How to Read Trading Chart on The Forex MetaTrader and Knowledge of the Bollinger Bands Indicator (2)
79Bollinger Bands are a type of statistical chart characterizing the prices and volatility over time of a financial instrument or commodity, using a formulaic method propounded by John Bollinger in the 1980s. Financial traders employ these charts as a methodical tool to inform trading decisions, control automated trading systems, or as a component of technical analysis. Bollinger Bands display a graphical band (the envelope maximum and minimum of moving averages, similar to Keltner or Donchian channels) and volatility (expressed by the width of the envelope) in one two-dimensional chart.
Two input parameters chosen independently by the user govern how a given chart summarizes the known historical price data, allowing the user to vary the response of the chart to the magnitude and frequency of price changes, similar to parametric formulas in signal processing or control systems. Bollinger Bands consist of an N-period moving average (MA), an upper band at K times an N-period standard deviation above the moving average (MA + Kσ), and a lower band at K times an N-period standard deviation below the moving average (MA − Kσ). The chart thus expresses arbitrary choices or assumptions of the user, and is not strictly about the price data alone.
Typical values for N and K are 20 and 2, respectively. The default choice for the average is a simple moving average, but other types of averages can be employed as needed. Exponential moving averages are a common second choice. Usually the same period is used for both the middle band and the calculation of standard deviation.
Bollinger registered the words "Bollinger Bands" as a U.S. trademark in 2011.
The purpose of Bollinger Bands is to provide a relative definition of high and low prices of a market. 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 2010, Bollinger introduced three new indicators based on Bollinger Bands. BBImpulse measures price change as a function of the bands; percent bandwidth (%b) normalizes the width of the bands over time; and bandwidth delta quantifies the changing width of the bands.
%b (pronounced "percent b") is derived from the formula for stochastics and shows where price is in relation to the bands. %b equals 1 at the upper band and 0 at the lower band. Writing upperBB for the upper Bollinger Band, lowerBB for the lower Bollinger Band, and last for the last (price) value:
%b = (last − lowerBB) / (upperBB − lowerBB)
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:
Bandwidth = (upperBB − lowerBB) / middleBB
Using the default parameters of a 20-period look back and plus/minus two standard deviations, bandwidth is equal to four times the 20-period coefficient of variation.
Uses for %b include system building and pattern recognition. Uses for bandwidth include identification of opportunities arising from relative extremes in volatility and trend identification.
Various studies of the effectiveness of the Bollinger Band strategy have been performed with mixed results. In 2007, Lento et al. published an analysis using a variety of formats (different moving average timescales, and standard deviation ranges) and markets (e.g., Dow Jones and Forex). Analysis of the trades, spanning a decade from 1995 onwards, found no evidence of consistent performance over the standard "buy and hold" approach. The authors did, however, find that a simple reversal of the strategy ("contrarian Bollinger Band") produced positive returns in a variety of markets.
A paper from 2005 uses Bollinger Bands to reduce variance in a Monte Carlo simulation used to forecast the Canadian treasury bill yield curve.
In 2012, Butler et al. published an approach to fitting the parameters of Bollinger Bands using particle swarm optimization method. 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.
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