Now let’s define the terminology we will be using:
1. Expert component: This combines two or more inputs into one output with a gain of information relating to that output. Components are designed to extract knowledge from the inputs. This can be as simple as a technical indicator or several indicators combined to produce a single output. It also can be used to produce simple forecasts.
2. Knowledge blocks: Apply multiple expert components to create a model using them. These models combine experts using methods like neural network, kernel regression and evolved solutions using genetic algorithms.
3. Collaboration block: These are multiple knowledge blocks that use different expert components and look at the solution slightly differently. These blocks can be combined like a voting scheme or trained using neural network/machine learning methods.
4. Meta-goal: This is a combination of collaboration blocks that create an end trading solution with buy/sell signals.
All of these components need to work in a walk-forward manner. The expert components might be long-term relationships and not based on walk-forward analysis, but the other levels need to be trained and created in a walk-forward manner. It’s possible that the knowledge block will not train correctly in some walk-forward windows. This means that collaboration blocks also need to be changed, sometimes as we walk forward.
We work around these issues with the following mechanisms:
1. Component supervisor: Controls which expert components are still valid on current walk forward window. Sometimes this can be omitted for long-term relationships that will be based on long-term fixed parameters.
2. Knowledge block supervisor: Selects components to use based on component supervisor screening.
3. Collaboration block supervisor: Evaluates these trained knowledge blocks and decides which ones to use in collaboration blocks.
This new paradigm allows us to break a solution into multiple pieces that work together and can adapt to the markets as a unit.
Consider the below variables that could be used to build expert components for an S&P 500 system based on the relationship between the 30-year Treasury and S&P 500:
• Inputs for expert components
• Intermarket indicators of divergences
• Correlation between intermarkets
• Long-term trend
• Intermediate-term trend
• Predictive correlation
• Trend indicators
We would take two to three versions of these concepts and use them as inputs to kernel regression. Because we need to keep the number of inputs low, we can’t do much sampling for the model. The sampling must be done at this expert component level. This means we need to develop an input which represents what we want.
This model represents the classic intermarket relationship for the S&P 500
• If ((Close of SP500) > (Average Close of SP500)) And ((Close of T-Bonds) < (Average Close of T-Bonds)) Then Sell SP500
• If ((Close of SP500) < (Average Close of SP500) And (Close of T-Bonds) >(Average Close of T-Bonds)) Then Buy SP500
For example, we can use this logic and build a component that will output a 1 when the buy signal is generated and -1 when we get a sell. These systems are always in the market in reversal strategy.
There also would be a time element. That is, when a signal is generated opposite of the previous signals, and we have another model that has not yet reversed, we could build either one or two components that can express this divergence as well as its current mode. These components could use fuzzy logic to create a single output based on divergence, time and the mode of the current divergence.
We also could take three sets of moving averages that work well but are far enough apart that they don’t produce the same results. We then simply add the output, and produce a final intermarket divergence number. This could be used as the component. We could optimize the ones to use based on creating a system that uses these rules. We also use generic optimizing to maximize n-bar returns when combining intermarket divergences using different moving average lengths.
In addition, we need to deal with intermarket correlation, predictive correlation, trend and strength of trend because intermarket relationships work differently based on these elements. Also, we should use several different measures of trend and use a different one in each knowledge block. The same is true for how we look at correlation. We then can combine four to 10 of these components per model and train them. Our collaboration block then will create one output. One or more of these collaboration blocks can be combined into a meta-goal, our final system.
This process represents more than just a new trading technique. It’s a new way of thinking about system development. As such, it doesn’t exploit new technologies to hammer different solutions out of old ideas. It leverages the potential of those technologies by forging brand new ways of building those solutions. In future articles, we will increasingly rely on these new methods to demonstrate the positive effect of technology on trading.
Murray A. Ruggiero Jr. is a consultant. His firm, Ruggiero Associates, develops
market timing systems. He is the author of "Cybernetic Trading Strategies" (Wiley). E-mail him at firstname.lastname@example.org.