feat(family-10): add 16 Ehlers / Cycle (DSP) indicators (#49)

Implements Family 10 (Ehlers / Cycle) end-to-end across Rust core,
Python / Node / WASM bindings, fuzz, tests, benches and docs. This
is an entirely new family covering John Ehlers' digital-signal-
processing school of cycle analytics — a strong differentiator
versus TA-Lib and pandas-ta, which ship only fragments.

Indicators:
- MAMA (Mesa Adaptive MA) — multi-output { mama, fama }
- FAMA (Following Adaptive MA) — scalar wrapper around MAMA's slow line
- Fisher Transform — Gaussian-normalising price transform
- Inverse Fisher Transform — bounded oscillator (tanh-based)
- SuperSmoother — 2-pole Butterworth lowpass
- Roofing Filter — high-pass + SuperSmoother bandpass
- Decycler — price minus 2-pole high-pass (lag-free trend)
- Decycler Oscillator — fast / slow Decycler difference (MACD-like)
- Hilbert Dominant Cycle — phase-derived period estimator [6, 50]
- Sine Wave Indicator — sin(phase) with 45° lead companion
- Adaptive Cycle Indicator — half-period driver for adaptive oscillators
- Center of Gravity Oscillator — weighted-mass momentum
- Cybernetic Cycle Component — EasyLanguage classic
- Empirical Mode Decomposition — bandpass + envelope mean
- Ehlers Stochastic — Stochastic on Roofing Filter input, [-1, +1]
- Instantaneous Trendline — Ehlers 2-pole lag-free trend

Indicator count rises 71 -> 87 across nine families (was eight).

All sixteen pass batch == streaming equivalence, expose the standard
Indicator surface (update / batch / reset / is_ready / warmup_period
/ name), are fuzz-tested, benchmarked against the checked-in BTCUSDT
1-minute dataset and reach across all four bindings.

Wiki deep-dive drafts for every indicator + Sidebar / Overview /
Home / Warmup updates are staged under indicator-ideas/families/
wiki/family-10-ehlers-cycle/ in the main repo (ghost-ignored) for
the maintainer to publish to the wiki repo manually.

bindings/node/__tests__/indicators.test.js

@@ -75,6 +75,22 @@ const scalarFactories = {
   LaguerreRSI: () => new wickra.LaguerreRSI(0.5),
   ConnorsRSI: () => new wickra.ConnorsRSI(3, 2, 100),
   RVIVolatility: () => new wickra.RVIVolatility(10),
+  // Family 10 — Ehlers / Cycle
+  SuperSmoother: () => new wickra.SuperSmoother(10),
+  FisherTransform: () => new wickra.FisherTransform(10),
+  InverseFisherTransform: () => new wickra.InverseFisherTransform(1.0),
+  Decycler: () => new wickra.Decycler(20),
+  DecyclerOscillator: () => new wickra.DecyclerOscillator(10, 30),
+  RoofingFilter: () => new wickra.RoofingFilter(10, 48),
+  CenterOfGravity: () => new wickra.CenterOfGravity(10),
+  CyberneticCycle: () => new wickra.CyberneticCycle(10),
+  InstantaneousTrendline: () => new wickra.InstantaneousTrendline(20),
+  EhlersStochastic: () => new wickra.EhlersStochastic(20),
+  EmpiricalModeDecomposition: () => new wickra.EmpiricalModeDecomposition(20, 0.5),
+  HilbertDominantCycle: () => new wickra.HilbertDominantCycle(),
+  AdaptiveCycle: () => new wickra.AdaptiveCycle(),
+  SineWave: () => new wickra.SineWave(),
+  FAMA: () => new wickra.FAMA(0.5, 0.05),
 };
 
 for (const [name, make] of Object.entries(scalarFactories)) {
@@ -213,6 +229,8 @@ const multi = {
   TDLines: { make: () => new wickra.TDLines(4, 9), fields: ['resistance', 'support'], step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
   TDRangeProjection: { make: () => new wickra.TDRangeProjection(), fields: ['high', 'low'], step: (ind, i) => ind.update(open[i], high[i], low[i], close[i]), batch: (ind) => ind.batch(open, high, low, close) },
   TDRiskLevel: { make: () => new wickra.TDRiskLevel(4, 9), fields: ['buyRisk', 'sellRisk'], step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
+  // Family 10: Ehlers / Cycle (multi-output)
+  MAMA: { make: () => new wickra.MAMA(0.5, 0.05), fields: ['mama', 'fama'], step: (ind, i) => ind.update(close[i]), batch: (ind) => ind.batch(close) },
 };
 
 for (const [name, d] of Object.entries(multi)) {