What Happens if We Increase or Decrease the Bin Size of Market Profiles (TPO & Volume)?
This applies specifically to the standard or free-tier market profiles available on most charting platforms. Market profiles are typically built on either the Time Price Opportunity (TPO) profile or the Volume Profile (VP), whether real or tick-based. Regardless of the type, the underlying calculation is the same: raw data is cleaned through dataframes (xlsx, csv, etc.) and represented through graphs (lines, bars, plots, etc.). A market profile is simply datapoints collapsed into a y-axis distribution, forming a "profile." That is it -- nothing more.
But what actually happens when we increase or decrease the bin size (the price-step) of the market profile?
Collapsing Price Action into a Profile
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In the figure above, we have one MP chart (left) and one line chart (right), both derived from the same dataset. The price action over time (line chart) moves from point A to B, C, and D. When we collapse those datapoints (A through D) into a y-axis distribution histogram, a Market Profile chart is formed.
If we use a bin size of 1.000 and the price range spans 3000.000 to 3010.000, then between those prices we get 10 bins worth of grouping. Same data, different representation.
Larger Datasets and Stacking
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With larger datasets, the principle remains the same. Datapoints collapse and stack to form a y-distribution. The more stacking occurs, the larger the profile becomes. In this example, the lowest profile value is 0 and the largest profile value is 4 stacks.
The Effect of Changing Bin Size
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The figure above uses the same dataset as the previous one, yet the profile looks different. It now has a lowest value of 0 and a largest value of only 2 stacks. If you are a beginner, this might feel suspicious when experimenting on your preferred charting platform -- but it is completely normal.
The reason is straightforward: more bins means more price groups for the datapoints to distribute across. As bin size decreases (more granular bins), each datapoint lands in a more specific price bucket. This spreads the data across more bins, resulting in shorter stacks and a flatter profile. Conversely, increasing the bin size consolidates datapoints into fewer groups, producing taller stacks and a more concentrated profile.
That is the kind of market profile you typically get on free-tier charting and trading platforms.
An Alternative Approach: Trail-Price Clustering
There is another way to model a market profile. Without going into full detail -- if you are an algorithmic trader or software developer familiar with feature engineering, this will be straightforward. The core idea is to add data to your original dataframes by clustering trail-prices (an original concept) to produce a more complete set of datapoints.
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This concept extends well beyond these illustrations. You can fill in missing data in dataframes (for any dataset) by applying a custom formula using your preferred programming language.
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The drawings may be rough, but the point stands. In our case, we model a market profile not based on TOCHL (Time, Open, Close, High, Low) or Volume (real, tick) but on mBA (microsecond raw bid/ask) formation.
Reference
@misc{continualquasars2026blog1,
title = {What Happens if We Increase or Decrease the Bin Size of Market Profiles (TPO \& Volume)?},
author = {ContinualQuasars},
year = {2026},
url = {https://github.com/ContinualQuasars/BLOG-1},
note = {What Happens if We Increase or Decrease the Bin Size of Market Profiles (TPO \& Volume)?}
}