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/filters/rls/Rls.md

@@ -1,5 +1,7 @@
 # RLS: Recursive Least Squares Adaptive Filter
 
+> *The man who has no patience has no wisdom.*
+
 | Property         | Value                            |
 | ---------------- | -------------------------------- |
 | **Category**     | Filter                        |
@@ -18,8 +20,6 @@
 - Requires `order + 1` bars of warmup before first valid output (IsHot = true).
 - Validated against TA-Lib, Skender, and Tulip reference implementations where available.
 
-> "The man who has no patience has no wisdom." — but waiting is not the same as convergence. RLS converges where LMS merely approaches.
-
 ## Introduction
 
 The Recursive Least Squares (RLS) adaptive filter is the Rolls-Royce of adaptive FIR filters. Where LMS crawls toward the Wiener solution one gradient step at a time, RLS arrives in approximately *order* iterations by maintaining an inverse correlation matrix $P$ that captures the full second-order statistics of the input signal. The trade-off is computational: $O(n^2)$ per bar versus LMS's $O(n)$, where $n$ is the filter order. For orders below 64, the convergence advantage typically outweighs the cost.