Bollinger Bands are placed over a price chart and consist of a moving average together with upper and lower bands. The earliest example of trading bands that I have been able to uncover comes from Wilfrid Ledoux in International Federation of Technical Analysts Journal: The single biggest mistake that many Bollinger Band novices make is that they sell the stock when the price touches the upper band or buy when it reaches the lower band.
This definition can aid in rigorous pattern recognition and is useful in comparing price action to the action of indicators to arrive at systematic trading decisions.
In Spring , Bollinger introduced three new indicators based on Bollinger Bands. Bandwidth tells how wide the Bollinger Bands are on a normalized basis. Writing the same symbols as before, and middleBB for the moving average, or middle Bollinger Band:. Uses for bandwidth include identification of opportunities arising from relative extremes in volatility and trend identification.
The use of Bollinger Bands varies widely among traders. Some traders buy when price touches the lower Bollinger Band and exit when price touches the moving average in the center of the bands.
Other traders buy when price breaks above the upper Bollinger Band or sell when price falls below the lower Bollinger Band. When the bands lie close together, a period of low volatility is indicated. Traders are often inclined to use Bollinger Bands with other indicators to confirm price action. In particular, the use of oscillator-like Bollinger Bands will often be coupled with a non-oscillator indicator-like chart patterns or a trendline. If these indicators confirm the recommendation of the Bollinger Bands, the trader will have greater conviction that the bands are predicting correct price action in relation to market volatility.
Various studies of the effectiveness of the Bollinger Band strategy have been performed with mixed results. In , Lento et al. The authors did, however, find that a simple reversal of the strategy "contrarian Bollinger Band" produced positive returns in a variety of markets. Similar results were found in another study, which concluded that Bollinger Band trading strategies may be effective in the Chinese marketplace, stating: A recent study examined the application of Bollinger Band trading strategies combined with the ADX for Equity Market indices with similar results.
A paper from uses Bollinger Bands to reduce variance in a Monte Carlo simulation used to forecast the Canadian treasury bill yield curve.
In , Butler et al. Their results indicated that by tuning the parameters to a particular asset for a particular market environment, the out-of-sample trading signals were improved compared to the default parameters. Companies like Forbes suggest that the use of Bollinger Bands is a simple and often an effective strategy but stop-loss orders should be used to mitigate losses from market pressure.
Security price returns have no known statistical distribution , normal or otherwise; they are known to have fat tails , compared to a normal distribution. Such techniques usually require the sample to be independent and identically distributed, which is not the case for a time series like security prices. Just the opposite is true; it is well recognized by practitioners that such price series are very commonly serially correlated [ citation needed ] —that is, each price will be closely related to its ancestor "most of the time".
Adjusting for serial correlation is the purpose of moving standard deviations , which use deviations from the moving average , but the possibility remains of high order price autocorrelation not accounted for by simple differencing from the moving average.
For such reasons, it is incorrect to assume that the long-term percentage of the data that will be observed in the future outside the Bollinger Bands range will always be constrained to a certain amount. Practitioners may also use related measures such as the Keltner channels , or the related Stoller average range channels, which base their band widths on different measures of price volatility, such as the difference between daily high and low prices, rather than on standard deviation.
Bollinger bands have been applied to manufacturing data to detect defects anomalies in patterned fabrics. The International Civil Aviation Organization is using Bollinger bands to measure the accident rate as a safety indicator to measure efficacy of global safety initiatives.
In Chester Keltner proposed a trading system, The Day Moving Average Rule, which later became Keltner bands in the hands of market technicians whose names we do not know.
Next comes the work of J. Hurst who used cycles to draw envelopes around the price structure. Hurst's work was so elegant that it became a sort of grail with many trying to replicate it, but few succeeding.
In the early '70s percentage bands became very popular, though we have no idea who created them. They were simply a moving average shifted up and down by a user-specified percent.
Percentage bands had the decided advantage of being easy to deploy by hand. Arthur Merrill suggested multiply and dividing by one plus the desired percentage. When I started using trading bands percentage bands were the most popular bands by far.
Along the way we got another fine example of envelopes, Donchian bands, which consist of the highest high and lowest low of the immediately prior n-days. Over the years there have been many variations on those ideas, some of which are still in use. Today the most popular approaches to trading bands are Donchian, Keltner, Percentage and, of course, Bollinger Bands. Percentage bands are fixed, they do not adapt to changing market conditions; Donchian bands use recent highs and lows and Keltner bands use Average True Range as adaptive mechanisms.
Bollinger Bands use standard deviation to adapt to changing market conditions and thereby hangs a tale. When I became active in the markets on a full time basis in I was mainly interested in options and technical analysis.
Information on both was hard to obtain in those days but I persisted; with the help of an early microcomputer I was able to make some progress. A touch of the upper band by price that was not confirmed by strength in the oscillator was a sell setup and a similarly unconfirmed tag of the lower band was a buy setup. The problem with that approach was that percentage bands needed to be adjusted over time to keep them germane to the price structure and the adjustment process let emotions into the analytical process.
If you were bullish, you had a natural tendency to draw the bands so they presented a bullish picture, if you were bearish the natural result was a picture with a bearish bias.
This was clearly a problem. We tried reset rules like lookbacks with some success, but what we really needed was an adaptive mechanism. I was trading options at the time and had built some volatility models in an early spreadsheet program called SuperCalc. One day I copied a volatility formula down a column of data and noticed that volatility was changing over time.
Seeing that, I wondered if volatility couldn't be used to set the width of trading bands. That idea may seem obvious now, but at the time it was a leap of faith. At that time volatility was thought to be a static quantity, a property of a security, and that if it changed at all, it did so only in a very long-term sense, over the life of a company for example. Today we know the volatility is a dynamic quantity, indeed very dynamic.