Reversal Chart Patterns: Double Tops and Bottoms

In the Chart Patterns Overview, we discussed that reversal chart patterns signal the ending on an ongoing trend, i.e., they signify a reversal of asset’s price direction.

In this article, we will discuss the two popular reversal patterns, namely, Double Top and Double Bottom

Double Tops: The Double Top pattern is formed when an uptrend reverses twice in a very short interval, at almost the same price level. The pattern is depicted below:

Some of the characteristics of double top are:

  • The price reversal happens at almost the same price high.
  • The volume on the second high is lower compared to the first high. This signifies a decrease in demand.
  • The distance between the two tops and the depth of the valley signify the strength of the formation.
  • At the end of the formation, the price is expected to fall below the neckline by atleast the difference between the valley low to the high of the tops. For example, suppose that for ABC stock, the first top is $90.1, the valley low is at $88, and the second top is at $90.2, the price will fall to atleast $88 – ($90.1 - $88) = $86.1.

Double Bottoms: The double bottom pattern is formed during a downtrend, when the price rebounds twice, and reaches back the same level. The pattern is depicted below:

The double bottoms are exactly the same as the double tops, except that they are observed in a downtrend, and traders use these to predict changes from downturns to uptrends in security prices.

At the end of the formation the price is expected to rise above he neckline. When a trader observes a double bottom, he can place a long order at the top of the formation's neckline. The target can be set as high as the distance from the bottoms to the neckline.

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Data Science in Finance: 9-Book Bundle

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Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
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  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

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