The key to developing intelligent trading systems is to start with a reliable rule-based strategy. The neural network, genetic algorithm, kernel regression or other machine-learning method should be designed to enhance the system’s already positive performance.
However, this core system does not have to be the Holy Grail. While this building block may not be tradeable as a standalone system, it should demonstrate an ability to generate a positive return over time, even if that return is lacking in terms of its risk-reward balance. For example, in trend-following markets, moving-average-crossover systems perform well. Similarly, we could employ a classic n-day breakout of highs or lows. Intermarket divergence strategies also are viable, as are those that employ traditional technical indicators or fundamental analysis, such as breadth data for the S&P 500, the Volatility Index or price/earnings ratios, to name a few.
What’s important is the trader must have a strong understanding of how a given trading system works, knows its strengths and is aware of its weaknesses. This understanding can provide the foundation for enhancing the strategy using advance technologies.
Let’s take a closer look at some of the better starting points for a technology-enhanced approach.
Consider the moving-average crossover strategy, composed of simple moving average difference oscillators. One of our goals using advanced analysis methods might be to predict these oscillators as a percentage of the shorter moving average some time in the future.
Here’s how the process would work:
- Optimize a moving average crossover system, finding several robust sets of parameters.
- Test what would happen if you predicted the moving average crossovers a few bars into the future. Also, evaluate the result of being a few bars late. We want the results of slightly late predictions to be reasonable still, and we want early predictions to be far better.
- When a viable system is identified, develop a model to predict the difference between the moving average or the crossovers a few bars ahead. This is what will generate the signals.
With respect to the channel breakout, we might attempt to use neural networks to predict where prices n bars into the future will fall within the range over a set period. We might only take those breakouts if the market is still in that extreme price range so many bars into the future.
With respect to intermarket systems, which compare the price fluctuations of two related markets with respect to their historical norms, we can improve our performance by filtering out periods when these models are likely to break down. We also may be able to predict periods of stronger correlation.