The Bollinger Bands Width Indicator.
Coding & Presenting the Bollinger Bands Width Indicator in Python.
We can transform Bollinger bands so that we measure their width, thus helping us understanding whether Bollinger bands-based strategies will work better or not. This article presents the details of the Bollinger bands width indicator.
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Trend Following Strategies in Python: How to Use Indicators to Follow the Trend.
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What are the Bollinger bands? When prices move, we can calculate a moving average (mean) around them so that we better understand their position regarding their mean. By doing this we can also calculate where do they stand statistically. But first we need to understand the concept of the normal distribution curve which is the essence of statistics and the foundation of statistical theory.
The above curve shows the number of values within a number of standard deviations. For example, the area shaded in red represents around 1.33x of standard deviations away from the mean of zero. We know that if data is normally distributed then:
- About 68% of the data falls within 1 standard deviation of the mean.
- About 95% of the data falls within 2 standard deviations of the mean.
- About 99% of the data falls within 3 standard deviations of the mean.