F13c: restructure the indicator catalogue into eight families

The original taxonomy was four classical families plus a statistics group,
with the F1-F12 expansion slotted in as sub-categories. This regroups the
whole 71-indicator catalogue into eight top-level families, each with at
least five members:

  Moving Averages (12), Momentum Oscillators (13), Trend & Directional (9),
  Price Oscillators (5), Volatility & Bands (12), Trailing Stops (5),
  Volume (9), Price Statistics (7).

- Wiki: docs/wiki/indicators/ reorganised into eight family folders; all 71
  indicator pages moved with `git mv`. Every internal cross-link is
  normalised to `../<family>/Indicator-X.md`, each page's `Family` field is
  set to its new family, and two pre-existing `../Indicator-Chaining.md`
  links (should have been `../../`) are corrected. A link check confirms
  every relative wiki link resolves.
- Indicators-Overview.md fully rewritten around the eight families;
  Home.md indicator reference and the README family table follow suit.
- Warmup-Periods.md gains the eight F13 indicators; CHANGELOG records the
  46-indicator expansion (25 -> 71) and the eight-family taxonomy.
- Tests: Node indicators.test.js and Python test_new_indicators.py cover
  all eight new indicators (Node 91/91, Python 117/117 green).

cargo fmt + clippy (core/wickra/data/wasm/node) clean; 508 core tests,
25 data tests and 74 doctests green.

bindings/python/tests/test_new_indicators.py

@@ -60,6 +60,9 @@ SCALAR = [
     (ta.PercentB, (20, 2.0)),
     (ta.LinearRegression, (14,)),
     (ta.LinRegSlope, (14,)),
+    (ta.VerticalHorizontalFilter, (28,)),
+    (ta.ZScore, (20,)),
+    (ta.LinRegAngle, (14,)),
 ]
 
 
@@ -131,6 +134,28 @@ CANDLE_SCALAR = {
         lambda: ta.WeightedClose(),
         lambda ind, h, l, c, v: ind.batch(h, l, c),
     ),
+    "AcceleratorOscillator": (
+        lambda: ta.AcceleratorOscillator(5, 34, 5),
+        lambda ind, h, l, c, v: ind.batch(h, l),
+    ),
+    "BalanceOfPower": (
+        # The streaming 6-tuple feeds open == close, so batch matches with
+        # the close column standing in for open.
+        lambda: ta.BalanceOfPower(),
+        lambda ind, h, l, c, v: ind.batch(c, h, l, c),
+    ),
+    "ChoppinessIndex": (
+        lambda: ta.ChoppinessIndex(14),
+        lambda ind, h, l, c, v: ind.batch(h, l, c),
+    ),
+    "TrueRange": (
+        lambda: ta.TrueRange(),
+        lambda ind, h, l, c, v: ind.batch(h, l, c),
+    ),
+    "ChaikinVolatility": (
+        lambda: ta.ChaikinVolatility(10, 10),
+        lambda ind, h, l, c, v: ind.batch(h, l),
+    ),
 }
 
 
@@ -239,6 +264,31 @@ def test_linreg_slope_reference():
     assert out[2] == pytest.approx(4.0)
 
 
+def test_balance_of_power_reference():
+    # (close - open) / (high - low) = (12 - 10) / (14 - 10) = 0.5.
+    bop = ta.BalanceOfPower()
+    assert bop.update((10.0, 14.0, 10.0, 12.0, 1.0, 0)) == pytest.approx(0.5)
+
+
+def test_true_range_reference():
+    tr = ta.TrueRange()
+    assert tr.update((11.0, 12.0, 8.0, 11.0, 1.0, 0)) == pytest.approx(4.0)
+    assert tr.update((9.5, 10.0, 9.0, 9.5, 1.0, 1)) == pytest.approx(2.0)
+
+
+def test_linreg_angle_reference():
+    # A series rising by 1 per step has slope 1, and atan(1) = 45 degrees.
+    out = ta.LinRegAngle(5).batch(np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]))
+    assert out[4] == pytest.approx(45.0)
+
+
+def test_z_score_reference():
+    # Window [1, 3]: mean 2, population stddev 1; latest 3 -> z = 1.
+    out = ta.ZScore(2).batch(np.array([1.0, 3.0]))
+    assert math.isnan(out[0])
+    assert out[1] == pytest.approx(1.0)
+
+
 # --- Lifecycle ------------------------------------------------------------