|  APR 1993

Oscillator Variations by Barbara Star

Oscillator Variations by Barbara Star Here, Barbara Star takes a 1985 STOCKS & COMMODITIES article by John Navarte called ""Reading between the lines"" and uses it as the basis for a tutorial on oscillators for trading. Oscillators are handy tools for traders. An oscillator plots the difference between two sets of data. Popular methods include the difference between the current closing price and a moving average, the current closing price and a closing price n periods ago or the difference between two moving averages. Traders use oscillators to identify changes in market momentum, temporary price extremes within trends and changes in market trends. Some traders smooth the oscillators with a moving average to identify changes in the direction of the oscillator. In ""Reading between the lines,"" John Navarte examined methods for calculating and interpreting oscillators. His first concept was price excess. PRICE EXCESS The price excess indicator shows when a tradable (that is, stocks, commodities and so forth) is too high (overbought) or too low (oversold) relative to historical extremes by comparing the price fluctuation to the fluctuation of its moving average. The price excess oscillator compares the difference between today's close and the security's 24-day moving average. This indicator can be closely approximated without a custom formula (see sidebar, ""MetaStock custom formula"") by selecting the price oscillator indicator offered on many charting packages and choosing the value ""1"" as the shorter-term moving average and ""24"" as the longer-term moving average, both as simple moving averages. Navarte suggested plotting a 12-unit simple moving average on the indicator to better see when the oscillator would turn. Buy and sell signals would be generated when the oscillator crossed the moving average. Figure 1 illustrates the price excess oscillator with a daily chart of the New York Stock Exchange (NYSE) Composite. In addition, the oscillator works well with weekly data.

by Barbara Star, Ph.D.