feat: Family 01 Moving Averages — ALMA / McGinley / FRAMA / VIDYA / JMA / Alligator / EVWMA (#39)

* feat(alma): add Arnaud Legoux Moving Average

Gaussian-weighted moving average with configurable centre (offset in
[0, 1]) and kernel width (sigma > 0). Pre-computes normalised weights
at construction so each update is a single rolling window dot product.

Reference: Arnaud Legoux and Dimitrios Kouzis-Loukas, 2009.

Touchpoints:
- crates/wickra-core: alma.rs + mod.rs + lib.rs re-export
- bindings/python: PyAlma + __init__.py + test_new_indicators +
  test_known_values reference
- bindings/node: AlmaNode + index.d.ts/index.js + indicators.test.js
  factory + reference value
- bindings/wasm: wasm_scalar_indicator! macro
- fuzz: indicator_update target covers ALMA(9, 0.85, 6.0)
- crates/wickra/benches: bench_scalar entry
- README + CHANGELOG: Moving Averages row + Unreleased entry

* feat(mcginley): add McGinley Dynamic moving average

John McGinley's self-adjusting moving average with the recurrence
MD + (price - MD) / (0.6 * period * (price / MD)^4). Speeds up when
price falls below the indicator and damps when price runs above the
indicator. Seeded with the simple average of the first period inputs.

Reference: McGinley, Technical Analysis of Stocks & Commodities, 1990.

Touchpoints:
- crates/wickra-core: mcginley_dynamic.rs + mod.rs + lib.rs re-export
- bindings/python: PyMcGinleyDynamic + __init__.py + test_new_indicators
  + test_known_values reference
- bindings/node: McGinleyDynamicNode (scalar macro) + index.d.ts/index.js
  + indicators.test.js factory + reference value
- bindings/wasm: wasm_scalar_indicator! macro
- fuzz: indicator_update target covers McGinleyDynamic(10)
- crates/wickra/benches: bench_scalar entry
- README + CHANGELOG: Moving Averages row + Unreleased entry

* 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

* feat(vidya): add Variable Index Dynamic Average

Chande's VIDYA — an EMA whose alpha scales with |CMO(cmo_period)| / 100.
Strong directional momentum lifts the smoothing constant toward the
EMA-of-period rate; flat or choppy windows shrink it toward zero so
VIDYA coasts on its previous value. Two parameters: period (14) and
cmo_period (9). Reuses the existing wickra-core Cmo internally.

Reference: Chande, Stocks & Commodities, 1992.

Also fixes a silent gap from d37fbd1 (feat(frama)): the PyFrama Python
class wrapper and its add_class registration were dropped because the
two edits hit "File has not been read yet" errors that scrolled past
in a batch. Adds them here alongside VIDYA's bindings.

Touchpoints (VIDYA): vidya.rs + mod.rs + lib.rs re-export, PyVidya +
__init__.py + test_new_indicators + test_known_values reference,
VidyaNode (manual two-param binding) + index.d.ts/index.js +
indicators.test.js factory + reference, wasm_scalar_indicator! macro,
fuzz target, bench, README + CHANGELOG.

* feat(jma): add Jurik Moving Average

Three-stage filter reconstruction of Mark Jurik's adaptive MA (the
algorithm is proprietary; this is the form used by most open-source
ports since the 1999 TASC article). Parameters: period (14), phase in
[-100, 100] (0), power in 1..=4 (2). State is seeded by setting
e0 = JMA = first input so a constant input stream is reproduced exactly.

Touchpoints: jma.rs + mod.rs + lib.rs re-export, PyJma + __init__.py +
test_new_indicators + test_known_values reference, JmaNode (manual
three-param binding) + index.d.ts/index.js + indicators.test.js factory
+ reference, wasm_scalar_indicator! macro, fuzz target, bench, README +
CHANGELOG.

* feat(alligator): add Bill Williams Alligator

Three SMMA lines (Jaw / Teeth / Lips) over the median price
(high + low) / 2 with default periods 13 / 8 / 5. Multi-output
indicator returning AlligatorOutput { jaw, teeth, lips }. The
original chart variant shifts each line forward for display; we
publish the unshifted SMMA values and leave the visual shift to
the consumer.

