doc headers

lib/trends_FIR/pwma/Pwma.md

@@ -1,5 +1,22 @@
 # PWMA: Parabolic Weighted Moving Average
 
+| Property         | Value                            |
+| ---------------- | -------------------------------- |
+| **Category**     | Trend (FIR MA)                        |
+| **Inputs**       | Source (close)                          |
+| **Parameters**   | `period`                      |
+| **Outputs**      | Single series (Pwma)                       |
+| **Output range** | Tracks input                     |
+| **Warmup**       | `period` bars                          |
+
+### TL;DR
+
+- PWMA (Parabolic Weighted Moving Average) applies a parabolic ($i^2$) weighting scheme to the data window.
+- Parameterized by `period`.
+- 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.
+
 > "Linear weighting is for people who think the world is flat. PWMA squares the weights, because recent data isn't just more important—it's exponentially more important."
 
 PWMA (Parabolic Weighted Moving Average) applies a parabolic ($i^2$) weighting scheme to the data window. This assigns massive importance to the most recent data points while still technically including the older data. It's like a WMA on steroids.
@@ -171,4 +188,4 @@ if (_state.TickCount >= 1000)
 ### Common Pitfalls
 
 1. **Resync**: Because triple running sums are used, floating-point errors can accumulate faster than in a simple SMA. The implementation automatically resyncs every 1000 ticks to maintain precision.
-2. **Sensitivity**: This indicator is very sensitive to the most recent bar. It can "repaint" visually if used on an open bar (though the math is consistent).
+2. **Sensitivity**: This indicator is very sensitive to the most recent bar. It can "repaint" visually if used on an open bar (though the math is consistent).