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# NW: Nadaraya-Watson Kernel Regression
# NW: Nadaraya-Watson Kernel Regression
| Property | Value |
| ---------------- | -------------------------------- |
| **Category** | Filter |
| **Inputs** | Source (close) |
| **Parameters** | `period` (default 64), `bandwidth` (default 8.0) |
| **Outputs** | Single series (Nw) |
| **Output range** | Tracks input |
| **Warmup** | `period` bars |
### TL;DR
- NW computes the Nadaraya-Watson kernel regression estimator with a Gaussian kernel, producing a nonparametric smooth of the price series.
- Parameterized by `period` (default 64), `bandwidth` (default 8.0).
- Output range: Tracks input.
- Requires `period` bars of warmup before first valid output (IsHot = true).
- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
> "Nadaraya and Watson independently discovered the same thing in 1964: weight each observation by how close it is, normalize, and average. Fifty years later, it became one of the most popular nonparametric smoothers on TradingView. The math did not change; only our ability to compute it in real time."