Reference: Bill Williams, Trading Chaos, 1995.

Touchpoints: alligator.rs + mod.rs + lib.rs re-export, PyAlligator
(Candle input, returns 3-tuple, ndarray (n, 3) batch) + __init__.py
+ test_new_indicators + test_known_values reference, AlligatorNode +
AlligatorValue + index.d.ts/index.js + indicators.test.js multi
factory + reference, WasmAlligator (manual JsValue object) +
candle-fuzz target + README + CHANGELOG.

* feat(evwma): add Elastic Volume-Weighted Moving Average

Christian P. Fries' elastic recurrence where the smoothing weight is the
bar's volume relative to the running window total:

  V_sum_t = sum of volumes over the last period candles
  EVWMA_t = ((V_sum_t - v_t) * EVWMA_{t-1} + v_t * close_t) / V_sum_t

A bar whose volume is small barely moves the average; a bar that
dominates the window pulls it strongly toward that bar's close. Seeded
with the close of the first full window; holds its previous value if
the entire window has zero volume.

Reference: Fries, Wilmott Magazine, 2001.

Touchpoints: evwma.rs + mod.rs + lib.rs re-export, PyEvwma (close +
volume batch) + __init__.py + test_new_indicators CANDLE_SCALAR +
test_known_values reference, EvwmaNode + index.d.ts/index.js +
indicators.test.js candleScalar factory + reference, WasmEvwma,
candle-fuzz target + README + CHANGELOG.

* ci: Force local wheel install in Python jobs

Use --no-index --no-deps so the Python matrix installs the freshly
built wheel from dist/ and never falls back to PyPI. Previously pip
sometimes picked the released 0.2.x wheel on macOS / Windows when its
platform tag was a wider match than the local build, which made the
job test the released package and miss any new symbols added in the
PR (e.g. AttributeError: module 'wickra' has no attribute 'ALMA').
numpy is already installed by the preceding pip step, so --no-deps
is safe.

bindings/wasm/src/lib.rs

@@ -79,6 +79,11 @@ wasm_scalar_indicator!(WasmSmma, "SMMA", wc::Smma, period: usize);
 wasm_scalar_indicator!(WasmTrima, "TRIMA", wc::Trima, period: usize);
 wasm_scalar_indicator!(WasmZlema, "ZLEMA", wc::Zlema, period: usize);
 wasm_scalar_indicator!(WasmT3, "T3", wc::T3, period: usize, v: f64);
+wasm_scalar_indicator!(WasmAlma, "ALMA", wc::Alma, period: usize, offset: f64, sigma: f64);
+wasm_scalar_indicator!(WasmMcGinleyDynamic, "McGinleyDynamic", wc::McGinleyDynamic, period: usize);
+wasm_scalar_indicator!(WasmFrama, "FRAMA", wc::Frama, period: usize);
+wasm_scalar_indicator!(WasmVidya, "VIDYA", wc::Vidya, period: usize, cmo_period: usize);
+wasm_scalar_indicator!(WasmJma, "JMA", wc::Jma, period: usize, phase: f64, power: u32);
 wasm_scalar_indicator!(WasmMom, "MOM", wc::Mom, period: usize);
 wasm_scalar_indicator!(WasmCmo, "CMO", wc::Cmo, period: usize);
 wasm_scalar_indicator!(WasmTsi, "TSI", wc::Tsi, long: usize, short: usize);
@@ -1372,6 +1377,47 @@ impl WasmMassIndex {
     }
 }
 
