doc headers

lib/trends_FIR/wma/Wma.md

@@ -1,5 +1,22 @@
 # WMA: Weighted Moving Average
 
+| Property         | Value                            |
+| ---------------- | -------------------------------- |
+| **Category**     | Trend (FIR MA)                        |
+| **Inputs**       | Source (close)                          |
+| **Parameters**   | `period`                      |
+| **Outputs**      | Single series (Wma)                       |
+| **Output range** | Tracks input                     |
+| **Warmup**       | `period` bars                          |
+
+### TL;DR
+
+- The Weighted Moving Average (WMA) assigns a linearly decreasing weight to data points.
+- 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.
+
 > "Because yesterday matters more than last Tuesday. WMA is the linear answer to the question: 'What have you done for me lately?'"
 
 The Weighted Moving Average (WMA) assigns a linearly decreasing weight to data points. The most recent price gets weight $N$, the one before it $N-1$, down to 1. This makes it more responsive to recent price changes than an SMA, but without the infinite tail of an EMA.
@@ -237,4 +254,4 @@ For WMA(200), total memory is approximately 1.75 KB per instance.
 
 1. **Drift**: Like SMA, the O(1) algorithm is susceptible to floating-point drift. QuanTAlib resets the sums every 10,000 ticks to guarantee accuracy.
 2. **Aggressiveness**: WMA reacts faster than SMA but can be "twitchy." It is often used as a component in other indicators (e.g., HMA) rather than a standalone trend filter.
-3. **Weights**: Users sometimes confuse WMA (linear weights) with EMA (exponential weights) or VWAP (volume weights).
+3. **Weights**: Users sometimes confuse WMA (linear weights) with EMA (exponential weights) or VWAP (volume weights).