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/node/__tests__/indicators.test.js

@@ -1,5 +1,5 @@
 // Comprehensive tests for the Wickra Node bindings: streaming-vs-batch
-// equivalence, reference values, and lifecycle methods across all 63
+// equivalence, reference values, and lifecycle methods across all 71
 // indicators. Ported from the Python test_streaming_vs_batch / test_known_values
 // suites.
 
@@ -13,6 +13,7 @@ const close = Array.from({ length: N }, (_, i) => 100 + Math.sin(i * 0.2) * 10 +
 const high = close.map((c) => c + 1.5);
 const low = close.map((c) => c - 1.5);
 const volume = Array.from({ length: N }, (_, i) => 1000 + (i % 7) * 50);
+const open = close.map((c) => c - 0.5);
 
 function eq(a, b) {
   if (Number.isNaN(a)) return Number.isNaN(b);
@@ -55,6 +56,9 @@ const scalarFactories = {
   PercentB: () => new wickra.PercentB(20, 2),
   LinearRegression: () => new wickra.LinearRegression(14),
   LinRegSlope: () => new wickra.LinRegSlope(14),
+  VerticalHorizontalFilter: () => new wickra.VerticalHorizontalFilter(28),
+  ZScore: () => new wickra.ZScore(20),
+  LinRegAngle: () => new wickra.LinRegAngle(14),
 };
 
 for (const [name, make] of Object.entries(scalarFactories)) {
@@ -95,6 +99,11 @@ const candleScalar = {
   TypicalPrice: { make: () => new wickra.TypicalPrice(), step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
   MedianPrice: { make: () => new wickra.MedianPrice(), step: (ind, i) => ind.update(high[i], low[i]), batch: (ind) => ind.batch(high, low) },
   WeightedClose: { make: () => new wickra.WeightedClose(), step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
+  AcceleratorOscillator: { make: () => new wickra.AcceleratorOscillator(5, 34, 5), step: (ind, i) => ind.update(high[i], low[i]), batch: (ind) => ind.batch(high, low) },
+  BalanceOfPower: { make: () => new wickra.BalanceOfPower(), step: (ind, i) => ind.update(open[i], high[i], low[i], close[i]), batch: (ind) => ind.batch(open, high, low, close) },
+  ChoppinessIndex: { make: () => new wickra.ChoppinessIndex(14), step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
+  TrueRange: { make: () => new wickra.TrueRange(), step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
+  ChaikinVolatility: { make: () => new wickra.ChaikinVolatility(10, 10), step: (ind, i) => ind.update(high[i], low[i]), batch: (ind) => ind.batch(high, low) },
 };
 
 for (const [name, d] of Object.entries(candleScalar)) {
@@ -232,3 +241,19 @@ test('SuperTrend flat market holds the lower band and an uptrend', () => {
   assert.ok(Math.abs(out[2 * n - 2] - 4) < 1e-9); // value
   assert.equal(out[2 * n - 1], 1); // direction
 });
+
+test('BalanceOfPower reference value', () => {
+  // (close - open) / (high - low) = (12 - 10) / (14 - 10) = 0.5.
+  assert.ok(Math.abs(new wickra.BalanceOfPower().update(10, 14, 10, 12) - 0.5) < 1e-9);
+});
+
+test('TrueRange reference values', () => {
+  const tr = new wickra.TrueRange();
+  assert.equal(tr.update(12, 8, 11), 4); // no prev close -> high - low
+  assert.equal(tr.update(10, 9, 9.5), 2); // prev close 11 -> max(1, 1, 2)
+});
+
+test('LinRegAngle of a unit-slope series is 45 degrees', () => {
+  const out = new wickra.LinRegAngle(5).batch([1, 2, 3, 4, 5, 6]);
+  assert.ok(Math.abs(out[4] - 45) < 1e-9);
+});

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 ------------------------------------------------------------