Adapting Moving Averages To Market Volatility
by Tushar S. Chande, Ph.D.
If a market is active, it has volatility: that cannot be avoided. And because the market is continuously
changing, an indicator that attempts to predict market activity must itself adapt and change. How?
Tushar Chande presents a dynamic—not static—indicators: a variable-length moving average, which
adapts to the volatility in question by exponentially smoothing data based on standard deviation.
Technicians can be trend followers or contrarians. Trend followers use price-based indicators, such as
moving averages, while contrarians prefer oscillators such as overbought-oversold indicators. But the
market never does quite the same thing twice, and so no indicator works all the time. The market is
dynamic, adjusting rapidly to information: a continuous tug of war between greed and fear, fact and
fiction. Technical indicators, on the other hand, are static, mechanically applying the same formula to the
relevant data. What is needed is a combination, dynamic indicators that will automatically adapt to the
changing nature of markets, a new class of dynamic indicators that combine exponential moving averages
with other technical indicators to adapt automatically to changing price behavior. What is needed is an
exponential moving average with a continuously variable smoothing index that adjusts rapidly to changes
in price behavior. The smoothing index can be tied to any market variable. It is the continuous, not
discrete, changes in the smoothing index that increases the sensitivity of these moving averages to
changes in price behavior. These new dynamic exponential averages can be referred to a variable index
dynamic average (VIDYA).
Let us first examine exponential moving averages and how they can be modified to obtain VIDYA, and in
turn compare VIDYA with conventional indicators to illustrate its dynamism. Then we will combine
dynamic averages to derive other indicators and then illustrate their effectiveness.