Adjusting for trading instrument volatility in technical indicators

In Chapter 2, Deciphering the Markets with Technical Analysis, we looked at generating trading signals with predetermined parameters. What we mean by that is we decided beforehand to use, say, 20 days moving average, or the number of time periods to use, or the smoothing constants to use, and these remained constant throughout the entire period of our analysis. These signals have the benefit of being simple, but suffer from the disadvantage of performing differently as the volatility of the trading instrument changed over the course of time.

Then we also looked at signals such as Bollinger Bands and standard deviation, which adjusted for trading instrument volatility, that is, during non-volatile periods, the lower standard deviation in price movements would make the signals more aggressive to entering positions and less aggressive when closing positions. Conversely, during volatile periods, the higher standard deviation in price movements makes the signals less aggressive to entering positions. This is because the bands that depend on standard deviation widen out from the moving average, which in itself has become more volatile. Thus, these signals implicitly had some aspects of adjusting for trading instrument volatility baked right into them.

In general, it is possible to take any of the technical indicators we have seen so far and combine a standard deviation signal with it to have a more sophisticated form of the basic technical indicator that has dynamic values for number of days, or number of time periods or smoothing factors. The parameters become dynamic by depending on the standard deviation as a volatility measure. Thus, moving averages can have a smaller history or number of time periods when volatility is high to capture more observations, and a larger history or number of time periods when volatility is low to capture fewer observations. Similarly, smoothing factors can be made higher or lower in magnitude depending on volatility. In essence, that controls how much weight is assigned to newer observations as compared to older ones. We won't go into any more detail here, but it is easy to apply these concepts to technical indicators once the basic idea of applying volatility measures to simple indicators to form complex indicators is clear.

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