We all have a few favorite technical indicators. However, almost all technical indicators calculate indicator values for a specific look-back period and then plot those values over time. With this approach, we can only analyze one look-back period at a time.
Of course, we could create several graphs for each indicator, each with a different look-back period. However, if we use several indicators, each with multiple periods, we would quickly be overwhelmed with data. I recently read about an innovative solution to this problem in a March 2012 Technical Analysis of Stocks & Commodities article titled "Introducing SwamiCharts," written by John Ehlers and Ric Way.
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We all use economic, fundamental, and/or technical data to gain insight into market movements. Some data may appear to lead the market, but there is often no plausible explanation for their supposed predictive abilities. This can be disconcerting for a trader. If we look at enough data over enough periods, some variables will appear to have explanatory power, but most do not hold up in actual trading.
However, the best tools come with a story. In other words, there is a simple (yet, hypothetical) explanation why the variable should have some level of predictive ability. These are my favorite types of indicators and they work well in practice, but they are hard to find. I got the inspiration for a surprisingly effective causal indicator in a bonus 2012 Technical Analysis of Stocks & Commodities article titled "10 Signs That Tell You When To Stop Trading," by Matt Blackman CMT.
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