RSI Overbought/Oversold Lines

The RSI (Relative Strength Index) Enables accurate Comparisons of different Securities on the same chart. The longer the time span, the narrower the RSI (Relative Strength Index) overbought and oversold lines should be constructed and vice versa.

The default time span for the calculation of an RSI (Relative Strength Index) is 14 periods. The overbought and oversold lines are typically drawn at 70 and 30, respectively. This research would indicate that the 70 and 30 levels recommended by Wilder should be moved farther apart to better reflect the average overbought and oversold values.

It is important to note that the magnitude of the oscillations of the RSI (Relative Strength Index) is inverse to that of most other momentum series. For example, the ROC indicator is subject to wider fluctuations the longer the time span. It works in an opposite way for the RSI (Relative Strength Index). For the RSI (Relative Strength Index), equilibrium is the halfway point, which in this case is the 50 level. It is therefore traditional to place the overbought and oversold lines equidistant from this point.

We should remember that longer time spans in the RSI (Relative Strength Index) calculation result in shallower swings and vice versa. Consequently, the 70/30 combination is inappropriate when the time span differs appreciably in either direction from the standard 14-day period.

The terms long and short time spans refer to the type of data under consideration in a relative sense. For example, a 60-day RSI (Relative Strength Index) would represent a long span for daily data, but for monthly numbers, a 60-day (2-month) span would be very short. Some consideration should therefore be given to this factor when the choice of a specific RSI (Relative Strength Index) time span is being made.

Because RSI (Relative Strength Index) based on shorter-term time spans experience greater volatility, they are more suitable for pointing out overbought and oversold conditions. On the other hand, longer-term spans are more stable in their trajectories and therefore lend themselves better to the purpose of constructing trendlines and price patterns.