Concept
Kernel Density Estimation
Kernel Density Estimation, also known as KDE, is a Machine Learning concept. The Library holds 2 implementations, each one a working definition you can pull into Quant.
Top Kernel Density Estimation indicators
2 total
What is Kernel Density Estimation?
Kernel density estimation (KDE) is a nonparametric way to estimate a probability distribution from data without assuming its shape. Each observation contributes a small kernel, commonly a Gaussian bump, centered on its value; summing the bumps and normalizing yields a smooth density curve. A single bandwidth parameter sets how wide each bump is, and therefore how smooth the estimate is: too narrow and the density chases noise, too wide and genuinely distinct peaks merge into one.
On charts, KDE is most often applied to traded prices or volume at price, producing a smooth alternative to the binned histogram of a volume profile. Peaks in the density, its modes, mark prices where activity concentrated and play the same role as high-volume nodes and the point of control; low-density valleys mark prices the market moved through quickly. The same tool is applied to returns to profile their distribution, where skew and fat tails show up directly instead of being assumed away.
How traders use it
- As a smoothed profile: density peaks locate heavily traded zones and valleys locate low-volume nodes, without the jagged edges and bin-size sensitivity of a histogram.
- As level generation: density modes are promoted to support and resistance candidates, with the prominence of each peak used as a rough strength score.
- As distribution profiling: applied to returns, the estimated density reveals skew and fat tails that a normality assumption would hide.
Related concepts · Learned models
Concept family
Machine Learning
32 concepts mapped · 21 in the Library
Kernel Density Estimation FAQ
How is KDE different from a volume profile histogram?
A histogram splits price into fixed bins, so its shape depends on bin width and where the bin edges fall, and it renders as steps. KDE replaces bins with overlapping kernels centered on each observation, producing one smooth curve free of bin-edge artifacts. Bandwidth takes over the role of bin width: it is the single control that decides how much detail survives smoothing.
What bandwidth should be used for kernel density estimation?
There is no universally correct value. Statistical rules of thumb such as Silverman's rule scale bandwidth to the data's spread and sample size, and charting tools usually expose it as a smoothing input. The practical test is stability: a useful bandwidth keeps the same major peaks when the window shifts slightly, while zones that merge or split with every new bar signal a poor setting.
Build Kernel Density Estimation your way.
Quant writes, tests, and refines it with you — then it runs on LuxAlgo charting or ports to TradingView.