What is it:
- A way that investors visualize and interpret open, high, low, and close (ohlc) price data and identify patterns.
- There are a number of different patterns that can occur individually or in a short sequence that are expected to presage a large up or down moves.
Flaws:
There are a number of different flaws the most obvious is the lack of proper definitions. Candlestick patterns are often defined with names like doji and subtypes of neutral, long-legged, gravestone, and dragonfly. In researching these name, I found they are defined with pictures... meaning you'll know it when you see it.
But that's the problem, when is a pattern more like one than another?
Will I really know it when I see it?
If I need to find these patterns by visual search, I have no real way to determine the effecacy of invest with this method, because these 'signals' really become a matter of how well an investor interprets market patterns, which may be more than just these chart patterns.
In order to test hypotheses using candlestick patterns concrete definitions for those patterns are needed.
This is an unsupervised learning problem.
We don't actually know what patterns exist in the ohlc data. Patterns like dojis may have been created looking for big moves, then trying to find similarities in the patterns that preceded it. However, whether we have an outcome from the pattern or not, we need to find similar patterns in ohlc data.
There are a number of different algorithms that can find similarities mostly by calculating the distance between vectors and the average of each cluster. Depending on the algorithm, the user may need to supply the number of clusters and perhaps use the elbow method to determine that number accurately.
In my own calculations, I discovered more than 50 different candlestick patterns by calculating a number of different market measures and normalizing the changes in stock prices.