Making of the Bands: Tips on Using Bollinger Bands Effectively as Technical Analysis

Bollinger Bands

The man who invented Bollinger Bands, John Bollinger himself, defines them as a straightforward tool for technical analysis, being basically a kind of trading envelope or band. Bollinger Bands make clever use of the statistical quantity referred to as standard deviation in order to accurately determine where certain points of interest known as resistance and support levels might lie on a trading chart.

The underlying knowledge base upon which this tool works is widely known as the volatility channel, which basically plots the lines above as well as below the central price measure. The band or envelope between these two parameters will contract or widen up according to the degree of market volatility. This is the absolutely basic background knowledge anyone seeking to make use of the Bollinger Band Strategy for their trading activity needs to have.

Let’s get into Bollinger’s playbook to see some of the basic rules and tips he has for us as we strive to put his invention to good, efficient, and profitable use.

  • Bollinger Bands are designed to provide an approximation of the highs and lows. The default designation is that high will be ranging towards the upper band while low will be closer to the lower band.
  • The relative definition above might be effectively used to arrive at a price action comparison as well as an indicator action in order to methodically arrive at buy or sell decisions.
  • You can derive useful indicators from quantities such as volume, open interest, sentiment, inter-market data, and so forth.
  • Should you have more than one indicator operational, ensure that they are not directly interacting. To illustrate this, a volume indicator might be able to complement a momentum indicator quite successfully, but two volume indicators will not give you anything more useful than if you just used one.
  • You can successfully use Bollinger Bands in the recognition of patterns in cases where you want to identify/clarify/define price patterns including momentum shifts, M’ tops, W’ bottoms, and more.
  • Do not confuse band tags with signals, as the upper Bollinger Bands is not a sell signal in-and-of-itself. Remember this well, as it may have a huge impact on your Bollinger Bands Strategy. The inverse is true as well, as the bottom Bollinger Band is not to be taken as a buy signal.
  • Within trending markets, prices may walk all the way up to the upper Band and travel down to the lower one.
  • When closes are outside the band limits, they should be taken as continuation signals rather than reversal signals, especially at first.
  • The established default parameters (twenty periods ascribed to the moving average as well as standard deviation calculation and 2 widths for the bands) shouldn’t be taken for more than that – defaults. You are at liberty to change these figures depending on the market you’re working in or the problem you’re trying to figure out.
  • The average that’s used as the middle Bollinger Band shouldn’t be the most convenient one for crossovers. It should, instead, be representative of the intermediate-term pattern.
  • To achieve consistent price containment follow this guide: lengthening the average calls for you to increase the standard deviation number (from 2 at twenty periods, to 2.1 at fifty periods). The reverse holds true as well.
  • Traditionally applied Bollinger Bands are founded upon simple moving averages. The reason for this is the fact that simple averages are used in the calculation of the standard deviation and so changing this metric would throw the logical consistency off.
  • Exponential Bands do away with the need for precipitate changes in the band width occasioned by large-scale changes that exit though the rear of the window of calculation. These averages need to be used BOTH in the middle band as well as the standard deviation calculation.
  • No statistical assumptions should be made founded on the standard deviation calculations used while setting up bands. The reason for this is that security price distributions are non-normal. In addition, the usual sample size in most Bollinger Band applications is too miniscule for use in statistical applications.

In Conclusion

This is by no means a comprehensive outline of what you can do with Bollinger Bands and how to do it, but a general starter’s guide. There is, of course, plenty of room for us to bend the rules to suit our own purposes and objectives, but this should serve as a handy guide and general resource, especially for those still getting used to working with the tool.