From the April 2013 issue of Futures Magazine • Subscribe!

Casting neural nets into modern markets

Predictive indicators, such as the ADX, the relative strength index and stochastics, also can be employed. More complex models also might layer in advance/decline data, put/call ratios and sentiment. Neural networks are effective in combining such seemingly unrelated indicators into a single comprehensive signal.

Another good core approach is a relative strength model. These rank percentage returns over a given period, trading the top n-ranked issues in the basket. For example, in a basket of 100 stocks, we might trade the 10 with the highest returns. These strategies are popular among individual stock and exchange-traded fund traders. Here’s how it works:

  1. Calculate momentum for each stock in the basket. This momentum needs to be normalized. For example, this is a 50-bar momentum calculation: Rawreturns=(Close-Close[50])/Close[50]. We also could filter out all issues trading below their 200-day moving average. 
  2. Sort these raw returns and select some number of the highest returning stocks. For example, if we have 100 stocks, we might take the top 10. 

We then can use the neural network to predict future returns for each of these top performers. We also can re-balance the portofolio dynamically as the top stocks change. 

Adding the net

Regardless of which core strategy you employ, there is a general framework for adding the neural network. The basic approach goes like this:

  1. Decide on a classic rule-based trading system. 
  2. Identify the weaknesses of the system.
  3. Decide how to address that weakness, such as filtering, ranking or weighting the output. 
  4. Determine a target to predict that will accomplish what is identified in step three. This is a critical point and could make or break this effort. The algorithm affects exactly how the target should be created.
  5. Check system fault tolerance. For example, if the network fails, does it destroy system performance? Test the effect of late or early predictions. Ideally, late signals should cause minor degradations in performance while early signals should significantly increase performance; this indicates the core logic is sound and that results are not caused by chance. 
  6. Pre-process the data so that it’s comparable across time periods. 
  7. Design your training and testing scheme for your model, including your retraining period and walk-forward period.
  8. Test and analyze your model. Decide if the predictive component improves performance enough and is stable enough to test in live markets. 

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