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/channels/ubands/ubands.md

@@ -1,5 +1,7 @@
 # UBANDS: Ehlers Ultimate Bands
 
+> *Ehlers' ultimate bands apply cycle-aware smoothing to define an envelope that resonates with dominant frequency.*
+
 | Property         | Value                            |
 | ---------------- | -------------------------------- |
 | **Category**     | Channel                        |
@@ -107,47 +109,6 @@ $$
 
 Cutoff frequency: approximately $f_c \approx 1/(2\pi n)$ cycles per bar. Rolloff: 12 dB/octave.
 
-### Pseudo-code
-
-```
-function ubands(source[], period, multiplier):
-    // precompute USF coefficients
-    arg = sqrt(2) * pi / period
-    c2 = 2 * exp(-arg) * cos(arg)
-    c3 = -exp(-2 * arg)
-    c1 = (1 + c2 - c3) / 4
-
-    usf_prev1 = NaN, usf_prev2 = NaN
-
-    for each bar t:
-        s0 = source[t]
-        s1 = source[t-1]  // or s0 if unavailable
-        s2 = source[t-2]  // or s1 if unavailable
-
-        if usf not initialized:
-            usf = s0
-        else:
-            usf = (1 - c1)*s0 + (2*c1 - c2)*s1
-                  - (c1 + c3)*s2 + c2*usf_prev1 + c3*usf_prev2
-
-        usf_prev2 = usf_prev1
-        usf_prev1 = usf
-
-        // RMS of residuals over window
-        sum_sq = 0, count = 0
-        for i = 0 to period-1:
-            r = source[t-i] - usf_at[t-i]  // residual at bar t-i
-            if r is valid:
-                sum_sq += r * r
-                count += 1
-
-        rms = count > 0 ? sqrt(sum_sq / count) : 0
-        upper = usf + multiplier * rms
-        lower = usf - multiplier * rms
-
-        emit (upper, usf, lower)
-```
-
 ### RMS vs Standard Deviation
 
 Standard deviation measures dispersion around the mean: $\sigma = \sqrt{E[(X - \mu)^2]}$. RMS measures dispersion around zero: $\text{RMS} = \sqrt{E[X^2]}$. Since the residuals $r_t = P_t - \text{USF}_t$ are already deviations from the smooth centerline, RMS is the correct measure. When the mean of residuals is zero (as it approximately is for a well-fitted filter), RMS equals standard deviation.