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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.

BUT FIRST, THE BACKGROUND

Exponential moving averages give greater weight to more recent data. An exponential moving average may be defined as:

(1) Ed = t ? Cd + (1-t) ? Ed-1
where
Ed is the new value of the moving average
Ed-1 is the previous value
Cd is the new data value
t is the smoothing constant of the average

The smoothing constant t of the average may be referred to as the smoothing index of the moving average, so it no longer has to be visualized as a numerical constant. Implicitly, t must be less than 1 for the term (1-t) to be positive. As the smoothing index t increases, the new value has a greater proportion of the most recent data and the exponential average moves more rapidly. Conversely, as t decreases, more weight is given to the previous value, giving a heavily smoothed average that changes quite slowly. Thus, a larger value of t makes the exponential moving average more sensitive to new data; a smaller index t makes it less sensitive.