Market Profile and Support/Resistance Levels With Python

In this video, I'll show a datadriven technique to find support and resistance levels implemented with Python. This technique extracts support and resistance levels from the market profile. The visualization you're seeing now shows the closing price on the left and the current estimation of the market profile on the right. The market profile is a charting technique to show where

price spends the most time. The market profile is calculated using the last 168 closing prices. This is hourly data, so 168 is one week of data. The significant peaks in the market profile are marked with red dots and the lengths of the orange lines beside the peaks are the prominences. We'll go over what the

market profile is and what peak prominence is in the video. The motivation of this technique is based around the idea that support and resistance are levels in price where price is slowed or stopped altogether. With this definition, if we find levels where the price tends to spend more time, then the price is slowed at those

levels or revisits those levels often and thus those levels are support and resistance. As an example, let's look at hourly Bitcoin tether data in 2022. Here is a histogram of the prices over the whole year. We can see that a chunk of closing prices happened here at 10.3. And on the price graph, we see that

price spent a chunk of time sitting at this level. This histogram is the market profile, but a histogram is not the best way to represent how price was distributed through time. A histogram groups prices into a number of bins, and the representation is blocky. It's hard to pick out exactly where the peaks are.