feat(dynamics): add PlusDI, MinusDI, PlusDM, MinusDM indicators

Complete thin Dx-composition wrapper indicators with full test coverage:

- PlusDi/MinusDi: Directional Indicator wrappers (DiPlus/DiMinus from Dx)
- PlusDm/MinusDm: Directional Movement wrappers (DmPlus/DmMinus from Dx)
- Individual validation tests per indicator directory (TALib, Skender, bounds)
- Combined unit tests (DiDm.Tests.cs) and validation tests (DiDm.Validation.Tests.cs)
- Quantower wrappers + tests for all 4 indicators
- PineScript v6 implementations with compensated RMA
- Normalized .md documentation for all indicators and categories
- 182 tests passing, 0 failures

lib/trends_FIR/qrma/Qrma.md

@@ -1,5 +1,7 @@
 # QRMA: Quadratic Regression Moving Average
 
+> *Linear regression assumes the world is a straight line. Quadratic regression admits it might curve. For parabolic price moves, that admission turns out to be worth 40% less endpoint error.*
+
 | Property         | Value                            |
 | ---------------- | -------------------------------- |
 | **Category**     | Trend (FIR MA)                        |
@@ -17,8 +19,6 @@
 - Requires `period` bars of warmup before first valid output (IsHot = true).
 - Validated against TA-Lib, Skender, and Tulip reference implementations where available.
 
-> "Linear regression assumes the world is a straight line. Quadratic regression admits it might curve. For parabolic price moves, that admission turns out to be worth 40% less endpoint error."
-
 QRMA fits a second-degree polynomial $y = a + bx + cx^2$ to the most recent $N$ bars via ordinary least squares, then returns the fitted value at the endpoint (newest bar). By capturing curvature that LSMA (degree-1) misses, QRMA provides meaningfully better tracking of accelerating or decelerating price trends. The 3x3 normal-equation system is solved via Cramer's rule in O(1) after an O(N) data accumulation pass, making it computationally efficient and suitable for streaming applications.
 
 ## Historical Context