+#[wasm_bindgen(js_name = EVWMA)]
+pub struct WasmEvwma {
+    inner: wc::Evwma,
+}
+
+#[wasm_bindgen(js_class = EVWMA)]
+impl WasmEvwma {
+    #[wasm_bindgen(constructor)]
+    pub fn new(period: usize) -> Result<WasmEvwma, JsError> {
+        Ok(Self {
+            inner: wc::Evwma::new(period).map_err(map_err)?,
+        })
+    }
+    pub fn update(&mut self, close: f64, volume: f64) -> Result<Option<f64>, JsError> {
+        let c = make_candle(close, close, close, volume)?;
+        Ok(self.inner.update(c))
+    }
+    pub fn batch(&mut self, close: &[f64], volume: &[f64]) -> Result<Float64Array, JsError> {
+        if close.len() != volume.len() {
+            return Err(JsError::new("close and volume must be equal length"));
+        }
+        let mut out = Vec::with_capacity(close.len());
+        for i in 0..close.len() {
+            let c = make_candle(close[i], close[i], close[i], volume[i])?;
+            out.push(self.inner.update(c).unwrap_or(f64::NAN));
+        }
+        Ok(Float64Array::from(out.as_slice()))
+    }
+    pub fn reset(&mut self) {
+        self.inner.reset();
+    }
+    #[wasm_bindgen(js_name = isReady)]
+    pub fn is_ready(&self) -> bool {
+        self.inner.is_ready()
+    }
+    #[wasm_bindgen(js_name = warmupPeriod)]
+    pub fn warmup_period(&self) -> usize {
+        self.inner.warmup_period()
+    }
+}
+
 #[wasm_bindgen(js_name = VWMA)]
 pub struct WasmVwma {
     inner: wc::Vwma,
@@ -1942,6 +1988,63 @@ impl WasmAo {
     }
 }
 
+#[wasm_bindgen(js_name = Alligator)]
+pub struct WasmAlligator {
+    inner: wc::Alligator,
+}
+
+#[wasm_bindgen(js_class = Alligator)]
+impl WasmAlligator {
+    #[wasm_bindgen(constructor)]
+    pub fn new(jaw: usize, teeth: usize, lips: usize) -> Result<WasmAlligator, JsError> {
+        Ok(Self {
+            inner: wc::Alligator::new(jaw, teeth, lips).map_err(map_err)?,
+        })
+    }
+    /// Returns `[jaw0, teeth0, lips0, jaw1, teeth1, lips1, ...]`, length `3n`.
+    pub fn batch(&mut self, high: &[f64], low: &[f64]) -> Result<Float64Array, JsError> {
+        if high.len() != low.len() {
+            return Err(JsError::new("high and low must be equal length"));
+        }
+        let n = high.len();
+        let mut out = vec![f64::NAN; n * 3];
+        for i in 0..n {
+            let c = make_candle(high[i], low[i], low[i], 0.0)?;
+            if let Some(o) = self.inner.update(c) {
+                out[i * 3] = o.jaw;
+                out[i * 3 + 1] = o.teeth;
+                out[i * 3 + 2] = o.lips;
+            }
+        }
+        Ok(Float64Array::from(out.as_slice()))
+    }
+    /// Streaming update. Returns `{ jaw, teeth, lips }` once warm, else `null`.
+    pub fn update(&mut self, high: f64, low: f64) -> Result<JsValue, JsError> {
+        let c = make_candle(high, low, low, 0.0)?;
+        Ok(match self.inner.update(c) {
+            Some(o) => {
+                let obj = Object::new();
+                Reflect::set(&obj, &"jaw".into(), &o.jaw.into()).ok();
+                Reflect::set(&obj, &"teeth".into(), &o.teeth.into()).ok();
+                Reflect::set(&obj, &"lips".into(), &o.lips.into()).ok();
+                obj.into()
+            }
+            None => JsValue::NULL,
+        })
+    }
+    pub fn reset(&mut self) {
+        self.inner.reset();
+    }
+    #[wasm_bindgen(js_name = isReady)]
+    pub fn is_ready(&self) -> bool {
+        self.inner.is_ready()
+    }
+    #[wasm_bindgen(js_name = warmupPeriod)]
+    pub fn warmup_period(&self) -> usize {
+        self.inner.warmup_period()
+    }
+}
+
 #[wasm_bindgen(js_name = Aroon)]
 pub struct WasmAroon {
     inner: wc::Aroon,