slight degrades, being a few bars late but capturing bigger profits by predicting a few bars early. When we have this type of modeling technology, predicting the crossovers can be effective.
Another trend-following strategy is to predict price highs or lows within a window. If the market breaks out of that window, you take the trade as a neurally assisted channel breakout.
An interesting strategy is to look at both positive and negative price excursion, buying when positive excursion is double the negative excursion and selling when negative excursion is double the positive excursion.
Or, assume we want to develop a stock trading model. We can combine fundamental and technical analysis with a target, such as relative strength of a given stock to the S&P 500, and use the combination as a stock-selection method.
Yet another strategy is to predict when the technical model fails: For example, to predict when not to follow the signals generated by an intermarket model, or when to exit a trade of an intermarket system.
In our final article in this series, we will develop one or more case studies of a real trading strategy using neural networks or kernel regression.