Hi! Different market conditions require different indicators. For example: trend-following indicators work well in trending times when volatility is low. According to 'Curtis Faith' book 'Way of the Turtle' you can classify a time series in two dimensions: 1. The strength of the trend 2. The volatility This gives you four market scenarios which require different indicators. 1. WEAK trend and LOW volatility 2. WEAK trend and HIGH volatility 3. STRONG trend and LOW volatility 4. STRONG trend and HIGH volatility I am trying to implement this in a system which automatically will classify the state of the time series. The idea is to look at the last 20 days and measure trend and volatility during this period and from that classify the time series. The classification then determines which indicators to use. Measure 1. I measure the strength of the trend by taking the slope of the linear regression line. The regression line is modeled after the prive values the last 20 days. After that I divide the slope value by the price of the first day of the period to get a percentual value of the trend. This also normalizes the data. Measure 2. I calculate the volatility for the past 20 days. Volatility is a already a normalized measure so I don't need to normalize the data. My question is if this is enough to be able to classify different equities from different markets? Concerns: * Indices or currencies will always be classified with a LOW volatility. * Imagine a time series with high volatility and a strong trend. Now produce a new time series with the same characteristics but with only a quarter of daily price changes. That is, if the original time series increased with 2% a particualr day, the new time series will increase with 0.5%. This new time series will have the same predictive qualities as the first one, but will be classified with a low volatility and a weak trend. Maybe I should classify by a relative volatility, that is measuring the relation between the short-time volatility and the long-time volatility? Does anybody have any suggestions of a good way to measure trend and volatility which works across different markets? With kind regards, Jossan

For a short term volatility versus long term volaility answer, how about 5 day ATR vs 50 period average of 5 day ATR? Or an alternative measure, 5 day $ Range vs 50 period average of 5 day $ Range? Or measure noise/goodness of fit to the regressed slope via R-squared?