feat(frama): add Fractal Adaptive Moving Average

Ehlers' FRAMA adapts its smoothing constant to the fractal dimension of
the recent window: tight tracking in trends, heavy smoothing in chop.
Uses the close-only variant where max/min over each window half drive
the dimension estimate. Period must be even (default 16).

Reference: Ehlers, Fractal Adaptive Moving Average, 2005.

Touchpoints:
- crates/wickra-core: frama.rs + mod.rs + lib.rs re-export
- bindings/python: PyFrama + __init__.py + test_new_indicators +
  test_known_values reference (constant series + uptrend tracking)
- bindings/node: FramaNode (scalar macro) + index.d.ts/index.js +
  indicators.test.js factory + reference value
- bindings/wasm: wasm_scalar_indicator! macro
- fuzz: indicator_update target covers Frama(16)
- crates/wickra/benches: bench_scalar entry
- README + CHANGELOG: Moving Averages row + Unreleased entry

fuzz/fuzz_targets/indicator_update.rs

@@ -15,7 +15,7 @@
 
 use libfuzzer_sys::fuzz_target;
 use wickra_core::{
-    Alma, BatchExt, BollingerBands, Cmo, Coppock, Dema, Dpo, Ema, HistoricalVolatility, Hma,
+    Alma, BatchExt, BollingerBands, Cmo, Coppock, Dema, Dpo, Ema, Frama, HistoricalVolatility, Hma,
     Indicator, Kama, LinRegAngle, LinRegSlope, LinearRegression, MacdIndicator, McGinleyDynamic,
     Mom, Pmo, Ppo, Roc, Rsi, Sma, Smma, StdDev, StochRsi, T3, Tema, Trima, Trix, Tsi, UlcerIndex,
     VerticalHorizontalFilter, Wma, ZScore, Zlema,
@@ -55,6 +55,7 @@ fuzz_target!(|data: Vec<f64>| {
     drive(|| Kama::new(10, 2, 30).unwrap(), &data);
     drive(|| Alma::new(9, 0.85, 6.0).unwrap(), &data);
     drive(|| McGinleyDynamic::new(10).unwrap(), &data);
+    drive(|| Frama::new(16).unwrap(), &data);
     drive(|| T3::new(14, 0.7).unwrap(), &data);
     drive(|| Mom::new(14).unwrap(), &data);
     drive(|| Cmo::new(14).unwrap(), &data);