Constructing a best fit line

# Methods for plotting trend lines

On the other hand, people are using these lines so there should also be some kind of self-fulfilling prophecy component with them. However, the secret ingredient is called Hough Transform.

## How to Draw Trend Lines Perfectly Every Time [ Update]

Methods for plotting trend lines idea of the Hough transformation is, that every edge point in the edge map is transformed into all possible lines that could pass through that point. The areas where most Hough space lines intersect is interpreted as true lines.

• Where you can make money quickly 500
• Additional Resources Trend Lines As technical analysis is built on the assumption that prices trend, the use of trend lines is important for both trend identification and confirmation.

It is not that complicated as it sounds. You need to know that Hough transformation is mostly used in image processing i.

Updated Nov 16, What Is a Trendline? Trendlines are easily recognizable lines that traders draw on charts to connect a series of prices together or show some data's best fit. The resulting line is then used to give the trader a good idea of the direction in which an investment's value might move. A trendline is a line drawn over pivot highs or under pivot lows to show the prevailing direction of price. Trendlines are a visual representation of support and resistance in any time frame.

Therefore you need to pre-process images using an edge detector. Well, of course, we use a discrete amount of lines where the number is somewhat arbitrary.

You can obviously find lots of tutorials and explanations of the Hough transformation on the internet if you want to dive deeper. Or you just take a look at this guy who tries to explain it for us.

• Defining trend line
• This article includes a list of general referencesbut it remains largely unverified because it lacks sufficient corresponding inline citations.

Implement a Hough Transform for a stock price chart The fully functional code and a Jupyter notebook can be found as part of a bigger library I am working on in my GitHub repository [2]. And just for visibility we only look at the most recent days.

Use the standard approach and rescale all prices to fall between 0 and 1 and round these numbers just to remove a bit of noise. But since we have stock prices and not an image we use a slightly different approach.

For the x-axis, we introduce a linear space from 0 to 1 and for the y-axis, we use the rescaled prices.

For our use case of finding trend lines we need to detect turning points. There we calculate the mean and check if the first and the latest data points are both above or below the mean.