Discussion of article "Measuring Indicator Information"
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New article Measuring Indicator Information has been published:
Machine learning has become a popular method for strategy development. Whilst there has been more emphasis on maximizing profitability and prediction accuracy , the importance of processing the data used to build predictive models has not received a lot of attention. In this article we consider using the concept of entropy to evaluate the appropriateness of indicators to be used in predictive model building as documented in the book Testing and Tuning Market Trading Systems by Timothy Masters.
As an example we examine some statistical properties of two indicators analyzed above.
The distribution of Williams's percent range reveals how almost all the values are spread across the entire range, apart from being multi modal the distribution is fairly uniform. Such a distribution is ideal and is reflected in the entropy value.
This is in contrast to the Market Facilitation Index distribution which has a long tail. Such an indicator would be problematic for most learning algorithms and requires transformation of the values. Transforming the values should lead to an improvement in indicator's relative entropy.
Author: Francis Dube