Bollinger Bands

That’s where Bollinger Bands come into play. We will soon get into how Bollinger Bands are calculated, but first you will need to understand some key .

February 15, at

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Bollinger Bands are a type of price envelope developed by John Bollinger. (Price envelopes define upper and lower price range levels.) Bollinger Bands are envelopes plotted at a standard deviation level above and below a simple moving average of the price. Because the distance of the bands is based.

By definition, prices are high at the upper band and low at 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. 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. After some experimentation I settled on the formulation we know today, an n period moving average with bands drawn above and below at intervals determined by a multiple of standard deviation We use the population calculation for standard deviation. The defaults today are the same as they were 35 years ago, 20 periods for the moving average with the bands set at plus and minus two standard deviations of the same data used for the average.

I had presented a chart showing an unconfirmed tag of my upper band and explained that the first down day would generate a sell signal. Bill then asked me what I called those lines around the price structure, a question that I was totally unprepared for, so I blurted out the alliteratively obvious choice: They are curves drawn in and around the price structure usually consisting of a moving average the middle band , an upper band, and a lower band that answer the question as to whether prices are high or low on a relative basis.

Bollinger Bands work best when the middle band is chosen to reflect the intermediate-term trend, so that trend information is combined with relative price level data. For many years that was the state of the art: Here are a couple of practical examples of the usage of Bollinger Bands and the classic Bollinger Band tools along with a volume indicator, Intraday Intensity:.

Click chart to enlarge. On 20 July prices tagged the upper Bollinger Band while day Intraday Intensity was deep in negative territory setting up a sell alert. The first down day was the sell signal and entry. The red triangle is a negative PowerShift. We have come a long way since the bands were developed. Today we have a suite of Bollinger Bands tools with at least one tool in every major technical analysis indicator category.

These tools form a logically consistent analytical framework and are in our Bollinger Bands Tool Kits for a wide variety of platforms: And with more on the way; wherever you are, whatever you trade, Bollinger Bands and the related tools will be there for you. Read the Rules for Trading with Bollinger Bands. Learn More about Bollinger Bands.