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.

crates/wickra-core/src/indicators/mod.rs

@@ -7,6 +7,8 @@
 mod accelerator_oscillator;
 mod adl;
 mod adx;
+mod alligator;
+mod alma;
 mod aroon;
 mod aroon_oscillator;
 mod atr;
@@ -29,9 +31,12 @@ mod donchian;
 mod dpo;
 mod ease_of_movement;
 mod ema;
+mod evwma;
 mod force_index;
+mod frama;
 mod historical_volatility;
 mod hma;
+mod jma;
 mod kama;
 mod keltner;
 mod linreg;
@@ -39,6 +44,7 @@ mod linreg_angle;
 mod linreg_slope;
 mod macd;
 mod mass_index;
+mod mcginley_dynamic;
 mod median_price;
 mod mfi;
 mod mom;
@@ -66,6 +72,7 @@ mod typical_price;
 mod ulcer_index;
 mod ultimate_oscillator;
 mod vertical_horizontal_filter;
+mod vidya;
 mod vortex;
 mod vpt;
 mod vwap;
@@ -79,6 +86,8 @@ mod zlema;
 pub use accelerator_oscillator::AcceleratorOscillator;
 pub use adl::Adl;
 pub use adx::{Adx, AdxOutput};
+pub use alligator::{Alligator, AlligatorOutput};
+pub use alma::Alma;
 pub use aroon::{Aroon, AroonOutput};
 pub use aroon_oscillator::AroonOscillator;
 pub use atr::Atr;
@@ -101,9 +110,12 @@ pub use donchian::{Donchian, DonchianOutput};
 pub use dpo::Dpo;
 pub use ease_of_movement::EaseOfMovement;
 pub use ema::Ema;
+pub use evwma::Evwma;
 pub use force_index::ForceIndex;
+pub use frama::Frama;
 pub use historical_volatility::HistoricalVolatility;
 pub use hma::Hma;
+pub use jma::Jma;
 pub use kama::Kama;
 pub use keltner::{Keltner, KeltnerOutput};
 pub use linreg::LinearRegression;
@@ -111,6 +123,7 @@ pub use linreg_angle::LinRegAngle;
 pub use linreg_slope::LinRegSlope;
 pub use macd::{MacdIndicator, MacdOutput};
 pub use mass_index::MassIndex;
+pub use mcginley_dynamic::McGinleyDynamic;
 pub use median_price::MedianPrice;
 pub use mfi::Mfi;
 pub use mom::Mom;
@@ -138,6 +151,7 @@ pub use typical_price::TypicalPrice;
 pub use ulcer_index::UlcerIndex;
 pub use ultimate_oscillator::UltimateOscillator;
 pub use vertical_horizontal_filter::VerticalHorizontalFilter;
+pub use vidya::Vidya;
 pub use vortex::{Vortex, VortexOutput};
 pub use vpt::VolumePriceTrend;
 pub use vwap::{RollingVwap, Vwap};